DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilchrist, Kristin H., E-mail: kgilchrist@rti.org; Lewis, Gregory F.; Gay, Elaine A.

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak formore » field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5 minute recordings at multiple time points (0.5, 1, 2 and 4 h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. - Highlights: • Six parameters describing arrhythmia were defined and measured for known compounds. • Software for efficient parameter extraction from large MEA data sets was developed. • The proposed cellular parameter set is predictive of clinical drug proarrhythmia.« less

Management of heart failure in the new era: the role of scores.

PubMed

Mantegazza, Valentina; Badagliacca, Roberto; Nodari, Savina; Parati, Gianfranco; Lombardi, Carolina; Di Somma, Salvatore; Carluccio, Erberto; Dini, Frank Lloyd; Correale, Michele; Magrì, Damiano; Agostoni, Piergiuseppe

2016-08-01

Heart failure is a widespread syndrome involving several organs, still characterized by high mortality and morbidity, and whose clinical course is heterogeneous and hardly predictable.In this scenario, the assessment of heart failure prognosis represents a fundamental step in clinical practice. A single parameter is always unable to provide a very precise prognosis. Therefore, risk scores based on multiple parameters have been introduced, but their clinical utility is still modest. In this review, we evaluated several prognostic models for acute, right, chronic, and end-stage heart failure based on multiple parameters. In particular, for chronic heart failure we considered risk scores essentially based on clinical evaluation, comorbidities analysis, baroreflex sensitivity, heart rate variability, sleep disorders, laboratory tests, echocardiographic imaging, and cardiopulmonary exercise test parameters. What is at present established is that a single parameter is not sufficient for an accurate prediction of prognosis in heart failure because of the complex nature of the disease. However, none of the scoring systems available is widely used, being in some cases complex, not user-friendly, or based on expensive or not easily available parameters. We believe that multiparametric scores for risk assessment in heart failure are promising but their widespread use needs to be experienced.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

NASA Astrophysics Data System (ADS)

Vallières, Martin; Laberge, Sébastien; Diamant, André; El Naqa, Issam

2017-11-01

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice (‘span’). Simulated T 1-weighted and T 2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of 0.84 +/- 0.01 in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters (p = 0.04 ), with an average AUC of 0.89 +/- 0.01 . Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

Inflammation-driven malnutrition: a new screening tool predicts outcome in Crohn's disease.

PubMed

Jansen, Irene; Prager, Matthias; Valentini, Luzia; Büning, Carsten

2016-09-01

Malnutrition is a frequent feature in Crohn's disease (CD), affects patient outcome and must be recognised. For chronic inflammatory diseases, recent guidelines recommend the development of combined malnutrition and inflammation risk scores. We aimed to design and evaluate a new screening tool that combines both malnutrition and inflammation parameters that might help predict clinical outcome. In a prospective cohort study, we examined fifty-five patients with CD in remission (Crohn's disease activity index (CDAI) <200) at 0 and 6 months. We assessed disease activity (CDAI, Harvey-Bradshaw index), inflammation (C-reactive protein (CRP), faecal calprotectin (FC)), malnutrition (BMI, subjective global assessment (SGA), serum albumin, handgrip strength), body composition (bioelectrical impedance analysis) and administered the newly developed 'Malnutrition Inflammation Risk Tool' (MIRT; containing BMI, unintentional weight loss over 3 months and CRP). All parameters were evaluated regarding their ability to predict disease outcome prospectively at 6 months. At baseline, more than one-third of patients showed elevated inflammatory markers despite clinical remission (36·4 % CRP ≥5 mg/l, 41·5 % FC ≥100 µg/g). Prevalence of malnutrition at baseline according to BMI, SGA and serum albumin was 2-16 %. At 6 months, MIRT significantly predicted outcome in numerous nutritional and clinical parameters (SGA, CD-related flares, hospitalisations and surgeries). In contrast, SGA, handgrip strength, BMI, albumin and body composition had no influence on the clinical course. The newly developed MIRT was found to reliably predict clinical outcome in CD patients. This screening tool might be used to facilitate clinical decision making, including treatment of both inflammation and malnutrition in order to prevent complications.

Predicting breast cancer using an expression values weighted clinical classifier.

PubMed

Thomas, Minta; De Brabanter, Kris; Suykens, Johan A K; De Moor, Bart

2014-12-31

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters. We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

Predicting the amount of intraperitoneal fluid accumulation by computed tomography and its clinical use in patients with perforated peptic ulcer.

PubMed

Ishiguro, Toru; Kumagai, Youichi; Baba, Hiroyuki; Tajima, Yusuke; Imaizumi, Hideko; Suzuki, Okihide; Kuwabara, Koki; Matsuzawa, Takeaki; Sobajima, Jun; Fukuchi, Minoru; Ishibashi, Keiichiro; Mochiki, Erito; Ishida, Hideyuki

2014-01-01

The correlation between the amount of peritoneal fluid and clinical parameters in patients with perforated peptic ulcer (PPU) has not been investigated. The authors' objective was to derive a reliable formula for determining the amount of peritoneal fluid in patients with PPU before surgery, and to evaluate the correlation between the estimated amount of peritoneal fluid and clinical parameters. We investigated 62 consecutive patients who underwent emergency surgery for PPU, and in whom prediction of the amount of accumulated intraperitoneal fluid was possible by computed tomography (CT) using the methods described by Oriuchi et al. We examined the relationship between the predicted amount of accumulated intraperitoneal fluid and that measured during surgery, and the relationship between the amount of fluid predicted preoperatively or measured during surgery and several clinical parameters. There was a significant positive correlation between the amount of fluid predicted by CT scan and that measured during surgery. When patients with gastric ulcer and duodenal ulcer were analyzed collectively, the predicted amount of intraperitoneal fluid and the amount measured during surgery were each associated with the period from onset until CT scan, perforation size, the Mannheim peritoneal index, and the severity of postoperative complications according to the Clavien-Dindo classification. Our present results suggest that the method of Oriuchi et al is useful for predicting the amount of accumulated intraperitoneal fluid in patients with PPU, and that this would be potentially helpful for treatment decision-making and estimating the severity of postoperative complications.

Predicting the Amount of Intraperitoneal Fluid Accumulation by Computed Tomography and Its Clinical Use in Patients With Perforated Peptic Ulcer

PubMed Central

Ishiguro, Toru; Kumagai, Youichi; Baba, Hiroyuki; Tajima, Yusuke; Imaizumi, Hideko; Suzuki, Okihide; Kuwabara, Koki; Matsuzawa, Takeaki; Sobajima, Jun; Fukuchi, Minoru; Ishibashi, Keiichiro; Mochiki, Erito; Ishida, Hideyuki

2014-01-01

The correlation between the amount of peritoneal fluid and clinical parameters in patients with perforated peptic ulcer (PPU) has not been investigated. The authors' objective was to derive a reliable formula for determining the amount of peritoneal fluid in patients with PPU before surgery, and to evaluate the correlation between the estimated amount of peritoneal fluid and clinical parameters. We investigated 62 consecutive patients who underwent emergency surgery for PPU, and in whom prediction of the amount of accumulated intraperitoneal fluid was possible by computed tomography (CT) using the methods described by Oriuchi et al. We examined the relationship between the predicted amount of accumulated intraperitoneal fluid and that measured during surgery, and the relationship between the amount of fluid predicted preoperatively or measured during surgery and several clinical parameters. There was a significant positive correlation between the amount of fluid predicted by CT scan and that measured during surgery. When patients with gastric ulcer and duodenal ulcer were analyzed collectively, the predicted amount of intraperitoneal fluid and the amount measured during surgery were each associated with the period from onset until CT scan, perforation size, the Mannheim peritoneal index, and the severity of postoperative complications according to the Clavien–Dindo classification. Our present results suggest that the method of Oriuchi et al is useful for predicting the amount of accumulated intraperitoneal fluid in patients with PPU, and that this would be potentially helpful for treatment decision-making and estimating the severity of postoperative complications. PMID:25437594

Pulsatility Index as a Diagnostic Parameter of Reciprocating Wall Shear Stress Parameters in Physiological Pulsating Waveforms

PubMed Central

Avrahami, Idit; Kersh, Dikla

2016-01-01

Arterial wall shear stress (WSS) parameters are widely used for prediction of the initiation and development of atherosclerosis and arterial pathologies. Traditional clinical evaluation of arterial condition relies on correlations of WSS parameters with average flow rate (Q) and heart rate (HR) measurements. We show that for pulsating flow waveforms in a straight tube with flow reversals that lead to significant reciprocating WSS, the measurements of HR and Q are not sufficient for prediction of WSS parameters. Therefore, we suggest adding a third quantity—known as the pulsatility index (PI)—which is defined as the peak-to-peak flow rate amplitude normalized by Q. We examine several pulsating flow waveforms with and without flow reversals using a simulation of a Womersley model in a straight rigid tube and validate the simulations through experimental study using particle image velocimetry (PIV). The results indicate that clinically relevant WSS parameters such as the percentage of negative WSS (P[%]), oscillating shear index (OSI) and the ratio of minimum to maximum shear stress rates (min/max), are better predicted when the PI is used in conjunction with HR and Q. Therefore, we propose to use PI as an additional and essential diagnostic quantity for improved predictability of the reciprocating WSS. PMID:27893801

Predicting quality of life in multiple sclerosis: accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change.

PubMed

Benedict, Ralph H B; Wahlig, Elizabeth; Bakshi, Rohit; Fishman, Inna; Munschauer, Frederick; Zivadinov, Robert; Weinstock-Guttman, Bianca

2005-04-15

Health-related quality of life (HQOL) is poor in multiple sclerosis (MS) but the clinical precipitants of the problem are not well understood. Previous correlative studies demonstrated relationships between various clinical parameters and diminished HQOL in MS. Unfortunately, these studies failed to account for multiple predictors in the same analysis. We endeavored to determine what clinical parameters account for most variance in predicting HQOL, and employability, while accounting for disease course, physical disability, fatigue, cognition, mood disorder, personality, and behavior disorder. In 120 MS patients, we measured HQOL (MS Quality of Life-54) and vocational status (employed vs. disabled) and then conducted detailed clinical testing. Data were analyzed by linear and logistic regression methods. MS patients reported lower HQOL (p<0.001) and were more likely to be disabled (45% of patients vs. 0 controls). Physical HQOL was predicted by fatigue, depression, and physical disability. Mental HQOL was associated with only depression and fatigue. In contrast, vocational status was predicted by three cognitive tests, conscientiousness, and disease duration (p<0.05). Thus, for the first time, we predicted HQOL in MS while accounting for measures from these many clinical domains. We conclude that self-report HQOL indices are most strongly predicted by measures of depression, whereas vocational status is predicted primarily by objective measures of cognitive function. The findings highlight core clinical problems that merit early identification and further research regarding the development of effective treatment.

[Fever and petechial exanthema in children].

PubMed

Soult Rubio, J A; Navarro González, J; Olano Claret, P

1992-11-01

In an attempt to determine clinical and analytical predictive parameters of a possible grave disease, we have carried out a retrospective study of 172 children admitted to our hospital with fever and petechiae as initial symptoms. The ages ranged between 1 month and 10 years. Even though we have not found a clinical symptom or analysis sufficiently sensitive as to predict all grave diseases, the general clinical state of the child associated with either a high or low white cell count and an abnormal coagulation study should be alert signals for a serious infectious disease. On the contrary, if the clinical and analytical parameters are within normal limits the risk of a grave disease is low. We emphasize the high incidence of meningococcal disease (26%).

Prediction of polycystic ovarian syndrome based on ultrasound findings and clinical parameters.

PubMed

Moschos, Elysia; Twickler, Diane M

2015-03-01

To determine the accuracy of sonographic-diagnosed polycystic ovaries and clinical parameters in predicting polycystic ovarian syndrome. Medical records and ultrasounds of 151 women with sonographically diagnosed polycystic ovaries were reviewed. Sonographic criteria for polycystic ovaries were based on 2003 Rotterdam European Society of Human Reproduction and Embryology/American Society for Reproductive Medicine guidelines: at least one ovary with 12 or more follicles measuring 2-9 mm and/or increased ovarian volume >10 cm(3) . Clinical variables of age, gravidity, ethnicity, body mass index, and sonographic indication were collected. One hundred thirty-five patients had final outcomes (presence/absence of polycystic ovarian syndrome). Polycystic ovarian syndrome was diagnosed if a patient had at least one other of the following two criteria: oligo/chronic anovulation and/or clinical/biochemical hyperandrogenism. A logistic regression model was constructed using stepwise selection to identify variables significantly associated with polycystic ovarian syndrome (p < .05). The validity of the model was assessed using receiver operating characteristics and Hosmer-Lemeshow χ(2) analyses. One hundred twenty-eight patients met official sonographic criteria for polycystic ovaries and 115 (89.8%) had polycystic ovarian syndrome (p = .009). Lower gravidity, abnormal bleeding, and body mass index >33 were significant in predicting polycystic ovarian syndrome (receiver operating characteristics curve, c = 0.86). Pain decreased the likelihood of polycystic ovarian syndrome. Polycystic ovaries on ultrasound were sensitive in predicting polycystic ovarian syndrome. Ultrasound, combined with clinical parameters, can be used to generate a predictive index for polycystic ovarian syndrome. © 2014 Wiley Periodicals, Inc.

Effect and clinical prediction of worsening renal function in acute decompensated heart failure.

PubMed

Breidthardt, Tobias; Socrates, Thenral; Noveanu, Markus; Klima, Theresia; Heinisch, Corinna; Reichlin, Tobias; Potocki, Mihael; Nowak, Albina; Tschung, Christopher; Arenja, Nisha; Bingisser, Roland; Mueller, Christian

2011-03-01

We aimed to establish the prevalence and effect of worsening renal function (WRF) on survival among patients with acute decompensated heart failure. Furthermore, we sought to establish a risk score for the prediction of WRF and externally validate the previously established Forman risk score. A total of 657 consecutive patients with acute decompensated heart failure presenting to the emergency department and undergoing serial creatinine measurements were enrolled. The potential of the clinical parameters at admission to predict WRF was assessed as the primary end point. The secondary end point was all-cause mortality at 360 days. Of the 657 patients, 136 (21%) developed WRF, and 220 patients had died during the first year. WRF was more common in the nonsurvivors (30% vs 41%, p = 0.03). Multivariate regression analysis found WRF to independently predict mortality (hazard ratio 1.92, p <0.01). In a single parameter model, previously diagnosed chronic kidney disease was the only independent predictor of WRF and achieved an area under the receiver operating characteristic curve of 0.60. After the inclusion of the blood gas analysis parameters into the model history of chronic kidney disease (hazard ratio 2.13, p = 0.03), outpatient diuretics (hazard ratio 5.75, p <0.01), and bicarbonate (hazard ratio 0.91, p <0.01) were all predictive of WRF. A risk score was developed using these predictors. On receiver operating characteristic curve analysis, the Forman and Basel prediction rules achieved an area under the curve of 0.65 and 0.71, respectively. In conclusion, WRF was common in patients with acute decompensated heart failure and was linked to significantly worse outcomes. However, the clinical parameters failed to adequately predict its occurrence, making a tailored therapy approach impossible. Copyright © 2011 Elsevier Inc. All rights reserved.

Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Ruyck, Kim, E-mail: kim.deruyck@UGent.be; Sabbe, Nick; Oberije, Cary

2011-10-01

Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidatemore » genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade {>=}2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.« less

Thyroid nodule management: clinical, ultrasound and cytopathological parameters for predicting malignancy

PubMed Central

Maia, Frederico F. R.; Zantut-Wittmann, Denise Engelbrecht

2012-01-01

Although fine-needle aspiration cytology is considered to be the reference method for evaluating thyroid nodules, the results are inaccurate in approximately 10-30% of cases. Several studies have attempted to predict the risk of malignancy in thyroid nodules based on age, nodularity, thyrotropin values, thyroid autoimmune disease, hot/cold nodule status, and ultrasound parameters. However, no consensus has been found, and none of these parameters has significantly affected patient management. The management of indeterminate thyroid nodules and re-biopsies of nodules with initially benign cytological results remain important and controversial topics of discussion. The Bethesda cytological system and several studies on the use of molecular markers to predict malignancy from cytological samples of thyroid nodules need further clarification. More in-depth discussions among and continuous education of the specialists involved in treating thyroid disease are necessary to improve the management of these patients. This review aims to examine the clinical, laboratory, ultrasound, and scintigraphic parameters that can be used for thyroid nodule management. PMID:22948464

Scintigraphic scoring system for grading severity of gastro-esophageal reflux on 99mTc sulfur colloid gastro-esophageal reflux scintigraphy: A prospective study of 39 cases with pre and post treatment assessment.

PubMed

Puranik, Ameya D; Nair, Gopinathan; Aggarwal, Rajiv; Bandyopadhyay, Abhijit; Shinto, Ajit; Zade, Anand

2013-04-01

The study aimed at developing a scoring system for scintigraphic grading of gastro-esophageal reflux (GER), on gastro-esophageal reflux scintigraphy (GERS) and comparison of clinical and scintigraphic scores, pre- and post-treatment. A total of 39 cases with clinically symptomatic GER underwent 99mTc sulfur colloid GERS; scores were assigned based on the clinical and scintigraphic parameters. Post domperidone GERS was performed after completion of treatment. Follow up GERS was performed and clinical and scintigraphic parameters were compared with baseline parameters. Paired t-test on pre and post domperidone treatment clinical scores showed that the decline in post-treatment scores was highly significant, with P value < 0.001. The scintigraphic scoring system had a sensitivity of 93.9% in assessing treatment response to domperidone, specificity of 83.3% i.e., 83.3% of children with no decline in scintigraphic scores show no clinical response to Domperidone. The scintigraphic scoring system had a positive predictive value of 96.9% and a negative predictive value of 71.4%. GERS with its quantitative parameters is a good investigation for assessing the severity of reflux and also for following children post-treatment.

Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and Gram-negative Bacteremia

PubMed Central

Ratzinger, Franz; Dedeyan, Michel; Rammerstorfer, Matthias; Perkmann, Thomas; Burgmann, Heinz; Makristathis, Athanasios; Dorffner, Georg; Loetsch, Felix; Blacky, Alexander; Ramharter, Michael

2015-01-01

Adequate early empiric antibiotic therapy is pivotal for the outcome of patients with bloodstream infections. In clinical practice the use of surrogate laboratory parameters is frequently proposed to predict underlying bacterial pathogens; however there is no clear evidence for this assumption. In this study, we investigated the discriminatory capacity of predictive models consisting of routinely available laboratory parameters to predict the presence of Gram-positive or Gram-negative bacteremia. Major machine learning algorithms were screened for their capacity to maximize the area under the receiver operating characteristic curve (ROC-AUC) for discriminating between Gram-positive and Gram-negative cases. Data from 23,765 patients with clinically suspected bacteremia were screened and 1,180 bacteremic patients were included in the study. A relative predominance of Gram-negative bacteremia (54.0%), which was more pronounced in females (59.1%), was observed. The final model achieved 0.675 ROC-AUC resulting in 44.57% sensitivity and 79.75% specificity. Various parameters presented a significant difference between both genders. In gender-specific models, the discriminatory potency was slightly improved. The results of this study do not support the use of surrogate laboratory parameters for predicting classes of causative pathogens. In this patient cohort, gender-specific differences in various laboratory parameters were observed, indicating differences in the host response between genders. PMID:26522966

Predictors of outcome for severe IgA Nephropathy in a multi-ethnic U.S. cohort.

PubMed

Arroyo, Ana Huerta; Bomback, Andrew S; Butler, Blake; Radhakrishnan, Jai; Herlitz, Leal; Stokes, M Barry; D'Agati, Vivette; Markowitz, Glen S; Appel, Gerald B; Canetta, Pietro A

2015-09-01

Although IgA nephropathy (IgAN) is the leading cause of glomerulonephritis worldwide, there are few large cohorts representative of U.S. Prognosis remains challenging, particularly as more patients are treated with RAAS blockade and immunosuppression. We analyzed a retrospective cohort of IgAN patients followed at Columbia University Medical Center from 1980 to 2010. We evaluated two outcomes - halving of eGFR and ESRD - using three proportional hazards models: 1) a model with only clinical parameters, 2) a model with only histopathologic parameters, and 3) a model combining clinical and histopathologic parameters. Of 154 patients with biopsy-proven IgAN, 126 had follow-up data available and 93 had biopsy slides re-read. Median follow-up was 47 months. The cohort was 64% male, 60% white, and the average age was 34 years at diagnosis. Median (IQR) eGFR and proteinuria at diagnosis were 64.1 (38.0 - 88.7) mL/min/1.73 m2 and 2.7 (1.3 - 4.5) g/day. Over 90% of subjects were treated with RAAS blockade, and over 66% received immunosuppression. In the clinical parameters-only model, baseline eGFR and African-American race predicted both halving of eGFR and ESRD. In the histopathologic parameters-only model, no parameter significantly predicted outcome. In the combined model, baseline eGFR remained the strongest predictor of both halving of eGFR (p = 0.03) and ESRD (p = 0.001), while the presence of IgG by immunofluorescence microscopy also predicted progression to ESRD. In this diverse U.S. IgAN cohort in which the majority of patients received RAAS blockade and immunosuppression, baseline eGFR, African-American race, and co-staining of IgG predicted poor outcome.

The alfa and beta of tumours: a review of parameters of the linear-quadratic model, derived from clinical radiotherapy studies.

PubMed

van Leeuwen, C M; Oei, A L; Crezee, J; Bel, A; Franken, N A P; Stalpers, L J A; Kok, H P

2018-05-16

Prediction of radiobiological response is a major challenge in radiotherapy. Of several radiobiological models, the linear-quadratic (LQ) model has been best validated by experimental and clinical data. Clinically, the LQ model is mainly used to estimate equivalent radiotherapy schedules (e.g. calculate the equivalent dose in 2 Gy fractions, EQD 2 ), but increasingly also to predict tumour control probability (TCP) and normal tissue complication probability (NTCP) using logistic models. The selection of accurate LQ parameters α, β and α/β is pivotal for a reliable estimate of radiation response. The aim of this review is to provide an overview of published values for the LQ parameters of human tumours as a guideline for radiation oncologists and radiation researchers to select appropriate radiobiological parameter values for LQ modelling in clinical radiotherapy. We performed a systematic literature search and found sixty-four clinical studies reporting α, β and α/β for tumours. Tumour site, histology, stage, number of patients, type of LQ model, radiation type, TCP model, clinical endpoint and radiobiological parameter estimates were extracted. Next, we stratified by tumour site and by tumour histology. Study heterogeneity was expressed by the I 2 statistic, i.e. the percentage of variance in reported values not explained by chance. A large heterogeneity in LQ parameters was found within and between studies (I 2  > 75%). For the same tumour site, differences in histology partially explain differences in the LQ parameters: epithelial tumours have higher α/β values than adenocarcinomas. For tumour sites with different histologies, such as in oesophageal cancer, the α/β estimates correlate well with histology. However, many other factors contribute to the study heterogeneity of LQ parameters, e.g. tumour stage, type of LQ model, TCP model and clinical endpoint (i.e. survival, tumour control and biochemical control). The value of LQ parameters for tumours as published in clinical radiotherapy studies depends on many clinical and methodological factors. Therefore, for clinical use of the LQ model, LQ parameters for tumour should be selected carefully, based on tumour site, histology and the applied LQ model. To account for uncertainties in LQ parameter estimates, exploring a range of values is recommended.

Configuration and validation of a novel prostate disease nomogram predicting prostate biopsy outcome: A prospective study correlating clinical indicators among Filipino adult males with elevated PSA level.

PubMed

Chua, Michael E; Tanseco, Patrick P; Mendoza, Jonathan S; Castillo, Josefino C; Morales, Marcelino L; Luna, Saturnino L

2015-04-01

To configure and validate a novel prostate disease nomogram providing prostate biopsy outcome probabilities from a prospective study correlating clinical indicators and diagnostic parameters among Filipino adult male with elevated serum total prostate specific antigen (PSA) level. All men with an elevated serum total PSA underwent initial prostate biopsy at our institution from January 2011 to August 2014 were included. Clinical indicators, diagnostic parameters, which include PSA level and PSA-derivatives, were collected as predictive factors for biopsy outcome. Multiple logistic-regression analysis involving a backward elimination selection procedure was used to select independent predictors. A nomogram was developed to calculate the probability of the biopsy outcomes. External validation of the nomogram was performed using separate data set from another center for determination of sensitivity and specificity. A receiver-operating characteristic (ROC) curve was used to assess the accuracy in predicting differential biopsy outcome. Total of 552 patients was included. One hundred and ninety-one (34.6%) patients had benign prostatic hyperplasia, and 165 (29.9%) had chronic prostatitis. The remaining 196 (35.5%) patients had prostate adenocarcinoma. The significant independent variables used to predict biopsy outcome were age, family history of prostate cancer, prior antibiotic intake, PSA level, PSA-density, PSA-velocity, echogenic findings on ultrasound, and DRE status. The areas under the receiver-operating characteristic curve for prostate cancer using PSA alone and the nomogram were 0.688 and 0.804, respectively. The nomogram configured based on routinely available clinical parameters, provides high predictive accuracy with good performance characteristics in predicting the prostate biopsy outcome such as presence of prostate cancer, high Gleason prostate cancer, benign prostatic hyperplasia, and chronic prostatitis.

Prediction of clinical behaviour and treatment for cancers.

PubMed

Futschik, Matthias E; Sullivan, Mike; Reeve, Anthony; Kasabov, Nikola

2003-01-01

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Radiomics in Oncological PET/CT: Clinical Applications.

PubMed

Lee, Jeong Won; Lee, Sang Mi

2018-06-01

18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

Predicting hepatic steatosis and liver fat content in obese children based on biochemical parameters and anthropometry.

PubMed

Zhang, H-X; Xu, X-Q; Fu, J-F; Lai, C; Chen, X-F

2015-04-01

Predictors of quantitative evaluation of hepatic steatosis and liver fat content (LFC) using clinical and laboratory variables available in the general practice in the obese children are poorly identified. To build predictive models of hepatic steatosis and LFC in obese children based on biochemical parameters and anthropometry. Hepatic steatosis and LFC were determined using proton magnetic resonance spectroscopy in 171 obese children aged 5.5-18.0 years. Routine clinical and laboratory parameters were also measured in all subjects. Group analysis, univariable correlation analysis, and multivariate logistic and linear regression analysis were used to develop a liver fat score to identify hepatic steatosis and a liver fat equation to predict LFC in each subject. The predictive model of hepatic steatosis in our participants based on waist circumference and alanine aminotransferase had an area under the receiver operating characteristic curve of 0.959 (95% confidence interval: 0.927-0.990). The optimal cut-off value of 0.525 for determining hepatic steatosis had sensitivity of 93% and specificity of 90%. A liver fat equation was also developed based on the same parameters of hepatic steatosis liver fat score, which would be used to calculate the LFC in each individual. The liver fat score and liver fat equation, consisting of routinely available variables, may help paediatricians to accurately determine hepatic steatosis and LFC in clinical practice, but external validation is needed before it can be employed for this purpose. © 2014 The Authors. Pediatric Obesity © 2014 World Obesity.

Pneumonia in multiple injured patients: a prospective controlled trial on early prediction using clinical and immunological parameters.

PubMed

Andermahr, J; Greb, A; Hensler, T; Helling, H J; Bouillon, B; Sauerland, S; Rehm, K E; Neugebauer, E

2002-05-01

In a prospective trial 266 multiple injured patients were included to evaluate clinical risk factors and immune parameters related to pneumonia. Clinical and humoral parameters were assessed and multivariate analysis performed. The multivariate analysis (odds ratio with 95% confidence interval (CI)) revealed male gender (3.65), traumatic brain injury (TBI) (2.52), thorax trauma (AIS(thorax) > or = 3) (2.05), antibiotic prophylaxis (1.30), injury severity score (ISS) (1.03 per ISS point) and the age (1.02 per year) as risk factors for pneumonia. The main pathogens were Acinetobacter Baumannii (40%) and Staphylococcus aureus (25%). A tendency towards higher Procalcitonin (PCT) and Interleukin (IL)-6 levels two days after trauma was observed for pneumonia patients. The immune parameters (PCT, IL-6, IL-10, soluble tumor necrosis factor p-55 and p-75) could not confirm the diagnosis of pneumonia earlier than the clinical parameters.

Outcome Prediction in Pneumonia Induced ALI/ARDS by Clinical Features and Peptide Patterns of BALF Determined by Mass Spectrometry

PubMed Central

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Background Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. Methodology/Principal Findings A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. Conclusions/Significance MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS. PMID:21991318

Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry.

PubMed

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS.

Personalized risk prediction of postoperative cognitive impairment - rationale for the EU-funded BioCog project.

PubMed

Winterer, G; Androsova, G; Bender, O; Boraschi, D; Borchers, F; Dschietzig, T B; Feinkohl, I; Fletcher, P; Gallinat, J; Hadzidiakos, D; Haynes, J D; Heppner, F; Hetzer, S; Hendrikse, J; Ittermann, B; Kant, I M J; Kraft, A; Krannich, A; Krause, R; Kühn, S; Lachmann, G; van Montfort, S J T; Müller, A; Nürnberg, P; Ofosu, K; Pietsch, M; Pischon, T; Preller, J; Renzulli, E; Scheurer, K; Schneider, R; Slooter, A J C; Spies, C; Stamatakis, E; Volk, H D; Weber, S; Wolf, A; Yürek, F; Zacharias, N

2018-04-01

Postoperative cognitive impairment is among the most common medical complications associated with surgical interventions - particularly in elderly patients. In our aging society, it is an urgent medical need to determine preoperative individual risk prediction to allow more accurate cost-benefit decisions prior to elective surgeries. So far, risk prediction is mainly based on clinical parameters. However, these parameters only give a rough estimate of the individual risk. At present, there are no molecular or neuroimaging biomarkers available to improve risk prediction and little is known about the etiology and pathophysiology of this clinical condition. In this short review, we summarize the current state of knowledge and briefly present the recently started BioCog project (Biomarker Development for Postoperative Cognitive Impairment in the Elderly), which is funded by the European Union. It is the goal of this research and development (R&D) project, which involves academic and industry partners throughout Europe, to deliver a multivariate algorithm based on clinical assessments as well as molecular and neuroimaging biomarkers to overcome the currently unsatisfying situation. Copyright © 2017. Published by Elsevier Masson SAS.

Data Assimilation and Propagation of Uncertainty in Multiscale Cardiovascular Simulation

NASA Astrophysics Data System (ADS)

Schiavazzi, Daniele; Marsden, Alison

2015-11-01

Cardiovascular modeling is the application of computational tools to predict hemodynamics. State-of-the-art techniques couple a 3D incompressible Navier-Stokes solver with a boundary circulation model and can predict local and peripheral hemodynamics, analyze the post-operative performance of surgical designs and complement clinical data collection minimizing invasive and risky measurement practices. The ability of these tools to make useful predictions is directly related to their accuracy in representing measured physiologies. Tuning of model parameters is therefore a topic of paramount importance and should include clinical data uncertainty, revealing how this uncertainty will affect the predictions. We propose a fully Bayesian, multi-level approach to data assimilation of uncertain clinical data in multiscale circulation models. To reduce the computational cost, we use a stable, condensed approximation of the 3D model build by linear sparse regression of the pressure/flow rate relationship at the outlets. Finally, we consider the problem of non-invasively propagating the uncertainty in model parameters to the resulting hemodynamics and compare Monte Carlo simulation with Stochastic Collocation approaches based on Polynomial or Multi-resolution Chaos expansions.

Furosemide/Fludrocortisone Test and Clinical Parameters to Diagnose Incomplete Distal Renal Tubular Acidosis in Kidney Stone Formers.

PubMed

Dhayat, Nasser A; Gradwell, Michael W; Pathare, Ganesh; Anderegg, Manuel; Schneider, Lisa; Luethi, David; Mattmann, Cedric; Moe, Orson W; Vogt, Bruno; Fuster, Daniel G

2017-09-07

Incomplete distal renal tubular acidosis is a well known cause of calcareous nephrolithiasis but the prevalence is unknown, mostly due to lack of accepted diagnostic tests and criteria. The ammonium chloride test is considered as gold standard for the diagnosis of incomplete distal renal tubular acidosis, but the furosemide/fludrocortisone test was recently proposed as an alternative. Because of the lack of rigorous comparative studies, the validity of the furosemide/fludrocortisone test in stone formers remains unknown. In addition, the performance of conventional, nonprovocative parameters in predicting incomplete distal renal tubular acidosis has not been studied. We conducted a prospective study in an unselected cohort of 170 stone formers that underwent sequential ammonium chloride and furosemide/fludrocortisone testing. Using the ammonium chloride test as gold standard, the prevalence of incomplete distal renal tubular acidosis was 8%. Sensitivity and specificity of the furosemide/fludrocortisone test were 77% and 85%, respectively, yielding a positive predictive value of 30% and a negative predictive value of 98%. Testing of several nonprovocative clinical parameters in the prediction of incomplete distal renal tubular acidosis revealed fasting morning urinary pH and plasma potassium as the most discriminative parameters. The combination of a fasting morning urinary threshold pH <5.3 with a plasma potassium threshold >3.8 mEq/L yielded a negative predictive value of 98% with a sensitivity of 85% and a specificity of 77% for the diagnosis of incomplete distal renal tubular acidosis. The furosemide/fludrocortisone test can be used for incomplete distal renal tubular acidosis screening in stone formers, but an abnormal furosemide/fludrocortisone test result needs confirmation by ammonium chloride testing. Our data furthermore indicate that incomplete distal renal tubular acidosis can reliably be excluded in stone formers by use of nonprovocative clinical parameters. Copyright © 2017 by the American Society of Nephrology.

Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests.

PubMed

Hu, Chen; Steingrimsson, Jon Arni

2018-01-01

A crucial component of making individualized treatment decisions is to accurately predict each patient's disease risk. In clinical oncology, disease risks are often measured through time-to-event data, such as overall survival and progression/recurrence-free survival, and are often subject to censoring. Risk prediction models based on recursive partitioning methods are becoming increasingly popular largely due to their ability to handle nonlinear relationships, higher-order interactions, and/or high-dimensional covariates. The most popular recursive partitioning methods are versions of the Classification and Regression Tree (CART) algorithm, which builds a simple interpretable tree structured model. With the aim of increasing prediction accuracy, the random forest algorithm averages multiple CART trees, creating a flexible risk prediction model. Risk prediction models used in clinical oncology commonly use both traditional demographic and tumor pathological factors as well as high-dimensional genetic markers and treatment parameters from multimodality treatments. In this article, we describe the most commonly used extensions of the CART and random forest algorithms to right-censored outcomes. We focus on how they differ from the methods for noncensored outcomes, and how the different splitting rules and methods for cost-complexity pruning impact these algorithms. We demonstrate these algorithms by analyzing a randomized Phase III clinical trial of breast cancer. We also conduct Monte Carlo simulations to compare the prediction accuracy of survival forests with more commonly used regression models under various scenarios. These simulation studies aim to evaluate how sensitive the prediction accuracy is to the underlying model specifications, the choice of tuning parameters, and the degrees of missing covariates.

Evaluation of dose response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy

NASA Astrophysics Data System (ADS)

Tsougos, Ioannis; Mavroidis, Panayiotis; Rajala, Juha; Theodorou, Kyriaki; Järvenpää, Ritva; Pitkänen, Maunu A.; Holli, Kaija; Ojala, Antti T.; Lind, Bengt K.; Hyödynmaa, Simo; Kappas, Constantin

2005-08-01

The purpose of this work is to evaluate the predictive strength of the relative seriality, parallel and LKB normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis, in a large group of patients following breast cancer radiotherapy, and furthermore, to illustrate statistical methods for examining whether certain published radiobiological parameters are compatible with a clinical treatment methodology and patient group characteristics. The study is based on 150 consecutive patients who received radiation therapy for breast cancer. For each patient, the 3D dose distribution delivered to lung and the clinical treatment outcome were available. Clinical symptoms and radiological findings, along with a patient questionnaire, were used to assess the manifestation of radiation-induced complications. Using this material, different methods of estimating the likelihood of radiation effects were evaluated. This was attempted by analysing patient data based on their full dose distributions and associating the calculated complication rates with the clinical follow-up records. Additionally, the need for an update of the criteria that are being used in the current clinical practice was also examined. The patient material was selected without any conscious bias regarding the radiotherapy treatment technique used. The treatment data of each patient were applied to the relative seriality, LKB and parallel NTCP models, using published parameter sets. Of the 150 patients, 15 experienced radiation-induced pneumonitis (grade 2) according to the radiation pneumonitis scoring criteria used. Of the NTCP models examined, the relative seriality model was able to predict the incidence of radiation pneumonitis with acceptable accuracy, although radiation pneumonitis was developed by only a few patients. In the case of modern breast radiotherapy, radiobiological modelling appears to be very sensitive to model and parameter selection giving clinically acceptable results in certain cases selectively (relative seriality model with Seppenwoolde et al (2003 Int. J. Radiat. Oncol. Biol. Phys. 55 724-35) and Gagliardi et al (2000 Int. J. Radiat. Oncol. Biol. Phys. 46 373-81) parameter sets). The use of published parameters should be considered as safe only after their examination using local clinical data. The variation of inter-patient radiosensitivity seems to play a significant role in the prediction of such low incidence rate complications. Scoring grades were combined to give stronger evidence of radiation pneumonitis since their differences could not be strictly associated with dose. This obviously reveals a weakness of the scoring related to this endpoint, and implies that the probability of radiation pneumonitis induction may be too low to be statistically analysed with high accuracy, at least with the latest advances of dose delivery in breast radiotherapy.

Ratio of urine and blood urea nitrogen concentration predicts the response of tolvaptan in congestive heart failure.

PubMed

Shimizu, Keisuke; Doi, Kent; Imamura, Teruhiko; Noiri, Eisei; Yahagi, Naoki; Nangaku, Masaomi; Kinugawa, Koichiro

2015-06-01

This study was conducted to evaluate the performance of the ratio of urine and blood urea nitrogen concentration (UUN/BUN) as a new predictive factor for the response of an arginine vasopressin receptor 2 antagonist tolvaptan (TLV) in decompensated heart failure patients. This study enrolled 70 decompensated heart failure patients who were administered TLV at University of Tokyo Hospital. We collected the data of clinical parameters including UUN/BUN before administering TLV. Two different outcomes were defined as follows: having over 300 mL increase in urine volume on the first day (immediate urine output response) and having any decrease in body weight within one week after starting TLV treatment (subsequent clinical response). Among the 70 enrolled patients, 37 patients (52.9%) showed immediate urine output response; 51 patients (72.9%) showed a subsequent clinical response of body weight decrease. Receiver operating characteristics (ROC) analysis showed good prediction by UUN/BUN for the immediate response (AUC-ROC 0.86 [0.75-0.93]) and a significantly better prediction by UUN/BUN for the subsequent clinical response compared with urinary osmolality (AUC-ROC 0.78 [0.63-0.88] vs. 0.68 [0.52-0.80], P < 0.05). We demonstrated that a clinical parameter of UUN/BUN can predict the response of TLV even when measured before TLV administration. UUN/BUN might enable identification of good responders for this new drug. © 2015 Asian Pacific Society of Nephrology.

Model-Based Approach to Predict Adherence to Protocol During Antiobesity Trials.

PubMed

Sharma, Vishnu D; Combes, François P; Vakilynejad, Majid; Lahu, Gezim; Lesko, Lawrence J; Trame, Mirjam N

2018-02-01

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave ® trials were included in this analysis. An indirect-response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose- and time-dependent pharmacodynamic (DTPD) model. Additionally, a population-pharmacokinetic parameter- and data (PPPD)-driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave ® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD-MM and PPPD-MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body-weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model-driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic-driven approach. © 2017, The Authors. The Journal of Clinical Pharmacology published by Wiley Periodicals, Inc. on behalf of American College of Clinical Pharmacology.

Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

PubMed

Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

2017-09-01

Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Natriuretic Peptide and Clinical Evaluation in the Diagnosis of Heart Failure Hemodynamic Profile: Comparison with Tissue Doppler Echocardiography.

PubMed

Almeida Junior, Gustavo Luiz Gouvêa de; Clausell, Nadine; Garcia, Marcelo Iorio; Esporcatte, Roberto; Rangel, Fernando Oswaldo Dias; Rocha, Ricardo Mourilhe; Beck-da-Silva, Luis; Silva, Fabricio Braga da; Gorgulho, Paula de Castro Carvalho; Xavier, Sergio Salles

2018-03-01

Physical examination and B-type natriuretic peptide (BNP) have been used to estimate hemodynamics and tailor therapy of acute decompensated heart failure (ADHF) patients. However, correlation between these parameters and left ventricular filling pressures is controversial. This study was designed to evaluate the diagnostic accuracy of physical examination, chest radiography (CR) and BNP in estimating left atrial pressure (LAP) as assessed by tissue Doppler echocardiogram. Patients admitted with ADHF were prospectively assessed. Diagnostic characteristics of physical signs of heart failure, CR and BNP in predicting elevation (> 15 mm Hg) of LAP, alone or combined, were calculated. Spearman test was used to analyze the correlation between non-normal distribution variables. The level of significance was 5%. Forty-three patients were included, with mean age of 69.9 ± 11.1years, left ventricular ejection fraction of 25 ± 8.0%, and BNP of 1057 ± 1024.21 pg/mL. Individually, all clinical, CR or BNP parameters had a poor performance in predicting LAP ≥ 15 mm Hg. A clinical score of congestion had the poorest performance [area under the receiver operating characteristic curve (AUC) 0.53], followed by clinical score + CR (AUC 0.60), clinical score + CR + BNP > 400 pg/mL (AUC 0.62), and clinical score + CR + BNP > 1000 pg/mL (AUC 0.66). Physical examination, CR and BNP had a poor performance in predicting a LAP ≥ 15 mm Hg. Using these parameters alone or in combination may lead to inaccurate estimation of hemodynamics.

Prognostic factors, pathophysiology and novel biomarkers in Crimean-Congo hemorrhagic fever.

PubMed

Akinci, Esragul; Bodur, Hurrem; Sunbul, Mustafa; Leblebicioglu, Hakan

2016-08-01

Crimean-Congo hemorrhagic fever (CCHF) is a geographically widespread tick-borne zoonosis. The clinical spectrum of the illness varies from mild infection to severe disease and death. In severe cases, hemorrhagic manifestations develop, with fatality rates of 4-20%, depending on the geographic region and quality of the health care. Although vast majority of the CCHF cases were reported from Turkey, mortality rate is lower than the other regions, which is 5% on average. Prediction of the clinical course of the disease enables appropriate management planning by the physician and prompt transportation, if needed, of the patient to a tertiary care hospital for an intensive therapy. Thus, predicting the outcome of the disease may avert potential mortality. There are numerous studies investigating the prognostic factors of CCHF in the literature. Majority of them were reported from Turkey and included investigations on clinical and biochemical parameters, severity scoring systems and some novel biomarkers. Somnolence, bleeding, thrombocytopenia, elevated liver enzymes and prolonged bleeding times are the most frequently reported prognostic factors to predict the clinical course of the disease earlier. High viral load seems to be the strongest predictor to make a clinical decision about the patient outcome. The severity scoring systems based on clinically important mortality-related parameters are especially useful for clinicians working in the field to predict the course of the disease and to decide which patient should be referred to a tertiary care hospital for intensive care. In the light of the pathophysiological characteristics of CCHF, some new biomarkers of prognosis including cytokines, soluble adhesion molecules, genetic polymorphisms and coagulopathy parameters were also investigated. However most of these tests are not available to clinicians and they were obtained mostly for research purposes. In spite of the various studies about prognostic factors, they have several inherent limitations, including large variability in the results and confusing data that are not useful for clinicians in routine practice. In this paper, the results of diverse studies of the prediction of the prognosis in CCHF based on epidemiological, clinical and laboratory findings of the disease were summarized and suggestions for future studies are provided. Copyright © 2016 Elsevier B.V. All rights reserved.

Photosensitizer fluorescence and singlet oxygen luminescence as dosimetric predictors of topical 5-aminolevulinic acid photodynamic therapy induced clinical erythema.

PubMed

Mallidi, Srivalleesha; Anbil, Sriram; Lee, Seonkyung; Manstein, Dieter; Elrington, Stefan; Kositratna, Garuna; Schoenfeld, David; Pogue, Brian; Davis, Steven J; Hasan, Tayyaba

2014-02-01

The need for patient-specific photodynamic therapy (PDT) in dermatologic and oncologic applications has triggered several studies that explore the utility of surrogate parameters as predictive reporters of treatment outcome. Although photosensitizer (PS) fluorescence, a widely used parameter, can be viewed as emission from several fluorescent states of the PS (e.g., minimally aggregated and monomeric), we suggest that singlet oxygen luminescence (SOL) indicates only the active PS component responsible for the PDT. Here, the ability of discrete PS fluorescence-based metrics (absolute and percent PS photobleaching and PS re-accumulation post-PDT) to predict the clinical phototoxic response (erythema) resulting from 5-aminolevulinic acid PDT was compared with discrete SOL (DSOL)-based metrics (DSOL counts pre-PDT and change in DSOL counts pre/post-PDT) in healthy human skin. Receiver operating characteristic curve (ROC) analyses demonstrated that absolute fluorescence photobleaching metric (AFPM) exhibited the highest area under the curve (AUC) of all tested parameters, including DSOL based metrics. The combination of dose-metrics did not yield better AUC than AFPM alone. Although sophisticated real-time SOL measurements may improve the clinical utility of SOL-based dosimetry, discrete PS fluorescence-based metrics are easy to implement, and our results suggest that AFPM may sufficiently predict the PDT outcomes and identify treatment nonresponders with high specificity in clinical contexts.

Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: Correlation between quantitative parameters and clinical stage.

PubMed

Xu, Xiao-Quan; Ma, Gao; Wang, Yan-Jun; Hu, Hao; Su, Guo-Yi; Shi, Hai-Bin; Wu, Fei-Yun

2017-07-18

To evaluate the correlation between histogram parameters derived from diffusion-kurtosis (DK) imaging and the clinical stage of nasopharyngeal carcinoma (NPC). High T-stage (T3/4) NPC showed significantly higher Kapp-mean (P = 0.018), Kapp-median (P = 0.029) and Kapp-90th (P = 0.003) than low T-stage (T1/2) NPC. High N-stage NPC (N2/3) showed significantly lower Dapp-mean (P = 0.002), Dapp-median (P = 0.002) and Dapp-10th (P < 0.001) than low N-stage NPC (N0/1). High AJCC-stage NPC (III/IV) showed significantly lower Dapp-10th (P = 0.038) than low AJCC-stage NPC (I/II). ROC analyses indicated that Kapp-90th was optimal for predicting high T-stage (AUC, 0.759; sensitivity, 0.842; specificity, 0.607), while Dapp-10th was best for predicting high N- and AJCC-stage (N-stage, AUC, 0.841; sensitivity, 0.875; specificity, 0.807; AJCC-stage, AUC, 0.671; sensitivity, 0.800; specificity, 0.588). DK imaging data of forty-seven consecutive NPC patients were retrospectively analyzed. Apparent diffusion for Gaussian distribution (Dapp) and apparent kurtosis coefficient (Kapp) were generated using diffusion-kurtosis model. Histogram parameters, including mean, median, 10th, 90th percentiles, skewness and kurtosis of Dapp and Kapp were calculated. Patients were divided into low and high T, N and clinical stage based on American Joint Committee on Cancer (AJCC) staging system. Differences of histogram parameters between low and high T, N and AJCC stages were compared using t test. Multiple receiver operating characteristic (ROC) curves were used to determine and compare the value of significant parameters in predicting high T, N and AJCC stage, respectively. DK imaging-derived parameters correlated well with clinical stage of NPC, therefore could serve as an adjunctive imaging technique for evaluating NPC.

Lactate Parameters Predict Clinical Outcomes in Patients with Nonvariceal Upper Gastrointestinal Bleeding.

PubMed

Lee, Seung Hoon; Min, Yang Won; Bae, Joohwan; Lee, Hyuk; Min, Byung Hoon; Lee, Jun Haeng; Rhee, Poong Lyul; Kim, Jae J

2017-11-01

The predictive role of lactate in patients with nonvariceal upper gastrointestinal bleeding (NVUGIB) has been suggested. This study evaluated several lactate parameters in terms of predicting outcomes of bleeding patients and sought to establish a new scoring model by combining lactate parameters and the AIMS65 score. A total of 114 patients with NVUGIB who underwent serum lactate level testing at least twice and endoscopic hemostasis within 24 hours after admission were retrospectively analyzed. The associations between five lactate parameters and clinical outcomes were evaluated and the predictive power of lactate parameter combined AIMS65s (L-AIMS65s) and AIMS56 scoring was compared. The most common cause of bleeding was gastric ulcer (48.2%). Lactate clearance rate (LCR) was associated with 30-day rebleeding (odds ratio [OR], 0.931; 95% confidence interval [CI], 0.872-0.994; P = 0.033). Initial lactate (OR, 1.313; 95% CI, 1.050-1.643; P = 0.017), maximal lactate (OR, 1.277; 95% CI, 1.037-1.573; P = 0.021), and average lactate (OR, 1.535; 95% CI, 1.137-2.072; P = 0.005) levels were associated with 30-day mortality. Initial lactate (OR, 1.213; 95% CI, 1.027-1.432; P = 0.023), maximal lactate (OR, 1.271; 95% CI, 1.074-1.504; P = 0.005), and average lactate (OR, 1.501; 95% CI, 1.150-1.959; P = 0.003) levels were associated with admission over 7 days. Although L-AIMS65s showed the highest area under the curve for prediction of each outcome, differences between L-AIMS65s and AIMS65 did not reach statistical significance. In conclusion, lactate parameters have a prognostic role in patients with NVUGIB. However, they do not increase the predictive power of AIMS65 when combined. © 2017 The Korean Academy of Medical Sciences.

Dosimetric and clinical predictors for radiation-induced esophageal injury.

PubMed

Ahn, Sung-Ja; Kahn, Daniel; Zhou, Sumin; Yu, Xiaoli; Hollis, Donna; Shafman, Timothy D; Marks, Lawrence B

2005-02-01

To evaluate the clinical and three-dimensional dosimetric parameters associated with esophageal injury after radiotherapy (RT) for non-small-cell lung cancer. The records of 254 patients treated for non-small-cell lung cancer between 1992 and 2001 were reviewed. A variety of metrics describing the esophageal dose were extracted. The Radiation Therapy Oncology Group toxicity criteria for grading of esophageal injury were used. The median follow-up time for all patients was 43 months (range, 0.5-120 months). Logistic regression analysis, contingency table analyses, and Fisher's exact tests were used for statistical analysis. Acute toxicity occurred in 199 (78%) of 254 patients. For acute toxicity of Grade 2 or worse, twice-daily RT, age, nodal stage of N2 or worse, and most dosimetric parameters were predictive. Late toxicity occurred in 17 (7%) of 238 patients. The median and maximal time to the onset of late toxicity was 5 and 40 months after RT, respectively. Late toxicity occurred in 2%, 3%, 17%, 26%, and 100% of patients with acute Grade 0, 1, 2, 3, and 4 toxicity, respectively. For late toxicity, the severity of acute toxicity was most predictive. A variety of dosimetric parameters are predictive of acute and late esophageal injury. A strong correlation between the dosimetric parameters prevented a comparison between the predictive abilities of these metrics. The presence of acute injury was the most predictive factor for the development of late injury. Additional studies to define better the predictors of RT-induced esophageal injury are needed.

An integrated biochemical prediction model of all-cause mortality in patients undergoing lower extremity bypass surgery for advanced peripheral artery disease

PubMed Central

Owens, Christopher D.; Kim, Ji Min; Hevelone, Nathanael D.; Gasper, Warren J.; Belkin, Michael; Creager, Mark A.; Conte, Michael S.

2012-01-01

Background Patients with advanced peripheral artery disease (PAD) have a high prevalence of cardiovascular (CV) risk factors and shortened life expectancy. However, CV risk factors poorly predict midterm (<5 years) mortality in this population. This study was designed to test the hypothesis that baseline biochemical parameters would add clinically meaningful predictive information in patients undergoing lower extremity bypass. Methods This was a prospective cohort study of subjects with clinically advanced PAD undergoing lower extremity bypass surgery. The Cox proportional hazard was used to assess the main outcome of all-cause mortality. A clinical model was constructed with known cardiovascular risk factors and the incremental value of the addition of clinical chemistry, lipid, and a panel of 11 inflammatory parameters were investigated using c-statistic, the integrated discrimination improvement (IDI) index and Akaike information criterion (AIC). Results 225 subjects were followed for a median 893 days; IQR 539–1315 days). In this study 50 (22.22%) subjects died during the follow-up period. By life table analysis (expressed as percent surviving ± standard error), survival at 1, 2, 3, 4, and 5 years respectively was 90.5 ± 1.9%, 83.4 ± 2.5%, 77.5 ± 3.1%, 71.0 ± 3.8%, and 65.3 ± 6.5%. Compared with survivors, decedents were older, diabetic, had extant CAD, and were more likely to present with CLI as their indication for bypass surgery, P<.05. After adjustment for the above, clinical chemistry and inflammatory parameters significant for all cause mortality were albumin, HR .43 (95% CI .26–.71); P=.001, estimated glomerular filtration rate (eGFR), HR .98 (95% CI .97–.99), P=.023, high sensitivity C-reactive protein (hsCRP), HR 3.21 (95% CI 1.21–8.55), P=.019, and soluble vascular cell adhesion molecule (sVCAM), HR 1.74 (1.04–2.91), P=.034. Of all inflammatory molecules investigated, hsCRP proved most robust and representative of the integrated inflammatory response. Albumin, eGFR, and hsCRP improved the c-statistic and IDI beyond that of the clinical model and produced a final c-statistic of .82. Conclusions A risk prediction model including traditional risk factors and parameters of inflammation, renal function and nutrition had excellent discriminatory ability in predicting all cause mortality in patients with clinically advanced PAD undergoing bypass surgery. PMID:22554422

An integrated biochemical prediction model of all-cause mortality in patients undergoing lower extremity bypass surgery for advanced peripheral artery disease.

PubMed

Owens, Christopher D; Kim, Ji Min; Hevelone, Nathanael D; Gasper, Warren J; Belkin, Michael; Creager, Mark A; Conte, Michael S

2012-09-01

Patients with advanced peripheral artery disease (PAD) have a high prevalence of cardiovascular (CV) risk factors and shortened life expectancy. However, CV risk factors poorly predict midterm (<5 years) mortality in this population. This study tested the hypothesis that baseline biochemical parameters would add clinically meaningful predictive information in patients undergoing lower extremity bypass operations. This was a prospective cohort study of patients with clinically advanced PAD undergoing lower extremity bypass surgery. The Cox proportional hazard model was used to assess the main outcome of all-cause mortality. A clinical model was constructed with known CV risk factors, and the incremental value of the addition of clinical chemistry, lipid assessment, and a panel of 11 inflammatory parameters was investigated using the C statistic, the integrated discrimination improvement index, and Akaike information criterion. The study monitored 225 patients for a median of 893 days (interquartile range, 539-1315 days). In this study, 50 patients (22.22%) died during the follow-up period. By life-table analysis (expressed as percent surviving ± standard error), survival at 1, 2, 3, 4, and 5 years, respectively, was 90.5% ± 1.9%, 83.4% ± 2.5%, 77.5% ± 3.1%, 71.0% ± 3.8%, and 65.3% ± 6.5%. Compared with survivors, decedents were older, diabetic, had extant coronary artery disease, and were more likely to present with critical limb ischemia as their indication for bypass surgery (P < .05). After adjustment for the above, clinical chemistry and inflammatory parameters significant (hazard ratio [95% confidence interval]) for all-cause mortality were albumin (0.43 [0.26-0.71]; P = .001), estimated glomerular filtration rate (0.98 [0.97-0.99]; P = .023), high-sensitivity C-reactive protein (hsCRP; 3.21 [1.21-8.55]; P = .019), and soluble vascular cell adhesion molecule (1.74 [1.04-2.91]; P = .034). Of the inflammatory molecules investigated, hsCRP proved most robust and representative of the integrated inflammatory response. Albumin, eGFR, and hsCRP improved the C statistic and integrated discrimination improvement index beyond that of the clinical model and produced a final C statistic of 0.82. A risk prediction model including traditional risk factors and parameters of inflammation, renal function, and nutrition had excellent discriminatory ability in predicting all-cause mortality in patients with clinically advanced PAD undergoing bypass surgery. Copyright © 2012 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

Impact of Humidity on In Vitro Human Skin Permeation Experiments for Predicting In Vivo Permeability.

PubMed

Ishida, Masahiro; Takeuchi, Hiroyuki; Endo, Hiromi; Yamaguchi, Jun-Ichi

2015-12-01

In vitro skin permeation studies have been commonly conducted to predict in vivo permeability for the development of transdermal therapeutic systems (TTSs). We clarified the impact of humidity on in vitro human skin permeation of two TTSs having different breathability and then elucidated the predictability of in vivo permeability based on in vitro experimental data. Nicotinell(®) TTS(®) 20 and Frandol(®) tape 40mg were used as model TTSs in this study. The in vitro human skin permeation experiments were conducted under humidity levels similar to those used in clinical trials (approximately 50%) as well as under higher humidity levels (approximately 95%). The skin permeability values of drugs at 95% humidity were higher than those at 50% humidity. The time profiles of the human plasma concentrations after TTS application fitted well with the clinical data when predicted based on the in vitro permeation parameters at 50% humidity. On the other hand, those profiles predicted based on the parameters at 95% humidity were overestimated. The impact of humidity was higher for the more breathable TTS; Frandol(®) tape 40mg. These results show that in vitro human skin permeation experiments should be investigated under realistic clinical humidity levels especially for breathable TTSs. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Analysis of clinically important factors on the performance of advanced hydraulic, microprocessor-controlled exo-prosthetic knee joints based on 899 trial fittings

PubMed Central

Hahn, Andreas; Lang, Michael; Stuckart, Claudia

2016-01-01

Abstract The objective of this work is to evaluate whether clinically important factors may predict an individual's capability to utilize the functional benefits provided by an advanced hydraulic, microprocessor-controlled exo-prosthetic knee component. This retrospective cross-sectional cohort analysis investigated the data of above knee amputees captured during routine trial fittings. Prosthetists rated the performance indicators showing the functional benefits of the advanced maneuvering capabilities of the device. Subjects were asked to rate their perception. Simple and multiple linear and logistic regression was applied. Data from 899 subjects with demographics typical for the population were evaluated. Ability to vary gait speed, perform toileting, and ascend stairs were identified as the most sensitive performance predictors. Prior C-Leg users showed benefits during advanced maneuvering. Variables showed plausible and meaningful effects, however, could not claim predictive power. Mobility grade showed the largest effect but also failed to be predictive. Clinical parameters such as etiology, age, mobility grade, and others analyzed here do not suffice to predict individual potential. Daily walking distance may pose a threshold value and be part of a predictive instrument. Decisions based solely on single parameters such as mobility grade rating or walking distance seem to be questionable. PMID:27828871

Analysis of clinically important factors on the performance of advanced hydraulic, microprocessor-controlled exo-prosthetic knee joints based on 899 trial fittings.

PubMed

Hahn, Andreas; Lang, Michael; Stuckart, Claudia

2016-11-01

The objective of this work is to evaluate whether clinically important factors may predict an individual's capability to utilize the functional benefits provided by an advanced hydraulic, microprocessor-controlled exo-prosthetic knee component.This retrospective cross-sectional cohort analysis investigated the data of above knee amputees captured during routine trial fittings. Prosthetists rated the performance indicators showing the functional benefits of the advanced maneuvering capabilities of the device. Subjects were asked to rate their perception. Simple and multiple linear and logistic regression was applied.Data from 899 subjects with demographics typical for the population were evaluated. Ability to vary gait speed, perform toileting, and ascend stairs were identified as the most sensitive performance predictors. Prior C-Leg users showed benefits during advanced maneuvering. Variables showed plausible and meaningful effects, however, could not claim predictive power. Mobility grade showed the largest effect but also failed to be predictive.Clinical parameters such as etiology, age, mobility grade, and others analyzed here do not suffice to predict individual potential. Daily walking distance may pose a threshold value and be part of a predictive instrument. Decisions based solely on single parameters such as mobility grade rating or walking distance seem to be questionable.

Classification of hepatocellular carcinoma stages from free-text clinical and radiology reports

PubMed Central

Yim, Wen-wai; Kwan, Sharon W; Johnson, Guy; Yetisgen, Meliha

2017-01-01

Cancer stage information is important for clinical research. However, they are not always explicitly noted in electronic medical records. In this paper, we present our work on automatic classification of hepatocellular carcinoma (HCC) stages from free-text clinical and radiology notes. To accomplish this, we defined 11 stage parameters used in the three HCC staging systems, American Joint Committee on Cancer (AJCC), Barcelona Clinic Liver Cancer (BCLC), and Cancer of the Liver Italian Program (CLIP). After aggregating stage parameters to the patient-level, the final stage classifications were achieved using an expert-created decision logic. Each stage parameter relevant for staging was extracted using several classification methods, e.g. sentence classification and automatic information structuring, to identify and normalize text as cancer stage parameter values. Stage parameter extraction for the test set performed at 0.81 F1. Cancer stage prediction for AJCC, BCLC, and CLIP stage classifications were 0.55, 0.50, and 0.43 F1.

Value of quantitative MRI parameters in predicting and evaluating clinical outcome in conservatively treated patients with chronic midportion Achilles tendinopathy: A prospective study.

PubMed

Tsehaie, J; Poot, D H J; Oei, E H G; Verhaar, J A N; de Vos, R J

2017-07-01

To evaluate whether baseline MRI parameters provide prognostic value for clinical outcome, and to study correlation between MRI parameters and clinical outcome. Observational prospective cohort study. Patients with chronic midportion Achilles tendinopathy were included and performed a 16-week eccentric calf-muscle exercise program. Outcome measurements were the validated Victorian Institute of Sports Assessment-Achilles (VISA-A) questionnaire and MRI parameters at baseline and after 24 weeks. The following MRI parameters were assessed: tendon volume (Volume), tendon maximum cross-sectional area (CSA), tendon maximum anterior-posterior diameter (AP), and signal intensity (SI). Intra-class correlation coefficients (ICCs) and minimum detectable changes (MDCs) for each parameter were established in a reliability analysis. Twenty-five patients were included and complete follow-up was achieved in 20 patients. The average VISA-A scores increased significantly with 12.3 points (27.6%). The reliability was fair-good for all MRI-parameters with ICCs>0.50. Average tendon volume and CSA decreased significantly with 0.28cm 3 (5.2%) and 4.52mm 2 (4.6%) respectively. Other MRI parameters did not change significantly. None of the baseline MRI parameters were univariately associated with VISA-A change after 24 weeks. MRI SI increase over 24 weeks was positively correlated with the VISA-A score improvement (B=0.7, R 2 =0.490, p=0.02). Tendon volume and CSA decreased significantly after 24 weeks of conservative treatment. As these differences were within the MDC limits, they could be a result of a measurement error. Furthermore, MRI parameters at baseline did not predict the change in symptoms, and therefore have no added value in providing a prognosis in daily clinical practice. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for Advanced Risk Modeling of Prostate Cancer-Patient-tailored Risk Stratification Can Reduce Unnecessary Biopsies.

PubMed

Radtke, Jan Philipp; Wiesenfarth, Manuel; Kesch, Claudia; Freitag, Martin T; Alt, Celine D; Celik, Kamil; Distler, Florian; Roth, Wilfried; Wieczorek, Kathrin; Stock, Christian; Duensing, Stefan; Roethke, Matthias C; Teber, Dogu; Schlemmer, Heinz-Peter; Hohenfellner, Markus; Bonekamp, David; Hadaschik, Boris A

2017-12-01

Multiparametric magnetic resonance imaging (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC; Gleason score≥3+4) detection. Decision making based on European Randomised Study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome prostate-specific antigen (PSA) limitations. We added pre-biopsy mpMRI to ERSPC-RC parameters and developed risk models (RMs) to predict individual sPC risk for biopsy-naïve men and men after previous biopsy. We retrospectively analyzed clinical parameters of 1159 men who underwent mpMRI prior to MRI/transrectal ultrasound fusion biopsy between 2012 and 2015. Multivariate regression analyses were used to determine significant sPC predictors for RM development. The prediction performance was compared with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus PI-RADSv1.0 using receiver-operating characteristics (ROCs). Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated based on resampling methods. PSA, prostate volume, digital-rectal examination, and PI-RADS were significant sPC predictors and included in the RMs together with age. The ROC area under the curve of the RM for biopsy-naïve men was comparable with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and PI-RADS (0.76). For postbiopsy men, the novel RM's discrimination (0.81) was higher, compared with PI-RADS (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4 plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies. Limitations include a monocentric design and a lack of PI-RADSv2.0. The novel RMs, incorporating clinical parameters and PI-RADS, performed significantly better compared with RMs without PI-RADS and provided measurable benefit in making the decision to biopsy men at a suspicion of PC. For biopsy-naïve patients, both our RM and ERSPC-RC3 plus PI-RADSv1.0 exceeded the prediction performance compared with clinical parameters alone. Combined risk models including clinical and imaging parameters predict clinically relevant prostate cancer significantly better than clinical risk calculators and multiparametric magnetic resonance imaging alone. The risk models demonstrate a benefit in making a decision about which patient needs a biopsy and concurrently help avoid unnecessary biopsies. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

The observed correlation between in vivo clinical pharmacokinetic parameters and in vitro potency of VEGFR-2 inhibitors. Can this be used as a prospective guide for the development of novel compounds?

PubMed

Benjamin, B; Sahu, M; Bhatnagar, U; Abhyankar, D; Srinivas, N R

2012-04-01

Literature data on the clinical pharmacokinetics of various VEGFR-2 inhibitors along with in vitro potency data were correlated and a linear relationship was established in spite of limited data set. In this work, a model set comprised of axitinib, recentin, sunitinib, pazopanib, and sorafenib were used. The in vitro potencies of the model set compounds were correlated with the published unbound plasma concentrations (Cmax, Cavg, Ctrough). The established linear regression (r2>0.90) equation was used to predict Cmax, Cavg, Ctrough of the 'prediction set' (motesanib, telatinib, CP547632, vatalanib, vandetanib) using in vitro potency and unbound protein free fraction. Cavg and Ctrough of prediction set were closely matched (0.2-1.8 fold of reported), demonstrating the usefulness of such predictions for tracking the target related modulation and/or efficacy signals within the clinically optimized population average. In case of Cmax where correlation was least anticipated, the predicted values were within 0.1-1.1 fold of those reported. Such predictions of appropriate parameters would provide rough estimates of whether or not therapeutically relevant dose(s) have been administered when clinical investigations of novel agents of this class are being performed. Therefore, it may aid in increasing clinical doses to a desired level if safety of the compound does not compromise such dose increases. In conclusion, the proposed model may prospectively guide the dosing strategies and would greatly aid the development of novel compounds in this class. © Georg Thieme Verlag KG Stuttgart · New York.

Correlation of pretreatment clinical parameters and PSA nadir after high-intensity focused ultrasound (HIFU) for localised prostate cancer.

PubMed

Ganzer, Roman; Bründl, Johannes; Koch, Daniel; Wieland, Wolf F; Burger, Maximilian; Blana, Andreas

2015-01-01

To determine which pretreatment clinical parameters were predictive of a low prostate-specific antigen (PSA) nadir following high-intensity focused ultrasound (HIFU) treatment. Retrospective study of patients with clinically localised prostate cancer undergoing HIFU at a single centre between December 1997 and September 2009. Whole-gland treatment was applied. Patients also included if they had previously undergone transurethral resection of the prostate (TURP). TURP was also conducted simultaneously to HIFU. Biochemical failure based on Phoenix definition (PSA nadir + 2). Univariate and multivariate analysis of pretreatment clinical parameters conducted to assess those factors predictive of a PSA nadir ≤0.2 and >0.2 ng/ml. Mean (SD) follow-up was 6.2 (2.8) years; median (range) was 6.3 (1.1-12.2) years. Kaplan-Meier estimate of biochemical disease-free survival rate at 8 years was 83 and 48 % for patients achieving a PSA nadir of ≤0.2 and >0.2 ng/ml, respectively. Prostate volume and incidental finding of cancer were significant predictors of low PSA nadir (≤0.2 ng/ml). Prostate volume and incidental finding of cancer could be predictors for oncologic success of HIFU based on post-treatment PSA nadir.

The predictive effect of inflammatory markers and lipid accumulation product index on clinical symptoms associated with polycystic ovary syndrome in nonobese adolescents and younger aged women.

PubMed

Tola, Esra Nur; Yalcin, Serenat Eris; Dugan, Nadiye

2017-07-01

The aim of our study is to analyse the inflammatory markers and lipid accumulation product (LAP) index in nonobese adolescents and younger aged women with polycystic ovary syndrome (PCOS) compared with age and body mass index (BMI)-matched healthy controls and to determine whether the investigated parameters are potential markers for the etiopathogenesis of PCOS. We also aim to determine whether these inflammatory markers are predictive for developing some clinical implications, such as cardiovascular disease (CVD) and insulin resistance (IR), associated with PCOS. A total of 34 adolescents and younger aged females with PCOS, and 33 age and BMI-matched healthy controls were recruited for our study. All participants were nonobese (BMI<25). Neopterin (NEO), C-reactive protein (CRP) levels and complete blood parameters were assessed. LAP index and homeostasis model assessment of IR (HOMA-IR) were calculated; anthropometric, clinical and biochemical parameters were also recorded. Serum NEO, CRP levels and LAP index were significantly increased in nonobese adolescents and younger aged females with PCOS compared to healthy controls. We could not found any predictive effect of investigated inflammatory markers and LAP index on CVD risk among PCOS patients after adjustment for abdominal obesity. We also found a positive predictive effect of WBC and a negative predictive effect of lymphocytes on IR in PCOS patients after adjustment for abdominal obesity. We did not find any predictor effect of NEO on IR, but it was a positive predictive marker for an elevated HOMA-IR index. Elevated NEO, CRP levels and LAP index could have potential roles in the etiopathogenesis of PCOS in nonobese adolescents and younger aged females,NEO could be a predictive marker for elevated HOMA-IR index, and WBC and lymphocytes could be predictive for the development of IR among nonobese adolescents and younger aged females with PCOS. Copyright © 2017 Elsevier B.V. All rights reserved.

Nomogram predicting response after chemoradiotherapy in rectal cancer using sequential PETCT imaging: a multicentric prospective study with external validation.

PubMed

van Stiphout, Ruud G P M; Valentini, Vincenzo; Buijsen, Jeroen; Lammering, Guido; Meldolesi, Elisa; van Soest, Johan; Leccisotti, Lucia; Giordano, Alessandro; Gambacorta, Maria A; Dekker, Andre; Lambin, Philippe

2014-11-01

To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential (18)F-FDG PETCT imaging. Prospective data (i.a. THUNDER trial) were used to train (N=112, MAASTRO Clinic) and validate (N=78, Università Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUVmax, SUVmean, metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined. The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUVmean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org. The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

High-throughput cardiac safety evaluation and multi-parameter arrhythmia profiling of cardiomyocytes using microelectrode arrays.

PubMed

Gilchrist, Kristin H; Lewis, Gregory F; Gay, Elaine A; Sellgren, Katelyn L; Grego, Sonia

2015-10-15

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak for field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5minute recordings at multiple time points (0.5, 1, 2 and 4h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. Copyright © 2015 Elsevier Inc. All rights reserved.

The BRICS (Bronchiectasis Radiologically Indexed CT Score): A Multicenter Study Score for Use in Idiopathic and Postinfective Bronchiectasis.

PubMed

Bedi, Pallavi; Chalmers, James D; Goeminne, Pieter C; Mai, Cindy; Saravanamuthu, Pira; Velu, Prasad Palani; Cartlidge, Manjit K; Loebinger, Michael R; Jacob, Joe; Kamal, Faisal; Schembri, Nicola; Aliberti, Stefano; Hill, Uta; Harrison, Mike; Johnson, Christopher; Screaton, Nicholas; Haworth, Charles; Polverino, Eva; Rosales, Edmundo; Torres, Antoni; Benegas, Michael N; Rossi, Adriano G; Patel, Dilip; Hill, Adam T

2018-05-01

The goal of this study was to develop a simplified radiological score that could assess clinical disease severity in bronchiectasis. The Bronchiectasis Radiologically Indexed CT Score (BRICS) was devised based on a multivariable analysis of the Bhalla score and its ability in predicting clinical parameters of severity. The score was then externally validated in six centers in 302 patients. A total of 184 high-resolution CT scans were scored for the validation cohort. In a multiple logistic regression model, disease severity markers significantly associated with the Bhalla score were percent predicted FEV 1 , sputum purulence, and exacerbations requiring hospital admission. Components of the Bhalla score that were significantly associated with the disease severity markers were bronchial dilatation and number of bronchopulmonary segments with emphysema. The BRICS was developed with these two parameters. The receiver operating-characteristic curve values for BRICS in the derivation cohort were 0.79 for percent predicted FEV 1 , 0.71 for sputum purulence, and 0.75 for hospital admissions per year; these values were 0.81, 0.70, and 0.70, respectively, in the validation cohort. Sputum free neutrophil elastase activity was significantly elevated in the group with emphysema on CT imaging. A simplified CT scoring system can be used as an adjunct to clinical parameters to predict disease severity in patients with idiopathic and postinfective bronchiectasis. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Do Urinary Cystine Parameters Predict Clinical Stone Activity?

PubMed

Friedlander, Justin I; Antonelli, Jodi A; Canvasser, Noah E; Morgan, Monica S C; Mollengarden, Daniel; Best, Sara; Pearle, Margaret S

2018-02-01

An accurate urinary predictor of stone recurrence would be clinically advantageous for patients with cystinuria. A proprietary assay (Litholink, Chicago, Illinois) measures cystine capacity as a potentially more reliable estimate of stone forming propensity. The recommended capacity level to prevent stone formation, which is greater than 150 mg/l, has not been directly correlated with clinical stone activity. We investigated the relationship between urinary cystine parameters and clinical stone activity. We prospectively followed 48 patients with cystinuria using 24-hour urine collections and serial imaging, and recorded stone activity. We compared cystine urinary parameters at times of stone activity with those obtained during periods of stone quiescence. We then performed correlation and ROC analysis to evaluate the performance of cystine parameters to predict stone activity. During a median followup of 70.6 months (range 2.2 to 274.6) 85 stone events occurred which could be linked to a recent urine collection. Cystine capacity was significantly greater for quiescent urine than for stone event urine (mean ± SD 48 ± 107 vs -38 ± 163 mg/l, p <0.001). Cystine capacity significantly correlated inversely with stone activity (r = -0.29, p <0.001). Capacity also correlated highly negatively with supersaturation (r = -0.88, p <0.001) and concentration (r = -0.87, p <0.001). Using the suggested cutoff of greater than 150 mg/l had only 8.0% sensitivity to predict stone quiescence. Decreasing the cutoff to 90 mg/l or greater improved sensitivity to 25.2% while maintaining specificity at 90.9%. Our results suggest that the target for capacity should be lower than previously advised. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Clinical-Radiological Parameters Improve the Prediction of the Thrombolysis Time Window by Both MRI Signal Intensities and DWI-FLAIR Mismatch.

PubMed

Madai, Vince Istvan; Wood, Carla N; Galinovic, Ivana; Grittner, Ulrike; Piper, Sophie K; Revankar, Gajanan S; Martin, Steve Z; Zaro-Weber, Olivier; Moeller-Hartmann, Walter; von Samson-Himmelstjerna, Federico C; Heiss, Wolf-Dieter; Ebinger, Martin; Fiebach, Jochen B; Sobesky, Jan

2016-01-01

With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. In a retrospective study, patients from 2 centers with proven stroke with onset <12 h were included. The DWI lesion was segmented and overlaid on ADC and FLAIR images. rSI mean and SD, were calculated as follows: (mean ROI value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models. Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction models. © 2016 S. Karger AG, Basel.

Predicting functional decline and survival in amyotrophic lateral sclerosis.

PubMed

Ong, Mei-Lyn; Tan, Pei Fang; Holbrook, Joanna D

2017-01-01

Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database. In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors. A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class. Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.

Doppler ultrasonography combined with transient elastography improves the non-invasive assessment of fibrosis in patients with chronic liver diseases.

PubMed

Alempijevic, Tamara; Zec, Simon; Nikolic, Vladimir; Veljkovic, Aleksandar; Stojanovic, Zoran; Matovic, Vera; Milosavljevic, Tomica

2017-01-31

Accurate clinical assessment of liver fibrosis is essential and the aim of our study was to compare and combine hemodynamic Doppler ultrasonography, liver stiffness by transient elastography, and non-invasive serum biomarkers with the degree of fibrosis confirmed by liver biopsy, and thereby to determine the value of combining non-invasive method in the prediction significant liver fibrosis. We included 102 patients with chronic liver disease of various etiology. Each patient was evaluated using Doppler ultrasonography measurements of the velocity and flow pattern at portal trunk, hepatic and splenic artery, serum fibrosis biomarkers, and transient elastography. These parameters were then input into a multilayer perceptron artificial neural network with two hidden layers, and used to create models for predicting significant fibrosis. According to METAVIR score, clinically significant fibrosis (≥F2) was detected in 57.8% of patients. A model based only on Doppler parameters (hepatic artery diameter, hepatic artery systolic and diastolic velocity, splenic artery systolic velocity and splenic artery Resistance Index), predicted significant liver fibrosis with a sensitivity and specificity of75.0% and 60.0%. The addition of unrelated non-invasive tests improved the diagnostic accuracy of Doppler examination. The best model for prediction of significant fibrosis was obtained by combining Doppler parameters, non-invasive markers (APRI, ASPRI, and FIB-4) and transient elastography, with a sensitivity and specificity of 88.9% and 100%. Doppler parameters alone predict the presence of ≥F2 fibrosis with fair accuracy. Better prediction rates are achieved by combining Doppler variables with non-invasive markers and liver stiffness by transient elastography.

The VCS parameters: Potential hematological indicators for predicting antituberculosis drug-induced neutropenia.

PubMed

Shen, Tian; Gu, Delin; Zhu, Yihua; Shi, Junwei; Xu, Dongsheng; Cao, Xingjian

2016-08-01

The morphological changes in activated neutrophils associated with antituberculosis drugs can be measured by volume, conductivity, and scatter (VCS) technology on the Coulter LH750 hematology analyzer. We conducted the current study to further validate the clinical usefulness of the neutrophil VCS parameters in predicting drug-induced neutropenia. Peripheral blood samples were collected from 52 patients with drug-induced neutropenia, 309 patients without any abnormal CBC, and 237 healthy controls. The mean neutrophil volume (MNV) with its distribution width (NDW) and the mean neutrophil scatter (MNS) were studied. We observed a significant increase in the MNV and NDW as well as a significant decrease in the MNS in neutropenia patients approximately one week prior to development of neutropenia compared to healthy controls as well as to case controls. In addition, the delta MNV and delta MNS were respectively correlated well with delta absolute neutrophil counts when neutropenia occurred. The ROC curve analyses showed that the MNV、NDW and MNS had larger areas under curves compared to conventional parameters. With a cutoff of 150.15 for the MNV, a sensitivity of 84.4% and specificity of 75.7% were achieved prior to neutropenia. The neutrophil VCS parameters may be clinically useful as potential hematological indicators for predicting antituberculosis drug-induced neutropenia. Copyright © 2016 Elsevier B.V. All rights reserved.

Ultraearly assessed reperfusion status after middle cerebral artery recanalization predicting clinical outcome.

PubMed

Gölitz, P; Muehlen, I; Gerner, S T; Knossalla, F; Doerfler, A

2018-06-01

Mechanical thrombectomy has high evidence in stroke therapy; however, successful recanalization guarantees not a favorable clinical outcome. We aimed to quantitatively assess the reperfusion status ultraearly after successful middle cerebral artery (MCA) recanalization to identify flow parameters that potentially allow predicting clinical outcome. Sixty-seven stroke patients with acute MCA occlusion, undergoing recanalization, were enrolled. Using parametric color coding, a post-processing algorithm, pre-, and post-interventional digital subtraction angiography series were evaluated concerning the following parameters: pre- and post-procedural cortical relative time to peak (rTTP) of MCA territory, reperfusion time, and index. Functional long-term outcome was assessed by the 90-day modified Rankin Scale score (mRS; favorable: 0-2). Cortical rTTP was significantly shorter before (3.33 ± 1.36 seconds; P = .03) and after intervention (2.05 ± 0.70 seconds; P = .003) in patients with favorable clinical outcome. Additionally, age (P = .005) and initial National Institutes of Health Stroke Scale score (P = .02) were significantly different between the patients, whereas reperfusion index and time as well as initially estimated infarct size were not. In multivariate analysis, only post-procedural rTTP (P = .005) was independently associated with favorable clinical outcome. 2.29 seconds for post-procedural rTTP might be a threshold to predict favorable clinical outcome. Ultraearly quantitative assessment of reperfusion status after successful MCA recanalization reveals post-procedural cortical rTTP as possible independent prognostic value in predicting favorable clinical outcome, even determining a threshold value might be possible. In consequence, focusing stroke therapy on microcirculatory patency could be valuable to improve outcome. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A focused ultrasound treatment system for moving targets (part I): generic system design and in-silico first-stage evaluation.

PubMed

Schwenke, Michael; Strehlow, Jan; Demedts, Daniel; Haase, Sabrina; Barrios Romero, Diego; Rothlübbers, Sven; von Dresky, Caroline; Zidowitz, Stephan; Georgii, Joachim; Mihcin, Senay; Bezzi, Mario; Tanner, Christine; Sat, Giora; Levy, Yoav; Jenne, Jürgen; Günther, Matthias; Melzer, Andreas; Preusser, Tobias

2017-01-01

Focused ultrasound (FUS) is entering clinical routine as a treatment option. Currently, no clinically available FUS treatment system features automated respiratory motion compensation. The required quality standards make developing such a system challenging. A novel FUS treatment system with motion compensation is described, developed with the goal of clinical use. The system comprises a clinically available MR device and FUS transducer system. The controller is very generic and could use any suitable MR or FUS device. MR image sequences (echo planar imaging) are acquired for both motion observation and thermometry. Based on anatomical feature tracking, motion predictions are estimated to compensate for processing delays. FUS control parameters are computed repeatedly and sent to the hardware to steer the focus to the (estimated) target position. All involved calculations produce individually known errors, yet their impact on therapy outcome is unclear. This is solved by defining an intuitive quality measure that compares the achieved temperature to the static scenario, resulting in an overall efficiency with respect to temperature rise. To allow for extensive testing of the system over wide ranges of parameters and algorithmic choices, we replace the actual MR and FUS devices by a virtual system. It emulates the hardware and, using numerical simulations of FUS during motion, predicts the local temperature rise in the tissue resulting from the controls it receives. With a clinically available monitoring image rate of 6.67 Hz and 20 FUS control updates per second, normal respiratory motion is estimated to be compensable with an estimated efficiency of 80%. This reduces to about 70% for motion scaled by 1.5. Extensive testing (6347 simulated sonications) over wide ranges of parameters shows that the main source of error is the temporal motion prediction. A history-based motion prediction method performs better than a simple linear extrapolator. The estimated efficiency of the new treatment system is already suited for clinical applications. The simulation-based in-silico testing as a first-stage validation reduces the efforts of real-world testing. Due to the extensible modular design, the described approach might lead to faster translations from research to clinical practice.

The reliability of the clinical examination in predicting hemodynamic status in acute febrile illness in a tropical, resource-limited setting.

PubMed

Moek, Felix; Poe, Poe; Charunwatthana, Prakaykaew; Pan-Ngum, Wirichada; Wattanagoon, Yupaporn; Chierakul, Wirongrong

2018-05-19

The clinical examination alone is widely considered unreliable when assessing fluid responsiveness in critically ill patients. Little evidence exists on the performance of the clinical examination to predict other hemodynamic derangements or more complex hemodynamic states. Patients with acute febrile illness were assessed on admission, both clinically and per non-invasive hemodynamic measurement. Correlations between clinical signs and hemodynamics patterns were analyzed, and the predictive capacity of the clinical signs was examined. Seventy-one patients were included; the most common diagnoses were bacterial sepsis, scrub typhus and dengue infection. Correlations between clinical signs and hemodynamic parameters were only statistically significant for Cardiac Index (r=0.75, p-value <0.01), Systemic Vascular Resistance Index (r=0.79, p-value <0.01) and flow time corrected (r=0.44, p-value 0.03). When assessing the predictive accuracy of clinical signs, the model identified only 62% of hemodynamic states correctly, even less if there was more than one hemodynamic abnormality. The clinical examination is not reliable to assess a patient's hemodynamic status in acute febrile illness. Fluid responsiveness, cardiodepression and more complex hemodynamic states are particularly easily missed.

Physiologically based pharmacokinetic model for 6-mercpatopurine: exploring the role of genetic polymorphism in TPMT enzyme activity

PubMed Central

Ogungbenro, Kayode; Aarons, Leon

2015-01-01

Aims To extend the physiologically based pharmacokinetic (PBPK) model developed for 6-mercaptopurine to account for intracellular metabolism and to explore the role of genetic polymorphism in the TPMT enzyme on the pharmacokinetics of 6-mercaptopurine. Methods The developed PBPK model was extended for 6-mercaptopurine to account for intracellular metabolism and genetic polymorphism in TPMT activity. System and drug specific parameters were obtained from the literature or estimated using plasma or intracellular red blood cell concentrations of 6-mercaptopurine and its metabolites. Age-dependent changes in parameters were implemented for scaling, and variability was also introduced for simulation. The model was validated using published data. Results The model was extended successfully. Parameter estimation and model predictions were satisfactory. Prediction of intracellular red blood cell concentrations of 6-thioguanine nucleotide for different TPMT phenotypes (in a clinical study that compared conventional and individualized dosing) showed results that were consistent with observed values and reported incidence of haematopoietic toxicity. Following conventional dosing, the predicted mean concentrations for homozygous and heterozygous variants, respectively, were about 10 times and two times the levels for wild-type. However, following individualized dosing, the mean concentration was around the same level for the three phenotypes despite different doses. Conclusions The developed PBPK model has been extended for 6-mercaptopurine and can be used to predict plasma 6-mercaptopurine and tissue concentration of 6-mercaptopurine, 6-thioguanine nucleotide and 6-methylmercaptopurine ribonucleotide in adults and children. Predictions of reported data from clinical studies showed satisfactory results. The model may help to improve 6-mercaptopurine dosing, achieve better clinical outcome and reduce toxicity. PMID:25614061

Validation and uncertainty analysis of a pre-treatment 2D dose prediction model

NASA Astrophysics Data System (ADS)

Baeza, Jose A.; Wolfs, Cecile J. A.; Nijsten, Sebastiaan M. J. J. G.; Verhaegen, Frank

2018-02-01

Independent verification of complex treatment delivery with megavolt photon beam radiotherapy (RT) has been effectively used to detect and prevent errors. This work presents the validation and uncertainty analysis of a model that predicts 2D portal dose images (PDIs) without a patient or phantom in the beam. The prediction model is based on an exponential point dose model with separable primary and secondary photon fluence components. The model includes a scatter kernel, off-axis ratio map, transmission values and penumbra kernels for beam-delimiting components. These parameters were derived through a model fitting procedure supplied with point dose and dose profile measurements of radiation fields. The model was validated against a treatment planning system (TPS; Eclipse) and radiochromic film measurements for complex clinical scenarios, including volumetric modulated arc therapy (VMAT). Confidence limits on fitted model parameters were calculated based on simulated measurements. A sensitivity analysis was performed to evaluate the effect of the parameter uncertainties on the model output. For the maximum uncertainty, the maximum deviating measurement sets were propagated through the fitting procedure and the model. The overall uncertainty was assessed using all simulated measurements. The validation of the prediction model against the TPS and the film showed a good agreement, with on average 90.8% and 90.5% of pixels passing a (2%,2 mm) global gamma analysis respectively, with a low dose threshold of 10%. The maximum and overall uncertainty of the model is dependent on the type of clinical plan used as input. The results can be used to study the robustness of the model. A model for predicting accurate 2D pre-treatment PDIs in complex RT scenarios can be used clinically and its uncertainties can be taken into account.

Can We Predict Patient Wait Time?

PubMed

Pianykh, Oleg S; Rosenthal, Daniel I

2015-10-01

The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors. To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments. All parameters were chosen as derivable from a typical Hospital Information System dataset. The parameters were fed into several time-predicting models, and the best parameter subsets, discovered through exhaustive model search, were applied to a large sample of actual patient wait data. We were able to discover the most efficient wait-time prediction factors and models, such as the line-size models introduced in this work. Moreover, these models proved to be equally accurate and computationally efficient. Finally, the selected models were implemented in our patient waiting areas, displaying predicted wait times on the monitors located at the front desks. The limitations of these models are also discussed. Optimal regression models based on wait-line sizes can provide accurate and efficient predictions for patient wait time. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Validation of the Singapore nomogram for outcome prediction in breast phyllodes tumours: an Australian cohort.

PubMed

Chng, Tze Wei; Lee, Jonathan Y H; Lee, C Soon; Li, HuiHua; Tan, Min-Han; Tan, Puay Hoon

2016-12-01

To validate the utility of the Singapore nomogram for outcome prediction in breast phyllodes tumours. Histological parameters, surgical margin status and clinical follow-up data of 34 women diagnosed with phyllodes tumours were analysed. Biostatistics modelling was performed, and the concordance between predicted and observed survivals was calculated. Women with a high nomogram score had an increased risk of developing relapse, which was predicted using the parameters defined by the Singapore nomogram. The Singapore nomogram is useful in predicting outcome in breast phyllodes tumours when applied to an Australian cohort of 34 women. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Assessing and predicting drug-induced anticholinergic risks: an integrated computational approach.

PubMed

Xu, Dong; Anderson, Heather D; Tao, Aoxiang; Hannah, Katia L; Linnebur, Sunny A; Valuck, Robert J; Culbertson, Vaughn L

2017-11-01

Anticholinergic (AC) adverse drug events (ADEs) are caused by inhibition of muscarinic receptors as a result of designated or off-target drug-receptor interactions. In practice, AC toxicity is assessed primarily based on clinician experience. The goal of this study was to evaluate a novel concept of integrating big pharmacological and healthcare data to assess clinical AC toxicity risks. AC toxicity scores (ATSs) were computed using drug-receptor inhibitions identified through pharmacological data screening. A longitudinal retrospective cohort study using medical claims data was performed to quantify AC clinical risks. ATS was compared with two previously reported toxicity measures. A quantitative structure-activity relationship (QSAR) model was established for rapid assessment and prediction of AC clinical risks. A total of 25 common medications, and 575,228 exposed and unexposed patients were analyzed. Our data indicated that ATS is more consistent with the trend of AC outcomes than other toxicity methods. Incorporating drug pharmacokinetic parameters to ATS yielded a QSAR model with excellent correlation to AC incident rate ( R 2 = 0.83) and predictive performance (cross validation Q 2 = 0.64). Good correlation and predictive performance ( R 2 = 0.68/ Q 2 = 0.29) were also obtained for an M2 receptor-specific QSAR model and tachycardia, an M2 receptor-specific ADE. Albeit using a small medication sample size, our pilot data demonstrated the potential and feasibility of a new computational AC toxicity scoring approach driven by underlying pharmacology and big data analytics. Follow-up work is under way to further develop the ATS scoring approach and clinical toxicity predictive model using a large number of medications and clinical parameters.

Predicting pneumococcal community-acquired pneumonia in the emergency department: evaluation of clinical parameters.

PubMed

Huijts, S M; Boersma, W G; Grobbee, D E; Gruber, W C; Jansen, K U; Kluytmans, J A J W; Kuipers, B A F; Palmen, F; Pride, M W; Webber, C; Bonten, M J M

2014-12-01

The aim of this study was to quantify the value of clinical predictors available in the emergency department (ED) in predicting Streptococcus pneumoniae as the cause of community-acquired pneumonia (CAP). A prospective, observational, cohort study of patients with CAP presenting in the ED was performed. Pneumococcal aetiology of CAP was based on either bacteraemia, or S. pneumoniae being cultured from sputum, or urinary immunochromatographic assay positivity, or positivity of a novel serotype-specific urinary antigen detection test. Multivariate logistic regression was used to identify independent predictors and various cut-off values of probability scores were used to evaluate the usefulness of the model. Three hundred and twenty-eight (31.0%) of 1057 patients with CAP had pneumococcal CAP. Nine independent predictors for pneumococcal pneumonia were identified, but the clinical utility of this prediction model was disappointing, because of low positive predictive values or a small yield. Clinical criteria have insufficient diagnostic capacity to predict pneumococcal CAP. Rapid antigen detection tests are needed to diagnose S. pneumoniae at the time of hospital admission. © 2014 The Authors Clinical Microbiology and Infection © 2014 European Society of Clinical Microbiology and Infectious Diseases.

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

PubMed

Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

2013-01-01

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

Introduction: the goals of antimicrobial therapy.

PubMed

Song, Jae-Hoon

2003-03-01

Antimicrobial agents are generally evaluated in preclinical studies assessing in vitro activity, animal models demonstrating in vivo bacteriologic efficacy, and clinical trials primarily investigating safety and clinical efficacy. However, large sample sizes are required to detect any differences in outcomes between antimicrobials in clinical trials, and, generally, studies are powered to show only clinical equivalence. In addition, diagnosis is often based on clinical symptoms, rather than microbiological evidence of bacterial infection, and the patients most likely to have resistant pathogens are often excluded. Clinical efficacy can be achieved in some bacterial infections in which antimicrobials are suboptimal or even not prescribed. However, bacterial eradication maximizes clinical efficacy and may also reduce the development and spread of resistant organisms. The goal of antimicrobial therapy is, therefore, to eradicate bacteria at the site of infection. Bacterial eradication is not usually assessed as a primary endpoint within the limits of currently recommended clinical trial design. However, pharmacokinetic (PK) (serum concentration profiles, penetration to site of infection) and pharmacodynamic (PD) (susceptibility, concentration- versus time-dependent killing, post-antimicrobial effects) criteria can be used to predict bacteriologic efficacy. PK/PD predictions should be confirmed during all phases of antimicrobial development and throughout clinical use in response to changing patterns of resistance. A clear rationale for dose recommendations can be determined preclinically based on PK/PD parameters, and correlated with efficacy, safety and resistance endpoints in clinical trials. The duration of treatment and dose should be the shortest that will reliably eradicate the pathogen(s), and that is safe and well tolerated. Currently available agents vary significantly in their ability to achieve PK/PD parameters necessary for bacteriologic eradication. Recommendations for appropriate antimicrobial therapy should be based on PK/PD parameters, with the aim of achieving the maximum potential for eradication of both existing and emerging resistant pathogens.

Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function.

PubMed

Perco, Paul; Heinzel, Andreas; Leierer, Johannes; Schneeberger, Stefan; Bösmüller, Claudia; Oberhuber, Rupert; Wagner, Silvia; Engler, Franziska; Mayer, Gert

2018-05-03

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

Prediction of treatment refractoriness in ulcerative colitis and Crohn's disease--do we have reliable markers?

PubMed

Gelbmann, C M

2000-05-01

Treatment refractoriness is a severe problem in the management of patients with ulcerative colitis and Crohn's disease. Despite some promising new therapeutic approaches, corticosteroids are still the preferential primary treatment for moderate to severe Crohn's disease and of severe ulcerative colitis. However, clinical response to corticosteroids varies, and many patients are resistant to such treatment. Since corticosteroids have frequent and even severe side effects, and toxicity increases with chronic steroid intake, factors predictive of response to such treatment would be very helpful for decisions on further management of these patients. At least in severe attacks of ulcerative colitis, the consensus seems to be that a high frequency of bowel movements as well as a high C-reactive protein and low serum albumin recorded after a few days of intensive medical treatment are important signs for early prediction of treatment failure in the majority of the patients. In Crohn's disease thus far, data on predictive factors are conflicting. No reliable marker with sufficient predictive value for treatment refractoriness could be identified. This might be due to the tremendous heterogeneity of Crohn's disease with many clinical phenotypes, which requires subgroup analysis with sufficient numbers of patients. Corticosteroids as well as other immunomodulating and immunosuppressive medications interfere with the immune system, which plays a central role in the mediation of intestinal inflammation. Treatment refractoriness might have its origin in specific immunological peculiarities eventually reflected in abnormal immunological, biochemical, and clinical parameters. Further exploration of those parameters to predict treatment refractoriness in patients with ulcerative colitis or Crohn's disease is of great clinical importance for safe and efficient management of patients.

What is the best ST-segment recovery parameter to predict clinical outcome and myocardial infarct size? Amplitude, speed, and completeness of ST-segment recovery after primary percutaneous coronary intervention for ST-segment elevation myocardial infarction.

PubMed

Kuijt, Wichert J; Green, Cindy L; Verouden, Niels J W; Haeck, Joost D E; Tzivoni, Dan; Koch, Karel T; Stone, Gregg W; Lansky, Alexandra J; Broderick, Samuel; Tijssen, Jan G P; de Winter, Robbert J; Roe, Matthew T; Krucoff, Mitchell W

ST-segment recovery (STR) is a strong mechanistic correlate of infarct size (IS) and outcome in ST-segment elevation myocardial infarction (STEMI). Characterizing measures of speed, amplitude, and completeness of STR may extend the use of this noninvasive biomarker. Core laboratory continuous 24-h 12-lead Holter ECG monitoring, IS by single-photon emission computed tomography (SPECT), and 30-day mortality of 2 clinical trials of primary percutaneous coronary intervention in STEMI were combined. Multiple ST measures (STR at last contrast injection (LC) measured from peak value; 30, 60, 90, 120, and 240min, residual deviation; time to steady ST recovery; and the 3-h area under the time trend curve [ST-AUC] from LC) were univariably correlated with IS and predictive of mortality. After multivariable adjustment for ST-parameters and GRACE risk factors, STR at 240min remained an additive predictor of mortality. Early STR, residual deviation, and ST-AUC remained associated with IS. Multiple parameters that quantify the speed, amplitude, and completeness of STR predict mortality and correlate with IS. Copyright © 2017. Published by Elsevier Inc.

SU-F-R-04: Radiomics for Survival Prediction in Glioblastoma (GBM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, H; Molitoris, J; Bhooshan, N

Purpose: To develop a quantitative radiomics approach for survival prediction of glioblastoma (GBM) patients treated with chemoradiotherapy (CRT). Methods: 28 GBM patients who received CRT at our institution were retrospectively studied. 255 radiomic features were extracted from 3 gadolinium-enhanced T1 weighted MRIs for 2 regions of interest (ROIs) (the surgical cavity and its surrounding enhancement rim). The 3 MRIs were at pre-treatment, 1-month and 3-month post-CRT. The imaging features comprehensively quantified the intensity, spatial variation (texture), geometric property and their spatial-temporal changes for the 2 ROIs. 3 demographics features (age, race, gender) and 12 clinical parameters (KPS, extent of resection,more » whether concurrent temozolomide was adjusted/stopped and radiotherapy related information) were also included. 4 Machine learning models (logistic regression (LR), support vector machine (SVM), decision tree (DT), neural network (NN)) were applied to predict overall survival (OS) and progression-free survival (PFS). The number of cases and percentage of cases predicted correctly were collected and AUC (area under the receiver operating characteristic (ROC) curve) were determined after leave-one-out cross-validation. Results: From univariate analysis, 27 features (1 demographic, 1 clinical and 25 imaging) were statistically significant (p<0.05) for both OS and PFS. Two sets of features (each contained 24 features) were algorithmically selected from all features to predict OS and PFS. High prediction accuracy of OS was achieved by using NN (96%, 27 of 28 cases were correctly predicted, AUC = 0.99), LR (93%, 26 of 28 cases were correctly predicted, AUC = 0.95) and SVM (93%, 26 of 28 cases were correctly predicted, AUC = 0.90). When predicting PFS, NN obtained the highest prediction accuracy (89%, 25 of 28 cases were correctly predicted, AUC = 0.92). Conclusion: Radiomics approach combined with patients’ demographics and clinical parameters can accurately predict survival in GBM patients treated with CRT.« less

Theory-based pharmacokinetics and pharmacodynamics of S- and R-warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients.

PubMed

Xue, Ling; Holford, Nick; Ding, Xiao-Liang; Shen, Zhen-Ya; Huang, Chen-Rong; Zhang, Hua; Zhang, Jing-Jing; Guo, Zhe-Ning; Xie, Cheng; Zhou, Ling; Chen, Zhi-Yao; Liu, Lin-Sheng; Miao, Li-Yan

2017-04-01

The aims of this study are to apply a theory-based mechanistic model to describe the pharmacokinetics (PK) and pharmacodynamics (PD) of S- and R-warfarin. Clinical data were obtained from 264 patients. Total concentrations for S- and R-warfarin were measured by ultra-high performance liquid tandem mass spectrometry. Genotypes were measured using pyrosequencing. A sequential population PK parameter with data method was used to describe the international normalized ratio (INR) time course. Data were analyzed with NONMEM. Model evaluation was based on parameter plausibility and prediction-corrected visual predictive checks. Warfarin PK was described using a one-compartment model. CYP2C9 *1/*3 genotype had reduced clearance for S-warfarin, but increased clearance for R-warfarin. The in vitro parameters for the relationship between prothrombin complex activity (PCA) and INR were markedly different (A = 0.560, B = 0.386) from the theory-based values (A = 1, B = 0). There was a small difference between healthy subjects and patients. A sigmoid E max PD model inhibiting PCA synthesis as a function of S-warfarin concentration predicted INR. Small R-warfarin effects was described by competitive antagonism of S-warfarin inhibition. Patients with VKORC1 AA and CYP4F2 CC or CT genotypes had lower C50 for S-warfarin. A theory-based PKPD model describes warfarin concentrations and clinical response. Expected PK and PD genotype effects were confirmed. The role of predicted fat free mass with theory-based allometric scaling of PK parameters was identified. R-warfarin had a minor effect compared with S-warfarin on PCA synthesis. INR is predictable from 1/PCA in vivo. © 2016 The British Pharmacological Society.

DCE-MRI as a predictor of clinical outcome in canine spontaneous soft-tissue sarcomas treated with thermoradiotherapy

PubMed Central

Viglianti, BL; Lora-Michiels, M; Poulson, JM; Lan, Lan; Yu, D; Sanders, L; Craciunescu, O; Vujaskovic, Z; Thrall, DE; MacFall, J; Charles, HC; Wong, T; Dewhirst, MW

2009-01-01

Purpose This study tests whether DCE-MRI parameters obtained from canine patients with soft tissue sarcomas, treated with hyperthermia and radiotherapy, are predictive of therapeutic outcome. Experimental Design 37 dogs with soft tissue sarcomas had DCE-MRI performed prior to and following the first hyperthermia. Signal enhancement for tumor and reference muscle were fitted empirically, yielding a washin/washout rate for the contrast agent, tumor AUC calculated from 0 to 60s, 90s, and the time of maximal enhancement in the reference muscle. These parameters were then compared to local tumor control, metastasis free survival, and overall survival. Results Pre-therapy rate of contrast agent washout was positively predictive of improved overall survival and metastasis free survival with hazard ratio of 0.67 (p = 0.015) and 0.68 (p = 0.012) respectively. After the first hyperthermia washin rate, AUC60, AUC90, and AUCt-max, were predictive of improved overall survivaloverall survival and metastasis free survival with hazard ratio ranging from 0.46 to 0.53 (p < 0.002) and 0.44 to 0.55 (p < 0.004), respectively. DCE-MRI parameters were compared with extracellular pH and 31-P-MR spectroscopy results (previously published) in the same patients demonstrating a correlation. This suggested that an increase in perfusion after therapy was effective in eliminating excess acid from the tumor. Conclusions This study demonstrates that DCE-MRI has utility predicting overall survivaloverall survival and metastasis free survival in canine patients with soft tissue sarcomas. To our knowledge, this is the first time that DCE-MRI parameters have been shown to be predictive of clinical outcome for soft tissue sarcomas. PMID:19622579

Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal {sup 18}F-FDG PET Features, Clinical Parameters, and Demographics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Hao; Tan, Shan; Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan

2014-01-01

Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]{sub max}, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changesmore » resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model. Conclusions: The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.« less

Risk Prediction Models for Acute Kidney Injury in Critically Ill Patients: Opus in Progressu.

PubMed

Neyra, Javier A; Leaf, David E

2018-05-31

Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality. Among critically ill patients admitted to intensive care units (ICUs), the incidence of AKI is as high as 50% and is associated with dismal outcomes. Thus, the development and validation of clinical risk prediction tools that accurately identify patients at high risk for AKI in the ICU is of paramount importance. We provide a comprehensive review of 3 clinical risk prediction tools that have been developed for incident AKI occurring in the first few hours or days following admission to the ICU. We found substantial heterogeneity among the clinical variables that were examined and included as significant predictors of AKI in the final models. The area under the receiver operating characteristic curves was ∼0.8 for all 3 models, indicating satisfactory model performance, though positive predictive values ranged from only 23 to 38%. Hence, further research is needed to develop more accurate and reproducible clinical risk prediction tools. Strategies for improved assessment of AKI susceptibility in the ICU include the incorporation of dynamic (time-varying) clinical parameters, as well as biomarker, functional, imaging, and genomic data. © 2018 S. Karger AG, Basel.

Early prediction of intensive care unit-acquired weakness using easily available parameters: a prospective observational study.

PubMed

Wieske, Luuk; Witteveen, Esther; Verhamme, Camiel; Dettling-Ihnenfeldt, Daniela S; van der Schaaf, Marike; Schultz, Marcus J; van Schaik, Ivo N; Horn, Janneke

2014-01-01

An early diagnosis of Intensive Care Unit-acquired weakness (ICU-AW) using muscle strength assessment is not possible in most critically ill patients. We hypothesized that development of ICU-AW can be predicted reliably two days after ICU admission, using patient characteristics, early available clinical parameters, laboratory results and use of medication as parameters. Newly admitted ICU patients mechanically ventilated ≥2 days were included in this prospective observational cohort study. Manual muscle strength was measured according to the Medical Research Council (MRC) scale, when patients were awake and attentive. ICU-AW was defined as an average MRC score <4. A prediction model was developed by selecting predictors from an a-priori defined set of candidate predictors, based on known risk factors. Discriminative performance of the prediction model was evaluated, validated internally and compared to the APACHE IV and SOFA score. Of 212 included patients, 103 developed ICU-AW. Highest lactate levels, treatment with any aminoglycoside in the first two days after admission and age were selected as predictors. The area under the receiver operating characteristic curve of the prediction model was 0.71 after internal validation. The new prediction model improved discrimination compared to the APACHE IV and the SOFA score. The new early prediction model for ICU-AW using a set of 3 easily available parameters has fair discriminative performance. This model needs external validation.

In Silico Screening Based on Predictive Algorithms as a Design Tool for Exon Skipping Oligonucleotides in Duchenne Muscular Dystrophy

PubMed Central

Echigoya, Yusuke; Mouly, Vincent; Garcia, Luis; Yokota, Toshifumi; Duddy, William

2015-01-01

The use of antisense ‘splice-switching’ oligonucleotides to induce exon skipping represents a potential therapeutic approach to various human genetic diseases. It has achieved greatest maturity in exon skipping of the dystrophin transcript in Duchenne muscular dystrophy (DMD), for which several clinical trials are completed or ongoing, and a large body of data exists describing tested oligonucleotides and their efficacy. The rational design of an exon skipping oligonucleotide involves the choice of an antisense sequence, usually between 15 and 32 nucleotides, targeting the exon that is to be skipped. Although parameters describing the target site can be computationally estimated and several have been identified to correlate with efficacy, methods to predict efficacy are limited. Here, an in silico pre-screening approach is proposed, based on predictive statistical modelling. Previous DMD data were compiled together and, for each oligonucleotide, some 60 descriptors were considered. Statistical modelling approaches were applied to derive algorithms that predict exon skipping for a given target site. We confirmed (1) the binding energetics of the oligonucleotide to the RNA, and (2) the distance in bases of the target site from the splice acceptor site, as the two most predictive parameters, and we included these and several other parameters (while discounting many) into an in silico screening process, based on their capacity to predict high or low efficacy in either phosphorodiamidate morpholino oligomers (89% correctly predicted) and/or 2’O Methyl RNA oligonucleotides (76% correctly predicted). Predictions correlated strongly with in vitro testing for sixteen de novo PMO sequences targeting various positions on DMD exons 44 (R2 0.89) and 53 (R2 0.89), one of which represents a potential novel candidate for clinical trials. We provide these algorithms together with a computational tool that facilitates screening to predict exon skipping efficacy at each position of a target exon. PMID:25816009

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500).

PubMed

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun; Seong, Gong Je

2017-03-01

To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R²=0.404). Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors.

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500)

PubMed Central

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun

2017-01-01

Purpose To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. Materials and Methods This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. Results In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R2=0.404). Conclusion Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors. PMID:28120576

Usefulness of Mitral Valve Prosthetic or Bioprosthetic Time Velocity Index Ratio to Detect Prosthetic or Bioprosthetic Mitral Valve Dysfunction.

PubMed

Luis, Sushil Allen; Blauwet, Lori A; Samardhi, Himabindu; West, Cathy; Mehta, Ramila A; Luis, Chris R; Scalia, Gregory M; Miller, Fletcher A; Burstow, Darryl J

2017-10-15

This study aimed to investigate the utility of transthoracic echocardiographic (TTE) Doppler-derived parameters in detection of mitral prosthetic dysfunction and to define optimal cut-off values for identification of such dysfunction by valve type. In total, 971 TTE studies (647 mechanical prostheses; 324 bioprostheses) were compared with transesophageal echocardiography for evaluation of mitral prosthesis function. Among all prostheses, mitral valve prosthesis (MVP) ratio (ratio of time velocity integral of MVP to that of left ventricular outflow tract; odds ratio [OR] 10.34, 95% confidence interval [95% CI] 6.43 to 16.61, p<0.001), E velocity (OR 3.23, 95% CI 1.61 to 6.47, p<0.001), and mean gradient (OR 1.13, 95% CI 1.02 to 1.25, p=0.02) provided good discrimination of clinically normal and clinically abnormal prostheses. Optimal cut-off values by receiver operating characteristic analysis for differentiating clinically normal and abnormal prostheses varied by prosthesis type. Combining MVP ratio and E velocity improved specificity (92%) and positive predictive value (65%) compared with either parameter alone, with minimal decline in negative predictive value (92%). Pressure halftime (OR 0.99, 95% CI 0.98 to 1.00, p=0.04) did not differentiate between clinically normal and clinically abnormal prostheses but was useful in discriminating obstructed from normal and regurgitant prostheses. In conclusion, cut-off values for TTE-derived Doppler parameters of MVP function were specific to prosthesis type and carried high sensitivity and specificity for identifying prosthetic valve dysfunction. MVP ratio was the best predictor of prosthetic dysfunction and, combined with E velocity, provided a useful parameter for determining likelihood of dysfunction and need for further assessment. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

PubMed

Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion: The predictive values of the constructed models were superior to the standard method (SUV max ). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Clinical and Vitamin Response to a Short-Term Multi-Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses.

PubMed

Mathias, Mariana Giaretta; Coelho-Landell, Carolina de Almeida; Scott-Boyer, Marie-Pier; Lacroix, Sébastien; Morine, Melissa J; Salomão, Roberta Garcia; Toffano, Roseli Borges Donegá; Almada, Maria Olímpia Ribeiro do Vale; Camarneiro, Joyce Moraes; Hillesheim, Elaine; de Barros, Tamiris Trevisan; Camelo-Junior, José Simon; Campos Giménez, Esther; Redeuil, Karine; Goyon, Alexandre; Bertschy, Emmanuelle; Lévêques, Antoine; Oberson, Jean-Marie; Giménez, Catherine; Carayol, Jerome; Kussmann, Martin; Descombes, Patrick; Métairon, Slyviane; Draper, Colleen Fogarty; Conus, Nelly; Mottaz, Sara Colombo; Corsini, Giovanna Zambianchi; Myoshi, Stephanie Kazu Brandão; Muniz, Mariana Mendes; Hernandes, Lívia Cristina; Venâncio, Vinícius Paula; Antunes, Lusania Maria Greggi; da Silva, Rosana Queiroz; Laurito, Taís Fontellas; Rossi, Isabela Ribeiro; Ricci, Raquel; Jorge, Jéssica Ré; Fagá, Mayara Leite; Quinhoneiro, Driele Cristina Gomes; Reche, Mariana Chinarelli; Silva, Paula Vitória Sozza; Falquetti, Letícia Lima; da Cunha, Thaís Helena Alves; Deminice, Thalia Manfrin Martins; Tambellini, Tâmara Hambúrguer; de Souza, Gabriela Cristina Arces; de Oliveira, Mariana Moraes; Nogueira-Pileggi, Vicky; Matsumoto, Marina Takemoto; Priami, Corrado; Kaput, Jim; Monteiro, Jacqueline Pontes

2018-03-01

Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals. A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures. The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spinal cord multi-parametric magnetic resonance imaging for survival prediction in amyotrophic lateral sclerosis.

PubMed

Querin, G; El Mendili, M M; Lenglet, T; Delphine, S; Marchand-Pauvert, V; Benali, H; Pradat, P-F

2017-08-01

Assessing survival is a critical issue in patients with amyotrophic lateral sclerosis (ALS). Neuroimaging seems to be promising in the assessment of disease severity and several studies also suggest a strong relationship between spinal cord (SC) atrophy described by magnetic resonance imaging (MRI) and disease progression. The aim of the study was to determine the predictive added value of multimodal SC MRI on survival. Forty-nine ALS patients were recruited and clinical data were collected. Patients were scored on the Revised ALS Functional Rating Scale and manual muscle testing. They were followed longitudinally to assess survival. The cervical SC was imaged using the 3 T MRI system. Cord volume and cross-sectional area (CSA) at each vertebral level were computed. Diffusion tensor imaging metrics were measured. Imaging metrics and clinical variables were used as inputs for a multivariate Cox regression survival model. On building a multivariate Cox regression model with clinical and MRI parameters, fractional anisotropy, magnetization transfer ratio and CSA at C2-C3, C4-C5, C5-C6 and C6-C7 vertebral levels were significant. Moreover, the hazard ratio calculated for CSA at the C3-C4 and C5-C6 levels indicated an increased risk for patients with SC atrophy (respectively 0.66 and 0.68). In our cohort, MRI parameters seem to be more predictive than clinical variables, which had a hazard ratio very close to 1. It is suggested that multimodal SC MRI could be a useful tool in survival prediction especially if used at the beginning of the disease and when combined with clinical variables. To validate it as a biomarker, confirmation of the results in bigger independent cohorts of patients is warranted. © 2017 EAN.

Impact of dosimetric and clinical parameters on clinical side effects in cervix cancer patients treated with 3D pulse-dose-rate intracavitary brachytherapy.

PubMed

Levitchi, Mihai; Charra-Brunaud, Claire; Quetin, Philippe; Haie-Meder, Christine; Kerr, Christine; Castelain, Bernard; Delannes, Martine; Thomas, Laurence; Desandes, Emmanuel; Peiffert, Didier

2012-06-01

To assess the association between dosimetric/clinical parameters and gastrointestinal/urinary grade 2-4 side effects in cervix cancer patients treated with 3D pulse dose rate brachytherapy. Three hundred and fifty-two patients received brachytherapy associated with external-beam radiotherapy (EBRT) for 266 of them; 236 patients underwent surgery. The doses for the most exposed 2, and 0.1 cm(3) (D(2cc) and D(0.1cc)) volumes of the rectum and bladder as well as bladder ICRU point dose (D(ICRU)) were converted into isoeffective doses in 2-Gy fractions. The clinical parameters analyzed were: age, smoking habits, arteritis, diabetes, previous pelvic surgery, FIGO stage, nodal status, pathology, pelvic surgery, EBRT and chemotherapy. Side effects were prospectively assessed using the CTCAEv3.0. Cutoff dose levels were defined separately for patients treated with EBRT and brachytherapy (Group 1) and with preoperative brachytherapy (Group 2). The median follow-up was 23.4months. In Group 1 a significant predictive value of rectum D(0.1cc) and D(2cc), bladder D(0.1cc) and D(ICRU) for gastrointestinal and urinary toxicity was found using as cutoff 83, 68, 109 and 68Gy(α)(/)(β)(3). In Group 2 a significant predictive value of bladder D(0.1cc), D(2cc) and D(ICRU) for urinary toxicity was found using as cutoff 141, 91 and 67Gy(α)(/)(β)(3), but not for the rectum D(0.1cc) and D(2cc); smoking had a significant predictive value on urinary toxicity. For patients treated with brachytherapy and EBRT, rectum D(0.1cc) and D(2cc) and bladder D(0.1cc) and D(ICRU) had a predictive value for toxicity. For patients treated with preoperative brachytherapy, bladder D(0.1cc), D(2cc) and D(ICRU) and smoking had a predictive value for urinary toxicity. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.

PubMed

Arsanjani, Reza; Dey, Damini; Khachatryan, Tigran; Shalev, Aryeh; Hayes, Sean W; Fish, Mathews; Nakanishi, Rine; Germano, Guido; Berman, Daniel S; Slomka, Piotr

2015-10-01

We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine learning (ML) approach. 713 rest (201)Thallium/stress (99m)Technetium MPS studies with correlating invasive angiography with 372 revascularization events (275 PCI/97 CABG) within 90 days after MPS (91% within 30 days) were considered. Transient ischemic dilation, stress combined supine/prone total perfusion deficit (TPD), supine rest and stress TPD, exercise ejection fraction, and end-systolic volume, along with clinical parameters including patient gender, history of hypertension and diabetes mellitus, ST-depression on baseline ECG, ECG and clinical response during stress, and post-ECG probability by boosted ensemble ML algorithm (LogitBoost) to predict revascularization events. These features were selected using an automated feature selection algorithm from all available clinical and quantitative data (33 parameters). Tenfold cross-validation was utilized to train and test the prediction model. The prediction of revascularization by ML algorithm was compared to standalone measures of perfusion and visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. The sensitivity of machine learning (ML) (73.6% ± 4.3%) for prediction of revascularization was similar to one reader (73.9% ± 4.6%) and standalone measures of perfusion (75.5% ± 4.5%). The specificity of ML (74.7% ± 4.2%) was also better than both expert readers (67.2% ± 4.9% and 66.0% ± 5.0%, P < .05), but was similar to ischemic TPD (68.3% ± 4.9%, P < .05). The receiver operator characteristics areas under curve for ML (0.81 ± 0.02) was similar to reader 1 (0.81 ± 0.02) but superior to reader 2 (0.72 ± 0.02, P < .01) and standalone measure of perfusion (0.77 ± 0.02, P < .01). ML approach is comparable or better than experienced readers in prediction of the early revascularization after MPS, and is significantly better than standalone measures of perfusion derived from MPS.

Predicting obstructive coronary artery disease using carotid ultrasound parameters: a nomogram from a large real-world clinical data.

PubMed

Wu, Na; Chen, Xinghua; Li, Mingyang; Qu, Xiaolong; Li, Yueli; Xie, Weijia; Wu, Long; Xiang, Ying; Li, Yafei; Zhong, Li

2018-05-21

Carotid ultrasound is a non-invasive tool for risk assessment of coronary artery disease (CAD). There is no consensus on which carotid ultrasound parameter constitutes the best measurement of atherosclerosis. We investigated which model of carotid ultrasound parameters and clinical risk factors (CRF) have the highest predictive value for CAD. We enrolled 2431 consecutive patients who have suspected CAD and underwent coronary angiography and carotid ultrasound with measurements of carotid intima-media thickness (CIMT), total number of plaques and areas of different types of plaques classified by echogenicity. Total number of plaques demonstrated the highest incremental prediction ability to predict CAD over CRF (area under the curve [AUC] 0.752 vs 0.701, net reclassification index [NRI] = 0.514, P < 0.001), followed by area of maximum mixed and soft plaques. CIMT had no significant incremental value over CRF (AUC 0.704 vs 0.701, P = 0.241; NRI = 0.062, P = 0.168). The model comprising total number of plaques, areas of maximum soft, hard and mixed plaques plus CRF had the highest discriminatory (AUC = 0.757) and reclassification value (NRI = 0.567) for CAD. A nomogram based on this model was developed to predict CAD. For subjects at low and intermediate risk, the model comprising total number of plaques plus CRF was the best. Total number of plaques, area of maximum soft, hard and mixed plaques showed significantly incremental prediction ability over CRF. A nomogram based on these factors provided an intuitive and practical method in detecting CAD. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Predicting Infected Bile Among Patients Undergoing Percutaneous Cholecystostomy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beardsley, Shannon L.; Shlansky-Goldberg, Richard D.; Patel, Aalpen

2005-04-15

Purpose. Patients may not achieve a clinical benefit after percutaneous cholecystostomy due to the inherent difficulty in identifying patients who truly have infected gallbladders. We attempted to identify imaging and biochemical parameters which would help to predict which patients have infected gallbladders. Methods. A retrospective review was performed of 52 patients undergoing percutaneous cholecystostomy for clinical suspicion of acute cholecystitis in whom bile culture results were available. Multiple imaging and biochemical variables were examined alone and in combination as predictors of infected bile, using logistic regression. Results. Of the 52 patients, 25 (48%) had infected bile. Organisms cultured included Enterococcus,more » Enterobacter, Klebsiella, Pseudomonas, E. coli, Citrobacter and Candida. No biochemical parameters were significantly predictive of infected bile; white blood cell count >15,000 was weakly associated with greater odds of infected bile (odds ratio 2.0, p = NS). The presence of gallstones, sludge, gallbladder wall thickening and pericholecystic fluid by ultrasound or CT were not predictive of infected bile, alone or in combination, although a trend was observed among patients with CT findings of acute cholecystitis toward a higher 30-day mortality. Radionuclide scans were performed in 31% of patients; all were positive and 66% of these patients had infected bile. Since no patient who underwent a radionuclide scan had a negative study, this variable could not be entered into the regression model due to collinearity. Conclusion. No single CT or ultrasound imaging variable was predictive of infected bile, and only a weak association of white blood cell count with infected bile was seen. No other biochemical parameters had any association with infected bile. The ability of radionuclide scanning to predict infected bile was higher than that of ultrasound or CT. This study illustrates the continued challenge to identify bacterial cholecystitis among patients referred for percutaneous cholecystostomy.« less

Characterization of Neutropenia in Advanced Cancer Patients Following Palbociclib Treatment Using a Population Pharmacokinetic-Pharmacodynamic Modeling and Simulation Approach.

PubMed

Sun, Wan; O'Dwyer, Peter J; Finn, Richard S; Ruiz-Garcia, Ana; Shapiro, Geoffrey I; Schwartz, Gary K; DeMichele, Angela; Wang, Diane

2017-09-01

Neutropenia is the most commonly reported hematologic toxicity following treatment with palbociclib, a cyclin-dependent kinase 4/6 inhibitor approved for metastatic breast cancer. Using data from 185 advanced cancer patients receiving palbociclib in 3 clinical trials, a pharmacokinetic-pharmacodynamic model was developed to describe the time course of absolute neutrophil count (ANC) and quantify the exposure-response relationship for neutropenia. These analyses help in understanding neutropenia associated with palbociclib and its comparison with chemotherapy-induced neutropenia. In the model, palbociclib plasma concentration was related to its antiproliferative effect on precursor cells through drug-related parameters (ie, maximum estimated drug effect and concentration corresponding to 50% of the maximum effect), and neutrophil physiology was mimicked through system-related parameters (ie, mean transit time, baseline ANC, and feedback parameter). Sex and baseline albumin level were significant covariates for baseline ANC. It was demonstrated by different model evaluation approaches (eg, prediction-corrected visual predictive check and standardized visual predictive check) that the final model adequately described longitudinal ANC with good predictive capability. The established model suggested that higher palbociclib exposure was associated with lower longitudinal neutrophil counts. The ANC nadir was reached approximately 21 days after palbociclib treatment initiation. Consistent with their mechanisms of action, neutropenia associated with palbociclib (cytostatic) was rapidly reversible and noncumulative, with a notably weaker antiproliferative effect on precursor cells relative to chemotherapies (cytotoxic). This pharmacokinetic-pharmacodynamic model aids in predicting neutropenia and optimizing dosing for future palbociclib trials with different dosing regimen combinations. © 2017, The American College of Clinical Pharmacology.

Progression of chronic periodontitis can be predicted by the levels of Porphyromonas gingivalis and Treponema denticola in subgingival plaque.

PubMed

Byrne, S J; Dashper, S G; Darby, I B; Adams, G G; Hoffmann, B; Reynolds, E C

2009-12-01

Chronic periodontitis is an inflammatory disease of the supporting tissues of the teeth associated with bacteria. Diagnosis is achieved retrospectively by clinical observation of attachment loss. Predicting disease progression would allow for targeted preventive therapy. The aim of this study was to monitor disease progression in patients on a maintenance program and determine the levels of specific bacteria in subgingival plaque samples and then examine the ability of the clinical parameters of disease and levels of specific bacteria in the plaque samples to predict disease progression. During a 12-month longitudinal study of 41 subjects, 25 sites in 21 subjects experienced disease progression indicated by at least 2 mm of clinical attachment loss. Real-time polymerase chain reaction was used to determine the levels of Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Fusobacterium nucleatum, and Prevotella intermedia in subgingival plaque samples. No clinical parameters were able to predict periodontal disease progression. In sites undergoing imminent periodontal disease progression within the next 3 months, significant partial correlations were found between P. gingivalis and T. forsythia (r = 0.55, P < 0.001) and T. denticola and T. forsythia (r = 0.43, P = 0.04). The odds of a site undergoing imminent periodontal disease progression increased with increasing levels of P. gingivalis and T. denticola. Monitoring the proportions of P. gingivalis and T. denticola in subgingival plaque has the potential to help identify sites at significant risk for progression of periodontitis, which would assist in the targeted treatment of disease.

Proposal of a clinical response score and predictors of clinical response to 2 years of GH replacement therapy in adult GH deficiency.

PubMed

Schneider, Harald J; Buchfelder, Michael; Wallaschofski, Henri; Luger, Anton; Johannsson, Gudmundur; Kann, Peter H; Mattsson, Anders

2015-12-01

There is no single clinical marker to reliably assess the clinical response to growth hormone replacement therapy (GHRT) in adults with growth hormone deficiency (GHD). The objective of this study was to propose a clinical response score to GHRT in adult GHD and to establish clinical factors that predict clinical response. This was a prospective observational cohort study from the international KIMS database (Pfizer International Metabolic Database). We included 3612 adult patients with GHD for proposing the response score and 844 patients for assessing predictors of response. We propose a clinical response score based on changes in total cholesterol, waist circumference and QoL-AGHDA quality of life measurements after 2 years of GHRT. A score point was added for each quintile of change in each variable, resulting in a sum score ranging from 3 to 15. For clinical response at 2 years, we analysed predictors at baseline and after 6 months using logistic regression analyses. In a baseline prediction model, IGF1, QoL-AGHDA, total cholesterol and waist circumference predicted response, with worse baseline parameters being associated with a favourable response (AUC 0.736). In a combined baseline and 6-month prediction model, baseline QoL-AGHDA, total cholesterol and waist circumference, and 6-month change in waist circumference were significant predictors of response (AUC 0.815). A simple clinical response score might be helpful in evaluating the success of GHRT. The baseline prediction model may aid in the decision to initiate GHRT and the combined prediction model may be helpful in the decision to continue GHRT. © 2015 European Society of Endocrinology.

The use of pharmacokinetic/pharmacodynamic principles to predict clinical outcome in paediatric acute otitis media.

PubMed

Dagan, Ron

2007-12-01

Double tympanocentesis studies of children with acute otitis media, carried out over an 11-year period, were used to confirm that pharmacokinetic (PK) and pharmacodynamic (PD) parameters can be used as predictors of the bacteriological and clinical efficacy of antimicrobial agents. Predicted susceptibilities of common respiratory pathogens, such as Streptococcus pneumoniae and Haemophilus influenzae, were compared with the bacteriological outcome of treatment in which the high-dose formulation of amoxicillin/clavulanate (90mg/kg/day) given twice daily achieved the greatest bacteriological eradication rates for an oral agent. Further analysis of the data has indicated that failure to eradicate bacteria from the middle ear fluid is strongly correlated with clinical failure.

Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis

PubMed Central

Hueber, Wolfgang; Tomooka, Beren H; Batliwalla, Franak; Li, Wentian; Monach, Paul A; Tibshirani, Robert J; Van Vollenhoven, Ronald F; Lampa, Jon; Saito, Kazuyoshi; Tanaka, Yoshiya; Genovese, Mark C; Klareskog, Lars; Gregersen, Peter K; Robinson, William H

2009-01-01

Introduction Anti-TNF therapies have revolutionized the treatment of rheumatoid arthritis (RA), a common systemic autoimmune disease involving destruction of the synovial joints. However, in the practice of rheumatology approximately one-third of patients demonstrate no clinical improvement in response to treatment with anti-TNF therapies, while another third demonstrate a partial response, and one-third an excellent and sustained response. Since no clinical or laboratory tests are available to predict response to anti-TNF therapies, great need exists for predictive biomarkers. Methods Here we present a multi-step proteomics approach using arthritis antigen arrays, a multiplex cytokine assay, and conventional ELISA, with the objective to identify a biomarker signature in three ethnically diverse cohorts of RA patients treated with the anti-TNF therapy etanercept. Results We identified a 24-biomarker signature that enabled prediction of a positive clinical response to etanercept in all three cohorts (positive predictive values 58 to 72%; negative predictive values 63 to 78%). Conclusions We identified a multi-parameter protein biomarker that enables pretreatment classification and prediction of etanercept responders, and tested this biomarker using three independent cohorts of RA patients. Although further validation in prospective and larger cohorts is needed, our observations demonstrate that multiplex characterization of autoantibodies and cytokines provides clinical utility for predicting response to the anti-TNF therapy etanercept in RA patients. PMID:19460157

Theory‐based pharmacokinetics and pharmacodynamics of S‐ and R‐warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients

PubMed Central

Xue, Ling; Holford, Nick; Ding, Xiao‐liang; Shen, Zhen‐ya; Huang, Chen‐rong; Zhang, Hua; Zhang, Jing‐jing; Guo, Zhe‐ning; Xie, Cheng; Zhou, Ling; Chen, Zhi‐yao; Liu, Lin‐sheng

2016-01-01

Aims The aims of this study are to apply a theory‐based mechanistic model to describe the pharmacokinetics (PK) and pharmacodynamics (PD) of S‐ and R‐warfarin. Methods Clinical data were obtained from 264 patients. Total concentrations for S‐ and R‐warfarin were measured by ultra‐high performance liquid tandem mass spectrometry. Genotypes were measured using pyrosequencing. A sequential population PK parameter with data method was used to describe the international normalized ratio (INR) time course. Data were analyzed with NONMEM. Model evaluation was based on parameter plausibility and prediction‐corrected visual predictive checks. Results Warfarin PK was described using a one‐compartment model. CYP2C9 *1/*3 genotype had reduced clearance for S‐warfarin, but increased clearance for R‐warfarin. The in vitro parameters for the relationship between prothrombin complex activity (PCA) and INR were markedly different (A = 0.560, B = 0.386) from the theory‐based values (A = 1, B = 0). There was a small difference between healthy subjects and patients. A sigmoid Emax PD model inhibiting PCA synthesis as a function of S‐warfarin concentration predicted INR. Small R‐warfarin effects was described by competitive antagonism of S‐warfarin inhibition. Patients with VKORC1 AA and CYP4F2 CC or CT genotypes had lower C50 for S‐warfarin. Conclusion A theory‐based PKPD model describes warfarin concentrations and clinical response. Expected PK and PD genotype effects were confirmed. The role of predicted fat free mass with theory‐based allometric scaling of PK parameters was identified. R‐warfarin had a minor effect compared with S‐warfarin on PCA synthesis. INR is predictable from 1/PCA in vivo. PMID:27763679

An Improved Model for Predicting Radiation Pneumonitis Incorporating Clinical and Dosimetric Variables;Lung cancer; Radiation pneumonitis; Dose-volume histogram; Angiotensin converting enzyme inhibitor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jenkins, Peter, E-mail: peter.jenkins@glos.nhs.uk; Watts, Joanne

2011-07-15

Purpose: Single dose-volume metrics are of limited value for the prediction of radiation pneumonitis (RP) in day-to-day clinical practice. We investigated whether multiparametric models that incorporate clinical and physiologic factors might have improved accuracy. Methods and Materials: The records of 160 patients who received radiation therapy for non-small-cell lung cancer were reviewed. All patients were treated to the same dose and with an identical technique. Dosimetric, pulmonary function, and clinical parameters were analyzed to determine their ability to predict for the subsequent development of RP. Results: Twenty-seven patients (17%) developed RP. On univariate analysis, the following factors were significantly correlatedmore » with the risk of pneumonitis: fractional volume of lung receiving >5-20 Gy, absolute volume of lung spared from receiving >5-15 Gy, mean lung dose, craniocaudal position of the isocenter, transfer coefficient for carbon monoxide (KCOc), total lung capacity, coadministration of angiotensin converting enzyme inhibitors, and coadministration of angiotensin receptor antagonists. By combining the absolute volume of lung spared from receiving >5 Gy with the KCOc, we defined a new parameter termed Transfer Factor Spared from receiving >5 Gy (TFS{sub 5}). The area under the receiver operator characteristic curve for TFS{sub 5} was 0.778, increasing to 0.846 if patients receiving modulators of the renin-angiotensin system were excluded from the analysis. Patients with a TFS{sub 5} <2.17 mmol/min/kPa had a risk of RP of 30% compared with 5% for the group with a TFS{sub 5} {>=}2.17. Conclusions: TFS{sub 5} represents a simple parameter that can be used in routine clinical practice to more accurately segregate patients into high- and low-risk groups for developing RP.« less

Clinico-pathological and biological prognostic variables in squamous cell carcinoma of the vulva.

PubMed

Gadducci, Angiolo; Tana, Roberta; Barsotti, Cecilia; Guerrieri, Maria Elena; Genazzani, Andrea Riccardo

2012-07-01

Several clinical-pathological parameters have been related to survival of patients with invasive squamous cell carcinoma of the vulva, whereas few studies have investigated the ability of biological variables to predict the clinical outcome of these patients. The present paper reviews the literature data on the prognostic relevance of lymph node-related parameters, primary tumor-related parameters, FIGO stage, blood variables, and tissue biological variables. Regarding these latter, the paper takes into account the analysis of DNA content, cell cycle-regulatory proteins, apoptosis-related proteins, epidermal growth factor receptor [EGFR], and proteins that are involved in tumor invasiveness, metastasis and angiogenesis. At present, the lymph node status and FIGO stage according to the new 2009 classification system are the main predictors for vulvar squamous cell carcinoma, whereas biological variables do not have yet a clinical relevance and their role is still investigational. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Clinically relevant hypoglycemia prediction metrics for event mitigation.

PubMed

Harvey, Rebecca A; Dassau, Eyal; Zisser, Howard C; Bevier, Wendy; Seborg, Dale E; Jovanovič, Lois; Doyle, Francis J

2012-08-01

The purpose of this study was to develop a method to compare hypoglycemia prediction algorithms and choose parameter settings for different applications, such as triggering insulin pump suspension or alerting for rescue carbohydrate treatment. Hypoglycemia prediction algorithms with different parameter settings were implemented on an ambulatory dataset containing 490 days from 30 subjects with type 1 diabetes mellitus using the Dexcom™ (San Diego, CA) SEVEN™ continuous glucose monitoring system. The performance was evaluated using a proposed set of metrics representing the true-positive ratio, false-positive rate, and distribution of warning times. A prospective, in silico study was performed to show the effect of using different parameter settings to prevent or rescue from hypoglycemia. The retrospective study results suggest the parameter settings for different methods of hypoglycemia mitigation. When rescue carbohydrates are used, a high true-positive ratio, a minimal false-positive rate, and alarms with short warning time are desired. These objectives were met with a 30-min prediction horizon and two successive flags required to alarm: 78% of events were detected with 3.0 false alarms/day and 66% probability of alarms occurring within 30 min of the event. This parameter setting selection was confirmed in silico: treating with rescue carbohydrates reduced the duration of hypoglycemia from 14.9% to 0.5%. However, for a different method, such as pump suspension, this parameter setting only reduced hypoglycemia to 8.7%, as can be expected by the low probability of alarming more than 30 min ahead. The proposed metrics allow direct comparison of hypoglycemia prediction algorithms and selection of parameter settings for different types of hypoglycemia mitigation, as shown in the prospective in silico study in which hypoglycemia was alerted or treated with rescue carbohydrates.

Assessment of general movements and heart rate variability in prediction of neurodevelopmental outcome in preterm infants.

PubMed

Dimitrijević, Lidija; Bjelaković, Bojko; Čolović, Hristina; Mikov, Aleksandra; Živković, Vesna; Kocić, Mirjana; Lukić, Stevo

2016-08-01

Adverse neurologic outcome in preterm infants could be associated with abnormal heart rate (HR) characteristics as well as with abnormal general movements (GMs) in the 1st month of life. To demonstrate to what extent GMs assessment can predict neurological outcome in preterm infants in our clinical setting; and to assess the clinical usefulness of time-domain indices of heart rate variability (HRV) in improving predictive value of poor repertoire (PR) GMs in writhing period. Qualitative assessment of GMs at 1 and 3 months corrected age; 24h electrocardiography (ECG) recordings and analyzing HRV at 1 month corrected age. Seventy nine premature infants at risk of neurodevelopmental impairments were included prospectively. Neurodevelopmental outcome was assessed at the age of 2 years corrected. Children were classified as having normal neurodevelopmental status, minor neurologic dysfunction (MND), or cerebral palsy (CP). We found that GMs in writhing period (1 month corrected age) predicted CP at 2 years with sensitivity of 100%, and specificity of 72.1%. Our results demonstrated the excellent predictive value of cramped synchronized (CS) GMs, but not of PR pattern. Analyzing separately a group of infants with PR GMs we found significantly lower values of HRV parameters in infants who later developed CP or MND vs. infants with PR GMs who had normal outcome. The quality of GMs was predictive for neurodevelopmental outcome at 2 years. Prediction of PR GMs was significantly enhanced with analyzing HRV parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A human microdose study of the antimalarial drug GSK3191607 in healthy volunteers.

PubMed

Okour, Malek; Derimanov, Geo; Barnett, Rodger; Fernandez, Esther; Ferrer, Santiago; Gresham, Stephanie; Hossain, Mohammad; Gamo, Francisco-Javier; Koh, Gavin; Pereira, Adrian; Rolfe, Katie; Wong, Deborah; Young, Graeme; Rami, Harshad; Haselden, John

2018-03-01

GSK3191607, a novel inhibitor of the Plasmodium falciparum ATP4 (PfATP4) pathway, is being considered for development in humans. However, a key problem encountered during the preclinical evaluation of the compound was its inconsistent pharmacokinetic (PK) profile across preclinical species (mouse, rat and dog), which prevented reliable prediction of PK parameters in humans and precluded a well-founded assessment of the potential for clinical development of the compound. Therefore, an open-label microdose (100 μg, six subjects) first time in humans study was conducted to assess the human PK of GSK3191607 following intravenous administration of [14C]-GSK3191607. A human microdose study was conducted to investigate the clinical PK of GSK3191607 and enable a Go/No Go decision on further progression of the compound. The PK disposition parameters estimated from the microdose study, combined with preclinical in vitro and in vivo pharmacodynamic parameters, were all used to estimate the potential efficacy of various oral dosing regimens in humans. The PK profile, based on the microdose data, demonstrated a half-life (~17 h) similar to other antimalarial compounds currently in clinical development. However, combining the microdose data with the pharmacodynamic data provided results that do not support further clinical development of the compound for a single dose cure. The information generated by this study provides a basis for predicting the expected oral PK profiles of GSK3191607 in man and supports decisions on the future clinical development of the compound. © 2017 The British Pharmacological Society.

Statistical methods for clinical verification of dose response parameters related to esophageal stricture and AVM obliteration from radiotherapy

NASA Astrophysics Data System (ADS)

Mavroidis, Panayiotis; Lind, Bengt K.; Theodorou, Kyriaki; Laurell, Göran; Fernberg, Jan-Olof; Lefkopoulos, Dimitrios; Kappas, Constantin; Brahme, Anders

2004-08-01

The purpose of this work is to provide some statistical methods for evaluating the predictive strength of radiobiological models and the validity of dose-response parameters for tumour control and normal tissue complications. This is accomplished by associating the expected complication rates, which are calculated using different models, with the clinical follow-up records. These methods are applied to 77 patients who received radiation treatment for head and neck cancer and 85 patients who were treated for arteriovenous malformation (AVM). The three-dimensional dose distribution delivered to esophagus and AVM nidus and the clinical follow-up results were available for each patient. Dose-response parameters derived by a maximum likelihood fitting were used as a reference to evaluate their compatibility with the examined treatment methodologies. The impact of the parameter uncertainties on the dose-response curves is demonstrated. The clinical utilization of the radiobiological parameters is illustrated. The radiobiological models (relative seriality and linear Poisson) and the reference parameters are validated to prove their suitability in reproducing the treatment outcome pattern of the patient material studied (through the probability of finding a worse fit, area under the ROC curve and khgr2 test). The analysis was carried out for the upper 5 cm of the esophagus (proximal esophagus) where all the strictures are formed, and the total volume of AVM. The estimated confidence intervals of the dose-response curves appear to have a significant supporting role on their clinical implementation and use.

Signs Indicating Imminent Death in Escherichia coli-Infected Broilers.

PubMed

Matthijs, M G R; Nieuwenhuis, J F; Dwars, R M

2017-09-01

Broilers were observed during 9 days for clinical signs after intratracheal inoculation at 8 days of age with 10 7 E. coli 506. It was determined if these signs were predictive for imminent death. Hourly observations were made daily from a distance of 1-2 m and nightly by camera observation, with respect to the following parameters: level of attention, locomotory activity, posture and appearance, interaction, and impairment of respiration. For deviations of the normal state for these five parameters (i.e., typical clinical signs of disease), scores were defined in up to four classes. The periods of time elapsing from attaining a score for the first time to death were registered per bird for each score for each parameter. Of 114 birds, 85 did not present typical signs of illness as described, and 29 presented the following clinical history: 25 died after presenting signs of illness, 2 died without previous signs, 1 fell ill but survived, and 1 fell ill and recovered. Extended clinical examination was performed in birds presenting clinical signs; temperature, heart rate, respiratory rate, and subcutaneous capillary refill time were measured. The level of attention, and posture and appearance were affected most often in ill birds; 25% of these birds died within 5 and 4 hr, respectively; 50% died within 12 hr; and 75% died within 20 and 19 hr, respectively. Any of these typical signs of illness visible from 1-2 m indicated imminent death, with 75% of the birds dying within 20 hr. Measurements resulting from extended clinical examination proved of lesser predictive value. From these observations, a protocol for intervention to prevent animal suffering may be designed.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

PubMed Central

Gangeh, Mehrdad; Tadayyon, Hadi; Sadeghi-Naini, Ali; Gandhi, Sonal; Wright, Frances C.; Slodkowska, Elzbieta; Curpen, Belinda; Tran, William; Czarnota, Gregory J.

2018-01-01

Background Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer. Methods The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times. Results Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology. Conclusions This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients. PMID:29298305

Upper gastrointestinal bleeding risk scores: Who, when and why?

PubMed Central

Monteiro, Sara; Gonçalves, Tiago Cúrdia; Magalhães, Joana; Cotter, José

2016-01-01

Upper gastrointestinal bleeding (UGIB) remains a significant cause of hospital admission. In order to stratify patients according to the risk of the complications, such as rebleeding or death, and to predict the need of clinical intervention, several risk scores have been proposed and their use consistently recommended by international guidelines. The use of risk scoring systems in early assessment of patients suffering from UGIB may be useful to distinguish high-risks patients, who may need clinical intervention and hospitalization, from low risk patients with a lower chance of developing complications, in which management as outpatients can be considered. Although several scores have been published and validated for predicting different outcomes, the most frequently cited ones are the Rockall score and the Glasgow Blatchford score (GBS). While Rockall score, which incorporates clinical and endoscopic variables, has been validated to predict mortality, the GBS, which is based on clinical and laboratorial parameters, has been studied to predict the need of clinical intervention. Despite the advantages previously reported, their use in clinical decisions is still limited. This review describes the different risk scores used in the UGIB setting, highlights the most important research, explains why and when their use may be helpful, reflects on the problems that remain unresolved and guides future research with practical impact. PMID:26909231

Relationship between intrapartum transperineal ultrasound measurement of angle of progression and head-perineum distance with correlation to conventional clinical parameters of labor progress and time to delivery.

PubMed

Chan, Ying Tze Viola; Ng, Vivian Kwun Sin; Yung, Wai Kuen; Lo, Tsz Kin; Leung, Wing Cheong; Lau, Wai Lam

2015-08-01

To assess whether angle of progression (AOP) and head-perineum distance (HPD) measured by intrapartum transperineal ultrasound (ITU) correlate with clinical fetal head station (station); and whether AOP versus HPD varies during uterine contraction and relaxation. In a subset of primiparous women, whether these ITU parameters correlate with time to normal spontaneous delivery (TD). We evaluated prospectively 100 primiparous and multiparous women at term in active labor. Transabdominal and transperineal ultrasound (sagittal and transverse plane) were used to measure fetal head position and ITU parameters, respectively. Digitally palpated station and cervical dilatation were also noted. The results were compared using regression and correlation coefficients. Station was moderately correlated with AOP (r = 0.579) and HPD (r = -0.497). AOP was highly correlated with HPD during uterine contraction (r = -0.703) and relaxation (r = -0.647). In the subgroup of primiparous women, natural log of TD has the highest correlation with HPD and AOP during uterine contraction (r = 0.742), making prediction of TD similar to that of using cervical dilatation. ITU parameters were moderately correlated with station. There was constant high correlation between AOP and HPD. Prediction of TD in primiparous women using ITU parameters was similar to that of using cervical dilatation.

P-wave characteristics on routine preoperative electrocardiogram improve prediction of new-onset postoperative atrial fibrillation in cardiac surgery.

PubMed

Wong, Jim K; Lobato, Robert L; Pinesett, Andre; Maxwell, Bryan G; Mora-Mangano, Christina T; Perez, Marco V

2014-12-01

To test the hypothesis that including preoperative electrocardiogram (ECG) characteristics with clinical variables significantly improves the new-onset postoperative atrial fibrillation prediction model. Retrospective analysis. Single-center university hospital. Five hundred twenty-six patients, ≥ 18 years of age, who underwent coronary artery bypass grafting, aortic valve replacement, mitral valve replacement/repair, or a combination of valve surgery and coronary artery bypass grafting requiring cardiopulmonary bypass. Retrospective review of medical records. Baseline characteristics and cardiopulmonary bypass times were collected. Digitally-measured timing and voltages from preoperative electrocardiograms were extracted. Postoperative atrial fibrillation was defined as atrial fibrillation requiring therapeutic intervention. Two hundred eight (39.5%) patients developed postoperative atrial fibrillation. Clinical predictors were age, ejection fraction<55%, history of atrial fibrillation, history of cerebral vascular event, and valvular surgery. Three ECG parameters associated with postoperative atrial fibrillation were observed: Premature atrial contraction, p-wave index, and p-frontal axis. Adding electrocardiogram variables to the prediction model with only clinical predictors significantly improved the area under the receiver operating characteristic curve, from 0.71 to 0.78 (p<0.01). Overall net reclassification improvement was 0.059 (p = 0.09). Among those who developed postoperative atrial fibrillation, the net reclassification improvement was 0.063 (p = 0.03). Several p-wave characteristics are independently associated with postoperative atrial fibrillation. Addition of these parameters improves the postoperative atrial fibrillation prediction model. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

P09.62 Towards individualized survival prediction in glioblastoma patients using machine learning methods

PubMed Central

Vera, L.; Pérez-Beteta, J.; Molina, D.; Borrás, J. M.; Benavides, M.; Barcia, J. A.; Velásquez, C.; Albillo, D.; Lara, P.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Machine learning methods are integrated in clinical research studies due to their strong capability to discover parameters having a high information content and their predictive combined potential. Several studies have been developed using glioblastoma patient’s imaging data. Many of them have focused on including large numbers of variables, mostly two-dimensional textural features and/or genomic data, regardless of their meaning or potential clinical relevance. Materials and methods: 193 glioblastoma patients were included in the study. Preoperative 3D magnetic resonance images were collected and semi-automatically segmented using an in-house software. After segmentation, a database of 90 parameters including geometrical and textural image-based measures together with patients’ clinical data (including age, survival, type of treatment, etc.) was constructed. The criterion for including variables in the study was that they had either shown individual impact on survival in single or multivariate analyses or have a precise clinical or geometrical meaning. These variables were used to perform several machine learning experiments. In a first set of computational cross-validation experiments based on regression trees, those attributes showing the highest information measures were extracted. In the second phase, more sophisticated learning methods were employed in order to validate the potential of the previous variables predicting survival. Concretely support vector machines, neural networks and sparse grid methods were used. Results: Variables showing high information measure in the first phase provided the best prediction results in the second phase. Specifically, patient age, Stupp regimen and a geometrical measure related with the irregularity of contrast-enhancing areas were the variables showing the highest information measure in the first stage. For the second phase, the combinations of patient age and Stupp regimen together with one tumor geometrical measure and one tumor heterogeneity feature reached the best quality prediction. Conclusions: Advanced machine learning methods identified the parameters with the highest information measure and survival predictive potential. The uninformed machine learning methods identified a novel feature measure with direct impact on survival. Used in combination with other previously known variables multi-indexes can be defined that can help in tumor characterization and prognosis prediction. Recent advances on the definition of those multi-indexes will be reported in the conference. Funding: James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Collaborative award 220020450 and planning grant 220020420], MINECO/FEDER [MTM2015-71200-R], JCCM [PEII-2014-031-P].

A TCP model for external beam treatment of intermediate-risk prostate cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, Sean; Putten, Wil van der

2013-03-15

Purpose: Biological models offer the ability to predict clinical outcomes. The authors describe a model to predict the clinical response of intermediate-risk prostate cancer to external beam radiotherapy for a variety of fractionation regimes. Methods: A fully heterogeneous population averaged tumor control probability model was fit to clinical outcome data for hyper, standard, and hypofractionated treatments. The tumor control probability model was then employed to predict the clinical outcome of extreme hypofractionation regimes, as utilized in stereotactic body radiotherapy. Results: The tumor control probability model achieves an excellent level of fit, R{sup 2} value of 0.93 and a root meanmore » squared error of 1.31%, to the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals Less-Than-Or-Slanted-Equal-To 1.0% are produced by the tumor control probability model when compared to clinical outcome data for stereotactic body radiotherapy. Conclusions: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.« less

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

PubMed

Johnson, Martin; Kozielska, Magdalena; Pilla Reddy, Venkatesh; Vermeulen, An; Barton, Hugh A; Grimwood, Sarah; de Greef, Rik; Groothuis, Geny M M; Danhof, Meindert; Proost, Johannes H

2016-04-01

To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Can plantar soft tissue mechanics enhance prognosis of diabetic foot ulcer?

PubMed

Naemi, R; Chatzistergos, P; Suresh, S; Sundar, L; Chockalingam, N; Ramachandran, A

2017-04-01

To investigate if the assessment of the mechanical properties of plantar soft tissue can increase the accuracy of predicting Diabetic Foot Ulceration (DFU). 40 patients with diabetic neuropathy and no DFU were recruited. Commonly assessed clinical parameters along with plantar soft tissue stiffness and thickness were measured at baseline using ultrasound elastography technique. 7 patients developed foot ulceration during a 12months follow-up. Logistic regression was used to identify parameters that contribute to predicting the DFU incidence. The effect of using parameters related to the mechanical behaviour of plantar soft tissue on the specificity, sensitivity, prediction strength and accuracy of the predicting models for DFU was assessed. Patients with higher plantar soft tissue thickness and lower stiffness at the 1st Metatarsal head area showed an increased risk of DFU. Adding plantar soft tissue stiffness and thickness to the model improved its specificity (by 3%), sensitivity (by 14%), prediction accuracy (by 5%) and prognosis strength (by 1%). The model containing all predictors was able to effectively (χ 2 (8, N=40)=17.55, P<0.05) distinguish between the patients with and without DFU incidence. The mechanical properties of plantar soft tissue can be used to improve the predictability of DFU in moderate/high risk patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation.

PubMed

Meertens, Linda J E; van Montfort, Pim; Scheepers, Hubertina C J; van Kuijk, Sander M J; Aardenburg, Robert; Langenveld, Josje; van Dooren, Ivo M A; Zwaan, Iris M; Spaanderman, Marc E A; Smits, Luc J M

2018-04-17

Prediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models. Prediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds. Four studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51-0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models. This review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth. © 2018 The Authors Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Preoperative Biometric Parameters Predict the Vault after ICL Implantation: A Retrospective Clinical Study.

PubMed

Zheng, Qian-Yin; Xu, Wen; Liang, Guan-Lu; Wu, Jing; Shi, Jun-Ting

2016-01-01

To investigate the correlation between the preoperative biometric parameters of the anterior segment and the vault after implantable Collamer lens (ICL) implantation via this retrospective study. Retrospective clinical study. A total of 78 eyes from 41 patients who underwent ICL implantation surgery were included in this study. Preoperative biometric parameters, including white-to-white (WTW) diameter, central corneal thickness, keratometer, pupil diameter, anterior chamber depth, sulcus-to-sulcus diameter, anterior chamber area (ACA) and central curvature radius of the anterior surface of the lens (Lenscur), were measured. Lenscur and ACA were measured with Rhinoceros 5.0 software on the image scanned with ultrasound biomicroscopy (UBM). The vault was assessed by UBM 3 months after surgery. Multiple stepwise regression analysis was employed to identify the variables that were correlated with the vault. The results showed that the vault was correlated with 3 variables: ACA (22.4 ± 4.25 mm2), WTW (11.36 ± 0.29 mm) and Lenscur (9.15 ± 1.21 mm). The regressive equation was: vault (mm) = 1.785 + 0.017 × ACA + 0.051 × Lenscur - 0.203 × WTW. Biometric parameters of the anterior segment (ACA, WTW and Lenscur) can predict the vault after ICL implantation using a new regression equation. © 2016 The Author(s) Published by S. Karger AG, Basel.

Volume effects of late term normal tissue toxicity in prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Bonta, Dacian Viorel

Modeling of volume effects for treatment toxicity is paramount for optimization of radiation therapy. This thesis proposes a new model for calculating volume effects in gastro-intestinal and genito-urinary normal tissue complication probability (NTCP) following radiation therapy for prostate carcinoma. The radiobiological and the pathological basis for this model and its relationship to other models are detailed. A review of the radiobiological experiments and published clinical data identified salient features and specific properties a biologically adequate model has to conform to. The new model was fit to a set of actual clinical data. In order to verify the goodness of fit, two established NTCP models and a non-NTCP measure for complication risk were fitted to the same clinical data. The method of fit for the model parameters was maximum likelihood estimation. Within the framework of the maximum likelihood approach I estimated the parameter uncertainties for each complication prediction model. The quality-of-fit was determined using the Aikaike Information Criterion. Based on the model that provided the best fit, I identified the volume effects for both types of toxicities. Computer-based bootstrap resampling of the original dataset was used to estimate the bias and variance for the fitted parameter values. Computer simulation was also used to estimate the population size that generates a specific uncertainty level (3%) in the value of predicted complication probability. The same method was used to estimate the size of the patient population needed for accurate choice of the model underlying the NTCP. The results indicate that, depending on the number of parameters of a specific NTCP model, 100 (for two parameter models) and 500 patients (for three parameter models) are needed for accurate parameter fit. Correlation of complication occurrence in patients was also investigated. The results suggest that complication outcomes are correlated in a patient, although the correlation coefficient is rather small.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Speech prosody impairment predicts cognitive decline in Parkinson's disease.

PubMed

Rektorova, Irena; Mekyska, Jiri; Janousova, Eva; Kostalova, Milena; Eliasova, Ilona; Mrackova, Martina; Berankova, Dagmar; Necasova, Tereza; Smekal, Zdenek; Marecek, Radek

2016-08-01

Impairment of speech prosody is characteristic for Parkinson's disease (PD) and does not respond well to dopaminergic treatment. We assessed whether baseline acoustic parameters, alone or in combination with other predominantly non-dopaminergic symptoms may predict global cognitive decline as measured by the Addenbrooke's cognitive examination (ACE-R) and/or worsening of cognitive status as assessed by a detailed neuropsychological examination. Forty-four consecutive non-depressed PD patients underwent clinical and cognitive testing, and acoustic voice analysis at baseline and at the two-year follow-up. Influence of speech and other clinical parameters on worsening of the ACE-R and of the cognitive status was analyzed using linear and logistic regression. The cognitive status (classified as normal cognition, mild cognitive impairment and dementia) deteriorated in 25% of patients during the follow-up. The multivariate linear regression model consisted of the variation in range of the fundamental voice frequency (F0VR) and the REM Sleep Behavioral Disorder Screening Questionnaire (RBDSQ). These parameters explained 37.2% of the variability of the change in ACE-R. The most significant predictors in the univariate logistic regression were the speech index of rhythmicity (SPIR; p = 0.012), disease duration (p = 0.019), and the RBDSQ (p = 0.032). The multivariate regression analysis revealed that SPIR alone led to 73.2% accuracy in predicting a change in cognitive status. Combining SPIR with RBDSQ improved the prediction accuracy of SPIR alone by 7.3%. Impairment of speech prosody together with symptoms of RBD predicted rapid cognitive decline and worsening of PD cognitive status during a two-year period. Copyright © 2016 Elsevier Ltd. All rights reserved.

Influence of pulmonary emphysema on COPD assessment test-oriented categorization in GOLD document.

PubMed

Suzuki, Toshio; Tada, Yuji; Kawata, Naoko; Ikari, Jun; Kasahara, Yasunori; Sakurai, Yoriko; Iesato, Ken; Nishimura, Rintaro; West, James; Tatsumi, Koichiro

2015-01-01

The COPD assessment test (CAT) score is a key component of the multifactorial assessment of COPD in the Global initiative for chronic Obstructive Lung Disease (GOLD) guidelines of 2014. Nevertheless, little is known regarding the differences among COPD categories in terms of clinical parameters such as pulmonary function or radiological findings. Thus, our aims in this study were to evaluate the associations between CAT scores and pulmonary clinical parameters, and to investigate factors that could discriminate between a "less symptomatic group" (categories A and C) and a "more symptomatic group" (categories B and D) among stable COPD patients. We enrolled 200 outpatients at Chiba University Hospital. Study subjects were assessed by CAT, pulmonary function testing, and multidetector computed tomography (MDCT). We assessed possible correlations between these indices. CAT scores were negatively correlated with percentage of the forced expiratory volume in 1 second predicted value (FEV1 %predicted) and percentage of the diffusing capacity for carbon monoxide per liter of lung volume predicted value (DLCO/VA [%predicted]) results and positively correlated with low attenuation volume percentage (LAV%) and residual volume to total lung capacity ratios (RV/TLC). In the "more symptomatic group" (category B or D), the mean DLCO/VA (%predicted) was significantly lower and the mean LAV% and RV/TLC was significantly higher than those in the "less symptomatic group" (category A or C), respectively. Interestingly, those in category B had higher mean LAV% compared to those in category C. CAT scores were significantly correlated with pulmonary function parameters and emphysematous changes on MDCT. The new GOLD classification system would be a step toward a phenotypic approach, especially taking into account the degree of emphysema and hyperinflation.

Predicting changes in clinical status of young asthmatics: clinical scores or objective parameters?

PubMed

Leung, Ting F; Ko, Fanny W S; Wong, Gary W K; Li, Chung Y; Yung, Edmund; Hui, David S C; Lai, Christopher K W

2009-05-01

Preventing asthma exacerbation is an important goal of asthma management. The existing clinical tools are not good in predicting asthma exacerbations in young children. Childhood Asthma Control Test (C-ACT) was recently published to be a simple tool for assessing disease control in young children. This study investigated C-ACT and other disease-related factors for indicating longitudinal changes in asthma status and predicting asthma exacerbations. During the same clinic visit, asthma patients aged 4-11 years completed the Chinese version of C-ACT and underwent exhaled nitric oxide and spirometric measurements. Blinded to these results, the same investigator assigned Disease Severity Score (DSS) and rated asthma control according to Global Initiative for Asthma. Asthma exacerbations during the next 6 months were recorded. Ninety-seven patients were recruited, with their mean (standard deviation [SD]) age being 9.2 (2.0) years. Thirty-six (37.1%) patients had uncontrolled asthma at baseline. C-ACT, DSS, and FEV(1) differed among patients with different control status (P < 0.001 for C-ACT and DSS; P = 0.028 for FEV(1)). Thirty-two patients had asthma exacerbations during the 6-month follow-up. Changes in patients' C-ACT scores correlated with changes in asthma control status, DSS, and FEV(1) (P = 0.019, 0.034, and 0.020, respectively). C-ACT score was lower among patients with asthma exacerbations (mean [SD]: 22.9 [4.2] vs. 24.5 [2.1]; P = 0.015). Logistic regression confirmed that the occurrence of asthma exacerbations was associated only with baseline C-ACT (B = -0.203, P = 0.042). In conclusion, C-ACT is better than DSS and objective parameters in reflecting changes in asthma status and predicting asthma exacerbations in young children. (c) 2009 Wiley-Liss, Inc.

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

PubMed

Erdoğan, Onur; Aydin Son, Yeşim

2014-01-01

Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

Which nerve conduction parameters can predict spontaneous electromyographic activity in carpal tunnel syndrome?

PubMed

Chang, Chia-Wei; Lee, Wei-Ju; Liao, Yi-Chu; Chang, Ming-Hong

2013-11-01

We investigate electrodiagnostic markers to determine which parameters are the best predictors of spontaneous electromyographic (EMG) activity in carpal tunnel syndrome (CTS). We enrolled 229 patients with clinically proven and nerve conduction study (NCS)-proven CTS, as well as 100 normal control subjects. All subjects were evaluated using electrodiagnostic techniques, including median distal sensory latencies (DSLs), sensory nerve action potentials (SNAPs), distal motor latencies (DMLs), compound muscle action potentials (CMAPs), forearm median nerve conduction velocities (FMCVs) and wrist-palm motor conduction velocities (W-P MCVs). All CTS patients underwent EMG examination of the abductor pollicis brevis (APB) muscle, and the presence or absence of spontaneous EMG activities was recorded. Normal limits were determined by calculating the means ± 2 standard deviations from the control data. Associations between parameters from the NCS and EMG findings were investigated. In patients with clinically diagnosed CTS, abnormal median CMAP amplitudes were the best predictors of spontaneous activity during EMG examination (p<0.001; OR 36.58; 95% CI 15.85-84.43). If the median CMAP amplitude was ≤ 2.1 mV, the rate of occurrence of spontaneous EMG activity was >95% (positive predictive rate >95%). If the median CMAP amplitude was higher than the normal limit (>4.9 mV), the rate of no spontaneous EMG activity was >94% (negative predictive rate >94%). An abnormal SNAP amplitude was the second best predictor of spontaneous EMG activity (p<0.001; OR 4.13; 95% CI 2.16-7.90), and an abnormal FMCV was the third best predictor (p=0.01; OR 2.10; 95% CI 1.20-3.67). No other nerve conduction parameters had significant power to predict spontaneous activity upon EMG examination. The CMAP amplitudes of the APB are the most powerful predictors of the occurrence of spontaneous EMG activity. Low CMAP amplitudes are strongly associated with spontaneous activity, whereas high CMAP amplitude are less associated with spontaneous activity, implying that needle EMG examination should be recommended for the detection of spontaneous activity in those CTS patients whose NCS reveals CMAP amplitudes between 2.1 mV and the lower normal limit (4.9mV in the present study). Using NCS, electromyographers can predict the presence of spontaneous EMG activity in CTS patients. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Prediction of Marginal Mass Required for Successful Islet Transplantation

PubMed Central

Papas, Klearchos K.; Colton, Clark K.; Qipo, Andi; Wu, Haiyan; Nelson, Rebecca A.; Hering, Bernhard J.; Weir, Gordon C.; Koulmanda, Maria

2013-01-01

Islet quality assessment methods for predicting diabetes reversal (DR) following transplantation are needed. We investigated two islet parameters, oxygen consumption rate (OCR) and OCR per DNA content, to predict transplantation outcome and explored the impact of islet quality on marginal islet mass for DR. Outcomes in immunosuppressed diabetic mice were evaluated by transplanting mixtures of healthy and purposely damaged rat islets for systematic variation of OCR/DNA over a wide range. The probability of DR increased with increasing transplanted OCR and OCR/DNA. On coordinates of OCR versus OCR/DNA, data fell into regions in which DR occurred in all, some, or none of the animals with a sharp threshold of around 150-nmol/min mg DNA. A model incorporating both parameters predicted transplantation outcome with sensitivity and specificity of 93% and 94%, respectively. Marginal mass was not constant, depended on OCR/DNA, and increased from 2,800 to over 100,000 islet equivalents/kg body weight as OCR/DNA decreased. We conclude that measurements of OCR and OCR/DNA are useful for predicting transplantation outcome in this model system, and OCR/DNA can be used to estimate the marginal mass required for reversing diabetes. Because human clinical islet preparations in a previous study had OCR/DNA values in the range of 100–150-nmol/min mg DNA, our findings suggest that substantial improvement in transplantation outcome may accompany increasedOCR/DNAin clinical islet preparations. PMID:20233002

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nesteruk, M; Riesterer, O; Veit-Haibach, P

2016-06-15

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, bloodmore » flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.« less

Sensitivity of corneal biomechanical and optical behavior to material parameters using design of experiments method.

PubMed

Xu, Mengchen; Lerner, Amy L; Funkenbusch, Paul D; Richhariya, Ashutosh; Yoon, Geunyoung

2018-02-01

The optical performance of the human cornea under intraocular pressure (IOP) is the result of complex material properties and their interactions. The measurement of the numerous material parameters that define this material behavior may be key in the refinement of patient-specific models. The goal of this study was to investigate the relative contribution of these parameters to the biomechanical and optical responses of human cornea predicted by a widely accepted anisotropic hyperelastic finite element model, with regional variations in the alignment of fibers. Design of experiments methods were used to quantify the relative importance of material properties including matrix stiffness, fiber stiffness, fiber nonlinearity and fiber dispersion under physiological IOP. Our sensitivity results showed that corneal apical displacement was influenced nearly evenly by matrix stiffness, fiber stiffness and nonlinearity. However, the variations in corneal optical aberrations (refractive power and spherical aberration) were primarily dependent on the value of the matrix stiffness. The optical aberrations predicted by variations in this material parameter were sufficiently large to predict clinically important changes in retinal image quality. Therefore, well-characterized individual variations in matrix stiffness could be critical in cornea modeling in order to reliably predict optical behavior under different IOPs or after corneal surgery.

Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy.

PubMed

Lucia, François; Visvikis, Dimitris; Desseroit, Marie-Charlotte; Miranda, Omar; Malhaire, Jean-Pierre; Robin, Philippe; Pradier, Olivier; Hatt, Mathieu; Schick, Ulrike

2018-05-01

The aim of this study is to determine if radiomics features from 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) images could contribute to prognoses in cervical cancer. One hundred and two patients (69 for training and 33 for testing) with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) from 08/2010 to 12/2016 were enrolled in this study. 18 F-FDG PET/CT and MRI examination [T1, T2, T1C, diffusion-weighted imaging (DWI)] were performed for each patient before CRT. Primary tumor volumes were delineated with the fuzzy locally adaptive Bayesian algorithm in the PET images and with 3D Slicer™ in the MRI images. Radiomics features (intensity, shape, and texture) were extracted and their prognostic value was compared with clinical parameters for recurrence-free and locoregional control. In the training cohort, median follow-up was 3.0 years (range, 0.43-6.56 years) and relapse occurred in 36% of patients. In univariate analysis, FIGO stage (I-II vs. III-IV) and metabolic response (complete vs. non-complete) were probably associated with outcome without reaching statistical significance, contrary to several radiomics features from both PET and MRI sequences. Multivariate analysis in training test identified Grey Level Non Uniformity GLRLM in PET and Entropy GLCM in ADC maps from DWI MRI as independent prognostic factors. These had significantly higher prognostic power than clinical parameters, as evaluated in the testing cohort with accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control (versus ~50-60% for clinical parameters). In LACC treated with CRT, radiomics features such as EntropyGLCM and GLNUGLRLM from functional imaging DWI-MRI and PET, respectively, are independent predictors of recurrence and loco-regional control with significantly higher prognostic power than usual clinical parameters. Further research is warranted for their validation, which may justify more aggressive treatment in patients identified with high probability of recurrence.

Physiologically based pharmacokinetic modelling of methotrexate and 6-mercaptopurine in adults and children. Part 2: 6-mercaptopurine and its interaction with methotrexate.

PubMed

Ogungbenro, Kayode; Aarons, Leon

2014-04-01

6-mercaptopurine (6-MP) is a purine antimetabolite and prodrug that undergoes extensive intracellular metabolism to produce thionucleotides, active metabolites which have cytotoxic and immunosuppressive properties. Combination therapies involving 6-MP and methotrexate have shown remarkable results in the cure of childhood acute lymphoblastic leukaemia (ALL) in the last 30 years. 6-MP undergoes very extensive intestinal and hepatic metabolism following oral dosing due to the activity of xanthine oxidase leading to very low and highly variable bioavailability and methotrexate has been demonstrated as an inhibitor of xanthine oxidase. Despite the success recorded in the use of 6-MP in ALL, there is still lack of effect and life threatening toxicity in some patients due to variability in the pharmacokinetics of 6-MP. Also, dose adjustment during treatment is still based on toxicity. The aim of the current work was to develop a mechanistic model that can be used to simulate trial outcomes and help to improve dose individualisation and dosage regimen optimisation. A physiological based pharmacokinetic model was proposed for 6-MP, this model has compartments for stomach, gut lumen, enterocyte, gut tissue, spleen, liver vascular, liver tissue, kidney vascular, kidney tissue, skin, bone marrow, thymus, muscle, rest of body and red blood cells. The model was based on the assumption of the same elimination pathways in adults and children. Parameters of the model include physiological parameters and drug-specific parameter which were obtained from the literature or estimated using plasma and red blood cell concentration data. Age-dependent changes in parameters were implemented for scaling and variability was also introduced on the parameters for prediction. Inhibition of 6-MP first-pass effect by methotrexate was implemented to predict observed clinical interaction between the two drugs. The model was developed successfully and plasma and red blood cell concentrations were adequately predicted both in terms of mean prediction and variability. The predicted interaction between 6-MP and methotrexate was slightly lower than the reported clinical interaction between the two drugs. The model can be used to predict plasma and tissue concentration in adults and children following oral and intravenous dosing and may ultimately help to improve treatment outcome in childhood ALL patients.

On the use of published radiobiological parameters and the evaluation of NTCP models regarding lung pneumonitis in clinical breast radiotherapy.

PubMed

Svolos, Patricia; Tsougos, Ioannis; Kyrgias, Georgios; Kappas, Constantine; Theodorou, Kiki

2011-04-01

In this study we sought to evaluate and accent the importance of radiobiological parameter selection and implementation to the normal tissue complication probability (NTCP) models. The relative seriality (RS) and the Lyman-Kutcher-Burman (LKB) models were studied. For each model, a minimum and maximum set of radiobiological parameter sets was selected from the overall published sets applied in literature and a theoretical mean parameter set was computed. In order to investigate the potential model weaknesses in NTCP estimation and to point out the correct use of model parameters, these sets were used as input to the RS and the LKB model, estimating radiation induced complications for a group of 36 breast cancer patients treated with radiotherapy. The clinical endpoint examined was Radiation Pneumonitis. Each model was represented by a certain dose-response range when the selected parameter sets were applied. Comparing the models with their ranges, a large area of coincidence was revealed. If the parameter uncertainties (standard deviation) are included in the models, their area of coincidence might be enlarged, constraining even greater their predictive ability. The selection of the proper radiobiological parameter set for a given clinical endpoint is crucial. Published parameter values are not definite but should be accompanied by uncertainties, and one should be very careful when applying them to the NTCP models. Correct selection and proper implementation of published parameters provides a quite accurate fit of the NTCP models to the considered endpoint.

A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

PubMed

Ouzounoglou, Eleftherios; Kolokotroni, Eleni; Stanulla, Martin; Stamatakos, Georgios S

2018-02-06

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

Using bioimpedance spectroscopy parameters as real-time feedback during tDCS.

PubMed

Nejadgholi, Isar; Caytak, Herschel; Bolic, Miodrag

2016-08-01

An exploratory analysis is carried out to investigate the feasibility of using BioImpedance Spectroscopy (BIS) parameters, measured on scalp, as real-time feedback during Transcranial Direct Current Stimulation (tDCS). TDCS is shown to be a potential treatment for neurological disorders. However, this technique is not considered as a reliable clinical treatment, due to the lack of a measurable indicator of treatment efficacy. Although the voltage that is applied on the head is very simple to measure during a tDCS session, changes of voltage are difficult to interpret in terms of variables that affect clinical outcome. BIS parameters are considered as potential feedback parameters, because: 1) they are shown to be associated with the DC voltage applied on the head, 2) they are interpretable in terms of conductive and capacitive properties of head tissues, 3) physical interpretation of BIS measurements makes them prone to be adjusted by clinically controllable variables, 4) BIS parameters are measurable in a cost-effective and safe way and do not interfere with DC stimulation. This research indicates that a quadratic regression model can predict the DC voltage between anode and cathode based on parameters extracted from BIS measurements. These parameters are extracted by fitting the measured BIS spectra to an equivalent electrical circuit model. The effect of clinical tDCS variables on BIS parameters needs to be investigated in future works. This work suggests that BIS is a potential method to be used for monitoring a tDCS session in order to adjust, tailor, or personalize tDCS treatment protocols.

Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status

PubMed Central

Zhao, Ping; Pan, Yuzhuo; Wagner, Christian

2017-01-01

A comprehensive search in literature and published US Food and Drug Administration reviews was conducted to assess whether physiologically based pharmacokinetic (PBPK) modeling could be prospectively used to predict clinical food effect on oral drug absorption. Among the 48 resulted food effect predictions, ∼50% were predicted within 1.25‐fold of observed, and 75% within 2‐fold. Dissolution rate and precipitation time were commonly optimized parameters when PBPK modeling was not able to capture the food effect. The current work presents a knowledgebase for documenting PBPK experience to predict food effect. PMID:29168611

Predictive relevance of clinical scores and inflammatory parameters in secondary peritonitis.

PubMed

Zügel, Nikolaus P; Kox, Martin; Lichtwark-Aschoff, Michael; Gippner-Steppert, Cornelia; Jochum, Marianne

2011-01-01

To measure and evaluate clinical scores and various inflammation parameters for providing a better outcome assessment of patients with secondary peritonitis. Prospective study. ICU of a university and a university affiliated hospital. Fifty-six patients with severe secondary peritonitis were enrolled in this study executed within 4 years. Blood samples were taken preoperatively and 2, 6, 8, 12, 18, 24, 30, 36, 42 and 48 hours post operation, thereafter every 12th hour until day 5 respectively once daily until day 14. Etiology of peritonitis, clinical score systems (APACHE II, MOF and SOFA), and 27 mainly with activity tests or enzyme-immunoassays measurable inflammation parameters were simultaneously analyzed and stratified into lethal outcome (n = 11) or survival (n = 45), respectively. The etiological distribution of peritonitis was identical among both groups. Proportion of intraperitoneal fungi, E. coli, and bacteroids was substantially higher during the primary operation in the group with lethal outcome. With increasing significance initial and follow-up APACHE II, MOF and SOFA scores provided higher values in this group. Various plasma/serum parameters of hemostasis, leukocyte proteolytic system, acute phase reaction, cytokine system, cell adhesion, opsonization, and main organ functions showed significantly different values between both groups at the preoperative stage and/or during observation period I (day 0-4). Logistic regression analysis revealed the SOFA score and neopterin concentration as the combination with the best sensitivity (63.6%) and specificity (93.2%) for predicting the patients' survival even at the preoperative stage. For the observation period I, the combination of SOFA score and TNF receptor II showed the highest predictive sensitivity (72.7%) and specificity (95.6%). Evaluation of the severity of secondary peritonitis using a scoring system with high prognostic relevance could conceivably result in an earlier and adequate application of intensive care such as hemofiltration, administration of immunoglobulins and serial abdominal lavage to improve successful outcome.

Validation of limited sampling strategy for the estimation of mycophenolic acid exposure in Chinese adult liver transplant recipients.

PubMed

Hao, Chen; Erzheng, Chen; Anwei, Mao; Zhicheng, Yu; Baiyong, Shen; Xiaxing, Deng; Weixia, Zhang; Chenghong, Peng; Hongwei, Li

2007-12-01

Mycophenolate mofetil (MMF) is indicated as immunosuppressive therapy in liver transplantation. The abbreviated models for the estimation of mycophenolic acid (MPA) area under the concentration-time curve (AUC) have been established by limited sampling strategies (LSSs) in adult liver transplant recipients. In the current study, the performance of the abbreviated models to predict MPA exposure was validated in an independent group of patients. A total of 30 MPA pharmacokinetic profiles from 30 liver transplant recipients receiving MMF in combination with tacrolimus were used to compare 8 models' performance with a full 10 time-point MPA-AUC. Linear regression analysis and Bland-Altman analysis were used to compare the estimated MPA-AUC0-12h from each model against the measured MPA-AUC0-12h. A wide range of agreement was shown when estimated MPA-AUC0-12h was compared with measured MPA-AUC0-12h, and the range of coefficient of determination (r2) was from 0.479 to 0.936. The model based on MPA pharmacokinetic parameters C1h, C2h, C6h, and C8h had the best ability to predict measured MPA-AUC0-12h, with the best coefficient of determination (r2=0.936), the excellent prediction bias (2.18%), the best prediction precision (5.11%), and the best prediction variation (2SD=+/-7.88 mg.h/L). However, the model based on MPA pharmacokinetic sampling time points C1h, C2h, and C4h was more suitable when concerned with clinical convenience, which had shorter sampling interval, an excellent coefficient of determination (r2=0.795), an excellent prediction bias (3.48%), an acceptable prediction precision (14.37%), and a good prediction variation (2SD=+/-13.23 mg.h/L). Measured MPA-AUC0-12h could be best predicted by using MPA pharmacokinetic parameters C1h, C2h, C6h, and C8h. The model based on MPA pharmacokinetic parameters C1h, C2h, and C4h was more feasible in clinical application. Copyright (c) 2007 AASLD.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

PubMed Central

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L.

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. PMID:26107615

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

PubMed

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

Risk Assessment Using Cytochrome P450 Time-Dependent Inhibition Assays at Single Time and Concentration in the Early Stage of Drug Discovery.

PubMed

Kosaka, Mai; Kosugi, Yohei; Hirabayashi, Hideki

2017-09-01

In this article, we proposed a risk assessment strategy for CYP3A time-dependent inhibition (TDI) during drug discovery based on a thorough retrospective study of 13 reference drugs, some of which are known to have in vitro TDI potential but have unknown clinical relevance. First, the traditional parameter k inact /K I , recommended by regulatory authorities for necessity decision making in clinical drug-drug interaction (DDI) studies, was investigated as a predictive index for clinical TDI liability. The cutoff value of 1.1 for k inact /K I , established by the Food and Drug Administration, tended to produce false-positive prediction results for clinical DDI occurrence. The value of 1.25 recommended in the European Medicines Evaluation Agency draft guideline yielded better predictions with only 1 false negative for diltiazem. Second, to enable earlier risk assessment, remaining activity, defined as the residual CYP3A activity in vitro obtained in the screening conditions, was investigated as an alternative index. As a result, the ratios of unbound C max or area under the curve to remaining activity precisely predicted clinical DDI occurrence. In conclusion, we demonstrated the predictive power of k inact /K I and remaining activity values for clinical DDIs. These findings provide insights that enable TDI risk assessment, even during drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus.

PubMed

Pena, Michelle J; Heinzel, Andreas; Rossing, Peter; Parving, Hans-Henrik; Dallmann, Guido; Rossing, Kasper; Andersen, Steen; Mayer, Bernd; Heerspink, Hiddo J L

2016-07-05

Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria. Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response. In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response. A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.

The significance of serum urea and renal function in patients with heart failure.

PubMed

Gotsman, Israel; Zwas, Donna; Planer, David; Admon, Dan; Lotan, Chaim; Keren, Andre

2010-07-01

Renal function and urea are frequently abnormal in patients with heart failure (HF) and are predictive of increased mortality. The relative importance of each parameter is less clear. We prospectively compared the predictive value of renal function and serum urea on clinical outcome in patients with HF. Patients hospitalized with definite clinical diagnosis of HF (n = 355) were followed for short-term (1 yr) and long-term (mean, 6.5 yr) survival and HF rehospitalization. Increasing tertiles of discharge estimated glomerular filtration rate (eGFR) were an independent predictor of increased long-term survival (hazard ratio [HR], 0.65; 95% confidence interval [CI], 0.47-0.91; p = 0.01) but not short-term survival. Admission and discharge serum urea and blood urea nitrogen (BUN)/creatinine ratio were predictors of reduced short- and long-term survival on multivariate Cox regression analysis. Increasing tertiles of discharge urea were a predictor of reduced 1-year survival (HR, 2.13; 95% CI, 1.21-3.73; p = 0.009) and long-term survival (HR, 1.93; 95% CI, 1.37-2.71; p < 0.0001). Multivariate analysis including discharge eGFR and serum urea demonstrated that only serum urea remained a significant predictor of long-term survival; however, eGFR and BUN/creatinine ratio were both independently predictive of survival. Urea was more discriminative than eGFR in predicting long-term survival by area under the receiver operating characteristic curve (0.803 vs. 0.787; p = 0.01). Increasing tertiles of discharge serum urea and BUN/creatinine were independent predictors of HF rehospitalization and combined death and HF rehospitalization. This study suggests that serum urea is a more powerful predictor of survival than eGFR in patients with HF. This may be due to urea's relation to key biological parameters including renal, hemodynamic, and neurohormonal parameters pertaining to the overall clinical status of the patient with chronic HF.

Clinical and molecular predictors of disease severity and survival in chronic lymphocytic leukemia.

PubMed

Weinberg, J Brice; Volkheimer, Alicia D; Chen, Youwei; Beasley, Bethany E; Jiang, Ning; Lanasa, Mark C; Friedman, Daphne; Vaccaro, Gina; Rehder, Catherine W; Decastro, Carlos M; Rizzieri, David A; Diehl, Louis F; Gockerman, Jon P; Moore, Joseph O; Goodman, Barbara K; Levesque, Marc C

2007-12-01

Several parameters may predict disease severity and overall survival in chronic lymphocytic leukemia (CLL). The purpose of our study of 190 CLL patients was to compare immunoglobulin heavy chain variable region (IgV(H)) mutation status, cytogenetic abnormalities, and leukemia cell CD38 and Zap-70 to older, traditional parameters. We also wanted to construct a simple, inexpensive prognosis score that would significantly predict TTT and survival in patients at the time of diagnosis and help practicing clinicians. In univariate analyses, patients with higher clinical stage, higher leukocyte count at diagnosis, shorter leukocyte doubling time, elevated serum lactate dehydrogenase (LDH), unmutated immunoglobulin heavy chain variable region (IgV(H)) genes, and higher CD38 had a shorter overall survival and time-to-treatment (TTT). CLL cell Zap-70 expression was higher in patients with unmutated IgV(H), and those with higher Zap-70 tended to have shorter survival. IgV(H)4-34 or IgV(H)1-69 was the most common IgV(H) genes used (16 and 12%, respectively). Of those with IgV(H)1-69, 86% had unmutated IgV(H) and had a significantly shorter TTT. A cytogenetic abnormality was noted in 71% of the patients tested. Patients with 11q22 del and 17p13 del or complex abnormalities were significantly more likely to have unmutated IgV(H). We found that a prognostic score constructed using modified Rai stage, cellular CD38, and serum LDH (parameters easily obtained clinically) significantly predicted TTT and survival in patients at the time of diagnosis and performed as well or better than models using the newer markers.

Can Fan-Beam Interactive Computed Tomography Accurately Predict Indirect Decompression in Minimally Invasive Spine Surgery Fusion Procedures?

PubMed

Janssen, Insa; Lang, Gernot; Navarro-Ramirez, Rodrigo; Jada, Ajit; Berlin, Connor; Hilis, Aaron; Zubkov, Micaella; Gandevia, Lena; Härtl, Roger

2017-11-01

Recently, novel mobile intraoperative fan-beam computed tomography (CT) was introduced, allowing for real-time navigation and immediate intraoperative evaluation of neural decompression in spine surgery. This study sought to investigate whether intraoperatively assessed neural decompression during minimally invasive spine surgery (MISS) has a predictive value for clinical and radiographic outcome. A retrospective study of patients undergoing intraoperative CT (iCT)-guided extreme lateral interbody fusion or transforaminal lumbar interbody fusion was conducted. 1) Preoperative, 2) intraoperative (after cage implantation, 3) postoperative, and 4) follow-up radiographic and clinical parameters obtained from radiography or CT were quantified. Thirty-four patients (41 spinal segments) were analyzed. iCT-based navigation was successfully accomplished in all patients. Radiographic parameters showed significant improvement from preoperatively to intraoperatively after cage implantation in both MISS procedures (extreme lateral interbody fusion/transforaminal lumbar interbody fusion) (P ≤ 0.05). Radiologic parameters for both MISS fusion procedures did not show significant differences to the assessed radiographic measures at follow-up (P > 0.05). Radiologic outcome values did not decrease when compared intraoperatively (after cage implantation) to latest follow-up. Intraoperative fan-beam CT is capable of assessing neural decompression intraoperatively with high accuracy, allowing for precise prediction of radiologic outcome and earliest possible feedback during MISS fusion procedures. These findings are highly valuable for routine practice and future investigations toward finding a threshold for neural decompression that translates into clinical improvement. If sufficient neural decompression has been confirmed with iCT imaging studies, additional postoperative and/or follow-up imaging studies might no longer be required if patients remain asymptomatic. Copyright © 2017 Elsevier Inc. All rights reserved.

[Clinical value of angiogenin in predicting the prognosis of patients with idiopathic pulmonary fibrosis].

PubMed

Bai, Yanling; Zhu, Haiyan; Sun, Qiyu; Gu, Guozhong; Zhang, Lingyu; Li, Ying; Yang, Baofeng

2017-09-01

To explore the relationship between angiogenin-1/2 (Ang-1/2) and clinical parameters of idiopathic pulmonary fibrosis (IPF), and to assess the value of Ang-1/2 in predicting the prognosis of patients with IPF. A retrospective analysis was conducted. Ninety-one patients diagnosed as IPF by high resolution CT (HRCT) and lung biopsy admitted to Daqing Oil Field General Hospital from March 2014 to January 2015 were enrolled. The general data, serum parameters and pulmonary function parameters of all patients were collected. After treatment, all of the 91 patients were followed-up to 2 years. The patients were divided into favorable prognosis group and unfavorable prognosis group according to follow-up results. The differences in all parameters between the two groups were compared. The relationship between Ang-1, Ang-2 and lung function parameters was analyzed by Pearson correlation analysis. Cox proportional hazard regression model was used to evaluate the effect of clinical parameters on the prognosis of patients with IPF. The effect of Ang-2 in predicting prognosis of patients with IPF was analyzed by receiver operating characteristic (ROC) curve. During the 2-year follow-up period, 30 of 91 patients showed a favorable prognosis, and 55 showed an unfavorable prognosis with a poor prognosis rate of 64.71%, and 6 patients withdrew from the study due to loss of follow-up and death. Compared with the favorable prognosis group, Ang-2 level in the unfavorable prognosis group was significantly increased (μg/L: 2.88±1.63 vs. 1.89±1.22, t = 2.909, P = 0.005), but Ang-1 only showed a slight increase (μg/L: 28.70±14.26 vs. 25.62±11.95, t = 1.005, P = 0.318). The results of Pearson correlation analysis showed that Ang-2 level was negatively correlated with forced expiratory volume in 1 second (FVC1) and the percentage of carbon monoxide diffusing capacity accounting for the expected value (DLCO%: r value was -0.227 and -0.206, and P value was 0.147 and 0.253, respectively), but no significant correlation between the level of Ang-1 and FVC1 as well as DLCO% was found (r value was -0.153 and -0.121, and P value was 0.147 and 0.253, respectively). Cox proportional hazard regression model analysis showed that the prognosis of patients with IPF was significantly affected by smoking time and Ang-2 (both P < 0.05), and the influence of Ang-2 was greater [relative risk (RR): 1.236 vs. 1.006, P = 0.037]. Age, gender, smoking and the levels of FVC1, DLCO% and Ang-1 had no significant effect on the prognosis of IPF patients (all P > 0.05). Prognostic analysis showed that the area under ROC curve (AUC) of Ang-2 for predicting prognosis of patients with IPF was 0.692, and the best diagnostic point was 0.35 μg/L, the sensitivity was 61.8%, the specificity was 73.3%, the positive predictive value was 69.8%, and the negative predictive value was 65.7% which indicated that Ang-2 could predict the prognosis of patients with IPF. Ang-2 could assess the prognosis of patients with IPF, which is expected to be used as an indicator of predicting the prognosis of patients with IPF.

Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

PubMed

Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

2016-07-01

Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological categories predict renal or patient survival. Age, renal function and proteinuria at presentation, histopathology, and infectious complications constitute the main outcome predictors and should be considered for individualized management.

Prediction of the First Variceal Haemorrhage

PubMed Central

1997-01-01

We followed 87 cirrhotic patients with esophageal varices and without previous hemorrhage for a mean period of 24 mo to prospectively evaluate the occurance of variceal bleeding within (early) or after (late) 6 mo from entry and the contribution of portal Doppler ultrasound parameters to the prediction of early and late hemorrhage. Clinical, biochemical, endoscopic and portal Doppler ultrasound parameters were recorded at entry. Variceal bleeding occurred in 22 patients (25.3%). Nine (40.9%) bled within the first 6 mo. Cox regression analysis identified variceal size, cherry-red spots, serum bilirubin and congestion index of the portal vein (the ratio of portal vein [cross-sectional area] and portal blood flow velocity) as the only independent predictors of first variceal hemorrhage. Discriminant analysis was used to find the prognostic index cut off points to identify patients who bled within 6 mo (prognostic group 1) or after 6 mo (prognostic group 2) or remained free of bleeding (prognostic group 3). The cumulative proportion of patients correctly classified was 73% in prognostic group 1, 47% in prognostic group 2 and more than 80% in prognostic group 3. The addition of Doppler ultrasound flowmetry to clinical, biochemical and endoscopic parameter only improved the classification of patients with early bleeding. PMID:9184882

Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy.

PubMed

Yamanouchi, Masayuki; Hoshino, Junichi; Ubara, Yoshifumi; Takaichi, Kenmei; Kinowaki, Keiichi; Fujii, Takeshi; Ohashi, Kenichi; Mise, Koki; Toyama, Tadashi; Hara, Akinori; Kitagawa, Kiyoki; Shimizu, Miho; Furuichi, Kengo; Wada, Takashi

2018-01-01

There have been a limited number of biopsy-based studies on diabetic nephropathy, and therefore the clinical importance of renal biopsy in patients with diabetes in late-stage chronic kidney disease (CKD) is still debated. We aimed to clarify the renal prognostic value of pathological information to clinical information in patients with diabetes and advanced CKD. We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic nephropathy in four centers in Japan. 296 patients with stage 3-5 CKD at the time of biopsy were identified and assigned two risk prediction scores for end-stage renal disease (ESRD): the Kidney Failure Risk Equation (KFRE, a score composed of clinical parameters) and the Diabetic Nephropathy Score (D-score, a score integrated pathological parameters of the Diabetic Nephropathy Classification by the Renal Pathology Society (RPS DN Classification)). They were randomized 2:1 to development and validation cohort. Hazard Ratios (HR) of incident ESRD were reported with 95% confidence interval (CI) of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk prediction with the addition of D-score to the KFRE was assessed using c-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of ESRD both in the development cohort and in the validation cohort. The c-statistics of the D-score was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker than that in the miscellaneous CKD cohort originally reported (c-statistics, 0.78 vs. 0.90) and was not significantly improved by adding the D-score (0.78 vs. 0.79, p = 0.83). Only continuous NRI was positive after adding the D-score to the KFRE (0.4%; CI: 0.0-0.8%). We found that the predict values of the KFRE and the D-score were not as good as reported, and combining the D-score with the KFRE did not significantly improve prediction of the risk of ESRD in advanced diabetic nephropathy. To improve prediction of renal prognosis for advanced diabetic nephropathy may require different approaches with combining clinical and pathological parameters that were not measured in the KFRE and the RPS DN Classification.

Optimal Futility Interim Design: A Predictive Probability of Success Approach with Time-to-Event Endpoint.

PubMed

Tang, Zhongwen

2015-01-01

An analytical way to compute predictive probability of success (PPOS) together with credible interval at interim analysis (IA) is developed for big clinical trials with time-to-event endpoints. The method takes account of the fixed data up to IA, the amount of uncertainty in future data, and uncertainty about parameters. Predictive power is a special type of PPOS. The result is confirmed by simulation. An optimal design is proposed by finding optimal combination of analysis time and futility cutoff based on some PPOS criteria.

Digital pathology in nephrology clinical trials, research, and pathology practice.

PubMed

Barisoni, Laura; Hodgin, Jeffrey B

2017-11-01

In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine. Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases. The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.

On better estimating and normalizing the relationship between clinical parameters: comparing respiratory modulations in the photoplethysmogram and blood pressure signal (DPOP versus PPV).

PubMed

Addison, Paul S; Wang, Rui; Uribe, Alberto A; Bergese, Sergio D

2015-01-01

DPOP (ΔPOP or Delta-POP) is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method). We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N) is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements.

On Better Estimating and Normalizing the Relationship between Clinical Parameters: Comparing Respiratory Modulations in the Photoplethysmogram and Blood Pressure Signal (DPOP versus PPV)

PubMed Central

Addison, Paul S.; Wang, Rui; Uribe, Alberto A.; Bergese, Sergio D.

2015-01-01

DPOP (ΔPOP or Delta-POP) is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method). We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N) is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements. PMID:25691912

Robust parameter extraction for decision support using multimodal intensive care data

PubMed Central

Clifford, G.D.; Long, W.J.; Moody, G.B.; Szolovits, P.

2008-01-01

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU. PMID:18936019

Limited diagnostic accuracy of magnetic resonance imaging and clinical tests for detecting partial-thickness tears of the rotator cuff.

PubMed

Brockmeyer, Matthias; Schmitt, Cornelia; Haupert, Alexander; Kohn, Dieter; Lorbach, Olaf

2017-12-01

The reliable diagnosis of partial-thickness tears of the rotator cuff is still elusive in clinical practise. Therefore, the purpose of the study was to determine the diagnostic accuracy of MR imaging and clinical tests for detecting partial-thickness tears of the rotator cuff as well as the combination of these parameters. 334 consecutive shoulder arthroscopies for rotator cuff pathologies performed during the time period between 2010 and 2012 were analyzed retrospectively for the findings of common clinical signs for rotator cuff lesions and preoperative MR imaging. These were compared with the intraoperative arthroscopic findings as "gold standard". The reports of the MR imaging were evaluated with regard to the integrity of the rotator cuff. The Ellman Classification was used to define partial-thickness tears of the rotator cuff in accordance with the arthroscopic findings. Descriptive statistics, sensitivity, specificity, positive and negative predictive value were calculated. MR imaging showed 80 partial-thickness and 70 full-thickness tears of the rotator cuff. The arthroscopic examination confirmed 64 partial-thickness tears of which 52 needed debridement or refixation of the rotator cuff. Sensitivity for MR imaging to identify partial-thickness tears was 51.6%, specificity 77.2%, positive predictive value 41.3% and negative predictive value 83.7%. For the Jobe-test, sensitivity was 64.1%, specificity 43.2%, positive predictive value 25.9% and negative predictive value 79.5%. Sensitivity for the Impingement-sign was 76.7%, specificity 46.6%, positive predictive value 30.8% and negative predictive value 86.5%. For the combination of MR imaging, Jobe-test and Impingement-sign sensitivity was 46.9%, specificity 85.4%, positive predictive value 50% and negative predictive value 83.8%. The diagnostic accuracy of MR imaging and clinical tests (Jobe-test and Impingement-sign) alone is limited for detecting partial-thickness tears of the rotator cuff. Additionally, the combination of MR imaging and clinical tests does not improve diagnostic accuracy. Level II, Diagnostic study.

ADAM-C score: New risk score for predicting diagnostic yield of transesophageal echocardiography after cerebral ischemia.

PubMed

Charbonnel, Clément; Jego, Christophe; Jourda, François; Vinsonneau, Ulric; Garçon, Philippe; Turlotte, Guillaume; Rivière, Jean François; Maurin, Marion; Lubret, Rémy; Meimoun, Patrick; Akret, Chrystelle; Cournot, Maxime; Sokic, Charles; Michel, Laurent; Lescure, Maryse; Kenizou, David; Melay, Marie; Fayard, Maxime; Gallet, Bruno; Fouche, Rémi; Janin-Manificat, Luc; Dijoux, Nicolas; Martin, Anne Céline; Tho-Agostini, Aurélia; Mann, Hubert; Ricard, Cécile; Pico, Fernando; Georges, Jean Louis; Belle, Loïc; Jourdain, Patrick

2018-05-14

The clinical utility of transesophageal echocardiography (TEE) after brain ischemia (BI) remains a matter of debate. We aimed to evaluate the clinical impact of TEE and to build a score that could help physicians to identify which patients should better benefit from TEE. This prospective, multicenter, observational study included patients over 18 years old, hospitalized for BI. TEE findings were judged discriminant if the results showed important information leading to major changes in the management of patients. Most patients with patent foramen ovale were excluded. Variables independently associated with a discriminant TEE were used to build the prediction model. Of the entire population (1479 patients), 255 patients (17%) were classified in the discriminant TEE group. Five parameters were selected as predictors of a discriminant TEE. Accordingly, the ADAM-C score could be calculated as follows: Score = 4 (if age ≥60) + 2 (if diabetes) + 2 (if aortic stenosis from any degrees) + 1 (if multi-territory stroke) + 2 (if history of coronary artery disease). At a threshold lower than 3, the sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of detecting discriminant TEE were 88% (95% CI 85-90), 44% (95% CI 41-47), 21% (95% CI 19-27), and 95% (95% CI 94-97), respectively. A simple score based on clinical and transthoracic echocardiographic parameters can help physicians to identify patients who might not benefit from TEE. Indeed, a score lower than 3 has an interesting NPV of 95% (95% CI 94-97). © 2018 Wiley Periodicals, Inc.

Model‐Based Approach to Predict Adherence to Protocol During Antiobesity Trials

PubMed Central

Sharma, Vishnu D.; Combes, François P.; Vakilynejad, Majid; Lahu, Gezim; Lesko, Lawrence J.

2017-01-01

Abstract Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave® trials were included in this analysis. An indirect‐response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose‐ and time‐dependent pharmacodynamic (DTPD) model. Additionally, a population‐pharmacokinetic parameter‐ and data (PPPD)‐driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD‐MM and PPPD‐MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body‐weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model‐driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic‐driven approach. PMID:28858397

Model identification using stochastic differential equation grey-box models in diabetes.

PubMed

Duun-Henriksen, Anne Katrine; Schmidt, Signe; Røge, Rikke Meldgaard; Møller, Jonas Bech; Nørgaard, Kirsten; Jørgensen, John Bagterp; Madsen, Henrik

2013-03-01

The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the "time to peak of meal response" parameter. We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the "peak time of meal absorption" parameter showed that the absorption rate varied according to meal type. This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development. © 2013 Diabetes Technology Society.

Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development.

PubMed

Rufibach, Kaspar; Burger, Hans Ulrich; Abt, Markus

2016-09-01

Bayesian predictive power, the expectation of the power function with respect to a prior distribution for the true underlying effect size, is routinely used in drug development to quantify the probability of success of a clinical trial. Choosing the prior is crucial for the properties and interpretability of Bayesian predictive power. We review recommendations on the choice of prior for Bayesian predictive power and explore its features as a function of the prior. The density of power values induced by a given prior is derived analytically and its shape characterized. We find that for a typical clinical trial scenario, this density has a u-shape very similar, but not equal, to a β-distribution. Alternative priors are discussed, and practical recommendations to assess the sensitivity of Bayesian predictive power to its input parameters are provided. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Diabetes and Impaired Fasting Glucose Prediction Using Anthropometric Indices in Adults from Maracaibo City, Venezuela.

PubMed

Bermúdez, Valmore; Salazar, Juan; Rojas, Joselyn; Calvo, María; Rojas, Milagros; Chávez-Castillo, Mervin; Añez, Roberto; Cabrera, Mayela

2016-12-01

To determine the predictive power of various anthropometric indices for the identification of dysglycemic states in Maracaibo, Venezuela. A cross-sectional study with randomized, multi-staged sampling was realized in 2230 adult subjects of both genders who had their body mass index (BMI), waist circumference (WC) and waist-height ratio (WHR) determined. Diagnoses of type 2 diabetes mellitus (DM2) and impaired fasting glucose (IFG) were made following ADA 2015 criteria. ROC curves were used to evaluate the predictive power of each anthropometric parameter. Area under the curve (AUC) values were compared through Delong's test. Of the total 2230 individuals (52.6 % females), 8.4 % were found to have DM2, and 19.5 % had IFG. Anthropometric parameters displayed greater predictive power regarding newly diagnosed diabetics, where WHR was the most important predictor in both females (AUC = 0.808; CI 95 % 0.715-0.900. Sensitivity: 82.8 %; specificity: 76.2 %) and males (AUC = 0.809; CI 95 % 0.736-0.882. Sensitivity: 78.6 %; specificity: 68.1 %), although all three parameters appeared to have comparable predictive power in this subset. In previously diagnosed diabetic subjects, WHR was superior to both WC and BMI in females, and WHR and WC were both superior to BMI in males. Lower predictive values were found for IFG in both genders. Accumulation of various altered anthropometric measurements was associated with increased odds ratios for both newly and previously diagnosed DM2. The predictive power of anthropometric measurements was greater for DM2 than IFG. We suggest assessment of as many available parameters as possible in the clinical setting.

Survivin expression in head and neck squamous cell carcinomas is frequent and correlates with clinical parameters and treatment outcomes.

PubMed

Münscher, Adrian; Prochnow, Sebastian; Gulati, Amit; Sauter, Guido; Lörincz, Balazs; Blessmann, Marco; Hanken, Henning; Böttcher, Arne; Clauditz, Till Sebastian

2018-04-18

Strong expression of survivin is associated with worse survival in many different tumours, and in cell culture, a correlation between radiation resistance and survivin expression can be seen. The potential of survivin expression as a prognostic/predictive marker or therapeutic target has not been examined in head and neck squamous cell carcinomas (HNSCC) yet. Retrospective study of 452 tissue samples and clinical data from patients with squamous cell carcinomas of the larynx/hypopharynx (LSCC), oral cavity (OSCC) and oropharynx (OPSCC) treated in the University Medical Centre Hamburg-Eppendorf between 2002 and 2006. The expression patterns were detected by tissue microarray technique and correlated with clinical parameters (sex, age, tumour location, TNM 7th edition, grading, recurrence-free and overall survival). 222 OSCC, 126 OPSCC and 105 LSCC tumours of 118 females and 335 males with a mean follow-up of 41.3 months were examined. Survivin expression correlates with pN, cM, pT and overall survival. The potential of survivin as a prognostic/predictive marker is very high. The findings have to be confirmed in a larger cohort of HNSCC esp. in those tumours treated primarily with radio/radiochemotherapy.

A new method to improve the clinical evaluation of cystic fibrosis patients by mucus viscoelastic properties.

PubMed

Tomaiuolo, Giovanna; Rusciano, Giulia; Caserta, Sergio; Carciati, Antonio; Carnovale, Vincenzo; Abete, Pasquale; Sasso, Antonio; Guido, Stefano

2014-01-01

In cystic fibrosis (CF) patients airways mucus shows an increased viscoelasticity due to the concentration of high molecular weight components. Such mucus thickening eventually leads to bacterial overgrowth and prevents mucus clearance. The altered rheological behavior of mucus results in chronic lung infection and inflammation, which causes most of the cases of morbidity and mortality, although the cystic fibrosis complications affect other organs as well. Here, we present a quantitative study on the correlation between cystic fibrosis mucus viscoelasticity and patients clinical status. In particular, a new diagnostic parameter based on the correlation between CF sputum viscoelastic properties and the severity of the disease, expressed in terms of FEV1 and bacterial colonization, was developed. By using principal component analysis, we show that the types of colonization and FEV1 classes are significantly correlated to the elastic modulus, and that the latter can be used for CF severity classification with a high predictive efficiency (88%). The data presented here show that the elastic modulus of airways mucus, given the high predictive efficiency, could be used as a new clinical parameter in the prognostic evaluation of cystic fibrosis.

The prognostic impact of clinical and CT parameters in patients with pontine hemorrhage.

PubMed

Dziewas, Rainer; Kremer, Marion; Lüdemann, Peter; Nabavi, Darius G; Dräger, Bianca; Ringelstein, Bernd

2003-01-01

In patients with pontine hemorrhage (PH), an accurate prognostic assessment is critical for establishing a reasonable therapeutic approach. The initial clinical symptoms and computed tomography (CT) features were analyzed with multivariate regression analysis in 39 consecutive patients with PH. PHs were classified into three types: (1) large paramedian, (2) basal or basotegmental and (3) lateral tegmental, and the hematomas' diameters were measured. The patients' outcome was evaluated. Twenty-seven patients (69%) died and 12 (31%) survived for more than 1 year after PH. The symptom most predictive of death was coma on admission. The large paramedian type of PH predicted a poor prognosis, whereas the lateral tegmental type was associated with a favorable outcome. The transverse hematoma diameter was also related to outcome, with the threshold value found to be 20 mm. We conclude that PH outcome can be estimated best by combining the CT parameters 'large paramedian PH' and 'transverse diameter >/=20 mm' with the clinical variable 'coma on admission'. Survival is unlikely if all 3 features are present, whereas survival may be expected if only 1 or none of these features is found. Copyright 2003 S. Karger AG, Basel

Defining probabilities of bowel resection in deep endometriosis of the rectum: Prediction with preoperative magnetic resonance imaging.

PubMed

Perandini, Alessio; Perandini, Simone; Montemezzi, Stefania; Bonin, Cecilia; Bellini, Gaia; Bergamini, Valentino

2018-02-01

Deep endometriosis of the rectum is a highly challenging disease, and a surgical approach is often needed to restore anatomy and function. Two kinds of surgeries may be performed: radical with segmental bowel resection or conservative without resection. Most patients undergo magnetic resonance imaging (MRI) before surgery, but there is currently no method to predict if conservative surgery is feasible or whether bowel resection is required. The aim of this study was to create an algorithm that could predict bowel resection using MRI images, that was easy to apply and could be useful in a clinical setting, in order to adequately discuss informed consent with the patient and plan the an appropriate and efficient surgical session. We collected medical records from 2010 to 2016 and reviewed the MRI results of 52 patients to detect any parameters that could predict bowel resection. Parameters that were reproducible and with a significant correlation to radical surgery were investigated by statistical regression and combined in an algorithm to give the best prediction of resection. The calculation of two parameters in MRI, impact angle and lesion size, and their use in a mathematical algorithm permit us to predict bowel resection with a positive predictive value of 87% and a negative predictive value of 83%. MRI could be of value in predicting the need for bowel resection in deep endometriosis of the rectum. Further research is required to assess the possibility of a wider application of this algorithm outside our single-center study. © 2017 Japan Society of Obstetrics and Gynecology.

Addition of 24-Hour Heart Rate Variability Parameters to the Cardiovascular Health Study Stroke Risk Score and Prediction of Incident Stroke: The Cardiovascular Health Study.

PubMed

Bodapati, Rohan K; Kizer, Jorge R; Kop, Willem J; Kamel, Hooman; Stein, Phyllis K

2017-07-21

Heart rate variability (HRV) characterizes cardiac autonomic functioning. The association of HRV with stroke is uncertain. We examined whether 24-hour HRV added predictive value to the Cardiovascular Health Study clinical stroke risk score (CHS-SCORE), previously developed at the baseline examination. N=884 stroke-free CHS participants (age 75.3±4.6), with 24-hour Holters adequate for HRV analysis at the 1994-1995 examination, had 68 strokes over ≤8 year follow-up (median 7.3 [interquartile range 7.1-7.6] years). The value of adding HRV to the CHS-SCORE was assessed with stepwise Cox regression analysis. The CHS-SCORE predicted incident stroke (HR=1.06 per unit increment, P =0.005). Two HRV parameters, decreased coefficient of variance of NN intervals (CV%, P =0.031) and decreased power law slope (SLOPE, P =0.033) also entered the model, but these did not significantly improve the c-statistic ( P =0.47). In a secondary analysis, dichotomization of CV% (LOWCV% ≤12.8%) was found to maximally stratify higher-risk participants after adjustment for CHS-SCORE. Similarly, dichotomizing SLOPE (LOWSLOPE <-1.4) maximally stratified higher-risk participants. When these HRV categories were combined (eg, HIGHCV% with HIGHSLOPE), the c-statistic for the model with the CHS-SCORE and combined HRV categories was 0.68, significantly higher than 0.61 for the CHS-SCORE alone ( P =0.02). In this sample of older adults, 2 HRV parameters, CV% and power law slope, emerged as significantly associated with incident stroke when added to a validated clinical risk score. After each parameter was dichotomized based on its optimal cut point in this sample, their composite significantly improved prediction of incident stroke during ≤8-year follow-up. These findings will require validation in separate, larger cohorts. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, H; Chen, W; Kligerman, S

2014-06-15

Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had {sup 18}F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associatedmore » changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features including quantitative PET/CT features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer. This work was supported in part by National Cancer Institute Grant R21 CA131979 and R01 CA172638. Shan Tan was supported in part by the National Natural Science Foundation of China 60971112 and 61375018, and by Fundamental Research Funds for the Central Universities 2012QN086.« less

Randomization in clinical trials: stratification or minimization? The HERMES free simulation software.

PubMed

Fron Chabouis, Hélène; Chabouis, Francis; Gillaizeau, Florence; Durieux, Pierre; Chatellier, Gilles; Ruse, N Dorin; Attal, Jean-Pierre

2014-01-01

Operative clinical trials are often small and open-label. Randomization is therefore very important. Stratification and minimization are two randomization options in such trials. The first aim of this study was to compare stratification and minimization in terms of predictability and balance in order to help investigators choose the most appropriate allocation method. Our second aim was to evaluate the influence of various parameters on the performance of these techniques. The created software generated patients according to chosen trial parameters (e.g., number of important prognostic factors, number of operators or centers, etc.) and computed predictability and balance indicators for several stratification and minimization methods over a given number of simulations. Block size and proportion of random allocations could be chosen. A reference trial was chosen (50 patients, 1 prognostic factor, and 2 operators) and eight other trials derived from this reference trial were modeled. Predictability and balance indicators were calculated from 10,000 simulations per trial. Minimization performed better with complex trials (e.g., smaller sample size, increasing number of prognostic factors, and operators); stratification imbalance increased when the number of strata increased. An inverse correlation between imbalance and predictability was observed. A compromise between predictability and imbalance still has to be found by the investigator but our software (HERMES) gives concrete reasons for choosing between stratification and minimization; it can be downloaded free of charge. This software will help investigators choose the appropriate randomization method in future two-arm trials.

Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters

PubMed Central

2013-01-01

Purpose Retrospective analysis of 3D clinical treatment plans to investigate qualitative, possible, clinical consequences of the use of PBC versus AAA. Methods The 3D dose distributions of 80 treatment plans at four different tumour sites, produced using PBC algorithm, were recalculated using AAA and the same number of monitor units provided by PBC and clinically delivered to each patient; the consequences of the difference on the dose-effect relations for normal tissue injury were studied by comparing different NTCP model/parameters extracted from a review of published studies. In this study the AAA dose calculation is considered as benchmark data. The paired Student t-test was used for statistical comparison of all results obtained from the use of the two algorithms. Results In the prostate plans, the AAA predicted lower NTCP value (NTCPAAA) for the risk of late rectal bleeding for each of the seven combinations of NTCP parameters, the maximum mean decrease was 2.2%. In the head-and-neck treatments, each combination of parameters used for the risk of xerostemia from irradiation of the parotid glands involved lower NTCPAAA, that varied from 12.8% (sd=3.0%) to 57.5% (sd=4.0%), while when the PBC algorithm was used the NTCPPBC’s ranging was from 15.2% (sd=2.7%) to 63.8% (sd=3.8%), according the combination of parameters used; the differences were statistically significant. Also NTCPAAA regarding the risk of radiation pneumonitis in the lung treatments was found to be lower than NTCPPBC for each of the eight sets of NTCP parameters; the maximum mean decrease was 4.5%. A mean increase of 4.3% was found when the NTCPAAA was calculated by the parameters evaluated from dose distribution calculated by a convolution-superposition (CS) algorithm. A markedly different pattern was observed for the risk relating to the development of pneumonitis following breast treatments: the AAA predicted higher NTCP value. The mean NTCPAAA varied from 0.2% (sd = 0.1%) to 2.1% (sd = 0.3%), while the mean NTCPPBC varied from 0.1% (sd = 0.0%) to 1.8% (sd = 0.2%) depending on the chosen parameters set. Conclusions When the original PBC treatment plans were recalculated using AAA with the same number of monitor units provided by PBC, the NTCPAAA was lower than the NTCPPBC, except for the breast treatments. The NTCP is strongly affected by the wide-ranging values of radiobiological parameters. PMID:23826854

Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters.

PubMed

Bufacchi, Antonella; Nardiello, Barbara; Capparella, Roberto; Begnozzi, Luisa

2013-07-04

Retrospective analysis of 3D clinical treatment plans to investigate qualitative, possible, clinical consequences of the use of PBC versus AAA. The 3D dose distributions of 80 treatment plans at four different tumour sites, produced using PBC algorithm, were recalculated using AAA and the same number of monitor units provided by PBC and clinically delivered to each patient; the consequences of the difference on the dose-effect relations for normal tissue injury were studied by comparing different NTCP model/parameters extracted from a review of published studies. In this study the AAA dose calculation is considered as benchmark data. The paired Student t-test was used for statistical comparison of all results obtained from the use of the two algorithms. In the prostate plans, the AAA predicted lower NTCP value (NTCPAAA) for the risk of late rectal bleeding for each of the seven combinations of NTCP parameters, the maximum mean decrease was 2.2%. In the head-and-neck treatments, each combination of parameters used for the risk of xerostemia from irradiation of the parotid glands involved lower NTCPAAA, that varied from 12.8% (sd=3.0%) to 57.5% (sd=4.0%), while when the PBC algorithm was used the NTCPPBC's ranging was from 15.2% (sd=2.7%) to 63.8% (sd=3.8%), according the combination of parameters used; the differences were statistically significant. Also NTCPAAA regarding the risk of radiation pneumonitis in the lung treatments was found to be lower than NTCPPBC for each of the eight sets of NTCP parameters; the maximum mean decrease was 4.5%. A mean increase of 4.3% was found when the NTCPAAA was calculated by the parameters evaluated from dose distribution calculated by a convolution-superposition (CS) algorithm. A markedly different pattern was observed for the risk relating to the development of pneumonitis following breast treatments: the AAA predicted higher NTCP value. The mean NTCPAAA varied from 0.2% (sd = 0.1%) to 2.1% (sd = 0.3%), while the mean NTCPPBC varied from 0.1% (sd = 0.0%) to 1.8% (sd = 0.2%) depending on the chosen parameters set. When the original PBC treatment plans were recalculated using AAA with the same number of monitor units provided by PBC, the NTCPAAA was lower than the NTCPPBC, except for the breast treatments. The NTCP is strongly affected by the wide-ranging values of radiobiological parameters.

Clinical, Electrocardiographic, and Echocardiographic Parameter Combination Predicts the Onset of Atrial Fibrillation.

PubMed

Soeki, Takeshi; Matsuura, Tomomi; Tobiume, Takeshi; Bando, Sachiko; Matsumoto, Kazuhisa; Nagano, Hiromi; Uematsu, Etsuko; Kusunose, Kenya; Ise, Takayuki; Yamaguchi, Koji; Yagi, Shusuke; Fukuda, Daiju; Yamada, Hirotsugu; Wakatsuki, Tetsuzo; Shimabukuro, Michio; Sata, Masataka

2018-05-30

The ability to identify risk markers for new-onset atrial fibrillation (AF) is critical to the development of preventive strategies, but it remains unknown whether a combination of clinical, electrocardiographic, and echocardiographic parameters predicts the onset of AF. In the present study, we evaluated the predictive value of a combined score that includes these parameters.Methods and Results:We retrospectively studied 1,040 patients without AF who underwent both echocardiography and 24-h Holter electrocardiography between May 2005 and December 2010. During a median follow-up period of 68.4 months (IQR, 49.9-93.3 months), we investigated the incidence of new-onset AF. Of the 1,040 patients, 103 (9.9%) developed AF. Patients who developed AF were older than patients who did not. Total heart beats, premature atrial contraction (PAC) count, maximum RR interval, and frequency of sinus pause quantified on 24-h electrocardiography were associated with new-onset AF. LA diameter (LAD) on echocardiography was also associated with the development of AF. On multivariate Cox analysis, age ≥58 years, PAC count ≥80 beats/day, maximum RR interval ≥1.64 s, and LAD ≥4.5 cm were independently associated with the development of AF. The incidence rate of new-onset AF significantly increased as the combined score (i.e., the sum of the risk score determined using hazard ratios) increased. A combined score that includes age, PAC count, maximum RR interval, and LAD could help characterize the risk of new-onset AF.

Risk Factors and Biomarkers of Age-Related Macular Degeneration

PubMed Central

Lambert, Nathan G.; Singh, Malkit K.; ElShelmani, Hanan; Mansergh, Fiona C.; Wride, Michael A.; Padilla, Maximilian; Keegan, David; Hogg, Ruth E.; Ambati, Balamurali K.

2016-01-01

A biomarker can be a substance or structure measured in body parts, fluids or products that can affect or predict disease incidence. As age-related macular degeneration (AMD) is the leading cause of blindness in the developed world, much research and effort has been invested in the identification of different biomarkers to predict disease incidence, identify at risk individuals, elucidate causative pathophysiological etiologies, guide screening, monitoring and treatment parameters, and predict disease outcomes. To date, a host of genetic, environmental, proteomic, and cellular targets have been identified as both risk factors and potential biomarkers for AMD. Despite this, their use has been confined to research settings and has not yet crossed into the clinical arena. A greater understanding of these factors and their use as potential biomarkers for AMD can guide future research and clinical practice. This article will discuss known risk factors and novel, potential biomarkers of AMD in addition to their application in both academic and clinical settings. PMID:27156982

Accounting for nonsampling error in estimates of HIV epidemic trends from antenatal clinic sentinel surveillance

PubMed Central

Eaton, Jeffrey W.; Bao, Le

2017-01-01

Objectives The aim of the study was to propose and demonstrate an approach to allow additional nonsampling uncertainty about HIV prevalence measured at antenatal clinic sentinel surveillance (ANC-SS) in model-based inferences about trends in HIV incidence and prevalence. Design Mathematical model fitted to surveillance data with Bayesian inference. Methods We introduce a variance inflation parameter σinfl2 that accounts for the uncertainty of nonsampling errors in ANC-SS prevalence. It is additive to the sampling error variance. Three approaches are tested for estimating σinfl2 using ANC-SS and household survey data from 40 subnational regions in nine countries in sub-Saharan, as defined in UNAIDS 2016 estimates. Methods were compared using in-sample fit and out-of-sample prediction of ANC-SS data, fit to household survey prevalence data, and the computational implications. Results Introducing the additional variance parameter σinfl2 increased the error variance around ANC-SS prevalence observations by a median of 2.7 times (interquartile range 1.9–3.8). Using only sampling error in ANC-SS prevalence ( σinfl2=0), coverage of 95% prediction intervals was 69% in out-of-sample prediction tests. This increased to 90% after introducing the additional variance parameter σinfl2. The revised probabilistic model improved model fit to household survey prevalence and increased epidemic uncertainty intervals most during the early epidemic period before 2005. Estimating σinfl2 did not increase the computational cost of model fitting. Conclusions: We recommend estimating nonsampling error in ANC-SS as an additional parameter in Bayesian inference using the Estimation and Projection Package model. This approach may prove useful for incorporating other data sources such as routine prevalence from Prevention of mother-to-child transmission testing into future epidemic estimates. PMID:28296801

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Modeling the shape and composition of the human body using dual energy X-ray absorptiometry images

PubMed Central

Shepherd, John A.; Fan, Bo; Schwartz, Ann V.; Cawthon, Peggy; Cummings, Steven R.; Kritchevsky, Stephen; Nevitt, Michael; Santanasto, Adam; Cootes, Timothy F.

2017-01-01

There is growing evidence that body shape and regional body composition are strong indicators of metabolic health. The purpose of this study was to develop statistical models that accurately describe holistic body shape, thickness, and leanness. We hypothesized that there are unique body shape features that are predictive of mortality beyond standard clinical measures. We developed algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans into body thickness and leanness images. We performed statistical appearance modeling (SAM) and principal component analysis (PCA) to efficiently encode the variance of body shape, leanness, and thickness across sample of 400 older Americans from the Health ABC study. The sample included 200 cases and 200 controls based on 6-year mortality status, matched on sex, race and BMI. The final model contained 52 points outlining the torso, upper arms, thighs, and bony landmarks. Correlation analyses were performed on the PCA parameters to identify body shape features that vary across groups and with metabolic risk. Stepwise logistic regression was performed to identify sex and race, and predict mortality risk as a function of body shape parameters. These parameters are novel body composition features that uniquely identify body phenotypes of different groups and predict mortality risk. Three parameters from a SAM of body leanness and thickness accurately identified sex (training AUC = 0.99) and six accurately identified race (training AUC = 0.91) in the sample dataset. Three parameters from a SAM of only body thickness predicted mortality (training AUC = 0.66, validation AUC = 0.62). Further study is warranted to identify specific shape/composition features that predict other health outcomes. PMID:28423041

Tumor heterogeneity measured on F-18 fluorodeoxyglucose positron emission tomography/computed tomography combined with plasma Epstein-Barr Virus load predicts prognosis in patients with primary nasopharyngeal carcinoma.

PubMed

Chan, Sheng-Chieh; Chang, Kai-Ping; Fang, Yu-Hua Dean; Tsang, Ngan-Ming; Ng, Shu-Hang; Hsu, Cheng-Lung; Liao, Chun-Ta; Yen, Tzu-Chen

2017-01-01

Plasma Epstein-Barr virus (EBV) DNA concentrations predict prognosis in patients with nasopharyngeal carcinoma (NPC). Recent evidence also indicates that intratumor heterogeneity on F-18 fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) scans is predictive of treatment outcomes in different solid malignancies. Here, we sought to investigate the prognostic value of heterogeneity parameters in patients with primary NPC. Retrospective cohort study. We examined 101 patients with primary NPC who underwent pretreatment 18 F-FDG PET/computed tomography. Circulating levels of EBV DNA were measured in all participants. The following PET heterogeneity parameters were collected: histogram-based heterogeneity parameters, second-order texture features (uniformity, contrast, entropy, homogeneity, dissimilarity, inverse difference moment), and higher-order (coarseness, contrast, busyness, complexity, strength) texture features. The median follow-up time was 5.14 years. Total lesion glycolysis (TLG), tumor heterogeneity measured by histogram-based parameter skewness, and the majority of second-order or higher-order texture features were significantly associated with overall survival (OS) and/or recurrence-free survival (RFS). In multivariate analysis, age (P =.005), EBV DNA load (P = .0002), and uniformity (P = .001) independently predicted OS. Only skewness retained the independent prognostic significance for RFS. Tumor stage, standardized uptake value, or TLG did not show an independent association with survival endpoints. The combination of uniformity, EBV DNA load, and age resulted in a more reliable prognostic stratification (P < .001). Tumor heterogeneity is superior to traditional PET parameters for predicting outcomes in primary NPC. The combination of uniformity with EBV DNA load can improve prognostic stratification in this clinical entity. 4 Laryngoscope, 127:E22-E28, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Feasibility of applying data mining techniques for predicting technical difficulties during laparoscopic cholecystectomy based on routine patient work-up in a small community hospital.

PubMed

Stanisic, Veselin; Andjelkovic, Igor; Vlaovic, Darko; Babic, Igor; Kocev, Nikola; Nikolic, Bosko; Milicevic, Miroslav

2013-10-01

Predicting technical difficulties in laparoscopic cholecystectomy (LC) in a small regional hospital increases efficacy, cost-benefit and safety of the procedure. The aim of the study was to assess whether it is possible to accurately predict a difficult LC (DLC) in a small regional hospital based only on the routine available clinical work-up parameters (patient history, ultrasound examination and blood chemistry) and their combinations. A prospective, cohort, of 369 consecutive patients operated by the same surgeon was analyzed. Conversion rate was 10 (2.7%). DLC was registered in 55 (14.90%). Various data mining techniques were applied and assessed. Seven significant predictors of DLC were identified: i) shrunken (fibrotic) gallbladder (GB); ii) ultrasound (US) GB wall thickness >4 mm; iii) >5 attacks of pain lasting >5 hours; iv) WBC >10x109 g/L; v) pericholecystic fluid; vi) urine amylase >380 IU/L, and vii) BMI >30kg/m2. Bayesian network was selected as the best classifier with accuracy of 94.57, specificity 0.98, sensitivity 0.77, AUC 0.96 and F-measure 0.81. It is possible to predict a DLC with high accuracy using data mining techniques, based on routine preoperative clinical parameters and their combinations. Use of sophisticated diagnostic equipment is not necessary.

In silico predictions of gastrointestinal drug absorption in pharmaceutical product development: application of the mechanistic absorption model GI-Sim.

PubMed

Sjögren, Erik; Westergren, Jan; Grant, Iain; Hanisch, Gunilla; Lindfors, Lennart; Lennernäs, Hans; Abrahamsson, Bertil; Tannergren, Christer

2013-07-16

Oral drug delivery is the predominant administration route for a major part of the pharmaceutical products used worldwide. Further understanding and improvement of gastrointestinal drug absorption predictions is currently a highly prioritized area of research within the pharmaceutical industry. The fraction absorbed (fabs) of an oral dose after administration of a solid dosage form is a key parameter in the estimation of the in vivo performance of an orally administrated drug formulation. This study discloses an evaluation of the predictive performance of the mechanistic physiologically based absorption model GI-Sim. GI-Sim deploys a compartmental gastrointestinal absorption and transit model as well as algorithms describing permeability, dissolution rate, salt effects, partitioning into micelles, particle and micelle drifting in the aqueous boundary layer, particle growth and amorphous or crystalline precipitation. Twelve APIs with reported or expected absorption limitations in humans, due to permeability, dissolution and/or solubility, were investigated. Predictions of the intestinal absorption for different doses and formulations were performed based on physicochemical and biopharmaceutical properties, such as solubility in buffer and simulated intestinal fluid, molecular weight, pK(a), diffusivity and molecule density, measured or estimated human effective permeability and particle size distribution. The performance of GI-Sim was evaluated by comparing predicted plasma concentration-time profiles along with oral pharmacokinetic parameters originating from clinical studies in healthy individuals. The capability of GI-Sim to correctly predict impact of dose and particle size as well as the in vivo performance of nanoformulations was also investigated. The overall predictive performance of GI-Sim was good as >95% of the predicted pharmacokinetic parameters (C(max) and AUC) were within a 2-fold deviation from the clinical observations and the predicted plasma AUC was within one standard deviation of the observed mean plasma AUC in 74% of the simulations. GI-Sim was also able to correctly capture the trends in dose- and particle size dependent absorption for the study drugs with solubility and dissolution limited absorption, respectively. In addition, GI-Sim was also shown to be able to predict the increase in absorption and plasma exposure achieved with nanoformulations. Based on the results, the performance of GI-Sim was shown to be suitable for early risk assessment as well as to guide decision making in pharmaceutical formulation development. Copyright © 2013 Elsevier B.V. All rights reserved.

Online adaptation and verification of VMAT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crijns, Wouter, E-mail: wouter.crijns@uzleuven.be; Defraene, Gilles; Depuydt, Tom

2015-07-15

Purpose: This work presents a method for fast volumetric modulated arc therapy (VMAT) adaptation in response to interfraction anatomical variations. Additionally, plan parameters extracted from the adapted plans are used to verify the quality of these plans. The methods were tested as a prostate class solution and compared to replanning and to their current clinical practice. Methods: The proposed VMAT adaptation is an extension of their previous intensity modulated radiotherapy (IMRT) adaptation. It follows a direct (forward) planning approach: the multileaf collimator (MLC) apertures are corrected in the beam’s eye view (BEV) and the monitor units (MUs) are corrected usingmore » point dose calculations. All MLC and MU corrections are driven by the positions of four fiducial points only, without need for a full contour set. Quality assurance (QA) of the adapted plans is performed using plan parameters that can be calculated online and that have a relation to the delivered dose or the plan quality. Five potential parameters are studied for this purpose: the number of MU, the equivalent field size (EqFS), the modulation complexity score (MCS), and the components of the MCS: the aperture area variability (AAV) and the leaf sequence variability (LSV). The full adaptation and its separate steps were evaluated in simulation experiments involving a prostate phantom subjected to various interfraction transformations. The efficacy of the current VMAT adaptation was scored by target mean dose (CTV{sub mean}), conformity (CI{sub 95%}), tumor control probability (TCP), and normal tissue complication probability (NTCP). The impact of the adaptation on the plan parameters (QA) was assessed by comparison with prediction intervals (PI) derived from a statistical model of the typical variation of these parameters in a population of VMAT prostate plans (n = 63). These prediction intervals are the adaptation equivalent of the tolerance tables for couch shifts in the current clinical practice. Results: The proposed adaptation of a two-arc VMAT plan resulted in the intended CTV{sub mean} (Δ ≤ 3%) and TCP (ΔTCP ≤ 0.001). Moreover, the method assures the intended CI{sub 95%} (Δ ≤ 11%) resulting in lowered rectal NTCP for all cases. Compared to replanning, their adaptation is faster (13 s vs 10 min) and more intuitive. Compared to the current clinical practice, it has a better protection of the healthy tissue. Compared to IMRT, VMAT is more robust to anatomical variations, but it is also less sensitive to the different correction steps. The observed variations of the plan parameters in their database included a linear dependence on the date of treatment planning and on the target radius. The MCS is not retained as QA metric due to a contrasting behavior of its components (LSV and AAV). If three out of four plan parameters (MU, EqFS, AAV, and LSV) need to lie inside a 50% prediction interval (3/4—50%PI), all adapted plans will be accepted. In contrast, all replanned plans do not meet this loose criterion, mainly because they have no connection to the initially optimized and verified plan. Conclusions: A direct (forward) VMAT adaptation performs equally well as (inverse) replanning but is faster and can be extended to real-time adaptation. The prediction intervals for the machine parameters are equivalent to the tolerance tables for couch shifts in the current clinical practice. A 3/4—50%PI QA criterion accepts all the adapted plans but rejects all the replanned plans.« less

Determination of physical height from crown dimensions of deciduous tooth: A dental morphometric study.

PubMed

Ramanna, C; Kamath, Venkatesh V; Sharada, C; Srikanth, N

2016-01-01

Dental morphometrics is a subject of great significance in forensic odontology in identification of an individual. Use of teeth to represent a physical profile is valuable for identification of an individual. The present study aims to assess the clinical crown length (CL) of erupted deciduous teeth and height of the child. A correlation of these parameters was attempted to arrive at a mathematical equation which would formulate a ratio of tooth CL to individual height that would support in estimating the probable height of the child. About 60 children (30 males and 30 females) of age ranged from 3-6 years were included in this study. Clinical vertical CLs of the deciduous dentition (tooth numbers 51, 52, 53, 54, and 55) were calculated using digital Vernier calipers (Aerospace Ltd., Bengaluru, Karnataka, India) on the cast models. Child height was measured using a standard measuring tape. Ratios of deciduous CL to height of the child were recorded. Linear stepwise forward regression analysis was applied to predict the probability of CL of a tooth most likely to support in prediction of physical height of the child. Tabulated results showed a probable correlation between tooth CL and height of the child. Tooth CLs of deciduous upper right second molar (55) among the males, lateral incisor (52) among females, and canine (53) using the combined male and female data were statistically significant, and they approximately predicted the child height with minimal variations. Mathematically derived equations based on linear stepwise forward regression analysis using sixty children data are height prediction (derived from combined data of male and female children) = 400.558 + 90.264 (53 CL), male child height prediction (derived from data of male children) = 660.290 + 72.970 (55 CL), and female child height prediction (derived from data of female children) = -187.942 + 194.818 (52 CL). In conclusion, clinical vertical CL is an important parameter in prediction of individual height and possible identification of the individual. An extension of the similar technique to all the deciduous dentition using a larger group of children would probably give us the best options available among vertical CLs for prediction of the child height.

Energy landscapes for a machine-learning prediction of patient discharge

NASA Astrophysics Data System (ADS)

Das, Ritankar; Wales, David J.

2016-06-01

The energy landscapes framework is applied to a configuration space generated by training the parameters of a neural network. In this study the input data consists of time series for a collection of vital signs monitored for hospital patients, and the outcomes are patient discharge or continued hospitalisation. Using machine learning as a predictive diagnostic tool to identify patterns in large quantities of electronic health record data in real time is a very attractive approach for supporting clinical decisions, which have the potential to improve patient outcomes and reduce waiting times for discharge. Here we report some preliminary analysis to show how machine learning might be applied. In particular, we visualize the fitting landscape in terms of locally optimal neural networks and the connections between them in parameter space. We anticipate that these results, and analogues of thermodynamic properties for molecular systems, may help in the future design of improved predictive tools.

Novel method to predict body weight in children based on age and morphological facial features.

PubMed

Huang, Ziyin; Barrett, Jeffrey S; Barrett, Kyle; Barrett, Ryan; Ng, Chee M

2015-04-01

A new and novel approach of predicting the body weight of children based on age and morphological facial features using a three-layer feed-forward artificial neural network (ANN) model is reported. The model takes in four parameters, including age-based CDC-inferred median body weight and three facial feature distances measured from digital facial images. In this study, thirty-nine volunteer subjects with age ranging from 6-18 years old and BW ranging from 18.6-96.4 kg were used for model development and validation. The final model has a mean prediction error of 0.48, a mean squared error of 18.43, and a coefficient of correlation of 0.94. The model shows significant improvement in prediction accuracy over several age-based body weight prediction methods. Combining with a facial recognition algorithm that can detect, extract and measure the facial features used in this study, mobile applications that incorporate this body weight prediction method may be developed for clinical investigations where access to scales is limited. © 2014, The American College of Clinical Pharmacology.

Computer-Assisted Sperm Analysis (CASA) parameters and their evolution during preparation as predictors of pregnancy in intrauterine insemination with frozen-thawed donor semen cycles.

PubMed

Fréour, Thomas; Jean, Miguel; Mirallié, Sophie; Dubourdieu, Sophie; Barrière, Paul

2010-04-01

To study the potential of CASA parameters in frozen-thawed donor semen before and after preparation on silica gradient as predictors of pregnancy in IUI with donor semen cycles. CASA parameters were measured in thawed donor semen before and after preparation on a silica gradient in 132 couples undergoing 168 IUI cycles with donor semen. The evolution of these parameters throughout this process was calculated. The relationship with cycle outcome was then studied. Clinical pregnancy rate was 18.4% per cycle. CASA parameters on donor semen before or after preparation were not significantly different between pregnancy and failure groups. However, amplitude of lateral head displacement (ALH) of spermatozoa improved in all cycles where pregnancy occurred, thus predicting pregnancy with a sensitivity of 100% and a specificity of 20%. Even if CASA parameters do not seem to predict pregnancy in IUI with donor semen cycles, their evolution during the preparation process should be evaluated, especially for ALH. However, the link between ALH improvement during preparation process and pregnancy remains to be explored. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Prediction of quantitative intrathoracic fluid volume to diagnose pulmonary oedema using LabVIEW.

PubMed

Urooj, Shabana; Khan, M; Ansari, A Q; Lay-Ekuakille, Aimé; Salhan, Ashok K

2012-01-01

Pulmonary oedema is a life-threatening disease that requires special attention in the area of research and clinical diagnosis. Computer-based techniques are rarely used to quantify the intrathoracic fluid volume (IFV) for diagnostic purposes. This paper discusses a software program developed to detect and diagnose pulmonary oedema using LabVIEW. The software runs on anthropometric dimensions and physiological parameters, mainly transthoracic electrical impedance (TEI). This technique is accurate and faster than existing manual techniques. The LabVIEW software was used to compute the parameters required to quantify IFV. An equation relating per cent control and IFV was obtained. The results of predicted TEI and measured TEI were compared with previously reported data to validate the developed program. It was found that the predicted values of TEI obtained from the computer-based technique were much closer to the measured values of TEI. Six new subjects were enrolled to measure and predict transthoracic impedance and hence to quantify IFV. A similar difference was also observed in the measured and predicted values of TEI for the new subjects.

Predictive variables for the occurrence of early clinical mastitis in primiparous Holstein cows under field conditions in France.

PubMed Central

Barnouin, J; Chassagne, M

2001-01-01

Holstein heifers from 47 dairy herds in France were enrolled in a field study to determine predictors for clinical mastitis within the first month of lactation. Precalving and calving variables (biochemical, hematological, hygienic, and disease indicators) were collected. Early clinical mastitis (ECM) predictive variables were analyzed by using a multiple logistic regression model (99 cows with ECM vs. 571 without clinical mastitis throughout the first lactation). Two variables were associated with a higher risk of ECM: a) difficult calving and b) medium and high white blood cell (WBC) counts in late gestation. Two prepartum indicators were associated with a lower ECM risk: a) medium and high serum concentrations of immunoglobulin G1 (IgG1) and b) high percentage of eosinophils among white blood cells. Calving difficulty and certain biological blood parameters (IgG1, eosinophils) could represent predictors that would merit further experimental studies, with the aim of designing programs for reducing the risk of clinical mastitis in the first lactation. PMID:11195522

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

PubMed Central

Serrancolí, Gil; Kinney, Allison L.; Fregly, Benjamin J.; Font-Llagunes, Josep M.

2016-01-01

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking. PMID:27210105

Comparison of Laser Doppler Imaging (LDI) and clinical assessment in differentiating between superficial and deep partial thickness burn wounds.

PubMed

Jan, Saadia Nosheen; Khan, Farid Ahmed; Bashir, Muhammad Mustehsan; Nasir, Muneeb; Ansari, Hamid Hussain; Shami, Hussan Birkhez; Nazir, Umer; Hanif, Asif; Sohail, Muhammad

2018-03-01

To compare the accuracy of Laser Doppler Imaging (LDI) and clinical assessment in differentiating between superficial and deep partial thickness burns to decide whether early tangential excision and grafting or conservative management should be employed to optimize burn and patient management. March 2015 to November 2016. Ninety two wounds in 34 patients reporting within 5days of less than 40% burn surface area were included. Unstable patients, pregnant females and those who expired were excluded. The wounds were clinically assessed and LDI done concomitantly Plastic Surgeons blinded to each other's findings. Wound appearance, color, blanching, pain, hair follicle dislodgement were the clinical parameters that distinguished between superficial and deep partial thickness burns. On day 21, the wounds were again assessed for the presence of healing by the same plastic surgeons. The findings were correlated with the initial findings on LDI and clinical assessment and the results statistically analyzed. The data of 92 burn wounds was analyzed using SPSS (ver. 17). Clinical assessment correctly identified the depth of 75 and LDI 83 wounds, giving diagnostic accuracies of 81.52% and 90.21% respectively. The sensitivity of clinical assessment was 81% and of LDI 92.75%, whereas the specificity was 82% for both. The positive predictive value was 93% for clinical assessment and 94% for LDI while the negative predictive value was 59% and 79% respectively. Predictive accuracy of LDI was found to be better than clinical assessment in the prediction of wound healing, the gold standard for wound healing being 21 days. As such it can prove to be a reliable and viable cost effective alternative per se to clinical assessment. Copyright © 2017 Elsevier Ltd and ISBI. All rights reserved.

A Bayesian framework for early risk prediction in traumatic brain injury

NASA Astrophysics Data System (ADS)

Chaganti, Shikha; Plassard, Andrew J.; Wilson, Laura; Smith, Miya A.; Patel, Mayur B.; Landman, Bennett A.

2016-03-01

Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

MicroRNAs and Glucocorticoid-Induced Apoptosis in Lymphoid Malignancies

PubMed Central

Sionov, Ronit Vogt

2013-01-01

The initial response of lymphoid malignancies to glucocorticoids (GCs) is a critical parameter predicting successful treatment. Although being known as a strong inducer of apoptosis in lymphoid cells for almost a century, the signaling pathways regulating the susceptibility of the cells to GCs are only partly revealed. There is still a need to develop clinical tests that can predict the outcome of GC therapy. In this paper, I discuss important parameters modulating the pro-apoptotic effects of GCs, with a specific emphasis on the microRNA world comprised of small players with big impacts. The journey through the multifaceted complexity of GC-induced apoptosis brings forth explanations for the differential treatment response and raises potential strategies for overcoming drug resistance. PMID:23431463

[Quantitative Prediction of Drug-Drug Interaction Caused by CYP Inhibition and Induction from In Vivo Data and Its Application in Daily Clinical Practices-Proposal for the Pharmacokinetic Interaction Significance Classification System (PISCS)].

PubMed

Ohno, Yoshiyuki

2018-01-01

　Drug-drug interactions (DDIs) can affect the clearance of various drugs from the body; however, these effects are difficult to sufficiently evaluate in clinical studies. This article outlines our approach to improving methods for evaluating and providing drug information relative to the effects of DDIs. In a previous study, total exposure changes to many substrate drugs of CYP caused by the co-administration of inhibitor or inducer drugs were successfully predicted using in vivo data. There are two parameters for the prediction: the contribution ratio of the enzyme to oral clearance for substrates (CR), and either the inhibition ratio for inhibitors (IR) or the increase in clearance of substrates produced by induction (IC). To apply these predictions in daily pharmacotherapy, the clinical significance of any pharmacokinetic changes must be carefully evaluated. We constructed a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered in a systematic manner, according to pharmacokinetic changes. The PISCS suggests that many current 'alert' classifications are potentially inappropriate, especially for drug combinations in which pharmacokinetics have not yet been evaluated. It is expected that PISCS would contribute to constructing a reliable system to alert pharmacists, physicians and consumers of a broad range of pharmacokinetic DDIs in order to more safely manage daily clinical practices.

A NTCP approach for estimating the outcome in radioiodine treatment of hyperthyroidism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Strigari, L.; Sciuto, R.; Benassi, M.

2008-09-15

Radioiodine has been in use for over 60 years as a treatment for hyperthyroidism. Major changes in clinical practice have led to accurate dosimetry capable of avoiding the risks of adverse effects and the optimization of the treatment. The aim of this study was to test the capability of a radiobiological model, based on normal tissue complication probability (NTCP), to predict the outcome after oral therapeutic {sup 131}I administration. Following dosimetric study, 79 patients underwent treatment for hyperthyroidism using radioiodine and then 67 had at least a one-year follow up. The delivered dose was calculated using the MIRD formula, takingmore » into account the measured maximum uptake of administered iodine transferred to the thyroid, U0, and the effective clearance rate, T{sub eff} and target mass. The dose was converted to normalized total dose delivered at 2 Gy per fraction (NTD{sub 2}). Furthermore, the method to take into account the reduction of the mass of the gland during radioiodine therapy was also applied. The clinical outcome and dosimetric parameters were analyzed in order to study the dose-response relationship for hypothyroidism. The TD{sub 50} and m parameters of the NTCP model approach were then estimated using the likelihood method. The TD{sub 50}, expressed as NTD{sub 2}, resulted in 60 Gy (95% C.I.: 45-75 Gy) and 96 Gy (95% C.I.: 86-109 Gy) for patients affected by Graves or autonomous/multinodular disease, respectively. This supports the clinical evidence that Graves' disease should be characterized by more radiosensitive cells compared to autonomous nodules. The m parameter for all patients was 0.27 (95% C.I.: 0.22-0.36). These parameters were compared with those reported in the literature for hypothyroidism induced after external beam radiotherapy. The NTCP model correctly predicted the clinical outcome after the therapeutic administration of radioiodine in our series.« less

US-guided percutaneous cholecystostomy: features predicting culture-positive bile and clinical outcome.

PubMed

Sosna, Jacob; Kruskal, Jonathan B; Copel, Laurian; Goldberg, S Nahum; Kane, Robert A

2004-03-01

To assess sonographic and clinical features that might be used to predict infected bile and/or patient outcome from ultrasonography (US)-guided percutaneous cholecystostomy. Between February 1997 and August 2002 at one institution, 112 patients underwent US-guided percutaneous cholecystostomy (59 men, 53 women; average age, 69.3 years). All US images were scored on a defined semiquantitative scale according to preset parameters: (a) gallbladder distention, (b) sludge and/or stones, (c) wall appearance, (d) pericholecystic fluid, and (e) common bile duct size and/or choledocholithiasis. Separate and total scores were generated. Retrospective evaluation of (a) the bacteriologic growth of aspirated bile and its color and (b) clinical indices (fever, white blood cell count, bilirubin level, liver function test results) was conducted by reviewing medical records. For each patient, the clinical manifestation was classified into four groups: (a) localized right upper quadrant symptoms, (b) generalized abdominal symptoms, (c) unexplained sepsis, or (d) sepsis with other known infection. Logistic regression models, exact Wilcoxon-Mann-Whitney test, and the Kruskal-Wallis test were used. Forty-seven (44%) of 107 patients had infected bile. A logistic regression model showed that wall appearance, distention, bile color, and pericholecystic fluid were not individually significant predictors for culture-positive bile, leaving sludge and/or stones (P =.003, odds ratio = 1.647), common bile duct status (P =.02, odds ratio = 2.214), and total score (P =.007, odds ratio = 1.267). No US covariates or clinical indices predicted clinical outcome. Clinical manifestation was predictive of clinical outcome (P =.001) and aspirating culture-positive bile (P =.008); specifically, 30 (86%) of 35 patients with right upper quadrant symptoms had their condition improve, compared with one (7%) of 15 asymptomatic patients with other known causes of infection. US variables can be used to predict culture-positive bile but not patient outcome. Clinical manifestation is important because patients with right upper quadrant symptoms have the best clinical outcome. Copyright RSNA, 2004

Chairside CAD/CAM materials. Part 3: Cyclic fatigue parameters and lifetime predictions.

PubMed

Wendler, Michael; Belli, Renan; Valladares, Diana; Petschelt, Anselm; Lohbauer, Ulrich

2018-06-01

Chemical and mechanical degradation play a key role on the lifetime of dental restorative materials. Therefore, prediction of their long-term performance in the oral environment should base on fatigue, rather than inert strength data, as commonly observed in the dental material's field. The objective of the present study was to provide mechanistic fatigue parameters of current dental CAD/CAM materials under cyclic biaxial flexure and assess their suitability in predicting clinical fracture behaviors. Eight CAD/CAM materials, including polycrystalline zirconia (IPS e.max ZirCAD), reinforced glasses (Vitablocs Mark II, IPS Empress CAD), glass-ceramics (IPS e.max CAD, Suprinity PC, Celtra Duo), as well as hybrid materials (Enamic, Lava Ultimate) were evaluated. Rectangular plates (12×12×1.2mm 3 ) with highly polished surfaces were prepared and tested in biaxial cyclic fatigue in water until fracture using the Ball-on-Three-Balls (B3B) test. Cyclic fatigue parameters n and A* were obtained from the lifetime data for each material and further used to build SPT diagrams. The latter were used to compare in-vitro with in-vivo fracture distributions for IPS e.max CAD and IPS Empress CAD. Susceptibility to subcritical crack growth under cyclic loading was observed for all materials, being more severe (n≤20) in lithium-based glass-ceramics and Vitablocs Mark II. Strength degradations of 40% up to 60% were predicted after only 1 year of service. Threshold stress intensity factors (K th ) representing the onset of subcritical crack growth (SCG), were estimated to lie in the range of 0.37-0.44 of K Ic for the lithium-based glass-ceramics and Vitablocs Mark II and between 0.51-0.59 of K Ic for the other materials. Failure distributions associated with mechanistic estimations of strength degradation in-vitro showed to be useful in interpreting failure behavior in-vivo. The parameter K th stood out as a better predictor of clinical performance in detriment to the SCG n parameter. Fatigue parameters obtained from cyclic loading experiments are more reliable predictors of the mechanical performance of contemporary dental CAD/CAM restoratives than quasi-static mechanical properties. Copyright © 2018 The Academy of Dental Materials. Published by Elsevier Inc. All rights reserved.

Biomarkers in bladder cancer: present status and perspectives.

PubMed

Kim, Wun-Jae; Park, Soongang; Kim, Yong-June

2007-03-27

Bladder cancers are a mixture of heterogeneous cell populations, and numerous factors are likely to be involved in dictating their recurrence, progression and the patient's survival. For any candidate prognostic marker to have considerable clinical relevance, it must add some predictive capacity beyond that offered by conventional clinical and pathologic parameters. Here, the current situation in bladder cancer research with respect to identification of suitable prognostic markers is reviewed. A number of individual molecular markers that might predict bladder cancer recurrence and progression have been identified but many are not sufficiently sensitive or specific for the whole spectrum of bladder cancer diseases seen in routine clinical practice. These limitations have led to interest in other molecular parameters that could enable more accurate prognosis for bladder cancer patients. Of particular interest is the epigenetic silencing of tumor suppressor genes. Since the methylation of these genes can correlate with a poor prognosis, the methylation profile may represent a new bio-marker that indicates the risk of transitional cell carcinoma development. In addition, bladder cancer research is likely to be revolutionized by high-throughput molecular technologies, which allow rapid and global gene expression analysis of thousands of tumor samples. Initial studies employing these technologies have considerably expanded our ability to classify bladder cancers with respect to their survivability. Future microarray analyses are likely to reveal particular gene expression signatures that predict the likelihood of bladder cancer progression and recurrence, as well as patient's survival and responsiveness to different anti-cancer therapies, with great specificity and sensitivity.

Upgrading Reference Set — EDRN Public Portal

Cancer.gov

We are proposing a multi-institutional study to identify molecular biomarkers and clinical measures that will predict presence of Gleason 7 or higher cancer (as evidence in the radical prostatectomy specimen) among patients with a biopsy diagnosis of Gleason score ≤ 6 prostate cancer. This proposal will be conducted in two phases. The first phase will assemble an “Upgrading Reference Set” that will include clinical information as well as biologics on a cohort of 600 men. The first phase will also assess the clinical parameters associated with upgrading, as well as, perform a central pathology review of both biopsies and prostatectomy specimens to confirm tumor grade. The second phase will use the biologics collected in phase 1 to evaluate a series of biomarkers to further refine the prediction of Gleason 7-10 cancer at radical prostatectomy.

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.

PubMed

van der Burgh, Hannelore K; Schmidt, Ruben; Westeneng, Henk-Jan; de Reus, Marcel A; van den Berg, Leonard H; van den Heuvel, Martijn P

2017-01-01

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

GCRBS score: a new scoring system for predicting outcome in severe falciparum malaria.

PubMed

Mohapatra, Biranchi Narayan; Jangid, Sanjay Kumar; Mohanty, Rina

2014-01-01

Severe falciparum malaria is a critical illness resulting in multi-organ dysfunction and death. Severe malaria is defined by the World Health Organisation as a qualitative variable. The purpose of this study is to devise a scoring system for predicting outcome in severe falciparum malaria. 112 cases of severe falciparum malaria diagnosed as per the WHO criteria, were evaluated to determine the parameters which were significantly associated with mortality. Of all the parameters studied, five variables namely cerebral malaria (GCS < 11), Renal failure (Creatinine > 3 mg/dl), Respiratory distress (Respiratory rate > 24/min), Jaundice (Bilirubin >10 mg/dl) and Shock (Systolic BP < 90 mm of Hg) were all found to be associated with a poor prognosis. The five selected parameters were analysed using the Odds ratio and a new scoring system named as GCRBS score was designed with a possible score from 0-10. With a cut-off score of 5, the GCRBS score predicted mortality with a sensitivity of 85.3% and a specificity of 95.6%. The GCRBS score is easy to calculate and apply. Of the 5 parameters, 3 are clinical which can be determined at bedside and only 2 are biochemical which can be done in any laboratory.The most important advantage of this scoring system is that all the 5 parameters are to be assessed quantitatively for allotting a score, which would eliminate the possibility of observer bias.

3P: Personalized Pregnancy Prediction in IVF Treatment Process

NASA Astrophysics Data System (ADS)

Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

Advanced non-contrasted computed tomography post-processing by CT-Calculometry (CT-CM) outperforms established predictors for the outcome of shock wave lithotripsy.

PubMed

Langenauer, J; Betschart, P; Hechelhammer, L; Güsewell, S; Schmid, H P; Engeler, D S; Abt, D; Zumstein, V

2018-05-29

To evaluate the predictive value of advanced non-contrasted computed tomography (NCCT) post-processing using novel CT-calculometry (CT-CM) parameters compared to established predictors of success of shock wave lithotripsy (SWL) for urinary calculi. NCCT post-processing was retrospectively performed in 312 patients suffering from upper tract urinary calculi who were treated by SWL. Established predictors such as skin to stone distance, body mass index, stone diameter or mean stone attenuation values were assessed. Precise stone size and shape metrics, 3-D greyscale measurements and homogeneity parameters such as skewness and kurtosis, were analysed using CT-CM. Predictive values for SWL outcome were analysed using logistic regression and receiver operating characteristics (ROC) statistics. Overall success rate (stone disintegration and no re-intervention needed) of SWL was 59% (184 patients). CT-CM metrics mainly outperformed established predictors. According to ROC analyses, stone volume and surface area performed better than established stone diameter, mean 3D attenuation value was a stronger predictor than established mean attenuation value, and parameters skewness and kurtosis performed better than recently emerged variation coefficient of stone density. Moreover, prediction of SWL outcome with 80% probability to be correct would be possible in a clearly higher number of patients (up to fivefold) using CT-CM-derived parameters. Advanced NCCT post-processing by CT-CM provides novel parameters that seem to outperform established predictors of SWL response. Implementation of these parameters into clinical routine might reduce SWL failure rates.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

The predictive value of quantitative DCE metrics for immediate therapeutic response of high-intensity focused ultrasound ablation (HIFU) of symptomatic uterine fibroids.

PubMed

Wei, Chao; Fang, Xin; Wang, Chuan-Bin; Chen, Yu; Xu, Xiao; Dong, Jiang-Ning

2017-12-04

The aim of this study was to investigate the value of quantitative DCE-MRI parameters for predicting the immediate non-perfused volume ratio (NPVR) of HIFU therapy in the treatment of symptomatic uterine fibroids. A total of 78 symptomatic uterine fibroids in 65 female patients were treated with US-HIFU therapy. All patients underwent conventional MRI and DCE-MRI scans 1 day before and 3 days after HIFU treatment. Permeability parameters K trans , K ep , V e , and V p and T1 perfusion parameters BF and BV of pretreatment were measured as a baseline, while NPVR was used to assess immediate ablation efficiency. Data were assigned to NPVR ≧ 70% and NPVR < 70% groups. Then, the predictive performances of different parameters for ablation efficacy were studied to seek the optimal cut-off value, and the length of time to calculate the variable parameters in each case was recorded. (1) It was observed that the pretreatment K trans , K ep , V e , and BF values of the NPVR ≧ 70% group were significantly lower compared to the NPVR < 70% group (p < 0.05). (2) The immediate NPVR was negatively correlated with the K trans , BF, and BV values before HIFU treatment (r = - 0.561, - 0.712, and - 0.528, respectively, p < 0.05 for all). (3) The AUCs of pretreatment K trans , BF, BV values, and K trans combined with BF used to predict the immediate NPVR were 0.810, 0.909, 0.795, and 0.922, respectively (p < 0.05 for all). (4) The mean time to calculate the variable parameters in each case was 7.5 min. Higher K trans , BF, and BV values at baseline DCE-MRI suggested a poor ablation efficacy of HIFU therapy for symptomatic uterine fibroids, while the pretreatment DCE-MRI parameters could be useful biomarkers for predicting the ablation efficacy in select cases. The software used to calculate DCE-MRI parameters was simpler, quicker, and easier to incorporate into clinical practice.

[Predictive value of postural and dynamic walking parameters after high-volume lumbar puncture in normal pressure hydrocephalus].

PubMed

Mary, P; Gallisa, J-M; Laroque, S; Bedou, G; Maillard, A; Bousquet, C; Negre, C; Gaillard, N; Dutray, A; Fadat, B; Jurici, S; Olivier, N; Cisse, B; Sablot, D

2013-04-01

Normal pressure hydrocephalus (NPH) was described by Adams et al. (1965). The common clinical presentation is the triad: gait disturbance, cognitive decline and urinary incontinence. Although these symptoms are suggestive, they are not specific to diagnosis. The improvement of symptoms after high-volume lumbar puncture (hVLP) could be a strong criterion for diagnosis. We tried to determine a specific pattern of dynamic walking and posture parameters in NPH. Additionally, we tried to specify the evolution of these criteria after hVLP and to determine predictive values of ventriculoperitoneal shunting (VPS) efficiency. Sixty-four patients were followed during seven years from January 2002 to June 2009. We identified three periods: before (S1), after hVLP (S2) and after VPS (S3). The following criteria concerned walking and posture parameters: walking parameters were speed, step length and step rhythm; posture parameters were statokinesigram total length and surface, length according to the surface (LFS), average value of equilibration for lateral movements (Xmoyen), anteroposterior movements (Ymoyen), total movement length in lateral axis (longX) and anteroposterior axis (longY). Among the 64 patients included, 22 had VPS and 16 were investigated in S3. All kinematic criteria are decreased in S1 compared with normal values. hVLP improved these criteria significantly (S2). Among posture parameters, only total length and surface of statokinesigram showed improvement in S1, but no improvement in S2. A gain in speed greater or equal to 0.15m/s between S1 and S2 predicted the efficacy of VPS with a positive predictive value (PPV) of 87.1% and a negative predictive value (NPV) of 69.7% (area under the ROC curve [AUC]: 0.86). Kinematic walking parameters are the most disruptive and are partially improved after hVLP. These parameters could be an interesting test for selecting candidates for VPS. These data have to be confirmed in a larger cohort. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Susceptible alleles of the CD40 and CTLA-4 genes are not associated with the relapse after antithyroid withdrawal in Graves' disease.

PubMed

Kim, Kyung Won; Park, Young Joo; Kim, Tae Yong; Park, Do Joon; Park, Kyong Soo; Cho, Bo Youn

2007-12-01

In this study, we investigated whether the CD40 or cytotoxic T lymphocyte-associated molecules-4 (CTLA-4) polymorphisms, which are associated with the susceptibility of Graves' disease (GD), can predict the clinical outcome after antithyroid drug (ATD) withdrawal. All patients with GD were treated with ATD. GD patients were divided into two groups: remission or failure. The remission group was defined as patients who maintained a euthyroid state for 1 year after ATD withdrawal. The failure group was defined as patients who relapsed within 1 year after the discontinuation of ATD or who could not discontinue their ATD treatment within 24 months. The rate of treatment failure after ATD withdrawal was 72.2%. For the susceptible genes, the CC genotype in the CD40, the GG genotype in the CTLA-4 exon 1, and the CC genotype in the CTLA-4 promoter region have shown no significant association with a clinical outcome after ATD withdrawal. However, clinical parameters, such as male gender, severe thyrotoxicosis, high thyroid-stimulating hormone-binding inhibitory immunoglobulin value, and a large goiter, were related to treatment failure. These findings suggest that the genetic markers associated with the development of GD cannot be used to predict the relapse of GD patients in place of clinical parameters.

Predictors of incident heart failure in patients after an acute coronary syndrome: The LIPID heart failure risk-prediction model.

PubMed

Driscoll, Andrea; Barnes, Elizabeth H; Blankenberg, Stefan; Colquhoun, David M; Hunt, David; Nestel, Paul J; Stewart, Ralph A; West, Malcolm J; White, Harvey D; Simes, John; Tonkin, Andrew

2017-12-01

Coronary heart disease is a major cause of heart failure. Availability of risk-prediction models that include both clinical parameters and biomarkers is limited. We aimed to develop such a model for prediction of incident heart failure. A multivariable risk-factor model was developed for prediction of first occurrence of heart failure death or hospitalization. A simplified risk score was derived that enabled subjects to be grouped into categories of 5-year risk varying from <5% to >20%. Among 7101 patients from the LIPID study (84% male), with median age 61years (interquartile range 55-67years), 558 (8%) died or were hospitalized because of heart failure. Older age, history of claudication or diabetes mellitus, body mass index>30kg/m 2 , LDL-cholesterol >2.5mmol/L, heart rate>70 beats/min, white blood cell count, and the nature of the qualifying acute coronary syndrome (myocardial infarction or unstable angina) were associated with an increase in heart failure events. Coronary revascularization was associated with a lower event rate. Incident heart failure increased with higher concentrations of B-type natriuretic peptide >50ng/L, cystatin C>0.93nmol/L, D-dimer >273nmol/L, high-sensitivity C-reactive protein >4.8nmol/L, and sensitive troponin I>0.018μg/L. Addition of biomarkers to the clinical risk model improved the model's C statistic from 0.73 to 0.77. The net reclassification improvement incorporating biomarkers into the clinical model using categories of 5-year risk was 23%. Adding a multibiomarker panel to conventional parameters markedly improved discrimination and risk classification for future heart failure events. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Magnetic Resonance Imaging-DRAGON score: 3-month outcome prediction after intravenous thrombolysis for anterior circulation stroke.

PubMed

Turc, Guillaume; Apoil, Marion; Naggara, Olivier; Calvet, David; Lamy, Catherine; Tataru, Alina M; Méder, Jean-François; Mas, Jean-Louis; Baron, Jean-Claude; Oppenheim, Catherine; Touzé, Emmanuel

2013-05-01

The DRAGON score, which includes clinical and computed tomographic scan parameters, showed a high specificity to predict 3-month outcome in patients with acute ischemic stroke treated by intravenous tissue plasminogen activator. We adapted the score for patients undergoing MRI as the first-line diagnostic tool. We reviewed patients with consecutive anterior circulation ischemic stroke treated ≤ 4.5 hour by intravenous tissue plasminogen activator between 2003 and 2012 in our center, where MRI is systematically implemented as first-line diagnostic work-up. We derived the MRI-DRAGON score keeping all clinical parameters of computed tomography-DRAGON (age, initial National Institutes of Health Stroke Scale and glucose level, prestroke handicap, onset to treatment time), and considering the following radiological variables: proximal middle cerebral artery occlusion on MR angiography instead of hyperdense middle cerebral artery sign, and diffusion-weighted imaging Alberta Stroke Program Early Computed Tomography Score (DWI ASPECTS) ≤ 5 instead of early infarct signs on computed tomography. Poor 3-month outcome was defined as modified Rankin scale >2. We calculated c-statistics as a measure of predictive ability and performed an internal cross-validation. Two hundred twenty-eight patients were included. Poor outcome was observed in 98 (43%) patients and was significantly associated with all parameters of the MRI-DRAGON score in multivariate analysis, except for onset to treatment time (nonsignificant trend). The c-statistic was 0.83 (95% confidence interval, 0.78-0.88) for poor outcome prediction. All patients with a MRI-DRAGON score ≤ 2 (n=22) had a good outcome, whereas all patients with a score ≥ 8 (n=11) had a poor outcome. The MRI-DRAGON score is a simple tool to predict 3-month outcome in acute stroke patients screened by MRI then treated by intravenous tissue plasminogen activator and may help for therapeutic decision.

Structure-activity relationships for serotonin transporter and dopamine receptor selectivity.

PubMed

Agatonovic-Kustrin, Snezana; Davies, Paul; Turner, Joseph V

2009-05-01

Antipsychotic medications have a diverse pharmacology with affinity for serotonergic, dopaminergic, adrenergic, histaminergic and cholinergic receptors. Their clinical use now also includes the treatment of mood disorders, thought to be mediated by serotonergic receptor activity. The aim of our study was to characterise the molecular properties of antipsychotic agents, and to develop a model that would indicate molecular specificity for the dopamine (D(2)) receptor and the serotonin (5-HT) transporter. Back-propagation artificial neural networks (ANNs) were trained on a dataset of 47 ligands categorically assigned antidepressant or antipsychotic utility. The structure of each compound was encoded with 63 calculated molecular descriptors. ANN parameters including hidden neurons and input descriptors were optimised based on sensitivity analyses, with optimum models containing between four and 14 descriptors. Predicted binding preferences were in excellent agreement with clinical antipsychotic or antidepressant utility. Validated models were further tested by use of an external prediction set of five drugs with unknown mechanism of action. The SAR models developed revealed the importance of simple molecular characteristics for differential binding to the D(2) receptor and the 5-HT transporter. These included molecular size and shape, solubility parameters, hydrogen donating potential, electrostatic parameters, stereochemistry and presence of nitrogen. The developed models and techniques employed are expected to be useful in the rational design of future therapeutic agents.

Improved modeling of clinical data with kernel methods.

PubMed

Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart

2012-02-01

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems. Copyright © 2011 Elsevier B.V. All rights reserved.

Models for patients' recruitment in clinical trials and sensitivity analysis.

PubMed

Mijoule, Guillaume; Savy, Stéphanie; Savy, Nicolas

2012-07-20

Taking a decision on the feasibility and estimating the duration of patients' recruitment in a clinical trial are very important but very hard questions to answer, mainly because of the huge variability of the system. The more elaborated works on this topic are those of Anisimov and co-authors, where they investigate modelling of the enrolment period by using Gamma-Poisson processes, which allows to develop statistical tools that can help the manager of the clinical trial to answer these questions and thus help him to plan the trial. The main idea is to consider an ongoing study at an intermediate time, denoted t(1). Data collected on [0,t(1)] allow to calibrate the parameters of the model, which are then used to make predictions on what will happen after t(1). This method allows us to estimate the probability of ending the trial on time and give possible corrective actions to the trial manager especially regarding how many centres have to be open to finish on time. In this paper, we investigate a Pareto-Poisson model, which we compare with the Gamma-Poisson one. We will discuss the accuracy of the estimation of the parameters and compare the models on a set of real case data. We make the comparison on various criteria : the expected recruitment duration, the quality of fitting to the data and its sensitivity to parameter errors. We discuss the influence of the centres opening dates on the estimation of the duration. This is a very important question to deal with in the setting of our data set. In fact, these dates are not known. For this discussion, we consider a uniformly distributed approach. Finally, we study the sensitivity of the expected duration of the trial with respect to the parameters of the model : we calculate to what extent an error on the estimation of the parameters generates an error in the prediction of the duration.

The clinical utility of health-related quality of life screening in a pediatric inflammatory bowel disease clinic.

PubMed

Ryan, Jamie L; Mellon, Michael W; Junger, Katherine W F; Hente, Elizabeth A; Denson, Lee A; Saeed, Shehzad A; Hommel, Kevin A

2013-11-01

Adjusting to symptom flares, treatment regimens, and side effects places youth with inflammatory bowel disease (IBD) at increased risk for emotional and behavioral problems and adverse disease outcomes. Implementation of psychosocial screening into clinical practice remains a challenge. This study examines the clinical utility of health-related quality of life (HRQOL) screening in predicting disease outcome and healthcare utilization. One hundred twelve youth of 7 to 18 years diagnosed with IBD and their parents. Youth completed standardized measures of HRQOL and depression. Parents completed a proxy report of HRQOL. Pediatric gastroenterologists provided the Physician Global Assessment. Families were recruited from a pediatric gastroenterology clinic. Retrospective chart reviews examined disease outcome and healthcare utilization for 12 months after baseline measurement. Linear regressions, controlling for demographic and disease parameters, revealed that baseline measurement of youth and parent proxy-reported HRQOL predicted the number of IBD-related hospital admissions, gastroenterology clinic visits, emergency department visits, psychology clinic visits, telephone contacts, and pain management referrals over the next 12 months. Disease outcome was not significant. Lower HRQOL was predictive of increased healthcare utilization among youth with IBD. Regular HRQOL screening may be the impetus to providing better case management and allocating resources based on ongoing care needs and costs. Proactive interventions focused on patients with poor HRQOL may be an efficient approach to saving on healthcare costs and resource utilization.

Clinical Response to Vedolizumab in Ulcerative Colitis Patients Is Associated with Changes in Integrin Expression Profiles.

PubMed

Fuchs, Friederike; Schillinger, Daniela; Atreya, Raja; Hirschmann, Simon; Fischer, Sarah; Neufert, Clemens; Atreya, Imke; Neurath, Markus F; Zundler, Sebastian

2017-01-01

Despite large clinical success, deeper insights into the immunological effects of vedolizumab therapy for inflammatory bowel diseases are scarce. In particular, the reasons for differential clinical response in individual patients, the precise impact on the equilibrium of integrin-expressing T cell subsets, and possible associations between these issues are not clear. Blood samples from patients receiving clinical vedolizumab therapy were sequentially collected and analyzed for expression of integrins and chemokine receptors on T cells. Moreover, clinical and laboratory data from the patients were collected, and changes between homing marker expression and clinical parameters were analyzed for possible correlations. While no significant correlation of changes in integrin expression and changes in outcome parameters were identified in Crohn's disease (CD), increasing α4β7 levels in ulcerative colitis (UC) seemed to be associated with favorable clinical development, whereas increasing α4β1 and αEβ7 correlated with negative changes in outcome parameters. Changes in α4β1 integrin expression after 6 weeks were significantly different in responders and non-responders to vedolizumab therapy as assessed after 16 weeks with a cutoff of +4.2% yielding 100% sensitivity and 100% specificity in receiver-operator-characteristic analysis. Our data show that clinical response to vedolizumab therapy in UC but not in CD is associated with specific changes in integrin expression profiles opening novel avenues for mechanistic research and possibly prediction of response to therapy.

Predictive model of outcome of targeted nodal assessment in colorectal cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton; Eberhardt, John; Peoples, George E; Stojadinovic, Alexander

2010-02-01

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

Ultrasound-detected subclinical inflammation was better reflected by the disease activity score (DAS-28) in patients with suspicion of inflammatory arthritis compared to established rheumatoid arthritis.

PubMed

Ciurtin, Coziana; Wyszynski, Karol; Clarke, Robert; Mouyis, Maria; Manson, Jessica; Marra, Giampiero

2016-10-01

Limited data are available about the ultrasound (US)-detected inflammatory features in patients with suspicion of inflammatory arthritis (S-IA) vs. established rheumatoid arthritis (RA). Our study aimed to assess if the presence of power Doppler (PD) can be predicted by a combination of clinical, laboratory and US parameters. We conducted a real-life, retrospective cohort study comparing clinical, laboratory and US parameters of 108 patients with established RA and 93 patients with S-IA. We propose a PD signal prediction model based on a beta-binomial distribution for PD variable using a mix of outcome measures. Patients with RA in clinical remission had significantly more active inflammation and erosions on US when compared with patients with S-IA with similar disease scores (p = 0.03 and p = 0.01, respectively); however, RA patients with different disease activity score (DAS-28) scores had similar PD scores (p = 0.058). The PD scores did not correlate with erosions (p = 0.38) or DAS-28 scores (p = 0.28) in RA patients, but they correlated with high disease activity in S-IA patients (p = 0.048). Subclinical inflammation is more common in patients with RA in clinical remission or with low disease activity than in patients with S-IA; therefore, US was more useful in assessing for true remission in RA rather than diagnosing IA in patients with low disease activity scores. This is the first study to propose a PD prediction model integrating several outcome measures in the two different groups of patients. Further research into validating this model can minimise the risk of underdiagnosing subclinical inflammation.

Improving the modelling of irradiation-induced brain activation for in vivo PET verification of proton therapy.

PubMed

Bauer, Julia; Chen, Wenjing; Nischwitz, Sebastian; Liebl, Jakob; Rieken, Stefan; Welzel, Thomas; Debus, Juergen; Parodi, Katia

2018-04-24

A reliable Monte Carlo prediction of proton-induced brain tissue activation used for comparison to particle therapy positron-emission-tomography (PT-PET) measurements is crucial for in vivo treatment verification. Major limitations of current approaches to overcome include the CT-based patient model and the description of activity washout due to tissue perfusion. Two approaches were studied to improve the activity prediction for brain irradiation: (i) a refined patient model using tissue classification based on MR information and (ii) a PT-PET data-driven refinement of washout model parameters. Improvements of the activity predictions compared to post-treatment PT-PET measurements were assessed in terms of activity profile similarity for six patients treated with a single or two almost parallel fields delivered by active proton beam scanning. The refined patient model yields a generally higher similarity for most of the patients, except in highly pathological areas leading to tissue misclassification. Using washout model parameters deduced from clinical patient data could considerably improve the activity profile similarity for all patients. Current methods used to predict proton-induced brain tissue activation can be improved with MR-based tissue classification and data-driven washout parameters, thus providing a more reliable basis for PT-PET verification. Copyright © 2018 Elsevier B.V. All rights reserved.

Mathematics as a Conduit for Translational Research in Post-Traumatic Osteoarthritis

PubMed Central

Ayati, Bruce P.; Kapitanov, Georgi I.; Coleman, Mitchell C.; Anderson, Donald D.; Martin, James A.

2016-01-01

Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a “conduit of translation”. The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge. PMID:27653021

Optical diagnosis of malaria infection in human plasma using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bilal, Muhammad; Saleem, Muhammad; Amanat, Samina Tufail; Shakoor, Huma Abdul; Rashid, Rashad; Mahmood, Arshad; Ahmed, Mushtaq

2015-01-01

We present the prediction of malaria infection in human plasma using Raman spectroscopy. Raman spectra of malaria-infected samples are compared with those of healthy and dengue virus infected ones for disease recognition. Raman spectra were acquired using a laser at 532 nm as an excitation source and 10 distinct spectral signatures that statistically differentiated malaria from healthy and dengue-infected cases were found. A multivariate regression model has been developed that utilized Raman spectra of 20 malaria-infected, 10 non-malarial with fever, 10 healthy, and 6 dengue-infected samples to optically predict the malaria infection. The model yields the correlation coefficient r2 value of 0.981 between the predicted values and clinically known results of trainee samples, and the root mean square error in cross validation was found to be 0.09; both these parameters validated the model. The model was further blindly tested for 30 unknown suspected samples and found to be 86% accurate compared with the clinical results, with the inaccuracy due to three samples which were predicted in the gray region. Standard deviation and root mean square error in prediction for unknown samples were found to be 0.150 and 0.149, which are accepted for the clinical validation of the model.

In Silico Modelling of Transdermal and Systemic Kinetics of Topically Applied Solutes: Model Development and Initial Validation for Transdermal Nicotine.

PubMed

Chen, Tao; Lian, Guoping; Kattou, Panayiotis

2016-07-01

The purpose was to develop a mechanistic mathematical model for predicting the pharmacokinetics of topically applied solutes penetrating through the skin and into the blood circulation. The model could be used to support the design of transdermal drug delivery systems and skin care products, and risk assessment of occupational or consumer exposure. A recently reported skin penetration model [Pharm Res 32 (2015) 1779] was integrated with the kinetic equations for dermis-to-capillary transport and systemic circulation. All model parameters were determined separately from the molecular, microscopic and physiological bases, without fitting to the in vivo data to be predicted. Published clinical studies of nicotine were used for model demonstration. The predicted plasma kinetics is in good agreement with observed clinical data. The simulated two-dimensional concentration profile in the stratum corneum vividly illustrates the local sub-cellular disposition kinetics, including tortuous lipid pathway for diffusion and the "reservoir" effect of the corneocytes. A mechanistic model for predicting transdermal and systemic kinetics was developed and demonstrated with published clinical data. The integrated mechanistic approach has significantly extended the applicability of a recently reported microscopic skin penetration model by providing prediction of solute concentration in the blood.

Integration of preclinical and clinical knowledge to predict intravenous PK in human: bilastine case study.

PubMed

Vozmediano, Valvanera; Ortega, Ignacio; Lukas, John C; Gonzalo, Ana; Rodriguez, Monica; Lucero, Maria Luisa

2014-03-01

Modern pharmacometrics can integrate and leverage all prior proprietary and public knowledge. Such methods can be used to scale across species or comparators, perform clinical trial simulation across alternative designs, confirm hypothesis and potentially reduce development burden, time and costs. Crucial yet typically lacking in integration is the pre-clinical stage. Prediction of PK in man, using in vitro and in vivo studies in different animal species, is increasingly well theorized but could still find wider application in drug development. The aim of the present work was to explore methods for bridging pharmacokinetic knowledge from animal species (i.v. and p.o.) and man (p.o.) into i.v. in man using the antihistamine drug bilastine as example. A model, predictive of i.v. PK in man, was developed on data from two pre-clinical species (rat and dog) and p.o. in man bilastine trials performed earlier. In the knowledge application stage, two different approaches were used to predict human plasma concentration after i.v. of bilastine: allometry (several scaling methods) and a semi-physiological method. Both approaches led to successful predictions of key i.v. PK parameters of bilastine in man. The predictive i.v. PK model was validated using later data from a clinical study of i.v. bilastine. Introduction of such knowledge in development permits proper leveraging of all emergent knowledge as well as quantification-based exploration of PK scenario, e.g. in special populations (pediatrics, renal insufficiency, comedication). In addition, the methods permit reduction or elimination and certainly optimization of learning trials, particularly those concerning alternative off-label administration routes.

Relapse prediction in Graves´ disease: Towards mathematical modeling of clinical, immune and genetic markers.

PubMed

Langenstein, Christoph; Schork, Diana; Badenhoop, Klaus; Herrmann, Eva

2016-12-01

Graves' disease (GD) is an important and prevalent thyroid autoimmune disorder. Standard therapy for GD consists of antithyroid drugs (ATD) with treatment periods of around 12 months but relapse is frequent. Since predictors for relapse are difficult to identify the individual decision making for optimal treatment is often arbitrary. After reviewing the literature on this topic we summarize important factors involved in GD and with respect to their potential for relapse prediction from markers before and after treatment. This information was used to design a mathematical model integrating thyroid hormone parameters, thyroid size, antibody titers and a complex algorithm encompassing genetic predisposition, environmental exposures and current immune activity in order to arrive at a prognostic index for relapse risk after treatment. In the search for a tool to analyze and predict relapse in GD mathematical modeling is a promising approach. In analogy to mathematical modeling approaches in other diseases such as viral infections, we developed a differential equation model on the basis of published clinical trials in patients with GD. Although our model needs further evaluation to be applicable in a clinical context, it provides a perspective for an important contribution to a final statistical prediction model.

Impaction and Prediction: Does Ureteral Wall Thickness Affect the Success of Medical Expulsive Therapy in Pediatric Ureteral Stones?

PubMed

Tuerxun, Aierken; Batuer, Abudukahaer; Erturhan, Sakip; Eryildirim, Bilal; Camur, Emre; Sarica, Kemal

2017-01-01

The study aimed to evaluate the predictive value of ureteral wall thickness (UWT) and stone-related parameters for medical expulsive therapy (MET) success with an alpha blocker in pediatric upper ureteral stones. A total of 35 children receiving MET ureteral stones (<10 mm) were evaluated. Patients were divided into 2 subgroups where MET was successful in 18 children (51.4%) and unsuccessful in 17 children (48.6%). Prior to management, stone size, stone density (in Hounsfield unit), degree of hydronephrosis, and UWT were evaluated with patient demographics and recorded. The possible predictive value of these parameters in success rates and time to stone expulsion were evaluated in a comparative manner between the 2 groups. The overall mean patient age and stone size values were 5.40 ± 0.51 years and 6.24 ± 0.28 mm, respectively. Regarding the predictive values of these parameters for the success of MET, while stone size and UWT were found to be highly predictive for MET success, patients age, body mass index, stone density, and degree of hydronephrosis had no predictive value on this aspect. Our findings indicated that some stone and anatomical factors may be used to predict the success of MET in pediatric ureteral stones in an effective manner. With this approach, unnecessary use of these drugs that may cause a delay in removing the stone will be avoided, and the possible adverse effects of obstruction as well as stone-related clinical symptoms could be minimized. © 2017 S. Karger AG, Basel.

Can baseline ultrasound results help to predict failure to achieve DAS28 remission after 1 year of tight control treatment in early RA patients?

PubMed

Ten Cate, D F; Jacobs, J W G; Swen, W A A; Hazes, J M W; de Jager, M H; Basoski, N M; Haagsma, C J; Luime, J J; Gerards, A H

2018-01-30

At present, there are no prognostic parameters unequivocally predicting treatment failure in early rheumatoid arthritis (RA) patients. We investigated whether baseline ultrasonography (US) findings of joints, when added to baseline clinical, laboratory, and radiographical data, could improve prediction of failure to achieve Disease Activity Score assessing 28 joints (DAS28) remission (<2.6) at 1 year in newly diagnosed RA patients. A multicentre cohort of newly diagnosed RA patients was followed prospectively for 1 year. US of the hands, wrists, and feet was performed at baseline. Clinical, laboratory, and radiographical parameters were recorded. Primary analysis was the prediction by logistic regression of the absence of DAS28 remission 12 months after diagnosis and start of therapy. Of 194 patients included, 174 were used for the analysis, with complete data available for 159. In a multivariate model with baseline DAS28 (odds ratio (OR) 1.6, 95% confidence interval (CI) 1.2-2.2), the presence of rheumatoid factor (OR 2.3, 95% CI 1.1-5.1), and type of monitoring strategy (OR 0.2, 95% CI 0.05-0.85), the addition of baseline US results for joints (OR 0.96, 95% CI 0.89-1.04) did not significantly improve the prediction of failure to achieve DAS28 remission (likelihood ratio test, 1.04; p = 0.31). In an early RA population, adding baseline ultrasonography of the hands, wrists, and feet to commonly available baseline characteristics did not improve prediction of failure to achieve DAS28 remission at 12 months. Clinicaltrials.gov, NCT01752309 . Registered on 19 December 2012.

Preoperative serum alkaline phosphatase: a predictive factor for early hypocalcaemia following parathyroidectomy of primary hyperparathyroidism.

PubMed

Sun, Longhao; He, Xianghui; Liu, Tong

2014-01-01

Postoperative hypocalcemia is one of the most common complications following parathyroidectomy for primary hyperparathyroidism (PHPT). The aim of this study was to analyze the predictive value of biochemical parameters as indicators for episodes of hypocalcemia in patients undergoing parathyroidectomy for PHPT. The patients with PHPT who underwent parathyroidectomy between February 2004 and February 2014 were studied retrospectively at a single medical center. The patients were divided into biochemical, clinical, and no postoperative hypocalcemia groups, based on different clinical manifestations. Potential risk factors for postoperative hypocalcemia were identified and investigated by univariate and multivariate Logistic regression analysis. Of the 139 cases, 25 patients (18.0%) were diagnosed with postoperative hypocalcemia according to the traditional criterion. Univariate analysis revealed only alkaline phosphatase (ALP) and the small area under the curve (AUC) of receiver operating characteristics (ROC) curve for ALP demonstrates low accuracy in predicting the occurrence of postoperative hypocalcemia. Based on new criteria, 22 patients were added to the postoperative hypocalcemia group and similar biochemical parameters were compared. The serum ALP was a significant independent risk factor for postoperative hypocalcemia (P = 0.000) and its AUC of ROC curve was 0.783. The optimal cutoff point was 269 U/L and the sensitivity and specificity for prediction were 89.2% and 64.3%, respectively. The risk of postoperative hypocalcemia after parathyroidectomy should be emphasized for patients with typical symptoms of hypocalcemia despite their serum calcium level is in normal or a little higher range. Serum ALP is a predictive factor for the occurrence of postoperative hypocalcemia.

Serum surfactant protein D predicts the outcome of patients with idiopathic pulmonary fibrosis treated with pirfenidone.

PubMed

Ikeda, Kimiyuki; Shiratori, Masanori; Chiba, Hirofumi; Nishikiori, Hirotaka; Yokoo, Keiki; Saito, Atsushi; Hasegawa, Yoshihiro; Kuronuma, Koji; Otsuka, Mitsuo; Yamada, Gen; Takahashi, Hiroki

2017-10-01

Idiopathic pulmonary fibrosis (IPF) is a fatal pulmonary disease with poor prognosis. Pirfenidone, the first antifibrotic drug, suppresses the decline in forced vital capacity (FVC) and improves prognosis in some, but not all, patients with IPF; therefore, an indicator for identifying improved outcomes in pirfenidone therapy is desirable. This study aims to clarify whether baseline parameters can be predictors of disease progression and prognosis in patients with IPF treated with pirfenidone. We retrospectively investigated patients with IPF who started treatment with pirfenidone between December 2008 and November 2014 at the Sapporo Medical University Hospital. Patients treated with pirfenidone for ≥6 months were enrolled in this study and were observed until November 2015. We investigated the association of clinical characteristics, pulmonary function test results, and blood examination results at the start of pirfenidone with the outcome of patients. Sixty patients were included in this study. In multivariate logistic regression analysis, % predicted FVC and serum surfactant protein (SP)-D levels were predictors of a ≥10% decline in FVC in the initial 12 months. In the Cox proportional hazards model, these two factors predicted progression-free survival. Pack-years, % predicted diffusing capacity for carbon monoxide, and SP-D levels predicted overall survival. The serum SP-D level was a predictor of disease progression and prognosis in patients with IPF treated with pirfenidone. In addition, this analysis describes the relative usefulness of other clinical parameters at baseline in estimating the prognosis of patients with IPF who are candidates for pirfenidone therapy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

PubMed Central

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

Objective To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Materials and methods Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. Results The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. Discussion With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Conclusions Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC. PMID:23616206

Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study.

PubMed

Kattou, Panayiotis; Lian, Guoping; Glavin, Stephen; Sorrell, Ian; Chen, Tao

2017-10-01

The development of a new two-dimensional (2D) model to predict follicular permeation, with integration into a recently reported multi-scale model of transdermal permeation is presented. The follicular pathway is modelled by diffusion in sebum. The mass transfer and partition properties of solutes in lipid, corneocytes, viable dermis, dermis and systemic circulation are calculated as reported previously [Pharm Res 33 (2016) 1602]. The mass transfer and partition properties in sebum are collected from existing literature. None of the model input parameters was fit to the clinical data with which the model prediction is compared. The integrated model has been applied to predict the published clinical data of transdermal permeation of caffeine. The relative importance of the follicular pathway is analysed. Good agreement of the model prediction with the clinical data has been obtained. The simulation confirms that for caffeine the follicular route is important; the maximum bioavailable concentration of caffeine in systemic circulation with open hair follicles is predicted to be 20% higher than that when hair follicles are blocked. The follicular pathway contributes to not only short time fast penetration, but also the overall systemic bioavailability. With such in silico model, useful information can be obtained for caffeine disposition and localised delivery in lipid, corneocytes, viable dermis, dermis and the hair follicle. Such detailed information is difficult to obtain experimentally.

Parameter predicting the recurrence of adhesive small bowel obstruction in patients managed with a long tube.

PubMed

Sakakibara, Takumi; Harada, Akio; Ishikawa, Tadao; Komatsu, Yoshinao; Yaguchi, Toyohisa; Kodera, Yasuhiro; Nakao, Akimasa

2007-01-01

Some of our patients showed a recurrence of adhesive small bowel obstruction (ASBO) with nonoperative management. The aim of this study was to evaluate the parameters predicting the recurrence of ASBO in patients managed with a long tube. Of 234 patients with ASBO admitted from April 1998 to September 2002, a total of 91 who recovered with nonoperative management after long tube placement were enrolled in this retrospective clinical study. We divided them into two groups for follow-up: the recurrence group and the no-recurrence group. We compared baseline characteristics, the number of previous ASBO admissions, the number of abdominal operations, the interval from the onset of symptoms to long-tube insertion, the duration of long-tube placement, the type of the contrasted intestine through the long tube, the location of the long-tube tip, and the drainage volume through the long tube between the two groups. We then examined the cumulative recurrence rate. A significant difference was found in the number of previous ASBO admissions, the duration of long-tube placement (77 hours vs. 43 hours), the contrasted intestine through the long tube, and the location of the long-tube tip. By multivariate analysis, the duration of long-tube placement was an independent parameter predicting the recurrence of ASBO. These results suggest that the duration of long-tube placement might serve as a parameter for predicting recurrence of ASBO in patients managed with a long tube.

We can predict postpalatoplasty velopharyngeal insufficiency in cleft palate patients.

PubMed

Leclerc, Jacques E; Godbout, Audrey; Arteau-Gauthier, Isabelle; Lacour, Sophie; Abel, Kati; McConnell, Elisa-Maude

2014-02-01

To find an anatomical measurement of the cleft palate (or a calculated parameter) that predicts the occurrence of velopharyngeal insufficiency (VPI) after palatal cleft repair. Retrospective cohort study. Charts were reviewed from cleft palate patients who underwent palatoplasty by the Von Langenbeck technique for isolated cleft palate or Bardach two-flap palatoplasty for cleft lip-palate. Seven anatomical cleft parameters were prospectively measured during the palatoplasty procedure. Three blinded speech-language pathologists retrospectively scored the clinically assessed VPI at 4 years of age. The recommendation of pharyngoplasty was also used as an indicator of VPI. From 1993 to 2008, 67 patients were enrolled in the study. The best predicting parameter was the ratio a/(30 - b1), in which a is defined as the posterior gap between the soft palate and the posterior pharyngeal wall and b1 is the width of the cleft at the hard palate level. An a/(30 - b1) ratio >0.7 to 0.8 is associated with a higher risk of developing VPI (relative risk = 2.2-5.1, sensitivity = 72%-81%, P < .03). The width of the cleft at the hard palate level and the posterior gap between the soft palate and the posterior pharyngeal wall were found to be the most significant parameters in predicting VPI. The best correlation was obtained with the ratio a/(30 - b1). 4. Copyright © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

Early variations of laboratory parameters predicting shunt-dependent hydrocephalus after subarachnoid hemorrhage.

PubMed

Na, Min Kyun; Won, Yu Deok; Kim, Choong Hyun; Kim, Jae Min; Cheong, Jin Hwan; Ryu, Je Il; Han, Myung-Hoon

2017-01-01

Hydrocephalus is a frequent complication following subarachnoid hemorrhage. Few studies investigated the association between laboratory parameters and shunt-dependent hydrocephalus. This study aimed to investigate the variations of laboratory parameters after subarachnoid hemorrhage. We also attempted to identify predictive laboratory parameters for shunt-dependent hydrocephalus. Multiple imputation was performed to fill the missing laboratory data using Bayesian methods in SPSS. We used univariate and multivariate Cox regression analyses to calculate hazard ratios for shunt-dependent hydrocephalus based on clinical and laboratory factors. The area under the receiver operating characteristic curve was used to determine the laboratory risk values predicting shunt-dependent hydrocephalus. We included 181 participants with a mean age of 54.4 years. Higher sodium (hazard ratio, 1.53; 95% confidence interval, 1.13-2.07; p = 0.005), lower potassium, and higher glucose levels were associated with higher shunt-dependent hydrocephalus. The receiver operating characteristic curve analysis showed that the areas under the curve of sodium, potassium, and glucose were 0.649 (cutoff value, 142.75 mEq/L), 0.609 (cutoff value, 3.04 mmol/L), and 0.664 (cutoff value, 140.51 mg/dL), respectively. Despite the exploratory nature of this study, we found that higher sodium, lower potassium, and higher glucose levels were predictive values for shunt-dependent hydrocephalus from postoperative day (POD) 1 to POD 12-16 after subarachnoid hemorrhage. Strict correction of electrolyte imbalance seems necessary to reduce shunt-dependent hydrocephalus. Further large studies are warranted to confirm our findings.

Reinforcement learning in depression: A review of computational research.

PubMed

Chen, Chong; Takahashi, Taiki; Nakagawa, Shin; Inoue, Takeshi; Kusumi, Ichiro

2015-08-01

Despite being considered primarily a mood disorder, major depressive disorder (MDD) is characterized by cognitive and decision making deficits. Recent research has employed computational models of reinforcement learning (RL) to address these deficits. The computational approach has the advantage in making explicit predictions about learning and behavior, specifying the process parameters of RL, differentiating between model-free and model-based RL, and the computational model-based functional magnetic resonance imaging and electroencephalography. With these merits there has been an emerging field of computational psychiatry and here we review specific studies that focused on MDD. Considerable evidence suggests that MDD is associated with impaired brain signals of reward prediction error and expected value ('wanting'), decreased reward sensitivity ('liking') and/or learning (be it model-free or model-based), etc., although the causality remains unclear. These parameters may serve as valuable intermediate phenotypes of MDD, linking general clinical symptoms to underlying molecular dysfunctions. We believe future computational research at clinical, systems, and cellular/molecular/genetic levels will propel us toward a better understanding of the disease. Copyright © 2015 Elsevier Ltd. All rights reserved.

Baseline predictors of persistence to first disease-modifying treatment in multiple sclerosis.

PubMed

Zettl, U K; Schreiber, H; Bauer-Steinhusen, U; Glaser, T; Hechenbichler, K; Hecker, M

2017-08-01

Patients with multiple sclerosis (MS) require lifelong therapy. However, success of disease-modifying therapies is dependent on patients' persistence and adherence to treatment schedules. In the setting of a large multicenter observational study, we aimed at assessing multiple parameters for their predictive power with respect to discontinuation of therapy. We analyzed 13 parameters to predict discontinuation of interferon beta-1b treatment during a 2-year follow-up period based on data from 395 patients with MS who were treatment-naïve at study onset. Besides clinical characteristics, patient-related psychosocial outcomes were assessed as well. Among patients without clinically relevant fatigue, males showed a higher persistence rate than females (80.3% vs 64.7%). Clinically relevant fatigue scores decreased the persistence rate in men and especially in women (71.4% and 51.2%). Besides gender and fatigue, univariable and multivariable analyses revealed further factors associated with interferon beta-1b therapy discontinuation, namely lower quality of life, depressiveness, and higher relapse rate before therapy initiation, while higher education, living without a partner, and higher age improved persistence. Patients with higher grades of fatigue and depressiveness are at higher risk to prematurely discontinue MS treatment; especially, women suffering from fatigue have an increased discontinuation rate. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Dosimetric and clinical predictors of radiation-induced lung toxicity in esophageal carcinoma.

PubMed

Zhu, Shu-Chai; Shen, Wen-Bin; Liu, Zhi-Kun; Li, Juan; Su, Jing-Wei; Wang, Yu-Xiang

2011-01-01

Radiation-induced lung toxicity occurs frequently in patients with esophageal carcinoma. This study aims to evaluate the clinical and three-dimensional dosimetric parameters associated with lung toxicity after radiotherapy for esophageal carcinoma. The records of 56 patients treated for esophageal carcinoma were reviewed. The Radiation Therapy Oncology Group criteria for grading of lung toxicity were followed. Spearman's correlation test, the chi-square test and logistic regression analyses were used for statistical analysis. Ten of the 56 patients developed acute toxicity. The toxicity grades were grade 2 in 7 patients and grade 3 in 3 patients; none of the patients developed grade 4 or worse toxicity. One case of toxicity occurred during radiotherapy and 9 occurred 2 weeks to 3 months after radiotherapy. The median time was 2.0 months after radiotherapy. Fourteen patients developed late irradiated lung injury, 3 after 3.5 months, 7 after 9 months, and 4 after 14 months. Radiographic imaging demonstrated patchy consolidation (n = 5), atelectasis with parenchymal distortion (n = 6), and solid consolidation (n = 3). For acute toxicity, the irradiated esophageal volume, number of fields, and most dosimetric parameters were predictive. For late toxicity, chemotherapy combined with radiotherapy and other dosimetric parameters were predictive. No obvious association between the occurrence of acute and late injury was observed. The percent of lung tissue receiving at least 25 Gy (V25), the number of fields, and the irradiated length of the esophagus can be used as predictors of the risk of acute toxicity. Lungs V30, as well as chemotherapy combined with radiotherapy, are predictive of late lung injury.

Predicting disease onset in clinically healthy people

PubMed Central

2016-01-01

Virtually all human disease is induced by oxidative stress. Oxidative stress, which is caused by toxic environmental exposure, the presence of disease, lifestyle choices, stress, chronic inflammation or combinations of these, is responsible for most disease. Oxidative stress from all sources is additive and it is the total oxidative stress from all sources that induces the onset of most disease. Oxidative stress leads to lipid peroxidation, which in turn produces Malondialdehyde. Serum malondialdehyde level is an additive parameter resulting from all sources of oxidative stress and, therefore, is a reliable indicator of total oxidative stress which can be used to predict the onset of disease in clinically asymptomatic individuals and to suggest the need for treatment that can prevent much human disease. PMID:28652846

Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages.

PubMed

Choi, Youn-Kyung; Kim, Jinmi; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Kim, Yong-Il

2016-01-01

This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5-18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P < 0.05). The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages

PubMed Central

Choi, Youn-Kyung; Kim, Jinmi; Maki, Koutaro; Ko, Ching-Chang

2016-01-01

This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P < 0.05). The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level. PMID:27340668

l-DOPA and Freezing of Gait in Parkinson’s Disease: Objective Assessment through a Wearable Wireless System

PubMed Central

Suppa, Antonio; Kita, Ardian; Leodori, Giorgio; Zampogna, Alessandro; Nicolini, Ettore; Lorenzi, Paolo; Rao, Rosario; Irrera, Fernanda

2017-01-01

Freezing of gait (FOG) is a leading cause of falls and fractures in Parkinson’s disease (PD). The episodic and rather unpredictable occurrence of FOG, coupled with the variable response to l-DOPA of this gait disorder, makes the objective evaluation of FOG severity a major clinical challenge in the therapeutic management of patients with PD. The aim of this study was to examine and compare gait, clinically and objectively, in patients with PD, with and without FOG, by means of a new wearable system. We also assessed the effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters in patients with and without FOG. To this purpose, we recruited 28 patients with FOG, 16 patients without FOG, and 16 healthy subjects. In all participants, gait was evaluated clinically by video recordings and objectively by means of the wearable wireless system, during a modified 3-m Timed Up and Go (TUG) test. All patients performed the modified TUG test under and not under dopaminergic therapy (ON and OFF therapy). By comparing instrumental data with the clinical identification of FOG based on offline video-recordings, we also assessed the performance of the wearable system to detect FOG automatically in terms of sensitivity, specificity, positive and negative predictive values, and finally accuracy. TUG duration was longer in patients than in controls, and the amount of gait abnormalities was prominent in patients with FOG compared with those without FOG. l-DOPA improved gait significantly in patients with PD and particularly in patients with FOG mainly by reducing FOG duration and increasing specific spatiotemporal gait parameters. Finally, the overall wireless system performance in automatic FOG detection was characterized by excellent sensitivity (93.41%), specificity (98.51%), positive predictive value (89.55%), negative predictive value (97.31%), and finally accuracy (98.51%). Our study overall provides new information on the beneficial effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters as objectively measured by a wearable sensory system. The algorithm here reported potentially opens to objective long-time sensing of FOG episodes in patients with PD. PMID:28855889

Comprehensive neuromechanical assessment in stroke patients: reliability and responsiveness of a protocol to measure neural and non-neural wrist properties.

PubMed

van der Krogt, Hanneke; Klomp, Asbjørn; de Groot, Jurriaan H; de Vlugt, Erwin; van der Helm, Frans Ct; Meskers, Carel Gm; Arendzen, J Hans

2015-03-13

Understanding movement disorder after stroke and providing targeted treatment for post stroke patients requires valid and reliable identification of biomechanical (passive) and neural (active and reflexive) contributors. Aim of this study was to assess test-retest reliability of passive, active and reflexive parameters and to determine clinical responsiveness in a cohort of stroke patients with upper extremity impairments and healthy volunteers. Thirty-two community-residing chronic stroke patients with an impairment of an upper limb and fourteen healthy volunteers were assessed with a comprehensive neuromechanical assessment protocol consisting of active and passive tasks and different stretch reflex-eliciting measuring velocities, using a haptic manipulator and surface electromyography of wrist flexor and extensor muscles (Netherlands Trial Registry number NTR1424). Intraclass correlation coefficients (ICC) and Standard Error of Measurement were calculated to establish relative and absolute test-retest reliability of passive, active and reflexive parameters. Clinical responsiveness was tested with Kruskal Wallis test for differences between groups. ICC of passive parameters were fair to excellent (0.45 to 0.91). ICC of active parameters were excellent (0.88-0.99). ICC of reflexive parameters were fair to good (0.50-0.74). Only the reflexive loop time of the extensor muscles performed poor (ICC 0.18). Significant differences between chronic stroke patients and healthy volunteers were found in ten out of fourteen parameters. Passive, active and reflexive parameters can be assessed with high reliability in post-stroke patients. Parameters were responsive to clinical status. The next step is longitudinal measurement of passive, active and reflexive parameters to establish their predictive value for functional outcome after stroke.

WE-H-BRA-02: Radiobiological Modeling of Tumor Control Probability (TCP) and Radiation-Induced Pneumonitis (RP) for Lung Cancer Patients Treated with Monte Carlo-Based Lung SBRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pokhrel, D; Sood, S; Shen, X

2016-06-15

Purpose: To present radiobiological modeling of TCP using tumor size-adjusted BED(s-BED)and PTV(D99) to lung SBRT patients treated with X-ray Voxel Monte Carlo(XVMC) algorithm, apply parameterized Lyman-NTCP model to predict grade-2 RP and subsequently, compare with clinical outcomes/observations. Methods: Dosimetric parameters and clinical follow-up for XVMC-based lung-SBRT patients were retrospectively evaluated. Patients were treated at Novalis-TX with hybrid(2 non-coplanar partial-arcs plus 3–6 static-beams)plan using HD-MLC/6MV-SRS-beam.For TCP,s-BED modelling was utilized: TCP=EXP[sBED-TCD50]/k/(1.0+EXP[sBED-TCD50]/k), where k=31Gy corresponding to TCD50=0Gy and s-BED was defined as BED10 minus 10 times the tumor diameter(in centimeters)by Ohri et al.(IJROBP,2012). For 2-yr local-control, we used more-realistic MC-computed PTVD99 as amore » predictive parameter, s-BED(D99).Due to relatively shorter median follow-up interval(12-months),Kaplan-Meier curves were generated to estimate 2-yr observed local-control and compared to predicted-rate by TCP modeling. For NTCP, we employed parameterized Lyman-NTCP model utilizing normal-lung DVH and α/β=3Gy fitted to predict grade-2 RP after lung-SBRT. Results: Total 108 patients (137 tumors) treated for 35–70Gy in 3–5 fractions, either primary-lung(n=74)or metastatic-lung(n=53)tumors were included.F or the given prescription dose with MC-computed MUs, 2-yr local-control rates with s-BED(D99) was 87±8%. Kaplan-Meier generated observed local-control rate at 2-yr was 87.5%,suggesting that PTV(D99) could be a potential predictor (p-value=0.38). Observed vs predicted TCP for primary-lung tumors and metastatic tumors were 97% vs 88±7% and 94% vs 86±9%.NTCP model predicted well for symptomatic-RP with predicted vs observed (3±5% vs 2%). Radiographic and clinically significant RP was observed in 13% and 2% of patients. Higher rates of radiographic change were observed in patients who received >50Gy compared to ≤50Gy(24% vs 10%). Conclusion: Utilizing MC-computed PTVD99, our TCP results were well correlated with clinical outcome. The predicted grade-2 RP rate was comparable to clinical observations. Clinical application of these radiobiological models may potentially allow for target dose escalation and/or lung-toxicity reduction. Further validation of these radiobiological models with longer follow up interval for large cohorts of lung-SBRT patients is anticipated.« less

Tumor Control Probability Modeling for Stereotactic Body Radiation Therapy of Early-Stage Lung Cancer Using Multiple Bio-physical Models

PubMed Central

Liu, Feng; Tai, An; Lee, Percy; Biswas, Tithi; Ding, George X.; El Naqa, Isaam; Grimm, Jimm; Jackson, Andrew; Kong, Feng-Ming (Spring); LaCouture, Tamara; Loo, Billy; Miften, Moyed; Solberg, Timothy; Li, X Allen

2017-01-01

Purpose To analyze pooled clinical data using different radiobiological models and to understand the relationship between biologically effective dose (BED) and tumor control probability (TCP) for stereotactic body radiotherapy (SBRT) of early-stage non-small cell lung cancer (NSCLC). Method and Materials The clinical data of 1-, 2-, 3-, and 5-year actuarial or Kaplan-Meier TCP from 46 selected studies were collected for SBRT of NSCLC in the literature. The TCP data were separated for Stage T1 and T2 tumors if possible, otherwise collected for combined stages. BED was calculated at isocenters using six radiobiological models. For each model, the independent model parameters were determined from a fit to the TCP data using the least chi-square (χ2) method with either one set of parameters regardless of tumor stages or two sets for T1 and T2 tumors separately. Results The fits to the clinic data yield consistent results of large α/β ratios of about 20 Gy for all models investigated. The regrowth model that accounts for the tumor repopulation and heterogeneity leads to a better fit to the data, compared to other 5 models where the fits were indistinguishable between the models. The models based on the fitting parameters predict that the T2 tumors require about additional 1 Gy physical dose at isocenters per fraction (≤5 fractions) to achieve the optimal TCP when compared to the T1 tumors. Conclusion This systematic analysis of a large set of published clinical data using different radiobiological models shows that local TCP for SBRT of early-stage NSCLC has strong dependence on BED with large α/β ratios of about 20 Gy. The six models predict that a BED (calculated with α/β of 20) of 90 Gy is sufficient to achieve TCP ≥ 95%. Among the models considered, the regrowth model leads to a better fit to the clinical data. PMID:27871671

A review of clinical and histological parameters associated with contralateral neck metastases in oral squamous cell carcinoma

PubMed Central

Fan, Song; Tang, Qiong-lan; Lin, Ying-jin; Chen, Wei-liang; Li, Jin-song; Huang, Zhi-quan; Yang, Zhao-hui; Wang, You-yuan; Zhang, Da-ming; Wang, Hui-jing; Dias-Ribeiro, Eduardo; Cai, Qiang; Wang, Lei

2011-01-01

Oral squamous cell carcinoma (OSCC) has a high incidence of cervical micrometastases and sometimes metastasizes contralaterally because of the rich lymphatic intercommunications relative to submucosal plexus of oral cavity that freely communicate across the midline, and it can facilitate the spread of neoplastic cells to any area of the neck consequently. Clinical and histopathologic factors continue to provide predictive information to contralateral neck metastases (CLNM) in OSCC, which determine prophylactic and adjuvant treatments for an individual patient. This review describes the predictive value of clinical-histopathologic factors, which relate to primary tumor and cervical lymph nodes, and surgical dissection and adjuvant treatments. In addition, the indications for elective contralateral neck dissection and adjuvant radiotherapy (aRT) and strategies for follow-up are offered, which is strongly focused by clinicians to prevent later CLNM and poor prognosis subsequently. PMID:22010576

From High-Throughput Microarray-Based Screening to Clinical Application: The Development of a Second Generation Multigene Test for Breast Cancer Prognosis

PubMed Central

Brase, Jan C.; Kronenwett, Ralf; Petry, Christoph; Denkert, Carsten; Schmidt, Marcus

2013-01-01

Several multigene tests have been developed for breast cancer patients to predict the individual risk of recurrence. Most of the first generation tests rely on proliferation-associated genes and are commonly carried out in central reference laboratories. Here, we describe the development of a second generation multigene assay, the EndoPredict test, a prognostic multigene expression test for estrogen receptor (ER) positive, human epidermal growth factor receptor (HER2) negative (ER+/HER2−) breast cancer patients. The EndoPredict gene signature was initially established in a large high-throughput microarray-based screening study. The key steps for biomarker identification are discussed in detail, in comparison to the establishment of other multigene signatures. After biomarker selection, genes and algorithms were transferred to a diagnostic platform (reverse transcription quantitative PCR (RT-qPCR)) to allow for assaying formalin-fixed, paraffin-embedded (FFPE) samples. A comprehensive analytical validation was performed and a prospective proficiency testing study with seven pathological laboratories finally proved that EndoPredict can be reliably used in the decentralized setting. Three independent large clinical validation studies (n = 2,257) demonstrated that EndoPredict offers independent prognostic information beyond current clinicopathological parameters and clinical guidelines. The review article summarizes several important steps that should be considered for the development process of a second generation multigene test and offers a means for transferring a microarray signature from the research laboratory to clinical practice. PMID:27605191

Proton MR spectroscopy in predicting the increase of perfusion MR imaging for WHO grade II gliomas.

PubMed

Guillevin, Remy; Menuel, Carole; Abud, Lucas; Costalat, Robert; Capelle, Laurent; Hoang-Xuan, Khê; Habas, Christophe; Chiras, Jacques; Vallée, Jean-Noel

2012-03-01

To investigate the correlation between the metabolite ratios obtained from proton magnetic resonance (MR) spectroscopy and those obtained from MR perfusion parameters (relative cerebral blood volume [rCBV]) in a cohort of low-grade glioma (LGG). Patients underwent prospectively conventional MR, proton magnetic resonance spectroscopy ((1) HMRS), and perfusion-weighted images (PWI). Statistical analyses were performed to determine the correlative and independent predictive factors of rCBVmax and the metabolite ratio thresholds with optimum sensitivity and specificity. Thirty-one patients were included in this study. Linear correlations were observed between the metabolic ratios (lactate [Lac]/creatine [Cr], choline [Cho]/N-acetyl-aspartate [NAA], free-lipids/Cr) and rCBVmax (P < 0.05). These metabolic ratios were determined to be independent predictive factors of rCBVmax (P = 0.027, 0.011 and 0.032, respectively). According to the receiver operating characteristic curves, the cutoff values of the metabolic ratios to discriminate between the two populations of rCBVmax (<1.7 versus = 1.7) were 1.72, 1.54, and 1.40, respectively, with a sensitivity = 75% and a specificity >95% for Lac/Cr. This study demonstrated consistent correlations between the data from (1) HMRS and PWI. The Lac/Cr ratio predicts regional hemodynamic changes, which are themselves a useful biomarker of clinical prognosis in patients with LGG. As such, this ratio may provide a new parameter for making improved clinical decisions. Copyright © 2011 Wiley-Liss, Inc.

A simple prediction tool for inhaled corticosteroid response in asthmatic children.

PubMed

Wu, Yi-Fan; Su, Ming-Wei; Chiang, Bor-Luen; Yang, Yao-Hsu; Tsai, Ching-Hui; Lee, Yungling L

2017-12-07

Inhaled corticosteroids are recommended as the first-line controller medication for childhood asthma owing to their multiple clinical benefits. However, heterogeneity in the response towards these drugs remains a significant clinical problem. Children aged 5 to 18 years with mild to moderate persistent asthma were recruited into the Taiwanese Consortium of Childhood Asthma Study. Their responses to inhaled corticosteroids were assessed based on their improvements in the asthma control test and peak expiratory flow. The predictors of responsiveness were demographic and clinical features that were available in primary care settings. We have developed a prediction model using logistic regression and have simplified it to formulate a practical tool. We assessed its predictive performance using the area under the receiver operating characteristic curve. Of the 73 asthmatic children with baseline and follow-up outcome measurements for inhaled corticosteroids treatment, 24 (33%) were defined as non-responders. The tool we have developed consisted of three predictors yielding a total score between 0 and 5, which are comprised of the following parameters: the age at physician-diagnosis of asthma, sex, and exhaled nitric oxide. Sensitivity and specificity of the tool for prediction of inhaled corticosteroids non-responsiveness, for a score of 3, were 0.75 and 0.69, respectively. The areas under the receiver operating characteristic curve for the prediction tool was 0.763. Our prediction tool represents a simple and low-cost method for predicting the response of inhaled corticosteroids treatment in asthmatic children.

Clinical Implications of a Dimensional Approach: The Normal:Abnormal Spectrum of Early Irritability.

PubMed

Wakschlag, Lauren S; Estabrook, Ryne; Petitclerc, Amelie; Henry, David; Burns, James L; Perlman, Susan B; Voss, Joel L; Pine, Daniel S; Leibenluft, Ellen; Briggs-Gowan, Margaret L

2015-08-01

The importance of dimensional approaches is widely recognized, but an empirical base for clinical application is lacking. This is particularly true for irritability, a dimensional phenotype that cuts across many areas of psychopathology and manifests early in life. We examine longitudinal, dimensional patterns of irritability and their clinical import in early childhood. Irritability was assessed longitudinally over an average of 16 months in a clinically enriched, diverse community sample of preschoolers (N = 497; mean = 4.2 years; SD = 0.8). Using the Temper Loss scale of the Multidimensional Assessment Profile of Disruptive Behavior (MAP-DB) as a developmentally sensitive indicator of early childhood irritability, we examined its convergent/divergent, clinical, and incremental predictive validity, and modeled its linear and nonlinear associations with clinical risk. The Temper Loss scale demonstrated convergent and divergent validity to child and maternal factors. In multivariate analyses, Temper Loss predicted mood (separation anxiety disorder [SAD], generalized anxiety disorder [GAD], and depression/dysthymia), disruptive (oppositional defiant disorder [ODD], attention-deficit/hyperactivity disorder [ADHD], and conduct disorder [CD]) symptoms. Preschoolers with even mildly elevated Temper Loss scale scores showed substantially increased risk of symptoms and disorders. For ODD, GAD, SAD, and depression, increases in Temper Loss scale scores at the higher end of the dimension had a greater impact on symptoms relative to increases at the lower end. Temper Loss scale scores also showed incremental validity over DSM-IV disorders in predicting subsequent impairment. Finally, accounting for the substantial heterogeneity in longitudinal patterns of Temper Loss significantly improved prediction of mood and disruptive symptoms. Dimensional, longitudinal characterization of irritability informs clinical prediction. A vital next step will be empirically generating parameters for the incorporation of dimensional information into clinical decision-making with reasonable certainty. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. All rights reserved.

A score for the differential diagnosis of bradykinin- and histamine-induced head and neck swellings.

PubMed

Lenschow, M; Bas, M; Johnson, F; Wirth, M; Strassen, U

2018-05-02

Acute edema of the head and neck region may lead to life-threatening dyspnea and require quick and targeted treatment. They can be subdivided in bradykinin- and histamine-mediated swellings, which require treatment with different classes of pharmaceuticals. Clinical pathways for differential diagnoses do not exist so far, although it is known that early treatment is decisive for faster symptom relief and reduced expression of the swellings. Aim of the study was the creation of a clinical algorithm for identification of bradykinin-mediated angioedema. 188 patients that presented to our outpatient department between 2010 and 2016 with an acute, non-inflammatory swelling of the head and neck region were included in our retrospective study. All available anamnestic and clinical parameters were obtained from patient files. Parameters showing significant differences between the two groups were included in our score. Utilization of the Youden's index allowed determination of an optimal cut-off value. 76 patients could be assigned to the histamine and 112 patients to bradykinin group. The following parameters were included in our score: age, dyspnea, itching or erythema, glucocorticoid response and intake of ACEi/AT-II blockers. The cut-off value is set at three points. The proposed score yielded a sensitivity for identification of bradykinin-mediated angioedema of 96%, a specificity of 84%, a positive predictive value of 91% and a negative predictive value of 93%. Utilization of the proposed score allows quick and reliable assignment of patients to the correct subgroup and thereby reduces time for treatment.

The lumbar lordosis index: a new ratio to detect spinal malalignment with a therapeutic impact for sagittal balance correction decisions in adult scoliosis surgery.

PubMed

Boissière, Louis; Bourghli, Anouar; Vital, Jean-Marc; Gille, Olivier; Obeid, Ibrahim

2013-06-01

Sagittal malalignment is frequently observed in adult scoliosis. C7 plumb line, lumbar lordosis and pelvic tilt are the main factors to evaluate sagittal balance and the need of a vertebral osteotomy to correct it. We described a ratio: the lumbar lordosis index (ratio lumbar lordosis/pelvic incidence) (LLI) and analyzed its relationships with spinal malalignment and vertebral osteotomies. 53 consecutive patients with a surgical adult scoliosis had preoperative and postoperative full spine EOS radiographies to measure spino-pelvic parameters and LLI. The lack of lordosis was calculated after prediction of theoretical lumbar lordosis. Correlation analysis between the different parameters was performed. All parameters were correlated with spinal malalignment but LLI is the most correlated parameter (r = -0.978). It is also the best parameter in this study to predict the need of a spinal osteotomy (r = 1 if LLI <0.5). LLI is a statistically validated parameter for sagittal malalignment analysis. It can be used as a mathematical tool to detect spinal malalignment in adult scoliosis and guides the surgeon decision of realizing a vertebral osteotomy for adult scoliosis sagittal correction. It can be used as well for the interpretation of clinical series in adult scoliosis.

[Predictive ability of clinical parameters of bacteremia in hemodialysed patients].

PubMed

Egea, Ana L; Vilaró, Mario; De la Fuente, Jorge; Cuestas, Eduardo; Bongiovanni, María E

2012-01-01

No clinical events to differentiate bacteteremia from other pathologies in hemodialysis patients therefore the physicians makes diagnosis and treatment decisions based on clinical evidence an local epidemiology. the aim of this work was to study the frequency of microorganism isolated from blood culture of hemodialysis patients with suspected bacteraemia and evaluate Sensitivity (S) and Specificity (E) of medical diagnostic orientation in this cases of suspected Materials and methods: we performed an observational and prospective study for one year in hemodialysis patient with suspected bacteremia. We evaluated blood pressure, temperature (Tº), altered conscious state (AEC), respiratory frequency (FR), chills (ESC),diarrhea (DIARR), blood culture results and microbiological identification. We work with the mean ± standar desviation for continuous variables and frequencies for categorical variables We analyzed S, E, negative predictive value (VPN), positive predictive value (VPP) RESULTADOS: a total of 87 events with suspected bacteremia 34 (39%) were confirmed with positive blood culture the most common microorganisms were cocci Gram positive (CGP) 65%, Most relevant clinical variables were PCP ≥ 2 (VPN 81%), Tº ≥ 38 (VPN 76%) and AEC (E 98% y VPP 80%). CGP were the most prevalent microorganisms None of the clinical variables shows high S and E indicating low usefulness as a predictive tool of bacteremia Excepting AEC with E98% and VPP 80% but it would be necessary to evaluate this variable with a more number patient. Results justify to routine HC use like diagnostic tool.

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

PubMed

Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei

2017-09-25

It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

Computational Electrocardiography: Revisiting Holter ECG Monitoring.

PubMed

Deserno, Thomas M; Marx, Nikolaus

2016-08-05

Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic method has not been changed. After 70 years of technologic developments, we revisit Holter ECG from recording to understanding. A fundamental change is fore-seen towards "computational ECG" (CECG), where continuous monitoring is producing big data volumes that are impossible to be inspected conventionally but require efficient computational methods. We draw parallels between CECG and computational biology, in particular with respect to computed tomography, computed radiology, and computed photography. From that, we identify technology and methodology needed for CECG. Real-time transfer of raw data into meaningful parameters that are tracked over time will allow prediction of serious events, such as sudden cardiac death. Evolved from Holter's technology, portable smartphones with Bluetooth-connected textile-embedded sensors will capture noisy raw data (recording), process meaningful parameters over time (analysis), and transfer them to cloud services for sharing (handling), predicting serious events, and alarming (understanding). To make this happen, the following fields need more research: i) signal processing, ii) cycle decomposition; iii) cycle normalization, iv) cycle modeling, v) clinical parameter computation, vi) physiological modeling, and vii) event prediction. We shall start immediately developing methodology for CECG analysis and understanding.

Physiologically-Based Pharmacokinetic Modeling of Macitentan: Prediction of Drug-Drug Interactions.

PubMed

de Kanter, Ruben; Sidharta, Patricia N; Delahaye, Stéphane; Gnerre, Carmela; Segrestaa, Jerome; Buchmann, Stephan; Kohl, Christopher; Treiber, Alexander

2016-03-01

Macitentan is a novel dual endothelin receptor antagonist for the treatment of pulmonary arterial hypertension (PAH). It is metabolized by cytochrome P450 (CYP) enzymes, mainly CYP3A4, to its active metabolite ACT-132577. A physiological-based pharmacokinetic (PBPK) model was developed by combining observations from clinical studies and physicochemical parameters as well as absorption, distribution, metabolism and excretion parameters determined in vitro. The model predicted the observed pharmacokinetics of macitentan and its active metabolite ACT-132577 after single and multiple dosing. It performed well in recovering the observed effect of the CYP3A4 inhibitors ketoconazole and cyclosporine, and the CYP3A4 inducer rifampicin, as well as in predicting interactions with S-warfarin and sildenafil. The model was robust enough to allow prospective predictions of macitentan-drug combinations not studied, including an alternative dosing regimen of ketoconazole and nine other CYP3A4-interacting drugs. Among these were the HIV drugs ritonavir and saquinavir, which were included because HIV infection is a known risk factor for the development of PAH. This example of the application of PBPK modeling to predict drug-drug interactions was used to support the labeling of macitentan (Opsumit).

Genetic analysis for mastitis resistance and milk somatic cell score in French Lacaune dairy sheep

PubMed Central

Barillet, Francis; Rupp, Rachel; Mignon-Grasteau, Sandrine; Astruc, Jean-Michel; Jacquin, Michèle

2001-01-01

Genetic analysis for mastitis resistance was studied from two data sets. Firstly, risk factors for different mastitis traits, i.e. culling due to clinical or chronic mastitis and subclinical mastitis predicted from somatic cell count (SCC), were explored using data from 957 first lactation Lacaune ewes of an experimental INRA flock composed of two divergent lines for milk yield. Secondly, genetic parameters for SCC were estimated from 5 272 first lactation Lacaune ewes recorded among 38 flocks, using an animal model. In the experimental flock, the frequency of culling due to clinical mastitis (5%) was lower than that of subclinical mastitis (10%) predicted from SCC. Predicted subclinical mastitis was unfavourably associated with the milk yield level. Such an antagonism was not detected for clinical mastitis, which could result, to some extent, from its low frequency or from the limited amount of data. In practice, however, selection for mastitis resistance could be limited in a first approach to selection against subclinical mastitis using SCC. The heritability estimate of SCC was 0.15 for the lactation mean trait and varied from 0.04 to 0.12 from the first to the fifth test-day. The genetic correlation between lactation SCC and milk yield was slightly positive (0.15) but showed a strong evolution during lactation, i.e. from favourable (-0.48) to antagonistic (0.27). On a lactation basis, our results suggest that selection for mastitis resistance based on SCC is feasible. Patterns for genetic parameters within first lactation, however, require further confirmation and investigation. PMID:11559483

Pharmacodynamics of temazepam in primary insomnia: assessment of the value of quantitative electroencephalography and saccadic eye movements in predicting improvement of sleep.

PubMed

Tuk, B; Oberyé, J J; Pieters, M S; Schoemaker, R C; Kemp, B; van Gerven, J; Danhof, M; Kamphuisen, H A; Cohen, A F; Breimer, D D; Peck, C C

1997-10-01

Quantitative electroencephalographic parameters and saccadic eye movements are frequently used as pharmacodynamic measures of benzodiazepine effect. We investigated the relationship between these measures and the hypnotic effect. The correlation between the pharmacodynamic measures and sleep quality was determined in 21 patients with primary insomnia. The pharmacokinetic-pharmacodynamic relationships were characterized after administration of 20 mg oral temazepam. The hypnotic effect was determined on the basis of polysomnographic sleep recordings and a subjective sleep evaluation questionnaire. Correlations between pharmacodynamic measures and the improvement of sleep were investigated. The pharmacokinetic-pharmacodynamic relationships for the parameters derived from electroencephalography and saccadic eye movements showed considerable interindividual variability. Administration of temazepam led to a significant improvement in the objective parameters sleep period efficiency, wake time after sleep onset, and sleep efficiency and in the subjective assessment of sleep quality. No significant correlations were observed between the pharmacokinetic-pharmacodynamic-derived parameters and the improvement in objective or subjective sleep parameters. In subjects with primary insomnia the administration of 20 mg oral temazepam results in changes in both the pharmacodynamic measures and in quality of sleep. No individual correlations between the pharmacodynamic measures and quality of sleep were observed. We concluded that the investigated pharmacodynamic measures are of value in the first assessment of clinical efficacy and for the selection of the dose(s) to be investigated in subsequent trials that aim at showing clinical efficacy. However, the conclusive quantification of clinical efficacy should be performed only on the basis of the clinical end point itself.

Lung disease severity in idiopathic pulmonary fibrosis is more strongly associated with impedance measures of bolus reflux than pH parameters of acid reflux alone.

PubMed

Gavini, S; Borges, L F; Finn, R T; Lo, W-K; Goldberg, H J; Burakoff, R; Feldman, N; Chan, W W

2017-05-01

Gastroesophageal reflux (GER) has been associated with idiopathic pulmonary fibrosis (IPF). Pathogenesis may be related to chronic micro-aspiration. We aimed to assess objective measures of GER on multichannel intraluminal impedance and pH study (MII-pH) and their relationship with pulmonary function testing (PFT) results, and to compare the performance of pH/acid reflux parameters vs corresponding MII/bolus parameters in predicting pulmonary dysfunction in IPF. This was a retrospective cohort study of IPF patients undergoing prelung transplant evaluation with MII-pH off acid suppression, and having received PFT within 3 months. Patients with prior fundoplication were excluded. Severe pulmonary dysfunction was defined using diffusion capacity of the lung for carbon monoxide (DLCO) ≤40%. Six pH/acid reflux parameters with corresponding MII/bolus reflux measures were specified a priori. Multivariate analyses were applied using forward stepwise logistic regression. Predictive value of each parameter for severe pulmonary dysfunction was calculated by area-under-the-receiver-operating-characteristic-curve or c-statistic. Forty-five subjects (67% M, age 59, 15 mild-moderate vs 30 severe) met criteria for inclusion. Patient demographics and clinical characteristics were similar between pulmonary dysfunction groups. Abnormal total reflux episodes and prolonged bolus clearance time were significantly associated with pulmonary dysfunction severity on univariate and multivariate analyses. No pH parameters were significant. The c-statistic of each pH parameter was lower than its MII counterpart in predicting pulmonary dysfunction. MII/bolus reflux, but not pH/acid reflux, was associated with pulmonary dysfunction in prelung transplant patients with IPF. MII-pH may be more valuable than pH testing alone in characterizing GER in IPF. © 2016 John Wiley & Sons Ltd.

Revisiting photodynamic therapy dosimetry: reductionist & surrogate approaches to facilitate clinical success

NASA Astrophysics Data System (ADS)

Pogue, Brian W.; Elliott, Jonathan T.; Kanick, Stephen C.; Davis, Scott C.; Samkoe, Kimberley S.; Maytin, Edward V.; Pereira, Stephen P.; Hasan, Tayyaba

2016-04-01

Photodynamic therapy (PDT) can be a highly complex treatment, with many parameters influencing treatment efficacy. The extent to which dosimetry is used to monitor and standardize treatment delivery varies widely, ranging from measurement of a single surrogate marker to comprehensive approaches that aim to measure or estimate as many relevant parameters as possible. Today, most clinical PDT treatments are still administered with little more than application of a prescribed drug dose and timed light delivery, and thus the role of patient-specific dosimetry has not reached widespread clinical adoption. This disconnect is at least partly due to the inherent conflict between the need to measure and understand multiple parameters in vivo in order to optimize treatment, and the need for expedience in the clinic and in the regulatory and commercialization process. Thus, a methodical approach to selecting primary dosimetry metrics is required at each stage of translation of a treatment procedure, moving from complex measurements to understand PDT mechanisms in pre-clinical and early phase I trials, towards the identification and application of essential dose-limiting and/or surrogate measurements in phase II/III trials. If successful, identifying the essential and/or reliable surrogate dosimetry measurements should help facilitate increased adoption of clinical PDT. In this paper, examples of essential dosimetry points and surrogate dosimetry tools that may be implemented in phase II/III trials are discussed. For example, the treatment efficacy as limited by light penetration in interstitial PDT may be predicted by the amount of contrast uptake in CT, and so this could be utilized as a surrogate dosimetry measurement to prescribe light doses based upon pre-treatment contrast. Success of clinical ALA-based skin lesion treatment is predicted almost uniquely by the explicit or implicit measurements of photosensitizer and photobleaching, yet the individualization of treatment based upon each patients measured bleaching needs to be attempted. In the case of ALA, lack of PpIX is more likely an indicator that alternative PpIX production methods must be implemented. Parsimonious dosimetry, using surrogate measurements that are clinically acceptable, might strategically help to advance PDT in a medical world that is increasingly cost and time sensitive. Careful attention to methodologies that can identify and advance the most critical dosimetric measurements, either direct or surrogate, are needed to ensure successful incorporation of PDT into niche clinical procedures.

Hyperbolic Discounting: Value and Time Processes of Substance Abusers and Non-Clinical Individuals in Intertemporal Choice

PubMed Central

2014-01-01

The single parameter hyperbolic model has been frequently used to describe value discounting as a function of time and to differentiate substance abusers and non-clinical participants with the model's parameter k. However, k says little about the mechanisms underlying the observed differences. The present study evaluates several alternative models with the purpose of identifying whether group differences stem from differences in subjective valuation, and/or time perceptions. Using three two-parameter models, plus secondary data analyses of 14 studies with 471 indifference point curves, results demonstrated that adding a valuation, or a time perception function led to better model fits. However, the gain in fit due to the flexibility granted by a second parameter did not always lead to a better understanding of the data patterns and corresponding psychological processes. The k parameter consistently indexed group and context (magnitude) differences; it is thus a mixed measure of person and task level effects. This was similar for a parameter meant to index payoff devaluation. A time perception parameter, on the other hand, fluctuated with contexts in a non-predicted fashion and the interpretation of its values was inconsistent with prior findings that supported enlarged perceived delays for substance abusers compared to controls. Overall, the results provide mixed support for hyperbolic models of intertemporal choice in terms of the psychological meaning afforded by their parameters. PMID:25390941

Quantitative in vivo imaging of tissue factor expression in glioma using dynamic contrast-enhanced MRI derived parameters.

PubMed

Chen, Xiao; Xie, Tian; Fang, Jingqin; Xue, Wei; Tong, Haipeng; Kang, Houyi; Wang, Sumei; Yang, Yizeng; Xu, Minhui; Zhang, Weiguo

2017-08-01

Tissue Factor (TF) has been well established in angiogenesis, invasion, metastasis, and prognosis in glioma. A noninvasive assessment of TF expression status in glioma is therefore of obvious clinical relevance. Dynamic contrast-enhanced (DCE) MRI parameters have been used to evaluate microvascular characteristics and predict molecular expression status in tumors. Our aim is to investigate whether quantitative DCE-MRI parameters could assess TF expression in glioma. Thirty-two patients with histopathologically diagnosed supratentorial glioma who underwent DCE-MRI were retrospectively recruited. Extended Tofts linear model was used for DCE-MRI post-processing. Hot-spot, whole tumor cross-sectional approaches, and histogram were used for analysis of model based parameters. Four serial paraffin sections of each case were stained with TF, CD105, CD34 and α-Sooth Muscle Actin, respectively for evaluating the association of TF and microvascular properties. Pearson correlation was performed between percentage of TF expression area and DCE-MRI parameters, multiple microvascular indexes. Volume transfer constant (K trans ) hot-spot value best correlated with TF (r=0.886, p<0.001), followed by 90th percentile K trans value (r=0.801, p<0.001). Moreover, histogram analysis of K trans value demonstrated that weak TF expression was associated with less heterogeneous and positively skewed distribution. Finally, pathology analysis revealed TF was associated with glioma grade and significantly correlated with these two dynamic angiogenic indexes which could be used to explain the strong correlation between K trans and TF expression. Our results indicate that K trans may serve as a potential clinical imaging biomarker to predict TF expression status preoperatively in gliomas. Copyright © 2017 Elsevier B.V. All rights reserved.

Root coverage using subepithelial connective tissue graft with platelet-rich plasma in the treatment of gingival recession: A clinical study

PubMed Central

Srinivas, B. V. V.; Rupa, N.; Halini Kumari, K. V.; Prasad, S. S. V.; Varalakshmi, U.; Sudhakar, K.

2015-01-01

Introduction: The presence of gingival recession associated with an insufficient amount of keratinized tissue may indicate gingival augmentation procedure. It is a multifaceted problem for which several treatment options are available. The most predictable technique used for gingival augmentation is the subepithelial connective tissue graft (SCTG). Platelet-rich plasma (PRP) is an enhanced source of growth factors and helps in accelerated periodontal repair and regeneration. Aims: The aim of this study was to evaluate the efficacy of SCTG along with PRP in the treatment of Miller's class I and II gingival recessions. Materials and Methods: Eleven subjects with Miller's class I and II gingival recessions were treated using SCTG with PRP. Clinical variables, including plaque index, gingival index, recession depth (RD), Recession width (RW), width of the keratinized gingiva, probing pocket depth (PD) and clinical attachment level (CAL) were recorded. Patients were recalled at baseline, 3 months, 6 months and 1-year after surgery and clinical recordings were taken. Root coverage percentage (%) was measured at the end of 1-year. Results: The clinical parameters were analyzed during the follow-up period by repeated measures ANOVA test. Twelve months follow-up results showed significant improvements in all the clinical parameters. Reduction of recession resulted in a significant decrease in CAL, PD, RW and RD at the end of 12 months. A statistically significant gain in width of keratinized gingiva and a mean root coverage of 84.72 ± 19.10 was obtained at the end of 12 months. Conclusion: From the results of this study, it may be concluded that SCTG with PRP is an effective and predictable method to treat miller's class I and II gingival recession. PMID:26538912

White Matter Microstructure Predicts Autistic Traits in Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Cooper, Miriam; Thapar, Anita; Jones, Derek K.

2014-01-01

Traits of autism spectrum disorder (ASD) in children with attention-deficit/hyperactivity disorder (ADHD) have previously been found to index clinical severity. This study examined the association of ASD traits with diffusion parameters in adolescent males with ADHD (n = 17), and also compared WM microstructure relative to controls (n = 17).…

Surgeon Reported Outcome Measure for Spine Trauma: An International Expert Survey Identifying Parameters Relevant for the Outcome of Subaxial Cervical Spine Injuries.

PubMed

Sadiqi, Said; Verlaan, Jorrit-Jan; Lehr, A Mechteld; Dvorak, Marcel F; Kandziora, Frank; Rajasekaran, S; Schnake, Klaus J; Vaccaro, Alexander R; Oner, F Cumhur

2016-12-15

International web-based survey. To identify clinical and radiological parameters that spine surgeons consider most relevant when evaluating clinical and functional outcomes of subaxial cervical spine trauma patients. Although an outcome instrument that reflects the patients' perspective is imperative, there is also a need for a surgeon reported outcome measure to reflect the clinicians' perspective adequately. A cross-sectional online survey was conducted among a selected number of spine surgeons from all five AOSpine International world regions. They were asked to indicate the relevance of a compilation of 21 parameters, both for the short term (3 mo-2 yr) and long term (≥2 yr), on a five-point scale. The responses were analyzed using descriptive statistics, frequency analysis, and Kruskal-Wallis test. Of the 279 AOSpine International and International Spinal Cord Society members who received the survey, 108 (38.7%) participated in the study. Ten parameters were identified as relevant both for short term and long term by at least 70% of the participants. Neurological status, implant failure within 3 months, and patient satisfaction were most relevant. Bony fusion was the only parameter for the long term, whereas five parameters were identified for the short term. The remaining six parameters were not deemed relevant. Minor differences were observed when analyzing the responses according to each world region, or spine surgeons' degree of experience. The perspective of an international sample of highly experienced spine surgeons was explored on the most relevant parameters to evaluate and predict outcomes of subaxial cervical spine trauma patients. These results form the basis for the development of a disease-specific surgeon reported outcome measure, which will be a helpful tool in research and clinical practice. 4.

Predicting birth weight with conditionally linear transformation models.

PubMed

Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

2016-12-01

Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

An implemented MRI program to eliminate radiation from the evaluation of pediatric appendicitis.

PubMed

Kulaylat, Afif N; Moore, Michael M; Engbrecht, Brett W; Brian, James M; Khaku, Aliasgher; Hollenbeak, Christopher S; Rocourt, Dorothy V; Hulse, Michael A; Olympia, Robert P; Santos, Mary C; Methratta, Sosamma T; Dillon, Peter W; Cilley, Robert E

2015-08-01

Recent efforts have been directed at reducing ionizing radiation delivered by CT scans to children in the evaluation of appendicitis. MRI has emerged as an alternative diagnostic modality. The clinical outcomes associated with MRI in this setting are not well-described. Review of a 30-month institutional experience with MRI as the primary diagnostic evaluation for suspected appendicitis (n=510). No intravenous contrast, oral contrast, or sedation was administered. Radiologic and clinical outcomes were abstracted. MRI diagnostic characteristics were: sensitivity 96.8% (95% CI: 92.1%-99.1%), specificity 97.4% (95% CI: 95.3-98.7), positive predictive value 92.4% (95% CI: 86.5-96.3), and negative predictive value 98.9% (95% CI: 97.3%-99.7%). Radiologic time parameters included: median time from request to scan, 71 minutes (IQR: 51-102), imaging duration, 11 minutes (IQR: 8-17), and request to interpretation, 2.0 hours (IQR: 1.6-2.6). Clinical time parameters included: median time from initial assessment to admit order, 4.1 hours (IQR: 3.1-5.1), assessment to antibiotic administration 4.7 hours (IQR: 3.9-6.7), and assessment to operating room 9.1 hours (IQR: 5.8-12.7). Median length of stay was 1.2 days (range: 0.2-19.5). Given the diagnostic accuracy and favorable clinical outcomes, without the potential risks of ionizing radiation, MRI may supplant the role of CT scans in pediatric appendicitis imaging. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting Malignant and Paramalignant Pleural Effusions by Combining Clinical, Radiological and Pleural Fluid Analytical Parameters.

PubMed

Herrera Lara, Susana; Fernández-Fabrellas, Estrella; Juan Samper, Gustavo; Marco Buades, Josefa; Andreu Lapiedra, Rafael; Pinilla Moreno, Amparo; Morales Suárez-Varela, María

2017-10-01

The usefulness of clinical, radiological and pleural fluid analytical parameters for diagnosing malignant and paramalignant pleural effusion is not clearly stated. Hence this study aimed to identify possible predictor variables of diagnosing malignancy in pleural effusion of unknown aetiology. Clinical, radiological and pleural fluid analytical parameters were obtained from consecutive patients who had suffered pleural effusion of unknown aetiology. They were classified into three groups according to their final diagnosis: malignant, paramalignant and benign pleural effusion. The CHAID (Chi-square automatic interaction detector) methodology was used to estimate the implication of the clinical, radiological and analytical variables in daily practice through decision trees. Of 71 patients, malignant (n = 31), paramalignant (n = 15) and benign (n = 25), smoking habit, dyspnoea, weight loss, radiological characteristics (mass, node, adenopathies and pleural thickening) and pleural fluid analytical parameters (pH and glucose) distinguished malignant and paramalignant pleural effusions (all with a p < 0.05). Decision tree 1 classified 77.8% of malignant and paramalignant pleural effusions in step 2. Decision tree 2 classified 83.3% of malignant pleural effusions in step 2, 73.3% of paramalignant pleural effusions and 91.7% of benign ones. The data herein suggest that the identified predictor values applied to tree diagrams, which required no extraordinary measures, have a higher rate of correct identification of malignant, paramalignant and benign effusions when compared to techniques available today and proved most useful for usual clinical practice. Future studies are still needed to further improve the classification of patients.

Evaluation of MELD score and Maddrey discriminant function for mortality prediction in patients with alcoholic hepatitis.

PubMed

Monsanto, Pedro; Almeida, Nuno; Lrias, Clotilde; Pina, Jos Eduardo; Sofia, Carlos

2013-01-01

Maddrey discriminant function (DF) is the traditional model for evaluating the severity and prognosis in alcoholic hepatitis (AH). However, MELD has also been used for this purpose. We aimed to determine the predictive parameters and compare the ability of Maddrey DF and MELD to predict short-term mortality in patients with AH. Retrospective study of 45 patients admitted in our department with AH between 2000 and 2010. Demographic, clinical and laboratory parameters were collected. MELD and Maddrey DF were calculated on admission. Short-term mortality was assessed at 30 and 90 days. Student t-test, χ2 test, univariate analysis, logistic regression and receiver operating characteristic curves were performed. Thirty-day and 90-day mortality was 27% and 42%, respectively. In multivariate analysis, Maddrey DF was the only independent predictor of mortality for these two periods. Receiver operating characteristic curves for Maddrey DF revealed an excellent discriminatory ability to predict 30-day and 90-day mortality for a Maddrey DF greater than 65 and 60, respectively. Discriminatory ability to predict 30-day and 90-day mortality for MELD was low. AH remains associated with a high short-term mortality. Maddrey DF is a more valuable model than MELD to predict short-term mortality in patients with AH.

Long non-coding RNA biomarker for human laryngeal squamous cell carcinoma prognosis.

PubMed

Chen, Jingjing; Shen, Zhisen; Deng, Hongxia; Zhou, Wei; Liao, Qi; Mu, Ying

2018-05-15

Long non-coding RNAs (lncRNA) were discovered in tumors. The regulation of lncRNA in human laryngeal squamous cell carcinoma (LSCC) remains incomplete. Uncovering the potential of lncRNA to stratify the prognosis of LSCC and streamline the vast amount of clinical information will affect medical interventions. The surgical resected LSCC tissues, adjacent non-cancerous tissues (ANCT) and lymph node metastatic tissue (LNM) were collected from 76 patients for lncRNA AC008440.10 expression assay. The stages of LSCC and LNM were classified accordingly. We integrated the epigenetic information with enhanced CT imaging and pathological evaluations to predict the patients' survival by comprehensive statistical algorithms using equal weighting. Significant downregulation of lncRNA AC008440.10 was detected in LSCC tumor and metastatic lymph node in advanced stage of patient samples compared with those in early stage. The pattern of differentially expressed AC008440.10 displayed a clear trend that significantly related to tumor progression. The downregulation of lncRNA AC008440.10 correlates with increasing risk of metastasis, poor prognosis and patient survival. The potential for lncRNA AC008440.10 to be developed as a novel biomarker for stratification of the prognosis was especially promising when clinic parameters were hybridized with equal weight, and using a panel of complementary parameters yielded a more powerful predictability of LSCC prognosis than any single parameter individually. Copyright © 2017. Published by Elsevier B.V.

Initial FDG-PET/CT predicts survival in adults Ewing sarcoma family of tumors

PubMed Central

Jamet, Bastien; Carlier, Thomas; Campion, Loic; Bompas, Emmanuelle; Girault, Sylvie; Borrely, Fanny; Ferrer, Ludovic; Rousseau, Maxime; Venel, Yann; Kraeber-Bodéré, Françoise; Rousseau, Caroline

2017-01-01

Purpose The aim of this retrospective study was to determine, at baseline, the prognostic value of different FDG-PET/CT quantitative parameters in a homogenous Ewing Sarcoma Family of Tumors (ESFT) adult population, compared with clinically relevant prognostic factors. Methods Adult patients from 3 oncological centers, all with proved ESFT, were retrospectively included. Quantitative FDG-PET/CT parameters (SUV (maximum, peak and mean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary lesion of each patient were recorded before treatment, as well as usual clinical prognostic factors (stage of disease, location, tumor size, gender and age). Then, their relation with progression free survival (PFS) and overall survival (OS) was evaluated. Results 32 patients were included. Median age was 21 years (range, 15 to 61). Nineteen patients (59%) were initially metastatic. On multivariate analysis, high SUVmax remained independent predictor of worst OS (p=0.02) and PFS (p=0.019), metastatic disease of worst PFS (p=0.01) and high SUVpeak of worst OS (p=0.01). Optimal prognostic cut-off of SUVpeak was found at 12.5 in multivariate analyses for PFS and OS (p=0.0001). Conclusions FDG-PET/CT, recommended at ESFT diagnosis for initial staging, can be a useful tool for predicting long-term adult patients outcome through semi-quantitative parameters. PMID:29100369

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours.

PubMed

Mariappan, Panchatcharam; Weir, Phil; Flanagan, Ronan; Voglreiter, Philip; Alhonnoro, Tuomas; Pollari, Mika; Moche, Michael; Busse, Harald; Futterer, Jurgen; Portugaller, Horst Rupert; Sequeiros, Roberto Blanco; Kolesnik, Marina

2017-01-01

Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.

Analysis of the Mechanism of Prolonged Persistence of Drug Interaction between Terbinafine and Amitriptyline or Nortriptyline.

PubMed

Mikami, Akiko; Hori, Satoko; Ohtani, Hisakazu; Sawada, Yasufumi

2017-01-01

The purpose of the study was to quantitatively estimate and predict drug interactions between terbinafine and tricyclic antidepressants (TCAs), amitriptyline or nortriptyline, based on in vitro studies. Inhibition of TCA-metabolizing activity by terbinafine was investigated using human liver microsomes. Based on the unbound K i values obtained in vitro and reported pharmacokinetic parameters, a pharmacokinetic model of drug interaction was fitted to the reported plasma concentration profiles of TCAs administered concomitantly with terbinafine to obtain the drug-drug interaction parameters. Then, the model was used to predict nortriptyline plasma concentration with concomitant administration of terbinafine and changes of area under the curve (AUC) of nortriptyline after cessation of terbinafine. The CYP2D6 inhibitory potency of terbinafine was unaffected by preincubation, so the inhibition seems to be reversible. Terbinafine competitively inhibited amitriptyline or nortriptyline E-10-hydroxylation, with unbound K i values of 13.7 and 12.4 nM, respectively. Observed plasma concentrations of TCAs administered concomitantly with terbinafine were successfully simulated with the drug interaction model using the in vitro parameters. Model-predicted nortriptyline plasma concentration after concomitant nortriptylene/terbinafine administration for two weeks exceeded the toxic level, and drug interaction was predicted to be prolonged; the AUC of nortriptyline was predicted to be increased by 2.5- or 2.0- and 1.5-fold at 0, 3 and 6 months after cessation of terbinafine, respectively. The developed model enables us to quantitatively predict the prolonged drug interaction between terbinafine and TCAs. The model should be helpful for clinical management of terbinafine-CYP2D6 substrate drug interactions, which are difficult to predict due to their time-dependency.

Prediction of New-Onset and Recurrent Atrial Fibrillation by Complete Blood Count Tests: A Comprehensive Systematic Review with Meta-Analysis

PubMed Central

Weymann, Alexander; Ali-Hasan-Al-Saegh, Sadeq; Sabashnikov, Anton; Popov, Aron-Frederik; Mirhosseini, Seyed Jalil; Liu, Tong; Lotfaliani, Mohammadreza; de Oliveira Sá, Michel Pompeu Barros; Baker, William L.; Yavuz, Senol; Zeriouh, Mohamed; Jang, Jae-Sik; Dehghan, Hamidreza; Meng, Lei; Testa, Luca; D’Ascenzo, Fabrizio; Benedetto, Umberto; Tse, Gary; Nombela-Franco, Luis; Dohmen, Pascal M.; Deshmukh, Abhishek J.; Linde, Cecilia; Biondi-Zoccai, Giuseppe; Stone, Gregg W.; Calkins, Hugh

2017-01-01

Background Atrial fibrillation (AF) is one of the most critical and frequent arrhythmias precipitating morbidities and mortalities. The complete blood count (CBC) test is an important blood test in clinical practice and is routinely used in the workup of cardiovascular diseases. This systematic review with meta-analysis aimed to determine the strength of evidence for evaluating the association of hematological parameters in the CBC test with new-onset and recurrent AF. Material/Methods We conducted a meta-analysis of observational studies evaluating hematologic parameters in patients with new-onset AF and recurrent AF. A comprehensive subgroup analysis was performed to explore potential sources of heterogeneity. Results The literature search of all major databases retrieved 2150 studies. After screening, 70 studies were analyzed in the meta-analysis on new-onset AF and 23 studies on recurrent AF. Pooled analysis on new-onset AF showed platelet count (PC) (weighted mean difference (WMD)=WMD of −26.39×109/L and p<0.001), mean platelet volume (MPV) (WMD=0.42 FL and p<0.001), white blood cell (WBC) (WMD=−0.005×109/L and p=0.83), neutrophil to lymphocyte ratio (NLR) (WMD=0.89 and p<0.001), and red blood cell distribution width (RDW) (WMD=0.61% and p<0.001) as associated factors. Pooled analysis on recurrent AF revealed PC (WMD=−2.71×109/L and p=0.59), WBC (WMD=0.20×109/L (95% CI: 0.08 to 0.32; p=0.002), NLR (WMD=0.37 and p<0.001), and RDW (WMD=0.28% and p<0.001). Conclusions Hematological parameters have significant ability to predict occurrence and recurrence of AF. Therefore, emphasizing the potential predictive role of hematological parameters for new-onset and recurrent AF, we recommend adding the CBC test to the diagnostic modalities of AF in clinical practice. PMID:28496093

Prediction of New-Onset and Recurrent Atrial Fibrillation by Complete Blood Count Tests: A Comprehensive Systematic Review with Meta-Analysis.

PubMed

Weymann, Alexander; Ali-Hasan-Al-Saegh, Sadeq; Sabashnikov, Anton; Popov, Aron-Frederik; Mirhosseini, Seyed Jalil; Liu, Tong; Lotfaliani, Mohammadreza; Sá, Michel Pompeu Barros de Oliveira; Baker, William L L; Yavuz, Senol; Zeriouh, Mohamed; Jang, Jae-Sik; Dehghan, Hamidreza; Meng, Lei; Testa, Luca; D'Ascenzo, Fabrizio; Benedetto, Umberto; Tse, Gary; Nombela-Franco, Luis; Dohmen, Pascal M; Deshmukh, Abhishek J; Linde, Cecilia; Biondi-Zoccai, Giuseppe; Stone, Gregg W; Calkins, Hugh; Surgery And Cardiology-Group Imcsc-Group, Integrated Meta-Analysis Of Cardiac

2017-05-12

BACKGROUND Atrial fibrillation (AF) is one of the most critical and frequent arrhythmias precipitating morbidities and mortalities. The complete blood count (CBC) test is an important blood test in clinical practice and is routinely used in the workup of cardiovascular diseases. This systematic review with meta-analysis aimed to determine the strength of evidence for evaluating the association of hematological parameters in the CBC test with new-onset and recurrent AF. MATERIAL AND METHODS We conducted a meta-analysis of observational studies evaluating hematologic parameters in patients with new-onset AF and recurrent AF. A comprehensive subgroup analysis was performed to explore potential sources of heterogeneity. RESULTS The literature search of all major databases retrieved 2150 studies. After screening, 70 studies were analyzed in the meta-analysis on new-onset AF and 23 studies on recurrent AF. Pooled analysis on new-onset AF showed platelet count (PC) (weighted mean difference (WMD)=WMD of -26.39×10^9/L and p<0.001), mean platelet volume (MPV) (WMD=0.42 FL and p<0.001), white blood cell (WBC) (WMD=-0.005×10^9/L and p=0.83), neutrophil to lymphocyte ratio (NLR) (WMD=0.89 and p<0.001), and red blood cell distribution width (RDW) (WMD=0.61% and p<0.001) as associated factors. Pooled analysis on recurrent AF revealed PC (WMD=-2.71×109/L and p=0.59), WBC (WMD=0.20×10^9/L (95% CI: 0.08 to 0.32; p=0.002), NLR (WMD=0.37 and p<0.001), and RDW (WMD=0.28% and p<0.001). CONCLUSIONS Hematological parameters have significant ability to predict occurrence and recurrence of AF. Therefore, emphasizing the potential predictive role of hematological parameters for new-onset and recurrent AF, we recommend adding the CBC test to the diagnostic modalities of AF in clinical practice.

Clinical Significance of Accounting for Tissue Heterogeneity in Permanent Breast Seed Implant Brachytherapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mashouf, Shahram; Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Ontario; Fleury, Emmanuelle

Purpose: The inhomogeneity correction factor (ICF) method provides heterogeneity correction for the fast calculation TG43 formalism in seed brachytherapy. This study compared ICF-corrected plans to their standard TG43 counterparts, looking at their capacity to assess inadequate coverage and/or risk of any skin toxicities for patients who received permanent breast seed implant (PBSI). Methods and Materials: Two-month postimplant computed tomography scans and plans of 140 PBSI patients were used to calculate dose distributions by using the TG43 and the ICF methods. Multiple dose-volume histogram (DVH) parameters of clinical target volume (CTV) and skin were extracted and compared for both ICF and TG43more » dose distributions. Short-term (desquamation and erythema) and long-term (telangiectasia) skin toxicity data were available on 125 and 110 of the patients, respectively, at the time of the study. The predictive value of each DVH parameter of skin was evaluated using the area under the receiver operating characteristic (ROC) curve for each toxicity endpoint. Results: Dose-volume histogram parameters of CTV, calculated using the ICF method, showed an overall decrease compared to TG43, whereas those of skin showed an increase, confirming previously reported findings of the impact of heterogeneity with low-energy sources. The ICF methodology enabled us to distinguish patients for whom the CTV V{sub 100} and V{sub 90} are up to 19% lower compared to TG43, which could present a risk of recurrence not detected when heterogeneity are not accounted for. The ICF method also led to an increase in the prediction of desquamation, erythema, and telangiectasia for 91% of skin DVH parameters studied. Conclusions: The ICF methodology has the advantage of distinguishing any inadequate dose coverage of CTV due to breast heterogeneity, which can be missed by TG43. Use of ICF correction also led to an increase in prediction accuracy of skin toxicities in most cases.« less

Clinical Significance of Accounting for Tissue Heterogeneity in Permanent Breast Seed Implant Brachytherapy Planning.

PubMed

Mashouf, Shahram; Fleury, Emmanuelle; Lai, Priscilla; Merino, Tomas; Lechtman, Eli; Kiss, Alex; McCann, Claire; Pignol, Jean-Philippe

2016-03-15

The inhomogeneity correction factor (ICF) method provides heterogeneity correction for the fast calculation TG43 formalism in seed brachytherapy. This study compared ICF-corrected plans to their standard TG43 counterparts, looking at their capacity to assess inadequate coverage and/or risk of any skin toxicities for patients who received permanent breast seed implant (PBSI). Two-month postimplant computed tomography scans and plans of 140 PBSI patients were used to calculate dose distributions by using the TG43 and the ICF methods. Multiple dose-volume histogram (DVH) parameters of clinical target volume (CTV) and skin were extracted and compared for both ICF and TG43 dose distributions. Short-term (desquamation and erythema) and long-term (telangiectasia) skin toxicity data were available on 125 and 110 of the patients, respectively, at the time of the study. The predictive value of each DVH parameter of skin was evaluated using the area under the receiver operating characteristic (ROC) curve for each toxicity endpoint. Dose-volume histogram parameters of CTV, calculated using the ICF method, showed an overall decrease compared to TG43, whereas those of skin showed an increase, confirming previously reported findings of the impact of heterogeneity with low-energy sources. The ICF methodology enabled us to distinguish patients for whom the CTV V100 and V90 are up to 19% lower compared to TG43, which could present a risk of recurrence not detected when heterogeneity are not accounted for. The ICF method also led to an increase in the prediction of desquamation, erythema, and telangiectasia for 91% of skin DVH parameters studied. The ICF methodology has the advantage of distinguishing any inadequate dose coverage of CTV due to breast heterogeneity, which can be missed by TG43. Use of ICF correction also led to an increase in prediction accuracy of skin toxicities in most cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluation of AUC(0-4) predictive methods for cyclosporine in kidney transplant patients.

PubMed

Aoyama, Takahiko; Matsumoto, Yoshiaki; Shimizu, Makiko; Fukuoka, Masamichi; Kimura, Toshimi; Kokubun, Hideya; Yoshida, Kazunari; Yago, Kazuo

2005-05-01

Cyclosporine (CyA) is the most commonly used immunosuppressive agent in patients who undergo kidney transplantation. Dosage adjustment of CyA is usually based on trough levels. Recently, trough levels have been replacing the area under the concentration-time curve during the first 4 h after CyA administration (AUC(0-4)). The aim of this study was to compare the predictive values obtained using three different methods of AUC(0-4) monitoring. AUC(0-4) was calculated from 0 to 4 h in early and stable renal transplant patients using the trapezoidal rule. The predicted AUC(0-4) was calculated using three different methods: the multiple regression equation reported by Uchida et al.; Bayesian estimation for modified population pharmacokinetic parameters reported by Yoshida et al.; and modified population pharmacokinetic parameters reported by Cremers et al. The predicted AUC(0-4) was assessed on the basis of predictive bias, precision, and correlation coefficient. The predicted AUC(0-4) values obtained using three methods through measurement of three blood samples showed small differences in predictive bias, precision, and correlation coefficient. In the prediction of AUC(0-4) measurement of one blood sample from stable renal transplant patients, the performance of the regression equation reported by Uchida depended on sampling time. On the other hand, the performance of Bayesian estimation with modified pharmacokinetic parameters reported by Yoshida through measurement of one blood sample, which is not dependent on sampling time, showed a small difference in the correlation coefficient. The prediction of AUC(0-4) using a regression equation required accurate sampling time. In this study, the prediction of AUC(0-4) using Bayesian estimation did not require accurate sampling time in the AUC(0-4) monitoring of CyA. Thus Bayesian estimation is assumed to be clinically useful in the dosage adjustment of CyA.

Retrospective use of PBPK modelling to understand a clinical drug-drug interaction between dextromethorphan and GSK1034702.

PubMed

Hobbs, Michael J; Bloomer, Jackie; Dear, Gordon

2017-08-01

1. In a clinical trial, a strong drug-drug interaction (DDI) was observed between dextromethorphan (DM, the object or victim drug) and GSK1034702 (the precipitant or perpetrator drug), following single and repeat doses. This study determined the inhibition parameters of GSK1034702 in vitro and applied PBPK modelling approaches to simulate the clinical observations and provide mechanistic hypotheses to understand the DDI. 2. In vitro assays were conducted to determine the inhibition parameters of human CYP2D6 by GSK1034702. PBPK models were populated with the in vitro parameters and DDI simulations conducted and compared to the observed data from a clinical study with DM and GSK1034702. 3. GSK1034702 was a potent direct and metabolism-dependent inhibitor of human CYP2D6, with inhibition parameters of: IC 50  =   1.6 μM, K inact  = 3.7 h -1 and K I  = 0.8 μM. Incorporating these data into PBPK models predicted a DDI after repeat, but not single, 5 mg doses of GSK1034702. 4. The DDI observed with repeat administration of GSK1034702 (5 mg) can be attributed to metabolism-dependent inhibition of CYP2D6. Further, in vitro data were generated and several potential mechanisms proposed to explain the interaction observed following a single dose of GSK1034702.

Interstage Assessment of Remnant Liver Function in ALPPS Using Hepatobiliary Scintigraphy: Prediction of Posthepatectomy Liver Failure and Introduction of the HIBA Index.

PubMed

Serenari, Matteo; Collaud, Carlos; Alvarez, Fernando A; de Santibañes, Martin; Giunta, Diego; Pekolj, Juan; Ardiles, Victoria; de Santibañes, Eduardo

2018-06-01

The aim of this study was to evaluate interstage liver function in associating liver partition and portal vein occlusion for staged hepatectomy (ALPPS) using hepatobiliary scintigraphy (HBS) and whether this may help to predict posthepatectomy liver failure (PHLF). ALPPS remains controversial given the high rate of liver-related mortality after stage 2. HBS combined with single photon emission computed tomography (SPECT) accurately estimates future liver remnant function and may be useful to predict PHLF. Between 2011 and 2016, 20 of 39 patients (51.3%) underwent SPECT-HBS before ALPPS stage 2 for primary (n = 3) or secondary liver tumors (n = 17) at the Hospital Italiano de Buenos Aires (HIBA). PHLF was defined by the International Study Group of Liver Surgery criteria, 50-50 criteria, or peak bilirubin >7 mg/dL. Grade A PHLF was excluded, as it requires no change in clinical management. Receiver-operating characteristic curves were used to determine cutoff for HBS parameters. Interstagely, 3 HBS parameters differed significantly between patients with (n = 4) and without PHLF (n = 16) after stage 2. Among these, the HIBA-index best predicted PHLF, with a cutoff value of 15%. The risk of PHLF in patients with cutoff <15% was 80%, whereas no patient with cutoff ≥15% developed PHLF. Interstage HBS could help to predict clinically significant PHLF after ALPPS stage 2. An HIBA-index cutoff of 15% seemed to give the best diagnostic performance. Although further studies are needed to confirm our findings, the routine application of this noninvasive low-cost examination could facilitate decision-making in institutions performing ALPPS.

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

PubMed

Surucu, Murat; Shah, Karan K; Mescioglu, Ibrahim; Roeske, John C; Small, William; Choi, Mehee; Emami, Bahman

2016-02-01

To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources. © The Author(s) 2015.

Early variations of laboratory parameters predicting shunt-dependent hydrocephalus after subarachnoid hemorrhage

PubMed Central

Kim, Choong Hyun; Kim, Jae Min; Cheong, Jin Hwan; Ryu, Je il

2017-01-01

Background and purpose Hydrocephalus is a frequent complication following subarachnoid hemorrhage. Few studies investigated the association between laboratory parameters and shunt-dependent hydrocephalus. This study aimed to investigate the variations of laboratory parameters after subarachnoid hemorrhage. We also attempted to identify predictive laboratory parameters for shunt-dependent hydrocephalus. Methods Multiple imputation was performed to fill the missing laboratory data using Bayesian methods in SPSS. We used univariate and multivariate Cox regression analyses to calculate hazard ratios for shunt-dependent hydrocephalus based on clinical and laboratory factors. The area under the receiver operating characteristic curve was used to determine the laboratory risk values predicting shunt-dependent hydrocephalus. Results We included 181 participants with a mean age of 54.4 years. Higher sodium (hazard ratio, 1.53; 95% confidence interval, 1.13–2.07; p = 0.005), lower potassium, and higher glucose levels were associated with higher shunt-dependent hydrocephalus. The receiver operating characteristic curve analysis showed that the areas under the curve of sodium, potassium, and glucose were 0.649 (cutoff value, 142.75 mEq/L), 0.609 (cutoff value, 3.04 mmol/L), and 0.664 (cutoff value, 140.51 mg/dL), respectively. Conclusions Despite the exploratory nature of this study, we found that higher sodium, lower potassium, and higher glucose levels were predictive values for shunt-dependent hydrocephalus from postoperative day (POD) 1 to POD 12–16 after subarachnoid hemorrhage. Strict correction of electrolyte imbalance seems necessary to reduce shunt-dependent hydrocephalus. Further large studies are warranted to confirm our findings. PMID:29232410

Phase-contrast MRI versus numerical simulation to quantify hemodynamical changes in cerebral aneurysms after flow diverter treatment

PubMed Central

Frolov, Sergey; Prothmann, Sascha; Liepsch, Dieter; Balasso, Andrea; Berg, Philipp; Kaczmarz, Stephan; Kirschke, Jan Stefan

2018-01-01

Cerebral aneurysms are a major risk factor for intracranial bleeding with devastating consequences for the patient. One recently established treatment is the implantation of flow-diverters (FD). Methods to predict their treatment success before or directly after implantation are not well investigated yet. The aim of this work was to quantitatively study hemodynamic parameters in patient-specific models of treated cerebral aneurysms and its correlation with the clinical outcome. Hemodynamics were evaluated using both computational fluid dynamics (CFD) and phase contrast (PC) MRI. CFD simulations and in vitro MRI measurements were done under similar flow conditions and results of both methods were comparatively analyzed. For preoperative and postoperative distribution of hemodynamic parameters, CFD simulations and PC-MRI velocity measurements showed similar results. In both cases where no occlusion of the aneurysm was observed after six months, a flow reduction of about 30-50% was found, while in the clinically successful case with complete occlusion of the aneurysm after 6 months, the flow reduction was about 80%. No vortex was observed in any of the three models after treatment. The results are in agreement with recent studies suggesting that CFD simulations can predict post-treatment aneurysm flow alteration already before implantation of a FD and PC-MRI could validate the predicted hemodynamic changes right after implantation of a FD. PMID:29304062

Modified locally weighted--partial least squares regression improving clinical predictions from infrared spectra of human serum samples.

PubMed

Perez-Guaita, David; Kuligowski, Julia; Quintás, Guillermo; Garrigues, Salvador; Guardia, Miguel de la

2013-03-30

Locally weighted partial least squares regression (LW-PLSR) has been applied to the determination of four clinical parameters in human serum samples (total protein, triglyceride, glucose and urea contents) by Fourier transform infrared (FTIR) spectroscopy. Classical LW-PLSR models were constructed using different spectral regions. For the selection of parameters by LW-PLSR modeling, a multi-parametric study was carried out employing the minimum root-mean square error of cross validation (RMSCV) as objective function. In order to overcome the effect of strong matrix interferences on the predictive accuracy of LW-PLSR models, this work focuses on sample selection. Accordingly, a novel strategy for the development of local models is proposed. It was based on the use of: (i) principal component analysis (PCA) performed on an analyte specific spectral region for identifying most similar sample spectra and (ii) partial least squares regression (PLSR) constructed using the whole spectrum. Results found by using this strategy were compared to those provided by PLSR using the same spectral intervals as for LW-PLSR. Prediction errors found by both, classical and modified LW-PLSR improved those obtained by PLSR. Hence, both proposed approaches were useful for the determination of analytes present in a complex matrix as in the case of human serum samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Using Poisson-gamma model to evaluate the duration of recruitment process when historical trials are available.

PubMed

Minois, Nathan; Lauwers-Cances, Valérie; Savy, Stéphanie; Attal, Michel; Andrieu, Sandrine; Anisimov, Vladimir; Savy, Nicolas

2017-10-15

At the design of clinical trial operation, a question of a paramount interest is how long it takes to recruit a given number of patients. Modelling the recruitment dynamics is the necessary step to answer this question. Poisson-gamma model provides very convenient, flexible and realistic approach. This model allows predicting the trial duration using data collected at an interim time with very good accuracy. A natural question arises: how to evaluate the parameters of recruitment model before the trial begins? The question is harder to handle as there are no recruitment data available for this trial. However, if there exist similar completed trials, it is appealing to use data from these trials to investigate feasibility of the recruitment process. In this paper, the authors explore the recruitment data of two similar clinical trials (Intergroupe Francais du Myélome 2005 and 2009). It is shown that the natural idea of plugging the historical rates estimated from the completed trial in the same centres of the new trial for predicting recruitment is not a relevant strategy. In contrast, using the parameters of a gamma distribution of the rates estimated from the completed trial in the recruitment dynamic model of the new trial provides reasonable predictive properties with relevant confidence intervals. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Simple case definition of clinical benign prostatic hyperplasia, based on International Prostate Symptom Score, predicts general practitioner consultation rates.

PubMed

Kok, Esther T; Bohnen, Arthur M; Jonkheijm, Rikkert; Gouweloos, Jochem; Groeneveld, Frans P M J; Thomas, Siep; Bosch, J L H Ruud

2006-10-01

To determine which case-definition of clinical benign prostatic hyperplasia (BPH) has the best predictive value for general practitioner visits for lower urinary tract symptoms (LUTS) suggestive of BPH. The incidence and prevalence rates of general practitioner visits for LUTS were also determined. A longitudinal, population-based study from 1995 to 2003 was conducted among 1688 men aged 50 to 78 years old. Data were collected on physical urologic parameters, quality of life, and symptom severity as determined from the International Prostate Symptom Score. Information on health-care-seeking behavior of all participants was collected from the general practitioner (GP) record using a computerized search engine and an additional manual check of the electronically selected files. The incidence and prevalence rate of the men at risk was 19.6% and 14.0%, respectively, and these rates increased with age. For sensitivity and the positive predictive value, the case-definition of clinical BPH as an International Prostate Symptom Score greater than 7 had the best predictive value for GP visits for LUTS within 2 years after baseline. Because only marginal improvement (greater specificity but lower sensitivity) in the prediction of GP visits for LUTS was possible by adding information on prostate volume and flow, for the prediction of future GP visits for LUTS suggestive of BPH, we suggest that the International Prostate Symptom Score questionnaire be used and that estimation of the prostate volume and flow is not required.

Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models.

PubMed

Molléro, Roch; Pennec, Xavier; Delingette, Hervé; Garny, Alan; Ayache, Nicholas; Sermesant, Maxime

2018-02-01

Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.

What must be the accuracy and target of optical sensor systems for patient monitoring?

NASA Astrophysics Data System (ADS)

Frank, Klaus H.; Kessler, Manfred D.

2002-06-01

Although the treatment in the intensive care unit has improved in recent years enabling greater surgical engagements and improving patients survival rate, no adequate monitoring is available in imminent severe pathological cases. Otherwise such kind of monitoring is necessary for early or prophylactic treatment in order to avoid or reduce the severity of the disease and protect the patient from sepsis or multiple organ failure. In these cases the common monitoring is limited, because clinical physiological and laboratory parameters indicate either the situation of macro-circulation or late disturbances of microcirculation, which arise previously on sub-cellular level. Optical sensor systems enable to reveal early variations in local capillary flow. The correlation between clinical parameters and changes in condition of oxygenation as a function of capillary flow disturbances is meaningful for the further treatment. The target should be to develop a predictive parameter, which is useful for detection and follow-up of changes in circulation.

Laboratory mechanical parameters of composite resins and their relation to fractures and wear in clinical trials-A systematic review.

PubMed

Heintze, Siegward D; Ilie, Nicoleta; Hickel, Reinhard; Reis, Alessandra; Loguercio, Alessandro; Rousson, Valentin

2017-03-01

To evaluate a range of mechanical parameters of composite resins and compare the data to the frequency of fractures and wear in clinical studies. Based on a search of PubMed and SCOPUS, clinical studies on posterior composite restorations were investigated with regard to bias by two independent reviewers using Cochrane Collaboration's tool for assessing risk of bias in randomized trials. The target variables were chipping and/or fracture, loss of anatomical form (wear) and a combination of both (summary clinical index). These outcomes were modelled by time and material in a linear mixed effect model including random study and experiment effects. The laboratory data from one test institute were used: flexural strength, flexural modulus, compressive strength, and fracture toughness (all after 24-h storage in distilled water). For some materials flexural strength data after aging in water/saliva/ethanol were available. Besides calculating correlations between clinical and laboratory outcomes, we explored whether a model including a laboratory predictor dichotomized at a cut-off value better predicted a clinical outcome than a linear model. A total of 74 clinical experiments from 45 studies were included involving 31 materials for which laboratory data were also available. A weak positive correlation between fracture toughness and clinical fractures was found (Spearman rho=0.34, p=0.11) in addition to a moderate and statistically significant correlation between flexural strength and clinical wear (Spearman rho=0.46, p=0.01). When excluding those studies with "high" risk of bias (n=18), the correlations were generally weaker with no statistically significant correlation. For aging in ethanol, a very strong correlation was found between flexural strength decrease and clinical index, but this finding was based on only 7 materials (Spearman rho=0.96, p=0.0001). Prediction was not consistently improved with cutoff values. Correlations between clinical and laboratory outcomes were moderately positive with few significant results, fracture toughness being correlated with clinical fractures and flexural strength with clinical wear. Whether artificial aging enhances the prognostic value needs further investigations. Copyright © 2016 The Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Visual Field Defects and Retinal Ganglion Cell Losses in Human Glaucoma Patients

PubMed Central

Harwerth, Ronald S.; Quigley, Harry A.

2007-01-01

Objective The depth of visual field defects are correlated with retinal ganglion cell densities in experimental glaucoma. This study was to determine whether a similar structure-function relationship holds for human glaucoma. Methods The study was based on retinal ganglion cell densities and visual thresholds of patients with documented glaucoma (Kerrigan-Baumrind, et al.) The data were analyzed by a model that predicted ganglion cell densities from standard clinical perimetry, which were then compared to histologic cell counts. Results The model, without free parameters, produced accurate and relatively precise quantification of ganglion cell densities associated with visual field defects. For 437 sets of data, the unity correlation for predicted vs. measured cell densities had a coefficient of determination of 0.39. The mean absolute deviation of the predicted vs. measured values was 2.59 dB, the mean and SD of the distribution of residual errors of prediction was -0.26 ± 3.22 dB. Conclusions Visual field defects by standard clinical perimetry are proportional to neural losses caused by glaucoma. Clinical Relevance The evidence for quantitative structure-function relationships provides a scientific basis of interpreting glaucomatous neuropathy from visual thresholds and supports the application of standard perimetry to establish the stage of the disease. PMID:16769839

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

PubMed

Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati

2018-05-07

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Effects of Anti-VEGF on Predicted Antibody Biodistribution: Roles of Vascular Volume, Interstitial Volume, and Blood Flow

PubMed Central

Boswell, C. Andrew; Ferl, Gregory Z.; Mundo, Eduardo E.; Bumbaca, Daniela; Schweiger, Michelle G.; Theil, Frank-Peter; Fielder, Paul J.; Khawli, Leslie A.

2011-01-01

Background The identification of clinically meaningful and predictive models of disposition kinetics for cancer therapeutics is an ongoing pursuit in drug development. In particular, the growing interest in preclinical evaluation of anti-angiogenic agents alone or in combination with other drugs requires a complete understanding of the associated physiological consequences. Methodology/Principal Findings Technescan™ PYP™, a clinically utilized radiopharmaceutical, was used to measure tissue vascular volumes in beige nude mice that were naïve or administered a single intravenous bolus dose of a murine anti-vascular endothelial growth factor (anti-VEGF) antibody (10 mg/kg) 24 h prior to assay. Anti-VEGF had no significant effect (p>0.05) on the fractional vascular volumes of any tissues studied; these findings were further supported by single photon emission computed tomographic imaging. In addition, apart from a borderline significant increase (p = 0.048) in mean hepatic blood flow, no significant anti-VEGF-induced differences were observed (p>0.05) in two additional physiological parameters, interstitial fluid volume and the organ blood flow rate, measured using indium-111-pentetate and rubidium-86 chloride, respectively. Areas under the concentration-time curves generated by a physiologically-based pharmacokinetic model changed substantially (>25%) in several tissues when model parameters describing compartmental volumes and blood flow rates were switched from literature to our experimentally derived values. However, negligible changes in predicted tissue exposure were observed when comparing simulations based on parameters measured in naïve versus anti-VEGF-administered mice. Conclusions/Significance These observations may foster an enhanced understanding of anti-VEGF effects in murine tissues and, in particular, may be useful in modeling antibody uptake alone or in combination with anti-VEGF. PMID:21436893

Predictive factors for pericardial effusion identified by heart dose-volume histogram analysis in oesophageal cancer patients treated with chemoradiotherapy.

PubMed

Hayashi, K; Fujiwara, Y; Nomura, M; Kamata, M; Kojima, H; Kohzai, M; Sumita, K; Tanigawa, N

2015-02-01

To identify predictive factors for the development of pericardial effusion (PCE) in patients with oesophageal cancer treated with chemotherapy and radiotherapy (RT). From March 2006 to November 2012, patients with oesophageal cancer treated with chemoradiotherapy (CRT) using the following criteria were evaluated: radiation dose >50 Gy; heart included in the radiation field; dose-volume histogram (DVH) data available for analysis; no previous thoracic surgery; and no PCE before treatment. The diagnosis of PCE was independently determined by two radiologists. Clinical factors, the percentage of heart volume receiving >5-60 Gy in increments of 5 Gy (V5-60, respectively), maximum heart dose and mean heart dose were analysed. A total of 143 patients with oesophageal cancer were reviewed retrospectively. The median follow-up by CT was 15 months (range, 2.1-72.6 months) after RT. PCE developed in 55 patients (38.5%) after RT, and the median time to develop PCE was 3.5 months (range, 0.2-9.9 months). On univariate analysis, DVH parameters except for V60 were significantly associated with the development of PCE (p < 0.001). No clinical factor was significantly related to the development of PCE. Recursive partitioning analysis including all DVH parameters as variables showed a V10 cut-off value of 72.8% to be the most influential factor. The present results showed that DVH parameters are strong independent predictive factors for the development of PCE in patients with oesophageal cancer treated with CRT. A heart dosage was associated with the development of PCE with radiation and without prophylactic nodal irradiation.

Longitudinal Trend in Lipid Profile of Incident Peritoneal Dialysis Patients is Not Influenced by the Use of Biocompatible Solutions

PubMed Central

Cho, Yeoungjee; Büchel, Janine; Steppan, Sonja; Passlick-Deetjen, Jutta; Hawley, Carmel M.; Dimeski, Goce; Clarke, Margaret; Johnson, David W.

2016-01-01

♦ Background: The longitudinal trends of lipid parameters and the impact of biocompatible peritoneal dialysis (PD) solutions on these levels remain to be fully defined. The present study aimed to a) evaluate the influence of neutral pH, low glucose degradation product (GDP) PD solutions on serum lipid parameters, and b) explore the capacity of lipid parameters (total cholesterol [TC], triglyceride [TG], high density lipoprotein [HDL], TC/HDL, low density lipoprotein [LDL], very low density lipoprotein [VLDL]) to predict cardiovascular events (CVE) and mortality in PD patients. ♦ Methods: The study included 175 incident participants from the balANZ trial with at least 1 stored serum sample. A composite CVE score was used as a primary clinical outcome measure. Multilevel linear regression and Poisson regression models were fitted to describe the trend of lipid parameters over time and its ability to predict composite CVE, respectively. ♦ Results: Small but statistically significant increases in serum TG (coefficient 0.006, p < 0.001), TC/HDL (coefficient 0.004, p = 0.001), and VLDL cholesterol (coefficient 0.005, p = 0.001) levels and a decrease in the serum HDL cholesterol levels (coefficient −0.004, p = 0.009) were observed with longer time on PD, whilst the type of PD solution (biocompatible vs standard) received had no significant effect on these levels. Peritoneal dialysis glucose exposure was significantly associated with trends in TG, TC/HDL, HDL and VLDL levels. Baseline lipid parameter levels were not predictive of composite CVEs or all-cause mortality. ♦ Conclusion: Serum TG, TC/HDL, and VLDL levels increased and the serum HDL levels decreased with increasing PD duration. None of the lipid parameters were significantly modified by biocompatible PD solution use over the time period studied or predictive of composite CVE or mortality. PMID:26429421

Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle.

PubMed

Miller, Miles A; Gadde, Suresh; Pfirschke, Christina; Engblom, Camilla; Sprachman, Melissa M; Kohler, Rainer H; Yang, Katherine S; Laughney, Ashley M; Wojtkiewicz, Gregory; Kamaly, Nazila; Bhonagiri, Sushma; Pittet, Mikael J; Farokhzad, Omid C; Weissleder, Ralph

2015-11-18

Therapeutic nanoparticles (TNPs) have shown heterogeneous responses in human clinical trials, raising questions of whether imaging should be used to identify patients with a higher likelihood of NP accumulation and thus therapeutic response. Despite extensive debate about the enhanced permeability and retention (EPR) effect in tumors, it is increasingly clear that EPR is extremely variable; yet, little experimental data exist to predict the clinical utility of EPR and its influence on TNP efficacy. We hypothesized that a 30-nm magnetic NP (MNP) in clinical use could predict colocalization of TNPs by magnetic resonance imaging (MRI). To this end, we performed single-cell resolution imaging of fluorescently labeled MNPs and TNPs and studied their intratumoral distribution in mice. MNPs circulated in the tumor microvasculature and demonstrated sustained uptake into cells of the tumor microenvironment within minutes. MNPs could predictably demonstrate areas of colocalization for a model TNP, poly(d,l-lactic-co-glycolic acid)-b-polyethylene glycol (PLGA-PEG), within the tumor microenvironment with >85% accuracy and circulating within the microvasculature with >95% accuracy, despite their markedly different sizes and compositions. Computational analysis of NP transport enabled predictive modeling of TNP distribution based on imaging data and identified key parameters governing intratumoral NP accumulation and macrophage uptake. Finally, MRI accurately predicted initial treatment response and drug accumulation in a preclinical efficacy study using a paclitaxel-encapsulated NP in tumor-bearing mice. These approaches yield valuable insight into the in vivo kinetics of NP distribution and suggest that clinically relevant imaging modalities and agents can be used to select patients with high EPR for treatment with TNPs. Copyright © 2015, American Association for the Advancement of Science.

Liver maximum capacity (LiMAx) test as a helpful prognostic tool in acute liver failure with sepsis: a case report.

PubMed

Buechter, Matthias; Gerken, Guido; Hoyer, Dieter P; Bertram, Stefanie; Theysohn, Jens M; Thodou, Viktoria; Kahraman, Alisan

2018-06-20

Acute liver failure (ALF) is a life-threatening entity particularly when infectious complications worsen the clinical course. Urgent liver transplantation (LT) is frequently the only curative treatment. However, in some cases, recovery is observed under conservative treatment. Therefore, prognostic tools for estimating course of the disease are of great clinical interest. Since laboratory parameters sometimes lack sensitivity and specificity, enzymatic liver function measured by liver maximum capacity (LiMAx) test may offer novel and valuable additional information in this setting. We here report the case of a formerly healthy 20-year old male caucasian patient who was admitted to our clinic for ALF of unknown origin in December 2017. Laboratory parameters confirmed the diagnosis with an initial MELD score of 28 points. Likewise, enzymatic liver function was significantly impaired with a value of 147 [> 315] μg/h/kg. Clinical and biochemical analyses for viral-, autoimmune-, or drug-induced hepatitis were negative. Liver synthesis parameters further deteriorated reaching a MELD score of 40 points whilst clinical course was complicated by septic pneumonia leading to severe hepatic encephalopathy grade III-IV, finally resulting in mechanical ventilation of the patient. Interestingly, although clinical course and laboratory data suggested poor outcome, serial LiMAx test revealed improvement of the enzymatic liver function at this time point increasing to 169 μg/h/kg. Clinical condition and laboratory data slowly improved likewise, however with significant time delay of 11 days. Finally, the patient could be dismissed from our clinic after 37 days. Estimating prognosis in patients with ALF is challenging by use of the established scores. In our case, improvement of enzymatic liver function measured by the LiMAx test was the first parameter predicting beneficial outcome in a patient with ALF complicated by sepsis.

The Benefits of Including Clinical Factors in Rectal Normal Tissue Complication Probability Modeling After Radiotherapy for Prostate Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Defraene, Gilles, E-mail: gilles.defraene@uzleuven.be; Van den Bergh, Laura; Al-Mamgani, Abrahim

2012-03-01

Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including themore » most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was 'previous abdominal surgery.' As second significant (p = 0.012-0.016) factor, 'cardiac history' was included in all three rectal bleeding fits, whereas including 'diabetes' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D{sub 50}. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. Conclusions: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.« less

Monitoring Doppler patterns and clinical parameters may predict feeding tolerance in intrauterine growth-restricted infants.

PubMed

Bozzetti, Valentina; Paterlini, Giuseppe; Gazzolo, Diego; Van Bel, Frank; Visser, Gerard H A; Roncaglia, Nadia; Tagliabue, Paolo E

2013-11-01

To detect predictors of feeding tolerance in intrauterine growth restriction (IUGR) infants with or without brain-sparing effect (BS). We conducted a case-control study in 70 IUGR infants (35 IUGR with BS, matched for gestational age with 35 IUGR infants with no BS). BS was classified as pulsatility index (PI) ratio [umbilical artery (UAPI) to middle cerebral artery (MCAPI) (U/C ratio)] > 1. Clinical parameters of feeding tolerance - days to achieve full enteral feeding (FEF) - were compared between the IUGR with BS and IUGR without BS infants. Age at the start of minimal enteral feeding (MEF) was analysed. Achievement of FEF was significantly shorter in IUGR infants without BS than in IUGR with BS. IUGR with BS started MEF later than IUGR without BS infants. Significant correlation of MEF and FEF with UA PI, U/C ratio and CRIB score was found. Multiple linear regression analysis showed significant correlations with CRIB score and caffeine administration (MEF only), and sepsis (FEF only) and U/C ratio (for both). Impaired gut function can be early detected by monitoring Doppler patterns and clinical parameters. ©2013 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram.

PubMed

Patel, Nikunjkumar; Wiśniowska, Barbara; Jamei, Masoud; Polak, Sebastian

2017-11-27

A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a 'virtual twin' of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (I Kr , I Ks , I CaL ); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment.

Epidemiological, clinical and climatic characteristics of dengue fever in Kaohsiung City, Taiwan with implication for prevention and control.

PubMed

Chang, Chiu-Jung; Chen, Colin S; Tien, Chien-Jung; Lu, Mei-Rou

2018-01-01

The early identification of dengue infection is essential for timely and effective quarantine and vector control measures for preventing outbreaks of the disease. Kaohsiung City is responsible for most of the dengue cases in Taiwan. Thus, this study aims to identify major factors involved in the prevalence of dengue fever by analyzing the epidemiological and clinical characteristics, and to establish associations between weather parameters and dengue occurrence in this City. A retrospective study was conducted with 3,322 confirmed dengue cases. Appropriate statistical methods were used to compare differences and correlations between dengue occurrence and demographic, clinical and weather parameters. The outbreak of dengue fever was found to be initiated by imported cases of dengue viruses from other endemic countries. Most of the confirmed cases were not reported to the health authority during the first visit to a doctor, and it took a median of 5 days after the appearance of the first syndromes for medical personnel to report suspected dengue cases. Accordingly, Aedes mosquitoes would have enough time to be infected and transmit the dengue virus. The diagnosis and notification criteria should not only include common symptoms of fever, myalgia, headache, skin rash and arthralgia, but should also be adjusted to include the most frequent symptoms of loss of appetite and feeling thirsty to shorten the notification time. Significantly positive correlations were found between the number of confirmed cases and weather parameters (i.e., temperature, rainfall and relative humidity) at a time lag of 1 month and 2 months. The predictive models for dengue occurrence using these three parameters at a 2-month lag time were established. The surveillance of imported cases, adjustment of notification criteria and application of climatic predictive models would be helpful in strengthening the dengue early warning surveillance system.

Morphological parameters of flat epithelial atypia (FEA) in stereotactic vacuum-assisted needle core biopsies do not predict the presence of malignancy on subsequent surgical excision.

PubMed

Bianchi, Simonetta; Bendinelli, Benedetta; Castellano, Isabella; Piubello, Quirino; Renne, Giuseppe; Cattani, Maria Grazia; Di Stefano, Domenica; Carrillo, Giovanna; Laurino, Licia; Bersiga, Alessandra; Giardina, Carmela; Dante, Stefania; Di Loreto, Carla; Quero, Carmela; Antonacci, Concetta Maria; Palli, Domenico

2012-10-01

Flat epithelial atypia (FEA) may represent the earliest precursor of low-grade breast cancer and often coexists with more advanced atypical proliferative breast lesions such as atypical ductal hyperplasia (ADH) and lobular intraepithelial neoplasia (LIN). The present study aims to investigate the association between morphological parameters of FEA and presence of malignancy at surgical excision (SE) and the clinical significance of the association of FEA with ADH and/or LIN. This study included 589 cases of stereotactic 11-gauge vacuum-assisted needle core biopsy (VANCB), reporting a diagnosis of FEA, ADH or LIN with subsequent SE from 14 pathology departments in Italy. Available slides were reviewed, with 114 (19.4 %) showing a malignant outcome at SE. Among the 190 cases of pure FEA, no statistically significant association emerged between clinical-pathological parameters of FEA and risk of malignancy. Logistic regression analyses showed an increased risk of malignancy according to the extension of ADH among the 275 cases of FEA associated with ADH (p = 0.004) and among the 34 cases of FEA associated with ADH and LIN (p = 0.02). In the whole series, a statistically significant increased malignancy risk emerged according to mammographic R1-R3/R4-R5 categories (OR = 1.56; p = 0.04), extension (OR = 1.24; p = 0.04) and grade (OR = 1.94; p = 0.004) of cytological atypia of FEA. The presence of ADH was associated with an increased malignancy risk (OR = 2.85; p < 0.0001). Our data confirm the frequent association of FEA with ADH and/or LIN. A diagnosis of pure FEA on VANCB carries a 9.5 % risk of concurrent malignancy and thus warrants follow-up excision because none of the clinical-pathological parameters predicts which cases will present carcinoma on SE.

Lymphatic vessel density and VEGF-C expression as independent predictors of melanoma metastases.

PubMed

Špirić, Zorica; Eri, Živka; Erić, Mirela

2017-11-01

In many patients, the clinical behaviour of cutaneous melanoma is very difficult to predict by traditional histologic and clinical parameters. This study aimed to examine the role of quantitative parameters of tumour lymphangiogenesis and vascular endothelial growth factor (VEGF)-C in predicting metastatic risk in patients with cutaneous melanoma. One hundred melanoma specimens were stained with lymphatic-specific antibody D2-40 and with anti-VEGF-C antibody. Quantitative parameters of lymphangiogenesis-lymphatic vessel density (LVD) and lymphatic vessel area (LVA)-were determined by computer-assisted morphometric analysis. Moderate or strong staining was assessed as a positive expression of VEGF-C in tumour cells. Univariate analysis revealed that intratumoural LVD, peritumoural LVD, VEGF-C expression in tumour cells, melanoma thickness, Clark level, ulceration, gender and histologic type were significant predictors of lymph node metastasis (p = 0.000, p = 0.000, p = 0.000, p = 0.000, p = 0.005, p = 0.005, p = 0.011 and p = 0.027, respectively). No significant association of intratumoural and peritumoural LVA with metastases was found. In multivariate analysis, independent predictors of metastatic risks were melanoma thickness [odds ratio OR = 1.655, 95% confidence interval (CI) 1.102-2.484, p = 0.015], intratumoural LVD (OR = 1.086, 95% CI 1.027-1.148, p = 0.004), peritumoural LVD (OR = 1.050, 95% CI 1.008-1.094, p = 0.020) and a positive VEGF-C expression in tumour cells (OR = 20.337, 95% CI 2.579-160.350, p = 0.004). This study identified intratumoural and peritumoural LVD and the VEGF-C expression in tumour cells as more significant predictors of metastatic risk than melanoma thickness, ulceration and other clinical-pathological parameters. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

NASA Astrophysics Data System (ADS)

Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

2017-03-01

Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

Bone volume fraction and structural parameters for estimation of mechanical stiffness and failure load of human cancellous bone samples; in-vitro comparison of ultrasound transit time spectroscopy and X-ray μCT.

PubMed

Alomari, Ali Hamed; Wille, Marie-Luise; Langton, Christian M

2018-02-01

Conventional mechanical testing is the 'gold standard' for assessing the stiffness (N mm -1 ) and strength (MPa) of bone, although it is not applicable in-vivo since it is inherently invasive and destructive. The mechanical integrity of a bone is determined by its quantity and quality; being related primarily to bone density and structure respectively. Several non-destructive, non-invasive, in-vivo techniques have been developed and clinically implemented to estimate bone density, both areal (dual-energy X-ray absorptiometry (DXA)) and volumetric (quantitative computed tomography (QCT)). Quantitative ultrasound (QUS) parameters of velocity and attenuation are dependent upon both bone quantity and bone quality, although it has not been possible to date to transpose one particular QUS parameter into separate estimates of quantity and quality. It has recently been shown that ultrasound transit time spectroscopy (UTTS) may provide an accurate estimate of bone density and hence quantity. We hypothesised that UTTS also has the potential to provide an estimate of bone structure and hence quality. In this in-vitro study, 16 human femoral bone samples were tested utilising three techniques; UTTS, micro computed tomography (μCT), and mechanical testing. UTTS was utilised to estimate bone volume fraction (BV/TV) and two novel structural parameters, inter-quartile range of the derived transit time (UTTS-IQR) and the transit time of maximum proportion of sonic-rays (TTMP). μCT was utilised to derive BV/TV along with several bone structure parameters. A destructive mechanical test was utilised to measure the stiffness and strength (failure load) of the bone samples. BV/TV was calculated from the derived transit time spectrum (TTS); the correlation coefficient (R 2 ) with μCT-BV/TV was 0.885. For predicting mechanical stiffness and strength, BV/TV derived by both μCT and UTTS provided the strongest correlation with mechanical stiffness (R 2 =0.567 and 0.618 respectively) and mechanical strength (R 2 =0.747 and 0.736 respectively). When respective structural parameters were incorporated to BV/TV, multiple regression analysis indicated that none of the μCT histomorphometric parameters could improve the prediction of mechanical stiffness and strength, while for UTTS, adding TTMP to BV/TV increased the prediction of mechanical stiffness to R 2 =0.711 and strength to R 2 =0.827. It is therefore envisaged that UTTS may have the ability to estimate BV/TV along with providing an improved prediction of osteoporotic fracture risk, within routine clinical practice in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

A behavioral economic measure of demand for alcohol predicts brief intervention outcomes.

PubMed

MacKillop, James; Murphy, James G

2007-07-10

Considerable basic and clinical research supports a behavioral economic conceptualization of alcohol and drug dependence. One behavioral economic approach to assess motivation for a drug is the use of demand curves, or quantitative representations of drug consumption and drug-reinforced responding across a range of prices. This study used a hypothetical alcohol purchase task to generate demand curves, and examined whether the resulting demand curve parameters predicted drinking outcomes following a brief intervention. Participants were 51 college student drinkers (67% female; 94% Caucasian; drinks/week: M=24.57, S.D.=8.77) who completed a brief alcohol intervention. Consistent with predictions, a number of demand curve indices significantly predicted post-intervention alcohol use and frequency of heavy drinking episodes, even after controlling for baseline drinking and other pertinent covariates. Most prominently, O(max) (i.e., maximum alcohol expenditure) and breakpoint (i.e., sensitivity of consumption to increasing price) predicted greater drinking at 6-month post-intervention follow-up. These results indicate that a behavioral economic measure of alcohol demand may have utility in characterizing the malleability of alcohol consumption. Moreover, these results support the utility of translating experimental assays of reinforcement into clinical research.

Can the efficiency of modified Alvarado scoring system in the diagnosis acute appendicitis be increased with tenesmus?

PubMed

Bulus, Hakan; Tas, Adnan; Morkavuk, Baris; Koklu, Seyfettin; Soy, Derya; Coskun, Ali

2013-01-01

Acute appendicitis is one of the main pathological conditions requiring emergency surgical intervention. The most widely accepted scoring system is modified Alvarado scoring system (MASS). In this study we aimed to improve the efficiency of MASS by adding a new parameter and to evaluate its efficiency in the diagnosis of acute appendicitis. This study included 158 patients who underwent acute appendectomy in Keçiören Training and Research Hospital General Surgery Department. In addition to criteria of MASS, all patients were questioned about the presence of tenesmus. The validity of MASS and MASS with additional parameter was evaluated with respect to sensitivity, specificity and positive and negative predictive values. Accuracy rates of MASS, clinical findings, ultrasonography and MASS with additional parameter in the diagnosis of acute appendicitis were 64, 76, 85 and 80 %. False positivity rates for clinical findings, MASS and MASS with additional parameter in the diagnosis of acute appendicitis were 17, 26 and 10 %, respectively. Sensitivity and specificity of clinical findings in the diagnosis of acute appendicitis were 83 and 66 %, respectively. Sensitivity and specificity of MASS in the diagnosis of acute appendicitis were 74 and 39 %, respectively, and those of MASS with additional parameter were appendicitis increased to 83 and 66 %, respectively. MASS is a simple, cheap and objective scoring system and does not require expertise. When tenesmus is added to standard MASS, rates of accuracy, sensitivity and specificity become better than those in MASS in the diagnosis of acute appendicitis.

Nomogram for suboptimal cytoreduction at primary surgery for advanced stage ovarian cancer.

PubMed

Gerestein, Cornelis G; Eijkemans, Marinus J; Bakker, Jeanette; Elgersma, Otto E; van der Burg, Maria E L; Kooi, Geertruida S; Burger, Curt W

2011-11-01

Maximal cytoreduction to minimal residual tumor is the most important determinant of prognosis in patients with advanced stage epithelial ovarian cancer (EOC). Preoperative prediction of suboptimal cytoreduction, defined as residual tumor >1 cm, could guide treatment decisions and improve counseling. The objective of this study was to identify predictive computed tomographic (CT) scan and clinical parameters for suboptimal cytoreduction at primary cytoreductive surgery for advanced stage EOC and to generate a nomogram with the identified parameters, which would be easy to use in daily clinical practice. Between October 2005 and December 2008, all patients with primary surgery for suspected advanced stage EOC at six participating teaching hospitals in the South Western part of the Netherlands entered the study protocol. To investigate independent predictors of suboptimal cytoreduction, a Cox proportional hazard model with backward stepwise elimination was utilized. One hundred and fifteen patients with FIGO stage III/IV EOC entered the study protocol. Optimal cytoreduction was achieved in 52 (45%) patients. A suboptimal cytoreduction was predicted by preoperative blood platelet count (p=0.1990; odds ratio (OR)=1.002), diffuse peritoneal thickening (DPT) (p=0.0074; OR=3.021), and presence of ascites on at least two thirds of CT scan slices (p=0.0385; OR=2.294) with a for-optimism corrected c-statistic of 0.67. Suboptimal cytoreduction was predicted by preoperative platelet count, DPT and presence of ascites. The generated nomogram can, after external validation, be used to estimate surgical outcome and to identify those patients, who might benefit from alternative treatment approaches.

Predictors of one and two years' mortality in patients with colon cancer: A prospective cohort study.

PubMed

Quintana, José M; Antón-Ladislao, Ane; González, Nerea; Lázaro, Santiago; Baré, Marisa; Fernández-de-Larrea, Nerea; Redondo, Maximino; Briones, Eduardo; Escobar, Antonio; Sarasqueta, Cristina; García-Gutierrez, Susana; Aróstegui, Inmaculada

2018-01-01

Tools to aid in the prognosis assessment of colon cancer patients in terms of risk of mortality are needed. Goals of this study are to develop and validate clinical prediction rules for 1- and 2-year mortality in these patients. This is a prospective cohort study of patients diagnosed with colon cancer who underwent surgery at 22 hospitals. The main outcomes were mortality at 1 and 2 years after surgery. Background, clinical parameters, and diagnostic tests findings were evaluated as possible predictors. Multivariable multilevel logistic regression and survival models were used in the analyses to create the clinical prediction rules. Models developed in the derivation sample were validated in another sample of the study. American Society of Anesthesiologists Physical Status Classification System (ASA), Charlson comorbidity index (> = 4), age (>75 years), residual tumor (R2), TNM stage IV and log of lymph nodes ratio (> = -0.53) were predictors of 1-year mortality (C-index (95% CI): 0.865 (0.792-0.938)). Adjuvant chemotherapy was an additional predictor. Again ASA, Charlson Index (> = 4), age (>75 years), log of lymph nodes ratio (> = -0.53), TNM, and residual tumor were predictors of 2-year mortality (C-index:0.821 (0.766-0.876). Chemotherapy was also an additional predictor. These clinical prediction rules show very good predictive abilities of one and two years survival and provide clinicians and patients with an easy and quick-to-use decision tool for use in the clinical decision process while the patient is still in the index admission.

Predictive value of the complex magnetocardiographic index in patients with intermediate pretest probability of chronic coronary artery disease: results of a two-center study.

PubMed

Chaikovsky, Illya; Hailer, Birgit; Sosnytskyy, Volodymyr; Lutay, Mykhaylo; Mjasnikov, Georgiy; Kazmirchuk, Anatoly; Bydnyk, Mykola; Lomakovskyy, Alexander; Sosnytskaja, Taisia

2014-09-01

The aim of this paper is to investigate the predictive value of the new integrated magnetocardiographic (MCG) index (CI) in the diagnosis of coronary artery disease (CAD) in patients with suspected CAD with intermediate pretest probability of the disease and uninformative results of routine tests. The study was carried out in the Clinic of Cardiology of the Main Military Clinical Hospital of Ukraine, Kiev (clinic 1), and in the Second Medical Clinic of the 'Katholisches Klinikum Essen', Germany (clinic 2).The main group (group 1) included 89 patients without a history of myocardial infarction. Coronary angiography was performed because of chest pain. Depending on the results of coronary angiography, this group was divided into two subgroups: (i) those with at least 70% stenosis in at least one of the main coronary arteries (subgroup 1a) and (ii) those without hemodynamically significant stenosis (subgroup 1b). The control group included 43 healthy volunteers.In all participants, the MCG examination was performed using a seven-channel MCG system located in an unshielded room. An integrated MCG index (CI), consisting of six parameters, was calculated. It can be shown that CI was significantly higher in patients with stenosis 70% or more compared with the patients without stenosis and healthy volunteers. Sensitivity was 93%, specificity was 84%, positive predictive value was 85%, and negative predictive value was 93%. The MCG test at rest has the potential to be useful in the noninvasive diagnosis of CAD in patients with intermediate pretest probability of disease and uninformative results of routine tests.

Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

PubMed Central

Motwani, Manish; Dey, Damini; Berman, Daniel S.; Germano, Guido; Achenbach, Stephan; Al-Mallah, Mouaz H.; Andreini, Daniele; Budoff, Matthew J.; Cademartiri, Filippo; Callister, Tracy Q.; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J.W.; Cury, Ricardo C.; Delago, Augustin; Gomez, Millie; Gransar, Heidi; Hadamitzky, Martin; Hausleiter, Joerg; Hindoyan, Niree; Feuchtner, Gudrun; Kaufmann, Philipp A.; Kim, Yong-Jin; Leipsic, Jonathon; Lin, Fay Y.; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert; Rubinshtein, Ronen; Shaw, Leslee J.; Stehli, Julia; Villines, Todd C.; Dunning, Allison; Min, James K.; Slomka, Piotr J.

2017-01-01

Aims Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. Methods and results The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001). Conclusions Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone. PMID:27252451

Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.

PubMed

Motwani, Manish; Dey, Damini; Berman, Daniel S; Germano, Guido; Achenbach, Stephan; Al-Mallah, Mouaz H; Andreini, Daniele; Budoff, Matthew J; Cademartiri, Filippo; Callister, Tracy Q; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J W; Cury, Ricardo C; Delago, Augustin; Gomez, Millie; Gransar, Heidi; Hadamitzky, Martin; Hausleiter, Joerg; Hindoyan, Niree; Feuchtner, Gudrun; Kaufmann, Philipp A; Kim, Yong-Jin; Leipsic, Jonathon; Lin, Fay Y; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert; Rubinshtein, Ronen; Shaw, Leslee J; Stehli, Julia; Villines, Todd C; Dunning, Allison; Min, James K; Slomka, Piotr J

2017-02-14

Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001). Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

A similarity based learning framework for interim analysis of outcome prediction of acupuncture for neck pain.

PubMed

Zhang, Gang; Liang, Zhaohui; Yin, Jian; Fu, Wenbin; Li, Guo-Zheng

2013-01-01

Chronic neck pain is a common morbid disorder in modern society. Acupuncture has been administered for treating chronic pain as an alternative therapy for a long time, with its effectiveness supported by the latest clinical evidence. However, the potential effective difference in different syndrome types is questioned due to the limits of sample size and statistical methods. We applied machine learning methods in an attempt to solve this problem. Through a multi-objective sorting of subjective measurements, outstanding samples are selected to form the base of our kernel-oriented model. With calculation of similarities between the concerned sample and base samples, we are able to make full use of information contained in the known samples, which is especially effective in the case of a small sample set. To tackle the parameters selection problem in similarity learning, we propose an ensemble version of slightly different parameter setting to obtain stronger learning. The experimental result on a real data set shows that compared to some previous well-known methods, the proposed algorithm is capable of discovering the underlying difference among different syndrome types and is feasible for predicting the effective tendency in clinical trials of large samples.

Gender and age related predictive value of walk test in heart failure: do anthropometrics matter in clinical practice?

PubMed

Frankenstein, L; Remppis, A; Graham, J; Schellberg, D; Sigg, C; Nelles, M; Katus, H A; Zugck, C

2008-07-21

The six-minute walk test (6 WT) is a valid and reliable predictor of morbidity and mortality in chronic heart failure (CHF) patients, frequently used as an endpoint or target in clinical trials. As opposed to spiroergometry, improvement of its prognostic accuracy by correction for height, weight, age and gender has not yet been attempted comprehensively despite known influences of these parameters. We recorded the 6 WT of 1035 CHF patients, attending clinic from 1995 to 2005. The 1-year prognostic value of 6 WT was calculated, alone and after correction for height, weight, BMI and/or age. Analysis was performed on the entire cohort, on males and females separately and stratified according to BMI (<25, 25-30 and >30 kg/m(2)). 6 WT weakly correlated with age (r=-0.32; p<0.0001), height (r=0.2; p<0.0001), weight (r=0.11; p<0.001), not with BMI (r=0.01; p=ns). The 6 WT was a strong predictor of 1-year mortality in both genders, both as a single and age corrected parameter. Parameters derived from correction of 6 WT for height, weight or BMI did not improve the prognostic value in univariate analysis for either gender. Comparison of the receiver operated characteristics showed no significant gain in prognostic accuracy from any derived variable, either for males or females. The six-minute walk test is a valid tool for risk prediction in both male and female CHF patients. In both genders, correcting 6 WT distance for height, weight or BMI alone, or adjusting for age, does not increase the prognostic power of this tool.

Perioperative echocardiography-derived right ventricle function parameters and early outcomes after tetralogy of Fallot repair in mid-childhood: a single-center, prospective observational study.

PubMed

Raj, Ravi; Puri, Goverdhan Dutt; Jayant, Aveek; Thingnam, Shyam Kumar Singh; Singh, Rana Sandip; Rohit, Manoj Kumar

2016-11-01

Right ventricular (RV) function alterations are invariably present in all patients after tetralogy of Fallot (TOF) repair. Unlike the developed world where most of the patients with TOF are corrected in infancy, average age of presentation and thus surgery for these patients in the developing world may be higher. We aimed to study the correlation between RV function parameters such as tricuspid annular peak systolic excursion (TAPSE), fractional area change (FAC), and tricuspid annular peak systolic velocity (S') with early outcome variables after intracardiac repair for TOF. Fifty patients with a preoperative diagnosis of tetralogy of Fallot scheduled for corrective surgery were included in this single-center, prospective observational study. A preoperative transthoracic echocardiogram was performed to measure RV function parameters (FAC0, TAPSE0, S'0). Transthoracic echocardiography was repeated postoperatively to measure FAC1, TAPSE1, S'1 (day 1) and FAC2, TAPSE2, and S'2 (day 3). The relationship between preoperative and postoperative RV function parameters with in-hospital mortality, duration of mechanical ventilation, and intensive care unit stay was studied. The median age of patients was 6 years (range 1-14 years). Multiple stepwise logistic regression analysis showed RV FAC as best predictor of clinical outcome. Area under the receiver operating characteristic curve for postoperative RV function parameters, that is, FAC, TAPSE, and S' to predict early or delayed recovery was 0.944, 0.875, and 0.655, respectively. Among the RV function parameters studied, RV FAC best predicted the early outcome variables after TOF repair, followed by TAPSE while lateral tricuspid annular velocity S' being the least predictive. © 2016, Wiley Periodicals, Inc.

Metabolic Tumor Volume on 18F-FDG PET/CT Improves Preoperative Identification of High-Risk Endometrial Carcinoma Patients.

PubMed

Husby, Jenny A; Reitan, Bernt C; Biermann, Martin; Trovik, Jone; Bjørge, Line; Magnussen, Inger J; Salvesen, Øyvind O; Salvesen, Helga B; Haldorsen, Ingfrid S

2015-08-01

Our objective was to prospectively explore the diagnostic value of (18)F-FDG PET/CT for preoperative staging in endometrial carcinomas and to investigate whether (18)F-FDG PET-specific quantitative tumor parameters reflect clinical and histologic characteristics. Preoperative (18)F-FDG PET/CT was prospectively performed on 129 consecutive endometrial carcinoma patients. Two physicians who did not know the clinical findings or staging results independently reviewed the images, assessing primary tumor, cervical stroma involvement and metastatic spread, and determining maximum and mean standardized uptake value (SUVmax and SUVmean, respectively) for tumor, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). All parameters were analyzed in relation to histomorphologic and clinical tumor characteristics. Receiver-operating-characteristic curves for identification of deep myometrial invasion and lymph node metastases were generated, and MTV cutoffs for predicting deep myometrial invasion and lymph node metastases were calculated. The sensitivity, specificity, and accuracy of (18)F-FDG PET/CT for the detection of lymph node metastases were 77%-85%, 91%-96%, and 89%-93%, respectively. SUVmax, SUVmean, MTV, and TLG were significantly related to deep myometrial invasion, presence of lymph node metastases, and high histologic grade (P < 0.015 for all) and independently predicted deep myometrial invasion (P < 0.015) and lymph node metastases (P < 0.025) after adjustment for preoperative histologic risk (based on subtype and grade) in endometrial biopsies. Optimal cutoffs for MTV in predicting deep myometrial invasion (20 mL) and the presence of lymph node metastases (30 mL) yielded odds ratios of 7.8 (P < 0.001) and 16.5 (P = 0.001), respectively. (18)F-FDG PET/CT represents a clinically valuable tool for preoperatively evaluating the presence of lymph node metastases in endometrial carcinoma patients. Applying MTV cutoffs for the prediction of deep myometrial invasion and lymph node metastases may increase diagnostic accuracy and aid preoperative identification of high-risk patients, enabling restriction of lymphadenectomy for patients with a low risk of aggressive disease. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

SU-F-T-681: Does the Biophysical Modeling for Immunological Aspects in Radiotherapy Precisely Predict Tumor and Normal Tissue Responses?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oita, M; Nakata, K; Sasaki, M

2016-06-15

Purpose: Recent advances in immunotherapy make possible to combine with radiotherapy. The aim of this study was to assess the TCP/NTCP model with immunological aspects including stochastic distribution as intercellular uncertainties. Methods: In the clinical treatment planning system (Eclipse ver.11.0, Varian medical systems, US), biological parameters such as α/β, D50, γ, n, m, TD50 including repair parameters (bi-exponential repair) can be set as any given values to calculate the TCP/NTCP. Using a prostate cancer patient data with VMAT commissioned as a 6-MV photon beam of Novalis-Tx (BrainLab, US) in clinical use, the fraction schedule were hypothesized as 70–78Gy/35–39fr, 72–81Gy/40–45fr, 52.5–66Gy/16–22fr,more » 35–40Gy/5fr of 5–7 fractions in a week. By use of stochastic biological model applying for Gaussian distribution, the effects of the TCP/NTCP variation of repair parameters of the immune system as well as the intercellular uncertainty of tumor and normal tissues have been evaluated. Results: As respect to the difference of the α/β, the changes of the TCP/NTCP were increased in hypo-fraction regimens. The difference between the values of n and m affect the variation of the NTCP with the fraction schedules, independently. The elongation of repair half-time (long) increased the TCP/NTCP twice or much higher in the case of hypo-fraction scheme. For tumor, the repopulation parameters such as Tpot and Tstart, which is immunologically working to the tumor, improved TCP. Conclusion: Compared to default fixed value, which has affected by the probability of cell death and cure, hypo-fractionation schemes seemed to have advantages for the variations of the values of m. The possibility of an increase of the α/β or TD50 and repair parameters in tumor and normal tissue by immunological aspects were highly expected. For more precise prediction, treatment planning systems should be incorporated the complicated biological optimization in clinical practice combined with basic experiments data.« less

Optimization of bone drilling parameters using Taguchi method based on finite element analysis

NASA Astrophysics Data System (ADS)

Rosidi, Ayip; Lenggo Ginta, Turnad; Rani, Ahmad Majdi Bin Abdul

2017-05-01

Thermal necrosis results fracture problems and implant failure if temperature exceeds 47 °C for one minute during bone drilling. To solve this problem, this work studied a new thermal model by using three drilling parameters: drill diameter, feed rate and spindle speed. Effects of those parameters to heat generation were studied. The drill diameters were 4 mm, 6 mm and 6 mm; the feed rates were 80 mm/min, 100 mm/min and 120 mm/min whereas the spindle speeds were 400 rpm, 500 rpm and 600 rpm then an optimization was done by Taguchi method to which combination parameter can be used to prevent thermal necrosis during bone drilling. The results showed that all the combination of parameters produce confidence results which were below 47 °C and finite element analysis combined with Taguchi method can be used for predicting temperature generation and optimizing bone drilling parameters prior to clinical bone drilling. All of the combination parameters can be used for surgeon to achieve sustainable orthopaedic surgery.

Identification of V122I (Val122Ile) transthyretin cardiac amyloidosis (ATTR) using serum retinol-binding protein 4 (RBP4) and a clinical prediction model

PubMed Central

Arvanitis, Marios; Koch, Clarissa M; Chan, Gloria G.; Arancivia, Celia M.T.; LaValley, Michael; Jacobson, Daniel; Berk, John L.; Connors, Lawreen H.; Ruberg, Frederick L.

2017-01-01

Importance Transthyretin amyloid cardiomyopathy (ATTR) is an under-recognized cause of heart failure (HF) in the elderly, owing in part to difficulty in diagnosis. ATTR can result from mutant TTR protein with one of the most common mutations in the United States, V122I, present in 3.43% of African Americans. Objective To determine whether serum retinol-binding protein 4 (RBP4), an endogenous TTR ligand, could be used as a diagnostic test for ATTR V122I amyloidosis. Design Combined prospective and retrospective cohort study Setting Tertiary care referral center Participants Fifty prospectively genotyped African American patients over age 60 years with non-amyloid HF and cardiac wall thickening, and a comparator cohort of biopsy proven ATTR V122I amyloidosis patients (n=25) comprised the development cohort. Twenty-seven prospectively genotyped African American patients and 9 ATTR V122I amyloidosis patients comprised the validation cohort. Main Outcomes and Measures Circulating RBP4, TTR, B-type natriuretic peptide (BNP) and troponin I (TnI) concentrations, electrocardiography (ECG), echocardiography, and clinical characteristics were assessed in all patients. Receiver operating characteristic (ROC) analysis was performed to identify optimal thresholds for ATTR V122I amyloidosis identification. A clinical prediction rule was developed using penalized logistic regression, evaluated using ROC analysis and validated in an independent cohort of cases and controls. Results Age, gender, BNP and TnI were similar between ATTR V122I amyloidosis patients and controls. Serum RBP4 concentration was lower in patients with ATTR V122I amyloidosis compared to non-amyloid controls (31.5 vs. 49.4 ug/ml, p < 0.001) and the difference persisted after controlling for potential confounding parameters. Left ventricular ejection fraction (LVEF) was lower in ATTR V122I amyloidosis (40% vs. 57%, p<0.001), while interventricular septal diameter (IVSd) was higher (16 vs. 14 mm, p<0.001). ROC analysis identified RBP4 as a sensitive identifier of ATTR V122I amyloidosis (AUC 0.78). A clinical prediction algorithm comprised of RBP4, TTR, LVEF, IVSd, mean limb lead ECG voltage and grade 3 diastolic dysfunction yielded excellent discriminatory capacity for ATTR V122I amyloidosis (AUC 0.97), while a 4 parameter model including RBP4 concentration retained excellent discrimination (AUC 0.92). The models maintained excellent discrimination in the validation cohort. Conclusions and Relevance A prediction model employing circulating RBP4 concentration and readily available clinical parameters accurately discriminated ATTR V122I amyloid cardiomyopathy from non-amyloid HF in a case matched cohort. We propose that this clinical algorithm may be useful for identification of ATTR V122I amyloidosis in elderly, African American patients with heart failure. PMID:28196196

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

A Bayesian predictive two-stage design for phase II clinical trials.

PubMed

Sambucini, Valeria

2008-04-15

In this paper, we propose a Bayesian two-stage design for phase II clinical trials, which represents a predictive version of the single threshold design (STD) recently introduced by Tan and Machin. The STD two-stage sample sizes are determined specifying a minimum threshold for the posterior probability that the true response rate exceeds a pre-specified target value and assuming that the observed response rate is slightly higher than the target. Unlike the STD, we do not refer to a fixed experimental outcome, but take into account the uncertainty about future data. In both stages, the design aims to control the probability of getting a large posterior probability that the true response rate exceeds the target value. Such a probability is expressed in terms of prior predictive distributions of the data. The performance of the design is based on the distinction between analysis and design priors, recently introduced in the literature. The properties of the method are studied when all the design parameters vary.

Choosing the appropriate forecasting model for predictive parameter control.

PubMed

Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars

2014-01-01

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Texture Analysis of T1-Weighted and Fluid-Attenuated Inversion Recovery Images Detects Abnormalities That Correlate With Cognitive Decline in Small Vessel Disease.

PubMed

Tozer, Daniel J; Zeestraten, Eva; Lawrence, Andrew J; Barrick, Thomas R; Markus, Hugh S

2018-06-04

Magnetic resonance imaging may be useful to assess disease severity in cerebral small vessel disease (SVD), identify those individuals who are most likely to progress to dementia, monitor disease progression, and act as surrogate markers to test new therapies. Texture analysis extracts information on the relationship between signal intensities of neighboring voxels. A potential advantage over techniques, such as diffusion tensor imaging, is that it can be used on clinically obtained magnetic resonance sequences. We determined whether texture parameters (TP) were abnormal in SVD, correlated with cognitive impairment, predicted cognitive decline, or conversion to dementia. In the prospective SCANS study (St George's Cognition and Neuroimaging in Stroke), we assessed TP in 121 individuals with symptomatic SVD at baseline, 99 of whom attended annual cognitive testing for 5 years. Conversion to dementia was recorded for all subjects during the 5-year period. Texture analysis was performed on fluid-attenuated inversion recovery and T1-weighted images. The TP obtained from the SVD cohort were cross-sectionally compared with 54 age-matched controls scanned on the same magnetic resonance imaging system. There were highly significant differences in several TP between SVD cases and controls. Within the SVD population, TP were highly correlated to other magnetic resonance imaging parameters (brain volume, white matter lesion volume, lacune count). TP correlated with executive function and global function at baseline and predicted conversion to dementia, after controlling for age, sex, premorbid intelligence quotient, and magnetic resonance parameters. TP, which can be obtained from routine clinical images, are abnormal in SVD, and the degree of abnormality correlates with executive dysfunction and global cognition at baseline and decline during 5 years. TP may be useful to assess disease severity in clinically collected data. This needs testing in data clinically acquired across multiple sites. © 2018 The Authors.

Effect of Normal Lung Definition on Lung Dosimetry and Lung Toxicity Prediction in Radiation Therapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Weili; Department of Radiation Oncology, the Fourth Affiliated Hospital, China Medical University, Shenyang; Xu, Yaping

2013-08-01

Purpose: This study aimed to compare lung dose–volume histogram (DVH) parameters such as mean lung dose (MLD) and the lung volume receiving ≥20 Gy (V20) of commonly used definitions of normal lung in terms of tumor/target subtraction and to determine to what extent they differ in predicting radiation pneumonitis (RP). Methods and Materials: One hundred lung cancer patients treated with definitive radiation therapy were assessed. The gross tumor volume (GTV) and clinical planning target volume (PTV{sub c}) were defined by the treating physician and dosimetrist. For this study, the clinical target volume (CTV) was defined as GTV with 8-mm uniformmore » expansion, and the PTV was defined as CTV with an 8-mm uniform expansion. Lung DVHs were generated with exclusion of targets: (1) GTV (DVH{sub G}); (2) CTV (DVH{sub C}); (3) PTV (DVH{sub P}); and (4) PTV{sub c} (DVH{sub Pc}). The lung DVHs, V20s, and MLDs from each of the 4 methods were compared, as was their significance in predicting radiation pneumonitis of grade 2 or greater (RP2). Results: There are significant differences in dosimetric parameters among the various definition methods (all Ps<.05). The mean and maximum differences in V20 are 4.4% and 12.6% (95% confidence interval 3.6%-5.1%), respectively. The mean and maximum differences in MLD are 3.3 Gy and 7.5 Gy (95% confidence interval, 1.7-4.8 Gy), respectively. MLDs of all methods are highly correlated with each other and significantly correlated with clinical RP2, although V20s are not. For RP2 prediction, on the receiver operating characteristic curve, MLD from DVH{sub G} (MLD{sub G}) has a greater area under curve of than MLD from DVH{sub C} (MLD{sub C}) or DVH{sub P} (MLD{sub P}). Limiting RP2 to 30%, the threshold is 22.4, 20.6, and 18.8 Gy, for MLD{sub G}, MLD{sub C}, and MLD{sub P}, respectively. Conclusions: The differences in MLD and V20 from various lung definitions are significant. MLD from the GTV exclusion method may be more accurate in predicting clinical significant radiation pneumonitis.« less

Prediction of the effect of atrasentan on renal and heart failure outcomes based on short-term changes in multiple risk markers.

PubMed

Schievink, Bauke; de Zeeuw, Dick; Smink, Paul A; Andress, Dennis; Brennan, John J; Coll, Blai; Correa-Rotter, Ricardo; Hou, Fan Fan; Kohan, Donald; Kitzman, Dalane W; Makino, Hirofumi; Parving, Hans-Henrik; Perkovic, Vlado; Remuzzi, Giuseppe; Tobe, Sheldon; Toto, Robert; Hoekman, Jarno; Lambers Heerspink, Hiddo J

2016-05-01

A recent phase II clinical trial (Reducing Residual Albuminuria in Subjects with Diabetes and Nephropathy with AtRasentan trial and an identical trial in Japan (RADAR/JAPAN)) showed that the endothelin A receptor antagonist atrasentan lowers albuminuria, blood pressure, cholesterol, hemoglobin, and increases body weight in patients with type 2 diabetes and nephropathy. We previously developed an algorithm, the Parameter Response Efficacy (PRE) score, which translates short-term drug effects into predictions of long-term effects on clinical outcomes. We used the PRE score on data from the RADAR/JAPAN study to predict the effect of atrasentan on renal and heart failure outcomes. We performed a post-hoc analysis of the RADAR/JAPAN randomized clinical trials in which 211 patients with type-2 diabetes and nephropathy were randomly assigned to atrasentan 0.75 mg/day, 1.25 mg/day, or placebo. A PRE score was developed in a background set of completed clinical trials using multivariate Cox models. The score was applied to baseline and week-12 risk marker levels of RADAR/JAPAN participants, to predict atrasentan effects on clinical outcomes. Outcomes were defined as doubling serum creatinine or end-stage renal disease and hospitalization for heart failure. The PRE score predicted renal risk changes of -23% and -30% for atrasentan 0.75 and 1.25 mg/day, respectively. PRE scores also predicted a small non-significant increase in heart failure risk for atrasentan 0.75 and 1.25 mg/day (+2% vs. +7%). Selecting patients with >30% albuminuria reduction from baseline (responders) improved renal outcome to almost 50% risk reduction, whereas non-responders showed no renal benefit. Based on the RADAR/JAPAN study, with short-term changes in risk markers, atrasentan is expected to decrease renal risk without increased risk of heart failure. Within this population albuminuria responders appear to contribute to the predicted improvements, whereas non-responders showed no benefit. The ongoing hard outcome trial (SONAR) in type 2 diabetic patients with >30% albuminuria reduction to atrasentan will allow us to assess the validity of these predictions. © The European Society of Cardiology 2015.

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

PubMed Central

Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami

2015-01-01

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach. PMID:26226448

Systematic reviews: I. The correlation between laboratory tests on marginal quality and bond strength. II. The correlation between marginal quality and clinical outcome.

PubMed

Heintze, Siegward D

2007-01-01

An accepted principle in restorative dentistry states that restorations should be placed with the best marginal quality possible to avoid postoperative sensitivity, marginal discoloration, and secondary caries. Different laboratory methods claim to predict the clinical performance of restorative materials, for example, tests of bond strength and microleakage and gap analysis. The purpose of this review was twofold: (1) find studies that correlated the results of bond strength tests with either microleakage or gap analysis for the same materials, and (2) find studies that correlated the results of microleakage and/or gaps with the clinical parameters for the same materials. Furthermore, influencing factors on the results of the laboratory tests were reviewed and assessed. For the first question, searches for studies were conducted in the MEDLINE database and IADR/AADR abtracts online with specific search and inclusion criteria. The outcome for each study was assessed on the basis of the statistical test applied in the study, and finally the number of studies with or without correlation was compiled. For the second question, results of the quantitative marginal analysis of Class V restorations published by the University of Zürich with the same test protocol and prospective clinical trials were searched that investigated the same materials for at least 2 years in Class V cavities. Pearson correlation coefficients were calculated for pooled data of materials and clinical outcome parameters such as retention loss, marginal discoloration, marginal integrity, and secondary caries. For the correlation of dye penetration and clinical outcome, studies on Class V restorations published by the same research institute were searched in MEDLINE that examined the same adhesive systems as the selected clinical trials. For the correlation bond strength/microleakage, 30 studies were included into the review, and for the correlation bond strength/gap analysis 18 studies. For both topics, about 80% of the studies revealed that there was no correlation between the two methods. For the correlation quantitative marginal analysis/clinical outcome, data were compared to the clinical outcome of 11 selected clinical studies. In only 2 out of the 11 studies (18%) did the clinical outcome match the prognosis based on the laboratory tests; the remaining studies did not show any correlation. When pooling data on 20 adhesive systems, no correlation was found between the percentage of continuous margin of restorations placed in extracted premolars and the percentage of teeth that showed no retention loss in clinical studies, no discoloured margins, acceptable margins, or absence of secondary caries. With regard to the correlation of dye penetration and clinical studies, no sufficient number of studies was found that matched the inclusion criteria. However, literature data suggest that there is no correlation between microleakage data as measured in the laboratory and clinical parameters. The results of bond strength tests did not correlate with laboratory tests that evaluated the marginal seal of restorations such as microleakage or gap analysis. The quantitative marginal analysis of Class V fillings in the laboratory was unable to predict the performance of the same materials in vivo. Therefore, microleakage tests or the quantitative marginal analysis should be abandoned and research should focus on laboratory tests that are validated with regard to their ability to satisfactorily predict the clinical performance of restorative materials.

The value of hemodynamic measurements by air plethysmography in diagnosing venous obstruction of the lower limb.

PubMed

Kurstjens, Ralph L M; de Wolf, Mark A F; Alsadah, Sarah A; Arnoldussen, Carsten W K P; Strijkers, Rob H W; Toonder, Irwin M; Wittens, Cees H A

2016-07-01

Air plethysmography (APG) is a functional, noninvasive test that can assess volumetric changes in the lower limb and might therefore be used as a diagnostic tool in chronic deep venous disease. However, use of APG in chronic deep venous obstructive disease remains debatable. This study assessed the clinical value of APG in identifying chronic deep venous obstruction. All patients referred to our tertiary, outpatient clinic between January 2011 and August 2013 with chronic venous complaints and suspected outflow obstruction underwent an outflow fraction (OF), ejection fraction (EF), and residual volume fraction (RVF) test using APG. Duplex ultrasound and magnetic resonance venography were used to establish whether and where obstruction was present. Diagnostic values of these tests were assessed for obstructions at different levels of the deep venous system. A total of 312 limbs in 248 patients were tested. Mean age was 45.5 ± 14.0 years, and 62.5% were female. In post-thrombotic disease, specificity and positive predictive value for OF were as high as 98.4% and 95.0%, respectively; however, sensitivity was 34.8% and negative predictive value was 29.6%, with no clinically relevant positive or negative likelihood ratios. No clinically relevant differences were observed in stratifying for level of obstruction. EF and RVF were as inconclusive. Neither could these parameters be used in diagnosing nonthrombotic iliac vein compression. We found a poor correlation between OF, EF, or RVF, determined by APG, and the presence of chronic deep venous obstruction. Therefore, use of its relative parameters is unwarranted in daily clinical practice. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Dengue score as a diagnostic predictor for pleural effusion and/or ascites: external validation and clinical application.

PubMed

Suwarto, Suhendro; Hidayat, Mohammad Jauharsyah; Widjaya, Bing

2018-02-23

The Dengue Score is a model for predicting pleural effusion and/or ascites and uses the hematocrit (Hct), albumin concentration, platelet count and aspartate aminotransferase (AST) ratio as independent variables. As this metric has not been validated, we conducted a study to validate the Dengue Score and assess its clinical application. A retrospective study was performed at a private hospital in Jakarta, Indonesia. Patients with dengue infection hospitalized from January 2011 through March 2016 were included. The Dengue Score was calculated using four parameters: Hct increase≥15.1%, serum albumin≤3.49 mg/dL, platelet count≤49,500/μL and AST ratio ≥ 2.51. Each parameter was scored as 1 if present and 0 if absent. To validate the Dengue Score, goodness-of-fit was used to assess calibration, and the area under the receiver operating characteristic curve (AROC) was used to assess discrimination. Associations between clinical parameters and Dengue Score groups were determined by bivariate analysis. A total of 207 patients were included in this study. The calibration of the Dengue Score was acceptable (Hosmer-Lemeshow test, p = 0.11), and the score's discriminative ability was good (AROC = 0.88 (95% CI: 0.83-0.92)). At a cutoff of ≥2, the Dengue Score had a positive predictive value (PPV) of 79.03% and a negative predictive value (NPV) of 90.36% for the diagnostic prediction of pleural effusion and/or ascites. Compared with the Dengue Score ≤ 1 group, the Dengue Score = 2 group was significantly associated with hemoconcentration> 20% (p = 0.029), severe thrombocytopenia (p = 0.029), and increased length of hospital stay (p = 0.003). Compared with the Dengue Score = 2 group, the Dengue Score ≥ 3 group was significantly associated with hemoconcentration> 20% (p = 0.001), severe thrombocytopenia (p = 0.024), severe dengue (p = 0.039), and increased length of hospital stay (p = 0.011). The Dengue Score performed well and can be used in daily practice to help clinicians identify patients who have plasma leakage associated with severe dengue.

A computer model of the pediatric circulatory system for testing pediatric assist devices.

PubMed

Giridharan, Guruprasad A; Koenig, Steven C; Mitchell, Michael; Gartner, Mark; Pantalos, George M

2007-01-01

Lumped parameter computer models of the pediatric circulatory systems for 1- and 4-year-olds were developed to predict hemodynamic responses to mechanical circulatory support devices. Model parameters, including resistance, compliance and volume, were adjusted to match hemodynamic pressure and flow waveforms, pressure-volume loops, percent systole, and heart rate of pediatric patients (n = 6) with normal ventricles. Left ventricular failure was modeled by adjusting the time-varying compliance curve of the left heart to produce aortic pressures and cardiac outputs consistent with those observed clinically. Models of pediatric continuous flow (CF) and pulsatile flow (PF) ventricular assist devices (VAD) and intraaortic balloon pump (IABP) were developed and integrated into the heart failure pediatric circulatory system models. Computer simulations were conducted to predict acute hemodynamic responses to PF and CF VAD operating at 50%, 75% and 100% support and 2.5 and 5 ml IABP operating at 1:1 and 1:2 support modes. The computer model of the pediatric circulation matched the human pediatric hemodynamic waveform morphology to within 90% and cardiac function parameters with 95% accuracy. The computer model predicted PF VAD and IABP restore aortic pressure pulsatility and variation in end-systolic and end-diastolic volume, but diminish with increasing CF VAD support.

An extended fatty liver index to predict non-alcoholic fatty liver disease.

PubMed

Kantartzis, K; Rettig, I; Staiger, H; Machann, J; Schick, F; Scheja, L; Gastaldelli, A; Bugianesi, E; Peter, A; Schulze, M B; Fritsche, A; Häring, H-U; Stefan, N

2017-06-01

In clinical practice, there is a strong interest in non-invasive markers of non-alcoholic fatty liver disease (NAFLD). Our hypothesis was that the fold-change in plasma triglycerides (TG) during a 2-h oral glucose tolerance test (fold-change TG OGTT ) in concert with blood glucose and lipid parameters, and the rs738409 C>G single nucleotide polymorphism (SNP) in PNPLA3 might improve the power of the widely used fatty liver index (FLI) to predict NAFLD. The liver fat content of 330 subjects was quantified by 1 H-magnetic resonance spectroscopy. Blood parameters were measured during fasting and after a 2-h OGTT. A subgroup of 213 subjects underwent these measurements before and after 9 months of a lifestyle intervention. The fold-change TG OGTT was closely associated with liver fat content (r=0.51, P<0.0001), but had less power to predict NAFLD (AUROC=0.75) than the FLI (AUROC=0.79). Not only was the fold-change TG OGTT independently associated with liver fat content and NAFLD, but so also were the 2-h blood glucose level and rs738409 C>G SNP in PNPLA3. In fact, a novel index (extended FLI) generated from these and the usual FLI parameters considerably increased its power to predict NAFLD (AUROC=0.79-0.86). The extended FLI also increased the power to predict changes in liver fat content with a lifestyle intervention (n=213; standardized beta coefficient: 0.23-0.29). This study has provided novel data confirming that the OGTT-derived fold-change TG OGTT and 2-h glucose level, together with the rs738409 C>G SNP in PNPLA3, allow calculation of an extended FLI that considerably improves its power to predict NAFLD. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Prediction of contact mechanics in metal-on-metal Total Hip Replacement for parametrically comprehensive designs and loads.

PubMed

Donaldson, Finn E; Nyman, Edward; Coburn, James C

2015-07-16

Manufacturers and investigators of Total Hip Replacement (THR) bearings require tools to predict the contact mechanics resulting from diverse design and loading parameters. This study provides contact mechanics solutions for metal-on-metal (MoM) bearings that encompass the current design space and could aid pre-clinical design optimization and evaluation. Stochastic finite element (FE) simulation was used to calculate the head-on-cup contact mechanics for five thousand combinations of design and loading parameters. FE results were used to train a Random Forest (RF) surrogate model to rapidly predict the contact patch dimensions, contact area, pressures and plastic deformations for arbitrary designs and loading. In addition to widely observed polar and edge contact, FE results included ring-polar, asymmetric-polar, and transitional categories which have previously received limited attention. Combinations of design and load parameters associated with each contact category were identified. Polar contact pressures were predicted in the range of 0-200 MPa with no permanent deformation. Edge loading (with subluxation) was associated with pressures greater than 500 MPa and induced permanent deformation in 83% of cases. Transitional-edge contact (with little subluxation) was associated with intermediate pressures and permanent deformation in most cases, indicating that, even with ideal anatomical alignment, bearings may face extreme wear challenges. Surrogate models were able to accurately predict contact mechanics 18,000 times faster than FE analyses. The developed surrogate models enable rapid prediction of MoM bearing contact mechanics across the most comprehensive range of loading and designs to date, and may be useful to those performing bearing design optimization or evaluation. Published by Elsevier Ltd.

Usefulness of elevated red cell distribution width for predicting systemic inflammatory response syndrome after extracorporeal circulation.

PubMed

Özeren, M; Aytaçoğlu, B; Vezir, Ö; Karaca, K; Akın, R; Sucu, N

2015-10-01

Cardiac surgical operations performed by using extracorporeal circulation (ECC) lead to a systemic inflammatory response (SIR). Sometimes SIR may turn into a severe state, the systemic inflammatory response syndrome (SIRS) that usually has a poor outcome with no specific clinical tools described for its prediction. Red cell distribution width (RDW) is a routine hematological parameter. It has been proposed as a marker of morbidity and mortality in various clinical conditions. We aimed to investigate the relationship between high RDW and SIRS which is triggered by ECC. Eleven hundred consecutive patients who underwent elective heart surgery with the use of ECC were retrospectively analyzed. A total of 19 patients fulfilled the described SIRS criteria and 20 consecutive patients were selected as the control group. RDW and other laboratory parameters, preoperative clinical status, operative data and postoperative data were compared between the SIRS and the control groups. Baseline characteristics of the patient groups were similar. Significant mortality was found in the SIRS group; 18 (94.73%) patients and 2 (10%) patients in the control group (p < 0.002). RDW was found to be significantly higher in the SIRS group vs the control group (15.02 ± 2.03 vs 13.01 ± 1.93, respectively, p < 0.003). Multiple logistic regression analyses showed an association between high RDW levels and SIRS development (OR for RDW levels exceeding 13.5%; 95% confidence limits of 1.0-1.3; p < 0.04). Total operation time and the need for inotropic support were also found to be significant against the SIRS group (p = 0.049). Increased RDW was significantly associated with increased risk of SIRS after ECC. The results of this study suggest that paying attention to RDW might provide valuable clinical information for predicting SIRS development among patients who are candidates for open heart surgery, without incurring additional costs. © The Author(s) 2015.

Methods for using groundwater model predictions to guide hydrogeologic data collection, with application to the Death Valley regional groundwater flow system

USGS Publications Warehouse

Tiedeman, C.R.; Hill, M.C.; D'Agnese, F. A.; Faunt, C.C.

2003-01-01

Calibrated models of groundwater systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogeologic data collection for improving model predictions by identifying model parameters that are most important to the predictions. Identification of these important parameters can help guide collection of field data about parameter values and associated flow system features and can lead to improved predictions. Methods for identifying parameters important to predictions include prediction scaled sensitivities (PSS), which account for uncertainty on individual parameters as well as prediction sensitivity to parameters, and a new "value of improved information" (VOII) method presented here, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. In this work, the PSS and VOII methods are demonstrated and evaluated using a model of the Death Valley regional groundwater flow system. The predictions of interest are advective transport paths originating at sites of past underground nuclear testing. Results show that for two paths evaluated the most important parameters include a subset of five or six of the 23 defined model parameters. Some of the parameters identified as most important are associated with flow system attributes that do not lie in the immediate vicinity of the paths. Results also indicate that the PSS and VOII methods can identify different important parameters. Because the methods emphasize somewhat different criteria for parameter importance, it is suggested that parameters identified by both methods be carefully considered in subsequent data collection efforts aimed at improving model predictions.

TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krafft, S; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Briere, T

2015-06-15

Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. Amore » total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted. This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK.« less

Impact of demographic and clinical parameters on video capsule transit time.

PubMed

Niv, Eva; Pinchasovich, Hadassa; Yanai, Henit

2016-10-01

Small bowel (SB) capsule endoscopy (CE) studies provide data on both gastric and SB transit times (GTT and SBTT, respectively). This study aimed to evaluate the influence of demographic and clinical parameters on the GTT and SBTT. Transit times for two generations of capsules (Pillcam SB2 and SB3) were also compared. Consecutive adult patients undergoing CE were included. GTT, SBTT, and cecum arrival rates were calculated and correlated to demographics and clinical characteristics. A total of 332 CE studies were analyzed. Neither GTT nor SBTT were impacted by age or sex. SBTT was prolonged in newly diagnosed Crohn's disease (CD) patients compared with all other patients (303.1±90.3 vs. 243.6±83.6 min, P=0.02 for SB2, 267.8±63 vs. 228.6±72.3, P=0.01 for SB3, respectively). Moreover, CD patients had higher incomplete study rates compared with patients with all other diagnoses (29.4 vs. 7.3%, respectively, P=0.0116) in the SB2 subgroup. Higher cecum arrival rates were achieved by the SB3 capsule compared with SB2 (97 vs. 91%, P=0.04). Patients with prolonged gastric time or patients with incomplete studies had similar demographic and clinical characteristics as others. Age and sex apparently do not influence intestinal kinetics. Newly diagnosed CD patients have relatively prolonged SBTTs. Demographic and clinical parameters cannot predict prolonged GTT or cecum nonarrival.

Comparison of Diffusion Tensor Tractography and Motor Evoked Potentials for the Estimation of Clinical Status in Subacute Stroke

PubMed Central

Chun, Kwang-Soo; Lee, Yong-Taek; Park, Jong-Wan; Lee, Joon-Youn; Park, Chul-Hyun

2016-01-01

Objective To compare diffusion tensor tractography (DTT) and motor evoked potentials (MEPs) for estimation of clinical status in patients in the subacute stage of stroke. Methods Patients with hemiplegia due to stroke who were evaluated using both DTT and MEPs between May 2012 and April 2015 were recruited. Clinical assessments investigated upper extremity motor and functional status. Motor status was evaluated using Medical Research Council grading and the Fugl-Meyer Assessment of upper limb and hand (FMA-U and FMA-H). Functional status was measured using the Modified Barthel Index (MBI). Patients were classified into subgroups according to DTT findings, MEP presence, fractional anisotropy (FA) value, FA ratio (rFA), and central motor conduction time (CMCT). Correlations of clinical assessments with DTT parameters and MEPs were estimated. Results Fifty-five patients with hemiplegia were recruited. In motor assessments (FMA-U), MEPs had the highest sensitivity and negative predictive value (NPV) as well as the second highest specificity and positive predictive value (PPV). CMCT showed the highest specificity and PPV. Regarding functional status (MBI), FA showed the highest sensitivity and NPV, whereas CMCT had the highest specificity and PPV. Correlation analysis showed that the resting motor threshold (RMT) ratio was strongly associated with motor status of the upper limb, and MEP parameters were not associated with MBI. Conclusion DTT and MEPs could be suitable complementary modalities for analyzing the motor and functional status of patients in the subacute stage of stroke. The RMT ratio was strongly correlated with motor status. PMID:26949679

Efficient temporal and interlayer parameter prediction for weighted prediction in scalable high efficiency video coding

NASA Astrophysics Data System (ADS)

Tsang, Sik-Ho; Chan, Yui-Lam; Siu, Wan-Chi

2017-01-01

Weighted prediction (WP) is an efficient video coding tool that was introduced since the establishment of the H.264/AVC video coding standard, for compensating the temporal illumination change in motion estimation and compensation. WP parameters, including a multiplicative weight and an additive offset for each reference frame, are required to be estimated and transmitted to the decoder by slice header. These parameters cause extra bits in the coded video bitstream. High efficiency video coding (HEVC) provides WP parameter prediction to reduce the overhead. Therefore, WP parameter prediction is crucial to research works or applications, which are related to WP. Prior art has been suggested to further improve the WP parameter prediction by implicit prediction of image characteristics and derivation of parameters. By exploiting both temporal and interlayer redundancies, we propose three WP parameter prediction algorithms, enhanced implicit WP parameter, enhanced direct WP parameter derivation, and interlayer WP parameter, to further improve the coding efficiency of HEVC. Results show that our proposed algorithms can achieve up to 5.83% and 5.23% bitrate reduction compared to the conventional scalable HEVC in the base layer for SNR scalability and 2× spatial scalability, respectively.

An oracle: antituberculosis pharmacokinetics-pharmacodynamics, clinical correlation, and clinical trial simulations to predict the future.

PubMed

Pasipanodya, Jotam; Gumbo, Tawanda

2011-01-01

Antimicrobial pharmacokinetic-pharmacodynamic (PK/PD) science and clinical trial simulations have not been adequately applied to the design of doses and dose schedules of antituberculosis regimens because many researchers are skeptical about their clinical applicability. We compared findings of preclinical PK/PD studies of current first-line antituberculosis drugs to findings from several clinical publications that included microbiologic outcome and pharmacokinetic data or had a dose-scheduling design. Without exception, the antimicrobial PK/PD parameters linked to optimal effect were similar in preclinical models and in tuberculosis patients. Thus, exposure-effect relationships derived in the preclinical models can be used in the design of optimal antituberculosis doses, by incorporating population pharmacokinetics of the drugs and MIC distributions in Monte Carlo simulations. When this has been performed, doses and dose schedules of rifampin, isoniazid, pyrazinamide, and moxifloxacin with the potential to shorten antituberculosis therapy have been identified. In addition, different susceptibility breakpoints than those in current use have been identified. These steps outline a more rational approach than that of current methods for designing regimens and predicting outcome so that both new and older antituberculosis agents can shorten therapy duration.

Current state of prognostication and risk stratification in myelodysplastic syndromes.

PubMed

Zeidan, Amer M; Gore, Steven D; Padron, Eric; Komrokji, Rami S

2015-03-01

Myelodysplastic syndromes (MDS) are characterized by significant biologic and clinical heterogeneity. Because of the wide outcome variability, accurate prognostication is vital to high-quality risk-adaptive care of MDS patients. In this review, we discuss the current state of prognostic schemes for MDS and overview efforts aimed at utilizing molecular aberrations for prognostication in clinical practice. Several prognostic instruments have been developed and validated with increasing accuracy and complexity. Oncologists should be aware of the inherent limitations of these prognostic tools as they counsel patients and make clinical decisions. As more therapies are becoming available for MDS, the focus of model development is shifting from prognostic to treatment-specific predictive instruments. In addition to providing additional prognostic data beyond traditional clinical and pathologic parameters, the improved understanding of the genetic landscape and pathophysiologic consequences in MDS may allow the construction of treatment-specific predictive instruments. How to best use the results of molecular mutation testing to inform clinical decision making in MDS is still a work in progress. Important steps in this direction include standardization in performance and interpretation of assays and better understanding of the independent prognostic importance of the recurrent mutations, especially the less frequent ones.

Specific prognostic factors for secondary pancreatic infection in severe acute pancreatitis.

PubMed

Armengol-Carrasco, M; Oller, B; Escudero, L E; Roca, J; Gener, J; Rodríguez, N; del Moral, P; Moreno, P

1999-01-01

The aim of the present study was to investigate whether there are specific prognostic factors to predict the development of secondary pancreatic infection (SPI) in severe acute pancreatitis in order to perform a computed tomography-fine needle aspiration with bacteriological sampling at the right moment and confirm the diagnosis. Twenty-five clinical and laboratory parameters were determined sequentially in 150 patients with severe acute pancreatitis (SAP) and univariate, and multivariate regression analyses were done looking for correlation with the development of SPI. Only APACHE II score and C-reactive protein levels were related to the development of SPI in the multivariate analysis. A regression equation was designed using these two parameters, and empiric cut-off points defined the subgroup of patients at high risk of developing secondary pancreatic infection. The results showed that it is possible to predict SPI during SAP allowing bacteriological confirmation and early treatment of this severe condition.

An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors

PubMed Central

Karageorgis, Anastassia; Dufort, Sandrine; Sancey, Lucie; Henry, Maxime; Hirsjärvi, Samuli; Passirani, Catherine; Benoit, Jean-Pierre; Gravier, Julien; Texier, Isabelle; Montigon, Olivier; Benmerad, Mériem; Siroux, Valérie; Barbier, Emmanuel L.; Coll, Jean-Luc

2016-01-01

Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines. PMID:26892874

An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors.

PubMed

Karageorgis, Anastassia; Dufort, Sandrine; Sancey, Lucie; Henry, Maxime; Hirsjärvi, Samuli; Passirani, Catherine; Benoit, Jean-Pierre; Gravier, Julien; Texier, Isabelle; Montigon, Olivier; Benmerad, Mériem; Siroux, Valérie; Barbier, Emmanuel L; Coll, Jean-Luc

2016-02-19

Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines.

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

PubMed Central

Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W. F.; Jeelani, Owase; Dunaway, David J.; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face. PMID:29742139

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.

PubMed

Knoops, Paul G M; Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W F; Jeelani, Owase; Dunaway, David J; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.

Anti AIDS drug design with the help of neural networks

NASA Astrophysics Data System (ADS)

Tetko, I. V.; Tanchuk, V. Yu.; Luik, A. I.

1995-04-01

Artificial neural networks were used to analyze and predict the human immunodefiency virus type 1 reverse transcriptase inhibitors. Training and control set included 44 molecules (most of them are well-known substances such as AZT, TIBO, dde, etc.) The biological activities of molecules were taken from literature and rated for two classes: active and inactive compounds according to their values. We used topological indices as molecular parameters. Four most informative parameters (out of 46) were chosen using cluster analysis and original input parameters' estimation procedure and were used to predict activities of both control and new (synthesized in our institute) molecules. We applied pruning network algorithm and network ensembles to obtain the final classifier and avoid chance correlation. The increasing of neural network generalization of the data from the control set was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly active. It was confirmed by further biological tests. The compound was as active as AZT and in order less toxic. The active compound is currently being evaluated in pre clinical trials as possible drug for anti-AIDS therapy.

Biological mechanisms of normal tissue damage: importance for the design of NTCP models.

PubMed

Trott, Klaus-Rüdiger; Doerr, Wolfgang; Facoetti, Angelica; Hopewell, John; Langendijk, Johannes; van Luijk, Peter; Ottolenghi, Andrea; Smyth, Vere

2012-10-01

The normal tissue complication probability (NTCP) models that are currently being proposed for estimation of risk of harm following radiotherapy are mainly based on simplified empirical models, consisting of dose distribution parameters, possibly combined with clinical or other treatment-related factors. These are fitted to data from retrospective or prospective clinical studies. Although these models sometimes provide useful guidance for clinical practice, their predictive power on individuals seems to be limited. This paper examines the radiobiological mechanisms underlying the most important complications induced by radiotherapy, with the aim of identifying the essential parameters and functional relationships needed for effective predictive NTCP models. The clinical features of the complications are identified and reduced as much as possible into component parts. In a second step, experimental and clinical data are considered in order to identify the gross anatomical structures involved, and which dose distributions lead to these complications. Finally, the pathogenic pathways and cellular and more specific anatomical parameters that have to be considered in this pathway are determined. This analysis is carried out for some of the most critical organs and sites in radiotherapy, i.e. spinal cord, lung, rectum, oropharynx and heart. Signs and symptoms of severe late normal tissue complications present a very variable picture in the different organs at risk. Only in rare instances is the entire organ the critical target which elicits the particular complication. Moreover, the biological mechanisms that are involved in the pathogenesis differ between the different complications, even in the same organ. Different mechanisms are likely to be related to different shapes of dose effect relationships and different relationships between dose per fraction, dose rate, and overall treatment time and effects. There is good reason to conclude that each type of late complication after radiotherapy depends on its own specific mechanism which is triggered by the radiation exposure of particular structures or sub-volumes of (or related to) the respective organ at risk. Hence each complication will need the development of an NTCP model designed to accommodate this structure. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Day-to-day measurement of patient-reported outcomes in exacerbations of chronic obstructive pulmonary disease

PubMed Central

Kocks, Jan Willem H; van den Berg, Jan Willem K; Kerstjens, Huib AM; Uil, Steven M; Vonk, Judith M; de Jong, Ynze P; Tsiligianni, Ioanna G; van der Molen, Thys

2013-01-01

Background Exacerbations of chronic obstructive pulmonary disease (COPD) are a major burden to patients and to society. Little is known about the possible role of day-to-day patient-reported outcomes during an exacerbation. This study aims to describe the day-to-day course of patient-reported health status during exacerbations of COPD and to assess its value in predicting clinical outcomes. Methods Data from two randomized controlled COPD exacerbation trials (n = 210 and n = 45 patients) were used to describe both the feasibility of daily collection of and the day-to-day course of patient-reported outcomes during outpatient treatment or admission to hospital. In addition to clinical parameters, the BORG dyspnea score, the Clinical COPD Questionnaire (CCQ), and the St George’s Respiratory Questionnaire were used in Cox regression models to predict treatment failure, time to next exacerbation, and mortality in the hospital study. Results All patient-reported outcomes showed a distinct pattern of improvement. In the multivariate models, absence of improvement in CCQ symptom score and impaired lung function were independent predictors of treatment failure. Health status and gender predicted time to next exacerbation. Five-year mortality was predicted by age, forced expiratory flow in one second % predicted, smoking status, and CCQ score. In outpatient management of exacerbations, health status was found to be less impaired than in hospitalized patients, while the rate and pattern of recovery was remarkably similar. Conclusion Daily health status measurements were found to predict treatment failure, which could help decision-making for patients hospitalized due to an exacerbation of COPD. PMID:23766644

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

PubMed

Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

2018-04-10

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

Mathematics as a conduit for translational research in post-traumatic osteoarthritis.

PubMed

Ayati, Bruce P; Kapitanov, Georgi I; Coleman, Mitchell C; Anderson, Donald D; Martin, James A

2017-03-01

Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a "conduit of translation." The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:566-572, 2017. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Histopathological Parameters predicting Occult Nodal Metastases in Tongue Carcinoma Cases: An Indian Perspective.

PubMed

Jacob, Tina Elizabeth; Malathi, N; Rajan, Sharada T; Augustine, Dominic; Manish, N; Patil, Shankargouda

2016-01-01

It is a well-established fact that in squamous cell carcinoma cases, the presence of lymph node metastases decreased the 5-year survival rate by 50% and also caused the recurrence of the primary tumor with development of distant metastases. Till date, the predictive factors for occult cervical lymph nodes metastases in cases of tongue squamous cell carcinoma remain inconclusive. Therefore, it is imperative to identify patients who are at the greatest risk for occult cervical metastases. This study was thus performed with the aim to identify various histopathologic parameters of the primary tumor that predict occult nodal metastases. The clinicopathologic features of 56 cases of lateral tongue squamous cell carcinoma with cT1NoMo/cT2NoMo as the stage and without prior radiotherapy or chemotherapy were considered. The surgical excision of primary tumor was followed by elective neck dissection. The glossectomy specimen along with the neck nodes were fixed in formalin and 5 urn thick sections were obtained. The hematoxylin & eosin stained sections were then subjected to microscopic examination. The primary tumor characteristics that were analyzed include tumor grade, invading front, depth of tumor, lymphovascular invasion, perineural invasion and inflammatory response. The nodes were examined for possible metastases using hematoxylin & eosin followed by cytokeratin immunohistochemistry. A total of 12 cases were found with positive occult nodal metastases. On performing univariate analysis, the histopathologic parameters that were found to be statistically significant were lymphovascular invasion (p = 0.004) and perineural invasion (p = 0.003) along with a cut-off depth of infiltration more than 5 mm (p = 0.01). Histopathologic assessment of the primary tumor specimen therefore continues to provide information that is central to guide clinical management, particularly in cases of occult nodal metastases. Clinical significance The study highlights the importance of extensive histopathological screening, which holds the key for establishing occult metastases. Pathological upgrading of tumors is possible following histopathological studies similar to the present one. Presence of occult metastases justify neck dissection in these clinically N0 cases. In an Indian setting, histopathological evaluation assumes a bigger role than other expensive and advanced techniques.

Factor VII Deficiency: Clinical Phenotype, Genotype and Therapy.

PubMed

Napolitano, Mariasanta; Siragusa, Sergio; Mariani, Guglielmo

2017-03-28

Factor VII deficiency is the most common among rare inherited autosomal recessive bleeding disorders, and is a chameleon disease due to the lack of a direct correlation between plasma levels of coagulation Factor VII and bleeding manifestations. Clinical phenotypes range from asymptomatic condition-even in homozygous subjects-to severe life-threatening bleedings (central nervous system, gastrointestinal bleeding). Prediction of bleeding risk is thus based on multiple parameters that challenge disease management. Spontaneous or surgical bleedings require accurate treatment schedules, and patients at high risk of severe hemorrhages may need prophylaxis from childhood onwards. The aim of the current review is to depict an updated summary of clinical phenotype, laboratory diagnosis, and treatment of inherited Factor VII deficiency.

Factor VII Deficiency: Clinical Phenotype, Genotype and Therapy

PubMed Central

Napolitano, Mariasanta; Siragusa, Sergio; Mariani, Guglielmo

2017-01-01

Factor VII deficiency is the most common among rare inherited autosomal recessive bleeding disorders, and is a chameleon disease due to the lack of a direct correlation between plasma levels of coagulation Factor VII and bleeding manifestations. Clinical phenotypes range from asymptomatic condition—even in homozygous subjects—to severe life-threatening bleedings (central nervous system, gastrointestinal bleeding). Prediction of bleeding risk is thus based on multiple parameters that challenge disease management. Spontaneous or surgical bleedings require accurate treatment schedules, and patients at high risk of severe hemorrhages may need prophylaxis from childhood onwards. The aim of the current review is to depict an updated summary of clinical phenotype, laboratory diagnosis, and treatment of inherited Factor VII deficiency. PMID:28350321

Anticoagulation therapy advisor: a decision-support system for heparin therapy during ECMO.

PubMed Central

Peverini, R. L.; Sale, M.; Rhine, W. D.; Fagan, L. M.; Lenert, L. A.

1992-01-01

We present a case study describing our development of a mathematical model to control a clinical parameter in a patient--in this case, the degree of anticoagulation during extracorporeal membrane oxygenation (ECMO) support. During ECMO therapy, an anticoagulant agent (heparin) is administered to prevent thrombosis. Under- or over-coagulation can have grave consequences. To improve control of anticoagulation, we developed a pharmacokinetic-pharmacodynamic (PK-PD) model that predicts activated clotting times (ACT) using the NONMEM program. We then integrated this model into a decision-support system, and validated it with an independent data set. The population model had a mean absolute error of prediction for ACT values of 33.5 seconds, with a mean bias in estimation of -14.3 seconds. Individualization of model-parameter estimates using nonlinear regression improved the absolute error prediction to 25.5 seconds, and lowered the mean bias to -3.1 seconds. The PK-PD model is coupled with software for heuristic interpretation of model results to provide a complete environment for the management of anticoagulation. PMID:1482937

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically significant differences being found between the two AUCs (p =0.079). The four-category proposal for PCO2 was ≤ 43;(43,52];(52,65];> 65, for which the following values were obtained: AIC=258.1 and AUC=0.81. No statistically significant differences were found between the AUC of the four-category option and that of the continuous predictor, which yielded an AIC of 250.3 and an AUC of 0.825 (p =0.115). Conclusions Our proposed method provides clinicians with the number and location of cut points for categorising variables, and performs as successfully as the original continuous predictor when it comes to developing clinical prediction rules. PMID:23802742

Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

PubMed

García-García, Isabel; Zeighami, Yashar; Dagher, Alain

2017-06-01

Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

Prediction of treatment outcomes to exercise in patients with nonremitted major depressive disorder.

PubMed

Rethorst, Chad D; South, Charles C; Rush, A John; Greer, Tracy L; Trivedi, Madhukar H

2017-12-01

Only one-third of patients with major depressive disorder (MDD) achieve remission with initial treatment. Consequently, current clinical practice relies on a "trial-and-error" approach to identify an effective treatment for each patient. The purpose of this report was to determine whether we could identify a set of clinical and biological parameters with potential clinical utility for prescription of exercise for treatment of MDD in a secondary analysis of the Treatment with Exercise Augmentation in Depression (TREAD) trial. Participants with nonremitted MDD were randomized to one of two exercise doses for 12 weeks. Participants were categorized as "remitters" (≤12 on the IDS-C), nonresponders (<30% drop in IDS-C), or neither. The least absolute shrinkage and selection operator (LASSO) and random forests were used to evaluate 30 variables as predictors of both remission and nonresponse. Predictors were used to model treatment outcomes using logistic regression. Of the 122 participants, 36 were categorized as remitters (29.5%), 56 as nonresponders (45.9%), and 30 as neither (24.6%). Predictors of remission were higher levels of brain-derived neurotrophic factor (BDNF) and IL-1B, greater depressive symptom severity, and higher postexercise positive affect. Predictors of treatment nonresponse were low cardiorespiratory fitness, lower levels of IL-6 and BDNF, and lower postexercise positive affect. Models including these predictors resulted in predictive values greater than 70% (true predicted remitters/all predicted remitters) with specificities greater than 25% (true predicted remitters/all remitters). Results indicate feasibility in identifying patients who will either remit or not respond to exercise as a treatment for MDD utilizing a clinical decision model that incorporates multiple patient characteristics. © 2017 Wiley Periodicals, Inc.

Clinical prediction model to identify vulnerable patients in ambulatory surgery: towards optimal medical decision-making.

PubMed

Mijderwijk, Herjan; Stolker, Robert Jan; Duivenvoorden, Hugo J; Klimek, Markus; Steyerberg, Ewout W

2016-09-01

Ambulatory surgery patients are at risk of adverse psychological outcomes such as anxiety, aggression, fatigue, and depression. We developed and validated a clinical prediction model to identify patients who were vulnerable to these psychological outcome parameters. We prospectively assessed 383 mixed ambulatory surgery patients for psychological vulnerability, defined as the presence of anxiety (state/trait), aggression (state/trait), fatigue, and depression seven days after surgery. Three psychological vulnerability categories were considered-i.e., none, one, or multiple poor scores, defined as a score exceeding one standard deviation above the mean for each single outcome according to normative data. The following determinants were assessed preoperatively: sociodemographic (age, sex, level of education, employment status, marital status, having children, religion, nationality), medical (heart rate and body mass index), and psychological variables (self-esteem and self-efficacy), in addition to anxiety, aggression, fatigue, and depression. A prediction model was constructed using ordinal polytomous logistic regression analysis, and bootstrapping was applied for internal validation. The ordinal c-index (ORC) quantified the discriminative ability of the model, in addition to measures for overall model performance (Nagelkerke's R (2) ). In this population, 137 (36%) patients were identified as being psychologically vulnerable after surgery for at least one of the psychological outcomes. The most parsimonious and optimal prediction model combined sociodemographic variables (level of education, having children, and nationality) with psychological variables (trait anxiety, state/trait aggression, fatigue, and depression). Model performance was promising: R (2)  = 30% and ORC = 0.76 after correction for optimism. This study identified a substantial group of vulnerable patients in ambulatory surgery. The proposed clinical prediction model could allow healthcare professionals the opportunity to identify vulnerable patients in ambulatory surgery, although additional modification and validation are needed. (ClinicalTrials.gov number, NCT01441843).

Resampling procedures to identify important SNPs using a consensus approach.

PubMed

Pardy, Christopher; Motyer, Allan; Wilson, Susan

2011-11-29

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then validate against the others. Our procedure performs well when predicting Q1 but is less successful for the other outcomes. We use resampling procedures where possible to guard against false positives and to improve generalizability. The approach is based on finding a consensus regarding important SNPs by applying random forests and the least absolute shrinkage and selection operator (LASSO) on multiple subsamples. Random forests are used first to discard unimportant predictors, narrowing our focus to roughly 100 important SNPs. A cross-validation LASSO is then used to further select variables. We combine these procedures to guarantee that cross-validation can be used to choose a shrinkage parameter for the LASSO. If the clinical variables were unavailable, this prefiltering step would be essential. We perform the SNP-based analyses simultaneously rather than one at a time to estimate SNP effects in the presence of other causal variants. We analyzed the first simulated replicate of Genetic Analysis Workshop 17 without knowledge of the true model. Post-conference knowledge of the simulation parameters allowed us to investigate the limitations of our approach. We found that many of the false positives we identified were substantially correlated with genuine causal SNPs.

Clinical and pathological tools for identifying microsatellite instability in colorectal cancer

PubMed Central

Krivokapić, Zoran; Marković, Srdjan; Antić, Jadranka; Dimitrijević, Ivan; Bojić, Daniela; Svorcan, Petar; Jojić, Njegica; Damjanović, Svetozar

2012-01-01

Aim To assess practical accuracy of revised Bethesda criteria (BGrev), pathological predictive model (MsPath), and histopathological parameters for detection of high-frequency of microsatellite instability (MSI-H) phenotype in patients with colorectal carcinoma (CRC). Method Tumors from 150 patients with CRC were analyzed for MSI using a fluorescence-based pentaplex polymerase chain reaction technique. For all patients, we evaluated age, sex, family history of cancer, localization, tumor differentiation, mucin production, lymphocytic infiltration (TIL), and Union for International Cancer Control stage. Patients were classified according to the BGrev, and the groups were compared. The utility of the BGrev, MsPath, and clinical and histopathological parameters for predicting microsatellite tumor status were assessed by univariate logistic regression analysis and by calculating the sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values. Results Fifteen out of 45 patients who met and 4 of 105 patients who did not meet the BGrev criteria had MSI-H CRC. Sensitivity, specificity, PPV, and NPV for BGrev were 78.9%, 77%, 30%, and 70%, respectively. MSI histology (the third BGrev criterion without age limit) was as sensitive as BGrev, but more specific. MsPath model was more sensitive than BGrev (86%), with similar specificity. Any BGrev criterion fulfillment, mucinous differentiation, and right-sided CRC were singled out as independent factors to identify MSI-H colorectal cancer. Conclusion The BGrev, MsPath model, and MSI histology are useful tools for selecting patients for MSI testing. PMID:22911525

SU-F-R-53: CT-Based Radiomics Analysis of Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huynh, E; Coroller, T; Narayan, V

Purpose: Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable non-small cell lung cancer (NSCLC) patients and has demonstrated excellent local control and survival. However, some patients still develop distant metastases and local recurrence, and therefore, there is a clinical need to identify patients at high-risk of disease recurrence. The aim of the current study is to use a radiomics approach to identify imaging biomarkers, based on tumor phenotype, for clinical outcomes in SBRT patients. Methods: Radiomic features were extracted from free breathing computed tomography (CT) images of 113 Stage I-II NSCLC patients treated with SBRT.more » Their association to and prognostic performance for distant metastasis (DM), locoregional recurrence (LRR) and survival was assessed and compared with conventional features (tumor volume and diameter) and clinical parameters (e.g. performance status, overall stage). The prognostic performance was evaluated using the concordance index (CI). Multivariate model performance was evaluated using cross validation. All p-values were corrected for multiple testing using the false discovery rate. Results: Radiomic features were associated with DM (one feature), LRR (one feature) and survival (four features). Conventional features were only associated with survival and one clinical parameter was associated with LRR and survival. One radiomic feature was significantly prognostic for DM (CI=0.670, p<0.1 from random), while none of the conventional and clinical parameters were significant for DM. The multivariate radiomic model had a higher median CI (0.671) for DM than the conventional (0.618) and clinical models (0.617). Conclusion: Radiomic features have potential to be imaging biomarkers for clinical outcomes that conventional imaging metrics and clinical parameters cannot predict in SBRT patients, such as distant metastasis. Development of a radiomics biomarker that can identify patients at high-risk of recurrence could facilitate personalization of their treatment regimen for an optimized clinical outcome. R.M. had consulting interest with Amgen (ended in 2015).« less

Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy.

PubMed

Fan, Jiawei; Wang, Jiazhou; Zhang, Zhen; Hu, Weigang

2017-06-01

To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle 3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy. © 2017 American Association of Physicists in Medicine.

Performance of a block detector PET scanner in imaging non-pure positron emitters—modelling and experimental validation with 124I

NASA Astrophysics Data System (ADS)

Robinson, S.; Julyan, P. J.; Hastings, D. L.; Zweit, J.

2004-12-01

The key performance measures of resolution, count rate, sensitivity and scatter fraction are predicted for a dedicated BGO block detector patient PET scanner (GE Advance) in 2D mode for imaging with the non-pure positron-emitting radionuclides 124I, 55Co, 61Cu, 62Cu, 64Cu and 76Br. Model calculations including parameters of the scanner, decay characteristics of the radionuclides and measured parameters in imaging the pure positron-emitter 18F are used to predict performance according to the National Electrical Manufacturers Association (NEMA) NU 2-1994 criteria. Predictions are tested with measurements made using 124I and show that, in comparison with 18F, resolution degrades by 1.2 mm radially and tangentially throughout the field-of-view (prediction: 1.2 mm), count-rate performance reduces considerably and in close accordance with calculations, sensitivity decreases to 23.4% of that with 18F (prediction: 22.9%) and measured scatter fraction increases from 10.0% to 14.5% (prediction: 14.7%). Model predictions are expected to be equally accurate for other radionuclides and may be extended to similar scanners. Although performance is worse with 124I than 18F, imaging is not precluded in 2D mode. The viability of 124I imaging and performance in a clinical context compared with 18F is illustrated with images of a patient with recurrent thyroid cancer acquired using both [124I]-sodium iodide and [18F]-2-fluoro-2-deoxyglucose.

Bayesian Weibull tree models for survival analysis of clinico-genomic data

PubMed Central

Clarke, Jennifer; West, Mike

2008-01-01

An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low- and high-risk categories. However, the need exists for models which examine how genomic predictors interact with existing clinical factors and provide personalized outcome predictions. We have developed clinico-genomic tree models for survival outcomes which use recursive partitioning to subdivide the current data set into homogeneous subgroups of patients, each with a specific Weibull survival distribution. These trees can provide personalized predictive distributions of the probability of survival for individuals of interest. Our strategy is to fit multiple models; within each model we adopt a prior on the Weibull scale parameter and update this prior via Empirical Bayes whenever the sample is split at a given node. The decision to split is based on a Bayes factor criterion. The resulting trees are weighted according to their relative likelihood values and predictions are made by averaging over models. In a pilot study of survival in advanced stage ovarian cancer we demonstrate that clinical and genomic data are complementary sources of information relevant to survival, and we use the exploratory nature of the trees to identify potential genomic biomarkers worthy of further study. PMID:18618012

Identifying predictive factors for long-term complications following button battery impactions: A case series and literature review.

PubMed

Eliason, Michael J; Melzer, Jonathan M; Winters, Jessica R; Gallagher, Thomas Q

2016-08-01

To complement a case series review of button battery impactions managed at our single military tertiary care center with a thorough literature review of laboratory research and clinical cases to develop a protocol to optimize patient care. Specifically, to identify predictive factors of long-term complications which can be used by the pediatric otolaryngologist to guide patient management after button battery impactions. A retrospective review of the Department of Defense's electronic medical record systems was conducted to identify patients with button battery ingestions and then characterize their treatment course. A thorough literature review complemented the lessons learned to identify potentially predictive clinical measures for long-term complications. Eight patients were identified as being treated for button battery impaction in the aerodigestive tract with two sustaining long-term complications. The median age of the patients treated was 33 months old and the median estimated time of impaction in the aerodigestive tract prior to removal was 10.5 h. Time of impaction, anatomic direction of the battery's negative pole, and identifying specific battery parameters were identified as factors that may be employed to predict sequelae. Based on case reviews, advancements in battery manufacturing, and laboratory research, there are distinct clinical factors that should be assessed at the time of initial therapy to guide follow-up management to minimize potential catastrophic sequelae of button battery ingestion. Published by Elsevier Ireland Ltd.

Clinical impact of quantitative left atrial vortex flow analysis in patients with atrial fibrillation: a comparison with invasive left atrial voltage mapping.

PubMed

Lee, Jung Myung; Hong, Geu-Ru; Pak, Hui-Nam; Shim, Chi Young; Houle, Helene; Vannan, Mani A; Kim, Minji; Chung, Namsik

2015-08-01

Recently, left atrial (LA) vortex flow analysis using contrast transesophageal echocardiography (TEE) has been shown to be feasible and has demonstrated significant differences in vortex flow morphology and pulsatility between normal subjects and patients with atrial fibrillation (AF). However, the relationship between LA vortex flow and electrophysiological properties and the clinical significance of LA vortex flow are unknown. The aims of this study were (1) to compare LA vortex flow parameters with LA voltage and (2) to assess the predictive value of LA vortex flow parameters for the recurrence of AF after radiofrequency catheter ablation (RFCA). Thirty-nine patients with symptomatic non-valvular AF underwent contrast TEE before undergoing RFCA for AF. Quantitative LA vortex flow parameters were analyzed by Omega flow (Siemens Medical Solution, Mountain View, CA, USA). The morphology and pulsatility of LA vortex flow were compared with electrophysiologic parameters that were measured invasively. Hemodynamic, electrophysiological, and vortex flow parameters were compared between patients with and without early recurrence of AF after RFCA. Morphologic parameters, including LA vortex depth, length, width, and sphericity index were not associated with LA voltage or hemodynamic parameters. The relative strength (RS), which represents the pulsatility power of LA, was positively correlated with LA voltage (R = 0.53, p = 0.01) and LA appendage flow velocity (R = 0.73, p < 0.001) and negatively correlated with LA volume index (R = -0.56, p < 0.001). Patients with recurrent AF after RFCA showed significantly lower RS (1.7 ± 0.2 vs 1.9 ± 0.4, p = 0.048) and LA voltage (0.9 ± 0.7 vs 1.7 ± 0.8, p = 0.004) than patients without AF recurrence. In the relatively small LA dimension group (LA volume index ≤ 33 ml/m(2)), RS was significantly lower (2.1 ± 0.3 vs 1.7 ± 0.1, p = 0.029) in patients with the recurrent AF. Quantitative LA vortex flow analysis, especially RS, correlated well with LA voltage. Decreased pulsatility strength in the LA was associated with recurrent AF. LA vortex may have incremental value in predicting the recurrence of AF.

Myocardial and Cardiocirculatory Reserve in Asymptomatic Aortic Stenosis and Preserved Ejection Fraction.

PubMed

Schulz, Olaf; Brala, Debora; Allison, Thomas G; Schimke, Ingolf

2015-07-01

Managing patients with asymptomatic severe aortic stenosis (AS) remains a major challenge. Myocardial as well as cardiocirculatory reserve have been hypothesized to predict outcome in patients with asymptomatic AS. A total of 48 patients (indexed aortic valve area 0.39 +/- 0.12 cm2/m2; ejection fraction (EF) 67 +/- 7%) underwent spiroergometry and dobutamine stress echocardiography. Death or valve surgery served as a combined endpoint for follow up. Thirty-seven patients reached the endpoint after a mean of 756 days (range: 100-2146 days). Age- and gender-corrected univariate Cox proportional analysis revealed the presence of mild obstructive lung disease, stroke work loss (SWL), end-systolic diameter index, and E/Flow propagation velocity as the best predictive clinical, valvular, cardiostructural, and left ventricular filling pressure parameters, respectively. After inclusion of these parameters into a baseline multivariable Cox proportional hazard model, SWL (HR 1.21 per rise of 1 unit, CI 1.08-1.35, p = 0.0005) and female gender (HR 3.37, CI 1.50-7.59, p = 0.0044) were independently predictive. Similarly, the best-performing myocardial parameter, EF after dobutamine, was independently predictive (HR 0.75 per 5 units, CI 0.57-0.99, p = 0.035) after inclusion. The best-performing exercise capacity parameter, Watt(max), was of borderline significance (HR 0.93 per 5 units, CI 0.86-1.00, p = 0.0505). For each parameter, cut-off values were determined by time-dependent receiver-operator characteristics. The Kaplan-Meier curves of the patients above versus below the cut-offs differed significantly for SWL (p = 0.001), Wattm (p = 0.001), and gender (p = 0.013). Besides SWL and female gender, the EF after dobutamine as well as highest exercise stress intensity reached are helpful in determining the prognosis of asymptomatic patients with moderate-severe AS.

A simple attention test in the acute phase of a major depressive episode is predictive of later functional remission.

PubMed

Cléry-Melin, Marie-Laure; Gorwood, Philip

2017-02-01

Functional recovery after a major depressive episode (MDE) requires both clinical remission and preservation of cognitive skills. As attentional deficit may persist after remission, leading to functional impairment, its role as a prognosis marker needs to be considered. Five hundred eight depressed outpatients (DSM-IV) were assessed at baseline for clinical symptoms (QIDS-SR), social functioning (Sheehan Disability Scale, SDS) and attention through the d2 test of attention and the trail making test, simple tests, respectively, requiring to quote or to interconnect relevant items. All patients were treated by agomelatine, and examined 6 to 8 weeks after baseline to assess clinical remission (QIDS-SR ≤ 5) and/or functional remission (SDS ≤ 6). At follow up, 154 patients (31%) were in clinical and functional remission. Shorter cumulative duration of prior depression, shorter present MDE, and two parameters of the d2 test of attention were predictive of such positive outcome, the number of omission mistakes (F1) being the only one still significantly predictive (P < .05) with a multivariate approach. F1 was unchanged after remission, patients with less than 11 mistakes had a 2.27 times increased chance to reach full remission, and a dose-response relationship was observed, with a regular increase of positive outcome for less mistakes. The number of omission mistakes (F1) of the d2 test of attention was a stable marker, being predictive of, and with a dose-effect for, clinical plus functional remission. It may constitute a specific marker of attentional deficit, involved in the resilience process that enables individuals to develop more adequate strategies to cope with everyday functional activities. © 2016 Wiley Periodicals, Inc.

Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Features

PubMed Central

Tamayo, Pablo; Cho, Yoon-Jae; Tsherniak, Aviad; Greulich, Heidi; Ambrogio, Lauren; Schouten-van Meeteren, Netteke; Zhou, Tianni; Buxton, Allen; Kool, Marcel; Meyerson, Matthew; Pomeroy, Scott L.; Mesirov, Jill P.

2011-01-01

Purpose Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis. Patients and Methods A Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression–derived features representing disease subtype–independent (model B) and disease subtype–dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78). Results On an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse. Conclusion The proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification. PMID:21357789

Changes in multimodality functional imaging parameters early during chemoradiation predict treatment response in patients with locally advanced head and neck cancer.

PubMed

Wong, Kee H; Panek, Rafal; Dunlop, Alex; Mcquaid, Dualta; Riddell, Angela; Welsh, Liam C; Murray, Iain; Koh, Dow-Mu; Leach, Martin O; Bhide, Shreerang A; Nutting, Christopher M; Oyen, Wim J; Harrington, Kevin J; Newbold, Kate L

2018-05-01

To assess the optimal timing and predictive value of early intra-treatment changes in multimodality functional and molecular imaging (FMI) parameters as biomarkers for clinical remission in patients receiving chemoradiation for head and neck squamous cell carcinoma (HNSCC). Thirty-five patients with stage III-IVb (AJCC 7th edition) HNSCC prospectively underwent 18 F-FDG-PET/CT, and diffusion-weighted (DW), dynamic contrast-enhanced (DCE) and susceptibility-weighted MRI at baseline, week 1 and week 2 of chemoradiation. Patients with evidence of persistent or recurrent disease during follow-up were classed as non-responders. Changes in FMI parameters at week 1 and week 2 were compared between responders and non-responders with the Mann-Whitney U test. The significance threshold was set at a p value of <0.05. There were 27 responders and 8 non-responders. Responders showed a greater reduction in PET-derived tumor total lesion glycolysis (TLG 40% ; p = 0.007) and maximum standardized uptake value (SUV max ; p = 0.034) after week 1 than non-responders but these differences were absent by week 2. In contrast, it was not until week 2 that MRI-derived parameters were able to discriminate between the two groups: larger fractional increases in primary tumor apparent diffusion coefficient (ADC; p < 0.001), volume transfer constant (K trans ; p = 0.012) and interstitial space volume fraction (V e ; p = 0.047) were observed in responders versus non-responders. ADC was the most powerful predictor (∆ >17%, AUC 0.937). Early intra-treatment changes in FDG-PET, DW and DCE MRI-derived parameters are predictive of ultimate response to chemoradiation in HNSCC. However, the optimal timing for assessment with FDG-PET parameters (week 1) differed from MRI parameters (week 2). This highlighted the importance of scanning time points for the design of FMI risk-stratified interventional studies.

Numerical simulations for tumor and cellular immune system interactions in lung cancer treatment

NASA Astrophysics Data System (ADS)

Kolev, M.; Nawrocki, S.; Zubik-Kowal, B.

2013-06-01

We investigate a new mathematical model that describes lung cancer regression in patients treated by chemotherapy and radiotherapy. The model is composed of nonlinear integro-differential equations derived from the so-called kinetic theory for active particles and a new sink function is investigated according to clinical data from carcinoma planoepitheliale. The model equations are solved numerically and the data are utilized in order to find their unknown parameters. The results of the numerical experiments show a good correlation between the predicted and clinical data and illustrate that the mathematical model has potential to describe lung cancer regression.

Prediction of clinically relevant hyperkalemia in patients treated with peptide receptor radionuclide therapy

PubMed Central

2014-01-01

Background Peptide receptor radionuclide therapy (PRRT) is applied in patients with advanced neuroendocrine tumors. Co-infused amino acids (AA) should prevent nephrotoxicity. The aims of this study were to correlate the incidence of AA-induced hyperkalemia (HK) (≥5.0 mmol/l) and to identify predictors of AA-induced severe HK (>6.0). Methods In 38 patients, standard activity of 177Lu-labelled somatostatin analogs was administered. Pre-therapeutic kidney function was assessed by renal scintigraphy and laboratory tests. For kidney protection, AA was co-infused. Biochemical parameters (potassium, glomerular filtration rate, creatinine, blood urea nitrogen (BUN), sodium, phosphate, chloride, and lactate dehydrogenase (LDH)) were obtained prior to 4 and 24 h after the AA infusion. Incidence of HK (≥5.0) was correlated with pre-therapeutic kidney function and serum parameters. Formulas for the prediction of severe hyperkalemia (>6.0) were computed and prospectively validated. Results At 4 h, HK (≥5.0) was present in 94.7% with severe HK (>6.0) in 36.1%. Values normalized after 24 h in 84.2%. Pre-therapeutic kidney function did not correlate with the incidence of severe HK. Increases in K+ were significantly correlated with decreases in phosphate (r = −0.444, p < 0.005) and increases in BUN (r = 0.313, p = 0.056). A baseline BUN of >28 mg/dl had a sensitivity of 84.6% and a specificity of 60.0% (AUC = 0.75) in predicting severe HK of >6.0 (phosphate, AUC = 0.37). Computing of five standard serum parameters (potassium, BUN, sodium, phosphate, LDH) resulted in a sensitivity of 88.9% and a specificity of 79.3% for the prediction of severe HK >6.0 (accuracy = 81.6%). Conclusions A combination of serum parameters predicted prospectively the occurrence of relevant HK with an accuracy of 81.6% underlining its potential utility for identifying ‘high-risk’ patients prone to PRRT. PMID:25977880

Bond Graph Model of Cerebral Circulation: Toward Clinically Feasible Systemic Blood Flow Simulations.

PubMed

Safaei, Soroush; Blanco, Pablo J; Müller, Lucas O; Hellevik, Leif R; Hunter, Peter J

2018-01-01

We propose a detailed CellML model of the human cerebral circulation that runs faster than real time on a desktop computer and is designed for use in clinical settings when the speed of response is important. A lumped parameter mathematical model, which is based on a one-dimensional formulation of the flow of an incompressible fluid in distensible vessels, is constructed using a bond graph formulation to ensure mass conservation and energy conservation. The model includes arterial vessels with geometric and anatomical data based on the ADAN circulation model. The peripheral beds are represented by lumped parameter compartments. We compare the hemodynamics predicted by the bond graph formulation of the cerebral circulation with that given by a classical one-dimensional Navier-Stokes model working on top of the whole-body ADAN model. Outputs from the bond graph model, including the pressure and flow signatures and blood volumes, are compared with physiological data.

A cross-sectional analysis of daytime versus nocturnal polysomnographic respiratory parameters in cystic fibrosis during early adolescence.

PubMed

Waters, K A; Lowe, A; Cooper, P; Vella, S; Selvadurai, Hiran

2017-03-01

In Cystic Fibrosis (CF), early detection and treatment of respiratory disease is considered the standard for respiratory care. Overnight polysomnography (PSG) may help identify respiratory deterioration in young patients with CF. A prospective cohort study of 46 patients with CF, aged 8-12years, from a specialist clinic in a tertiary paediatric hospital. Daytime pulmonary function, shuttle test exercise testing and overnight PSG were studied. Of 81 children aged 8-12years, 46 (57%) agreed to participate. FEV 1 (% predicted, mean 74.6%) was normal in 23 (50%), mildly abnormal in 12 (26.1%), moderately abnormal in 10 (21.7%) and severely abnormal in 1 (2.2%). Amongst sleep study parameters, FEV 1 (% predicted) showed significant correlation with the respiratory rate (RR) in slow wave sleep (SWS), CO 2 change in REM, baseline SaO 2 , and the arousal index (h -1 ). Backward, stepwise linear regression modelling for FEV 1 (% predicted) included the entire group with a wide spectrum of clinical severity. From sleep, variables remaining in the multivariate model for FEV 1 (F=16.81, p<0.001) were the RR in SWS (min -1 ) and the CO 2 change in REM (p=0.003, and 0.014, respectively). When daytime tests were included, the variables remaining were RR in SWS and SD score for BMI (BMIsds) (F=18.70, p<0.001). Respiratory abnormalities on overnight sleep studies included elevated respiratory rates during SWS and mild CO 2 retention in REM sleep, and these incorporated into a model correlating with FEV 1 (% predicted). Thus, mild mechanical impairment of ventilation is evident on overnight sleep studies in children with cystic fibrosis although the significance of this finding will require further investigation. Copyright © 2016 European Cystic Fibrosis Society. All rights reserved.

An integrated approach to patient-specific predictive modeling for single ventricle heart palliation.

PubMed

Corsini, Chiara; Baker, Catriona; Kung, Ethan; Schievano, Silvia; Arbia, Gregory; Baretta, Alessia; Biglino, Giovanni; Migliavacca, Francesco; Dubini, Gabriele; Pennati, Giancarlo; Marsden, Alison; Vignon-Clementel, Irene; Taylor, Andrew; Hsia, Tain-Yen; Dorfman, Adam

2014-01-01

In patients with congenital heart disease and a single ventricle (SV), ventricular support of the circulation is inadequate, and staged palliative surgery (usually 3 stages) is needed for treatment. In the various palliative surgical stages individual differences in the circulation are important and patient-specific surgical planning is ideal. In this study, an integrated approach between clinicians and engineers has been developed, based on patient-specific multi-scale models, and is here applied to predict stage 2 surgical outcomes. This approach involves four distinct steps: (1) collection of pre-operative clinical data from a patient presenting for SV palliation, (2) construction of the pre-operative model, (3) creation of feasible virtual surgical options which couple a three-dimensional model of the surgical anatomy with a lumped parameter model (LPM) of the remainder of the circulation and (4) performance of post-operative simulations to aid clinical decision making. The pre-operative model is described, agreeing well with clinical flow tracings and mean pressures. Two surgical options (bi-directional Glenn and hemi-Fontan operations) are virtually performed and coupled to the pre-operative LPM, with the hemodynamics of both options reported. Results are validated against postoperative clinical data. Ultimately, this work represents the first patient-specific predictive modeling of stage 2 palliation using virtual surgery and closed-loop multi-scale modeling.

Pharmacogenetic versus clinical dosing of warfarin in individuals of Chinese and African-American ancestry: assessment using data simulation.

PubMed

Syn, Nicholas L X; Lee, Soo-Chin; Brunham, Liam R; Goh, Boon-Cher

2015-10-01

Clinical trials of genotype-guided dosing of warfarin have yielded mixed results, which may in part reflect ethnic differences among study participants. However, no previous study has compared genotype-guided versus clinically guided or standard-of-care dosing in a Chinese population, whereas those involving African-Americans were underpowered to detect significant differences. We present a preclinical strategy that integrates pharmacogenetics (PG) and pharmacometrics to predict the outcome or guide the design of dosing strategies for drugs that show large interindividual variability. We use the example of warfarin and focus on two underrepresented groups in warfarin research. We identified the parameters required to simulate a patient population and the outcome of dosing strategies. PG and pharmacogenetic plus loading (PG+L) algorithms that take into account a patient's VKORC1 and CYP2C9 genotype status were considered and compared against a clinical (CA) algorithm for a simulated Chinese population using a predictive Monte Carlo and pharmacokinetic-pharmacodynamic framework. We also examined a simulated population of African-American ancestry to assess the robustness of the model in relation to real-world clinical trial data. The simulations replicated similar trends observed with clinical data in African-Americans. They further predict that the PG+L regimen is superior to both the CA and the PG regimen in maximizing percentage time in therapeutic range in a Chinese cohort, whereas the CA regimen poses the highest risk of overanticoagulation during warfarin initiation. The findings supplement the literature with an unbiased comparison of warfarin dosing algorithms and highlights interethnic differences in anticoagulation control.

Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

PubMed

Zanderigo, Francesca; Sparacino, Giovanni; Kovatchev, Boris; Cobelli, Claudio

2007-09-01

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data. We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference. Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges. For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.

The relationship between dental implant stability and trabecular bone structure using cone-beam computed tomography

PubMed Central

2016-01-01

Purpose The objective of this study was to investigate the relationships between primary implant stability as measured by impact response frequency and the structural parameters of trabecular bone using cone-beam computed tomography(CBCT), excluding the effect of cortical bone thickness. Methods We measured the impact response of a dental implant placed into swine bone specimens composed of only trabecular bone without the cortical bone layer using an inductive sensor. The peak frequency of the impact response spectrum was determined as an implant stability criterion (SPF). The 3D microstructural parameters were calculated from CT images of the bone specimens obtained using both micro-CT and CBCT. Results SPF had significant positive correlations with trabecular bone structural parameters (BV/TV, BV, BS, BSD, Tb.Th, Tb.N, FD, and BS/BV) (P<0.01) while SPF demonstrated significant negative correlations with other microstructural parameters (Tb.Sp, Tb.Pf, and SMI) using micro-CT and CBCT (P<0.01). Conclusions There was an increase in implant stability prediction by combining BV/TV and SMI in the stepwise forward regression analysis. Bone with high volume density and low surface density shows high implant stability. Well-connected thick bone with small marrow spaces also shows high implant stability. The combination of bone density and architectural parameters measured using CBCT can predict the implant stability more accurately than the density alone in clinical diagnoses. PMID:27127692

A biomechanical model for fibril recruitment: Evaluation in tendons and arteries.

PubMed

Bevan, Tim; Merabet, Nadege; Hornsby, Jack; Watton, Paul N; Thompson, Mark S

2018-06-06

Simulations of soft tissue mechanobiological behaviour are increasingly important for clinical prediction of aneurysm, tendinopathy and other disorders. Mechanical behaviour at low stretches is governed by fibril straightening, transitioning into load-bearing at recruitment stretch, resulting in a tissue stiffening effect. Previous investigations have suggested theoretical relationships between stress-stretch measurements and recruitment probability density function (PDF) but not derived these rigorously nor evaluated these experimentally. Other work has proposed image-based methods for measurement of recruitment but made use of arbitrary fibril critical straightness parameters. The aim of this work was to provide a sound theoretical basis for estimating recruitment PDF from stress-stretch measurements and to evaluate this relationship using image-based methods, clearly motivating the choice of fibril critical straightness parameter in rat tail tendon and porcine artery. Rigorous derivation showed that the recruitment PDF may be estimated from the second stretch derivative of the first Piola-Kirchoff tissue stress. Image-based fibril recruitment identified the fibril straightness parameter that maximised Pearson correlation coefficients (PCC) with estimated PDFs. Using these critical straightness parameters the new method for estimating recruitment PDF showed a PCC with image-based measures of 0.915 and 0.933 for tendons and arteries respectively. This method may be used for accurate estimation of fibril recruitment PDF in mechanobiological simulation where fibril-level mechanical parameters are important for predicting cell behaviour. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of thrombus-related mechanical prosthetic valve dysfunction using transesophageal echocardiography

NASA Technical Reports Server (NTRS)

Lin, S. S.; Tiong, I. Y.; Asher, C. R.; Murphy, M. T.; Thomas, J. D.; Griffin, B. P.

2000-01-01

Identification of thrombus-related mechanical prosthetic valve dysfunction (MPVD) has important therapeutic implications. We sought to develop an algorithm, combining clinical and echocardiographic parameters, for prediction of thrombus-related MPVD in a series of 53 patients (24 men, age 52 +/- 16 years) who had intraoperative diagnosis of thrombus or pannus from 1992 to 1997. Clinical and echocardiographic parameters were analyzed to identify predictors of thrombus and pannus. Prevalence of thrombus and diagnostic yields relative to the number of predictors were determined. There were 22 patients with thrombus, 19 patients with pannus, and 12 patients with both. Forty-two of 53 masses were visualized using transesophageal echocardiography (TEE), including 29 of 34 thrombi or both thrombi and panni and 13 of 19 isolated panni. Predictors of thrombus or mixed presentation include mobile mass (p = 0.009), attachment to occluder (p = 0.02), elevated gradients (p = 0.04), and an international normalized ratio of < or = 2.5 (p = 0.03). All 34 patients with thrombus or mixed presentation had > or = 1 predictor. The prevalence of thrombus in the presence of < or = 1, 2, and > or = 3 predictors is 14%, 69%, and 91%, respectively. Thus, TEE is sensitive in the identification of abnormal mass in the setting of MPVD. An algorithm based on clinical and transesophageal echocardiographic predictors may be useful to estimate the likelihood of thrombus in the setting of MPVD. In the presence of > or = 3 predictors, the probability of thrombus is high.

Motor impairment predicts falls in specialized Alzheimer care units.

PubMed

Camicioli, Richard; Licis, Lisa

2004-01-01

We sought to identify clinical risk factors for falls in people with advanced Alzheimer disease (AD) in a prospective longitudinal observational study set in specialized AD care units. Forty-two patients with probable or possible AD were recruited. Age, sex, Mini-Mental Status Examination, Clinical Dementia Rating Scale, Neuropsychiatric Inventory/Nursing Home, Morse Fall Scale (MFS), modified Unified Parkinson's Rating Scale (mUPDRS), and gait parameters using a GAITRite Gold Walkway System with and without dual-task performance were examined. Time to a first fall was the primary outcome measure, and independent risk factors were identified. Participating subjects were old (non-fallers age, 82.3 +/- 6.7 years; fallers: 83.1 +/- 9.6 years; p = 0.76) and predominantly women (36 female/6 male). Fallers did not differ from non-fallers on any parameter except the MFS (non-fallers: 35.6 +/- 26.1; fallers: 54.4 +/- 29.8; p = 0.04), the UPDRS (non-fallers: 4.75 +/- 3.98; fallers: 7.61 +/- 4.3, p = 0.03) and cadence (steps per minute: non-fallers: 102.3 +/- 12.3; fallers: 91.7 +/- 16, p = 0.02). Fallers and non-fallers were equally affected by dual-task performance. The hazard ratios for MFS, UPDRS, and cadence were not affected by adjusting for age, sex, MMSE, or NPI scores. In conclusion, falls in advanced AD can be predicted using simple clinical measures of motor impairment or cadence. These measures may be useful for targeting interventions.

Serum bilirubin: a simple routine surrogate marker of the progression of chronic kidney disease.

PubMed

Moolchandani, K; Priyadarssini, M; Rajappa, M; Parameswaran, S; Revathy, G

2016-10-01

Studies suggest that Chronic Kidney Disease (CKD) is a global burden health associated with significant comorbid conditions. Few biochemical parameters have gained significance in predicting the disease progression. The present work aimed to study the association of the simple biochemical parameter of serum bilirubin level with the estimated glomerular filtration rate (eGFR), and to assess their association with the co-morbid conditions in CKD. We recruited 188 patients with CKD who attended a Nephrology out-patient department. eGFR values were calculated based on the serum creatinine levels using CKD-EPI formula. Various biochemical parameters including glucose, creatinine, uric acid, total and direct bilirubin were assayed in all study subjects. Study subjects were categorized into subgroups based on their eGFR values and their diabetic status and the parameters were compared among the different subgroups. We observed a significantly decreased serum bilirubin levels (p < 0.001) in patients with lower eGFR values, compared to those with higher eGFR levels. There was a significant positive correlation between the eGFR levels and the total bilirubin levels (r = 0.92). We also observed a significant positive correlation between the eGFR levels and the direct bilirubin levels (r = 0.76). On multivariate linear regression analysis, we found that total and direct bilirubin independently predict eGFR, after adjusting for potential confounders (p < 0.001). Our results suggest that there is significant hypobilirubinemia in CKD, especially with increasing severity and co-existing diabetes mellitus. This finding has importance in the clinical setting, as assay of simple routine biochemical parameters such as serum bilirubin may help in predicting the early progression of CKD and more so in diabetic CKD.

FSI simulation of CSF hydrodynamic changes in a large population of non-communicating hydrocephalus patients during treatment process with regard to their clinical symptoms

PubMed Central

2018-01-01

3D fluid-structure interaction modelling was utilized for simulation of 13 normal subjects, 11 non-communicating hydrocephalus (NCH) patients at pre-treatment phase, and 3 patients at five post-treatment phases. Evaluation of ventricles volume and maximum CSF pressure (before shunting) following results validation indicated that these parameters were the most proper hydrodynamic indices and the NCH type doesn’t have any significant effect on changes in two indices. The results confirmed an appropriate correlation between these indices although the correlation decreased slightly after the occurrence of disease. NCH raises the intensity of vortex and pulsatility (2.4 times) of CSF flow while the flow remains laminar. On day 18 after shunting, the CSF pressure decreased 81.0% and all clinical symptoms of patients vanished except for headache. Continuing this investigation during the treatment process showed that maximum CSF pressure is the most sensitive parameter to patients’ clinical symptoms. Maximum CSF pressure has decreased proportional to the level of decrease in clinical symptoms and has returned close to the pressure range in normal subjects faster than other parameters and simultaneous with disappearance of patients’ clinical symptoms (from day 81 after shunting). However, phase lag between flow rate and pressure gradient functions and the degree of CSF pulsatility haven’t returned to normal subjects’ conditions even 981 days after shunting and NCH has also caused a permanent volume change (of 20.1%) in ventricles. Therefore, patients have experienced a new healthy state in new hydrodynamic conditions after shunting and healing. Increase in patients’ intracranial compliance was predicted with a more accurate non-invasive method than previous experimental methods up to more than 981 days after shunting. The changes in hydrodynamic parameters along with clinical reports of patients can help to gain more insight into the pathophysiology of NCH patients. PMID:29708982

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

PubMed Central

Cheng, Ningtao; Wu, Leihong; Cheng, Yiyu

2013-01-01

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability. PMID:23861920

Predictive Value of CTA Spot Sign on Hematoma Expansion in Intracerebral Hemorrhage Patients

PubMed Central

Peng, Wen-Jie; Reis, Cesar; Reis, Haley

2017-01-01

Hematoma expansion (HE) occurs in approximately one-third of patients with intracerebral hemorrhage and leads to high rates of mortality and morbidity. Currently, contrast extravasation within hematoma, termed the spot sign on computed tomography angiography (CTA), has been identified as a strong independent predictor of early hematoma expansion. Past studies indicate that the spot sign is a dynamic entity and is indicative of active hemorrhage. Furthermore, to enhance the spot sign's accuracy of predicting HE, spot parameters observed on CTA or dynamic CTA were used for its quantification. In addition, spot signs detected on multiphase CTA and dynamic CTA are shown to have higher sensitivity and specificity when compared with simple standardized spot sign detection in recent studies. Based on the spot sign, novel methods such as leakage sign and rate of contrast extravasation were explored to redefine HE prediction in combination with clinical characteristics and spot sign on CTA to assist clinical judgment. The spot sign is an accepted independent predictor of active hemorrhage and is used in both secondary intracerebral hemorrhage and the process of surgical assessment for hemorrhagic risk in patients with ischemic stroke. Spot sign predicts patients at high risk for hematoma expansion. PMID:28852647

Predictive Value of CTA Spot Sign on Hematoma Expansion in Intracerebral Hemorrhage Patients.

PubMed

Peng, Wen-Jie; Reis, Cesar; Reis, Haley; Zhang, John; Yang, Jun

2017-01-01

Hematoma expansion (HE) occurs in approximately one-third of patients with intracerebral hemorrhage and leads to high rates of mortality and morbidity. Currently, contrast extravasation within hematoma, termed the spot sign on computed tomography angiography (CTA), has been identified as a strong independent predictor of early hematoma expansion. Past studies indicate that the spot sign is a dynamic entity and is indicative of active hemorrhage. Furthermore, to enhance the spot sign's accuracy of predicting HE, spot parameters observed on CTA or dynamic CTA were used for its quantification. In addition, spot signs detected on multiphase CTA and dynamic CTA are shown to have higher sensitivity and specificity when compared with simple standardized spot sign detection in recent studies. Based on the spot sign, novel methods such as leakage sign and rate of contrast extravasation were explored to redefine HE prediction in combination with clinical characteristics and spot sign on CTA to assist clinical judgment. The spot sign is an accepted independent predictor of active hemorrhage and is used in both secondary intracerebral hemorrhage and the process of surgical assessment for hemorrhagic risk in patients with ischemic stroke. Spot sign predicts patients at high risk for hematoma expansion.

Perioprosthetic and Implant-Supported Rehabilitation of Complex Cases: Clinical Management and Timing Strategy.

PubMed

Landi, Luca; Piccinelli, Stefano; Raia, Roberto; Marinotti, Fabio; Manicone, Paolo Francesco

2016-01-01

Treatment of complex perioprosthetic cases is one of the clinical challenges of everyday practice. Only a complete and thorough diagnostic setup may allow the clinician to formulate a realistic prognosis to select the abutments to support prosthetic rehabilitation. Clinical, radiographic, or laboratory parameters used separately are useless to correctly assign a reliable prognosis to single teeth except in the case of a clearly hopeless tooth. Therefore, it is crucial to gather the greatest quantity of data to determine the role that every single element can play in the prosthetic rehabilitation of the case. The following report deals with the management of a multidisciplinary periodontally compromised case in which a treatment strategy and chronology were designed to reach clinical predictability while reducing the duration of the therapy.

Clinical and cytological features predictive of malignancy in thyroid follicular neoplasms.

PubMed

Lubitz, Carrie C; Faquin, William C; Yang, Jingyun; Mekel, Michal; Gaz, Randall D; Parangi, Sareh; Randolph, Gregory W; Hodin, Richard A; Stephen, Antonia E

2010-01-01

The preoperative diagnosis of malignancy in nodules suspicious for a follicular neoplasm remains challenging. A number of clinical and cytological parameters have been previously studied; however, none have significantly impacted clinical practice. The aim of this study was to determine predictive characteristics of follicular neoplasms useful for clinical application. Four clinical (age, sex, nodule size, solitary nodule) and 17 cytological variables were retrospectively reviewed for 144 patients with a nodule suspicious for follicular neoplasm, diagnosed preoperatively by fine-needle aspiration (FNA), from a single institution over a 2-year period (January 2006 to December 2007). The FNAs were examined by a single, blinded pathologist and compared with final surgical pathology. Significance of clinical and cytological variables was determined by univariate analysis and backward stepwise logistic regression. Odds ratios (ORs) for malignancy, a receiver operating characteristic curve, and predicted probabilities of combined features were determined. There was an 11% incidence of malignancy (16/144). On univariate analysis, nodule size >OR=4.0 cm nears significance (p = 0.054) and 9 of 17 cytological features examined were significantly associated with malignancy. Three variables stay in the final model after performing backward stepwise selection in logistic regression: nodule size (OR = 0.25, p = 0.05), presence of a transgressing vessel (OR = 23, p < 0.0001), and nuclear grooves (OR = 4.3, p = 0.03). The predicted probability of malignancy was 88.4% with the presence of all three variables on preoperative FNA. When the two papillary carcinomas were excluded from the analysis, the presence of nuclear grooves was no longer significant, and anisokaryosis (OR = 12.74, p = 0.005) and presence of nucleolus (OR = 0.11, p = 0.04) were significantly associated with malignancy. Excluding the two papillary thyroid carcinomas, a nodule size >or=4 cm, with a transgressing vessel and anisokaryosis and lacking a nucleolus, has a predicted probability of malignancy of 96.5%. A combination of larger nodule size, transgressing vessels, and specific nuclear features are predictive of malignancy in patients with follicular neoplasms. These findings enhance our current limited predictive armamentarium and can be used to guide surgical decision making. Further study may result in the inclusion of these variables to the systematic evaluation of follicular neoplasms.

Antiangiogenic Therapy for Glioblastoma: Current Status and Future Prospects

PubMed Central

Batchelor, Tracy T.; Reardon, David A.; de Groot, John F.; Wick, Wolfgang; Weller, Michael

2014-01-01

Glioblastoma is characterized by high expression levels of pro-angiogenic cytokines and microvascular proliferation, highlighting the potential value of treatments targeting angiogenesis. Antiangiogenic treatment likely achieves a beneficial impact through multiple mechanisms of action. Ultimately, however, alternative pro-angiogenic signal transduction pathways are activated leading to the development of resistance, even in tumors that initially respond. The identification of biomarkers or imaging parameters to predict response and to herald resistance is of high priority. Despite promising phase 2 clinical trial results and patient benefit in terms of clinical improvement and longer progression-free survival, an overall survival benefit has not been demonstrated in 4 randomized phase 3 trials of bevacizumab or cilengitide in newly diagnosed glioblastoma or cediranib or enzastaurin recurrent glioblastoma. However, future studies are warranted: predictive markers may allow appropriate patient enrichment, combination with chemotherapy may ultimately prove successful in improving overall survival, and novel agents targeting multiple pro-angiogenic pathways may prove effective. PMID:25398844

Clinical evaluation of the efficacy of a GTR membrane (HEALIGUIDE) and demineralised bone matrix (OSSEOGRAFT) as a space maintainer in the treatment of Miller's Class I gingival recession.

PubMed

Nanditha, S; Priya, M S; Sabitha, S; Arun, K V; Avaneendra, T

2011-04-01

Periodontal plastic surgical procedures aimed at coverage of exposed root surface have evolved into routine treatment modalities. The present study was designed to evaluate the effectiveness and predictability of using a collagen barrier along with a demineralized bone matrix in the treatment of recession defects in a single surgical procedure. Seventeen patients with Miller's class I recession were treated with a combination of a collagen barrier used along with a bone graft and coronally advanced flap technique. Clinical parameters were recorded at baseline, 3 months, 6 months, and 9 months. The study showed a highly significant reduction in the recession depth (70.29 ± 21.96%) at the end of the study. This study showed that the use of this technique for recession coverage is highly predictable and highly esthetic root coverage can be obtained.

Decreased expression of IDH1-R132H correlates with poor survival in gastrointestinal cancer.

PubMed

Li, Jieying; Huang, Jianfei; Huang, Fang; Jin, Qing; Zhu, Huijun; Wang, Xudong; Chen, Meng

2016-11-08

Isocitrate dehydrogenase (IDH1) is an NADP-dependent enzyme that catalyzes the decarboxylation of isocitrate to alpha-ketoglutarate. The IDH1-R132H mutation predicts a better clinical outcome for glioma patients, and the expression of IDH1-R132H correlates with a favorable outcome in patients with brain tumors. Here, we investigated IDH1-R132H expression in both gastric (n=526) and colorectal (n=399) tissues by performing immunohistochemistry analyses on tissue microarrays. We also tested whether IDH1-R132H expression correlated with various clinical parameters. In both gastric and colorectal cancer, expression of IDH1-R132H was associated with tumor stage. Patients with low IDH1-R132H expression had a poor overall survival. Our data indicate that IDH1-R132H expression could be used as a predictive marker of prognosis for patients with gastrointestinal cancer.

Decreased expression of IDH1-R132H correlates with poor survival in gastrointestinal cancer

PubMed Central

Li, Jieying; Huang, Jianfei; Huang, Fang; Jin, Qing; Zhu, Huijun; Wang, Xudong; Chen, Meng

2016-01-01

Isocitrate dehydrogenase (IDH1) is an NADP-dependent enzyme that catalyzes the decarboxylation of isocitrate to alpha-ketoglutarate. The IDH1-R132H mutation predicts a better clinical outcome for glioma patients, and the expression of IDH1-R132H correlates with a favorable outcome in patients with brain tumors. Here, we investigated IDH1-R132H expression in both gastric (n=526) and colorectal (n=399) tissues by performing immunohistochemistry analyses on tissue microarrays. We also tested whether IDH1-R132H expression correlated with various clinical parameters. In both gastric and colorectal cancer, expression of IDH1-R132H was associated with tumor stage. Patients with low IDH1-R132H expression had a poor overall survival. Our data indicate that IDH1-R132H expression could be used as a predictive marker of prognosis for patients with gastrointestinal cancer. PMID:27655638

Adaptive control of bivalirudin in the cardiac intensive care unit.

PubMed

Zhao, Qi; Edrich, Thomas; Paschalidis, Ioannis Ch

2015-02-01

Bivalirudin is a direct thrombin inhibitor used in the cardiac intensive care unit when heparin is contraindicated due to heparin-induced thrombocytopenia. Since it is not a commonly used drug, clinical experience with its dosing is sparse. In earlier work [1], we developed a dynamic system model that accurately predicts the effect of bivalirudin given dosage over time and patient physiological characteristics. This paper develops adaptive dosage controllers that regulate its effect to desired levels. To that end, and in the case that bivalirudin model parameters are available, we develop a Model Reference Control law. In the case that model parameters are unknown, an indirect Model Reference Adaptive Control scheme is applied to estimate model parameters first and then adapt the controller. Alternatively, direct Model Reference Adaptive Control is applied to adapt the controller directly without estimating model parameters first. Our algorithms are validated using actual patient data from a large hospital in the Boston area.

Serum transferrin as a prognostic indicator of spontaneous closure and mortality in gastrointestinal cutaneous fistulas.

PubMed Central

Kuvshinoff, B W; Brodish, R J; McFadden, D W; Fischer, J E

1993-01-01

OBJECTIVE: This study determined whether there are any laboratory or other features that will enable prediction of spontaneous closure in patients with gastrointestinal cutaneous fistulas. SUMMARY BACKGROUND DATA: Although the anatomic criteria for spontaneous closure of gastrointestinal cutaneous fistulas have been presented by several authors, less than 50% of such fistulas tend to close, even in the most recent series. METHODS: A group of patients with gastrointestinal cutaneous fistulas with anatomical features favorable to study were investigated with respect to a series of parameters including the usual demographic parameters, plus fistula output, number of blood transfusions, presence of sepsis, as well as metabolic parameters including serum transferrin, retinol-binding protein, thyroxin-binding prealbumin, and serum albumin. RESULTS: Of 79 patients with 116 fistulas, 16 (20.3%) died. Causes of death were uncontrolled sepsis in eight patients and cancer in five patients. Postoperative fistulas constituted 80% of the group. The presence of local sepsis, systemic sepsis, remote sepsis (such as pneumonia or line sepsis), the number of fistulas, fistula output, and the number of blood transfusions were not predictive of spontaneous closure, whereas serum transferrin was predictive of spontaneous closure. Serum transferrin, retinol-binding protein, and thyroxin-binding prealbumin were predictive of mortality. CONCLUSIONS: Serum transferrin does not appear to be an entirely independent variable, but seems to identify those patients with significant remote sepsis, systemic sepsis, and neoplasia in whom these processes are clinically significant. The results, if confirmed, and provided that nutritional needs are met, suggest that short-turnover proteins, particularly serum transferrin, might be useful in predicting which patients with gastrointestinal cutaneous fistulas should undergo surgery despite anatomic criteria favorable for spontaneous closure. PMID:8507110

Diagnostic accuracy of uriSed automated urine microscopic sediment analyzer and dipstick parameters in predicting urine culture test results.

PubMed

Huysal, Kağan; Budak, Yasemin U; Karaca, Ayse Ulusoy; Aydos, Murat; Kahvecioğlu, Serdar; Bulut, Mehtap; Polat, Murat

2013-01-01

Urinary tract infection (UTI) is one of the most common types of infection. Currently, diagnosis is primarily based on microbiologic culture, which is time- and labor-consuming. The aim of this study was to assess the diagnostic accuracy of urinalysis results from UriSed (77 Electronica, Budapest, Hungary), an automated microscopic image-based sediment analyzer, in predicting positive urine cultures. We examined a total of 384 urine specimens from hospitalized patients and outpatients attending our hospital on the same day for urinalysis, dipstick tests and semi-quantitative urine culture. The urinalysis results were compared with those of conventional semiquantitative urine culture. Of 384 urinary specimens, 68 were positive for bacteriuria by culture, and were thus considered true positives. Comparison of these results with those obtained from the UriSed analyzer indicated that the analyzer had a specificity of 91.1%, a sensitivity of 47.0%, a positive predictive value (PPV) of 53.3% (95% confidence interval (CI) = 40.8-65.3), and a negative predictive value (NPV) of 88.8% (95% CI = 85.0-91.8%). The accuracy was 83.3% when the urine leukocyte parameter was used, 76.8% when bacteriuria analysis of urinary sediment was used, and 85.1% when the bacteriuria and leukocyturia parameters were combined. The presence of nitrite was the best indicator of culture positivity (99.3% specificity) but had a negative likelihood ratio of 0.7, indicating that it was not a reliable clinical test. Although the specificity of the UriSed analyzer was within acceptable limits, the sensitivity value was low. Thus, UriSed urinalysis resuIts do not accurately predict the outcome of culture.

Predictors of survival in critically ill patients with acute respiratory distress syndrome (ARDS): an observational study.

PubMed

Balzer, Felix; Menk, Mario; Ziegler, Jannis; Pille, Christian; Wernecke, Klaus-Dieter; Spies, Claudia; Schmidt, Maren; Weber-Carstens, Steffen; Deja, Maria

2016-11-08

Currently there is no ARDS definition or classification system that allows optimal prediction of mortality in ARDS patients. This study aimed to examine the predictive values of the AECC and Berlin definitions, as well as clinical and respiratory parameters obtained at onset of ARDS and in the course of the first seven consecutive days. The observational study was conducted at a 14-bed intensive care unit specialized on treatment of ARDS. Predictive validity of the AECC and Berlin definitions as well as P a O 2 /F i O 2 and F i O 2 /P a O 2 *P mean (oxygenation index) on mortality of ARDS patients was assessed and statistically compared. Four hundred forty two critically-ill patients admitted for ARDS were analysed. Multivariate Cox regression indicated that the oxygenation index was the most accurate parameter for mortality prediction. The third day after ARDS criteria were met at our hospital was found to represent the best compromise between earliness and accuracy of prognosis of mortality regarding the time of assessment. An oxygenation index of 15 or greater was associated with higher mortality, longer length of stay in ICU and hospital and longer duration of mechanical ventilation. In addition, non-survivors had a significantly longer length of stay and duration of mechanical ventilation in referring hospitals before admitted to the national reference centre than survivors. The oxygenation index is suggested to be the most suitable parameter to predict mortality in ARDS, preferably assessed on day 3 after admission to a specialized centre. Patients might benefit when transferred to specialized ICU centres as soon as possible for further treatment.

[Correlations of 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging Parameters with the Pathological Differentiation of Head and Neck Squamous Cell Carcinoma and Their Diagnostic Efficiencies].

PubMed

Dang, Hao Dan; Chen, Yu; Shi, Xiao Hua; Hou, Bo; Xing, Hai Qun; Zhang, Tao; Chen, Xing Ming; Zhang, Zhu Hua; Xue, Hua Dan; Jin, Zheng Yu

2018-04-28

Objective To evaluate the correlation of the positron emission tomography/magnetic resonance imaging (PET/MR) parameters with the pathological differentiation of head and neck squamous cell carcinoma(HNSCC) and the diagnostic efficiencies of PET/MR parameters. Methods Patients with clinical suspicion of HNSCC were included and underwent PET/MR scan. HNSCC was pathologically confirmed in all these patients. The PET/MR examination included PET and MR sequences of diffusion-weighted imaging (DWI) and T2-and T1-weighted imaging. The multiple parameters of PET/MR included the mean values of apparent diffusion coefficient(ADC mean ) and the maximum and mean values of standardized uptake value (SUV max and SUV mean ) were measured and estimated. The correlations of all the parameters and distribution between the different tumor differentiation groups were analyzed. Logistic regression was utilized to build the model as the PET/MR combined parameter for predicting the differentiation by multiple parameters of PET/MR. The receiver operating characteristic curve was calculated for each parameter and the combination. Results Totally 23 patients were included in this study:9 patients (9 males and 0 female) had well-differentiated tumor,with an average age of (61.0±6.8)years;14 cases had moderately-differentiated (n=10) or poorly-differentiated tumors (n=4),with an average age of (62.0±9.1) years. All the patients were males. There was statistical correlation between SUV mean and SUV max (P<0.001);however,ADC mean showed no statistical correlation with SUV max and with SUV mean (P=0.42,P=0.13). ADC mean and SUV mean showed significant difference between well-differentiated group and moderately-poorly-differentiated group (P=0.005,P=0.007). Compared with the individual parameters,the combination of PET/MR parameters with SUV mean and ADC mean had higher efficacy in predicting tumor differentiation,with an area under curve of 0.84. Conclusions The distributions of ADC mean ,SUV max and SUV mean differ among HNSCC with different pathological differentiation. Compared with the individual parameters,the combination of the PET/MR parameters has higher efficiency in predicting tumor differentiation.

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

PubMed Central

Ho, Wen-Hsien; Lee, King-Teh; Chen, Hong-Yaw; Ho, Te-Wei; Chiu, Herng-Chia

2012-01-01

Background A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection. PMID:22235270

Immunohistochemical expression of p53 and its clinicopathological correlation with modified Anneroth's histological grading system.

PubMed

Dave, Kajal V; Chalishazar, Monali; Dave, Vishal R; Panja, Pritam; Singh, Manisha; Modi, Tapan G

2016-01-01

Oral squamous cell carcinoma (OSCC) is an epithelial neoplasm generally beginning as focal overgrowth of altered stem cells near the basement membrane, moving upward and laterally, replacing the normal epithelium. Histopathological grading has been used for many decades in an attempt to predict the clinical behavior of oral squamous cell carcinoma. In the present study, Forty biopsies were studied for histological grading and p53 expression. The p53 expression was studied in relation to clinical parameters such as age, sex of patient and site of tumors. Relation between histological grade of malignancy and p53 protein expression was analysed. All cases were classified according to Anneroth's histological malignancy grading system (1987). 40 cases of OSCC were assessed for clinical parameters, Anneroth's histological grading and immunohistochemically stained with p53 protien. The results obtained were analyzed using Spearman's Co-relation. The positive expression of p53 was found in 62% of carcinomas studied. Positivity of p53 showed correlation with histological grade of malignancy and with individual parameters like degree of keratinization, nuclear polymorphism, number of mitoses and lymphoplasmacytic infiltration while showed a negative correlation with pattern of invasion. Our study showed a significant correlation between parameters of tumor cell population, lymphoplasmacytic infiltration and p53 expression. A significant association between high grade of malignancy and p53 overexpression and insignificant correlation of p53 with age, sex of the patient and site of the tumor was found.

Dynamic Contrast-Enhanced MRI of Cervical Cancers: Temporal Percentile Screening of Contrast Enhancement Identifies Parameters for Prediction of Chemoradioresistance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andersen, Erlend K.F.; Hole, Knut Hakon; Lund, Kjersti V.

Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test,more » resulting in p value and relative risk maps of all percentile-time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile-time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile-time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile-time interval of nRSI was associated with progression-free survival. Conclusions: The percentile-time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.« less

Pulmonary arterial enlargement predicts long-term survival in COPD patients.

PubMed

de-Torres, Juan P; Ezponda, Ana; Alcaide, Ana B; Campo, Arantza; Berto, Juan; Gonzalez, Jessica; Zulueta, Javier J; Casanova, Ciro; Rodriguez-Delgado, Luisa Elena; Celli, Bartolome R; Bastarrika, Gorka

2018-01-01

Pulmonary artery enlargement (PAE) is associated with exacerbations in Chronic Obstructive Pulmonary Disease (COPD) and with survival in moderate to severe patients. The potential role of PAE in survival prediction has not been compared with other clinical and physiological prognostic markers. In 188 patients with COPD, PA diameter was measured on a chest CT and the following clinical and physiological parameters registered: age, gender, smoking status, pack-years history, dyspnea, lung function, exercise capacity, Body Mass Index, BODE index and history of exacerbations in year prior to enrolment. Proportional Cox regression analysis determined the best predictor of all cause survival. During 83 months (±42), 43 patients died. Age, pack-years history, smoking status, BMI, FEV1%, six minute walking distance, Modified Medical Research Council dyspnea scale, BODE index, exacerbation rate prior to enrollment, PA diameter and PAE (diameter≥30mm) were associated with survival. In the multivariable analysis, age (HR: 1.08; 95%CI: 1.03-1.12, p<0.001) and PAE (HR: 2.78; 95%CI: 1.35-5.75, p = 0.006) were the most powerful parameters associated with all-cause mortality. In this prospective observational study of COPD patients with mild to moderate airflow limitation, PAE was the best predictor of long-term survival along with age.

Modeling the Risk of Radiation-Induced Acute Esophagitis for Combined Washington University and RTOG Trial 93-11 Lung Cancer Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Ellen X.; Bradley, Jeffrey D.; El Naqa, Issam

2012-04-01

Purpose: To construct a maximally predictive model of the risk of severe acute esophagitis (AE) for patients who receive definitive radiation therapy (RT) for non-small-cell lung cancer. Methods and Materials: The dataset includes Washington University and RTOG 93-11 clinical trial data (events/patients: 120/374, WUSTL = 101/237, RTOG9311 = 19/137). Statistical model building was performed based on dosimetric and clinical parameters (patient age, sex, weight loss, pretreatment chemotherapy, concurrent chemotherapy, fraction size). A wide range of dose-volume parameters were extracted from dearchived treatment plans, including Dx, Vx, MOHx (mean of hottest x% volume), MOCx (mean of coldest x% volume), and gEUDmore » (generalized equivalent uniform dose) values. Results: The most significant single parameters for predicting acute esophagitis (RTOG Grade 2 or greater) were MOH85, mean esophagus dose (MED), and V30. A superior-inferior weighted dose-center position was derived but not found to be significant. Fraction size was found to be significant on univariate logistic analysis (Spearman R = 0.421, p < 0.00001) but not multivariate logistic modeling. Cross-validation model building was used to determine that an optimal model size needed only two parameters (MOH85 and concurrent chemotherapy, robustly selected on bootstrap model-rebuilding). Mean esophagus dose (MED) is preferred instead of MOH85, as it gives nearly the same statistical performance and is easier to compute. AE risk is given as a logistic function of (0.0688 Asterisk-Operator MED+1.50 Asterisk-Operator ConChemo-3.13), where MED is in Gy and ConChemo is either 1 (yes) if concurrent chemotherapy was given, or 0 (no). This model correlates to the observed risk of AE with a Spearman coefficient of 0.629 (p < 0.000001). Conclusions: Multivariate statistical model building with cross-validation suggests that a two-variable logistic model based on mean dose and the use of concurrent chemotherapy robustly predicts acute esophagitis risk in combined-data WUSTL and RTOG 93-11 trial datasets.« less

Self-learning computers for surgical planning and prediction of postoperative alignment.

PubMed

Lafage, Renaud; Pesenti, Sébastien; Lafage, Virginie; Schwab, Frank J

2018-02-01

In past decades, the role of sagittal alignment has been widely demonstrated in the setting of spinal conditions. As several parameters can be affected, identifying the driver of the deformity is the cornerstone of a successful treatment approach. Despite the importance of restoring sagittal alignment for optimizing outcome, this task remains challenging. Self-learning computers and optimized algorithms are of great interest in spine surgery as in that they facilitate better planning and prediction of postoperative alignment. Nowadays, computer-assisted tools are part of surgeons' daily practice; however, the use of such tools remains to be time-consuming. NARRATIVE REVIEW AND RESULTS: Computer-assisted methods for the prediction of postoperative alignment consist of a three step analysis: identification of anatomical landmark, definition of alignment objectives, and simulation of surgery. Recently, complex rules for the prediction of alignment have been proposed. Even though this kind of work leads to more personalized objectives, the number of parameters involved renders it difficult for clinical use, stressing the importance of developing computer-assisted tools. The evolution of our current technology, including machine learning and other types of advanced algorithms, will provide powerful tools that could be useful in improving surgical outcomes and alignment prediction. These tools can combine different types of advanced technologies, such as image recognition and shape modeling, and using this technique, computer-assisted methods are able to predict spinal shape. The development of powerful computer-assisted methods involves the integration of several sources of information such as radiographic parameters (X-rays, MRI, CT scan, etc.), demographic information, and unusual non-osseous parameters (muscle quality, proprioception, gait analysis data). In using a larger set of data, these methods will aim to mimic what is actually done by spine surgeons, leading to real tailor-made solutions. Integrating newer technology can change the current way of planning/simulating surgery. The use of powerful computer-assisted tools that are able to integrate several parameters and learn from experience can change the traditional way of selecting treatment pathways and counseling patients. However, there is still much work to be done to reach a desired level as noted in other orthopedic fields, such as hip surgery. Many of these tools already exist in non-medical fields and their adaptation to spine surgery is of considerable interest.

Association of physical examination with pulmonary artery catheter parameters in acute lung injury.

PubMed

Grissom, Colin K; Morris, Alan H; Lanken, Paul N; Ancukiewicz, Marek; Orme, James F; Schoenfeld, David A; Thompson, B Taylor

2009-10-01

To correlate physical examination findings, central venous pressure, fluid output, and central venous oxygen saturation with pulmonary artery catheter parameters. Retrospective study. Data from the multicenter Fluid and Catheter Treatment Trial of the National Institutes of Health Acute Respiratory Distress Syndrome Network. Five hundred thirteen patients with acute lung injury randomized to treatment with a pulmonary artery catheter. Correlation of physical examination findings (capillary refill time >2 secs, knee mottling, or cool extremities), central venous pressure, fluid output, and central venous oxygen saturation with parameters from a pulmonary artery catheter. We determined association of baseline physical examination findings and on-study parameters of central venous pressure and central venous oxygen saturation with cardiac index <2.5 L/min/m2 and mixed venous oxygen saturation <60%. We determined correlation of baseline central venous oxygen saturation and mixed venous oxygen saturation and predictive value of a low central venous oxygen saturation for a low mixed venous oxygen saturation. Prevalence of cardiac index <2.5 and mixed venous oxygen saturation <60% was 8.1% and 15.5%, respectively. Baseline presence of all three physical examination findings had low sensitivity (12% and 8%), high specificity (98% and 99%), low positive predictive value (40% and 56%), but high negative predictive value (93% and 86%) for cardiac index <2.5 and mixed venous oxygen saturation <60%, respectively. Central venous oxygen saturation <70% predicted a mixed venous oxygen saturation <60% with a sensitivity 84%,specificity 70%, positive predictive value 31%, and negative predictive value of 96%. Low cardiac index correlated with cool extremities, high central venous pressure, and low 24-hr fluid output; and low mixed venous oxygen saturation correlated with knee mottling and high central venous pressure, but these correlations were not found to be clinically useful. In this subset of patients with acute lung injury, there is a high prior probability that cardiac index and mixed venous oxygen saturation are normal and physical examination findings of ineffective circulation are not useful for predicting low cardiac index or mixed venous oxygen saturation. Central venous oxygen saturation <70% does not accurately predict mixed venous oxygen saturation <60%, but a central venous oxygen saturation >or=70% may be useful to exclude mixed venous oxygen saturation <60%.

Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

PubMed

Reddy, Bhargava K; Delen, Dursun; Agrawal, Rupesh K

2018-01-01

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Association of lean body mass with nutritional parameters and mortality in hemodialysis patients: A long-term follow-up clinical study.

PubMed

Zhou, Dong Chi; Yang, Xiu Hong; Zhan, Xiao Li; Gu, Yan Hong; Guo, Li Li; Jin, Hui Min

2018-06-01

This study aimed to evaluate the correlation between lean body mass (LBM) and nutritional status in hemodialysis (HD) patients to better predict their long-term prognosis. Anthropometric body measurements and biochemical parameters were recorded from 222 patients on maintenance hemodialysis (MHD) at the Shanghai Pudong Hospital Hemodialysis Center. LBM was calculated using the serum creatinine index (LBM-SCR), mid-arm muscle circumference (LBM-MAMC), and dominant-arm hand-grip strength (LBM-HGS). Patient mortality and hospitalization were observed after 24 months. LBMs measured from LBM-SCR and LBM-MAMC were associated with sex, body mass index (BMI), serum albumin, and serum creatinine (SCR) ( p < 0.05). Through three methods of LBM evaluation, low LBM was shown to be associated with a higher mortality in patients undergoing HD ( p < 0.05). In addition, the rate of hospitalization among these patients was significantly increased ( p < 0.05). Performing multivariate regression analysis using mortality and hospitalization as the dependent variable, we found LBM-SCR and LBM-HGS are strongly associated with hospitalization and mortality in HD patients, indicating LBM is an important factor in prediction of outcomes in those patients. LBM is associated with nutritional parameters in HD patients, and LBM-SCR, HGS, and MAMC are simple approaches for accurately predicting the patient's risk of hospitalization and/or death.

Predicting umbilical artery pH during labour: Development and validation of a nomogram using fetal heart rate patterns.

PubMed

Ramanah, Rajeev; Omar, Sikiyah; Guillien, Alicia; Pugin, Aurore; Martin, Alain; Riethmuller, Didier; Mottet, Nicolas

2018-06-01

Nomograms are statistical models that combine variables to obtain the most accurate and reliable prediction for a particular risk. Fetal heart rate (FHR) interpretation alone has been found to be poorly predictive for fetal acidosis while other clinical risk factors exist. The aim of this study was to create and validate a nomogram based on FHR patterns and relevant clinical parameters to provide a non-invasive individualized prediction of umbilical artery pH during labour. A retrospective observational study was conducted on 4071 patients in labour presenting singleton pregnancies at >34 gestational weeks and delivering vaginally. Clinical characteristics, FHR patterns and umbilical cord gas of 1913 patients were used to construct a nomogram predicting an umbilical artery (Ua) pH <7.18 (10th centile of the study population) after an univariate and multivariate stepwise logistic regression analysis. External validation was obtained from an independent cohort of 2158 patients. Area under the receiver operating characteristics (ROC) curve, sensitivity, specificity, positive and negative predictive values of the nomogram were determined. Upon multivariate analysis, parity (p < 0.01), induction of labour (p = 0.01), a prior uterine scar (p = 0.02), maternal fever (p = 0.02) and the type of FHR (p < 0.01) were significantly associated with an Ua pH <7.18 (p < 0.05). Apgar score at 1, 5 and 10 min were significantly lower in the group with an Ua pH <7.18 (p < 0.01). The nomogram constructed had a Concordance Index of 0.75 (area under the curve) with a sensitivity of 57%, a specificity of 91%, a negative predictive value of 5% and a positive predictive value of 99%. Calibration found no difference between the predicted probabilities and the observed rate of Ua pH <7.18 (p = 0.63). The validation set had a Concordance Index of 0.72 and calibration with a p < 0.77. We successfully developed and validated a nomogram to predict Ua pH by combining easily available clinical variables and FHR. Discrimination and calibration of the model were statistically good. This mathematical tool can help clinicians in the management of labour by predicting umbilical artery pH based on FHR tracings. Copyright © 2018 Elsevier B.V. All rights reserved.

Normal Tissue Complication Probability (NTCP) modeling of late rectal bleeding following external beam radiotherapy for prostate cancer: A Test of the QUANTEC-recommended NTCP model.

PubMed

Liu, Mitchell; Moiseenko, Vitali; Agranovich, Alexander; Karvat, Anand; Kwan, Winkle; Saleh, Ziad H; Apte, Aditya A; Deasy, Joseph O

2010-10-01

Validating a predictive model for late rectal bleeding following external beam treatment for prostate cancer would enable safer treatments or dose escalation. We tested the normal tissue complication probability (NTCP) model recommended in the recent QUANTEC review (quantitative analysis of normal tissue effects in the clinic). One hundred and sixty one prostate cancer patients were treated with 3D conformal radiotherapy for prostate cancer at the British Columbia Cancer Agency in a prospective protocol. The total prescription dose for all patients was 74 Gy, delivered in 2 Gy/fraction. 159 3D treatment planning datasets were available for analysis. Rectal dose volume histograms were extracted and fitted to a Lyman-Kutcher-Burman NTCP model. Late rectal bleeding (>grade 2) was observed in 12/159 patients (7.5%). Multivariate logistic regression with dose-volume parameters (V50, V60, V70, etc.) was non-significant. Among clinical variables, only age was significant on a Kaplan-Meier log-rank test (p=0.007, with an optimal cut point of 77 years). Best-fit Lyman-Kutcher-Burman model parameters (with 95% confidence intervals) were: n = 0.068 (0.01, +infinity); m =0.14 (0.0, 0.86); and TD50 = 81 (27, 136) Gy. The peak values fall within the 95% QUANTEC confidence intervals. On this dataset, both models had only modest ability to predict complications: the best-fit model had a Spearman's rank correlation coefficient of rs = 0.099 (p = 0.11) and area under the receiver operating characteristic curve (AUC) of 0.62; the QUANTEC model had rs=0.096 (p= 0.11) and a corresponding AUC of 0.61. Although the QUANTEC model consistently predicted higher NTCP values, it could not be rejected according to the χ(2) test (p = 0.44). Observed complications, and best-fit parameter estimates, were consistent with the QUANTEC-preferred NTCP model. However, predictive power was low, at least partly because the rectal dose distribution characteristics do not vary greatly within this patient cohort.

Physiology-Based Modeling May Predict Surgical Treatment Outcome for Obstructive Sleep Apnea

PubMed Central

Li, Yanru; Ye, Jingying; Han, Demin; Cao, Xin; Ding, Xiu; Zhang, Yuhuan; Xu, Wen; Orr, Jeremy; Jen, Rachel; Sands, Scott; Malhotra, Atul; Owens, Robert

2017-01-01

Study Objectives: To test whether the integration of both anatomical and nonanatomical parameters (ventilatory control, arousal threshold, muscle responsiveness) in a physiology-based model will improve the ability to predict outcomes after upper airway surgery for obstructive sleep apnea (OSA). Methods: In 31 patients who underwent upper airway surgery for OSA, loop gain and arousal threshold were calculated from preoperative polysomnography (PSG). Three models were compared: (1) a multiple regression based on an extensive list of PSG parameters alone; (2) a multivariate regression using PSG parameters plus PSG-derived estimates of loop gain, arousal threshold, and other trait surrogates; (3) a physiological model incorporating selected variables as surrogates of anatomical and nonanatomical traits important for OSA pathogenesis. Results: Although preoperative loop gain was positively correlated with postoperative apnea-hypopnea index (AHI) (P = .008) and arousal threshold was negatively correlated (P = .011), in both model 1 and 2, the only significant variable was preoperative AHI, which explained 42% of the variance in postoperative AHI. In contrast, the physiological model (model 3), which included AHIREM (anatomy term), fraction of events that were hypopnea (arousal term), the ratio of AHIREM and AHINREM (muscle responsiveness term), loop gain, and central/mixed apnea index (control of breathing terms), was able to explain 61% of the variance in postoperative AHI. Conclusions: Although loop gain and arousal threshold are associated with residual AHI after surgery, only preoperative AHI was predictive using multivariate regression modeling. Instead, incorporating selected surrogates of physiological traits on the basis of OSA pathophysiology created a model that has more association with actual residual AHI. Commentary: A commentary on this article appears in this issue on page 1023. Clinical Trial Registration: ClinicalTrials.Gov; Title: The Impact of Sleep Apnea Treatment on Physiology Traits in Chinese Patients With Obstructive Sleep Apnea; Identifier: NCT02696629; URL: https://clinicaltrials.gov/show/NCT02696629 Citation: Li Y, Ye J, Han D, Cao X, Ding X, Zhang Y, Xu W, Orr J, Jen R, Sands S, Malhotra A, Owens R. Physiology-based modeling may predict surgical treatment outcome for obstructive sleep apnea. J Clin Sleep Med. 2017;13(9):1029–1037. PMID:28818154

MUC1 Expression by Immunohistochemistry Is Associated with Adverse Pathologic Features in Prostate Cancer: A Multi-Institutional Study.

PubMed

Eminaga, Okyaz; Wei, Wei; Hawley, Sarah J; Auman, Heidi; Newcomb, Lisa F; Simko, Jeff; Hurtado-Coll, Antonio; Troyer, Dean A; Carroll, Peter R; Gleave, Martin E; Lin, Daniel W; Nelson, Peter S; Thompson, Ian M; True, Lawrence D; McKenney, Jesse K; Feng, Ziding; Fazli, Ladan; Brooks, James D

2016-01-01

The uncertainties inherent in clinical measures of prostate cancer (CaP) aggressiveness endorse the investigation of clinically validated tissue biomarkers. MUC1 expression has been previously reported to independently predict aggressive localized prostate cancer. We used a large cohort to validate whether MUC1 protein levels measured by immunohistochemistry (IHC) predict aggressive cancer, recurrence and survival outcomes after radical prostatectomy independent of clinical and pathological parameters. MUC1 IHC was performed on a multi-institutional tissue microarray (TMA) resource including 1,326 men with a median follow-up of 5 years. Associations with clinical and pathological parameters were tested by the Chi-square test and the Wilcoxon rank sum test. Relationships with outcome were assessed with univariable and multivariable Cox proportional hazard models and the Log-rank test. The presence of MUC1 expression was significantly associated with extracapsular extension and higher Gleason score, but not with seminal vesicle invasion, age, positive surgical margins or pre-operative serum PSA levels. In univariable analyses, positive MUC1 staining was significantly associated with a worse recurrence free survival (RFS) (HR: 1.24, CI 1.03-1.49, P = 0.02), although not with disease specific survival (DSS, P>0.5). On multivariable analyses, the presence of positive surgical margins, extracapsular extension, seminal vesicle invasion, as well as higher pre-operative PSA and increasing Gleason score were independently associated with RFS, while MUC1 expression was not. Positive MUC1 expression was not independently associated with disease specific survival (DSS), but was weakly associated with overall survival (OS). In our large, rigorously designed validation cohort, MUC1 protein expression was associated with adverse pathological features, although it was not an independent predictor of outcome after radical prostatectomy.

Medication possession ratio predicts antiretroviral regimens persistence in Peru.

PubMed

Salinas, Jorge L; Alave, Jorge L; Westfall, Andrew O; Paz, Jorge; Moran, Fiorella; Carbajal-Gonzalez, Danny; Callacondo, David; Avalos, Odalie; Rodriguez, Martin; Gotuzzo, Eduardo; Echevarria, Juan; Willig, James H

2013-01-01

In developing nations, the use of operational parameters (OPs) in the prediction of clinical care represents a missed opportunity to enhance the care process. We modeled the impact of multiple measurements of antiretroviral treatment (ART) adherence on antiretroviral treatment outcomes in Peru. Retrospective cohort study including ART naïve, non-pregnant, adults initiating therapy at Hospital Nacional Cayetano Heredia, Lima-Peru (2006-2010). Three OPs were defined: 1) Medication possession ratio (MPR): days with antiretrovirals dispensed/days on first-line therapy; 2) Laboratory monitory constancy (LMC): proportion of 6 months intervals with ≥1 viral load or CD4 reported; 3) Clinic visit constancy (CVC): proportion of 6 months intervals with ≥1 clinic visit. Three multi-variable Cox proportional hazard (PH) models (one per OP) were fit for (1) time of first-line ART persistence and (2) time to second-line virologic failure. All models were adjusted for socio-demographic, clinical and laboratory variables. 856 patients were included in first-line persistence analyses, median age was 35.6 years [29.4-42.9] and most were male (624; 73%). In multivariable PH models, MPR (per 10% increase HR=0.66; 95%CI=0.61-0.71) and LMC (per 10% increase 0.83; 0.71-0.96) were associated with prolonged time on first-line therapies. Among 79 individuals included in time to second-line virologic failure analyses, MPR was the only OP independently associated with prolonged time to second-line virologic failure (per 10% increase 0.88; 0.77-0.99). The capture and utilization of program level parameters such as MPR can provide valuable insight into patient-level treatment outcomes.

Estimating retinal vascular permeability using the adiabatic approximation to the tissue homogeneity model with fluorescein videoangiography

NASA Astrophysics Data System (ADS)

Tichauer, Kenneth M.; Osswald, Christian R.; Dosmar, Emily; Guthrie, Micah J.; Hones, Logan; Sinha, Lagnojita; Xu, Xiaochun; Mieler, William F.; St. Lawrence, Keith; Kang-Mieler, Jennifer J.

2015-06-01

Clinical symptoms of diabetic retinopathy are not detectable until damage to the retina reaches an irreversible stage, at least by today's treatment standards. As a result, there is a push to develop new, "sub-clinical" methods of predicting the onset of diabetic retinopathy before the onset of irreversible damage. With diabetic retinopathy being associated with the accumulation of long-term mild damage to the retinal vasculature, retinal blood vessel permeability has been proposed as a key parameter for detecting preclinical stages of retinopathy. In this study, a kinetic modeling approach used to quantify vascular permeability in dynamic contrast-enhanced medical imaging was evaluated in noise simulations and then applied to retinal videoangiography data in a diabetic rat for the first time to determine the potential for this approach to be employed clinically as an early indicator of diabetic retinopathy. Experimental levels of noise were found to introduce errors of less than 15% in estimates of blood flow and extraction fraction (a marker of vascular permeability), and fitting of rat retinal fluorescein angiography data provided stable maps of both parameters.

[Morphological index for prediction of cervix uteri cancer].

PubMed

Gatenadze, Ts Z; Ungiadze, D Iu; Chakhoian, O P; Nakashidze, M G; Sulaberidze, I M

2010-01-01

The aim of the research is evaluation of morphological parameters which would allow predicting cervix uteri cancer. The case histories of 505 patients (from 20 to 70 years old) with I-III clinical stages of primary cervical uteri cancer (PUC), which got surgical and combined treatment in oncological center in Batumi from 1970-2005 were evaluated. The factors that influence on the prognosis of PUC are revealed: cancer cell emboli in vessels of tumour stroma, sharpness of the tumour edges, the tumor tissue types, and the depth of invasion. The imaging characteristics of the tumours are described. Planocellular cancer has more favourable prognosis in comparison with glandular cancer.

Diffuse optical measurements of head and neck tumor hemodynamics for early prediction of chemoradiation therapy outcomes

NASA Astrophysics Data System (ADS)

Dong, Lixin; Kudrimoti, Mahesh; Irwin, Daniel; Chen, Li; Kumar, Sameera; Shang, Yu; Huang, Chong; Johnson, Ellis L.; Stevens, Scott D.; Shelton, Brent J.; Yu, Guoqiang

2016-08-01

This study used a hybrid near-infrared diffuse optical instrument to monitor tumor hemodynamic responses to chemoradiation therapy for early prediction of treatment outcomes in patients with head and neck cancer. Forty-seven patients were measured once per week to evaluate the hemodynamic status of clinically involved cervical lymph nodes as surrogates for the primary tumor response. Patients were classified into two groups: complete response (CR) (n=29) and incomplete response (IR) (n=18). Tumor hemodynamic responses were found to be associated with clinical outcomes (CR/IR), wherein the associations differed depending on human papillomavirus (HPV-16) status. In HPV-16 positive patients, significantly lower levels in tumor oxygenated hemoglobin concentration ([HbO2]) at weeks 1 to 3, total hemoglobin concentration at week 3, and blood oxygen saturation (StO2) at week 3 were found in the IR group. In HPV-16 negative patients, significantly higher levels in tumor blood flow index and reduced scattering coefficient (μs‧) at week 3 were observed in the IR group. These hemodynamic parameters exhibited significantly high accuracy for early prediction of clinical outcomes, within the first three weeks of therapy, with the areas under the receiver operating characteristic curves (AUCs) ranging from 0.83 to 0.96.

Four hundred or more participants needed for stable contingency table estimates of clinical prediction rule performance.

PubMed

Kent, Peter; Boyle, Eleanor; Keating, Jennifer L; Albert, Hanne B; Hartvigsen, Jan

2017-02-01

To quantify variability in the results of statistical analyses based on contingency tables and discuss the implications for the choice of sample size for studies that derive clinical prediction rules. An analysis of three pre-existing sets of large cohort data (n = 4,062-8,674) was performed. In each data set, repeated random sampling of various sample sizes, from n = 100 up to n = 2,000, was performed 100 times at each sample size and the variability in estimates of sensitivity, specificity, positive and negative likelihood ratios, posttest probabilities, odds ratios, and risk/prevalence ratios for each sample size was calculated. There were very wide, and statistically significant, differences in estimates derived from contingency tables from the same data set when calculated in sample sizes below 400 people, and typically, this variability stabilized in samples of 400-600 people. Although estimates of prevalence also varied significantly in samples below 600 people, that relationship only explains a small component of the variability in these statistical parameters. To reduce sample-specific variability, contingency tables should consist of 400 participants or more when used to derive clinical prediction rules or test their performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Diffuse optical measurements of head and neck tumor hemodynamics for early prediction of chemoradiation therapy outcomes

PubMed Central

Dong, Lixin; Kudrimoti, Mahesh; Irwin, Daniel; Chen, Li; Kumar, Sameera; Shang, Yu; Huang, Chong; Johnson, Ellis L.; Stevens, Scott D.; Shelton, Brent J.; Yu, Guoqiang

2016-01-01

Abstract. This study used a hybrid near-infrared diffuse optical instrument to monitor tumor hemodynamic responses to chemoradiation therapy for early prediction of treatment outcomes in patients with head and neck cancer. Forty-seven patients were measured once per week to evaluate the hemodynamic status of clinically involved cervical lymph nodes as surrogates for the primary tumor response. Patients were classified into two groups: complete response (CR) (n=29) and incomplete response (IR) (n=18). Tumor hemodynamic responses were found to be associated with clinical outcomes (CR/IR), wherein the associations differed depending on human papillomavirus (HPV-16) status. In HPV-16 positive patients, significantly lower levels in tumor oxygenated hemoglobin concentration ([HbO2]) at weeks 1 to 3, total hemoglobin concentration at week 3, and blood oxygen saturation (StO2) at week 3 were found in the IR group. In HPV-16 negative patients, significantly higher levels in tumor blood flow index and reduced scattering coefficient (μs′) at week 3 were observed in the IR group. These hemodynamic parameters exhibited significantly high accuracy for early prediction of clinical outcomes, within the first three weeks of therapy, with the areas under the receiver operating characteristic curves (AUCs) ranging from 0.83 to 0.96. PMID:27564315

Evolving biomarkers improve prediction of long-term mortality in patients with stable coronary artery disease: the BIO-VILCAD score.

PubMed

Kleber, M E; Goliasch, G; Grammer, T B; Pilz, S; Tomaschitz, A; Silbernagel, G; Maurer, G; März, W; Niessner, A

2014-08-01

Algorithms to predict the future long-term risk of patients with stable coronary artery disease (CAD) are rare. The VIenna and Ludwigshafen CAD (VILCAD) risk score was one of the first scores specifically tailored for this clinically important patient population. The aim of this study was to refine risk prediction in stable CAD creating a new prediction model encompassing various pathophysiological pathways. Therefore, we assessed the predictive power of 135 novel biomarkers for long-term mortality in patients with stable CAD. We included 1275 patients with stable CAD from the LUdwigshafen RIsk and Cardiovascular health study with a median follow-up of 9.8 years to investigate whether the predictive power of the VILCAD score could be improved by the addition of novel biomarkers. Additional biomarkers were selected in a bootstrapping procedure based on Cox regression to determine the most informative predictors of mortality. The final multivariable model encompassed nine clinical and biochemical markers: age, sex, left ventricular ejection fraction (LVEF), heart rate, N-terminal pro-brain natriuretic peptide, cystatin C, renin, 25OH-vitamin D3 and haemoglobin A1c. The extended VILCAD biomarker score achieved a significantly improved C-statistic (0.78 vs. 0.73; P = 0.035) and net reclassification index (14.9%; P < 0.001) compared to the original VILCAD score. Omitting LVEF, which might not be readily measureable in clinical practice, slightly reduced the accuracy of the new BIO-VILCAD score but still significantly improved risk classification (net reclassification improvement 12.5%; P < 0.001). The VILCAD biomarker score based on routine parameters complemented by novel biomarkers outperforms previous risk algorithms and allows more accurate classification of patients with stable CAD, enabling physicians to choose more personalized treatment regimens for their patients.

Predicting individual contrast sensitivity functions from acuity and letter contrast sensitivity measurements

PubMed Central

Thurman, Steven M.; Davey, Pinakin Gunvant; McCray, Kaydee Lynn; Paronian, Violeta; Seitz, Aaron R.

2016-01-01

Contrast sensitivity (CS) is widely used as a measure of visual function in both basic research and clinical evaluation. There is conflicting evidence on the extent to which measuring the full contrast sensitivity function (CSF) offers more functionally relevant information than a single measurement from an optotype CS test, such as the Pelli–Robson chart. Here we examine the relationship between functional CSF parameters and other measures of visual function, and establish a framework for predicting individual CSFs with effectively a zero-parameter model that shifts a standard-shaped template CSF horizontally and vertically according to independent measurements of high contrast acuity and letter CS, respectively. This method was evaluated for three different CSF tests: a chart test (CSV-1000), a computerized sine-wave test (M&S Sine Test), and a recently developed adaptive test (quick CSF). Subjects were 43 individuals with healthy vision or impairment too mild to be considered low vision (acuity range of −0.3 to 0.34 logMAR). While each test demands a slightly different normative template, results show that individual subject CSFs can be predicted with roughly the same precision as test–retest repeatability, confirming that individuals predominantly differ in terms of peak CS and peak spatial frequency. In fact, these parameters were sufficiently related to empirical measurements of acuity and letter CS to permit accurate estimation of the entire CSF of any individual with a deterministic model (zero free parameters). These results demonstrate that in many cases, measuring the full CSF may provide little additional information beyond letter acuity and contrast sensitivity. PMID:28006065

Simulating environmental and psychological acoustic factors of the operating room.

PubMed

Bennett, Christopher L; Dudaryk, Roman; Ayers, Andrew L; McNeer, Richard R

2015-12-01

In this study, an operating room simulation environment was adapted to include quadraphonic speakers, which were used to recreate a composed clinical soundscape. To assess validity of the composed soundscape, several acoustic parameters of this simulated environment were acquired in the presence of alarms only, background noise only, or both. These parameters were also measured for comparison from size-matched operating rooms at Jackson Memorial Hospital. The parameters examined included sound level, reverberation time, and predictive metrics of speech intelligibility in quiet and noise. It was found that the sound levels and acoustic parameters were comparable between the simulated environment and the actual operating rooms. The impact of the background noise on the perception of medical alarms was then examined, and was found to have little impact on the audibility of the alarms. This study is a first in kind report of a comparison between the environmental and psychological acoustical parameters of a hospital simulation environment and actual operating rooms.

An Algorithm and R Program for Fitting and Simulation of Pharmacokinetic and Pharmacodynamic Data.

PubMed

Li, Jijie; Yan, Kewei; Hou, Lisha; Du, Xudong; Zhu, Ping; Zheng, Li; Zhu, Cairong

2017-06-01

Pharmacokinetic/pharmacodynamic link models are widely used in dose-finding studies. By applying such models, the results of initial pharmacokinetic/pharmacodynamic studies can be used to predict the potential therapeutic dose range. This knowledge can improve the design of later comparative large-scale clinical trials by reducing the number of participants and saving time and resources. However, the modeling process can be challenging, time consuming, and costly, even when using cutting-edge, powerful pharmacological software. Here, we provide a freely available R program for expediently analyzing pharmacokinetic/pharmacodynamic data, including data importation, parameter estimation, simulation, and model diagnostics. First, we explain the theory related to the establishment of the pharmacokinetic/pharmacodynamic link model. Subsequently, we present the algorithms used for parameter estimation and potential therapeutic dose computation. The implementation of the R program is illustrated by a clinical example. The software package is then validated by comparing the model parameters and the goodness-of-fit statistics generated by our R package with those generated by the widely used pharmacological software WinNonlin. The pharmacokinetic and pharmacodynamic parameters as well as the potential recommended therapeutic dose can be acquired with the R package. The validation process shows that the parameters estimated using our package are satisfactory. The R program developed and presented here provides pharmacokinetic researchers with a simple and easy-to-access tool for pharmacokinetic/pharmacodynamic analysis on personal computers.

Pilot Mental Workload with Predictive System Status Information

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.

1998-01-01

Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, the mental workload associated with using this predictive information has not been ascertained. The study described here attempted to measure mental workload. In this simulator experiment, three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and four initial times to a parameter alert range (1 minute, 5 minutes, 15 minutes, and ETA+45 minutes) were tested to determine their effects on subjects mental workload. Subjective workload ratings increased with increasing predictive information (whether a parameter was changing abnormally or the time for a parameter to reach an alert range). Subjective situation awareness decreased with more predictive information but it became greater with increasing initial times to a parameter alert range. Also, subjective focus changed depending on the type of predictive information. Lastly, skin temperature fluctuated less as the initial time to a parameter alert range increased.

Stepped approach for prediction of syndrome Z in patients attending sleep clinic: a north Indian hospital-based study.

PubMed

Agrawal, Swastik; Sharma, Surendra Kumar; Sreenivas, Vishnubhatla; Lakshmy, Ramakrishnan; Mishra, Hemant K

2012-09-01

Syndrome Z is the occurrence of metabolic syndrome (MS) with obstructive sleep apnea. Knowledge of its risk factors is useful to screen patients requiring further evaluation for syndrome Z. Consecutive patients referred from sleep clinic undergoing polysomnography in the Sleep Laboratory of AIIMS Hospital, New Delhi were screened between June 2008 and May 2010, and 227 patients were recruited. Anthropometry, body composition analysis, blood pressure, fasting blood sugar, and lipid profile were measured. MS was defined using the National Cholesterol Education Program (adult treatment panel III) criteria, with Asian cutoff values for abdominal obesity. Prevalence of MS and syndrome Z was 74% and 65%, respectively. Age, percent body fat, excessive daytime sleepiness (EDS), and ΔSaO(2) (defined as difference between baseline and minimum SaO(2) during polysomnography) were independently associated with syndrome Z. Using a cutoff of 15% for level of desaturation, the stepped predictive score using these risk factors had sensitivity, specificity, positive predictive value, and negative predictive value of 75%, 73%, 84%, and 61%, respectively for the diagnosis of syndrome Z. It correctly characterized presence of syndrome Z 75% of the time and obviated need for detailed evaluation in 42% of the screened subjects. A large proportion of patients presenting to sleep clinics have MS and syndrome Z. Age, percent body fat, EDS, and ΔSaO(2) are independent risk factors for syndrome Z. A stepped predictive score using these parameters is cost-effective and useful in diagnosing syndrome Z in resource-limited settings.

Application of a Physiologically Based Pharmacokinetic Model to Assess Propofol Hepatic and Renal Glucuronidation in Isolation: Utility of In Vitro and In Vivo Data

PubMed Central

Gill, Katherine L.; Gertz, Michael; Houston, J. Brian

2013-01-01

A physiologically based pharmacokinetic (PBPK) modeling approach was used to assess the prediction accuracy of propofol hepatic and extrahepatic metabolic clearance and to address previously reported underprediction of in vivo clearance based on static in vitro–in vivo extrapolation methods. The predictive capacity of propofol intrinsic clearance data (CLint) obtained in human hepatocytes and liver and kidney microsomes was assessed using the PBPK model developed in MATLAB software. Microsomal data obtained by both substrate depletion and metabolite formation methods and in the presence of 2% bovine serum albumin were considered in the analysis. Incorporation of hepatic and renal in vitro metabolic clearance in the PBPK model resulted in underprediction of propofol clearance regardless of the source of in vitro data; the predicted value did not exceed 35% of the observed clearance. Subsequently, propofol clinical data from three dose levels in intact patients and anhepatic subjects were used for the optimization of hepatic and renal CLint in a simultaneous fitting routine. Optimization process highlighted that renal glucuronidation clearance was underpredicted to a greater extent than liver clearance, requiring empirical scaling factors of 17 and 9, respectively. The use of optimized clearance parameters predicted hepatic and renal extraction ratios within 20% of the observed values, reported in an additional independent clinical study. This study highlights the complexity involved in assessing the contribution of extrahepatic clearance mechanisms and illustrates the application of PBPK modeling, in conjunction with clinical data, to assess prediction of clearance from in vitro data for each tissue individually. PMID:23303442

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

PubMed

Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

2014-08-15

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

PubMed Central

Ziegler, G.; Ridgway, G.R.; Dahnke, R.; Gaser, C.

2014-01-01

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

[Clinical end-points and surrogate markers of pulmonary arterial hypertension in the light of evidence-based treatment].

PubMed

Can, Mehmet Mustafa; Kaymaz, Cihangir

2010-08-01

Pulmonary arterial hypertension (PAH) is a rare, fatal and progressive disease. There is an acceleration in the advent of new therapies in parallel to the development of the knowledge about etiogenesis and pathogenesis of PAH. Therefore, to optimize the goals of PAH-specific treatment and to determine the time to shift from monotherapy to combination therapy, simple, objective and reproducible end-points, which may predict the disease severity, progression rate and life expectancy are needed. The adventure of end points in PAH has started with six minute walk distance and functional capacity, and continues with new parameters (biochemical marker, time to clinical worsening, echocardiography and magnetic resonance imaging etc.), which can better reflect the clinical outcome.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I - pre-analytical and analytical validation.

PubMed

Masucci, Giuseppe V; Cesano, Alessandra; Hawtin, Rachael; Janetzki, Sylvia; Zhang, Jenny; Kirsch, Ilan; Dobbin, Kevin K; Alvarez, John; Robbins, Paul B; Selvan, Senthamil R; Streicher, Howard Z; Butterfield, Lisa H; Thurin, Magdalena

2016-01-01

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question. Although numerous candidate biomarkers have been described, there are currently three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated to identify patients who are more likely to benefit from a single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accurate prediction of clinical benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy. To illustrate the requirements for validation, we discuss examples of biomarker assays that have shown preliminary evidence of an association with clinical benefit from immunotherapeutic interventions. The scope includes only those assays and technologies that have established a certain level of validation for clinical use (fit-for-purpose). Recommendations to meet challenges and strategies to guide the choice of analytical and clinical validation design for specific assays are also provided.

Metabolic and anthropometric parameters contribute to ART-mediated CD4+ T cell recovery in HIV-1-infected individuals: an observational study.

PubMed

Azzoni, Livio; Foulkes, Andrea S; Firnhaber, Cynthia; Yin, Xiangfan; Crowther, Nigel J; Glencross, Deborah; Lawrie, Denise; Stevens, Wendy; Papasavvas, Emmanouil; Sanne, Ian; Montaner, Luis J

2011-07-29

The degree of immune reconstitution achieved in response to suppressive ART is associated with baseline individual characteristics, such as pre-treatment CD4 count, levels of viral replication, cellular activation, choice of treatment regimen and gender. However, the combined effect of these variables on long-term CD4 recovery remains elusive, and no single variable predicts treatment response. We sought to determine if adiposity and molecules associated with lipid metabolism may affect the response to ART and the degree of subsequent immune reconstitution, and to assess their ability to predict CD4 recovery. We studied a cohort of 69 (48 females and 21 males) HIV-infected, treatment-naïve South African subjects initiating antiretroviral treatment (d4T, 3Tc and lopinavir/ritonavir). We collected information at baseline and six months after viral suppression, assessing anthropometric parameters, dual energy X-ray absorptiometry and magnetic resonance imaging scans, serum-based clinical laboratory tests and whole blood-based flow cytometry, and determined their role in predicting the increase in CD4 count in response to ART. We present evidence that baseline CD4+ T cell count, viral load, CD8+ T cell activation (CD95 expression) and metabolic and anthropometric parameters linked to adiposity (LDL/HDL cholesterol ratio and waist/hip ratio) significantly contribute to variability in the extent of CD4 reconstitution (ΔCD4) after six months of continuous ART. Our final model accounts for 44% of the variability in CD4+ T cell recovery in virally suppressed individuals, representing a workable predictive model of immune reconstitution.

A Combined Pharmacokinetic and Radiologic Assessment of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Predicts Response to Chemoradiation in Locally Advanced Cervical Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Semple, Scott; Harry, Vanessa N. MRCOG.; Parkin, David E.

2009-10-01

Purpose: To investigate the combination of pharmacokinetic and radiologic assessment of dynamic contrast-enhanced magnetic resonance imaging (MRI) as an early response indicator in women receiving chemoradiation for advanced cervical cancer. Methods and Materials: Twenty women with locally advanced cervical cancer were included in a prospective cohort study. Dynamic contrast-enhanced MRI was carried out before chemoradiation, after 2 weeks of therapy, and at the conclusion of therapy using a 1.5-T MRI scanner. Radiologic assessment of uptake parameters was obtained from resultant intensity curves. Pharmacokinetic analysis using a multicompartment model was also performed. General linear modeling was used to combine radiologic andmore » pharmacokinetic parameters and correlated with eventual response as determined by change in MRI tumor size and conventional clinical response. A subgroup of 11 women underwent repeat pretherapy MRI to test pharmacokinetic reproducibility. Results: Pretherapy radiologic parameters and pharmacokinetic K{sup trans} correlated with response (p < 0.01). General linear modeling demonstrated that a combination of radiologic and pharmacokinetic assessments before therapy was able to predict more than 88% of variance of response. Reproducibility of pharmacokinetic modeling was confirmed. Conclusions: A combination of radiologic assessment with pharmacokinetic modeling applied to dynamic MRI before the start of chemoradiation improves the predictive power of either by more than 20%. The potential improvements in therapy response prediction using this type of combined analysis of dynamic contrast-enhanced MRI may aid in the development of more individualized, effective therapy regimens for this patient group.« less

Predictors of long-term renal outcome in lupus nephritis trials: lessons learned from the Euro-Lupus Nephritis cohort.

PubMed

Dall'Era, Maria; Cisternas, Miriam G; Smilek, Dawn E; Straub, Laura; Houssiau, Frédéric A; Cervera, Ricard; Rovin, Brad H; Mackay, Meggan

2015-05-01

There is a need to determine which response measures in lupus nephritis trials are most predictive of good long-term renal function. We used data from the Euro-Lupus Nephritis Trial to evaluate the performance of proteinuria, serum creatinine (Cr), and urinary red blood cells (RBCs) as predictors of good long-term renal outcome. Patients from the Euro-Lupus Nephritis Trial with proteinuria, serum Cr, and urinary RBC measurements at 3, 6, or 12 months and with a minimum of 7 years of followup were included (n = 76). We assessed the ability of these clinical biomarkers at 3, 6, and 12 months after randomization to predict good long-term renal outcome (defined as a serum Cr value ≤1.0 mg/dl) at 7 years. Receiver operating characteristic curves were generated to assess parameter performance at these time points and to select the best cutoff for individual parameters. Sensitivity and specificity were calculated for the parameters alone and in combination. A proteinuria value of <0.8 gm/day at 12 months after randomization was the single best predictor of good long-term renal function (sensitivity 81% and specificity 78%). The addition of serum Cr to proteinuria as a composite predictor did not improve the performance of the outcome measure; addition of urinary RBCs as a predictor significantly decreased the sensitivity to 47%. This study demonstrates that the level of proteinuria at 12 months is the individual best predictor of long-term renal outcome in patients with lupus nephritis. Inclusion of urinary RBCs as part of a composite outcome measure actually undermined the predictive value of the trial data. We therefore suggest that urinary RBCs should not be included as a component of clinical trial response criteria in lupus nephritis. © 2015, American College of Rheumatology.

SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thwaites, D; Holloway, L; Bailey, M

2015-06-15

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction andmore » mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.« less

Brain atrophy and lesion load measures over 1 year relate to clinical status after 6 years in patients with clinically isolated syndromes.

PubMed

Di Filippo, M; Anderson, V M; Altmann, D R; Swanton, J K; Plant, G T; Thompson, A J; Miller, D H

2010-02-01

Conventional MRI lesion measures modestly predict long term disability in some clinically isolated syndrome (CIS) studies. Brain atrophy suggests neuroaxonal loss in multiple sclerosis (MS) with the potential to reflect disease progression to a greater extent than lesion measures. To investigate whether brain atrophy and lesion load, during the first year in patients presenting with CIS, independently predict clinical outcome (development of MS and disability at 6 years). 99 patients presenting with CIS were included in the study. T1 gadolinium enhanced and T2 weighted brain MRI was acquired at baseline and approximately 1 year later. Percentage brain atrophy rate between baseline and follow-up scans was analysed using SIENA. Mean annual brain atrophy rates were -0.38% for all patients, -0.50% in patients who had developed MS at 6 years and -0.26% in those who had not. Brain atrophy rate (p = 0.005) and baseline T2 lesion load (p<0.001) were independent predictors of clinically definite MS. While brain atrophy rate was a predictor of Expanded Disability Status Scale (EDSS) score in a univariate analysis, only 1 year T2 lesion load change (p = 0.007) and baseline gadolinium enhancing lesion number (p = 0.03) were independent predictors of EDSS score at the 6 year follow-up. T1 lesion load was the only MRI parameter which predicted Multiple Sclerosis Functional Composite score at the 6 year follow-up. The findings confirm that brain atrophy occurs during the earliest phases of MS and suggest that 1 year longitudinal measures of MRI change, if considered together with baseline MRI variables, might help to predict clinical status 6 years after the first demyelinating event in CIS patients, better than measurements such as lesion or brain volumes on baseline MRI alone.

Numerical Modelling of Effects of Biphasic Layers of Corrosion Products to the Degradation of Magnesium Metal In Vitro

PubMed Central

Ahmed, Safia K.; Ward, John P.; Liu, Yang

2017-01-01

Magnesium (Mg) is becoming increasingly popular for orthopaedic implant materials. Its mechanical properties are closer to bone than other implant materials, allowing for more natural healing under stresses experienced during recovery. Being biodegradable, it also eliminates the requirement of further surgery to remove the hardware. However, Mg rapidly corrodes in clinically relevant aqueous environments, compromising its use. This problem can be addressed by alloying the Mg, but challenges remain at optimising the properties of the material for clinical use. In this paper, we present a mathematical model to provide a systematic means of quantitatively predicting Mg corrosion in aqueous environments, providing a means of informing standardisation of in vitro investigation of Mg alloy corrosion to determine implant design parameters. The model describes corrosion through reactions with water, to produce magnesium hydroxide Mg(OH)2, and subsequently with carbon dioxide to form magnesium carbonate MgCO3. The corrosion products produce distinct protective layers around the magnesium block that are modelled as porous media. The resulting model of advection–diffusion equations with multiple moving boundaries was solved numerically using asymptotic expansions to deal with singular cases. The model has few free parameters, and it is shown that these can be tuned to predict a full range of corrosion rates, reflecting differences between pure magnesium or magnesium alloys. Data from practicable in vitro experiments can be used to calibrate the model’s free parameters, from which model simulations using in vivo relevant geometries provide a cheap first step in optimising Mg-based implant materials. PMID:29267244

Comparative evaluation of guided tissue regeneration with use of collagen-based barrier freeze-dried dura mater allograft for mandibular class 2 furcation defects (a comparative controlled clinical study).

PubMed

Patel, Sandeep; Kubavat, Ajay; Ruparelia, Brijesh; Agarwal, Arvind; Panda, Anup

2012-01-01

The aim of periodontal surgery is complete regeneration. The present study was designed to evaluate and compare clinically soft tissue changes in form of probing pocket depth, gingival shrinkage, attachment level and hard tissue changes in form of horizontal and vertical bone level using resorbable membranes. Twelve subjects with bilateral class 2 furcation defects were selected. After initial phase one treatment, open debridement was performed in control site while freezedried dura mater allograft was used in experimental site. Soft and hard tissue parameters were registered intrasurgically. Nine months reentry ensured better understanding and evaluation of the final outcome of the study. Guided tissue regeneration is a predictable treatment modality for class 2 furcation defect. There was statistically significant reduction in pocket depth as compared to control (p < 0.01). There is statistically significant increase in periodontal attachment level within control and experimental sites showed better results (p < 0.01). For hard tissue parameter, significant defect fill resulted in experimental group, while in control group, less significant defect fill was found in horizontal direction and nonsignificant defect fill was found in vertical direction. The results showed statistically significant improvement in soft and hard tissue parameters and less gingival shrinkage in experimental sites compared to control site. The use of FDDMA in furcation defects helps us to achieve predictable results. This cross-linked collagen membrane has better handling properties and ease of procurement as well as economic viability making it a logical material to be used in regenerative surgeries.

A New Parameter for Cardiac Efficiency Analysis

NASA Astrophysics Data System (ADS)

Borazjani, Iman; Rajan, Navaneetha Krishnan; Song, Zeying; Hoffmann, Kenneth; MacMahon, Eileen; Belohlavek, Marek

2014-11-01

Detecting and evaluating a heart with suboptimal pumping efficiency is a significant clinical goal. However, the routine parameters such as ejection fraction, quantified with current non-invasive techniques are not predictive of heart disease prognosis. Furthermore, they only represent left-ventricular (LV) ejection function and not the efficiency, which might be affected before apparent changes in the function. We propose a new parameter, called the hemodynamic efficiency (H-efficiency) and defined as the ratio of the useful to total power, for cardiac efficiency analysis. Our results indicate that the change in the shape/motion of the LV will change the pumping efficiency of the LV even if the ejection fraction is kept constant at 55% (normal value), i.e., H-efficiency can be used for suboptimal cardiac performance diagnosis. To apply H-efficiency on a patient-specific basis, we are developing a system that combines echocardiography (echo) and computational fluid dynamics (CFD) to provide the 3D pressure and velocity field to directly calculate the H-efficiency parameter. Because the method is based on clinically used 2D echo, which has faster acquisition time and lower cost relative to other imaging techniques, it can have a significant impact on a large number of patients. This work is partly supported by the American Heart Association.

Validation of the harmless acute pancreatitis score in predicting nonsevere course of acute pancreatitis.

PubMed

Oskarsson, V; Mehrabi, M; Orsini, N; Hammarqvist, F; Segersvärd, R; Andrén-Sandberg, A; Sadr Azodi, O

2011-01-01

The Harmless Acute Pancreatitis Score (HAPS) is a scoring algorithm to identify patients with nonsevere acute pancreatitis. The aim of this study was to evaluate the reproducibility of HAPS outside its original study setting. Baseline information of all hospitalized patients with acute pancreatitis at Karolinska University Hospital, Stockholm, Sweden, between 2004 and 2009 was collected. The parameters constituting HAPS were signs of peritonitis, hematocrit and serum creatinine levels. Since hematocrit was not available in all patients, complete sample analysis was performed by replacing hematocrit with hemoglobin (strongly correlated with hematocrit; r = 0.86). In total, 531 patients with a first-time or a recurrent attack of acute pancreatitis were included. Among 353 patients with complete information on parameters constituting HAPS, 79 patients were predicted to have a nonsevere course, of whom 1 patient developed severe acute pancreatitis. The specificity of HAPS in predicting a nonsevere course of acute pancreatitis was 96.3% (95% CI: 81.0-99.9) with a corresponding positive predictive value of 98.7% (95% CI: 93.1-100). Complete sample analysis replacing hematocrit with hemoglobin level predicted a nonsevere course in 182 patients, of whom 2 patients had severe acute pancreatitis (94.3% specificity and 98.9% positive predictive value). HAPS is a highly specific scoring algorithm that predicts a nonsevere course of acute pancreatitis. Therefore, HAPS might be an additional tool in the clinical assessment of acute pancreatitis where early screening is important to treat the patients at an optimal level of care. Copyright © 2011 S. Karger AG, Basel.

Cognitive models of risky choice: parameter stability and predictive accuracy of prospect theory.

PubMed

Glöckner, Andreas; Pachur, Thorsten

2012-04-01

In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are individual differences as measured by model parameters stable enough to improve the ability to predict behavior as compared to modeling without adjustable parameters? We examined this issue in cumulative prospect theory (CPT), arguably the most widely used framework to model decisions under risk. Specifically, we examined (a) the temporal stability of CPT's parameters; and (b) how well different implementations of CPT, varying in the number of adjustable parameters, predict individual choice relative to models with no adjustable parameters (such as CPT with fixed parameters, expected value theory, and various heuristics). We presented participants with risky choice problems and fitted CPT to each individual's choices in two separate sessions (which were 1 week apart). All parameters were correlated across time, in particular when using a simple implementation of CPT. CPT allowing for individual variability in parameter values predicted individual choice better than CPT with fixed parameters, expected value theory, and the heuristics. CPT's parameters thus seem to pick up stable individual differences that need to be considered when predicting risky choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Assessing value of innovative molecular diagnostic tests in the concept of predictive, preventive, and personalized medicine.

PubMed

Akhmetov, Ildar; Bubnov, Rostyslav V

2015-01-01

Molecular diagnostic tests drive the scientific and technological uplift in the field of predictive, preventive, and personalized medicine offering invaluable clinical and socioeconomic benefits to the key stakeholders. Although the results of diagnostic tests are immensely influential, molecular diagnostic tests (MDx) are still grudgingly reimbursed by payers and amount for less than 5 % of the overall healthcare costs. This paper aims at defining the value of molecular diagnostic test and outlining the most important components of "value" from miscellaneous assessment frameworks, which go beyond accuracy and feasibility and impact the clinical adoption, informing healthcare resource allocation decisions. The authors suggest that the industry should facilitate discussions with various stakeholders throughout the entire assessment process in order to arrive at a consensus about the depth of evidence required for positive marketing authorization or reimbursement decisions. In light of the evolving "value-based healthcare" delivery practices, it is also recommended to account for social and ethical parameters of value, since these are anticipated to become as critical for reimbursement decisions and test acceptance as economic and clinical criteria.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

PubMed Central

Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set. PMID:21423775

Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

PubMed Central

2011-01-01

Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

PubMed

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting.

Age, tumor size, and in-office ultrasonography are predictive parameters of malignancy in follicular neoplasms of the thyroid.

PubMed

Paramo, Juan C; Mesko, Thomas

2008-01-01

To identify clinical predictors of malignancy in patients with intraoperative frozen-section diagnosis of follicular neoplasm of the thyroid. We performed a retrospective cross-sectional study of 71 patients with intraoperative frozen-section diagnosis of follicular neoplasm who underwent thyroidectomy between January 1992 and December 2000. Age, sex, tumor size, and in-office ultrasonography characteristics of the lesions were assessed. These clinical factors were compared between cases that had benign definitive pathologic findings and those that were found to be carcinomas on permanent sections. Nine (13%) of the 71 follicular neoplasms were found to be carcinomas after definitive pathologic evaluation. The incidence of malignancy was 13% (2/16) in men and 13% (7/55) in women (P>.5). Patients younger than 45 years had a 27% (8/30) incidence of malignancy compared with 2% (1/41) in patients 45 years or older (P<.01). Of tumors smaller than 4 cm, 7% (4/55) were ultimately diagnosed as carcinomas compared with 31% (5/16) of those 4 cm or larger (P = .05). When the in-office ultrasonography findings were interpreted as benign, only 7% (3/46) of cases were malignant compared with 40% (4/10) when the ultrasonography findings were suspicious (P = .02). Age and tumor size are predictive parameters of malignancy in follicular neoplasm of the thyroid. Suspicious ultrasonography findings also have an important predictive role. Total thyroidectomy is reasonable in patients with follicular neoplasm on frozen section if they are young (<45 years old), with large (>4 cm) tumors or if there are suspicious findings on in-office ultrasonography.

Citrate Pharmacokinetics in Critically Ill Patients with Acute Kidney Injury

PubMed Central

Zhu, Qiuyu; Liu, Junfeng; Qian, Jing; You, Huaizhou; Gu, Yong; Hao, Chuanming; Jiao, Zheng; Ding, Feng

2013-01-01

Introduction Regional citrate anticoagulation (RCA) is gaining popularity in continous renal replacement therapy (CRRT) for critically ill patients. The risk of citrate toxicity is a primary concern during the prolonged process. The aim of this study was to assess the pharmacokinetics of citrate in critically ill patients with AKI, and used the kinetic parameters to predict the risk of citrate accumulation in this population group undergoing continuous veno-venous hemofiltration (CVVH) with RCA. Methods Critically ill patients with AKI (n = 12) and healthy volunteers (n = 12) were investigated during infusing comparative dosage of citrate. Serial blood samples were taken before, during 120 min and up to 120 min after infusion. Citrate pharmacokinetics were calculated and compared between groups. Then the estimated kinetic parameters were applied to the citrate kinetic equation for validation in other ten patients’ CVVH sessions with citrate anticoagulation. Results Total body clearance of citrate was similar in critically ill patients with AKI and healthy volunteers (648.04±347.00 L/min versus 686.64±353.60 L/min; P = 0.624). Basal and peak citrate concentrations were similar in both groups (p = 0.423 and 0.247, respectively). The predicted citrate curve showed excellent fit to the measurements. Conclusions Citrate clearance is not impaired in critically ill patients with AKI in the absence of severe liver dysfunction. Citrate pharmacokinetic data can provide a basis for the clinical use of predicting the risk of citrate accumulation. Trial Registration ClinicalTrials.gov Identifier NCT00948558 PMID:23824037

Estimation of treatment effects in all-comers randomized clinical trials with a predictive marker.

PubMed

Choai, Yuki; Matsui, Shigeyuki

2015-03-01

Recent advances in genomics and biotechnologies have accelerated the development of molecularly targeted treatments and accompanying markers to predict treatment responsiveness. However, it is common at the initiation of a definitive phase III clinical trial that there is no compelling biological basis or early trial data for a candidate marker regarding its capability in predicting treatment effects. In this case, it is reasonable to include all patients as eligible for randomization, but to plan for prospective subgroup analysis based on the marker. One analysis plan in such all-comers designs is the so-called fallback approach that first tests for overall treatment efficacy and then proceeds to testing in a biomarker-positive subgroup if the first test is not significant. In this approach, owing to the adaptive nature of the analysis and a correlation between the two tests, a bias will arise in estimating the treatment effect in the biomarker-positive subgroup after a non-significant first overall test. In this article, we formulate the bias function and show a difficulty in obtaining unbiased estimators for a whole range of an associated parameter. To address this issue, we propose bias-corrected estimation methods, including those based on an approximation of the bias function under a bounded range of the parameter using polynomials. We also provide an interval estimation method based on a bivariate doubly truncated normal distribution. Simulation experiments demonstrated a success in bias reduction. Application to a phase III trial for lung cancer is provided. © 2014, The International Biometric Society.

Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis.

PubMed

Uher, Tomas; Vaneckova, Manuela; Sobisek, Lukas; Tyblova, Michaela; Seidl, Zdenek; Krasensky, Jan; Ramasamy, Deepa; Zivadinov, Robert; Havrdova, Eva; Kalincik, Tomas; Horakova, Dana

2017-01-01

Disease progression and treatment efficacy vary among individuals with multiple sclerosis. Reliable predictors of individual disease outcomes are lacking. To examine the accuracy of the early prediction of 12-year disability outcomes using clinical and magnetic resonance imaging (MRI) parameters. A total of 177 patients from the original Avonex-Steroids-Azathioprine study were included. Participants underwent 3-month clinical follow-ups. Cox models were used to model the associations between clinical and MRI markers at baseline or after 12 months with sustained disability progression (SDP) over the 12-year observation period. At baseline, T2 lesion number, T1 and T2 lesion volumes, corpus callosum (CC), and thalamic fraction were the best predictors of SDP (hazard ratio (HR) = 1.7-4.6; p ⩽ 0.001-0.012). At 12 months, Expanded Disability Status Scale (EDSS) and its change, number of new or enlarging T2 lesions, and CC volume % change were the best predictors of SDP over the follow-up (HR = 1.7-3.5; p ⩽  0.001-0.017). A composite score was generated from a subset of the best predictors of SDP. Scores of ⩾4 had greater specificity (90%-100%) and were associated with greater cumulative risk of SDP (HR = 3.2-21.6; p < 0.001) compared to the individual predictors. The combination of established MRI and clinical indices with MRI volumetric predictors improves the prediction of SDP over long-term follow-up and may provide valuable information for therapeutic decisions.

Chikungunya Virus: In Vitro Response to Combination Therapy With Ribavirin and Interferon Alfa 2a.

PubMed

Gallegos, Karen M; Drusano, George L; D Argenio, David Z; Brown, Ashley N

2016-10-15

We evaluated the antiviral activities of ribavirin (RBV) and interferon (IFN) alfa as monotherapy and combination therapy against chikungunya virus (CHIKV). Vero cells were infected with CHIKV in the presence of RBV and/or IFN alfa, and viral production was quantified by plaque assay. A mathematical model was fit to the data to identify drug interactions for effect. We ran simulations using the best-fit model parameters to predict the antiviral activity associated with clinically relevant regimens of RBV and IFN alfa as combination therapy. The model predictions were validated using the hollow fiber infection model (HFIM) system. RBV and IFN alfa were effective against CHIKV as monotherapy at supraphysiological concentrations. However, RBV and IFN alfa were highly synergistic for antiviral effect when administered as combination therapy. Simulations with our mathematical model predicted that a standard clinical regimen of RBV plus IFN alfa would inhibit CHIKV burden by 2.5 log10 following 24 hours of treatment. In the HFIM system, RBV plus IFN alfa at clinical exposures resulted in a 2.1-log10 decrease in the CHIKV burden following 24 hours of therapy. These findings validate the prediction made by the mathematical model. These studies illustrate the promise of RBV plus IFN alfa as a potential therapeutic strategy for the treatment of CHIKV infections. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

New onset status epilepticus in older patients: Clinical characteristics and outcome.

PubMed

Malter, M P; Nass, R D; Kaluschke, T; Fink, G R; Burghaus, L; Dohmen, C

2017-10-01

We here evaluated (1) the differential characteristics of status epilepticus (SE) in older (≥60 years) compared to younger adults (18-59 years). In particular, we were interested in (2) the proportion and characteristics of new onset SE in patients with no history of epilepsy (NOSE) in older compared to younger adults, and (3) predictive parameters for clinical outcome in older subjects with NOSE. We performed a monocentric retrospective analysis of all adult patients (≥18years) admitted with SE to our tertiary care centre over a period of 10 years (2006-2015) to evaluate clinical characteristics and short-time outcome at discharge. One-hundred-thirty-five patients with SE were included in the study. Mean age at onset was 64 years (range 21-90), eighty-seven of the patients (64%) were older than 60 years. In 76 patients (56%), SE occurred as NOSE, sixty-seven percent of them were aged ≥60 years. There was no age-dependent predominance for NOSE. NOSE was not a relevant outcome predictor, especially regarding age-related subgroups. Older patients with NOSE had less frequently general tonic clonic SE (GTCSE; p=0.001) and were more often female (p=0.01). Regarding outcome parameters and risk factors in older patients with NOSE, unfavourable outcome was associated with infections during in-hospital treatment (0.04), extended stay in ICU (p=0.001), and generally in hospital (p<0.001). In our cohort, older patients represented the predominant subgroup in patients with SE. Older patients suffered more often from non-convulsive semiology and had a less favourable short-time outcome. NOSE was not a predictive outcome parameter in older patients. Data suggest that avoiding infections should have a priority because higher infection rates were associated with unfavourable outcome. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Breast dosimetry in clinical mammography

NASA Astrophysics Data System (ADS)

Benevides, Luis Alberto Do Rego

The objective of this study was show that a clinical dosimetry protocol that utilizes a dosimetric breast phantom series based on population anthropometric measurements can reliably predict the average glandular dose (AGD) imparted to the patient during a routine screening mammogram. In the study, AGD was calculated using entrance skin exposure and dose conversion factors based on fibroglandular content, compressed breast thickness, mammography unit parameters and modifying parameters for homogeneous phantom (phantom factor), compressed breast lateral dimensions (volume factor) and anatomical features (anatomical factor). The protocol proposes the use of a fiber-optic coupled (FOCD) or Metal Oxide Semiconductor Field Effect Transistor (MOSFET) dosimeter to measure the entrance skin exposure at the time of the mammogram without interfering with diagnostic information of the mammogram. The study showed that FOCD had sensitivity with less than 7% energy dependence, linear in all tube current-time product stations, and was reproducible within 2%. FOCD was superior to MOSFET dosimeter in sensitivity, reusability, and reproducibility. The patient fibroglandular content was evaluated using a calibrated modified breast tissue equivalent homogeneous phantom series (BRTES-MOD) designed from anthropomorphic measurements of a screening mammography population and whose elemental composition was referenced to International Commission on Radiation Units and Measurements Report 44 tissues. The patient fibroglandular content, compressed breast thickness along with unit parameters and spectrum half-value layer were used to derive the currently used dose conversion factor (DgN). The study showed that the use of a homogeneous phantom, patient compressed breast lateral dimensions and patient anatomical features can affect AGD by as much as 12%, 3% and 1%, respectively. The protocol was found to be superior to existing methodologies. In addition, the study population anthropometric measurements enabled the development of analytical equations to calculate the whole breast area, estimate for the skin layer thickness and optimal location for automatic exposure control ionization chamber. The clinical dosimetry protocol developed in this study can reliably predict the AGD imparted to an individual patient during a routine screening mammogram.

A clinically parameterized mathematical model of Shigella immunity to inform vaccine design

PubMed Central

Wahid, Rezwanul; Toapanta, Franklin R.; Simon, Jakub K.; Sztein, Marcelo B.

2018-01-01

We refine and clinically parameterize a mathematical model of the humoral immune response against Shigella, a diarrheal bacteria that infects 80-165 million people and kills an estimated 600,000 people worldwide each year. Using Latin hypercube sampling and Monte Carlo simulations for parameter estimation, we fit our model to human immune data from two Shigella EcSf2a-2 vaccine trials and a rechallenge study in which antibody and B-cell responses against Shigella′s lipopolysaccharide (LPS) and O-membrane proteins (OMP) were recorded. The clinically grounded model is used to mathematically investigate which key immune mechanisms and bacterial targets confer immunity against Shigella and to predict which humoral immune components should be elicited to create a protective vaccine against Shigella. The model offers insight into why the EcSf2a-2 vaccine had low efficacy and demonstrates that at a group level a humoral immune response induced by EcSf2a-2 vaccine or wild-type challenge against Shigella′s LPS or OMP does not appear sufficient for protection. That is, the model predicts an uncontrolled infection of gut epithelial cells that is present across all best-fit model parameterizations when fit to EcSf2a-2 vaccine or wild-type challenge data. Using sensitivity analysis, we explore which model parameter values must be altered to prevent the destructive epithelial invasion by Shigella bacteria and identify four key parameter groups as potential vaccine targets or immune correlates: 1) the rate that Shigella migrates into the lamina propria or epithelium, 2) the rate that memory B cells (BM) differentiate into antibody-secreting cells (ASC), 3) the rate at which antibodies are produced by activated ASC, and 4) the Shigella-specific BM carrying capacity. This paper underscores the need for a multifaceted approach in ongoing efforts to design an effective Shigella vaccine. PMID:29304144

A clinically parameterized mathematical model of Shigella immunity to inform vaccine design.

PubMed

Davis, Courtney L; Wahid, Rezwanul; Toapanta, Franklin R; Simon, Jakub K; Sztein, Marcelo B

2018-01-01

We refine and clinically parameterize a mathematical model of the humoral immune response against Shigella, a diarrheal bacteria that infects 80-165 million people and kills an estimated 600,000 people worldwide each year. Using Latin hypercube sampling and Monte Carlo simulations for parameter estimation, we fit our model to human immune data from two Shigella EcSf2a-2 vaccine trials and a rechallenge study in which antibody and B-cell responses against Shigella's lipopolysaccharide (LPS) and O-membrane proteins (OMP) were recorded. The clinically grounded model is used to mathematically investigate which key immune mechanisms and bacterial targets confer immunity against Shigella and to predict which humoral immune components should be elicited to create a protective vaccine against Shigella. The model offers insight into why the EcSf2a-2 vaccine had low efficacy and demonstrates that at a group level a humoral immune response induced by EcSf2a-2 vaccine or wild-type challenge against Shigella's LPS or OMP does not appear sufficient for protection. That is, the model predicts an uncontrolled infection of gut epithelial cells that is present across all best-fit model parameterizations when fit to EcSf2a-2 vaccine or wild-type challenge data. Using sensitivity analysis, we explore which model parameter values must be altered to prevent the destructive epithelial invasion by Shigella bacteria and identify four key parameter groups as potential vaccine targets or immune correlates: 1) the rate that Shigella migrates into the lamina propria or epithelium, 2) the rate that memory B cells (BM) differentiate into antibody-secreting cells (ASC), 3) the rate at which antibodies are produced by activated ASC, and 4) the Shigella-specific BM carrying capacity. This paper underscores the need for a multifaceted approach in ongoing efforts to design an effective Shigella vaccine.

HIV Model Parameter Estimates from Interruption Trial Data including Drug Efficacy and Reservoir Dynamics

PubMed Central

Luo, Rutao; Piovoso, Michael J.; Martinez-Picado, Javier; Zurakowski, Ryan

2012-01-01

Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3–5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients. PMID:22815727

A statistical analysis of RNA folding algorithms through thermodynamic parameter perturbation.

PubMed

Layton, D M; Bundschuh, R

2005-01-01

Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction algorithms are to changes in these parameters. We found already that for changes corresponding to the actual experimental error to which these parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using parameter perturbation is proposed, and its limitations are discussed.

Body mass index and other anthropometric parameters in patients with diffuse large B-cell lymphoma: physiopathological significance and predictive value in the immunochemotherapy era.

PubMed

Sarkozy, Clémentine; Camus, Vincent; Tilly, Hervé; Salles, Gilles; Jardin, Fabrice

2015-07-01

Diffuse large B-cell lymphoma (DLBCL) is the most common form of aggressive non-Hodgkin lymphoma, accounting for 30-40% of newly diagnosed cases. Obesity is a well-defined risk factor for DLBCL. However, the impact of body mass index (BMI) on DLBCL prognosis is controversial. Recent studies suggest that skeletal muscle wasting (sarcopenia) or loss of fat mass can be detected by computed tomography (CT) images and is useful for predicting the clinical outcome in several types of cancer including DLBCL. Several hypotheses have been proposed to explain the differences in DLBCL outcome according to BMI or weight that include tolerance to treatment, inflammatory background and chemotherapy or rituximab metabolism. In this review, we summarize the available literature, addressing the impact and physiopathological relevance of simple anthropometric tools including BMI and tissue distribution measurements. We also discuss their relationship with other nutritional parameters and their potential role in the management of patients with DLBCL.

Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126.

PubMed

Moore, Kevin L; Schmidt, Rachel; Moiseenko, Vitali; Olsen, Lindsey A; Tan, Jun; Xiao, Ying; Galvin, James; Pugh, Stephanie; Seider, Michael J; Dicker, Adam P; Bosch, Walter; Michalski, Jeff; Mutic, Sasa

2015-06-01

The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. A total of 219 IMRT patients from the high-dose arm (79.2 Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH0126,top10%). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed "high-quality," "low-quality," and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol. Existing models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP reductions while maintaining the volume of the PTV receiving prescription dose. An equivalent sample of high-quality plans showed fewer toxicities than low-quality plans, 6 of 73 versus 10 of 73 respectively, although these differences were not significant (P=.21) due to insufficient statistical power in this retrospective study. Plan quality deficiencies in RTOG 0126 exposed patients to substantial excess risk for rectal complications. Copyright © 2015 Elsevier Inc. All rights reserved.

Qualitative and quantitative descriptions of glenohumeral motion.

PubMed

Hill, A M; Bull, A M J; Wallace, A L; Johnson, G R

2008-02-01

Joint modelling plays an important role in qualitative and quantitative descriptions of both normal and abnormal joints, as well as predicting outcomes of alterations to joints in orthopaedic practice and research. Contemporary efforts in modelling have focussed upon the major articulations of the lower limb. Well-constrained arthrokinematics can form the basis of manageable kinetic and dynamic mathematical predictions. In order to contain computation of shoulder complex modelling, glenohumeral joint representations in both limited and complete shoulder girdle models have undergone a generic simplification. As such, glenohumeral joint models are often based upon kinematic descriptions of inadequate degrees of freedom (DOF) for clinical purposes and applications. Qualitative descriptions of glenohumeral motion range from the parody of a hinge joint to the complex realism of a spatial joint. In developing a model, a clear idea of intention is required in order to achieve a required application. Clinical applicability of a model requires both descriptive and predictive output potentials, and as such, a high level of validation is required. Without sufficient appreciation of the clinical intention of the arthrokinematic foundation to a model, error is all too easily introduced. Mathematical description of joint motion serves to quantify all relevant clinical parameters. Commonly, both the Euler angle and helical (screw) axis methods have been applied to the glenohumeral joint, although concordance between these methods and classical anatomical appreciation of joint motion is limited, resulting in miscommunication between clinician and engineer. Compounding these inconsistencies in motion quantification is gimbal lock and sequence dependency.

Identification of clinical predictive factors of oxaliplatin-induced chronic peripheral neuropathy in colorectal cancer patients treated with adjuvant Folfox IV.

PubMed

Vincenzi, Bruno; Frezza, Anna Maria; Schiavon, Gaia; Spoto, Chiara; Silvestris, Nicola; Addeo, Raffaele; Catalano, Vincenzo; Graziano, Francesco; Santini, Daniele; Tonini, Giuseppe

2013-05-01

Oxaliplatin-induced neuropathy is a dose-related side effect which occurs in almost 40 % of patients treated with oxaliplatin. Aim of the present study was to identify reliable clinical factors predicting its development and duration. One hundred sixty-nine completely resected colorectal cancer patients treated with adjuvant Folfox IV regimen were retrospectively included. The following pre-treatment clinical parameters were collected: hypocalcaemia, hypomagnesaemia, hypoalbuminaemia, anaemia, diabetes, chronic renal failure (CRF), folate deficiency, vitamin B(12) deficiency, number of cycles received and habit to alcohol consumption. Incidence, grade (NCI-CTCAE v.3) and duration of neuropathy were recorded. Incidence of neuropathy was found to be higher in patients with pre-treatment anaemia (p = 0.001), hypoalbuminaemia (p = 0.01) and hypomagnesaemia (p = 0.001) as well in those with habit to alcohol consumption (p = 0.003). Neuropathy durations were conversely associated with age, being longer in younger patients (p = 0.03), and again with hypoalbuminaemia (p = 0.04) and hypomagnesaemia (p = 0.002). No correlation was found with gender, hypocalcaemia, diabetes and CRF. The correlation between vitamin B(12) and folate levels and the development of neurotoxicity were not analysed because of the high number of missing data in the population. Age, anaemia, hypoalbuminaemia, hypomagnesaemia and alcohol consumption are reliable and easily assessable clinical factors predicting incidence and length of oxaliplatin-induced neuropathy.

NWP model forecast skill optimization via closure parameter variations

NASA Astrophysics Data System (ADS)

Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

2012-04-01

We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

Intrinsic Molecular Subtypes of Glioma Are Prognostic and Predict Benefit From Adjuvant Procarbazine, Lomustine, and Vincristine Chemotherapy in Combination With Other Prognostic Factors in Anaplastic Oligodendroglial Brain Tumors: A Report From EORTC Study 26951

PubMed Central

Erdem-Eraslan, Lale; Gravendeel, Lonneke A.; de Rooi, Johan; Eilers, Paul H.C.; Idbaih, Ahmed; Spliet, Wim G.M.; den Dunnen, Wilfred F.A.; Teepen, Johannes L.; Wesseling, Pieter; Sillevis Smitt, Peter A.E.; Kros, Johan M.; Gorlia, Thierry; van den Bent, Martin J.; French, Pim J.

2013-01-01

Purpose Intrinsic glioma subtypes (IGSs) are molecularly similar tumors that can be identified based on unsupervised gene expression analysis. Here, we have evaluated the clinical relevance of these subtypes within European Organisation for Research and Treatment of Cancer (EORTC) 26951, a randomized phase III clinical trial investigating adjuvant procarbazine, lomustine, and vincristine (PCV) chemotherapy in anaplastic oligodendroglial tumors. Our study includes gene expression profiles of formalin-fixed, paraffin-embedded (FFPE) clinical trial samples. Patients and Methods Gene expression profiling was performed in 140 samples, 47 fresh frozen samples and 93 FFPE samples, on HU133_Plus_2.0 and HuEx_1.0_st arrays, respectively. Results All previously identified six IGSs are present in EORTC 26951. This confirms that different molecular subtypes are present within a well-defined histologic subtype. Intrinsic subtypes are highly prognostic for overall survival (OS) and progression-free survival (PFS). They are prognostic for PFS independent of clinical (age, performance status, and tumor location), molecular (1p/19q loss of heterozygosity [LOH], IDH1 mutation, and MGMT methylation), and histologic parameters. Combining known molecular (1p/19q LOH, IDH1) prognostic parameters with intrinsic subtypes improves outcome prediction (proportion of explained variation, 30% v 23% for each individual group of factors). Specific genetic changes (IDH1, 1p/19q LOH, and EGFR amplification) segregate into different subtypes. We identified one subtype, IGS-9 (characterized by a high percentage of 1p/19q LOH and IDH1 mutations), that especially benefits from PCV chemotherapy. Median OS in this subtype was 5.5 years after radiotherapy (RT) alone versus 12.8 years after RT/PCV (P = .0349; hazard ratio, 2.18; 95% CI, 1.06 to 4.50). Conclusion Intrinsic subtypes are highly prognostic in EORTC 26951 and improve outcome prediction when combined with other prognostic factors. Tumors assigned to IGS-9 benefit from adjuvant PCV. PMID:23269986

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

DOE PAGES

Alderman, Phillip D.; Stanfill, Bryan

2016-10-06

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relativemore » contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, this study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.« less

[Current Possibilities for Predicting Responses to EGFR Blockade in Metastatic Colorectal Cancer].

PubMed

Němeček, R; Svoboda, M; Slabý, O

2016-01-01

The combination of modern systemic chemotherapy and anti-EGFR monoclonal antibodies improves overall survival and quality of life for patients with metastatic colorecal cancer. By contrast, the addition of anti-EGFR therapy to the treatment regime of resistant patients may lead to worse progression-free survival and overall survival. Therefore, identifying sensitive and resistant patients prior to targeted therapy of metastatic colorecal cancer is a key point during the initial decision making process. Previous research shows that primary resistance to EGFR blockade is in most cases caused by constitutive activation of signaling pathways downstream of EGFR. Of all relevant factors (mutation of KRAS, NRAS, BRAF, and PIK3CA oncogenes, inactivation of tumor suppressors PTEN and TP53, amplification of EGFR and HER2, and expression of epiregulin and amphiregulin, mikroRNA miR-31-3p, and miR-31-5p), only evaluation of KRAS and NRAS mutations has entered routine clinical practice. The role of the other markers still needs to be validated. The ongoing benefit of anti-EGFR therapy could be indicated by specific clinical parameters measured after the initiation of targeted therapy, including early tumor shrinkage, the deepness of the response, or hypomagnesemia. The accuracy of predictive dia-gnostic tools could be also increased by examining a combination of predictive markers using next generation sequencing methods. However, unjustified investigation of many molecular markers should be resisted as this may complicate interpretation of the results, particularly in terms of their specific clinical relevance. The aim of this review is to describe current possibilities with respect to predicting responses to EGFR blockade in the context of the EGFR pathway, and the utilization of such results in routine clinical practice.

Drug-drug interactions via mechanism-based cytochrome P450 inactivation: points to consider for risk assessment from in vitro data and clinical pharmacologic evaluation.

PubMed

Venkatakrishnan, Karthik; Obach, R Scott

2007-06-01

This commentary discusses the approaches to, and key considerations in the in vitro-in vivo extrapolation of drug-drug interactions (DDI) resulting from mechanism-based inactivation (MBI) of cytochrome P450 (CYP) enzymes and clinical pharmacologic implications. In vitro kinetic assessment and prediction of DDI produced via reversible inhibition and MBI rely on operationally and conceptually distinct approaches. DDI risk assessment for inactivators requires estimation of maximal inactivation rate (k(inact)) and inactivator potency (KI) in vitro, that need to be considered in context of the biological turnover rate of the enzyme (kdeg) and clinical exposures of the inactivator (I), respectively, to predict interaction magnitude. Risk assessment cannot be performed by a simple comparison of inactivator potency against in vivo exposure since inactivation is both concentration and time-dependent. MBI contour plots tracking combinations of I:KI and k(inact):k(deg) resulting in identical fold-reductions in intrinsic clearance are proposed as a useful framework for DDI risk assessment. Additionally, substrate-specific factors like fraction of the total clearance of the object drug via the enzyme being inactivated (f(m(CYP) )) and the bioavailability fraction across the intestine for CYP3A substrates (F(G)) are important determinants of interaction magnitude. Sensitivity analysis of predicted DDI magnitude to uncertainty in input parameters is recommended to inform confidence in predictions. The time course of reversal of DDI resulting from CYP inactivation is determined by the half-life of the enzyme which is an important consideration in the design and interpretation of clinical DDI studies with inactivators.

Method for Automatic Selection of Parameters in Normal Tissue Complication Probability Modeling.

PubMed

Christophides, Damianos; Appelt, Ane L; Gusnanto, Arief; Lilley, John; Sebag-Montefiore, David

2018-07-01

To present a fully automatic method to generate multiparameter normal tissue complication probability (NTCP) models and compare its results with those of a published model, using the same patient cohort. Data were analyzed from 345 rectal cancer patients treated with external radiation therapy to predict the risk of patients developing grade 1 or ≥2 cystitis. In total, 23 clinical factors were included in the analysis as candidate predictors of cystitis. Principal component analysis was used to decompose the bladder dose-volume histogram into 8 principal components, explaining more than 95% of the variance. The data set of clinical factors and principal components was divided into training (70%) and test (30%) data sets, with the training data set used by the algorithm to compute an NTCP model. The first step of the algorithm was to obtain a bootstrap sample, followed by multicollinearity reduction using the variance inflation factor and genetic algorithm optimization to determine an ordinal logistic regression model that minimizes the Bayesian information criterion. The process was repeated 100 times, and the model with the minimum Bayesian information criterion was recorded on each iteration. The most frequent model was selected as the final "automatically generated model" (AGM). The published model and AGM were fitted on the training data sets, and the risk of cystitis was calculated. The 2 models had no significant differences in predictive performance, both for the training and test data sets (P value > .05) and found similar clinical and dosimetric factors as predictors. Both models exhibited good explanatory performance on the training data set (P values > .44), which was reduced on the test data sets (P values < .05). The predictive value of the AGM is equivalent to that of the expert-derived published model. It demonstrates potential in saving time, tackling problems with a large number of parameters, and standardizing variable selection in NTCP modeling. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

Prognostic Utility of Cell Cycle Progression Score in Men With Prostate Cancer After Primary External Beam Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freedland, Stephen J., E-mail: steve.freedland@duke.edu; Department of Surgery; Department of Pathology, Duke University School of Medicine, Durham, North Carolina

Purpose: To evaluate the prognostic utility of the cell cycle progression (CCP) score, a RNA signature based on the average expression level of 31 CCP genes, for predicting biochemical recurrence (BCR) in men with prostate cancer treated with external beam radiation therapy (EBRT) as their primary curative therapy. Methods and Materials: The CCP score was derived retrospectively from diagnostic biopsy specimens of men diagnosed with prostate cancer from 1991 to 2006 (n=141). All patients were treated with definitive EBRT; approximately half of the cohort was African American. Outcome was time from EBRT to BCR using the Phoenix definition. Median follow-upmore » for patients without BCR was 4.8 years. Association with outcome was evaluated by Cox proportional hazards survival analysis and likelihood ratio tests. Results: Of 141 patients, 19 (13%) had BCR. The median CCP score for patient samples was 0.12. In univariable analysis, CCP score significantly predicted BCR (P=.0017). The hazard ratio for BCR was 2.55 for 1-unit increase in CCP score (equivalent to a doubling of gene expression). In a multivariable analysis that included Gleason score, prostate-specific antigen, percent positive cores, and androgen deprivation therapy, the hazard ratio for CCP changed only marginally and remained significant (P=.034), indicating that CCP provides prognostic information that is not provided by standard clinical parameters. With 10-year censoring, the CCP score was associated with prostate cancer-specific mortality (P=.013). There was no evidence for interaction between CCP and any clinical variable, including ethnicity. Conclusions: Among men treated with EBRT, the CCP score significantly predicted outcome and provided greater prognostic information than was available with clinical parameters. If validated in a larger cohort, CCP score could identify high-risk men undergoing EBRT who may need more aggressive therapy.« less

Fluid dynamics in flexible tubes: An application to the study of the pulmonary circulation

NASA Technical Reports Server (NTRS)

Kuchar, N. R.

1971-01-01

Based on an analysis of unsteady, viscous flow through distensible tubes, a lumped-parameter model for the dynamics of blood flow through the pulmonary vascular bed was developed. The model is nonlinear, incorporating the variation of flow resistance with transmural pressure. Solved using a hybrid computer, the model yields information concerning the time-dependent behavior of blood pressures, flow rates, and volumes in each important class of vessels in each lobe of each lung in terms of the important physical and environmental parameters. Simulations of twenty abnormal or pathological situations of interest in environmental physiology and clinical medicine were performed. The model predictions agree well with physiological data.

Antibodies to the HIV-1 Tat protein correlated with nonprogression to AIDS: a rationale for the use of Tat toxoid as an HIV-1 vaccine.

PubMed

Zagury, J F; Sill, A; Blattner, W; Lachgar, A; Le Buanec, H; Richardson, M; Rappaport, J; Hendel, H; Bizzini, B; Gringeri, A; Carcagno, M; Criscuolo, M; Burny, A; Gallo, R C; Zagury, D

1998-01-01

To investigate which immune parameters, such as antibodies against HIV-1 specificities, or viral parameters, such as p24 antigenemia, are predictive of disease progression. We performed studies on serum collected from individuals exhibiting two extremes of disease evolution--67 fast progressors (FP) and 182 nonprogressors (NP)--at their enrollment. After a 1- to 2-year clinical follow-up of 104 nonprogressors after their enrollment, we could determine the best serologic predictors for disease progression. We investigated levels of antibodies to tetanus toxoid and to HIV antigens including Env, Gag, Nef, and Tat proteins, as well as p24 antigenemia, viremia, CD4 cell count, and interferon-alpha (IFN-alpha) titers in FPs and NPs, and we correlated these data with clinical and biologic signs of progression. p24 Antigenemia, a marker of viral replication, and anti-Tat antibodies were highly and inversely correlated in both groups (P < .001). Furthermore, anti-p24 antibodies and low serum IFN-alpha levels were correlated to the NP versus the FP cohort. Finally, among NPs, only antibodies to Tat and not to the other HIV specificities (Env, Nef, Gag) were significantly predictive of clinical stability during their follow-up. Antibodies toward HIV-1 Tat, which are inversely correlated to p24 antigenemia, appear as a critical marker for a lack of disease progression. This study strongly suggests that rising anti-Tat antibodies through active immunization may be beneficial in AIDS vaccine development to control viral replication.

Assessment of imaging parameters correlated with the effects of cryopreservation on embryo development

NASA Astrophysics Data System (ADS)

Zarnescu, Livia; Abeyta, Mike; Baer, Thomas M.; Behr, Barry; Ellerbee, Audrey K.

2014-03-01

Embryo cryopreservation is an increasingly common technique that allows patients to undergo multiple cycles of in vitro fertilization (IVF) without being subjected to repeated ovarian stimulation and oocyte retrieval. There are two types of cryopreservation commonly used in IVF clinics today: slow freezing and vitrification. Because vitrification has been shown to result in higher rates of embryo survival post-thaw compared to slow freezing, it is rapidly gaining popularity in clinics worldwide. However, several studies have shown that vitrification can still cause damage to embryos in the form of DNA fragmentation, altered mitochondrial distribution and changes in transcriptional activity, all of which are impossible to assess noninvasively. In this paper we demonstrate a new method of quantitatively and noninvasively assessing changes in embryo appearance due to vitrification. Using full-field optical coherence tomography (FF-OCT), we show that vitrification causes striking changes in the appearance of the cytoplasm that are not visible under conventional brightfield microscopy. Using an automated algorithm that extracts parameters to describe these changes, we show that these parameters can also predict viability in embryos that have undergone vitrification. An automated, noninvasive assessment of embryo viability after vitrification and thawing could have significant clinical impact: allowing clinicians to more accurately choose the most viable embryos to transfer back to patients could reduce the average number of IVF cycles that patients must undergo to achieve pregnancy.

The effect of word prediction settings (frequency of use) on text input speed in persons with cervical spinal cord injury: a prospective study.

PubMed

Pouplin, Samuel; Roche, Nicolas; Antoine, Jean-Yves; Vaugier, Isabelle; Pottier, Sandra; Figere, Marjorie; Bensmail, Djamel

2017-06-01

To determine whether activation of the frequency of use and automatic learning parameters of word prediction software has an impact on text input speed. Forty-five participants with cervical spinal cord injury between C4 and C8 Asia A or B accepted to participate to this study. Participants were separated in two groups: a high lesion group for participants with lesion level is at or above C5 Asia AIS A or B and a low lesion group for participants with lesion is between C6 and C8 Asia AIS A or B. A single evaluation session was carried out for each participant. Text input speed was evaluated during three copying tasks: • without word prediction software (WITHOUT condition) • with automatic learning of words and frequency of use deactivated (NOT_ACTIV condition) • with automatic learning of words and frequency of use activated (ACTIV condition) Results: Text input speed was significantly higher in the WITHOUT than the NOT_ACTIV (p< 0.001) or ACTIV conditions (p = 0.02) for participants with low lesions. Text input speed was significantly higher in the ACTIV than in the NOT_ACTIV (p = 0.002) or WITHOUT (p < 0.001) conditions for participants with high lesions. Use of word prediction software with the activation of frequency of use and automatic learning increased text input speed in participants with high-level tetraplegia. For participants with low-level tetraplegia, the use of word prediction software with frequency of use and automatic learning activated only decreased the number of errors. Implications in rehabilitation   Access to technology can be difficult for persons with disabilities such as cervical spinal cord injury (SCI). Several methods have been developed to increase text input speed such as word prediction software.This study show that parameter of word prediction software (frequency of use) affected text input speed in persons with cervical SCI and differed according to the level of the lesion. • For persons with high-level lesion, our results suggest that this parameter must be activated so that text input speed is increased. • For persons with low lesion group, this parameter must be activated so that the numbers of errors are decreased. • In all cases, the activation of the parameter of frequency of use is essential in order to improve the efficiency of the word prediction software. • Health-related professionals should use these results in their clinical practice for better results and therefore better patients 'satisfaction.

Enhancing coronary Wave Intensity Analysis robustness by high order central finite differences.

PubMed

Rivolo, Simone; Asrress, Kaleab N; Chiribiri, Amedeo; Sammut, Eva; Wesolowski, Roman; Bloch, Lars Ø; Grøndal, Anne K; Hønge, Jesper L; Kim, Won Y; Marber, Michael; Redwood, Simon; Nagel, Eike; Smith, Nicolas P; Lee, Jack

2014-09-01

Coronary Wave Intensity Analysis (cWIA) is a technique capable of separating the effects of proximal arterial haemodynamics from cardiac mechanics. Studies have identified WIA-derived indices that are closely correlated with several disease processes and predictive of functional recovery following myocardial infarction. The cWIA clinical application has, however, been limited by technical challenges including a lack of standardization across different studies and the derived indices' sensitivity to the processing parameters. Specifically, a critical step in WIA is the noise removal for evaluation of derivatives of the acquired signals, typically performed by applying a Savitzky-Golay filter, to reduce the high frequency acquisition noise. The impact of the filter parameter selection on cWIA output, and on the derived clinical metrics (integral areas and peaks of the major waves), is first analysed. The sensitivity analysis is performed either by using the filter as a differentiator to calculate the signals' time derivative or by applying the filter to smooth the ensemble-averaged waveforms. Furthermore, the power-spectrum of the ensemble-averaged waveforms contains little high-frequency components, which motivated us to propose an alternative approach to compute the time derivatives of the acquired waveforms using a central finite difference scheme. The cWIA output and consequently the derived clinical metrics are significantly affected by the filter parameters, irrespective of its use as a smoothing filter or a differentiator. The proposed approach is parameter-free and, when applied to the 10 in-vivo human datasets and the 50 in-vivo animal datasets, enhances the cWIA robustness by significantly reducing the outcome variability (by 60%).

Progression of trunk imbalance in adolescent idiopathic scoliosis with a thoracolumbar/lumbar curve: is it predictable at the initial visit?

PubMed

Hwang, Chang Ju; Lee, Choon Sung; Lee, Dong-Ho; Cho, Jae Hwan

2017-11-01

OBJECTIVE Progression of trunk imbalance is an important finding during follow-up of patients with adolescent idiopathic scoliosis (AIS). Nevertheless, no factors that predict progression of trunk imbalance have been identified. The purpose of this study was to identify parameters that predict progression of trunk imbalance in cases of AIS with a structural thoracolumbar/lumbar (TL/L) curve. METHODS This study included 105 patients with AIS and a structural TL/L curve who were followed up at an outpatient clinic. Patients with trunk imbalance (trunk shift ≥ 20 mm) at the initial visit were excluded. All patients were followed up for more than 2 years. Patients were divided into the following groups according to progression of trunk imbalance: 1) Group P, trunk shift ≥ 20 mm at the final visit and degree of progression ≥ 10 mm; and 2) Group NP, trunk shift < 20 mm at the final visit or degree of progression < 10 mm. Radiological parameters included Cobb angle, upper end vertebrae and lower end vertebrae (LEV), LEV tilt, disc wedge angle between LEV and LEV+1, trunk shift, apical vertebral translation, and apical vertebral rotation (AVR). Each parameter was compared between groups. Radiological parameters were assessed at every visit using whole-spine standing anteroposterior radiographs. RESULTS Among the 105 patients examined, 13 showed trunk imbalance with progression ≥ 10 mm at the final visit (Group P). Multivariate logistic regression analysis identified a lower Risser grade (p = 0.002) and a greater initial AVR (p = 0.020) as predictors of progressive trunk imbalance. A change in LEV tilt during follow-up was associated with trunk imbalance (p = 0.001). CONCLUSIONS Risser grade and AVR measured at the initial visit may predict progression of trunk imbalance. Surgeons should consider the risk of progressive trunk imbalance if patients show skeletal immaturity and a greater AVR at the initial visit.

Discovery and Validation of Novel Expression Signature for Postcystectomy Recurrence in High-Risk Bladder Cancer

PubMed Central

Lam, Lucia L.; Ghadessi, Mercedeh; Erho, Nicholas; Vergara, Ismael A.; Alshalalfa, Mohammed; Buerki, Christine; Haddad, Zaid; Sierocinski, Thomas; Triche, Timothy J.; Skinner, Eila C.; Davicioni, Elai; Daneshmand, Siamak; Black, Peter C.

2014-01-01

Background Nearly half of muscle-invasive bladder cancer patients succumb to their disease following cystectomy. Selecting candidates for adjuvant therapy is currently based on clinical parameters with limited predictive power. This study aimed to develop and validate genomic-based signatures that can better identify patients at risk for recurrence than clinical models alone. Methods Transcriptome-wide expression profiles were generated using 1.4 million feature-arrays on archival tumors from 225 patients who underwent radical cystectomy and had muscle-invasive and/or node-positive bladder cancer. Genomic (GC) and clinical (CC) classifiers for predicting recurrence were developed on a discovery set (n = 133). Performances of GC, CC, an independent clinical nomogram (IBCNC), and genomic-clinicopathologic classifiers (G-CC, G-IBCNC) were assessed in the discovery and independent validation (n = 66) sets. GC was further validated on four external datasets (n = 341). Discrimination and prognostic abilities of classifiers were compared using area under receiver-operating characteristic curves (AUCs). All statistical tests were two-sided. Results A 15-feature GC was developed on the discovery set with area under curve (AUC) of 0.77 in the validation set. This was higher than individual clinical variables, IBCNC (AUC = 0.73), and comparable to CC (AUC = 0.78). Performance was improved upon combining GC with clinical nomograms (G-IBCNC, AUC = 0.82; G-CC, AUC = 0.86). G-CC high-risk patients had elevated recurrence probabilities (P < .001), with GC being the best predictor by multivariable analysis (P = .005). Genomic-clinicopathologic classifiers outperformed clinical nomograms by decision curve and reclassification analyses. GC performed the best in validation compared with seven prior signatures. GC markers remained prognostic across four independent datasets. Conclusions The validated genomic-based classifiers outperform clinical models for predicting postcystectomy bladder cancer recurrence. This may be used to better identify patients who need more aggressive management. PMID:25344601

Normal tissue complication probability modelling of tissue fibrosis following breast radiotherapy

NASA Astrophysics Data System (ADS)

Alexander, M. A. R.; Brooks, W. A.; Blake, S. W.

2007-04-01

Cosmetic late effects of radiotherapy such as tissue fibrosis are increasingly regarded as being of importance. It is generally considered that the complication probability of a radiotherapy plan is dependent on the dose uniformity, and can be reduced by using better compensation to remove dose hotspots. This work aimed to model the effects of improved dose homogeneity on complication probability. The Lyman and relative seriality NTCP models were fitted to clinical fibrosis data for the breast collated from the literature. Breast outlines were obtained from a commercially available Rando phantom using the Osiris system. Multislice breast treatment plans were produced using a variety of compensation methods. Dose-volume histograms (DVHs) obtained for each treatment plan were reduced to simple numerical parameters using the equivalent uniform dose and effective volume DVH reduction methods. These parameters were input into the models to obtain complication probability predictions. The fitted model parameters were consistent with a parallel tissue architecture. Conventional clinical plans generally showed reducing complication probabilities with increasing compensation sophistication. Extremely homogenous plans representing idealized IMRT treatments showed increased complication probabilities compared to conventional planning methods, as a result of increased dose to areas receiving sub-prescription doses using conventional techniques.

Novel biomarkers for predicting intrauterine growth restriction: a systematic review and meta-analysis.

PubMed

Conde-Agudelo, A; Papageorghiou, A T; Kennedy, S H; Villar, J

2013-05-01

Several biomarkers for predicting intrauterine growth restriction (IUGR) have been proposed in recent years. However, the predictive performance of these biomarkers has not been systematically evaluated. To determine the predictive accuracy of novel biomarkers for IUGR in women with singleton gestations. Electronic databases, reference list checking and conference proceedings. Observational studies that evaluated the accuracy of novel biomarkers proposed for predicting IUGR. Data were extracted on characteristics, quality and predictive accuracy from each study to construct 2×2 tables. Summary receiver operating characteristic curves, sensitivities, specificities and likelihood ratios (LRs) were generated. A total of 53 studies, including 39,974 women and evaluating 37 novel biomarkers, fulfilled the inclusion criteria. Overall, the predictive accuracy of angiogenic factors for IUGR was minimal (median pooled positive and negative LRs of 1.7, range 1.0-19.8; and 0.8, range 0.0-1.0, respectively). Two small case-control studies reported high predictive values for placental growth factor and angiopoietin-2 only when IUGR was defined as birthweight centile with clinical or pathological evidence of fetal growth restriction. Biomarkers related to endothelial function/oxidative stress, placental protein/hormone, and others such as serum levels of vitamin D, urinary albumin:creatinine ratio, thyroid function tests and metabolomic profile had low predictive accuracy. None of the novel biomarkers evaluated in this review are sufficiently accurate to recommend their use as predictors of IUGR in routine clinical practice. However, the use of biomarkers in combination with biophysical parameters and maternal characteristics could be more useful and merits further research. © 2013 The Authors BJOG An International Journal of Obstetrics and Gynaecology © 2013 RCOG.

Predicting deterioration in previously healthy infants hospitalized with respiratory syncytial virus infection.

PubMed

Brooks, A M; McBride, J T; McConnochie, K M; Aviram, M; Long, C; Hall, C B

1999-09-01

To estimate the incidence of clinical deterioration leading to intensive care unit transfer in previously healthy infants with respiratory syncytial virus (RSV) infection hospitalized on a general pediatric unit and, to assess the hypothesis that history, physical examination, oximetry, and chest radiographic findings at time of presentation can accurately identify these infants. A virology database was used to identify and determine the disposition of all children 80 and an O(2) saturation <85% at time of presentation each had a specificity >97% for predicting subsequent deterioration. Each parameter, however, had a sensitivity

Use of bioimpedance vector analysis in critically ill and cardiorenal patients.

PubMed

Peacock, W Frank

2010-01-01

Prospective outcome prediction and volume status assessment are difficult tasks in the acute care environment. Rapidly available, non-invasive, bioimpedance vector analysis (BIVA) may offer objective measures to improve clinical decision-making and predict outcomes. Performed by the placement of bipolar electrodes at the wrist and ankle, data is graphically displayed such that short-term morality risk and volume status can be accurately quantified. BIVA is able to provide indices of general cellular health, which has significant prognostic implications, as well as total body volume. Knowledge of these parameters can provide insight as to the short-term prognosis, as well as the presenting volume status. 2010 S. Karger AG, Basel.

Improving hot region prediction by parameter optimization of density clustering in PPI.

PubMed

Hu, Jing; Zhang, Xiaolong

2016-11-01

This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius. Copyright © 2016. Published by Elsevier Inc.

Prediction of stream volatilization coefficients

USGS Publications Warehouse

Rathbun, Ronald E.

1990-01-01

Equations are developed for predicting the liquid-film and gas-film reference-substance parameters for quantifying volatilization of organic solutes from streams. Molecular weight and molecular-diffusion coefficients of the solute are used as correlating parameters. Equations for predicting molecular-diffusion coefficients of organic solutes in water and air are developed, with molecular weight and molal volume as parameters. Mean absolute errors of prediction for diffusion coefficients in water are 9.97% for the molecular-weight equation, 6.45% for the molal-volume equation. The mean absolute error for the diffusion coefficient in air is 5.79% for the molal-volume equation. Molecular weight is not a satisfactory correlating parameter for diffusion in air because two equations are necessary to describe the values in the data set. The best predictive equation for the liquid-film reference-substance parameter has a mean absolute error of 5.74%, with molal volume as the correlating parameter. The best equation for the gas-film parameter has a mean absolute error of 7.80%, with molecular weight as the correlating parameter.

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

PubMed Central

Parker, Joel S.; Mullins, Michael; Cheang, Maggie C.U.; Leung, Samuel; Voduc, David; Vickery, Tammi; Davies, Sherri; Fauron, Christiane; He, Xiaping; Hu, Zhiyuan; Quackenbush, John F.; Stijleman, Inge J.; Palazzo, Juan; Marron, J.S.; Nobel, Andrew B.; Mardis, Elaine; Nielsen, Torsten O.; Ellis, Matthew J.; Perou, Charles M.; Bernard, Philip S.

2009-01-01

Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy. PMID:19204204

First-trimester prediction of birth weight.

PubMed

Boucoiran, Isabelle; Djemli, Anissa; Taillefer, Catherine; Rypens, Françoise; Delvin, Edgard; Audibert, François

2013-09-01

To determine whether the parameters used in first-trimester screening for aneuploidies contribute significantly to the prediction of birth weight. In this retrospective cohort study (n = 4110), nuchal translucency (NT), free β-chorionic gonadotropin (fβ-hCG), and pregnancy-associated plasma protein-A (PAPP-A) blood concentrations were measured between 11 + 0 and 13 + 6 weeks. Multiple pregnancies, chromosomal anomalies, major fetal defects, and deliveries before 24 weeks were excluded. NT (0.95 versus 0.98 multiples of the expected median [MoM], p < 0.001) and PAPP-A (0.93 versus 1.06 MoM, p = 0.005) were significantly lower in small-for-gestational-age (SGA) newborns (<10th percentile) than the unaffected group, but not fβ-hCG (0.89 versus 0.93 MoM, p = 0.113). NT was significantly higher (1.03 versus 0.98 MoM, p < 0.001) in the large-for-gestational-age (LGA) group (>90th percentile) compared with the unaffected group, and biomarkers did not differ. After controlling for gestational age, maternal weight, smoking status, ethnicity, and fetal sex, first-trimester markers contributed to the prediction of birth weight in a multiple linear model but did not significantly improved the prediction of SGA and LGA compared with maternal characteristics alone. Parameters used in first-trimester screening for aneuploidies contribute to the prediction of birth weight but their clinical utility to detect women at risk of SGA or LGA baby is limited. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

The BPAQ: a bone-specific physical activity assessment instrument.

PubMed

Weeks, B K; Beck, B R

2008-11-01

A newly developed bone-specific physical activity questionnaire (BPAQ) was compared with other common measures of physical activity for its ability to predict parameters of bone strength in healthy, young adults. The BPAQ predicted indices of bone strength at clinically relevant sites in both men and women, while other measures did not. Only certain types of physical activity (PA) are notably osteogenic. Most methods to quantify levels of PA fail to account for bone relevant loading. Our aim was to examine the ability of several methods of PA assessment and a new bone-specific measure to predict parameters of bone strength in healthy adults. We recruited 40 men and women (mean age 24.5). Subjects completed the modifiable activity questionnaire, Bouchard 3-day activity record, a recently published bone loading history questionnaire (BLHQ), and wore a pedometer for 14 days. We also administered our bone-specific physical activity questionnaire (BPAQ). Calcaneal broadband ultrasound attenuation (BUA) (QUS-2, Quidel) and densitometric measures (XR-36, Norland) were examined. Multiple regression and correlation analyses were performed on the data. The current activity component of BPAQ was a significant predictor of variance in femoral neck bone mineral density (BMD), lumbar spine BMD, and whole body BMD (R(2) = 0.36-0.68, p < 0.01) for men, while the past activity component of BPAQ predicted calcaneal BUA (R(2) = 0.48, p = 0.001) for women. The BPAQ predicted indices of bone strength at skeletal sites at risk of osteoporotic fracture while other PA measurement tools did not.

Combined periodontal and restorative approach to the treatment of gingival recessions with noncarious cervical lesions: a case treated with acellular dermal matrix allograft and compomer restorations.

PubMed

Efeoğlu, Ahmet; Hanzade, Mete; Sari, Esra; Alpay, Hande; Karakaş, Ozan; Koray, Fatma

2012-08-01

Treatment of gingival recessions has become one of the most challenging procedures in periodontal plastic surgery. Various surgical options with predictable outcomes are available, but in cases with cervical lesions or restorations, optimal functional and esthetic results may require the combination of periodontal and restorative procedures. In this case report, one patient treated with acellular dermal matrix allograft and a coronally positioned flap in combination with compomer cervical restorations is presented. Clinical parameters were recorded immediately prior to surgery and after 12 months. Postoperatively, significant root coverage, reductions in probing depths, and gains in clinical attachment were observed. The final clinical results, esthetics, color match, and tissue contours were acceptable to both the patient and clinicians.

Sarcopenia-related parameters and incident disability in older persons: results from the "invecchiare in Chianti" study.

PubMed

Cesari, Matteo; Rolland, Yves; Abellan Van Kan, Gabor; Bandinelli, Stefania; Vellas, Bruno; Ferrucci, Luigi

2015-04-01

Current operational definitions of sarcopenia are based on algorithms' simultaneous considering measures of skeletal muscle mass and muscle-specific as well as global function. We hypothesize that quantitative and qualitative sarcopenia-related parameters may not be equally predictive of incident disability, thus presenting different clinical relevance. Data are from 922 elder adults (mean age = 73.9 years) with no activities of daily living (ADL) impairment recruited in the "Invecchiare in Chianti" study. Incident disability in ≥1 ADL defined the outcome of interest. The specific capacities of following sarcopenia-related parameters at predicting incident ADL disability were compared: residuals of skeletal muscle mass, fat-adjusted residuals of skeletal muscle mass, muscle density, ankle extension strength, ratio ankle extension strength/muscle mass, gait speed, and handgrip strength. During the follow-up (median = 9.1 years), 188 (20.4%) incident ADL disability events were reported. Adjusted models showed that only gait speed was significantly associated with the outcome in both men (per standard deviation [SD] = 0.23 m/s increase, hazard ratio [HR] = 0.46, 95% confidence interval [CI] = 0.33-0.63; p < .001) and women (per SD = 0.24 m/s increase, HR = 0.64, 95% CI = 0.50-0.82; p < .001). In women, the fat-adjusted lean mass residual (per SD = 4.41 increase, HR = 0.79, 95% CI = 0.65-0.96; p = .02) and muscle density (per SD = 3.60 increase, HR = 0.76, 95% CI = 0.61-0.93; p = .01) were the only other parameters that predicted disability. In men, several of the tested variables (except muscle mass measures) reported significant results. Gender strongly influences which sarcopenia-related parameters predict disability. Gait speed was a powerful predictor of disability in both men and women, but its nonmuscle-specific nature should impose caution about its inclusion in definitions of sarcopenia. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Diagnostic importance of 18F-FDG PET/CT parameters and total lesion glycolysis in differentiating between benign and malignant adrenal lesions.

PubMed

Ciftci, Esra; Turgut, Bulent; Cakmakcilar, Ali; Erturk, Seyit A

2017-09-01

Benign adrenal lesions are prevalent in oncologic imaging and make metastatic disease diagnoses difficult. This study evaluates the diagnostic importance of metabolic, volumetric, and metabolovolumetric parameters measured by fluorine-18-fluorodeoxyglucose (F-FDG) PET/CT in differentiating between benign and malignant adrenal lesions in cancer patients. In this retrospective study, we evaluated F-FDG PET/CT parameters of adrenal lesions of follow-up cancer patients referred to our clinic between January 2012 and November 2016. The diagnosis of adrenal malignant lesions was made on the basis of interval growth or reduction after chemotherapy. Patient demographics, analysis of metabolic parameters such as maximum standard uptake value (SUVmax), tumor SUVmax/liver SUVmean ratio (T/LR), morphologic parameters such as size, Hounsfield Units, and computed tomography (CT) volume, and metabolovolumetric parameters such as metabolic tumor volume and total lesion glycolysis (TLG) of adrenal lesions were calculated. PET/CT parameters were assessed using the Mann-Whitney U-test and receiving operating characteristic analysis. In total, 186 adrenal lesions in 163 cancer patients (108 men/54 women; mean±SD age: 64±10.9 years) were subjected to F-FDG PET/CT for tumor evaluation. SUVmax values (mean±SD) were 2.8±0.8 and 10.6±6; TLG were 10.8±9.2 and 124.4±347.9; and T/LR were 1±0.3 and 4.1±2.6 in benign and malignant adrenal lesions, respectively. On the basis of the area under the curve, adrenal lesion SUVmax and T/LR had similar highest diagnostic performance for predicting malignant lesions (area under the curve: 0.993 and 0.991, respectively, P<0.001). Multivariate logistic regression analysis showed that T/LR, adrenal lesion SUVmax, and Hounsfield Units were independent predictive factors for malignancy rather than TLG. Irrespective of whether TLG was statistically highly significant for differentiating benign from malignant adrenal lesions, it did not reach the expected performance with a low negative predictive value. This may be because of the malignant but small and benign but large lesions on metabolovolumetric calculation.

Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the ECLIPSE cohort using cluster analysis.

PubMed

Rennard, Stephen I; Locantore, Nicholas; Delafont, Bruno; Tal-Singer, Ruth; Silverman, Edwin K; Vestbo, Jørgen; Miller, Bruce E; Bakke, Per; Celli, Bartolomé; Calverley, Peter M A; Coxson, Harvey; Crim, Courtney; Edwards, Lisa D; Lomas, David A; MacNee, William; Wouters, Emiel F M; Yates, Julie C; Coca, Ignacio; Agustí, Alvar

2015-03-01

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. To identify subgroups of COPD in ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) subjects using cluster analysis and to assess clinically meaningful outcomes of the clusters during 3 years of longitudinal follow-up. Factor analysis was used to reduce 41 variables determined at recruitment in 2,164 patients with COPD to 13 main factors, and the variables with the highest loading were used for cluster analysis. Clusters were evaluated for their relationship with clinically meaningful outcomes during 3 years of follow-up. The relationships among clinical parameters were evaluated within clusters. Five subgroups were distinguished using cross-sectional clinical features. These groups differed regarding outcomes. Cluster A included patients with milder disease and had fewer deaths and hospitalizations. Cluster B had less systemic inflammation at baseline but had notable changes in health status and emphysema extent. Cluster C had many comorbidities, evidence of systemic inflammation, and the highest mortality. Cluster D had low FEV1, severe emphysema, and the highest exacerbation and COPD hospitalization rate. Cluster E was intermediate for most variables and may represent a mixed group that includes further clusters. The relationships among clinical variables within clusters differed from that in the entire COPD population. Cluster analysis using baseline data in ECLIPSE identified five COPD subgroups that differ in outcomes and inflammatory biomarkers and show different relationships between clinical parameters, suggesting the clusters represent clinically and biologically different subtypes of COPD.

Reference values for clinical laboratory parameters in young adults in Maputo, Mozambique.

PubMed

Tembe, Nelson; Joaquim, Orvalho; Alfai, Eunice; Sitoe, Nádia; Viegas, Edna; Macovela, Eulalia; Gonçalves, Emilia; Osman, Nafissa; Andersson, Sören; Jani, Ilesh; Nilsson, Charlotta

2014-01-01

Clinical laboratory reference values from North American and European populations are currently used in most Africans countries due to the absence of locally derived reference ranges, despite previous studies reporting significant differences between populations. Our aim was to define reference ranges for both genders in 18 to 24 year-old Mozambicans in preparation for clinical vaccine trials. A cross-sectional study including 257 volunteers (102 males and 155 females) between 18 and 24 years was performedat a youth clinic in Maputo, Mozambique. All volunteers were clinically healthy and human immunodeficiency virus, Hepatitis B virus and syphilis negative.Median and 95% reference ranges were calculated for immunological, hematological and chemistry parameters. Ranges were compared with those reported based on populations in other African countries and the US. The impact of applying US NIH Division of AIDS (DAIDS) toxicity tables was assessed. The immunology ranges were comparable to those reported for the US and western Kenya.There were significant gender differences in CD4+ T cell values 713 cells/µL in males versus 824 cells/µL in females (p<0.0001). Hematologic values differed from the US values but were similar to reports of populations in western Kenya and Uganda. The lower and upper limits of the ranges for hemoglobin, hematocrit, red blood cells, white blood cells and lymphocytes were somewhat lower than those from these African countries. The chemistry values were comparable to US values, with few exceptions. The upper limits for ALT, AST, bilirubin, cholesterol and triglycerides were higher than those from the US. DAIDStables for adverse events predicted 297 adverse events and 159 (62%) of the volunteers would have been excluded. This study is the first to determine normal laboratory parameters in Mozambique. Our results underscore the necessity of establishing region-specific clinical reference ranges for proper patient management and safe conduct of clinical trials.

Biomarkers from distinct biological pathways improve early risk stratification in medical emergency patients: the multinational, prospective, observational TRIAGE study.

PubMed

Schuetz, Philipp; Hausfater, Pierre; Amin, Devendra; Amin, Adina; Haubitz, Sebastian; Faessler, Lukas; Kutz, Alexander; Conca, Antoinette; Reutlinger, Barbara; Canavaggio, Pauline; Sauvin, Gabrielle; Bernard, Maguy; Huber, Andreas; Mueller, Beat

2015-10-29

Early risk stratification in the emergency department (ED) is vital to reduce time to effective treatment in high-risk patients and to improve patient flow. Yet, there is a lack of investigations evaluating the incremental usefulness of multiple biomarkers measured upon admission from distinct biological pathways for predicting fatal outcome and high initial treatment urgency in unselected ED patients in a multicenter and multinational setting. We included consecutive, adult, medical patients seeking ED care into this observational, cohort study in Switzerland, France and the USA. We recorded initial clinical parameters and batch-measured prognostic biomarkers of inflammation (pro-adrenomedullin [ProADM]), stress (copeptin) and infection (procalcitonin). During a 30-day follow-up, 331 of 7132 (4.6 %) participants reached the primary endpoint of death within 30 days. In logistic regression models adjusted for conventional risk factors available at ED admission, all three biomarkers strongly predicted the risk of death (AUC 0.83, 0.78 and 0.75), ICU admission (AUC 0.67, 0.69 and 0.62) and high initial triage priority (0.67, 0.66 and 0.58). For the prediction of death, ProADM significantly improved regression models including (a) clinical information available at ED admission (AUC increase from 0.79 to 0.84), (b) full clinical information at ED discharge (AUC increase from 0.85 to 0.88), and (c) triage information (AUC increase from 0.67 to 0.83) (p <0.01 for each comparison). Similarly, ProADM also improved clinical models for prediction of ICU admission and high initial treatment urgency. Results were robust in regard to predefined patient subgroups by center, main diagnosis, presenting symptoms, age and gender. Combination of clinical information with results of blood biomarkers measured upon ED admission allows early and more adequate risk stratification in individual unselected medical ED patients. A randomized trial is needed to answer the question whether biomarker-guided initial patient triage reduces time to initial treatment of high-risk patients in the ED and thereby improves patient flow and clinical outcomes. ClinicalTrials.gov NCT01768494 . Registered January 9, 2013.

Diffusion-weighted imaging score of the brain stem: A predictor of outcome in acute basilar artery occlusion treated with the Solitaire FR device.

PubMed

Mourand, I; Machi, P; Nogué, E; Arquizan, C; Costalat, V; Picot, M-C; Bonafé, A; Milhaud, D

2014-06-01

The prognosis for ischemic stroke due to acute basilar artery occlusion is very poor: Early recanalization remains the main factor that can improve outcomes. The baseline extent of brain stem ischemic damage can also influence outcomes. We evaluated the validity of an easy-to-use DWI score to predict clinical outcome in patients with acute basilar artery occlusion treated by mechanical thrombectomy. We analyzed the baseline clinical and DWI parameters of 31 patients with acute basilar artery occlusion, treated within 24 hours of symptom onset by using a Solitaire FR device. The DWI score of the brain stem was assessed with a 12-point semiquantitative score that separately considered each side of the medulla, pons, and midbrain. Clinical outcome was assessed at 180 days by using the mRS. According to receiver operating characteristic analyses, the cutoff score determined the optimal positive predictive value for outcome. The Spearman rank correlation coefficient assessed the correlation between the DWI brain stem score and baseline characteristics. Successful recanalization (Thrombolysis in Cerebral Infarction 3-2b) was achieved in 23 patients (74%). A favorable outcome (mRS ≤ 2) was observed in 11 patients (35%). An optimal DWI brain stem score of <3 predicted a favorable outcome. The probability of a very poor outcome (mRS ≥ 5) if the DWI brain stem score was ≥5 reached 80% (positive predictive value) and 100% if this score was ≥6. Interobserver reliability of the DWI brain stem score was excellent, with an intraclass correlation coefficient of 0.97 (95% CI, 0.96-0.99). The DWI brain stem score was significantly associated with baseline tetraplegia (P = .001) and coma (P = .005). In patients with acute basilar artery occlusion treated by mechanical thrombectomy, the baseline DWI brain lesion score seems to predict clinical outcome. © 2014 by American Journal of Neuroradiology.

Graphical user interface for yield and dose estimations for cyclotron-produced technetium

NASA Astrophysics Data System (ADS)

Hou, X.; Vuckovic, M.; Buckley, K.; Bénard, F.; Schaffer, P.; Ruth, T.; Celler, A.

2014-07-01

The cyclotron-based 100Mo(p,2n)99mTc reaction has been proposed as an alternative method for solving the shortage of 99mTc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with 99mTc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced 99mTc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.

Graphical user interface for yield and dose estimations for cyclotron-produced technetium.

PubMed

Hou, X; Vuckovic, M; Buckley, K; Bénard, F; Schaffer, P; Ruth, T; Celler, A

2014-07-07

The cyclotron-based (100)Mo(p,2n)(99m)Tc reaction has been proposed as an alternative method for solving the shortage of (99m)Tc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with (99m)Tc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced (99m)Tc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.

Bond Graph Model of Cerebral Circulation: Toward Clinically Feasible Systemic Blood Flow Simulations

PubMed Central

Safaei, Soroush; Blanco, Pablo J.; Müller, Lucas O.; Hellevik, Leif R.; Hunter, Peter J.

2018-01-01

We propose a detailed CellML model of the human cerebral circulation that runs faster than real time on a desktop computer and is designed for use in clinical settings when the speed of response is important. A lumped parameter mathematical model, which is based on a one-dimensional formulation of the flow of an incompressible fluid in distensible vessels, is constructed using a bond graph formulation to ensure mass conservation and energy conservation. The model includes arterial vessels with geometric and anatomical data based on the ADAN circulation model. The peripheral beds are represented by lumped parameter compartments. We compare the hemodynamics predicted by the bond graph formulation of the cerebral circulation with that given by a classical one-dimensional Navier-Stokes model working on top of the whole-body ADAN model. Outputs from the bond graph model, including the pressure and flow signatures and blood volumes, are compared with physiological data. PMID:29551979

The Prognostic Influence of BRAF Mutation and other Molecular, Clinical and Laboratory Parameters in Stage IV Colorectal Cancer.

PubMed

Karadima, Maria L; Saetta, Angelica A; Chatziandreou, Ilenia; Lazaris, Andreas C; Patsouris, Efstratios; Tsavaris, Nikolaos

2016-10-01

Our aim was to evaluate the predictive and prognostic influence of BRAF mutation and other molecular, clinical and laboratory parameters in stage IV colorectal cancer (CRC). 60 patients were included in this retrospective analysis, and 17 variables were examined for their relation with treatment response and survival. KRAS mutation was identified in 40.3 % of cases, BRAF and PIK3CA in 8.8 % and 10.5 % respectively. 29.8 % of patients responded to treatment. Median survival time was 14.3 months. Weight loss, fever, abdominal metastases, blood transfusion, hypoalbuminaimia, BRAF and PIK3CA mutations, CRP and DNA Index were associated with survival. In multivariate analysis, male patients had 3.8 times higher probability of response, increased DNA Index was inversely correlated with response and one unit raise of DNA Index augmented 6 times the probability of death. Our findings potentiate the prognostic role of BRAF, PIK3CA mutations and ploidy in advanced CRC.

Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

NASA Astrophysics Data System (ADS)

Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

718F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin's lymphoma as predictors of treatment outcome and survival.

PubMed

Parvez, Aatif; Tau, Noam; Hussey, Douglas; Maganti, Manjula; Metser, Ur

2018-05-12

To determine whether metabolic tumor parameters and radiomic features extracted from 18 F-FDG PET/CT (PET) can predict response to therapy and outcome in patients with aggressive B-cell lymphoma. This institutional ethics board-approved retrospective study included 82 patients undergoing PET for aggressive B-cell lymphoma staging. Whole-body metabolic tumor volume (MTV) using various thresholds and tumor radiomic features were assessed on representative tumor sites. The extracted features were correlated with treatment response, disease-free survival (DFS) and overall survival (OS). At the end of therapy, 66 patients (80.5%) had shown complete response to therapy. The parameters correlating with response to therapy were bulky disease > 6 cm at baseline (p = 0.026), absence of a residual mass > 1.5 cm at the end of therapy CT (p = 0.028) and whole-body MTV with best performance using an SUV threshold of 3 and 6 (p = 0.015 and 0.009, respectively). None of the tumor texture features were predictive of first-line therapy response, while a few of them including GLNU correlated with disease-free survival (p = 0.013) and kurtosis correlated with overall survival (p = 0.035). Whole-body MTV correlates with response to therapy in patient with aggressive B-cell lymphoma. Tumor texture features could not predict therapy response, although several features correlated with the presence of a residual mass at the end of therapy CT and others correlated with disease-free and overall survival. These parameters should be prospectively validated in a larger cohort to confirm clinical prognostication.

Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

NASA Astrophysics Data System (ADS)

Ray, Shonket; Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

2016-03-01

This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

PubMed

Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2015-08-01

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score.

PubMed

Tabak, Ying P; Johannes, Richard S; Sun, Xiaowu; Nunez, Carlos M; McDonald, L Clifford

2015-06-01

To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission Retrospective data analysis Six US acute care hospitals Adult inpatients We used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations. Among 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76-0.81) with good calibration. Among 79% of patients with risk scores of 0-7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001). Using clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.

PREOPERATIVE MRI IMPROVES PREDICTION OF EXTENSIVE OCCULT AXILLARY LYMPH NODE METASTASES IN BREAST CANCER PATIENTS WITH A POSITIVE SENTINEL LYMPH NODE BIOPSY

PubMed Central

Loiselle, Christopher; Eby, Peter R.; Kim, Janice N.; Calhoun, Kristine E.; Allison, Kimberly H.; Gadi, Vijayakrishna K.; Peacock, Sue; Storer, Barry; Mankoff, David A.; Partridge, Savannah C.; Lehman, Constance D.

2014-01-01

Rationale and Objectives To test the ability of quantitative measures from preoperative Dynamic Contrast Enhanced MRI (DCE-MRI) to predict, independently and/or with the Katz pathologic nomogram, which breast cancer patients with a positive sentinel lymph node biopsy will have ≥ 4 positive axillary lymph nodes upon completion axillary dissection. Methods and Materials A retrospective review was conducted to identify clinically node-negative invasive breast cancer patients who underwent preoperative DCE-MRI, followed by sentinel node biopsy with positive findings and complete axillary dissection (6/2005 – 1/2010). Clinical/pathologic factors, primary lesion size and quantitative DCE-MRI kinetics were collected from clinical records and prospective databases. DCE-MRI parameters with univariate significance (p < 0.05) to predict ≥ 4 positive axillary nodes were modeled with stepwise regression and compared to the Katz nomogram alone and to a combined MRI-Katz nomogram model. Results Ninety-eight patients with 99 positive sentinel biopsies met study criteria. Stepwise regression identified DCE-MRI total persistent enhancement and volume adjusted peak enhancement as significant predictors of ≥4 metastatic nodes. Receiver operating characteristic (ROC) curves demonstrated an area under the curve (AUC) of 0.78 for the Katz nomogram, 0.79 for the DCE-MRI multivariate model, and 0.87 for the combined MRI-Katz model. The combined model was significantly more predictive than the Katz nomogram alone (p = 0.003). Conclusion Integration of DCE-MRI primary lesion kinetics significantly improved the Katz pathologic nomogram accuracy to predict presence of metastases in ≥ 4 nodes. DCE-MRI may help identify sentinel node positive patients requiring further localregional therapy. PMID:24331270