Reagent-free bacterial identification using multivariate analysis of transmission spectra

NASA Astrophysics Data System (ADS)

Smith, Jennifer M.; Huffman, Debra E.; Acosta, Dayanis; Serebrennikova, Yulia; García-Rubio, Luis; Leparc, German F.

2012-10-01

The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

Preoperative Electrocardiogram Score for Predicting New-Onset Postoperative Atrial Fibrillation in Patients Undergoing Cardiac Surgery.

PubMed

Gu, Jiwei; Andreasen, Jan J; Melgaard, Jacob; Lundbye-Christensen, Søren; Hansen, John; Schmidt, Erik B; Thorsteinsson, Kristinn; Graff, Claus

2017-02-01

To investigate if electrocardiogram (ECG) markers from routine preoperative ECGs can be used in combination with clinical data to predict new-onset postoperative atrial fibrillation (POAF) following cardiac surgery. Retrospective observational case-control study. Single-center university hospital. One hundred consecutive adult patients (50 POAF, 50 without POAF) who underwent coronary artery bypass grafting, valve surgery, or combinations. Retrospective review of medical records and registration of POAF. Clinical data and demographics were retrieved from the Western Denmark Heart Registry and patient records. Paper tracings of preoperative ECGs were collected from patient records, and ECG measurements were read by two independent readers blinded to outcome. A subset of four clinical variables (age, gender, body mass index, and type of surgery) were selected to form a multivariate clinical prediction model for POAF and five ECG variables (QRS duration, PR interval, P-wave duration, left atrial enlargement, and left ventricular hypertrophy) were used in a multivariate ECG model. Adding ECG variables to the clinical prediction model significantly improved the area under the receiver operating characteristic curve from 0.54 to 0.67 (with cross-validation). The best predictive model for POAF was a combined clinical and ECG model with the following four variables: age, PR-interval, QRS duration, and left atrial enlargement. ECG markers obtained from a routine preoperative ECG may be helpful in predicting new-onset POAF in patients undergoing cardiac surgery. Copyright © 2017 Elsevier Inc. All rights reserved.

Clinical predictors of cardiac magnetic resonance late gadolinium enhancement in patients with atrial fibrillation.

PubMed

Chrispin, Jonathan; Ipek, Esra Gucuk; Habibi, Mohammadali; Yang, Eunice; Spragg, David; Marine, Joseph E; Ashikaga, Hiroshi; Rickard, John; Berger, Ronald D; Zimmerman, Stefan L; Calkins, Hugh; Nazarian, Saman

2017-03-01

This study aims to examine the association of clinical co-morbidities with the presence of left atrial (LA) late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR). Previous studies have established the severity of LA LGE to be associated with atrial fibrillation (AF) recurrence following AF ablation. We sought to determine whether baseline clinical characteristics were associated with LGE extent among patients presenting for an initial AF ablation. The cohort consisted of 179 consecutive patients with no prior cardiac ablation procedures who underwent pre-procedure LGE-CMR. The extent of LA LGE for each patient was calculated using the image intensity ratio, normalized to the mean blood pool intensity, corresponding to a bipolar voltage ≤0.3 mV. The association of LGE extent with baseline clinical characteristics was examined using non-parametric and multivariable models. The mean age of the cohort was 60.9 ± 9.6 years and 128 (72%) were male. In total, 56 (31%) patients had persistent AF. The mean LA volume was 118.4 ± 41.6 mL, and the mean LA LGE extent was 14.1 ± 10.4%. There was no association with any clinical variables with LGE extent by quartiles in the multivariable model. Extent of LGE as a continuous variable was positively, but weakly associated with LA volume in a multivariable model adjusting for age, body mass index, AF persistence, and left ventricular ejection fraction (1.5% scar/mL, P = 0.038). In a cohort of patients presenting for initial AF ablation, the presence of pre-ablation LA LGE extent was weakly, but positively associated with increasing LA volume. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth.

PubMed

Menon, Ramkumar; Bhat, Geeta; Saade, George R; Spratt, Heidi

2014-04-01

To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians. Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method. Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians. Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery. Data were partitioned into training and testing sets. Variable importance, a relative indicator (0-100%) and area under the receiver operating characteristic curve (AUC) characterized results. Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-α, angiopoietin 2, TNFRI, IL-5, MIP1α, IL-1β and TGF-α modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-β1 , IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-α, MCP3, TGF-β3 , TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79). Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

General Multivariate Linear Modeling of Surface Shapes Using SurfStat

PubMed Central

Chung, Moo K.; Worsley, Keith J.; Nacewicz, Brendon, M.; Dalton, Kim M.; Davidson, Richard J.

2010-01-01

Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper present a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used as to parameterize, to smooth out, and to normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects. PMID:20620211

Decision-support models for empiric antibiotic selection in Gram-negative bloodstream infections.

PubMed

MacFadden, D R; Coburn, B; Shah, N; Robicsek, A; Savage, R; Elligsen, M; Daneman, N

2018-04-25

Early empiric antibiotic therapy in patients can improve clinical outcomes in Gram-negative bacteraemia. However, the widespread prevalence of antibiotic-resistant pathogens compromises our ability to provide adequate therapy while minimizing use of broad antibiotics. We sought to determine whether readily available electronic medical record data could be used to develop predictive models for decision support in Gram-negative bacteraemia. We performed a multi-centre cohort study, in Canada and the USA, of hospitalized patients with Gram-negative bloodstream infection from April 2010 to March 2015. We analysed multivariable models for prediction of antibiotic susceptibility at two empiric windows: Gram-stain-guided and pathogen-guided treatment. Decision-support models for empiric antibiotic selection were developed based on three clinical decision thresholds of acceptable adequate coverage (80%, 90% and 95%). A total of 1832 patients with Gram-negative bacteraemia were evaluated. Multivariable models showed good discrimination across countries and at both Gram-stain-guided (12 models, areas under the curve (AUCs) 0.68-0.89, optimism-corrected AUCs 0.63-0.85) and pathogen-guided (12 models, AUCs 0.75-0.98, optimism-corrected AUCs 0.64-0.95) windows. Compared to antibiogram-guided therapy, decision-support models of antibiotic selection incorporating individual patient characteristics and prior culture results have the potential to increase use of narrower-spectrum antibiotics (in up to 78% of patients) while reducing inadequate therapy. Multivariable models using readily available epidemiologic factors can be used to predict antimicrobial susceptibility in infecting pathogens with reasonable discriminatory ability. Implementation of sequential predictive models for real-time individualized empiric antibiotic decision-making has the potential to both optimize adequate coverage for patients while minimizing overuse of broad-spectrum antibiotics, and therefore requires further prospective evaluation. Readily available epidemiologic risk factors can be used to predict susceptibility of Gram-negative organisms among patients with bacteraemia, using automated decision-making models. Copyright © 2018 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Nurses' decision making in heart failure management based on heart failure certification status.

PubMed

Albert, Nancy M; Bena, James F; Buxbaum, Denise; Martensen, Linda; Morrison, Shannon L; Prasun, Marilyn A; Stamp, Kelly D

Research findings on the value of nurse certification were based on subjective perceptions or biased by correlations of certification status and global clinical factors. In heart failure, the value of certification is unknown. Examine the value of certification based nurses' decision-making. Cross-sectional study of nurses who completed heart failure clinical vignettes that reflected decision-making in clinical heart failure scenarios. Statistical tests included multivariable linear, logistic and proportional odds logistic regression models. Of nurses (N = 605), 29.1% were heart failure certified, 35.0% were certified in another specialty/job role and 35.9% were not certified. In multivariable modeling, nurses certified in heart failure (versus not heart failure certified) had higher clinical vignette scores (p = 0.002), reflecting higher evidence-based decision making; nurses with another specialty/role certification (versus no certification) did not (p = 0.62). Heart failure certification, but not in other specialty/job roles was associated with decisions that reflected delivery of high-quality care. Copyright © 2018 Elsevier Inc. All rights reserved.

Predictive features of chronic kidney disease in atypical haemolytic uremic syndrome

PubMed Central

Jamme, Matthieu; Raimbourg, Quentin; Chauveau, Dominique; Seguin, Amélie; Presne, Claire; Perez, Pierre; Gobert, Pierre; Wynckel, Alain; Provôt, François; Delmas, Yahsou; Mousson, Christiane; Servais, Aude; Vrigneaud, Laurence; Veyradier, Agnès

2017-01-01

Chronic kidney disease (CKD) is a frequent and serious complication of atypical haemolytic uremic syndrome (aHUS). We aimed to develop a simple accurate model to predict the risk of renal dysfunction in aHUS based on clinical and biological features available at hospital admission. Renal function at 1-year follow-up, based on an estimated glomerular filtration rate < 60mL/min/1.73m2 as assessed by the Modification of Diet in Renal Disease equation, was used as an indicator of significant CKD. Prospectively collected data from a cohort of 156 aHUS patients who did not receive eculizumab were used to identify predictors of CKD. Covariates associated with renal impairment were identified by multivariate analysis. The model performance was assessed and a scoring system for clinical practice was constructed from the regression coefficient. Multivariate analyses identified three predictors of CKD: a high serum creatinine level, a high mean arterial pressure and a mildly decreased platelet count. The prognostic model had a good discriminative ability (area under the curve = .84). The scoring system ranged from 0 to 5, with corresponding risks of CKD ranging from 18% to 100%. This model accurately predicts development of 1-year CKD in patients with aHUS using clinical and biological features available on admission. After further validation, this model may assist in clinical decision making. PMID:28542627

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

Development of a multivariate model to predict the likelihood of carcinoma in patients with indeterminate peripheral lung nodules after a nondiagnostic bronchoscopic evaluation.

PubMed

Voss, Jesse S; Iqbal, Seher; Jenkins, Sarah M; Henry, Michael R; Clayton, Amy C; Jett, James R; Kipp, Benjamin R; Halling, Kevin C; Maldonado, Fabien

2014-01-01

Studies have shown that fluorescence in situ hybridization (FISH) testing increases lung cancer detection on cytology specimens in peripheral nodules. The goal of this study was to determine whether a predictive model using clinical features and routine cytology with FISH results could predict lung malignancy after a nondiagnostic bronchoscopic evaluation. Patients with an indeterminate peripheral lung nodule that had a nondiagnostic bronchoscopic evaluation were included in this study (N = 220). FISH was performed on residual bronchial brushing cytology specimens diagnosed as negative (n = 195), atypical (n = 16), or suspicious (n = 9). FISH results included hypertetrasomy (n = 30) and negative (n = 190). Primary study end points included lung cancer status along with time to diagnosis of lung cancer or date of last clinical follow-up. Hazard ratios (HRs) were calculated using Cox proportional hazards regression model analyses, and P values < .05 were considered statistically significant. The mean age of the 220 patients was 66.7 years (range, 35-91), and most (58%) were men. Most patients (79%) were current or former smokers with a mean pack year history of 43.2 years (median, 40; range, 1-200). After multivariate analysis, hypertetrasomy FISH (HR = 2.96, P < .001), pack years (HR = 1.03 per pack year up to 50, P = .001), age (HR = 1.04 per year, P = .02), atypical or suspicious cytology (HR = 2.02, P = .04), and nodule spiculation (HR = 2.36, P = .003) were independent predictors of malignancy over time and were used to create a prediction model (C-statistic = 0.78). These results suggest that this multivariate model including test results and clinical features may be useful following a nondiagnostic bronchoscopic examination. © 2013.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

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.

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Radiation Therapy Noncompliance and Clinical Outcomes in an Urban Academic Cancer Center

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

Ohri, Nitin; Rapkin, Bruce D.; Guha, Chandan

Purpose: To examine associations between radiation therapy (RT) noncompliance and clinical outcomes. Methods and Materials: We reviewed all patients who completed courses of external beam RT with curative intent in our department from the years 2007 to 2012 for cancers of the head and neck, breast, lung, cervix, uterus, or rectum. Patients who missed 2 or more scheduled RT appointments (excluding planned treatment breaks) were deemed noncompliant. Univariate, multivariable, and propensity-matched analyses were performed to examine associations between RT noncompliance and clinical outcomes. Results: Of 1227 patients, 266 (21.7%) were noncompliant. With median follow-up of 50.9 months, 108 recurrences (8.8%) and 228more » deaths (18.6%) occurred. In univariate analyses, RT noncompliance was associated with increased recurrence risk (5-year cumulative incidence 16% vs 7%, P<.001), inferior recurrence-free survival (5-year actuarial rate 63% vs 79%, P<.001), and inferior overall survival (5-year actuarial rate 72% vs 83%, P<.001). In multivariable analyses that were adjusted for disease site and stage, comorbidity score, gender, ethnicity, race, and socioeconomic status (SES), RT noncompliance was associated with inferior recurrence, recurrence-free survival, and overall survival rates. Propensity score–matched models yielded results nearly identical to those seen in univariate analyses. Low SES was associated with RT noncompliance and was associated with inferior clinical outcomes in univariate analyses, but SES was not associated with inferior outcomes in multivariable models. Conclusion: For cancer patients being treated with curative intent, RT noncompliance is associated with inferior clinical outcomes. The magnitudes of these effects demonstrate that RT noncompliance can serve as a behavioral biomarker to identify high-risk patients who require additional interventions. Treatment compliance may mediate the associations that have been observed linking SES and clinical outcomes.« less

Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review.

PubMed

Hickey, Graeme L; Philipson, Pete; Jorgensen, Andrea; Kolamunnage-Dona, Ruwanthi

2018-01-31

Methodological development and clinical application of joint models of longitudinal and time-to-event outcomes have grown substantially over the past two decades. However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome. In clinical and public health research, patients who are followed up over time may often experience multiple, recurrent, or a succession of clinical events. Models that utilise such multivariate event time outcomes are quite valuable in clinical decision-making. We comprehensively review the literature for implementation of joint models involving more than a single event time per subject. We consider the distributional and modelling assumptions, including the association structure, estimation approaches, software implementations, and clinical applications. Research into this area is proving highly promising, but to-date remains in its infancy.

An analytics approach to designing patient centered medical homes.

PubMed

Ajorlou, Saeede; Shams, Issac; Yang, Kai

2015-03-01

Recently the patient centered medical home (PCMH) model has become a popular team based approach focused on delivering more streamlined care to patients. In current practices of medical homes, a clinical based prediction frame is recommended because it can help match the portfolio capacity of PCMH teams with the actual load generated by a set of patients. Without such balances in clinical supply and demand, issues such as excessive under and over utilization of physicians, long waiting time for receiving the appropriate treatment, and non-continuity of care will eliminate many advantages of the medical home strategy. In this paper, by using the hierarchical generalized linear model with multivariate responses, we develop a clinical workload prediction model for care portfolio demands in a Bayesian framework. The model allows for heterogeneous variances and unstructured covariance matrices for nested random effects that arise through complex hierarchical care systems. We show that using a multivariate approach substantially enhances the precision of workload predictions at both primary and non primary care levels. We also demonstrate that care demands depend not only on patient demographics but also on other utilization factors, such as length of stay. Our analyses of a recent data from Veteran Health Administration further indicate that risk adjustment for patient health conditions can considerably improve the prediction power of the model.

Incremental Prognostic Value of Apparent Diffusion Coefficient Histogram Analysis in Head and Neck Squamous Cell Carcinoma.

PubMed

Li, Xiaoxia; Yuan, Ying; Ren, Jiliang; Shi, Yiqian; Tao, Xiaofeng

2018-03-26

We aimed to investigate the incremental prognostic value of apparent diffusion coefficient (ADC) histogram analysis in patients with head and neck squamous cell carcinoma (HNSCC) and integrate it into a multivariate prognostic model. A retrospective review of magnetic resonance imaging findings was conducted in patients with pathologically confirmed HNSCC between June 2012 and December 2015. For each tumor, six histogram parameters were derived: the 10th, 50th, and 90th percentiles of ADC (ADC 10 , ADC 50 , and ADC 90 ); mean ADC values (ADC mean ); kurtosis; and skewness. The clinical variables included age, sex, smoking status, tumor volume, and tumor node metastasis stage. The association of these histogram and clinical variables with overall survival (OS) was determined. Further validation of the histogram parameters as independent biomarkers was performed using multivariate Cox proportional hazard models combined with clinical variables, which was compared to the clinical model. Models were assessed with C index and receiver operating characteristic curve analyses for the 12- and 36-month OS. Ninety-six patients were eligible for analysis. Median follow-up was 877 days (range, 54-1516 days). A total of 29 patients died during follow-up (30%). Patients with higher ADC values (ADC 10  > 0.958 × 10 -3 mm 2 /s, ADC 50  > 1.089 × 10 -3 mm 2 /s, ADC 90  > 1.152 × 10 -3 mm 2 /s, ADC mean  > 1.047 × 10 -3 mm 2 /s) and lower kurtosis (≤0.967) were significant predictors of poor OS (P < .100 for all). After adjusting for sex and tumor node metastasis stage, the ADC 90 and kurtosis are both significant predictors of OS with hazard ratios = 1.00 (95% confidence interval: 1.001-1.004) and 0.58 (95% confidence interval: 0.37-0.90), respectively. By adding the ADC parameters into the clinical model, the C index and diagnostic accuracies for the 12- and 36-month OS showed significant improvement. ADC histogram analysis has incremental prognostic value in patients with HNSCC and increases the performance of a multivariable prognostic model in addition to clinical variables. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Clinical and physiological assessments for elucidating falls risk in Parkinson's disease.

PubMed

Latt, Mark D; Lord, Stephen R; Morris, John G L; Fung, Victor S C

2009-07-15

The study aims were to devise (1) a fall risk screen for people with PD using routine clinical measures and (2) an explanatory (physiological) fall risk assessment for guiding fall prevention interventions. One hundred thirteen people with PD (age 66 +/- 95% CI 1.6 years) underwent clinical assessments and quantitative tests of sway, gait, strength, reaction time, and lower limb sensation. Participants were then followed up for 12 months to determine fall incidence. In the follow-up year, 51 participants (45%) fell one or more times whereas 62 participants (55%) did not fall. Multivariate analyses of routine clinical measures revealed that a fall in the past year, abnormal axial posture, cognitive impairment, and freezing of gait were independent risk factors for falls and predicted 38/51 fallers (75%) and 45/62 non-fallers (73%). A multivariate model combining clinical and physiological measures that elucidate the pathophysiology of falls identified abnormal posture, freezing of gait, frontal impairment, poor leaning balance, and leg weakness as independent risk factors. This model correctly classified 39/51 fallers (77%) and 51/62 non-fallers (82%). Patients with PD at risk of falls can be identified accurately with routine clinical assessments and quantitative physiological tests. Many of the risk factors identified are amenable to targeted intervention. 2009 Movement Disorder Society.

Willingness to Participate in Clinical Trials Among African Americans and Whites Previously Exposed To Clinical Research

PubMed Central

Durant, Raegan W.; Legedza, Anna T.; Marcantonio, Edward R.; Freeman, Marcie B.; Landon, Bruce E.

2011-01-01

The objective of this study was to identify racial differences in willingness to participate in a population with previous exposure to clinical research. A survey instrument was administered to community-dwelling whites and African Americans who were voluntarily receiving a lay research and health education newsletter from a local Boston geriatric clinical research institution. The survey instrument assessed willingness to participate in 3 hypothetical clinical trials (diet trial for obesity, medication trial for hypertension [HTN], chemotherapy trial for cancer). Surveys were received from 473 whites and 279 African Americans (53% response rate) with mean age 74 (SD ± 9). In multivariate models, race was not significantly related to willingness to participate in the multivariate models for any of the 3 trials. Previous trial participation was related to a higher odds of willingness to participate in the diet trial only (OR 1.8, 95% CI 1.2,2.6). Lower levels of trust in one’s primary care physician were associated with a lower odds of willingness to participate in clinical trials for the diet and HTN trials (OR 0.5, 95% CI 0.3,0.8 and OR 0.6, 95% CI 0.3,0.9, respectively). These findings suggest that, within populations previously exposed to clinical research, African Americans are no less willing to participate in clinical trials compared to whites. PMID:21526582

The impact of undergraduate clinical teaching models on the perceptions of work-readiness among new graduate nurses: A cross sectional study.

PubMed

Patterson, Emma E B; Boyd, Leanne; Mnatzaganian, George

2017-08-01

Clinical Placements are an essential component of bridging the gap between academic theory and nursing practice. There are multiple clinical models designed to ease the transition from student to professional, yet there has been little exploration of such models and their impact on graduates' perceptions of work-readiness. This cross sectional study examined perceptions of work-readiness of new graduate nurses who attended one of the following clinical teaching models: the University Fellowship Program (UFP), the Traditional Multi-facility Clinical Model (TMCPM), and the Mixed Program (MP). Three groups of first year graduate nurses (UFP, TMCPM, and MP) were compared using the Work-readiness Scale, a validated and reliable tool, which assessed nurses' perceptions of work-readiness in four domains: organizational acumen, personal work characteristics, social intelligence, and work competence. A multivariable Generalized Estimating Equations regression investigated socio-demographic and teaching-modelrelated factors associated with work-readiness. Of 43 nurses approached, 28 completed the survey (65% response rate) of whom 6 were UFP attendants, 8 attended the TMCPM and 14 the MP. Those who had attended the UFP scored higher than the other two in all four domains; however, the crude between-group comparisons did not yield statistically significant results. Only after accounting for age, gender, teaching setting and prior work experience, the multivariable model showed that undertaking the UFP was likely to increase perceptions of work-readiness by 1.4 points (95% CI 0.11-2.69), P=0.03). The UFP was superior to the other two placement models. The study suggests that the UFP may enhance graduate nurses' perceptions of work readiness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

PubMed Central

Daws, Richard E.; Soreq, Eyal; Johnson, Eileanoir B.; Scahill, Rachael I.; Tabrizi, Sarah J.; Barker, Roger A.; Hampshire, Adam

2018-01-01

Objective Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD. Method A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. Results Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. Interpretation We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543 PMID:29405351

When Does Neoadjuvant Chemotherapy Really Avoid Radiotherapy? Clinical Predictors of Adjuvant Radiotherapy in Cervical Cancer.

PubMed

Papadia, Andrea; Bellati, Filippo; Bogani, Giorgio; Ditto, Antonino; Martinelli, Fabio; Lorusso, Domenica; Donfrancesco, Cristina; Gasparri, Maria Luisa; Raspagliesi, Francesco

2015-12-01

The aim of this study was to identify clinical variables that may predict the need for adjuvant radiotherapy after neoadjuvant chemotherapy (NACT) and radical surgery in locally advanced cervical cancer patients. A retrospective series of cervical cancer patients with International Federation of Gynecology and Obstetrics (FIGO) stages IB2-IIB treated with NACT followed by radical surgery was analyzed. Clinical predictors of persistence of intermediate- and/or high-risk factors at final pathological analysis were investigated. Statistical analysis was performed using univariate and multivariate analysis and using a model based on artificial intelligence known as artificial neuronal network (ANN) analysis. Overall, 101 patients were available for the analyses. Fifty-two (51 %) patients were considered at high risk secondary to parametrial, resection margin and/or lymph node involvement. When disease was confined to the cervix, four (4 %) patients were considered at intermediate risk. At univariate analysis, FIGO grade 3, stage IIB disease at diagnosis and the presence of enlarged nodes before NACT predicted the presence of intermediate- and/or high-risk factors at final pathological analysis. At multivariate analysis, only FIGO grade 3 and tumor diameter maintained statistical significance. The specificity of ANN models in evaluating predictive variables was slightly superior to conventional multivariable models. FIGO grade, stage, tumor diameter, and histology are associated with persistence of pathological intermediate- and/or high-risk factors after NACT and radical surgery. This information is useful in counseling patients at the time of treatment planning with regard to the probability of being subjected to pelvic radiotherapy after completion of the initially planned treatment.

Comparison of the prognostic value of pretreatment measurements of systemic inflammatory response in patients undergoing curative resection of clear cell renal cell carcinoma.

PubMed

Lucca, Ilaria; de Martino, Michela; Hofbauer, Sebastian L; Zamani, Nura; Shariat, Shahrokh F; Klatte, Tobias

2015-12-01

Pretreatment measurements of systemic inflammatory response, including the Glasgow prognostic score (GPS), the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), the platelet-to-lymphocyte ratio (PLR) and the prognostic nutritional index (PNI) have been recognized as prognostic factors in clear cell renal cell carcinoma (CCRCC), but there is at present no study that compared these markers. We evaluated the pretreatment GPS, NLR, MLR, PLR and PNI in 430 patients, who underwent surgery for clinically localized CCRCC (pT1-3N0M0). Associations with disease-free survival were assessed with Cox models. Discrimination was measured with the C-index, and a decision curve analysis was used to evaluate the clinical net benefit. On multivariable analyses, all measures of systemic inflammatory response were significant prognostic factors. The increase in discrimination compared with the stage, size, grade and necrosis (SSIGN) score alone was 5.8 % for the GPS, 1.1-1.4 % for the NLR, 2.9-3.4 % for the MLR, 2.0-3.3 % for the PLR and 1.4-3.0 % for the PNI. On the simultaneous multivariable analysis of all candidate measures, the final multivariable model contained the SSIGN score (HR 1.40, P < 0.001), the GPS (HR 2.32, P < 0.001) and the MLR (HR 5.78, P = 0.003) as significant variables. Adding both the GPS and the MLR increased the discrimination of the SSIGN score by 6.2 % and improved the clinical net benefit. In patients with clinically localized CCRCC, the GPS and the MLR appear to be the most relevant prognostic measures of systemic inflammatory response. They may be used as an adjunct for patient counseling, tailoring management and clinical trial design.

Optimization of large animal MI models; a systematic analysis of control groups from preclinical studies.

PubMed

Zwetsloot, P P; Kouwenberg, L H J A; Sena, E S; Eding, J E; den Ruijter, H M; Sluijter, J P G; Pasterkamp, G; Doevendans, P A; Hoefer, I E; Chamuleau, S A J; van Hout, G P J; Jansen Of Lorkeers, S J

2017-10-27

Large animal models are essential for the development of novel therapeutics for myocardial infarction. To optimize translation, we need to assess the effect of experimental design on disease outcome and model experimental design to resemble the clinical course of MI. The aim of this study is therefore to systematically investigate how experimental decisions affect outcome measurements in large animal MI models. We used control animal-data from two independent meta-analyses of large animal MI models. All variables of interest were pre-defined. We performed univariable and multivariable meta-regression to analyze whether these variables influenced infarct size and ejection fraction. Our analyses incorporated 246 relevant studies. Multivariable meta-regression revealed that infarct size and cardiac function were influenced independently by choice of species, sex, co-medication, occlusion type, occluded vessel, quantification method, ischemia duration and follow-up duration. We provide strong systematic evidence that commonly used endpoints significantly depend on study design and biological variation. This makes direct comparison of different study-results difficult and calls for standardized models. Researchers should take this into account when designing large animal studies to most closely mimic the clinical course of MI and enable translational success.

Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study.

PubMed

Dienstmann, R; Mason, M J; Sinicrope, F A; Phipps, A I; Tejpar, S; Nesbakken, A; Danielsen, S A; Sveen, A; Buchanan, D D; Clendenning, M; Rosty, C; Bot, B; Alberts, S R; Milburn Jessup, J; Lothe, R A; Delorenzi, M; Newcomb, P A; Sargent, D; Guinney, J

2017-05-01

TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation. After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n = 3016)-N0147 (NCT00079274) and PETACC3 (NCT00026273)-was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n = 1499), and also externally in different population cohorts of chemotherapy-treated (n = 949) or -untreated (n = 1080) CC patients, and an additional series without treatment annotation (n = 782). TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61-0.68 in the TNM alone model to 0.63-0.71 in models with added molecular markers, 0.65-0.73 with clinicopathological features and 0.66-0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively. Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Diagnosis of rheumatoid arthritis: multivariate analysis of biomarkers.

PubMed

Wild, Norbert; Karl, Johann; Grunert, Veit P; Schmitt, Raluca I; Garczarek, Ursula; Krause, Friedemann; Hasler, Fritz; van Riel, Piet L C M; Bayer, Peter M; Thun, Matthias; Mattey, Derek L; Sharif, Mohammed; Zolg, Werner

2008-02-01

To test if a combination of biomarkers can increase the classification power of autoantibodies to cyclic citrullinated peptides (anti-CCP) in the diagnosis of rheumatoid arthritis (RA) depending on the diagnostic situation. Biomarkers were subject to three inclusion/exclusion criteria (discrimination between RA patients and healthy blood donors, ability to identify anti-CCP-negative RA patients, specificity in a panel with major non-rheumatological diseases) before univariate ranking and multivariate analysis was carried out using a modelling panel (n = 906). To enable the evaluation of the classification power in different diagnostic settings the disease controls (n = 542) were weighted according to the admission rates in rheumatology clinics modelling a clinic panel or according to the relative prevalences of musculoskeletal disorders in the general population seen by general practitioners modelling a GP panel. Out of 131 biomarkers considered originally, we evaluated 32 biomarkers in this study, of which only seven passed the three inclusion/exclusion criteria and were combined by multivariate analysis using four different mathematical models. In the modelled clinic panel, anti-CCP was the lead marker with a sensitivity of 75.8% and a specificity of 94.0%. Due to the lack in specificity of the markers other than anti-CCP in this diagnostic setting, any gain in sensitivity by any marker combination is off-set by a corresponding loss in specificity. In the modelled GP panel, the best marker combination of anti-CCP and interleukin (IL)-6 resulted in a sensitivity gain of 7.6% (85.9% vs. 78.3%) at a minor loss in specificity of 1.6% (90.3% vs. 91.9%) compared with anti-CCP as the best single marker. Depending on the composition of the sample panel, anti-CCP alone or anti-CCP in combination with IL-6 has the highest classification power for the diagnosis of established RA.

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients.

PubMed

Jacobsen, Ramune; Vadstrup, Eva; Røder, Michael; Frølich, Anne

2012-01-01

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

Affective temperaments and suicidal ideation and behavior in mood and anxiety disorder patients.

PubMed

Baldessarini, Ross J; Vázquez, Gustavo H; Tondo, Leonardo

2016-07-01

Clinical characteristics proposed to be associated with suicidal risk include affective temperament types. We tested this proposal with two methods in a large sample of subjects with mood and anxiety disorders. We assessed consecutive, consenting subjects clinically for affective temperament types and by TEMPS-A self-ratings for associations of temperament with suicidal ideation and acts, using standard bivariate methods, and multivariate logistic regression models. Among 2561 subjects (major depressive, 1171; bipolar, 919, anxiety disorders, 471), temperament-types and TEMPS-A (39-item Italian version) subscale scores differed by risk of suicidal acts or ideation. Suicidal acts and ideation were most associated with cyclothymic and dysthymic, and less with hyperthymic temperaments. These associations were sustained by multivariate modeling that included diagnosis, age, sex, and diagnosis. Not all subjects completed TEMPS-A self-ratings; clinical assessments of temperaments were not standardized, and long-term stability of temperament assessments was not tested. The findings support and extend associations of cyclothymic-dysthymic temperaments with suicidal acts and ideation, whereas hyperthymic temperament may be protective. Copyright © 2016 Elsevier B.V. All rights reserved.

Pediatric Irritable Bowel Syndrome Patient and Parental Characteristics Differ by Care Management Type.

PubMed

Hollier, John M; Czyzewski, Danita I; Self, Mariella M; Weidler, Erica M; Smith, E O'Brian; Shulman, Robert J

2017-03-01

This study evaluates whether certain patient or parental characteristics are associated with gastroenterology (GI) referral versus primary pediatrics care for pediatric irritable bowel syndrome (IBS). A retrospective clinical trial sample of patients meeting pediatric Rome III IBS criteria was assembled from a single metropolitan health care system. Baseline socioeconomic status (SES) and clinical symptom measures were gathered. Various instruments measured participant and parental psychosocial traits. Study outcomes were stratified by GI referral versus primary pediatrics care. Two separate analyses of SES measures and GI clinical symptoms and psychosocial measures identified key factors by univariate and multiple logistic regression analyses. For each analysis, identified factors were placed in unadjusted and adjusted multivariate logistic regression models to assess their impact in predicting GI referral. Of the 239 participants, 152 were referred to pediatric GI, and 87 were managed in primary pediatrics care. Of the SES and clinical symptom factors, child self-assessment of abdominal pain duration and lower percentage of people living in poverty were the strongest predictors of GI referral. Among the psychosocial measures, parental assessment of their child's functional disability was the sole predictor of GI referral. In multivariate logistic regression models, all selected factors continued to predict GI referral in each model. Socioeconomic environment, clinical symptoms, and functional disability are associated with GI referral. Future interventions designed to ameliorate the effect of these identified factors could reduce unnecessary specialty consultations and health care overutilization for IBS.

Cytogenetic prognostication within medulloblastoma subgroups.

PubMed

Shih, David J H; Northcott, Paul A; Remke, Marc; Korshunov, Andrey; Ramaswamy, Vijay; Kool, Marcel; Luu, Betty; Yao, Yuan; Wang, Xin; Dubuc, Adrian M; Garzia, Livia; Peacock, John; Mack, Stephen C; Wu, Xiaochong; Rolider, Adi; Morrissy, A Sorana; Cavalli, Florence M G; Jones, David T W; Zitterbart, Karel; Faria, Claudia C; Schüller, Ulrich; Kren, Leos; Kumabe, Toshihiro; Tominaga, Teiji; Shin Ra, Young; Garami, Miklós; Hauser, Peter; Chan, Jennifer A; Robinson, Shenandoah; Bognár, László; Klekner, Almos; Saad, Ali G; Liau, Linda M; Albrecht, Steffen; Fontebasso, Adam; Cinalli, Giuseppe; De Antonellis, Pasqualino; Zollo, Massimo; Cooper, Michael K; Thompson, Reid C; Bailey, Simon; Lindsey, Janet C; Di Rocco, Concezio; Massimi, Luca; Michiels, Erna M C; Scherer, Stephen W; Phillips, Joanna J; Gupta, Nalin; Fan, Xing; Muraszko, Karin M; Vibhakar, Rajeev; Eberhart, Charles G; Fouladi, Maryam; Lach, Boleslaw; Jung, Shin; Wechsler-Reya, Robert J; Fèvre-Montange, Michelle; Jouvet, Anne; Jabado, Nada; Pollack, Ian F; Weiss, William A; Lee, Ji-Yeoun; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Leonard, Jeffrey R; Rubin, Joshua B; de Torres, Carmen; Lavarino, Cinzia; Mora, Jaume; Cho, Yoon-Jae; Tabori, Uri; Olson, James M; Gajjar, Amar; Packer, Roger J; Rutkowski, Stefan; Pomeroy, Scott L; French, Pim J; Kloosterhof, Nanne K; Kros, Johan M; Van Meir, Erwin G; Clifford, Steven C; Bourdeaut, Franck; Delattre, Olivier; Doz, François F; Hawkins, Cynthia E; Malkin, David; Grajkowska, Wieslawa A; Perek-Polnik, Marta; Bouffet, Eric; Rutka, James T; Pfister, Stefan M; Taylor, Michael D

2014-03-20

Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.

Cytogenetic Prognostication Within Medulloblastoma Subgroups

PubMed Central

Shih, David J.H.; Northcott, Paul A.; Remke, Marc; Korshunov, Andrey; Ramaswamy, Vijay; Kool, Marcel; Luu, Betty; Yao, Yuan; Wang, Xin; Dubuc, Adrian M.; Garzia, Livia; Peacock, John; Mack, Stephen C.; Wu, Xiaochong; Rolider, Adi; Morrissy, A. Sorana; Cavalli, Florence M.G.; Jones, David T.W.; Zitterbart, Karel; Faria, Claudia C.; Schüller, Ulrich; Kren, Leos; Kumabe, Toshihiro; Tominaga, Teiji; Shin Ra, Young; Garami, Miklós; Hauser, Peter; Chan, Jennifer A.; Robinson, Shenandoah; Bognár, László; Klekner, Almos; Saad, Ali G.; Liau, Linda M.; Albrecht, Steffen; Fontebasso, Adam; Cinalli, Giuseppe; De Antonellis, Pasqualino; Zollo, Massimo; Cooper, Michael K.; Thompson, Reid C.; Bailey, Simon; Lindsey, Janet C.; Di Rocco, Concezio; Massimi, Luca; Michiels, Erna M.C.; Scherer, Stephen W.; Phillips, Joanna J.; Gupta, Nalin; Fan, Xing; Muraszko, Karin M.; Vibhakar, Rajeev; Eberhart, Charles G.; Fouladi, Maryam; Lach, Boleslaw; Jung, Shin; Wechsler-Reya, Robert J.; Fèvre-Montange, Michelle; Jouvet, Anne; Jabado, Nada; Pollack, Ian F.; Weiss, William A.; Lee, Ji-Yeoun; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Leonard, Jeffrey R.; Rubin, Joshua B.; de Torres, Carmen; Lavarino, Cinzia; Mora, Jaume; Cho, Yoon-Jae; Tabori, Uri; Olson, James M.; Gajjar, Amar; Packer, Roger J.; Rutkowski, Stefan; Pomeroy, Scott L.; French, Pim J.; Kloosterhof, Nanne K.; Kros, Johan M.; Van Meir, Erwin G.; Clifford, Steven C.; Bourdeaut, Franck; Delattre, Olivier; Doz, François F.; Hawkins, Cynthia E.; Malkin, David; Grajkowska, Wieslawa A.; Perek-Polnik, Marta; Bouffet, Eric; Rutka, James T.; Pfister, Stefan M.; Taylor, Michael D.

2014-01-01

Purpose Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. Patients and Methods Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. Results Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. Conclusion Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials. PMID:24493713

What's in a title? An assessment of whether randomized controlled trial in a title means that it is one.

PubMed

Koletsi, Despina; Pandis, Nikolaos; Polychronopoulou, Argy; Eliades, Theodore

2012-06-01

In this study, we aimed to investigate whether studies published in orthodontic journals and titled as randomized clinical trials are truly randomized clinical trials. A second objective was to explore the association of journal type and other publication characteristics on correct classification. American Journal of Orthodontics and Dentofacial Orthopedics, European Journal of Orthodontics, Angle Orthodontist, Journal of Orthodontics, Orthodontics and Craniofacial Research, World Journal of Orthodontics, Australian Orthodontic Journal, and Journal of Orofacial Orthopedics were hand searched for clinical trials labeled in the title as randomized from 1979 to July 2011. The data were analyzed by using descriptive statistics, and univariable and multivariable examinations of statistical associations via ordinal logistic regression modeling (proportional odds model). One hundred twelve trials were identified. Of the included trials, 33 (29.5%) were randomized clinical trials, 52 (46.4%) had an unclear status, and 27 (24.1%) were not randomized clinical trials. In the multivariable analysis among the included journal types, year of publication, number of authors, multicenter trial, and involvement of statistician were significant predictors of correctly classifying a study as a randomized clinical trial vs unclear and not a randomized clinical trial. From 112 clinical trials in the orthodontic literature labeled as randomized clinical trials, only 29.5% were identified as randomized clinical trials based on clear descriptions of appropriate random number generation and allocation concealment. The type of journal, involvement of a statistician, multicenter trials, greater numbers of authors, and publication year were associated with correct clinical trial classification. This study indicates the need of clear and accurate reporting of clinical trials and the need for educating investigators on randomized clinical trial methodology. Copyright © 2012 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

Does clinical presentation predict response to a nonsurgical chronic disease management program for endstage hip and knee osteoarthritis?

PubMed

Eyles, Jillian P; Lucas, Barbara R; Patterson, Jillian A; Williams, Matthew J; Weeks, Kate; Fransen, Marlene; Hunter, David J

2014-11-01

To identify baseline characteristics of participants who will respond favorably following 6 months of participation in a chronic disease management program for hip and knee osteoarthritis (OA). This prospective cohort study assessed 559 participants at baseline and following 6 months of participation in the Osteoarthritis Chronic Care Program. Response was defined as the minimal clinically important difference of an 18% and 9-point absolute improvement in the Western Ontario and McMaster Universities Arthritis Index global score. Multivariate logistic regression modeling was used to identify predictors of response. Complete data were available for 308 participants. Those who withdrew within the study period were imputed as nonresponders. Three variables were independently associated with response: signal joint (knee vs hip), sex, and high level of comorbidity. Index joint and sex were significant in the multivariate model, but the model was not a sensitive predictor of response. Strong predictors of response to a chronic disease management program for hip and knee OA were not identified. The significant predictors that were found should be considered in future studies.

Predictive factors of clinical response in steroid-refractory ulcerative colitis treated with granulocyte-monocyte apheresis

PubMed Central

D'Ovidio, Valeria; Meo, Donatella; Viscido, Angelo; Bresci, Giampaolo; Vernia, Piero; Caprilli, Renzo

2011-01-01

AIM: To identify factors predicting the clinical response of ulcerative colitis patients to granulocyte-monocyte apheresis (GMA). METHODS: Sixty-nine ulcerative colitis patients (39 F, 30 M) dependent upon/refractory to steroids were treated with GMA. Steroid dependency, clinical activity index (CAI), C reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), values at baseline, use of immunosuppressant, duration of disease, and age and extent of disease were considered for statistical analysis as predictive factors of clinical response. Univariate and multivariate logistic regression models were used. RESULTS: In the univariate analysis, CAI (P = 0.039) and ESR (P = 0.017) levels at baseline were singled out as predictive of clinical remission. In the multivariate analysis steroid dependency [Odds ratio (OR) = 0.390, 95% Confidence interval (CI): 0.176-0.865, Wald 5.361, P = 0.0160] and low CAI levels at baseline (4 < CAI < 7) (OR = 0.770, 95% CI: 0.425-1.394, Wald 3.747, P = 0.028) proved to be effective as factors predicting clinical response. CONCLUSION: GMA may be a valid therapeutic option for steroid-dependent ulcerative colitis patients with mild-moderate disease and its clinical efficacy seems to persist for 12 mo. PMID:21528055

LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma

PubMed Central

Lee, Tsair-Fwu; Liou, Ming-Hsiang; Huang, Yu-Jie; Chao, Pei-Ju; Ting, Hui-Min; Lee, Hsiao-Yi

2014-01-01

To predict the incidence of moderate-to-severe patient-reported xerostomia among head and neck squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC) patients treated with intensity-modulated radiotherapy (IMRT). Multivariable normal tissue complication probability (NTCP) models were developed by using quality of life questionnaire datasets from 152 patients with HNSCC and 84 patients with NPC. The primary endpoint was defined as moderate-to-severe xerostomia after IMRT. The numbers of predictive factors for a multivariable logistic regression model were determined using the least absolute shrinkage and selection operator (LASSO) with bootstrapping technique. Four predictive models were achieved by LASSO with the smallest number of factors while preserving predictive value with higher AUC performance. For all models, the dosimetric factors for the mean dose given to the contralateral and ipsilateral parotid gland were selected as the most significant predictors. Followed by the different clinical and socio-economic factors being selected, namely age, financial status, T stage, and education for different models were chosen. The predicted incidence of xerostomia for HNSCC and NPC patients can be improved by using multivariable logistic regression models with LASSO technique. The predictive model developed in HNSCC cannot be generalized to NPC cohort treated with IMRT without validation and vice versa. PMID:25163814

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

PubMed

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Biomarkers for Early Detection of Clinically Relvant Prostate Cancer: A Multi-Institutional Validation Trial - Genomic Health, Inc. — EDRN Public Portal

Cancer.gov

Validate a panel of tissue-based biomarkers to determine the presence of or progression to clinically relevant prostate cancer at the time of diagnosis. Utilize a novel, biopsy based multi-gene quantitative RT-PCR assay developed by Genomic Health, Oncotype DX Prostate Cancer Assay, which discriminates aggressive from indolent cancer on multivariate modeling of PCa patients.

Disparate molecular, histopathology, and clinical factors in HNSCC racial groups

PubMed Central

Worsham, Maria J.; Stephen, Josena K.; Lu, Mei; Chen, Kang Mei; Havard, Shaleta; Shah, Veena; Schweitzer, Vanessa P.

2013-01-01

Objective The causes of the differences in the higher incidence of and the mortality from head and neck squamous cell carcinoma (HNSCC) in African American (AA) versus Caucasian Americans (CA) lack a consensus. We examined a comprehensive array of risk factors influencing health and disease in an access to care, racially diverse, primary HNSCC cohort. Study Design Cross-sectional study. Setting Primary care academic health care system. Subjects and Methods The cohort of 673 comprised 391 CA and 282 AA (42%). Risk variables included demographic, histopathology, and clinical/epidemiologic factors. Tumor DNA was interrogated for loss and gain of 113 genes with known involvement in HNSCC/cancer. Logistic regression for univariate analysis was followed by multivariate modeling with determination of model predictability (c-index). Results Of the 39 univariate differences between AA and CA, multivariate modeling (c-index=0.81) retained seven (p<0.05). AA were less likely to be married, more likely to have tumor lymphocytic response, undergo radiation treatment, and smoke. Insurance type was a significant predictor of race. AA were more likely to have Medicaid, Medicare, and other HMO types. AA tumors were more likely to have loss of CDKN2A and gain of SCYA3 versus CA. Conclusions Multivariate modeling indicated significant differences between AA and CA HNSCC for histopathology, treatment, smoking, marital status, type of insurance, as well as tumor gene copy number alterations. Our data reiterate that for HNSCC as in the case of other complex diseases, tumor genetics or biology is only one of many potential contributors to differences among racial groups. PMID:22412179

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

The Contribution of Missed Clinic Visits to Disparities in HIV Viral Load Outcomes

PubMed Central

Westfall, Andrew O.; Gardner, Lytt I.; Giordano, Thomas P.; Wilson, Tracey E.; Drainoni, Mari-Lynn; Keruly, Jeanne C.; Rodriguez, Allan E.; Malitz, Faye; Batey, D. Scott; Mugavero, Michael J.

2015-01-01

Objectives. We explored the contribution of missed primary HIV care visits (“no-show”) to observed disparities in virological failure (VF) among Black persons and persons with injection drug use (IDU) history. Methods. We used patient-level data from 6 academic clinics, before the Centers for Disease Control and Prevention and Health Resources and Services Administration Retention in Care intervention. We employed staged multivariable logistic regression and multivariable models stratified by no-show visit frequency to evaluate the association of sociodemographic factors with VF. We used multiple imputations to assign missing viral load values. Results. Among 10 053 patients (mean age = 46 years; 35% female; 64% Black; 15% with IDU history), 31% experienced VF. Although Black patients and patients with IDU history were significantly more likely to experience VF in initial analyses, race and IDU parameter estimates were attenuated after sequential addition of no-show frequency. In stratified models, race and IDU were not statistically significantly associated with VF at any no-show level. Conclusions. Because missed clinic visits contributed to observed differences in viral load outcomes among Black and IDU patients, achieving an improved understanding of differential visit attendance is imperative to reducing disparities in HIV. PMID:26270301

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Clinical characterisation of pneumonia caused by atypical pathogens combining classic and novel predictors.

PubMed

Masiá, M; Gutiérrez, F; Padilla, S; Soldán, B; Mirete, C; Shum, C; Hernández, I; Royo, G; Martin-Hidalgo, A

2007-02-01

The aim of this study was to characterise community-acquired pneumonia (CAP) caused by atypical pathogens by combining distinctive clinical and epidemiological features and novel biological markers. A population-based prospective study of consecutive patients with CAP included investigation of biomarkers of bacterial infection, e.g., procalcitonin, C-reactive protein and lipopolysaccharide-binding protein (LBP) levels. Clinical, radiological and laboratory data for patients with CAP caused by atypical pathogens were compared by univariate and multivariate analysis with data for patients with typical pathogens and patients from whom no organisms were identified. Two predictive scoring models were developed with the most discriminatory variables from multivariate analysis. Of 493 patients, 94 had CAP caused by atypical pathogens. According to multivariate analysis, patients with atypical pneumonia were more likely to have normal white blood cell counts, have repetitive air-conditioning exposure, be aged <65 years, have elevated aspartate aminotransferase levels, have been exposed to birds, and have lower serum levels of LBP. Two different scoring systems were developed that predicted atypical pathogens with sensitivities of 35.2% and 48.8%, and specificities of 93% and 91%, respectively. The combination of selected patient characteristics and laboratory data identified up to half of the cases of atypical pneumonia with high specificity, which should help clinicians to optimise initial empirical therapy for CAP.

Ibrutinib versus previous standard of care: an adjusted comparison in patients with relapsed/refractory chronic lymphocytic leukaemia.

PubMed

Hansson, Lotta; Asklid, Anna; Diels, Joris; Eketorp-Sylvan, Sandra; Repits, Johanna; Søltoft, Frans; Jäger, Ulrich; Österborg, Anders

2017-10-01

This study explored the relative efficacy of ibrutinib versus previous standard-of-care treatments in relapsed/refractory patients with chronic lymphocytic leukaemia (CLL), using multivariate regression modelling to adjust for baseline prognostic factors. Individual patient data were collected from an observational Stockholm cohort of consecutive patients (n = 144) diagnosed with CLL between 2002 and 2013 who had received at least second-line treatment. Data were compared with results of the RESONATE clinical trial. A multivariate Cox proportional hazards regression model was used which estimated the hazard ratio (HR) of ibrutinib versus previous standard of care. The adjusted HR of ibrutinib versus the previous standard-of-care cohort was 0.15 (p < 0.0001) for progression-free survival (PFS) and 0.36 (p < 0.0001) for overall survival (OS). A similar difference was observed also when patients treated late in the period (2012-) were compared separately. Multivariate analysis showed that later line of therapy, male gender, older age and poor performance status were significant independent risk factors for worse PFS and OS. Our results suggest that PFS and OS with ibrutinib in the RESONATE study were significantly longer than with previous standard-of-care regimens used in second or later lines in routine healthcare. The approach used, which must be interpreted with caution, compares patient-level data from a clinical trial with outcomes observed in a daily clinical practice and may complement results from randomised trials or provide preliminary wider comparative information until phase 3 data exist.

Multivariable normal tissue complication probability model-based treatment plan optimization for grade 2-4 dysphagia and tube feeding dependence in head and neck radiotherapy.

PubMed

Kierkels, Roel G J; Wopken, Kim; Visser, Ruurd; Korevaar, Erik W; van der Schaaf, Arjen; Bijl, Hendrik P; Langendijk, Johannes A

2016-12-01

Radiotherapy of the head and neck is challenged by the relatively large number of organs-at-risk close to the tumor. Biologically-oriented objective functions (OF) could optimally distribute the dose among the organs-at-risk. We aimed to explore OFs based on multivariable normal tissue complication probability (NTCP) models for grade 2-4 dysphagia (DYS) and tube feeding dependence (TFD). One hundred head and neck cancer patients were studied. Additional to the clinical plan, two more plans (an OF DYS and OF TFD -plan) were optimized per patient. The NTCP models included up to four dose-volume parameters and other non-dosimetric factors. A fully automatic plan optimization framework was used to optimize the OF NTCP -based plans. All OF NTCP -based plans were reviewed and classified as clinically acceptable. On average, the Δdose and ΔNTCP were small comparing the OF DYS -plan, OF TFD -plan, and clinical plan. For 5% of patients NTCP TFD reduced >5% using OF TFD -based planning compared to the OF DYS -plans. Plan optimization using NTCP DYS - and NTCP TFD -based objective functions resulted in clinically acceptable plans. For patients with considerable risk factors of TFD, the OF TFD steered the optimizer to dose distributions which directly led to slightly lower predicted NTCP TFD values as compared to the other studied plans. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Mortality Prediction Model of Septic Shock Patients Based on Routinely Recorded Data

PubMed Central

Carrara, Marta; Baselli, Giuseppe; Ferrario, Manuela

2015-01-01

We studied the problem of mortality prediction in two datasets, the first composed of 23 septic shock patients and the second composed of 73 septic subjects selected from the public database MIMIC-II. For each patient we derived hemodynamic variables, laboratory results, and clinical information of the first 48 hours after shock onset and we performed univariate and multivariate analyses to predict mortality in the following 7 days. The results show interesting features that individually identify significant differences between survivors and nonsurvivors and features which gain importance only when considered together with the others in a multivariate regression model. This preliminary study on two small septic shock populations represents a novel contribution towards new personalized models for an integration of multiparameter patient information to improve critical care management of shock patients. PMID:26557154

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

PubMed

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando; Treviño, Victor

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

PubMed Central

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. PMID:29596496

Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor.

PubMed

Jain, Rajan; Poisson, Laila M; Gutman, David; Scarpace, Lisa; Hwang, Scott N; Holder, Chad A; Wintermark, Max; Rao, Arvind; Colen, Rivka R; Kirby, Justin; Freymann, John; Jaffe, C Carl; Mikkelsen, Tom; Flanders, Adam

2014-08-01

To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor

PubMed Central

Poisson, Laila M.; Gutman, David; Scarpace, Lisa; Hwang, Scott N.; Holder, Chad A.; Wintermark, Max; Rao, Arvind; Colen, Rivka R.; Kirby, Justin; Freymann, John; Jaffe, C. Carl; Mikkelsen, Tom; Flanders, Adam

2014-01-01

Purpose To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. Materials and Methods An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material–enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Results Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49–1.79 years). Conclusion Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features. © RSNA, 2014 Online supplemental material is available for this article. PMID:24646147

Higher clinical performance during a surgical clerkship is independently associated with matriculation of medical students into general surgery.

PubMed

Daly, Shaun C; Deal, Rebecca A; Rinewalt, Daniel E; Francescatti, Amanda B; Luu, Minh B; Millikan, Keith W; Anderson, Mary C; Myers, Jonathan A

2014-04-01

The purpose of our study was to determine the predictive impact of individual academic measures for the matriculation of senior medical students into a general surgery residency. Academic records were evaluated for third-year medical students (n = 781) at a single institution between 2004 and 2011. Cohorts were defined by student matriculation into either a general surgery residency program (n = 58) or a non-general surgery residency program (n = 723). Multivariate logistic regression was performed to evaluate independently significant academic measures. Clinical evaluation raw scores were predictive of general surgery matriculation (P = .014). In addition, multivariate modeling showed lower United States Medical Licensing Examination Step 1 scores to be independently associated with matriculation into general surgery (P = .007). Superior clinical aptitude is independently associated with general surgical matriculation. This is in contrast to the negative correlation United States Medical Licensing Examination Step 1 scores have on general surgery matriculation. Recognizing this, surgical clerkship directors can offer opportunities for continued surgical education to students showing high clinical aptitude, increasing their likelihood of surgical matriculation. Copyright © 2014 Elsevier Inc. All rights reserved.

Neonatal Pulmonary MRI of Bronchopulmonary Dysplasia Predicts Short-term Clinical Outcomes.

PubMed

Higano, Nara S; Spielberg, David R; Fleck, Robert J; Schapiro, Andrew H; Walkup, Laura L; Hahn, Andrew D; Tkach, Jean A; Kingma, Paul S; Merhar, Stephanie L; Fain, Sean B; Woods, Jason C

2018-05-23

Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current NICHD/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes. We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes. Forty-two neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD controls; 40±3 weeks post-menstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo-time and gradient echo) in a NICU-sited, neonatal-sized 1.5T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared to clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models including clinical data and scores. MRI scores significantly correlated with severities and predicted respiratory support at NICU discharge (P<0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support. Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this non-ionizing technique can be implemented to phenotype disease and has potential to serially assess efficacy of individualized therapies.

Elements of the Veterans Health Administration Patient-Centered Medical Home are Associated with Greater Adherence to Oral Hypoglycemic Agents in Patients with Diabetes.

PubMed

Meo, Nicholas; Wong, Edwin; Sun, Haili; Curtis, Idamay; Batten, Adam; Fihn, Stephan D; Nelson, Karin

2018-04-01

In 2010, Veterans Health Administration (VHA) primary care clinics adopted a patient-centered medical home (PCMH) model. This study sought to examine the association between the organizational features related to adoption of PCMH and the level of adherence to oral hypoglycemic agents (OHAs) among patients with diabetes. This retrospective cohort study involved 757 VA clinics that provide primary care to 440,971 patients with diabetes who were taking OHAs in fiscal year 2012. One-year refill-based medication possession ratios (MPRs) were calculated at the patient level. Clinic-level adherence was defined as the proportion of clinics with MPR ≥80%. Risk adjustment of adherence was performed using logistic regression to account for differences in patient populations at clinics. Eight domains of the PCMH model (ie, access, continuity, coordination, teamwork, comprehensive care, self-management, communication, shared decision making) were assessed using items from a previously validated index. Multivariate linear regression was applied to identify PCMH components associated with clinic-level adherence. Patients with diabetes per clinic ranged from 100 to 5011. The average level of adherence to OHAs among clinics ranged from 52.8% to 61.9% (interquartile range = 57.9% to 59.4%). In multivariate analysis, organizational features associated with higher clinic-level adherence included access to routine care (standardized beta [Sβ] = .21, P = .004), having a respectful office staff (Sβ = 0.21, P = .002), and utilization of telephone encounters (Sβ = 0.23, P < .001). Among a national cohort of veterans with diabetes, overall PCMH implementation did not significantly increase adherence to oral hypoglycemic agents, although aspects of implementation were associated with increased adherence. Measures of access to care appear the most significant.

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

PubMed

Samad, Manar D; Ulloa, Alvaro; Wehner, Gregory J; Jing, Linyuan; Hartzel, Dustin; Good, Christopher W; Williams, Brent A; Haggerty, Christopher M; Fornwalt, Brandon K

2018-06-09

The goal of this study was to use machine learning to more accurately predict survival after echocardiography. Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data. Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. We investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant International Classification of Diseases, Tenth Revision, codes, and age, sex, height, weight, heart rate, blood pressures, low-density lipoprotein, high-density lipoprotein, and smoking; 2) clinical variables plus physician-reported EF; and 3) clinical variables and EF, plus 57 additional echocardiographic measurements. Missing data were imputed with a multivariate imputation by using a chained equations algorithm (MICE). We compared models versus each other and baseline clinical scoring systems by using a mean area under the curve (AUC) over 10 cross-validation folds and across 10 survival durations (6 to 60 months). Machine learning models achieved significantly higher prediction accuracy (all AUC >0.82) over common clinical risk scores (AUC = 0.61 to 0.79), with the nonlinear random forest models outperforming logistic regression (p < 0.01). The random forest model including all echocardiographic measurements yielded the highest prediction accuracy (p < 0.01 across all models and survival durations). Only 10 variables were needed to achieve 96% of the maximum prediction accuracy, with 6 of these variables being derived from echocardiography. Tricuspid regurgitation velocity was more predictive of survival than LVEF. In a subset of studies with complete data for the top 10 variables, multivariate imputation by chained equations yielded slightly reduced predictive accuracies (difference in AUC of 0.003) compared with the original data. Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Robust tumor morphometry in multispectral fluorescence microscopy

NASA Astrophysics Data System (ADS)

Tabesh, Ali; Vengrenyuk, Yevgen; Teverovskiy, Mikhail; Khan, Faisal M.; Sapir, Marina; Powell, Douglas; Mesa-Tejada, Ricardo; Donovan, Michael J.; Fernandez, Gerardo

2009-02-01

Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy images of prostate biopsy specimens. To enable feature extraction, EN and cytoplasm regions were first segmented from the IF images. Then, feature sets consisting of the characteristics of the minimum spanning tree (MST) connecting the EN and the fractal dimension (FD) of gland boundaries were obtained from the segmented compartments. We demonstrated the utility of the proposed features in prostate cancer recurrence prediction on a multi-institution cohort of 1027 patients. Univariate analysis revealed that both FD and one of the MST features were highly effective for predicting cancer recurrence (p <= 0.0001). In multivariate analysis, an MST feature was selected for a model incorporating clinical and image features. The model achieved a concordance index (CI) of 0.73 on the validation set, which was significantly higher than the CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice (p < 0.0001). The contributions of this work are twofold. First, it is the first demonstration of the utility of the proposed features in morphometric analysis of IF images. Second, this is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.

Risk factors of significant pain syndrome 90 days after minor thoracic injury: trajectory analysis.

PubMed

Daoust, Raoul; Emond, Marcel; Bergeron, Eric; LeSage, Natalie; Camden, Stéphanie; Guimont, Chantal; Vanier, Laurent; Chauny, Jean-Marc

2013-11-01

The objective was to identify the risk factors of clinically significant pain at 90 days in patients with minor thoracic injury (MTI) discharged from the emergency department (ED). A prospective, multicenter, cohort study was conducted in four Canadian EDs from November 2006 to November 2010. All consecutive patients aged 16 years or older with MTI were eligible at discharge from EDs. They underwent standardized clinical and radiologic evaluations at 1 and 2 weeks, followed by standardized telephone interviews at 30 and 90 days. A pain trajectory model characterized groups of patients with different pain evolutions and ascertained specific risk factors in each group through multivariate analysis. In this cohort of 1,132 patients, 734 were eligible for study inclusion. The authors identified a pain trajectory that characterized 18.2% of the study population experiencing clinically significant pain (>3 of 10) at 90 days after a MTI. Multivariate modeling found two or more rib fractures, smoking, and initial oxygen saturation below 95% to be predictors of this group of patients. To the authors' knowledge, this is the first prospective study of trajectory modeling to detect risk factors associated with significant pain at 90 days after MTI. These factors may help in planning specific treatment strategies and should be validated in another prospective cohort. © 2013 by the Society for Academic Emergency Medicine.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

Power analysis to detect treatment effects in longitudinal clinical trials for Alzheimer's disease.

PubMed

Huang, Zhiyue; Muniz-Terrera, Graciela; Tom, Brian D M

2017-09-01

Assessing cognitive and functional changes at the early stage of Alzheimer's disease (AD) and detecting treatment effects in clinical trials for early AD are challenging. Under the assumption that transformed versions of the Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Alzheimer's Disease Assessment Scale-Cognitive Subscale tests'/components' scores are from a multivariate linear mixed-effects model, we calculated the sample sizes required to detect treatment effects on the annual rates of change in these three components in clinical trials for participants with mild cognitive impairment. Our results suggest that a large number of participants would be required to detect a clinically meaningful treatment effect in a population with preclinical or prodromal Alzheimer's disease. We found that the transformed Mini-Mental State Examination is more sensitive for detecting treatment effects in early AD than the transformed Clinical Dementia Rating Scale-Sum of Boxes and Alzheimer's Disease Assessment Scale-Cognitive Subscale. The use of optimal weights to construct powerful test statistics or sensitive composite scores/endpoints can reduce the required sample sizes needed for clinical trials. Consideration of the multivariate/joint distribution of components' scores rather than the distribution of a single composite score when designing clinical trials can lead to an increase in power and reduced sample sizes for detecting treatment effects in clinical trials for early AD.

Model for investigating the benefits of clinical supervision in psychiatric nursing: a survey study.

PubMed

Gonge, Henrik; Buus, Niels

2011-04-01

The objective of this study was to test a model for analysing the possible benefits of clinical supervision. The model suggested a pathway from participation to effectiveness to benefits of clinical supervision, and included possible influences of individual and workplace factors. The study sample was 136 nursing staff members in permanent employment on nine general psychiatric wards and at four community mental health centres at a Danish psychiatric university hospital. Data were collected by means of a set of questionnaires. Participation in clinical supervision was associated with the effectiveness of clinical supervision, as measured by the Manchester Clinical Supervision Scale (MCSS). Furthermore, MCSS scores were associated with benefits, such as increased job satisfaction, vitality, rational coping and less stress, emotional exhaustion, and depersonalization. Multivariate analyses indicated that certain individual and workplace factors were related to subscales of the MCSS, as well as some of the benefits. The study supported the suggested model, but methodological limitations apply. © 2011 The Authors. International Journal of Mental Health Nursing © 2011 Australian College of Mental Health Nurses Inc.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

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

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

A comparison of risk assessment models for term and preterm low birthweight.

PubMed

Michielutte, R; Ernest, J M; Moore, M L; Meis, P J; Sharp, P C; Wells, H B; Buescher, P A

1992-01-01

Most epidemiological research dealing with the assessment of risk for low birthweight has focused on all low birthweight births. Studies that have attempted to distinguish between term and preterm low birthweights have tended to examine preterm low birthweight, since the risk of perinatal mortality and morbidity is greatest for this group of infants. This study uses data from 25,408 singleton births in a 20-county region in North Carolina to identify and compare risk factors for term and preterm low birthweights, and also examines the usefulness of separate multivariate risk assessment systems for term and preterm low birthweights that could be used in the clinical setting. Risk factors that overlap as significant predictors of both types of low birthweight include race, no previous live births, smoking, weight under 100 lb, and previous preterm or low birthweight birth. Age also is a significant predictor of both types of low birthweight, but in opposite directions. Younger age is associated with reduced risk of term low birthweight and increased risk of pattern low birthweight. Comparison of all risk factors indicates that different multivariate models are needed to understand the epidemiology of preterm and term low birthweights. In terms of clinical value, a general risk assessment model that combines all low birthweight births is as effective as the separate models.

Socioeconomic disparities in outcomes after acute myocardial infarction.

PubMed

Bernheim, Susannah M; Spertus, John A; Reid, Kimberly J; Bradley, Elizabeth H; Desai, Rani A; Peterson, Eric D; Rathore, Saif S; Normand, Sharon-Lise T; Jones, Philip G; Rahimi, Ali; Krumholz, Harlan M

2007-02-01

Patients of low socioeconomic status (SES) have higher mortality after acute myocardial infarction (AMI). Little is known about the underlying mechanisms or the relationship between SES and rehospitalization after AMI. We analyzed data from the PREMIER observational study, which included 2142 patients hospitalized with AMI from 18 US hospitals. Socioeconomic status was measured by self-reported household income and education level. Sequential multivariable modeling assessed the relationship of socioeconomic factors with 1-year all-cause mortality and all-cause rehospitalization after adjustment for demographics, clinical factors, and quality-of-care measures. Both household income and education level were associated with higher risk of mortality (hazard ratio 2.80, 95% CI 1.37-5.72, lowest to highest income group) and rehospitalization after AMI (hazard ratio 1.55, 95% CI 1.17-2.05). Patients with low SES had worse clinical status at admission and received poorer quality of care. In multivariable modeling, the relationship between household income and mortality was attenuated by adjustment for demographic and clinical factors (hazard ratio 1.19, 95% CI 0.54-2.62), with a further small decrement in the hazard ratio after adjustment for quality of care. The relationship between income and rehospitalization was only partly attenuated by demographic and clinical factors (hazard ratio 1.38, 95% CI 1.01-1.89) and was not influenced by adjustment for quality of care. Patients' baseline clinical status largely explained the relationship between SES and mortality, but not rehospitalization, among patients with AMI.

Clinical predictors of rectal lymphogranuloma venereum infection: results from a multicentre case–control study in the UK

PubMed Central

Pallawela, S N S; Sullivan, A K; Macdonald, N; French, P; White, J; Dean, G; Smith, A; Winter, A J; Mandalia, S; Alexander, S; Ison, C; Ward, H

2014-01-01

Objective Since 2003, over 2000 cases of lymphogranuloma venereum (LGV) have been diagnosed in the UK in men who have sex with men (MSM). Most cases present with proctitis, but there are limited data on how to differentiate clinically between LGV and other pathology. We analysed the clinical presentations of rectal LGV in MSM to identify clinical characteristics predictive of LGV proctitis and produced a clinical prediction model. Design A prospective multicentre case–control study was conducted at six UK hospitals from 2008 to 2010. Cases of rectal LGV were compared with controls with rectal symptoms but without LGV. Methods Data from 98 LGV cases and 81 controls were collected from patients and clinicians using computer-assisted self-interviews and clinical report forms. Univariate and multivariate logistic regression was used to compare symptoms and signs. Clinical prediction models for LGV were compared using receiver operating curves. Results Tenesmus, constipation, anal discharge and weight loss were significantly more common in cases than controls. In multivariate analysis, tenesmus and constipation alone were suggestive of LGV (OR 2.98, 95% CI 0.99 to 8.98 and 2.87, 95% CI 1.01 to 8.15, respectively) and that tenesmus alone or in combination with constipation was a significant predictor of LGV (OR 6.97, 95% CI 2.71 to 17.92). The best clinical prediction was having one or more of tenesmus, constipation and exudate on proctoscopy, with a sensitivity of 77% and specificity of 65%. Conclusions This study indicates that tenesmus alone or in combination with constipation makes a diagnosis of LGV in MSM presenting with rectal symptoms more likely. PMID:24687130

Prognostic value of the new Grade Groups in Prostate Cancer: a multi-institutional European validation study.

PubMed

Mathieu, R; Moschini, M; Beyer, B; Gust, K M; Seisen, T; Briganti, A; Karakiewicz, P; Seitz, C; Salomon, L; de la Taille, A; Rouprêt, M; Graefen, M; Shariat, S F

2017-06-01

We aimed to assess the prognostic relevance of the new Grade Groups in Prostate Cancer (PCa) within a large cohort of European men treated with radical prostatectomy (RP). Data from 27 122 patients treated with RP at seven European centers were analyzed. We investigated the prognostic performance of the new Grade Groups (based on Gleason score 3+3, 3+4, 4+3, 8 and 9-10) on biopsy and RP specimen, adjusted for established clinical and pathological characteristics. Multivariable Cox proportional hazards regression models assessed the association of new Grade Groups with biochemical recurrence (BCR). Prognostic accuracies of the models were assessed using Harrell's C-index. Median follow-up was 29 months (interquartile range, 13-54). The 4-year estimated BCR-free survival (bRFS) for biopsy Grade Groups 1-5 were 91.3, 81.6, 69.8, 60.3 and 44.4%, respectively. The 4-year estimated bRFS for RP Grade Groups 1-5 were 96.1%, 86.7%, 67.0%, 63.1% and 41.0%, respectively. Compared with Grade Group 1, all other Grade Groups based both on biopsy and RP specimen were independently associated with a lower bRFS (all P<0.01). Adjusted pairwise comparisons revealed statistically differences between all Grade Groups, except for group 3 and 4 on RP specimen (P=0.10). The discriminations of the multivariable base prognostic models based on the current three-tier and the new five-tier systems were not clinically different (0.3 and 0.9% increase in discrimination for clinical and pathological model). We validated the independent prognostic value of the new Grade Groups on biopsy and RP specimen from European PCa men. However, it does not improve the accuracies of prognostic models by a clinically significant margin. Nevertheless, this new classification may help physicians and patients estimate disease aggressiveness with a user-friendly, clinically relevant and reproducible method.

Improved Accuracy of Automated Estimation of Cardiac Output Using Circulation Time in Patients with Heart Failure.

PubMed

Dajani, Hilmi R; Hosokawa, Kazuya; Ando, Shin-Ichi

2016-11-01

Lung-to-finger circulation time of oxygenated blood during nocturnal periodic breathing in heart failure patients measured using polysomnography correlates negatively with cardiac function but possesses limited accuracy for cardiac output (CO) estimation. CO was recalculated from lung-to-finger circulation time using a multivariable linear model with information on age and average overnight heart rate in 25 patients who underwent evaluation of heart failure. The multivariable model decreased the percentage error to 22.3% relative to invasive CO measured during cardiac catheterization. This improved automated noninvasive CO estimation using multiple variables meets a recently proposed performance criterion for clinical acceptability of noninvasive CO estimation, and compares very favorably with other available methods. Copyright © 2016 Elsevier Inc. All rights reserved.

Clinic Design and Continuity in Internal Medicine Resident Clinics: Findings of the Educational Innovations Project Ambulatory Collaborative.

PubMed

Francis, Maureen D; Wieland, Mark L; Drake, Sean; Gwisdalla, Keri Lyn; Julian, Katherine A; Nabors, Christopher; Pereira, Anne; Rosenblum, Michael; Smith, Amy; Sweet, David; Thomas, Kris; Varney, Andrew; Warm, Eric; Wininger, David; Francis, Mark L

2015-03-01

Many internal medicine (IM) programs have reorganized their resident continuity clinics to improve trainees' ambulatory experience. Downstream effects on continuity of care and other clinical and educational metrics are unclear. This multi-institutional, cross-sectional study included 713 IM residents from 12 programs. Continuity was measured using the usual provider of care method (UPC) and the continuity for physician method (PHY). Three clinic models (traditional, block, and combination) were compared using analysis of covariance. Multivariable linear regression analysis was used to analyze the effect of practice metrics and clinic model on continuity. UPC, reflecting continuity from the patient perspective, was significantly different, and was highest in the block model, midrange in combination model, and lowest in the traditional model programs. PHY, reflecting continuity from the perspective of the resident provider, was significantly lower in the block model than in combination and traditional programs. Panel size, ambulatory workload, utilization, number of clinics attended in the study period, and clinic model together accounted for 62% of the variation found in UPC and 26% of the variation found in PHY. Clinic model appeared to have a significant effect on continuity measured from both the patient and resident perspectives. Continuity requires balance between provider availability and demand for services. Optimizing this balance to maximize resident education, and the health of the population served, will require consideration of relevant local factors and priorities in addition to the clinic model.

Clinic Design and Continuity in Internal Medicine Resident Clinics: Findings of the Educational Innovations Project Ambulatory Collaborative

PubMed Central

Francis, Maureen D.; Wieland, Mark L.; Drake, Sean; Gwisdalla, Keri Lyn; Julian, Katherine A.; Nabors, Christopher; Pereira, Anne; Rosenblum, Michael; Smith, Amy; Sweet, David; Thomas, Kris; Varney, Andrew; Warm, Eric; Wininger, David; Francis, Mark L.

2015-01-01

Background Many internal medicine (IM) programs have reorganized their resident continuity clinics to improve trainees' ambulatory experience. Downstream effects on continuity of care and other clinical and educational metrics are unclear. Methods This multi-institutional, cross-sectional study included 713 IM residents from 12 programs. Continuity was measured using the usual provider of care method (UPC) and the continuity for physician method (PHY). Three clinic models (traditional, block, and combination) were compared using analysis of covariance. Multivariable linear regression analysis was used to analyze the effect of practice metrics and clinic model on continuity. Results UPC, reflecting continuity from the patient perspective, was significantly different, and was highest in the block model, midrange in combination model, and lowest in the traditional model programs. PHY, reflecting continuity from the perspective of the resident provider, was significantly lower in the block model than in combination and traditional programs. Panel size, ambulatory workload, utilization, number of clinics attended in the study period, and clinic model together accounted for 62% of the variation found in UPC and 26% of the variation found in PHY. Conclusions Clinic model appeared to have a significant effect on continuity measured from both the patient and resident perspectives. Continuity requires balance between provider availability and demand for services. Optimizing this balance to maximize resident education, and the health of the population served, will require consideration of relevant local factors and priorities in addition to the clinic model. PMID:26217420

Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.

PubMed

Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo

2017-07-01

The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients.

[Modeling in value-based medicine].

PubMed

Neubauer, A S; Hirneiss, C; Kampik, A

2010-03-01

Modeling plays an important role in value-based medicine (VBM). It allows decision support by predicting potential clinical and economic consequences, frequently combining different sources of evidence. Based on relevant publications and examples focusing on ophthalmology the key economic modeling methods are explained and definitions are given. The most frequently applied model types are decision trees, Markov models, and discrete event simulation (DES) models. Model validation includes besides verifying internal validity comparison with other models (external validity) and ideally validation of its predictive properties. The existing uncertainty with any modeling should be clearly stated. This is true for economic modeling in VBM as well as when using disease risk models to support clinical decisions. In economic modeling uni- and multivariate sensitivity analyses are usually applied; the key concepts here are tornado plots and cost-effectiveness acceptability curves. Given the existing uncertainty, modeling helps to make better informed decisions than without this additional information.

Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal.

PubMed

Conlon, Anna S C; Taylor, Jeremy M G; Elliott, Michael R

2014-04-01

In clinical trials, a surrogate outcome variable (S) can be measured before the outcome of interest (T) and may provide early information regarding the treatment (Z) effect on T. Using the principal surrogacy framework introduced by Frangakis and Rubin (2002. Principal stratification in causal inference. Biometrics 58, 21-29), we consider an approach that has a causal interpretation and develop a Bayesian estimation strategy for surrogate validation when the joint distribution of potential surrogate and outcome measures is multivariate normal. From the joint conditional distribution of the potential outcomes of T, given the potential outcomes of S, we propose surrogacy validation measures from this model. As the model is not fully identifiable from the data, we propose some reasonable prior distributions and assumptions that can be placed on weakly identified parameters to aid in estimation. We explore the relationship between our surrogacy measures and the surrogacy measures proposed by Prentice (1989. Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in Medicine 8, 431-440). The method is applied to data from a macular degeneration study and an ovarian cancer study.

Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal

PubMed Central

Conlon, Anna S. C.; Taylor, Jeremy M. G.; Elliott, Michael R.

2014-01-01

In clinical trials, a surrogate outcome variable (S) can be measured before the outcome of interest (T) and may provide early information regarding the treatment (Z) effect on T. Using the principal surrogacy framework introduced by Frangakis and Rubin (2002. Principal stratification in causal inference. Biometrics 58, 21–29), we consider an approach that has a causal interpretation and develop a Bayesian estimation strategy for surrogate validation when the joint distribution of potential surrogate and outcome measures is multivariate normal. From the joint conditional distribution of the potential outcomes of T, given the potential outcomes of S, we propose surrogacy validation measures from this model. As the model is not fully identifiable from the data, we propose some reasonable prior distributions and assumptions that can be placed on weakly identified parameters to aid in estimation. We explore the relationship between our surrogacy measures and the surrogacy measures proposed by Prentice (1989. Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in Medicine 8, 431–440). The method is applied to data from a macular degeneration study and an ovarian cancer study. PMID:24285772

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

PubMed

Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

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.

[Value of the albumin to globulin ratio in predicting severity and prognosis in myasthenia gravis patients].

PubMed

Yang, D H; Su, Z Q; Chen, Y; Chen, Z B; Ding, Z N; Weng, Y Y; Li, J; Li, X; Tong, Q L; Han, Y X; Zhang, X

2016-03-08

To assess the predictive value of the albumin to globulin ratio (AGR) in evaluation of disease severity and prognosis in myasthenia gravis patients. A total of 135 myasthenia gravis (MG) patients were enrolled between February 2009 and March 2015. The AGR was detected on the first day of hospitalization and ranked from lowest to highest, and the patients were divided into three equal tertiles according to the AGR values, which were T1 (AGR <1.34), T2 (1.34≤AGR≤1.53) and T3 (AGR>1.53). The Kaplan-Meier curve was used to evaluate the prognostic value of AGR. Cox model analysis was used to evaluate the relevant factors. Multivariate Logistic regression analysis was used to find the predictors of myasthenia crisis during hospitalization. The median length of hospital stay for each tertile was: for the T1 21 days (15-35.5), T2 18 days (14-27.5), and T3 16 days (12-22.5) (P<0.01), and Kaplan-Meier curves showed significant difference among the three groups. In the univariate model, serum albumin, creatinine, AGR and MGFA clinical classification were related to prognosis of myasthenia gravis. At the multivariate Cox regression analysis, the AGR (P<0.001) and MGFA clinical classification (P<0.001) were independent predictive factors of disease severity and prognosis in myasthenia gravis patients. Respectively, the hazard ratio (HR) were 4.655 (95% CI: 2.355-9.202) and 0.596 (95% CI: 0.492-0.723). Multivariate Logistic regression analysis showed the AGR (P<0.001) and MGFA clinical classification were related to myasthenia crisis. The AGR may represent a simple, potentially useful predictive biomarker for evaluating the disease severity and prognosis of patients with myasthenia gravis.

Abandonment of antiretroviral therapy among HIV-positive patients attended at the reference center for HIV/AIDS in Vitória, Brazil.

PubMed

Zago, Adriana Marchon; Morelato, Paola; Endringer, Emmanuele de Angeli; Dan, Germano de Freitas; Ribeiro, Evanira Mendes; Miranda, Angelica Espinosa

2012-01-01

This study evaluates the risk factors for the abandonment of antiretroviral therapy (ART) among patients receiving care in an AIDS clinic in Vitória, Brazil. We conducted a case-control study of patients with AIDS attending a reference center for sexually transmitted disease (STD)/AIDS. A total of 62 patients, who abandoned therapy in 2008, and 188 HIV-infected patients answered an interview including demographic, social, and clinical characteristics. Risk factors associated with abandon in univariate analysis were entered into logistic regression models. A total of 250 patients were included in the study. Groups were similar regarding age, gender, and monthly income. In the final multivariate model, illicit drug use (adjusted odds ratio [AOR], 2.3; 95% confidence interval [CI], 1.03-5.07), previous abandon of medication (AOR 38.6; 95% CI 10.49-142.25), last CD4 count <200 cells/mm(3) (AOR 1.5; 95% CI 1.03-2.10), and viral load higher than 1000 copies/mL (AOR 2.0 (95% CI 1.34-3.09) were independent predictors of abandonment of ART. In addition to the clinical indicators, behavioral factors remained important throughout the multivariate analysis in our study.

Relationships between heart rate and age, bodyweight and breed in 10,849 dogs.

PubMed

Hezzell, M J; Dennis, S G; Humm, K; Agee, L; Boswood, A

2013-06-01

To evaluate relationships between heart rate and clinical variables in healthy dogs and dogs examined at a referral hospital. Clinical data were extracted from the electronic patient records of a first opinion group (5000 healthy dogs) and a referral hospital (5849 dogs). Univariable and multi-variable general linear models were used to assess associations between heart rate and clinical characteristics. Separate multi-variable models were constructed for first opinion and referral populations. In healthy dogs, heart rate was negatively associated with bodyweight (P<0.001) but was higher in Chihuahuas. The mean difference in heart rate between a 5 and 55 kg dog was 10.5 beats per minute. In dogs presenting to a referral hospital, heart rate was negatively associated with bodyweight (P<0.001) and the following breeds; border collie, golden retriever, Labrador retriever, springer spaniel and West Highland white terrier and positively associated with age, admitting service (emergency and critical care, emergency first opinion and cardiology) and the following breeds; Cavalier King Charles spaniel, Staffordshire bull terrier and Yorkshire terrier. Bodyweight, age, breed and disease status all influence heart rate in dogs, although these factors account for a relatively small proportion of the overall variability in heart rate. © 2013 British Small Animal Veterinary Association.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

PubMed

Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial.

PubMed

Bartlett, John M S; Christiansen, Jason; Gustavson, Mark; Rimm, David L; Piper, Tammy; van de Velde, Cornelis J H; Hasenburg, Annette; Kieback, Dirk G; Putter, Hein; Markopoulos, Christos J; Dirix, Luc Y; Seynaeve, Caroline; Rea, Daniel W

2016-01-01

Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

Imaging muscle as a potential biomarker of denervation in motor neuron disease

PubMed Central

Jenkins, Thomas M; Alix, James J P; David, Charlotte; Pearson, Eilish; Rao, D Ganesh; Hoggard, Nigel; O’Brien, Eoghan; Baster, Kathleen; Bradburn, Michael; Bigley, Julia; McDermott, Christopher J; Wilkinson, Iain D; Shaw, Pamela J

2018-01-01

Objective To assess clinical, electrophysiological and whole-body muscle MRI measurements of progression in patients with motor neuron disease (MND), as tools for future clinical trials, and to probe pathophysiological mechanisms in vivo. Methods A prospective, longitudinal, observational, clinicoelectrophysiological and radiological cohort study was performed. Twenty-nine patients with MND and 22 age-matched and gender-matched healthy controls were assessed with clinical measures, electrophysiological motor unit number index (MUNIX) and T2-weighted whole-body muscle MRI, at first clinical presentation and 4 months later. Between-group differences and associations were assessed using age-adjusted and gender-adjusted multivariable regression models. Within-subject longitudinal changes were assessed using paired t-tests. Patterns of disease spread were modelled using mixed-effects multivariable regression, assessing associations between muscle relative T2 signal and anatomical adjacency to site of clinical onset. Results Patients with MND had 30% higher relative T2 muscle signal than controls at baseline (all regions mean, 95% CI 15% to 45%, p<0.001). Higher T2 signal was associated with greater overall disability (coefficient −0.009, 95% CI −0.017 to –0.001, p=0.023) and with clinical weakness and lower MUNIX in multiple individual muscles. Relative T2 signal in bilateral tibialis anterior increased over 4 months in patients with MND (right: 10.2%, 95% CI 2.0% to 18.4%, p=0.017; left: 14.1%, 95% CI 3.4% to 24.9%, p=0.013). Anatomically, contiguous disease spread on MRI was not apparent in this model. Conclusions Whole-body muscle MRI offers a new approach to objective assessment of denervation over short timescales in MND and enables investigation of patterns of disease spread in vivo. Muscles inaccessible to conventional clinical and electrophysiological assessment may be investigated using this methodology. PMID:29089397

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Prediction of higher cost of antiretroviral therapy (ART) according to clinical complexity. A validated clinical index.

PubMed

Velasco, Cesar; Pérez, Inaki; Podzamczer, Daniel; Llibre, Josep Maria; Domingo, Pere; González-García, Juan; Puig, Inma; Ayala, Pilar; Martín, Mayte; Trilla, Antoni; Lázaro, Pablo; Gatell, Josep Maria

2016-03-01

The financing of antiretroviral therapy (ART) is generally determined by the cost incurred in the previous year, the number of patients on treatment, and the evidence-based recommendations, but not the clinical characteristics of the population. To establish a score relating the cost of ART and patient clinical complexity in order to understand the costing differences between hospitals in the region that could be explained by the clinical complexity of their population. Retrospective analysis of patients receiving ART in a tertiary hospital between 2009 and 2011. Factors potentially associated with a higher cost of ART were assessed by bivariate and multivariate analysis. Two predictive models of "high-cost" were developed. The normalized estimated (adjusted for the complexity scores) costs were calculated and compared with the normalized real costs. In the Hospital Index, 631 (16.8%) of the 3758 patients receiving ART were responsible for a "high-cost" subgroup, defined as the highest 25% of spending on ART. Baseline variables that were significant predictors of high cost in the Clinic-B model in the multivariate analysis were: route of transmission of HIV, AIDS criteria, Spanish nationality, year of initiation of ART, CD4+ lymphocyte count nadir, and number of hospital admissions. The Clinic-B score ranged from 0 to 13, and the mean value (5.97) was lower than the overall mean value of the four hospitals (6.16). The clinical complexity of the HIV patient influences the cost of ART. The Clinic-B and Clinic-BF scores predicted patients with high cost of ART and could be used to compare and allocate costs corrected for the patient clinical complexity. Copyright © 2015 Elsevier España, S.L.U. y Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Delivering a sustainable trauma management training programme tailored for low-resource settings in East, Central and Southern African countries using a cascading course model.

PubMed

Peter, N A; Pandit, H; Le, G; Nduhiu, M; Moro, E; Lavy, C

2016-05-01

Injuries cause five million deaths and 279 Disability Adjusted Life Years (DALYS) each year worldwide. The COSECSA Oxford Orthopaedic Link (COOL) is a multi-country partnership programme that has delivered training in trauma management to nine sub-Saharan countries across a wide-cadre of health-workers using a model of "primary" courses delivered by UK instructors, followed by "cascading" courses led by local faculty. This study examines the impact on knowledge and clinical confidence among health-workers, and compares the performance of "cascading" and "primary" courses delivered in low-resource settings. Data was collated from 1030 candidates (119 Clinical Officers, 540 Doctors, 260 Nurses and 111 Medical Students) trained over 28 courses (9 "primary" and 19 "cascading" courses) in nine sub-Saharan countries between 2012 and 2013. Knowledge and clinical confidence of candidates were assessed using pre- and post-course MCQs and confidence matrix rating of clinical scenarios. Changes were measured in relation to co-variants of gender, job roles and primary versus cascading courses. Multivariate regression modelling and cost analysis was performed to examine the impact of primary versus cascading courses on candidates' performance. There was a significant improvement in knowledge (58% to 77%, p<0.05) and clinical confidence (68% to 90%, p<0.05) post-course. "Non-doctors" demonstrated a greater improvement in knowledge (22%) and confidence (24%) following the course (p<0.05). The degree of improvement of MCQ scores differed significantly, with the cascading courses (21%) outperforming primary courses (15%) (p<0.002). This is further supported by multivariate regression modelling where cascading courses are a strong predictor for improvement in MCQ scores (Coef=4.83, p<0.05). Trauma management training of health-workers plays a pivotal role in tackling the ever-growing trauma burden in Africa. Our study suggests cascading PTC courses may be an effective model in delivering trauma training in low-resource settings, however further studies are required to determine its efficacy in improving clinical competence and retention of knowledge and skills in the long term. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Community-Based Addiction Treatment Staff Attitudes about the Usefulness of Evidence-Based Addiction Treatment and CBO Organizational Linkages to Research Institutions

ERIC Educational Resources Information Center

Lundgren, Lena; Krull, Ivy; Zerden, Lisa de Saxe; McCarty, Dennis

2011-01-01

This national study of community-based addiction-treatment organizations' (CBOs) implementation of evidence-based practices explored CBO Program Directors' (n = 296) and clinical staff (n = 518) attitudes about the usefulness of science-based addiction treatment. Through multivariable regression modeling, the study identified that identical…

Can patient comorbidities be included in clinical performance measures for radiation oncology?

PubMed

Owen, Jean B; Khalid, Najma; Ho, Alex; Kachnic, Lisa A; Komaki, Ritsuko; Tao, May Lin; Currey, Adam; Wilson, J Frank

2014-05-01

Patient comorbidities may affect the applicability of performance measures that are inherent in multidisciplinary cancer treatment guidelines. This article describes the distribution of common comorbid conditions by disease site and by patient and facility characteristics in patients who received radiation therapy as part of treatment for cancer of the breast, cervix, lung, prostate, and stomach, and investigates the association of comorbidities with treatment decisions. Stratified two-stage cluster sampling provided a random sample of radiation oncology facilities. Eligible patients were randomly sampled from each participating facility for each disease site, and data were abstracted from medical records. The Adult Comorbidity Evaluation Index (ACE-27) was used to measure comorbid conditions and their severity. National estimates were calculated using SUDAAN statistical software. Multivariable logistic regression models predicted the dependent variable "treatment changed or contraindicated due to comorbidities." The final model showed that ACE-27 was highly associated with change in treatment for patients with severe or moderate index values compared to those with none or mild (P < .001). Two other covariates, age and medical coverage, had no (age) or little (medical coverage) significant contribution to predicting treatment change in the multivariable model. Disease site was associated with treatment change after adjusting for other covariates in the model. ACE-27 is highly predictive of treatment modifications for patients treated for these cancers who receive radiation as part of their care. A standardized tool identifying patients who should be excluded from clinical performance measures allows more accurate use of these measures. Copyright © 2014 by American Society of Clinical Oncology.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study.

PubMed

Hamilton, C A; Miller, A; Casablanca, Y; Horowitz, N S; Rungruang, B; Krivak, T C; Richard, S D; Rodriguez, N; Birrer, M J; Backes, F J; Geller, M A; Quinn, M; Goodheart, M J; Mutch, D G; Kavanagh, J J; Maxwell, G L; Bookman, M A

2018-02-01

To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). The analysis dataset included 3010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. Published by Elsevier Inc.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study

PubMed Central

Hamilton, C. A.; Miller, A.; Casablanca, Y.; Horowitz, N. S.; Rungruang, B.; Krivak, T. C.; Richard, S. D.; Rodriguez, N.; Birrer, M.J.; Backes, F.J.; Geller, M.A.; Quinn, M.; Goodheart, M.J.; Mutch, D.G.; Kavanagh, J.J.; Maxwell, G. L.; Bookman, M. A.

2018-01-01

Objective To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Methods Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10 years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). Results The analysis dataset included 3,010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. Conclusions The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. PMID:29195926

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

PubMed Central

Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

PubMed Central

Powell, Graham A.; Tudur Smith, Catrin; Marson, Anthony G.

2017-01-01

Objectives To develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment. Methods We analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike’s Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model. Results Significant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision. Conclusions This is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials. PMID:29267375

Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.

PubMed

Moss, Travis J; Lake, Douglas E; Calland, J Forrest; Enfield, Kyle B; Delos, John B; Fairchild, Karen D; Moorman, J Randall

2016-09-01

Patients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness. We report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation). University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data. None. We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. Subacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes.

Prognostic nutritional index is associated with survival after total gastrectomy for patients with gastric cancer.

PubMed

Ishizuka, Mitsuru; Oyama, Yusuke; Abe, Akihito; Tago, Kazuma; Tanaka, Genki; Kubota, Keiichi

2014-08-01

To investigate the influence of clinical characteristics including nutritional markers on postoperative survival in patients undergoing total gastrectomy (TG) for gastric cancer (GC). One hundred fifty-four patients were enrolled. Uni- and multivariate analyses using the Cox proportional hazard model were performed to explore the most valuable clinical characteristic that was associated with postoperative survival. Multivariate analysis using twelve clinical characteristics selected from univariate analyses revealed that age (≤ 72/>72), carcinoembryonic antigen (≤ 20/>20) (ng/ml), white blood cell count (≤ 9.5/>9.5) (× 10(3)/mm(3)), prognostic nutritional index (PNI) (≤ 45/>45) and lymph node metastasis (negative/positive) were associated with postoperative survival. Kaplan-Meier analysis and log-rank test showed that patients with higher PNI (>45) had a higher postoperative survival rate than those with lower PNI (≤ 45) (p<0.001). PNI is associated with postoperative survival of patients undergoing TG for GC and is able to divide such patients into two independent groups before surgery. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Epidemiology of clinical feline herpesvirus infection in zoo-housed cheetahs (Acinonyx jubatus).

PubMed

Witte, Carmel L; Lamberski, Nadine; Rideout, Bruce A; Vaida, Florin; Citino, Scott B; Barrie, Michael T; Haefele, Holly J; Junge, Randall E; Murray, Suzan; Hungerford, Laura L

2017-10-15

OBJECTIVE To determine the incidence of and risk factors for clinical feline herpesvirus (FHV) infection in zoo-housed cheetahs and determine whether dam infection was associated with offspring infection. DESIGN Retrospective cohort study. ANIMALS 144 cheetah cubs born in 6 zoos from 1988 through 2007. PROCEDURES Data were extracted from the health records of cheetahs and their dams to identify incident cases of clinical FHV infection and estimate incidence from birth to 18 months of age. Univariate and multivariable Cox proportional hazards models, controlling for correlations among cheetahs with the same dam, were used to identify risk factors for incident FHV infection. RESULTS Cumulative incidence of FHV infection in cheetah cubs was 35% (50/144). No significant association between dam and offspring infection was identified in any model. Factors identified as significant through multivariable analysis varied by age group. For cheetahs up to 3 months of age, the most important predictor of FHV infection was having a dam that had received a preparturition FHV vaccine regimen that included a modified-live virus vaccine versus a dam that had received no preparturition vaccine. Other risk factors included being from a small litter, being born to a primiparous dam, and male sex. CONCLUSIONS AND CLINICAL RELEVANCE This study provided the first population-level characterization of the incidence of and risk factors for FHV infection in cheetahs, and findings confirmed the importance of this disease. Recognition that clinical FHV infection in the dam was not a significant predictor of disease in cubs and identification of other significant factors have implications for disease management.

Effect of metallo-β-lactamase production and multidrug resistance on clinical outcomes in patients with Pseudomonas aeruginosa bloodstream infection: a retrospective cohort study.

PubMed

Willmann, Matthias; Kuebart, Ines; Marschal, Matthias; Schröppel, Klaus; Vogel, Wichard; Flesch, Ingo; Markert, Uwe; Autenrieth, Ingo B; Hölzl, Florian; Peter, Silke

2013-11-01

Blood stream infections (BSI) with Pseudomonas aeruginosa lead to poor clinical outcomes. The worldwide emergence and spread of metallo-β-lactamase (MBL) producing, often multidrug-resistant organisms may further aggravate this problem. Our study aimed to investigate the effect of MBL-producing P. aeruginosa (MBL-PA) and various other resistance phenotypes on clinical outcomes. A retrospective cohort study was conducted in three German hospitals. Medical files from 2006 until 2012 were studied, and a number of 113 patients with P. aeruginosa BSI were included. The presence of VIM, IMP and NDM genes was detected using molecular techniques. Genetic relatedness was assessed through multilocus sequence typing (MLST). The effect of resistance patterns or MBL production on clinical outcomes was investigated by using multivariate Cox regression models. In-hospital mortality was significantly higher in patients with MBL-PA and multidrug-resistant P. aeruginosa. However, neither BSI with MBL-PA nor BSI with various resistance phenotypes of P. aeruginosa were independently associated with mortality or length of hospital stay. In multivariate models, the SAPS II score (HR 1.046), appropriate definitive treatment (HR range 0.25-0.26), and cardiovascular disease (HR range 0.44-0.46) were independent predictors of mortality. Concomitant infections were associated with an excess length of stay (HR < 1). Medication with appropriate antimicrobial agents at any time during the course of infection remains the key for improving clinical outcomes in patients with P. aeruginosa BSI and should be combined with a strict implementation of routine infection control measures.

Study for Updated Gout Classification Criteria (SUGAR): identification of features to classify gout

PubMed Central

Taylor, William J.; Fransen, Jaap; Jansen, Tim L.; Dalbeth, Nicola; Schumacher, H. Ralph; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Vargas-Santos, Ana Beatriz; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Cimmino, Marco A.; Uhlig, Till; Neogi, Tuhina

2015-01-01

Objective To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. Methods A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. Results In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6 mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Conclusion Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. PMID:25777045

Prognosis Relevance of Serum Cytokines in Pancreatic Cancer

PubMed Central

Alejandre, Maria José; Palomino-Morales, Rogelio J.; Prados, Jose; Aránega, Antonia; Delgado, Juan R.; Irigoyen, Antonio; Martínez-Galán, Joaquina; Ortuño, Francisco M.

2015-01-01

The overall survival of patients with pancreatic ductal adenocarcinoma is extremely low. Although gemcitabine is the standard used chemotherapy for this disease, clinical outcomes do not reflect significant improvements, not even when combined with adjuvant treatments. There is an urgent need for prognosis markers to be found. The aim of this study was to analyze the potential value of serum cytokines to find a profile that can predict the clinical outcome in patients with pancreatic cancer and to establish a practical prognosis index that significantly predicts patients' outcomes. We have conducted an extensive analysis of serum prognosis biomarkers using an antibody array comprising 507 human cytokines. Overall survival was estimated using the Kaplan-Meier method. Univariate and multivariate Cox's proportional hazard models were used to analyze prognosis factors. To determine the extent that survival could be predicted based on this index, we used the leave-one-out cross-validation model. The multivariate model showed a better performance and it could represent a novel panel of serum cytokines that correlates to poor prognosis in pancreatic cancer. B7-1/CD80, EG-VEGF/PK1, IL-29, NRG1-beta1/HRG1-beta1, and PD-ECGF expressions portend a poor prognosis for patients with pancreatic cancer and these cytokines could represent novel therapeutic targets for this disease. PMID:26346854

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

PTEN Loss as Determined by Clinical-grade Immunohistochemistry Assay Is Associated with Worse Recurrence-free Survival in Prostate Cancer.

PubMed

Lotan, Tamara L; Wei, Wei; Morais, Carlos L; Hawley, Sarah T; Fazli, Ladan; Hurtado-Coll, Antonio; Troyer, Dean; McKenney, Jesse K; Simko, Jeffrey; Carroll, Peter R; Gleave, Martin; Lance, Raymond; Lin, Daniel W; Nelson, Peter S; Thompson, Ian M; True, Lawrence D; Feng, Ziding; Brooks, James D

2016-06-01

PTEN is the most commonly deleted tumor suppressor gene in primary prostate cancer (PCa) and its loss is associated with poor clinical outcomes and ERG gene rearrangement. We tested whether PTEN loss is associated with shorter recurrence-free survival (RFS) in surgically treated PCa patients with known ERG status. A genetically validated, automated PTEN immunohistochemistry (IHC) protocol was used for 1275 primary prostate tumors from the Canary Foundation retrospective PCa tissue microarray cohort to assess homogeneous (in all tumor tissue sampled) or heterogeneous (in a subset of tumor tissue sampled) PTEN loss. ERG status as determined by a genetically validated IHC assay was available for a subset of 938 tumors. Associations between PTEN and ERG status were assessed using Fisher's exact test. Kaplan-Meier and multivariate weighted Cox proportional models for RFS were constructed. When compared to intact PTEN, homogeneous (hazard ratio [HR] 1.66, p = 0.001) but not heterogeneous (HR 1.24, p = 0.14) PTEN loss was significantly associated with shorter RFS in multivariate models. Among ERG-positive tumors, homogeneous (HR 3.07, p < 0.0001) but not heterogeneous (HR 1.46, p = 0.10) PTEN loss was significantly associated with shorter RFS. Among ERG-negative tumors, PTEN did not reach significance for inclusion in the final multivariate models. The interaction term for PTEN and ERG status with respect to RFS did not reach statistical significance ( p = 0.11) for the current sample size. These data suggest that PTEN is a useful prognostic biomarker and that there is no statistically significant interaction between PTEN and ERG status for RFS. We found that loss of the PTEN tumor suppressor gene in prostate tumors as assessed by tissue staining is correlated with shorter time to prostate cancer recurrence after radical prostatectomy.

Psychological distress, health and treatment-related factors among individuals initiating ART in Oromia, Ethiopia.

PubMed

Parcesepe, Angela M; Tymejczyk, Olga; Remien, Robert; Gadisa, Tsigereda; Kulkarni, Sarah Gorrell; Hoffman, Susie; Melaku, Zenebe; Elul, Batya; Nash, Denis

2018-03-01

HIV diagnosis may be a source of psychological distress. Late initiation of antiretroviral therapy (ART) and treatment-related beliefs may intensify psychological distress among those recently diagnosed. This analysis describes the prevalence of psychological distress among people living with HIV (PLWH) and examines the association of recent HIV diagnosis, late ART initiation and treatment-related beliefs with psychological distress. The sample includes 1175 PLWH aged 18 or older initiating ART at six HIV clinics in Ethiopia. Psychological distress was assessed with Kessler Psychological Distress Scale. Scores ≥ 29 were categorized as severe psychological distress. Individuals who received their first HIV diagnosis in the past 90 days were categorized as recently diagnosed. Multivariable logistic regression modeled the association of recent diagnosis, late ART initiation and treatment-related beliefs on severe psychological distress, controlling for age, sex, education, area of residence, relationship status, and health facility. Among respondents, 29.5% reported severe psychological distress, 46.6% were recently diagnosed and 31.0% initiated ART late. In multivariable models, relative to those who did not initiate ART late and had longer time since diagnosis, odds of severe psychological distress was significantly greater among those with recent diagnosis and late ART initiation (adjusted OR [aOR]: 1.9 [95% CI 1.4, 2.8]). Treatment-related beliefs were not associated with severe psychological distress in multivariable models. Severe psychological distress was highly prevalent, particularly among those who were recently diagnosed and initiated ART late. Greater understanding of the relationship between psychological distress, recent diagnosis, and late ART initiation can inform interventions to reduce psychological distress among this population. Mental health screening and interventions should be incorporated into routine HIV clinical care from diagnosis through treatment.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

Clinical risk factors associated with incidence and progression of periodontal conditions in pregnant women.

PubMed

Moss, Kevin L; Beck, James D; Offenbacher, Steven

2005-05-01

Few large studies have investigated the progression of periodontal conditions during pregnancy in a comprehensive manner. This study aimed to identify clinical factors that were predictive of incidence/progression of periodontal measures in pregnant women adjusting for relevant predictors. Periodontal examinations were conducted on 891 pregnant women prior to 26 weeks gestational age and within 48 h after delivery. Gingivitis/periodontitis incidence/progression (GPIP) was defined as four plus sites with 2+ mm increase in probing depth (PD) that resulted in PD of at least 4 mm at delivery. Multivariable models including relevant clinical variables and significant covariates were developed. While several clinical measures were significantly associated with the outcome, having >/=10% of sites with bleeding on probing (BOP) and four plus sites with PD >/=4 mm (PD4) were the best two predictors of GPIP (odds ratio (OR)=2.8, 95% confidence interval (CI)=1.8-4.2; OR=2.0, 95% CI=1.4-2.9, respectively), adjusting for maternal race, age, enrollment weight, smoking during pregnancy, marital status, food stamp eligibility, and private health insurance. Multivariable models assessed the impact of BOP on the PD4-GPIP relationship. PD4 was significant in the presence of BOP (low BOP OR=1.3, 95% CI=0.5-3.3; high BOP OR=3.0, 95% CI=2.2-4.3). Enrollment BOP and PD4 were significant predictors of PD in pregnant women, however; PD4 is only a predictor with BOP.

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

Predicting early cognitive decline in newly-diagnosed Parkinson's patients: A practical model.

PubMed

Hogue, Olivia; Fernandez, Hubert H; Floden, Darlene P

2018-06-19

To create a multivariable model to predict early cognitive decline among de novo patients with Parkinson's disease, using brief, inexpensive assessments that are easily incorporated into clinical flow. Data for 351 drug-naïve patients diagnosed with idiopathic Parkinson's disease were obtained from the Parkinson's Progression Markers Initiative. Baseline demographic, disease history, motor, and non-motor features were considered as candidate predictors. Best subsets selection was used to determine the multivariable baseline symptom profile that most accurately predicted individual cognitive decline within three years. Eleven per cent of the sample experienced cognitive decline. The final logistic regression model predicting decline included five baseline variables: verbal memory retention, right-sided bradykinesia, years of education, subjective report of cognitive impairment, and REM behavior disorder. Model discrimination was good (optimism-adjusted concordance index = .749). The associated nomogram provides a tool to determine individual patient risk of meaningful cognitive change in the early stages of the disease. Through the consideration of easily-implemented or routinely-gathered assessments, we have identified a multidimensional baseline profile and created a convenient, inexpensive tool to predict cognitive decline in the earliest stages of Parkinson's disease. The use of this tool would generate prediction at the individual level, allowing clinicians to tailor medical management for each patient and identify at-risk patients for clinical trials aimed at disease modifying therapies. Copyright © 2018. Published by Elsevier Ltd.

Magnitude of virologic blips is associated with a higher risk for virologic rebound in HIV-infected individuals: a recurrent events analysis.

PubMed

Grennan, J Troy; Loutfy, Mona R; Su, DeSheng; Harrigan, P Richard; Cooper, Curtis; Klein, Marina; Machouf, Nima; Montaner, Julio S G; Rourke, Sean; Tsoukas, Christos; Hogg, Bob; Raboud, Janet

2012-04-15

The importance of human immunodeficiency virus (HIV) blip magnitude on virologic rebound has been raised in clinical guidelines relating to viral load assays. Antiretroviral-naive individuals initiating combination antiretroviral therapy (cART) after 1 January 2000 and achieving virologic suppression were studied. Negative binomial models were used to identify blip correlates. Recurrent event models were used to determine the association between blips and rebound by incorporating multiple periods of virologic suppression per individual. 3550 participants (82% male; median age, 40 years) were included. In a multivariable negative binomial regression model, the Amplicor assay was associated with a lower blip rate than branched DNA (rate ratio, 0.69; P < .01), controlling for age, sex, region, baseline HIV-1 RNA and CD4 count, AIDS-defining illnesses, year of cART initiation, cART type, and HIV-1 RNA testing frequency. In a multivariable recurrent event model controlling for age, sex, intravenous drug use, cART start year, cART type, assay type, and HIV-1 RNA testing frequency, blips of 500-999 copies/mL were associated with virologic rebound (hazard ratio, 2.70; P = .002), whereas blips of 50-499 were not. HIV-1 RNA assay was an important determinant of blip rates and should be considered in clinical guidelines. Blips ≥500 copies/mL were associated with increased rebound risk.

C-reactive protein as an adverse prognostic marker for men with castration-resistant prostate cancer (CRPC): confirmatory results

PubMed Central

Prins, Renee C.; Rademacher, Brooks L.; Mongoue-Tchokote, Solange; Alumkal, Joshi J.; Graff, Julie N.; Eilers, Kristine M.; Beer, Tomasz M.

2010-01-01

We previously reported that higher serum concentrations of C-reactive protein (CRP) are associated with shorter survival in men with castration-resistant prostate cancer (CRPC). To confirm this finding in an independent data set, we used 119 CRPC patients enrolled in 6 phase II clinical trials and examined the relationship of CRP, alkaline phosphatase, hemoglobin, age, ECOG PS, and prostate specific antigen (PSA) with survival. Median follow-up was 19.7 months (0.9–98.5 months) and 89% have died. After analyzing the form of the risk function using the generalized additive model method, univariate and multivariate Cox proportional hazard models were used to assess associations between baseline individual categorical and continuous variables. Quartiles of CRP were: 1: 0–1.0, 1.1–4.9, 5.0–17.0, and 17.1 to 311 mg/L. In a Cox multivariate model, log2(CRP) (HR 1.106 p=0.013) as well as hemoglobin and alkaline phosphatase were independently associated with survival, confirming that higher CRP is associated with shorter survival in CRPC. Since CRP is a marker of inflammation, this finding suggests that inflammation may play an important role in the natural history of advanced prostate cancer. CRP is a readily measurable biomarker that has the potential to improve prognostic models and should be validated in a prospective clinical trial. PMID:20207556

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

ERIC Educational Resources Information Center

Rohner, Ronald P.; Rohner, Evelyn C.

This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

Calorie intake and patient outcomes in severe acute kidney injury: findings from The Randomized Evaluation of Normal vs. Augmented Level of Replacement Therapy (RENAL) study trial

PubMed Central

2014-01-01

Introduction Current practice in the delivery of caloric intake (DCI) in patients with severe acute kidney injury (AKI) receiving renal replacement therapy (RRT) is unknown. We aimed to describe calorie administration in patients enrolled in the Randomized Evaluation of Normal vs. Augmented Level of Replacement Therapy (RENAL) study and to assess the association between DCI and clinical outcomes. Methods We performed a secondary analysis in 1456 patients from the RENAL trial. We measured the dose and evolution of DCI during treatment and analyzed its association with major clinical outcomes using multivariable logistic regression, Cox proportional hazards models, and time adjusted models. Results Overall, mean DCI during treatment in ICU was low at only 10.9 ± 9 Kcal/kg/day for non-survivors and 11 ± 9 Kcal/kg/day for survivors. Among patients with a lower DCI (below the median) 334 of 729 (45.8%) had died at 90-days after randomization compared with 316 of 727 (43.3%) patients with a higher DCI (above the median) (P = 0.34). On multivariable logistic regression analysis, mean DCI carried an odds ratio of 0.95 (95% confidence interval (CI): 0.91-1.00; P = 0.06) per 100 Kcal increase for 90-day mortality. DCI was not associated with significant differences in renal replacement (RRT) free days, mechanical ventilation free days, ICU free days and hospital free days. These findings remained essentially unaltered after time adjusted analysis and Cox proportional hazards modeling. Conclusions In the RENAL study, mean DCI was low. Within the limits of such low caloric intake, greater DCI was not associated with improved clinical outcomes. Trial registration ClinicalTrials.gov number, NCT00221013 PMID:24629036

Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain

PubMed Central

Konstantinou, Kika; Ogollah, Reuben; Hay, Elaine M.; Dunn, Kate M.

2018-01-01

Background Identification of sciatica may assist timely management but can be challenging in clinical practice. Diagnostic models to identify sciatica have mainly been developed in secondary care settings with conflicting reference standard selection. This study explores the challenges of reference standard selection and aims to ascertain which combination of clinical assessment items best identify sciatica in people seeking primary healthcare. Methods Data on 394 low back-related leg pain consulters were analysed. Potential sciatica indicators were seven clinical assessment items. Two reference standards were used: (i) high confidence sciatica clinical diagnosis; (ii) high confidence sciatica clinical diagnosis with confirmatory magnetic resonance imaging findings. Multivariable logistic regression models were produced for both reference standards. A tool predicting sciatica diagnosis in low back-related leg pain was derived. Latent class modelling explored the validity of the reference standard. Results Model (i) retained five items; model (ii) retained six items. Four items remained in both models: below knee pain, leg pain worse than back pain, positive neural tension tests and neurological deficit. Model (i) was well calibrated (p = 0.18), discrimination was area under the receiver operating characteristic curve (AUC) 0.95 (95% CI 0.93, 0.98). Model (ii) showed good discrimination (AUC 0.82; 0.78, 0.86) but poor calibration (p = 0.004). Bootstrapping revealed minimal overfitting in both models. Agreement between the two latent classes and clinical diagnosis groups defined by model (i) was substantial, and fair for model (ii). Conclusion Four clinical assessment items were common in both reference standard definitions of sciatica. A simple scoring tool for identifying sciatica was developed. These criteria could be used clinically and in research to improve accuracy of identification of this subgroup of back pain patients. PMID:29621243

Is there a relationship between periodontal conditions and number of medications among the elderly?

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alshaeri, Heba K; Alasqah, Mohammed; Papas, Athena

2016-03-01

To investigate possible correlations of clinical attachment level and pocket depth with number of medications in elderly individuals. Intra-oral examinations for 139 patients visiting Tufts dental clinic were done. Periodontal assessments were performed with a manual UNC-15 periodontal probe to measure probing depth (PD) and clinical attachment level (CAL) at 6 sites. Complete lists of patients' medications were obtained during the examinations. Statistical analysis involved Kruskal-Wallis, chi square and multivariate logistic regression analyses. Age and health status attained statistical significance (p< 0.05), in contingency table analysis with number of medications. Number of medications had an effect on CAL: increased attachment loss was observed when 4 or more medications were being taken by the patient. Number of medications did not have any effect on periodontal PD. In multivariate logistic regression analysis, 6 or more medications had a higher risk of attachment loss (>3mm) when compared to the no-medication group, in crude OR (1.20, 95% CI:0.22-6.64), and age adjusted (OR=1.16, 95% CI:0.21-6.45), but not with the multivariate model (OR=0.71, 95% CI:0.11-4.39). CAL seems to be more sensitive to the number of medications taken, when compared to PD. However, it is not possible to discriminate at exactly what number of drug combinations the breakdown in CAL will happen. We need to do further analysis, including more subjects, to understand the possible synergistic mechanisms for different drug and periodontal responses.

The Developmental Association between Eating Disorders Symptoms and Symptoms of Depression and Anxiety in Juvenile Twin Girls

ERIC Educational Resources Information Center

Silberg, Judy L.; Bulik, Cynthia M.

2005-01-01

Objective: We investigated the role of genetic and environmental factors in the developmental association among symptoms of eating disorders, depression, and anxiety syndromes in 8-13-year-old and 14-17-year-old twin girls. Methods: Multivariate genetic models were fitted to child-reported longitudinal symptom data gathered from clinical interview…

Seven protective miRNA signatures for prognosis of cervical cancer.

PubMed

Liu, Bei; Ding, Jin-Feng; Luo, Jian; Lu, Li; Yang, Fen; Tan, Xiao-Dong

2016-08-30

Cervical cancer is the second cause of cancer death in females in their 20s and 30s, but there were limited studies about its prognosis. This study aims to identify miRNA related to prognosis and study their functions. TCGA data of patients with cervical cancer were used to build univariate Cox's model with single clinical parameter or miRNA expression level. Multivariate Cox's model was built using both clinical information and miRNA expression levels. At last, STRING was used to enrich gene ontology or pathway for validated targets of significant miRNAs, and visualize the interactions among them. Using univariate Cox's model with clinical parameters, we found that two clinical parameters, tobacco use and clinical stage, and seven miRNAs were highly correlated with the survival status. Only using the expression level of miRNA signatures, the model could separate patients into high-risk and low-risk groups successfully. An optimal feature-selected model was proposed based on two clinical parameters and seven miRNAs. Functional analysis of these seven miRNAs showed they were associated to various pathways related to cancer, including MAPK, VEGF and P53 pathways. These results helped the research of identifying targets for targeted therapy which could potentially allow tailoring of treatment for cervical cancer patients.

Early warning indicators for first-line virologic failure independent of adherence measures in a South African urban clinic.

PubMed

Marconi, Vincent C; Wu, Baohua; Hampton, Jane; Ordóñez, Claudia E; Johnson, Brent A; Singh, Dinesh; John, Sally; Gordon, Michelle; Hare, Anna; Murphy, Richard; Nachega, Jean; Kuritzkes, Daniel R; del Rio, Carlos; Sunpath, Henry

2013-12-01

We sought to develop individual-level Early Warning Indicators (EWI) of virologic failure (VF) for clinicians to use during routine care complementing WHO population-level EWI. A case-control study was conducted at a Durban clinic. Patients after ≥ 5 months of first-line antiretroviral therapy (ART) were defined as cases if they had VF [HIV-1 viral load (VL)>1000 copies/mL] and controls (2:1) if they had VL ≤ 1000 copies/mL. Pharmacy refills and pill counts were used as adherence measures. Participants responded to a questionnaire including validated psychosocial and symptom scales. Data were also collected from the medical record. Multivariable logistic regression models of VF included factors associated with VF (p<0.05) in univariable analyses. We enrolled 158 cases and 300 controls. In the final multivariable model, male gender, not having an active religious faith, practicing unsafe sex, having a family member with HIV, not being pleased with the clinic experience, symptoms of depression, fatigue, or rash, low CD4 counts, family recommending HIV care, and using a TV/radio as ART reminders (compared to mobile phones) were associated with VF independent of adherence measures. In this setting, we identified several key individual-level EWI associated with VF including novel psychosocial factors independent of adherence measures.

Healthcare in the New Vietnam: comparing patients' satisfaction with outpatient care in a traditional neighborhood clinic and a new, western-style clinic in Ho Chi Minh City.

PubMed

Tat, Sonny; Barr, Donald

2006-03-01

As Vietnam opens its economy to privatization, its system of healthcare will face a series of crucial tests. Vietnam's system of private healthcare--once comprised only of individual physicians holding clinic hours in their homes--has come to also include larger customer-oriented clinics based on an American business model. As the two models compete in the expanding private market, it becomes increasingly important to understand patients' perceptions of the alternative models of care. This study reports on interviews with 194 patients in two different types of private-sector clinics in Vietnam: a western-style clinic and a traditional style, after-hours clinic. In bivariate and multivariate analyses, we found that patients at the western style clinic reported both higher expectations of the facility and higher satisfaction with many aspects of care than patients at the after-hours clinic. These different perceptions appear to be based on the interpersonal manner of the physician seen and the clinic's delivery methods rather than perceptions of the physician's technical skill and method of treatment. These findings were unaffected by the ethnicity of physician seen. These findings suggest that patients in Vietnam recognize and prefer more customer-oriented care and amenities, regardless of physician ethnicity and perceive no significant differences in technical skill between the private delivery models.

Concurrent Anemia and Elevated C-Reactive Protein Predicts HIV Clinical Treatment Failure, Including Tuberculosis, After Antiretroviral Therapy Initiation

PubMed Central

Shivakoti, Rupak; Yang, Wei-Teng; Gupte, Nikhil; Berendes, Sima; Rosa, Alberto La; Cardoso, Sandra W.; Mwelase, Noluthando; Kanyama, Cecilia; Pillay, Sandy; Samaneka, Wadzanai; Riviere, Cynthia; Sugandhavesa, Patcharaphan; Santos, Brento; Poongulali, Selvamuthu; Tripathy, Srikanth; Bollinger, Robert C.; Currier, Judith S.; Tang, Alice M.; Semba, Richard D.; Christian, Parul; Campbell, Thomas B.; Gupta, Amita

2015-01-01

Background. Anemia is a known risk factor for clinical failure following antiretroviral therapy (ART). Notably, anemia and inflammation are interrelated, and recent studies have associated elevated C-reactive protein (CRP), an inflammation marker, with adverse human immunodeficiency virus (HIV) treatment outcomes, yet their joint effect is not known. The objective of this study was to assess prevalence and risk factors of anemia in HIV infection and to determine whether anemia and elevated CRP jointly predict clinical failure post-ART. Methods. A case-cohort study (N = 470 [236 cases, 234 controls]) was nested within a multinational randomized trial of ART efficacy (Prospective Evaluation of Antiretrovirals in Resource Limited Settings [PEARLS]). Cases were incident World Health Organization stage 3, 4, or death by 96 weeks of ART treatment (clinical failure). Multivariable logistic regression was used to determine risk factors for pre-ART (baseline) anemia (females: hemoglobin <12.0 g/dL; males: hemoglobin <13.0 g/dL). Association of anemia as well as concurrent baseline anemia and inflammation (CRP ≥10 mg/L) with clinical failure were assessed using multivariable Cox models. Results. Baseline anemia prevalence was 51% with 15% prevalence of concurrent anemia and inflammation. In analysis of clinical failure, multivariate-adjusted hazard ratios were 6.41 (95% confidence interval [CI], 2.82–14.57) for concurrent anemia and inflammation, 0.77 (95% CI, .37–1.58) for anemia without inflammation, and 0.45 (95% CI, .11–1.80) for inflammation without anemia compared to those without anemia and inflammation. Conclusions. ART-naive, HIV-infected individuals with concurrent anemia and inflammation are at particularly high risk of failing treatment, and understanding the pathogenesis could lead to new interventions. Reducing inflammation and anemia will likely improve HIV disease outcomes. Alternatively, concurrent anemia and inflammation could represent individuals with occult opportunistic infections in need of additional screening. PMID:25828994

Multivariate Normal Tissue Complication Probability Modeling of Heart Valve Dysfunction in Hodgkin Lymphoma Survivors

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

Cella, Laura, E-mail: laura.cella@cnr.it; Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples; Liuzzi, Raffaele

Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced asymptomatic heart valvular defects (RVD). Methods and Materials: Fifty-six patients treated with sequential chemoradiation therapy for Hodgkin lymphoma (HL) were retrospectively reviewed for RVD events. Clinical information along with whole heart, cardiac chambers, and lung dose distribution parameters was collected, and the correlations to RVD were analyzed by means of Spearman's rank correlation coefficient (Rs). For the selection of the model order and parameters for NTCP modeling, a multivariate logistic regression method using resampling techniques (bootstrapping) was applied. Model performance was evaluated using the area under themore » receiver operating characteristic curve (AUC). Results: When we analyzed the whole heart, a 3-variable NTCP model including the maximum dose, whole heart volume, and lung volume was shown to be the optimal predictive model for RVD (Rs = 0.573, P<.001, AUC = 0.83). When we analyzed the cardiac chambers individually, for the left atrium and for the left ventricle, an NTCP model based on 3 variables including the percentage volume exceeding 30 Gy (V30), cardiac chamber volume, and lung volume was selected as the most predictive model (Rs = 0.539, P<.001, AUC = 0.83; and Rs = 0.557, P<.001, AUC = 0.82, respectively). The NTCP values increase as heart maximum dose or cardiac chambers V30 increase. They also increase with larger volumes of the heart or cardiac chambers and decrease when lung volume is larger. Conclusions: We propose logistic NTCP models for RVD considering not only heart irradiation dose but also the combined effects of lung and heart volumes. Our study establishes the statistical evidence of the indirect effect of lung size on radio-induced heart toxicity.« less

Association of Increased Urinary Albumin With Risk of Incident Clinical Fracture and Rate of Hip Bone Loss: the Osteoporotic Fractures in Men Study.

PubMed

Fink, Howard A; Vo, Tien N; Langsetmo, Lisa; Barzilay, Joshua I; Cauley, Jane A; Schousboe, John T; Orwoll, Eric S; Canales, Muna T; Ishani, Areef; Lane, Nancy E; Ensrud, Kristine E

2017-05-01

Prior studies suggest that increased urine albumin is associated with a heightened fracture risk in women, but results in men are unclear. We used data from Osteoporotic Fractures in Men (MrOS), a prospective cohort study of community-dwelling men aged ≥65 years, to evaluate the association of increased urine albumin with subsequent fractures and annualized rate of hip bone loss. We calculated albumin/creatinine ratio (ACR) from urine collected at the 2003-2005 visit. Subsequent clinical fractures were ascertained from triannual questionnaires and centrally adjudicated by review of radiographic reports. Total hip BMD was measured by DXA at the 2003-2005 visit and again an average of 3.5 years later. We estimated risk of incident clinical fracture using Cox proportional hazards models, and annualized BMD change using ANCOVA. Of 2982 men with calculable ACR, 9.4% had ACR ≥30 mg/g (albuminuria) and 1.0% had ACR ≥300 mg/g (macroalbuminuria). During a mean of 8.7 years of follow-up, 20.0% of men had an incident clinical fracture. In multivariate-adjusted models, neither higher ACR quintile (p for trend 0.75) nor albuminuria (HR versus no albuminuria, 0.89; 95% CI, 0.65 to 1.20) was associated with increased risk of incident clinical fracture. Increased urine albumin had a borderline significant, multivariate-adjusted, positive association with rate of total hip bone loss when modeled in ACR quintiles (p = 0.06), but not when modeled as albuminuria versus no albuminuria. Macroalbuminuria was associated with a higher rate of annualized hip bone loss compared to no albuminuria (-1.8% more annualized loss than in men with ACR <30 mg/g; p < 0.001), but the limited prevalence of macroalbuminuria precluded reliable estimates of its fracture associations. In these community-dwelling older men, we found no association between urine albumin levels and risk of incident clinical fracture, but found a borderline significant, positive association with rate of hip bone loss. © 2016 American Society for Bone and Mineral Research. © 2016 American Society for Bone and Mineral Research.

PharmML in Action: an Interoperable Language for Modeling and Simulation.

PubMed

Bizzotto, R; Comets, E; Smith, G; Yvon, F; Kristensen, N R; Swat, M J

2017-10-01

PharmML is an XML-based exchange format created with a focus on nonlinear mixed-effect (NLME) models used in pharmacometrics, but providing a very general framework that also allows describing mathematical and statistical models such as single-subject or nonlinear and multivariate regression models. This tutorial provides an overview of the structure of this language, brief suggestions on how to work with it, and use cases demonstrating its power and flexibility. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.

PubMed

Carroll, Rachel; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Aregay, Mehreteab; Watjou, Kevin

2017-05-09

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Sociodemographic profile and predictors of outpatient clinic attendance among HIV-positive patients initiating antiretroviral therapy in Selangor, Malaysia.

PubMed

Abdulrahman, Surajudeen Abiola; Rampal, Lekhraj; Othman, Norlijah; Ibrahim, Faisal; Hayati, Kadir Shahar; Radhakrishnan, Anuradha P

2017-01-01

Inconsistent literature evidence suggests that sociodemographic, economic, and system- and patient-related factors are associated with clinic attendance among the HIV-positive population receiving antiretroviral therapy (ART) around the world. We examined the factors that predict outpatient clinic attendance among a cohort of HIV-positive patients initiating ART in Selangor, Malaysia. This cross-sectional study analyzed secondary data on outpatient clinic attendance and sociodemographic, economic, psychosocial, and patient-related factors among 242 adult Malaysian patients initiating ART in Selangor, Malaysia. Study cohort was enrolled in a parent randomized controlled trial (RCT) in Hospital Sungai Buloh Malaysia between January and December 2014, during which peer counseling, medication, and clinic appointment reminders were provided to the intervention group through short message service (SMS) and telephone calls for 24 consecutive weeks. Data on outpatient clinic attendance were extracted from the hospital electronic medical records system, while other patient-level data were extracted from pre-validated Adult AIDS Clinical Trial Group (AACTG) adherence questionnaires in which primary data were collected. Outpatient clinic attendance was categorized into binary outcome - regular attendee and defaulter categories - based on the number of missed scheduled outpatient clinic appointments within a 6-month period. Multivariate regression models were fitted to examine predictors of outpatient clinic attendance using SPSS version 22 and R software. A total of 224 (93%) patients who completed 6-month assessment were included in the model. Out of those, 42 (18.7%) defaulted scheduled clinic attendance at least once. Missed appointments were significantly more prevalent among females (n=10, 37.0%), rural residents (n=10, 38.5%), and bisexual respondents (n=8, 47.1%). Multivariate binary logistic regression analysis showed that Indian ethnicity (adjusted odds ratio [AOR] =0.235; 95% CI [0.063-0.869]; P =0.030) and heterosexual orientation (AOR =4.199; 95% CI [1.040-16.957]; P =0.044) were significant predictors of outpatient clinic attendance among HIV-positive patients receiving ART in Malaysia. Ethnicity and sexual orientation of Malaysian patients may play a significant role in their level of adherence to scheduled clinic appointments. These factors should be considered during collaborative adherence strategy planning at ART initiation.

Clinical predictors of rectal lymphogranuloma venereum infection: results from a multicentre case-control study in the U.K.

PubMed

Pallawela, S N S; Sullivan, A K; Macdonald, N; French, P; White, J; Dean, G; Smith, A; Winter, A J; Mandalia, S; Alexander, S; Ison, C; Ward, H

2014-06-01

Since 2003, over 2000 cases of lymphogranuloma venereum (LGV) have been diagnosed in the U.K. in men who have sex with men (MSM). Most cases present with proctitis, but there are limited data on how to differentiate clinically between LGV and other pathology. We analysed the clinical presentations of rectal LGV in MSM to identify clinical characteristics predictive of LGV proctitis and produced a clinical prediction model. A prospective multicentre case-control study was conducted at six U.K. hospitals from 2008 to 2010. Cases of rectal LGV were compared with controls with rectal symptoms but without LGV. Data from 98 LGV cases and 81 controls were collected from patients and clinicians using computer-assisted self-interviews and clinical report forms. Univariate and multivariate logistic regression was used to compare symptoms and signs. Clinical prediction models for LGV were compared using receiver operating curves. Tenesmus, constipation, anal discharge and weight loss were significantly more common in cases than controls. In multivariate analysis, tenesmus and constipation alone were suggestive of LGV (OR 2.98, 95% CI 0.99 to 8.98 and 2.87, 95% CI 1.01 to 8.15, respectively) and that tenesmus alone or in combination with constipation was a significant predictor of LGV (OR 6.97, 95% CI 2.71 to 17.92). The best clinical prediction was having one or more of tenesmus, constipation and exudate on proctoscopy, with a sensitivity of 77% and specificity of 65%. This study indicates that tenesmus alone or in combination with constipation makes a diagnosis of LGV in MSM presenting with rectal symptoms more likely. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Predictors of clinical outcome in pediatric oligodendroglioma: meta-analysis of individual patient data and multiple imputation.

PubMed

Wang, Kevin Yuqi; Vankov, Emilian R; Lin, Doris Da May

2018-02-01

OBJECTIVE Oligodendroglioma is a rare primary CNS neoplasm in the pediatric population, and only a limited number of studies in the literature have characterized this entity. Existing studies are limited by small sample sizes and discrepant interstudy findings in identified prognostic factors. In the present study, the authors aimed to increase the statistical power in evaluating for potential prognostic factors of pediatric oligodendrogliomas and sought to reconcile the discrepant findings present among existing studies by performing an individual-patient-data (IPD) meta-analysis and using multiple imputation to address data not directly available from existing studies. METHODS A systematic search was performed, and all studies found to be related to pediatric oligodendrogliomas and associated outcomes were screened for inclusion. Each study was searched for specific demographic and clinical characteristics of each patient and the duration of event-free survival (EFS) and overall survival (OS). Given that certain demographic and clinical information of each patient was not available within all studies, a multivariable imputation via chained equations model was used to impute missing data after the mechanism of missing data was determined. The primary end points of interest were hazard ratios for EFS and OS, as calculated by the Cox proportional-hazards model. Both univariate and multivariate analyses were performed. The multivariate model was adjusted for age, sex, tumor grade, mixed pathologies, extent of resection, chemotherapy, radiation therapy, tumor location, and initial presentation. A p value of less than 0.05 was considered statistically significant. RESULTS A systematic search identified 24 studies with both time-to-event and IPD characteristics available, and a total of 237 individual cases were available for analysis. A median of 19.4% of the values among clinical, demographic, and outcome variables in the compiled 237 cases were missing. Multivariate Cox regression analysis revealed subtotal resection (p = 0.007 [EFS] and 0.043 [OS]), initial presentation of headache (p = 0.006 [EFS] and 0.004 [OS]), mixed pathologies (p = 0.005 [EFS] and 0.049 [OS]), and location of the tumor in the parietal lobe (p = 0.044 [EFS] and 0.030 [OS]) to be significant predictors of tumor progression or recurrence and death. CONCLUSIONS The use of IPD meta-analysis provides a valuable means for increasing statistical power in investigations of disease entities with a very low incidence. Missing data are common in research, and multiple imputation is a flexible and valid approach for addressing this issue, when it is used conscientiously. Undergoing subtotal resection, having a parietal tumor, having tumors with mixed pathologies, and suffering headaches at the time of diagnosis portended a poorer prognosis in pediatric patients with oligodendroglioma.

[Analysis of variance of repeated data measured by water maze with SPSS].

PubMed

Qiu, Hong; Jin, Guo-qin; Jin, Ru-feng; Zhao, Wei-kang

2007-01-01

To introduce the method of analyzing repeated data measured by water maze with SPSS 11.0, and offer a reference statistical method to clinical and basic medicine researchers who take the design of repeated measures. Using repeated measures and multivariate analysis of variance (ANOVA) process of the general linear model in SPSS and giving comparison among different groups and different measure time pairwise. Firstly, Mauchly's test of sphericity should be used to judge whether there were relations among the repeatedly measured data. If any (P

Characteristics and associations of pain intensity in patients referred to a specialist cancer pain clinic.

PubMed

Pina, Paulo; Sabri, Elham; Lawlor, Peter G

2015-01-01

Uncontrolled cancer pain (CP) may impair quality of life. Given the multidimensional nature of CP, its poor control is often attributed to poor assessment and classification. To determine the characteristics and associations of pain intensity in a specialist CP clinic. Consecutive patients referred to the CP clinic of the Portuguese Cancer Institute (Lisbon, Portugal) had standardized initial assessments and status documentation of the following: Brief Pain Inventory ratings for 'pain now' as the outcome variable; initial pain intensity (iPI) on a 0 to 10 scale; pain mechanism (using the Douleur Neuropathique 4 tool to assess neuropathic pain); episodic pain; Eastern Cooperative Oncology Group rating; oral morphine equivalent daily dose (MEDD); Hospital Anxiety Depression Scale and Emotional Thermometer scores; and cancer diagnosis, metastases, treatment and pain duration. Univariable analyses were conducted to test the association of independent variables with iPI. Variables with P<0.1 were entered into a multivariable regression model, using backward elimination and a cut-point of P=0.2 for final model selection. Of 371 participants, 285 (77%) had moderate (4 to 6) or severe (7 to 10) iPI. The initial median MEDD was relatively low (30 mg [range 20 mg to 60 mg]). In the multivariable model, higher income, Eastern Cooperative Oncology Group rating 3 to 4, cancer diagnosis (head and neck, genitourinary and gastrointestinal), adjuvant use and initial MEDD were associated with iPI (P<0.05). The model's R2 was 18.6, which explained only 19% of iPI variance. The diversity of factors associated with pain intensity and their limited explanation of its variance underscore the biopsychosocial complexity of CP. Adequacy of CP management warrants further exploration.

A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects.

PubMed

Henschel, Volkmar; Engel, Jutta; Hölzel, Dieter; Mansmann, Ulrich

2009-02-10

Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome. Likelihood inference for models which additionally include random effects are not available at all. Developed algorithms bear problems for practical users like: matrix inversion, slow convergence, no assessment of statistical uncertainty. MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework. Two examples from clinical practice demonstrate the handling of clustered interval censored event times as well as multilayer random effects for inter-institutional quality assessment. The software developed is called survBayes and is freely available at CRAN. The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.

Impact of Stress Cardiac Magnetic Resonance Imaging on Clinical Care

PubMed Central

McGraw, Sloane; Romano, Simone; Jue, Jennifer; Bauml, Michael A; Chung, Jaehoon; Farzaneh-Far, Afshin

2016-01-01

Given the rising costs of imaging, there is increasing pressure to provide evidence for direct additive impact on clinical care. Appropriate use criteria (AUC) were developed to optimize test-patient selection, and are increasingly used by payers to assess reimbursement. However, these criteria were created by expert consensus with limited systematic validation. The aims of this study were therefore to determine: 1) rates of active clinical change resulting from stress cardiovascular magnetic resonance (CMR) imaging; and 2) whether the AUC can predict these changes. We prospectively enrolled 350 consecutive outpatients referred for stress CMR. Categories of “active changes in clinical care” due to stress CMR were pre-defined. Appropriateness was classified according to the 2013 AUC. Multivariable logistic regression analysis was used to identify factors independently associated with active change. Overall, stress CMR led to an active change in clinical care in about 70% of patients. Rates of change in clinical care did not vary significantly across AUC categories (p=0.767). In a multivariable model adjusting for clinical variables and AUC, only ischemia (OR 6.896, 95% CI 2.637–18.032, p<0.001), known CAD (OR 0.300, 95% CI 0.161–0.559, p<0.001), and age (OR 0.977, 95% CI 0.954–1.000, p=0.050) independently predicted significant clinical change. In conclusion, stress CMR made a significant impact on clinical management, resulting in active change in clinical care in about 70% of patients. AUC categories were not an independent predictor of clinical change. Clinical change was independently associated with presence of ischemia, absence of known CAD, and younger age. PMID:27476576

Herd-level risk factors for lameness in freestall farms in the northeastern United States and California.

PubMed

Chapinal, N; Barrientos, A K; von Keyserlingk, M A G; Galo, E; Weary, D M

2013-01-01

The objective was to investigate the association between herd-level management and facility design factors and the prevalence of lameness in high-producing dairy cows in freestall herds in the northeastern United States (NE; Vermont, New York, Pennsylvania) and California (CA). Housing and management measures such as pen space, stall design, bedding type, and milking routine were collected for the high-producing pen in 40 farms in NE and 39 farms in CA. All cows in the pen were gait scored using a 1-to-5 scale and classified as clinically lame (score ≥3) or severely lame (score ≥4). Measures associated with the (logit-transformed) proportion of clinically or severely lame cows at the univariable level were submitted to multivariable general linear models. In NE, lameness increased on farms that used sawdust bedding [odds ratio (OR)=1.71; 95% confidence interval (CI)=1.06-2.76] and decreased with herd size (OR=0.94; CI=0.90-0.97, for a 100-cow increase), use of deep bedding (OR=0.48; CI=0.29-0.79), and access to pasture (OR=0.52; CI=0.32-0.85). The multivariable model included herd size, access to pasture, and provision of deep bedding, and explained 50% of the variation in clinical lameness. Severe lameness increased with the percentage of stalls with fecal contamination (OR=1.15; CI=1.06-1.25, for a 10% increase) and with use of sawdust bedding (OR=2.13; CI=1.31-3.47), and decreased with use of deep bedding (OR=0.31; CI=0.19-0.50), sand bedding (OR=0.32; CI=0.19-0.53), herd size (OR=0.93; CI=-0.89-0.97, for a 100-cow increase), and rearing replacement heifers on site (OR=0.57; CI=0.32-0.99). The multivariable model included deep bedding and herd size, and explained 59% of the variation of severe lameness. In CA, clinical lameness increased with the percentage of stalls containing fecal contamination (OR=1.15; CI=1.05-1.26, for a 10% increase), and decreased with herd size (OR=0.96; CI=0.94-0.99, for a 100-cow increase), presence of rubber in the alley to the milking parlor (OR=0.46; CI=0.28-0.76), distance of the neck rail from the rear curb (OR=0.97; CI=0.95-0.99, for a 1-cm increase), water space per cow (OR=0.92; CI=0.85-0.99, for a 1-cm increase), and increased frequency of footbaths per week (OR=0.90; CI=081-0.99, for a 1-unit increase). The multivariable model included herd size, percentage of stalls containing fecal contamination, and presence of rubber in the alley to the milking parlor, and explained 44% of the variation of clinical lameness. Severe lameness increased with the percentage of stalls containing fecal contamination (OR=1.23; CI=1.06-1.42, for a 10% increase) and decreased with frequency of manure removal in the pen per day (OR=0.72; CI=0.53-0.97, for a 1-unit increase). The final model included both variables and explained 28% of the variation in severe lameness. In conclusion, changes in housing and management may help decrease the prevalence of lameness on dairy farms, but key risk factors vary across regions. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predicting Dural Tear in Compound Depressed Skull Fractures: A Prospective Multicenter Correlational Study.

PubMed

Salia, Shemsedin Musefa; Mersha, Hagos Biluts; Aklilu, Abenezer Tirsit; Baleh, Abat Sahlu; Lund-Johansen, Morten

2018-06-01

Compound depressed skull fracture (DSF) is a neurosurgical emergency. Preoperative knowledge of dural status is indispensable for treatment decision making. This study aimed to determine predictors of dural tear from clinical and imaging characteristics in patients with compound DSF. This prospective, multicenter correlational study in neurosurgical hospitals in Addis Ababa, Ethiopia, included 128 patients operated on from January 1, 2016, to October 31, 2016. Clinical, imaging, and intraoperative findings were evaluated. Univariate and multivariate analyses were used to establish predictors of dural tear. A logistic regression model was developed to predict probability of dural tear. Model validation was done using the receiver operating characteristic curve. Dural tear was seen in 55.5% of 128 patients. Demographics, injury mechanism, clinical presentation, and site of DSF had no significant correlation with dural tear. In univariate and multivariate analyses, depth of fracture depression (odds ratio 1.3, P < 0.001), pneumocephalus (odds ratio 2.8, P = 0.005), and brain contusions/intracerebral hematoma (odds ratio 5.5, P < 0.001) were significantly correlated with dural tear. We developed a logistic regression model (diagnostic test) to calculate probability of dural tear. Using the receiver operating characteristic curve, we determined the cutoff value for a positive test giving the highest accuracy to be 30% with a corresponding sensitivity of 93.0% and specificity of 43.9%. Dural tear in compound DSF can be predicted with 93.0% sensitivity using preoperative findings and may guide treatment decision making in resource-limited settings where risk of extensive cranial surgery outweighs the benefit. Copyright © 2018 Elsevier Inc. All rights reserved.

Development and validation of a risk calculator predicting exercise-induced ventricular arrhythmia in patients with cardiovascular disease.

PubMed

Hermes, Ilarraza-Lomelí; Marianna, García-Saldivia; Jessica, Rojano-Castillo; Carlos, Barrera-Ramírez; Rafael, Chávez-Domínguez; María Dolores, Rius-Suárez; Pedro, Iturralde

2016-10-01

Mortality due to cardiovascular disease is often associated with ventricular arrhythmias. Nowadays, patients with cardiovascular disease are more encouraged to take part in physical training programs. Nevertheless, high-intensity exercise is associated to a higher risk for sudden death, even in apparently healthy people. During an exercise testing (ET), health care professionals provide patients, in a controlled scenario, an intense physiological stimulus that could precipitate cardiac arrhythmia in high risk individuals. There is still no clinical or statistical tool to predict this incidence. The aim of this study was to develop a statistical model to predict the incidence of exercise-induced potentially life-threatening ventricular arrhythmia (PLVA) during high intensity exercise. 6415 patients underwent a symptom-limited ET with a Balke ramp protocol. A multivariate logistic regression model where the primary outcome was PLVA was performed. Incidence of PLVA was 548 cases (8.5%). After a bivariate model, thirty one clinical or ergometric variables were statistically associated with PLVA and were included in the regression model. In the multivariate model, 13 of these variables were found to be statistically significant. A regression model (G) with a X(2) of 283.987 and a p<0.001, was constructed. Significant variables included: heart failure, antiarrhythmic drugs, myocardial lower-VD, age and use of digoxin, nitrates, among others. This study allows clinicians to identify patients at risk of ventricular tachycardia or couplets during exercise, and to take preventive measures or appropriate supervision. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Effect of metallo-β-lactamase production and multidrug resistance on clinical outcomes in patients with Pseudomonas aeruginosa bloodstream infection: a retrospective cohort study

PubMed Central

2013-01-01

Background Blood stream infections (BSI) with Pseudomonas aeruginosa lead to poor clinical outcomes. The worldwide emergence and spread of metallo-β-lactamase (MBL) producing, often multidrug-resistant organisms may further aggravate this problem. Our study aimed to investigate the effect of MBL-producing P. aeruginosa (MBL-PA) and various other resistance phenotypes on clinical outcomes. Methods A retrospective cohort study was conducted in three German hospitals. Medical files from 2006 until 2012 were studied, and a number of 113 patients with P. aeruginosa BSI were included. The presence of VIM, IMP and NDM genes was detected using molecular techniques. Genetic relatedness was assessed through multilocus sequence typing (MLST). The effect of resistance patterns or MBL production on clinical outcomes was investigated by using multivariate Cox regression models. Results In-hospital mortality was significantly higher in patients with MBL-PA and multidrug-resistant P. aeruginosa. However, neither BSI with MBL-PA nor BSI with various resistance phenotypes of P. aeruginosa were independently associated with mortality or length of hospital stay. In multivariate models, the SAPS II score (HR 1.046), appropriate definitive treatment (HR range 0.25-0.26), and cardiovascular disease (HR range 0.44-0.46) were independent predictors of mortality. Concomitant infections were associated with an excess length of stay (HR < 1). Conclusions Medication with appropriate antimicrobial agents at any time during the course of infection remains the key for improving clinical outcomes in patients with P. aeruginosa BSI and should be combined with a strict implementation of routine infection control measures. PMID:24176052

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.

Pattern of Utilisation of Dental Health Care Among HIV-positive Adult Nigerians.

PubMed

Adedigba, Michael A; Adekanmbi, Victor T; Asa, Sola; Fakande, Ibiyemi

2016-01-01

To determine the pattern of dental care utilisation of people living with HIV (PLHIV). A cross-sectional questionnaire survey of 239 PLHIV patients in three care centres was done. Information on sociodemographics, dental visit, risk groups, living arrangement, medical insurance and need of dental care was recorded. The EC Clearinghouse and WHO clinical staging was used to determine the stage of HIV/AIDS infection following routine oral examinations under natural daylight. Multivariate logistic regression models were created after adjusting for all the covariates that were statistically significant at univariate/bivariate levels. The majority of subjects were younger than 50 years, about 93% had not seen a dentist before being diagnosed HIV positive and 92% reported no dental visit after contracting HIV. Among nonusers of dental care, 14.3% reported that they wanted care but were afraid to seek it. Other reasons included poor awareness, lack of money and stigmatisation. Multivariate analysis showed that lack of dental care was associated with employment status, living arrangements, educational status, income per annum and presenting with oral symptoms. The area under the receiver operating curve was 84% for multivariate logistic regression model 1, 70% for model 2, 67% for model 3 and 71% for model 4, which means that the predictive power of the models were good. Contrary to our expectations, dental utilisation among PLHIV was generally poor among this group of patients. There is serious and immediate need to improve the awareness of PLHIVs in African settings and barriers to dental care utilisation should also be removed or reduced.

A model for prediction of color change after tooth bleaching based on CIELAB color space

NASA Astrophysics Data System (ADS)

Herrera, Luis J.; Santana, Janiley; Yebra, Ana; Rivas, María. José; Pulgar, Rosa; Pérez, María. M.

2017-08-01

An experimental study aiming to develop a model based on CIELAB color space for prediction of color change after a tooth bleaching procedure is presented. Multivariate linear regression models were obtained to predict the L*, a*, b* and W* post-bleaching values using the pre-bleaching L*, a*and b*values. Moreover, univariate linear regression models were obtained to predict the variation in chroma (C*), hue angle (h°) and W*. The results demonstrated that is possible to estimate color change when using a carbamide peroxide tooth-bleaching system. The models obtained can be applied in clinic to predict the colour change after bleaching.

Clinical performance of the Prostate Health Index (PHI) for the prediction of prostate cancer in obese men: data from the PROMEtheuS project, a multicentre European prospective study.

PubMed

Abrate, Alberto; Lazzeri, Massimo; Lughezzani, Giovanni; Buffi, Nicolòmaria; Bini, Vittorio; Haese, Alexander; de la Taille, Alexandre; McNicholas, Thomas; Redorta, Joan Palou; Gadda, Giulio M; Lista, Giuliana; Kinzikeeva, Ella; Fossati, Nicola; Larcher, Alessandro; Dell'Oglio, Paolo; Mistretta, Francesco; Freschi, Massimo; Guazzoni, Giorgio

2015-04-01

To test serum prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA), p2PSA/free PSA (%p2PSA) and Prostate Health Index (PHI) accuracy in predicting prostate cancer in obese men and to test whether PHI is more accurate than PSA in predicting prostate cancer in obese patients. The analysis consisted of a nested case-control study from the pro-PSA Multicentric European Study (PROMEtheuS) project. The study is registered at http://www.controlled-trials.com/ISRCTN04707454. The primary outcome was to test sensitivity, specificity and accuracy (clinical validity) of serum p2PSA, %p2PSA and PHI, in determining prostate cancer at prostate biopsy in obese men [body mass index (BMI) ≥30 kg/m(2) ], compared with total PSA (tPSA), free PSA (fPSA) and fPSA/tPSA ratio (%fPSA). The number of avoidable prostate biopsies (clinical utility) was also assessed. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 965 patients, 383 (39.7%) were normal weight (BMI <25 kg/m(2) ), 440 (45.6%) were overweight (BMI 25-29.9 kg/m(2) ) and 142 (14.7%) were obese (BMI ≥30 kg/m(2) ). Among obese patients, prostate cancer was found in 65 patients (45.8%), with a higher percentage of Gleason score ≥7 diseases (67.7%). PSA, p2PSA, %p2PSA and PHI were significantly higher, and %fPSA significantly lower in patients with prostate cancer (P < 0.001). In multivariable logistic regression models, PHI significantly increased accuracy of the base multivariable model by 8.8% (P = 0.007). At a PHI threshold of 35.7, 46 (32.4%) biopsies could have been avoided. In obese patients, PHI is significantly more accurate than current tests in predicting prostate cancer. © 2014 The Authors. BJU International © 2014 BJU International.

Management of dairy heifers and its relationships with the incidence of clinical mastitis.

PubMed

Parker, K I; Compton, C W R; Anniss, F M; Weir, A M; McDougal, S

2007-10-01

To describe aspects of management of dairy heifers before calving and determine risk factors for clinical mastitis postpartum in heifers, at the herd level, under pasture-based management systems in the Waikato and Taranaki regions of New Zealand. Dairy herdowners (n=578) provided information via a prospective survey about their practices for rearing heifers and management of mastitis. A proportion of herdowners (n=250) subsequently provided data on the cases of clinical mastitis in their herds, including the date, cow identification, age and quarter affected from cases occurring in the 4 months after the planned start of calving (PSC) in the subsequent lactation. The relationship between management factors and the proportion of heifers diagnosed with clinical mastitis within a herd was examined using bivariate and multivariate analyses. The herd average percentage of heifers with clinical mastitis was 13.6 (95% confidence interval (CI)=12.3-14.9)%, and multiparous cows with clinical mastitis was 9.0 (95% CI=8.2-9.8)% in the first 4 months of lactation. There were positive relationships between the proportion of heifers with clinical mastitis and average milk production per cow (kg milksolids/ lactation; p<0.001), number of cows milked per labour unit (p=0.003), stocking rate (<> 3.30 cows/ha; p=0.002), and incidence of clinical mastitis in multiparous cows (%/120 days; p<0.04), in the final multivariate model. The proportion of heifers with clinical mastitis per herd was lower in herds that milked their lactating cows in multiple groups (p=0.02). The risk of clinical mastitis in heifers was significantly associated with management practices. It may be possible to reduce the incidence of clinical mastitis in heifers by modification of management practices at the herd level, and further studies are required to investigate this.

Pre-Antiretroviral Therapy Serum Selenium Concentrations Predict WHO Stages 3, 4 or Death but not Virologic Failure Post-Antiretroviral Therapy

PubMed Central

Shivakoti, Rupak; Gupte, Nikhil; Yang, Wei-Teng; Mwelase, Noluthando; Kanyama, Cecilia; Tang, Alice M.; Pillay, Sandy; Samaneka, Wadzanai; Riviere, Cynthia; Berendes, Sima; Lama, Javier R.; Cardoso, Sandra W.; Sugandhavesa, Patcharaphan; Semba, Richard D.; Christian, Parul; Campbell, Thomas B.; Gupta, Amita

2014-01-01

A case-cohort study, within a multi-country trial of antiretroviral therapy (ART) efficacy (Prospective Evaluation of Antiretrovirals in Resource Limited Settings (PEARLS)), was conducted to determine if pre-ART serum selenium deficiency is independently associated with human immunodeficiency virus (HIV) disease progression after ART initiation. Cases were HIV-1 infected adults with either clinical failure (incident World Health Organization (WHO) stage 3, 4 or death by 96 weeks) or virologic failure by 24 months. Risk factors for serum selenium deficiency (<85 μg/L) pre-ART and its association with outcomes were examined. Median serum selenium concentration was 82.04 μg/L (Interquartile range (IQR): 57.28–99.89) and serum selenium deficiency was 53%, varying widely by country from 0% to 100%. In multivariable models, risk factors for serum selenium deficiency were country, previous tuberculosis, anemia, and elevated C-reactive protein. Serum selenium deficiency was not associated with either clinical failure or virologic failure in multivariable models. However, relative to people in the third quartile (74.86–95.10 μg/L) of serum selenium, we observed increased hazards (adjusted hazards ratio (HR): 3.50; 95% confidence intervals (CI): 1.30–9.42) of clinical failure but not virologic failure for people in the highest quartile. If future studies confirm this relationship of high serum selenium with increased clinical failure, a cautious approach to selenium supplementation might be needed, especially in HIV-infected populations with sufficient or unknown levels of selenium. PMID:25401501

The impact of family planning clinic programs on adolescent pregnancy.

PubMed

Forrest, J D; Hermalin, A I; Henshaw, S K

1981-01-01

During the 1970s, there was a decline in adolescent childbearing in the United States and, among teenagers who were sexually active, there was a decline in pregnancy rates as well. To what extent was increased enrollment by teenagers in federally funded family planning clinics responsible for these declines? Areal multivariate analysis reveals that adolescent birthrates were reduced between 1970 and 1975 as the result of enrollment by teenagers in family planning clinics, independent of the effects of other factors also affecting fertility, such as poverty status, education and urbanization. Using a model which controls for differences in adolescent sexual activity in different areas in 1970 and 1975, the analysis found that for every 10 teenage patients enrolled in family planning clinics in 1975, about one birth was averted in 1976. Other multivariate models, which did not control for differences in sexual activity, showed changes in the same direction, though of smaller dimension. Since the family planning program averts not only births but also pregnancies that result in abortions and miscarriages, an estimate was made of the total number of pregnancies averted by the program. Based on the proportion of unintended pregnancies among adolescents that resulted in live births in 1976 (36 percent), it was estimated that for every 10 teen patients enrolled in 1975, almost three pregnancies were averted in the following year. Over the 1970s, an estimated 2.6 million unintended adolescent pregnancies were averted by the program--944,000 births, 1,376,000 abortions and 326,000 miscarriages. In 1979 alone, an estimated 417,000 unintended pregnancies were prevented by the program.

Risk of fall-related injury in people with lower limb amputations: A prospective cohort study.

PubMed

Wong, Christopher Kevin; Chihuri, Stanford T; Li, Guohua

2016-01-01

To assess fall-related injury risk and risk factors in people with lower limb amputation. Prospective longitudinal cohort with follow-up every 6 months for up to 41 months. Community-dwelling adults with lower limb amputations of any etiology and level recruited from support groups and prosthetic clinics. Demographic and clinical characteristics were obtained by self-reported questionnaire and telephone or in-person follow-up. Fall-related injury incidence requiring medical care per person-month and adjusted hazard ratio of fall-related injury were calculated using multivariable proportional hazards regression modeling. A total of 41 subjects, with 782 follow-up person-months in total, had 11 fall-related injury incidents (14.1/1,000 person-months). During follow-up, 56.1% of subjects reported falling and 26.8% reported fall-related injury. Multivariable proportional hazard modeling showed that women were nearly 6 times more likely as men to experience fall-related injury and people of non-white race were 13 times more likely than people of white race to experience fall-related injury. The final predictive model also included vascular amputation and age. Risk of fall-related injury requiring medical care in people with lower limb amputation appears to be higher than in older adult inpatients. Intervention programs to prevent fall-related injury in people with lower limb amputation should target women and racial minorities.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Analysis of multivariate longitudinal kidney function outcomes using generalized linear mixed models.

PubMed

Jaffa, Miran A; Gebregziabher, Mulugeta; Jaffa, Ayad A

2015-06-14

Renal transplant patients are mandated to have continuous assessment of their kidney function over time to monitor disease progression determined by changes in blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate (eGFR). Multivariate analysis of these outcomes that aims at identifying the differential factors that affect disease progression is of great clinical significance. Thus our study aims at demonstrating the application of different joint modeling approaches with random coefficients on a cohort of renal transplant patients and presenting a comparison of their performance through a pseudo-simulation study. The objective of this comparison is to identify the model with best performance and to determine whether accuracy compensates for complexity in the different multivariate joint models. We propose a novel application of multivariate Generalized Linear Mixed Models (mGLMM) to analyze multiple longitudinal kidney function outcomes collected over 3 years on a cohort of 110 renal transplantation patients. The correlated outcomes BUN, Cr, and eGFR and the effect of various covariates such patient's gender, age and race on these markers was determined holistically using different mGLMMs. The performance of the various mGLMMs that encompass shared random intercept (SHRI), shared random intercept and slope (SHRIS), separate random intercept (SPRI) and separate random intercept and slope (SPRIS) was assessed to identify the one that has the best fit and most accurate estimates. A bootstrap pseudo-simulation study was conducted to gauge the tradeoff between the complexity and accuracy of the models. Accuracy was determined using two measures; the mean of the differences between the estimates of the bootstrapped datasets and the true beta obtained from the application of each model on the renal dataset, and the mean of the square of these differences. The results showed that SPRI provided most accurate estimates and did not exhibit any computational or convergence problem. Higher accuracy was demonstrated when the level of complexity increased from shared random coefficient models to the separate random coefficient alternatives with SPRI showing to have the best fit and most accurate estimates.

Development of multivariate NTCP models for radiation-induced hypothyroidism: a comparative analysis.

PubMed

Cella, Laura; Liuzzi, Raffaele; Conson, Manuel; D'Avino, Vittoria; Salvatore, Marco; Pacelli, Roberto

2012-12-27

Hypothyroidism is a frequent late side effect of radiation therapy of the cervical region. Purpose of this work is to develop multivariate normal tissue complication probability (NTCP) models for radiation-induced hypothyroidism (RHT) and to compare them with already existing NTCP models for RHT. Fifty-three patients treated with sequential chemo-radiotherapy for Hodgkin's lymphoma (HL) were retrospectively reviewed for RHT events. Clinical information along with thyroid gland dose distribution parameters were collected and their correlation to RHT was analyzed by Spearman's rank correlation coefficient (Rs). Multivariate logistic regression method using resampling methods (bootstrapping) was applied to select model order and parameters for NTCP modeling. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC). Models were tested against external published data on RHT and compared with other published NTCP models. If we express the thyroid volume exceeding X Gy as a percentage (Vx(%)), a two-variable NTCP model including V30(%) and gender resulted to be the optimal predictive model for RHT (Rs = 0.615, p < 0.001. AUC = 0.87). Conversely, if absolute thyroid volume exceeding X Gy (Vx(cc)) was analyzed, an NTCP model based on 3 variables including V30(cc), thyroid gland volume and gender was selected as the most predictive model (Rs = 0.630, p < 0.001. AUC = 0.85). The three-variable model performs better when tested on an external cohort characterized by large inter-individuals variation in thyroid volumes (AUC = 0.914, 95% CI 0.760-0.984). A comparable performance was found between our model and that proposed in the literature based on thyroid gland mean dose and volume (p = 0.264). The absolute volume of thyroid gland exceeding 30 Gy in combination with thyroid gland volume and gender provide an NTCP model for RHT with improved prediction capability not only within our patient population but also in an external cohort.

Inter-hospital variations in caesarean sections. A risk adjusted comparison in the Valencia public hospitals

PubMed Central

Librero, J.; Peiro, S.; Calderon, S. M.

2000-01-01

BACKGROUND—The aim of this study was to describe the variability in caesarean rates in the public hospitals in the Valencia Region, Spain, and to analyse the association between caesarean sections and clinical and extra-clinical factors.
METHODS—Analysis of data contained in the Minimum Basic Data Set (MBDS) compiled for all births in 11 public hospitals in Valencia during 1994-1995 (n=36 819). Bivariate and multivariate analyses were used to evaluate the association between caesarean section rates and specific risk factors. The multivariate model was used to construct predictions about caesarean rates for each hospital, for comparison with rates observed.
RESULTS—Caesarean rates were 17.6% (inter-hospital range: 14.7% to 25.0%), with ample variability between hospitals in the diagnosis of maternal-fetal risk factors (particularly dystocia and fetal distress), and the indication for caesarean in the presence of these factors. Multivariate analysis showed that maternal-fetal risk factors correlated strongly with caesarean section, although extra-clinical factors, such as the day of the week, also correlated positively. After adjusting for the risk factors, the inter-hospital variation in caesarean rates persisted.
CONCLUSIONS—Although certain limitations (imprecision of some diagnoses and information biases in the MBDS) make it impossible to establish unequivocal conclusions, results show a high degree of variability among hospitals when opting for caesarean section. This variability cannot be justified by differences in obstetric risks.


Keywords: hospital utilisation; medical practice variation; caesarean section; administrative databases PMID:10890876

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

A panel of kallikrein markers can reduce unnecessary biopsy for prostate cancer: data from the European Randomized Study of Prostate Cancer Screening in Göteborg, Sweden

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Aus, Gunnar; Pihl, Carl-Gustav; Becker, Charlotte; Pettersson, Kim; Scardino, Peter T; Hugosson, Jonas; Lilja, Hans

2008-01-01

Background Prostate-specific antigen (PSA) is widely used to detect prostate cancer. The low positive predictive value of elevated PSA results in large numbers of unnecessary prostate biopsies. We set out to determine whether a multivariable model including four kallikrein forms (total, free, and intact PSA, and human kallikrein 2 (hK2)) could predict prostate biopsy outcome in previously unscreened men with elevated total PSA. Methods The study cohort comprised 740 men in Göteborg, Sweden, undergoing biopsy during the first round of the European Randomized study of Screening for Prostate Cancer. We calculated the area-under-the-curve (AUC) for predicting prostate cancer at biopsy. AUCs for a model including age and PSA (the 'laboratory' model) and age, PSA and digital rectal exam (the 'clinical' model) were compared with those for models that also included additional kallikreins. Results Addition of free and intact PSA and hK2 improved AUC from 0.68 to 0.83 and from 0.72 to 0.84, for the laboratory and clinical models respectively. Using a 20% risk of prostate cancer as the threshold for biopsy would have reduced the number of biopsies by 424 (57%) and missed only 31 out of 152 low-grade and 3 out of 40 high-grade cancers. Conclusion Multiple kallikrein forms measured in blood can predict the result of biopsy in previously unscreened men with elevated PSA. A multivariable model can determine which men should be advised to undergo biopsy and which might be advised to continue screening, but defer biopsy until there was stronger evidence of malignancy. PMID:18611265

Multivariate Associations Among Behavioral, Clinical, and Multimodal Imaging Phenotypes in Patients With Psychosis.

PubMed

Moser, Dominik A; Doucet, Gaelle E; Lee, Won Hee; Rasgon, Alexander; Krinsky, Hannah; Leibu, Evan; Ing, Alex; Schumann, Gunter; Rasgon, Natalie; Frangou, Sophia

2018-04-01

Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r = 0.63, P < .001), which was independent of diagnosis. Among the nonimaging variables examined, age (r = -0.53), IQ (r = 0.36), and body mass index (r = -0.25) were associated with multiple imaging phenotypes; cannabis use (r = 0.23) and other substance use (r = 0.33) were associated with subcortical volumes, and alcohol use was associated with white matter integrity (r = -0.15). Within the multivariate models, positive symptoms retained associations with the global neuroimaging (r = -0.13), the cortical thickness (r = -0.22), and the task-related activation variates (r = -0.18); negative symptoms were mostly associated with measures of subcortical volume (r = 0.23), and depression/anxiety was associated with measures of white matter integrity (r = 0.12). Multivariate analyses provide a more accurate characterization of the association between brain alterations and psychosis because they enable the modeling of other key factors that influence neuroimaging phenotypes.

Psycho-Cognitive Intervention for ASD from Cross-Species Behavioral Analyses of Infants, Chicks and Common Marmosets.

PubMed

Koshiba, Mamiko; Karino, Genta; Mimura, Koki; Nakamura, Shun; Yui, Kunio; Kunikata, Tetsuya; Yamanouchi, Hideo

2016-01-01

Educational treatment to support social development of children with autism spectrum disorder (ASD) is an important topic in developmental psychiatry. However, it remains difficult to objectively quantify the socio-emotional development of ASD children. To address this problem, we developed a novel analytical method that assesses subjects' complex behaviors using multivariate analysis, 'Behavior Output analysis for Quantitative Emotional State Translation' (BOUQUET). Here, we examine the potential for psycho-cognitive ASD therapy based on comparative evaluations of clinical (human) and experimental (animal) models. Our observations of ASD children (vs. their normally developing siblings) and the domestic chick in socio-sensory deprivation models show the importance of unimodal sensory stimulation, particularly important for tactile- and auditory-biased socialization. Identifying psycho-cognitive elements in early neural development, human newborn infants in neonatal intensive care unit as well as a New World monkey, the common marmoset, also prompted us to focus on the development of voluntary movement against gravity. In summary, striking behavioral similarities between children with ASD and domestic chicks' socio-sensory deprivation models support the role of multimodal sensory-motor integration as a prerequisite step for normal development of socio-emotional and psycho-cognitive functions. Data obtained in the common marmoset model also suggest that switching from primitive anti-gravity reflexes to complex voluntary movement may be a critical milestone for psycho-cognitive development. Combining clinical findings with these animal models, and using multivariate integrative analyses may facilitate the development of effective interventions to improve social functions in infants and in children with neurodevelopmental disorders.

Dosimetric Predictors of Hypothyroidism After Radical Intensity-modulated Radiation Therapy for Non-metastatic Nasopharyngeal Carcinoma.

PubMed

Lee, V; Chan, Sum-Yin; Choi, Cheuk-Wai; Kwong, D; Lam, Ka-On; Tong, Chi-Chung; Sze, Chun-Kin; Ng, S; Leung, To-Wai; Lee, A

2016-08-01

To investigate dosimetric predictors of hypothyroidism after radical intensity-modulated radiation therapy (IMRT) for non-metastatic nasopharyngeal carcinoma (NPC). Patients with non-metastatic NPC treated with radical IMRT from 2008 to 2013 were reviewed. Serum thyroid function tests before and after IMRT were regularly monitored. Univariable and multivariable analyses were carried out for predictors of biochemical and clinical hypothyroidism. In total, 149 patients were recruited. After a median follow-up duration of 3.1 years, 33 (22.1%) and 21 (14.1%) patients developed biochemical and clinical hypothyroidism, respectively. Eight (24.2%) patients who had biochemical hypothyroidism developed clinical hypothyroidism later. Univariable and multivariable analyses revealed that the volume of the thyroid (P=0.002, multivariable), VS60 (the absolute thyroid volume spared from 60 Gy or less) (P<0.001, multivariable) and VS45 (P<0.001, multivariable) of the thyroid were significant predictors of biochemical hypothyroidism. The freedom from biochemical hypothyroidism was longer for those whose VS60 ≥ 10 cm(3) (mean 90.9 versus 62.6 months; P<0.001) and VS45 ≥ 5 cm(3) (mean 91.9 versus 65.2 months; P=0.001). Similarly multivariable analyses revealed that VS60 (P=0.001) and VS45 (P=0.003) were significant predictors of clinical hypothyroidism. The freedom from clinical hypothyroidism was longer for those whose VS60 ≥ 10 cm(3) (91.5 versus 73.3 months; P=0.002) and VS45 ≥ 5 cm(3) (91.5 versus 75.9 months; P=0.007). VS60 and VS45 of the thyroid should be considered important dose constraints against hypothyroidism without compromising target coverage during IMRT optimisation for NPC. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis.

PubMed

Jensen, Dan B; Hogeveen, Henk; De Vries, Albert

2016-09-01

Rapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis. We also evaluated reductions in the number of sensors for detecting mastitis. With the DLM, we co-modeled 7 sources of sensor data (milk yield, fat, protein, lactose, conductivity, blood, body weight) collected at each milking for individual cows to produce one-step-ahead forecasts for each sensor. The observations were subsequently categorized according to the errors of the forecasted values and the estimated forecast variance. The categorized sensor data were combined with other data pertaining to the cow (week in milk, parity, mastitis history, somatic cell count category, and season) using Bayes' theorem, which produced a combined probability of the cow having clinical mastitis. If this probability was above a set threshold, the cow was classified as mastitis positive. To illustrate the performance of our method, we used sensor data from 1,003,207 milkings from the University of Florida Dairy Unit collected from 2008 to 2014. Of these, 2,907 milkings were associated with recorded cases of clinical mastitis. Using the DLM/NBC method, we reached an area under the receiver operating characteristic curve of 0.89, with a specificity of 0.81 when the sensitivity was set at 0.80. Specificities with omissions of sensor data ranged from 0.58 to 0.81. These results are comparable to other studies, but differences in data quality, definitions of clinical mastitis, and time windows make comparisons across studies difficult. We found the DLM/NBC method to be a flexible method for combining multiple sensor and nonsensor data sources to predict clinical mastitis and accommodate missing observations. Further research is needed before practical implementation is possible. In particular, the performance of our method needs to be improved in the first 2 wk of lactation. The DLM method produces forecasts that are based on continuously estimated multivariate normal distributions, which makes forecasts and forecast errors easy to interpret, and new sensors can easily be added. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Prospective inverse associations of sex hormone concentrations in men with biomarkers of inflammation and oxidative stress.

PubMed

Haring, Robin; Baumeister, Sebastian E; Völzke, Henry; Dörr, Marcus; Kocher, Thomas; Nauck, Matthias; Wallaschofski, Henri

2012-01-01

The suggested associations between sex hormone concentrations and inflammatory biomarkers in men originate from cross-sectional studies and small-scale clinical trials. But prior studies have not investigated longitudinal associations. Overall, 1344 men aged 20-79 years from the population-based cohort Study of Health in Pomerania were followed up for 5.0 (median) years. We used multivariable regression models to analyze cross-sectional and longitudinal associations of serum sex hormone concentrations (total testosterone [TT], sex hormone-binding globulin [SHBG], calculated free testosterone [free T], and dehydroepiandrosterone sulfate [DHEAS]) with biomarkers of inflammation (fibrinogen, high-sensitive C-reactive protein [hsCRP], and white blood cell count [WBC]) and oxidative stress (γ-glutamyl transferase [GGT]) using ordinary least square regression and generalized estimating equation models, respectively. Cross-sectional models revealed borderline associations of sex hormone concentrations with hsCRP, WBC, and GGT levels that were not retained after multivariable adjustment. Longitudinal multivariable analyses revealed an inverse association of baseline TT, free T, and DHEAS concentrations with change in fibrinogen levels (per SD decrement in TT, 0.25 [95% confidence interval, 0.04-0.45]; in free T, 0.30 [0.09-0.51]; and in DHEAS, 0.23 [0.11-0.36]). Furthermore, baseline DHEAS concentrations were inversely associated with change in WBC levels (per SD decrement, 0.53 [0.24-0.82]). Baseline TT, SHBG, free T, and DHEAS concentrations were also inversely associated with change in GGT after multivariable adjustment. The present study is the first to demonstrate prospective inverse associations between sex hormone concentrations and markers of inflammation and oxidative stress in men. Additional studies are warranted to elucidate potential mechanisms underlying the revealed associations.

Pharmacodynamics and effectiveness of topical nitroglycerin at lowering blood pressure during autonomic dysreflexia.

PubMed

Solinsky, R; Bunnell, A E; Linsenmeyer, T A; Svircev, J N; Engle, A; Burns, S P

2017-10-01

Secondary analysis of prospectively collected observational data assessing the safety of an autonomic dysreflexia (AD) management protocol. To estimate the time to onset of action, time to full clinical effect (sustained systolic blood pressure (SBP) <160 mm Hg) and effectiveness of nitroglycerin ointment at lowering blood pressure for patients with spinal cord injuries experiencing AD. US Veterans Affairs inpatient spinal cord injury (SCI) unit. Episodes of AD recalcitrant to nonpharmacologic interventions that were given one to two inches of 2% topical nitroglycerin ointment were recorded. Pharmacodynamics as above and predictive characteristics (through a mixed multivariate logistic regression model) were calculated. A total of 260 episodes of pharmacologically managed AD were recorded in 56 individuals. Time to onset of action for nitroglycerin ointment was 9-11 min. Time to full clinical effect was 14-20 min. Topical nitroglycerin controlled SBP <160 mm Hg in 77.3% of pharmacologically treated AD episodes with the remainder requiring additional antihypertensive medications. A multivariate logistic regression model was unable to identify statistically significant factors to predict which patients would respond to nitroglycerin ointment (odds ratios 95% confidence intervals 0.29-4.93). The adverse event rate, entirely attributed to hypotension, was 3.6% with seven of the eight events resolving with close observation alone and one episode requiring normal saline. Nitroglycerin ointment has a rapid onset of action and time to full clinical effect with high efficacy and relatively low adverse event rate for patients with SCI experiencing AD.

Testing for clinical inertia in medication treatment of bipolar disorder.

PubMed

Hodgkin, Dominic; Merrick, Elizabeth L; O'Brien, Peggy L; McGuire, Thomas G; Lee, Sue; Deckersbach, Thilo; Nierenberg, Andrew A

2016-11-15

Clinical inertia has been defined as lack of change in medication treatment at visits where a medication adjustment appears to be indicated. This paper seeks to identify the extent of clinical inertia in medication treatment of bipolar disorder. A second goal is to identify patient characteristics that predict this treatment pattern. Data describe 23,406 visits made by 1815 patients treated for bipolar disorder during the STEP-BD practical clinical trial. Visits were classified in terms of whether a medication adjustment appears to be indicated, and also whether or not one occurred. Multivariable regression analyses were conducted to find which patient characteristics were predictive of whether adjustment occurred. 36% of visits showed at least 1 indication for adjustment. The most common indications were non-response to medication, side effects, and start of a new illness episode. Among visits with an indication for adjustment, no adjustment occurred 19% of the time, which may be suggestive of clinical inertia. In multivariable models, presence of any indication for medication adjustment was a predictor of receiving one (OR=1.125, 95% CI =1.015, 1.246), although not as strong as clinical status measures. The associations observed are not necessarily causal, given the study design. The data also lack information about physician-patient communication. Many patients remained on the same medication regimen despite indications of side effects or non-response to treatment. Although lack of adjustment does not necessarily reflect clinical inertia in all cases, the reasons for this treatment pattern merit further examination. Copyright © 2016 Elsevier B.V. All rights reserved.

Testing for Clinical Inertia in Medication Treatment of Bipolar Disorder

PubMed Central

Hodgkin, Dominic; Merrick, Elizabeth L.; O'Brien, Peggy L.; McGuire, Thomas G.; Lee, Sue; Deckersbach, Thilo; Nierenberg, Andrew A.

2016-01-01

Background Clinical inertia has been defined as lack of change in medication treatment at visits where a medication adjustment appears to be indicated. This paper seeks to identify the extent of clinical inertia in medication treatment of bipolar disorder. A second goal is to identify patient characteristics that predict this treatment pattern. Method Data describe 23,406 visits made by 1,815 patients treated for bipolar disorder during the STEP-BD practical clinical trial. Visits were classified in terms of whether a medication adjustment appears to be indicated, and also whether or not one occurred. Multivariable regression analyses were conducted to find which patient characteristics were predictive of whether adjustment occurred. Results 36% of visits showed at least 1 indication for adjustment. The most common indications were non-response to medication, side effects, and start of a new illness episode. Among visits with an indication for adjustment, no adjustment occurred 19% of the time, which may be suggestive of clinical inertia. In multivariable models, presence of any indication for medication adjustment was a predictor of receiving one (OR=1.125, 95% CI = 1.015, 1.246), although not as strong as clinical status measures. Limitations The associations observed are not necessarily causal, given the study design. The data also lack information about physician-patient communication. Conclusions Many patients remained on the same medication regimen despite indications of side effects or non-response to treatment. Although lack of adjustment does not necessarily reflect clinical inertia in all cases, the reasons for this treatment pattern merit further examination. PMID:27391267

A Shortened Version of the Suicide Cognitions Scale for Identifying Chronic Pain Patients at Risk for Suicide.

PubMed

Bryan, Craig J; Kanzler, Kathryn E; Grieser, Emily; Martinez, Annette; Allison, Sybil; McGeary, Donald

2017-03-01

Research in psychiatric outpatient and inpatient populations supports the utility of the Suicide Cognitions Scale (SCS) as an indicator of current and future risk for suicidal thoughts and behaviors. Designed to assess suicide-specific thoughts and beliefs, the SCS has yet to be evaluated among chronic pain patients, a group with elevated risk for suicide. The purpose of the present study was to develop and test a shortened version of the SCS (the SCS-S). A total of 228 chronic pain patients completed a battery of self-report surveys before or after a scheduled appointment. Three outpatient medical clinics (pain medicine, orofacial pain, and clinical health psychology). Confirmatory factor analysis, multivariate regression, and graded item response theory model analyses. Results of the CFAs suggested that a 3-factor solution was optimal. A shortened 9-item scale was identified based on the results of graded item response theory model analyses. Correlation and multivariate analyses supported the construct and incremental validity of the SCS-S. Results support the reliability and validity of the SCS-S among chronic pain patients, and suggest the scale may be a useful method for identifying high-risk patients in medical settings. © 2016 World Institute of Pain.

Estimating family planning program effects on U.S. fertility rates.

PubMed

Cutright, P; Jaffe, F S

1977-08-01

An evaluation was undertaken of the effects on U. S. fertility rates of the national family planning program. 1968-1969 family planning enrollment data were linked to 1970 census data in the same areas to derive an objective measure of the impact of organized clinical family planning programs on the 1969-70 fertility rates of subgroups of women defined by age, race, marital status, economic status, and racial composition of their area. Multivariate modelling was used to control for spurious effects of irrelevant variables. Results of the multivariate modelling show significant reductions of marital fertility among the low socioeconomic groups served by the program; no effects were exhibited by groups not served. Cumulative fertility of all groups, black and white, at all age and socioeconomic levels was affected by the program. A plausible explanation for these results lies in antecedent factors which led to the presence or absence of family planning clinics in any particular area in 1969 and its 1969 enrollment level. Communities more favorably disposed to provision of birth control services would have been more likely than other areas to apply for federal funding of family planning programs when it became available in the middle 1960s. Due to an earlier start, their programs were flourishing by 1969.

Canine dilated cardiomyopathy: a retrospective study of prognostic findings in 367 clinical cases.

PubMed

Martin, M W S; Stafford Johnson, M J; Strehlau, G; King, J N

2010-08-01

To review the association between clinical signs and diagnostic findings and the survival time of dogs with dilated cardiomyopathy (DCM), and any influence of treatment prescribed. A retrospective observational study of 367 dogs with DCM. Survival times until death or euthanasia for cardiac reasons were analysed using the Kaplan-Meier method plus univariate and multivariate Cox proportional hazards models. Two-tailed P values less than 0.05 were considered statistically significant. In the multivariate model, left ventricular diameter (LVDs)-index (P=0.0067), presence of pulmonary oedema on radiography (P=0.043), presence of ventricular premature complexes (VPCs) (P=0.0012), higher plasma creatinine (P=0.0002), lower plasma protein (P=0.029) and great Dane breed (P=0.0003) were negatively associated with survival. Most dogs were treated with angiotensin-converting enzyme inhibitors (93%) or furosemide (86%), and many received digoxin (50%) and/or pimobendan (30%). Thirteen dogs were lost to follow-up. No conclusions could be made in this study on the association between use of drugs and survival. The LVDs-index was the single best variable for assessing the prognosis in this group of dogs with DCM. Other variables that were negatively associated with survival were presence of pulmonary oedema on radiography, presence of VPCs, higher plasma creatinine, lower plasma protein and great Dane breed.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Relationship between patient-reported outcomes and clinical outcomes in metastatic castration-resistant prostate cancer: post hoc analysis of COU-AA-301 and COU-AA-302.

PubMed

Cella, D; Traina, S; Li, T; Johnson, K; Ho, K F; Molina, A; Shore, N D

2018-02-01

Patient-reported outcomes (PROs) are used to assess benefit-risk in drug development. The relationship between PROs and clinical outcomes is not well understood. We aim to elucidate the relationships between changes in PRO measures and clinical outcomes in metastatic castration-resistant prostate cancer (mCRPC). We investigated relationships between changes in self-reported fatigue, pain, functional well-being (FWB), physical well-being (PWB) and prostate cancer-specific symptoms with overall survival (OS) and radiographic progression-free survival (rPFS) after 6 and 12 months of treatment in COU-AA-301 (N = 1195) or COU-AA-302 (N = 1088). Eligible COU-AA-301 patients had progressed after docetaxel and had Eastern Cooperative Oncology Group performance status (ECOG PS) ≤ 2. Eligible COU-AA-302 patients had no prior chemotherapy and ECOG PS 0 or 1. Patients were treated with abiraterone acetate (1000 mg/day) plus prednisone (10 mg/day) or prednisone alone daily. Association between self-reported fatigue, pain and functional status, and OS and/or rPFS, using pooled data regardless of treatment, was assessed. Cox proportional hazard regression modeled time to death or radiographic progression. In COU-AA-301 patients, PRO improvements were associated with longer OS and longer time to radiographic progression versus worsening or stable PROs (P < 0.0001). In multivariate models, all except pain intensity remained associated with OS. Pain intensity, PWB and FWB improvements remained associated with rPFS. In COU-AA-302 patients, worsening PROs were associated with higher likelihood of radiographic progression (P ≤ 0.025) compared with improved or stable PROs. In multivariate models, worsening PWB remained associated with worse rPFS. The 12-month analysis confirmed the 6-month results. PROs are significantly associated with clinically relevant time-to-event efficacy outcomes in clinical trials and may complement and help predict traditional clinical practice methods for monitoring patients for disease progression. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals.

PubMed

Zhang, Ying-Ying; Zhou, Xiao-Bin; Wang, Qiu-Zhen; Zhu, Xiao-Yan

2017-05-01

Multivariable logistic regression (MLR) has been increasingly used in Chinese clinical medical research during the past few years. However, few evaluations of the quality of the reporting strategies in these studies are available.To evaluate the reporting quality and model accuracy of MLR used in published work, and related advice for authors, readers, reviewers, and editors.A total of 316 articles published in 5 leading Chinese clinical medical journals with high impact factor from January 2010 to July 2015 were selected for evaluation. Articles were evaluated according 12 established criteria for proper use and reporting of MLR models.Among the articles, the highest quality score was 9, the lowest 1, and the median 5 (4-5). A total of 85.1% of the articles scored below 6. No significant differences were found among these journals with respect to quality score (χ = 6.706, P = .15). More than 50% of the articles met the following 5 criteria: complete identification of the statistical software application that was used (97.2%), calculation of the odds ratio and its confidence interval (86.4%), description of sufficient events (>10) per variable, selection of variables, and fitting procedure (78.2%, 69.3%, and 58.5%, respectively). Less than 35% of the articles reported the coding of variables (18.7%). The remaining 5 criteria were not satisfied by a sufficient number of articles: goodness-of-fit (10.1%), interactions (3.8%), checking for outliers (3.2%), collinearity (1.9%), and participation of statisticians and epidemiologists (0.3%). The criterion of conformity with linear gradients was applicable to 186 articles; however, only 7 (3.8%) mentioned or tested it.The reporting quality and model accuracy of MLR in selected articles were not satisfactory. In fact, severe deficiencies were noted. Only 1 article scored 9. We recommend authors, readers, reviewers, and editors to consider MLR models more carefully and cooperate more closely with statisticians and epidemiologists. Journals should develop statistical reporting guidelines concerning MLR.

The clinical performance evaluation of novel protein chips for eleven biomarkers detection and the diagnostic model study.

PubMed

Luo, Yuan; Zhu, Xu; Zhang, Pengjun; Shen, Qian; Wang, Zi; Wen, Xinyu; Wang, Ling; Gao, Jing; Dong, Jin; Yang, Caie; Wu, Tangming; Zhu, Zheng; Tian, Yaping

2015-01-01

We aimed to develop and validate two novel protein chips, which are based on microarray chemiluminescence immunoassay and can simultaneously detected 11 biomarkers, and then to evaluate their clinical diagnostic value by comparing with the traditional methods. Protein chips were evaluated for limit of detection, specificity, common interferences, linearity, precision and accuracy. 11 biomarkers were simultaneously detected by traditional methods and protein chips in 3683 samples, which included 1723 cancer patients, 1798 benign diseases patients and 162 healthy controls. After assay validation, protein chips demonstrated high sensitivity, high specificity, good linearity, low imprecision and were free of common interferences. Compared with the traditional methods, protein chips have good correlation in the detection of all the 13 kinds of biomarkers (r≥0.935, P<0.001). For specific cancer detection, there were no statistically significant differences between the traditional method and novel protein chips, except that male protein chip showed significantly better diagnostic value on NSE detection (P=0.004) but significantly worse value on pro-GRP detection (P=0.012), female chip showed significantly better diagnostic value on pro-GRP detection (P=0.005). Furthermore, both male and female multivariate diagnostic models had significantly better diagnostic value than single detection of PGI, PG II, pro-GRP, NSE and CA125 (P<0.05). In addition, male models had significantly better diagnostic value than single CA199 and free-PSA (P<0.05), while female models observed significantly better diagnostic value than single CA724 and β-HCG (P<0.05). For total disease or cancer detection, the AUC of multivariate logistic regression for the male and female disease detection was 0.981 (95% CI: 0.975-0.987) and 0.836 (95% CI: 0.798-0.874), respectively. While, that for total cancer detection was 0.691 (95% CI: 0.666-0.717) and 0.753 (95% CI: 0.731-0.775), respectively. The new designed protein chips are simple, multiplex and reliable clinical assays and the multi-parameter diagnostic models based on them could significantly improve their clinical performance.

The clinical performance evaluation of novel protein chips for eleven biomarkers detection and the diagnostic model study

PubMed Central

Luo, Yuan; Zhu, Xu; Zhang, Pengjun; Shen, Qian; Wang, Zi; Wen, Xinyu; Wang, Ling; Gao, Jing; Dong, Jin; Yang, Caie; Wu, Tangming; Zhu, Zheng; Tian, Yaping

2015-01-01

We aimed to develop and validate two novel protein chips, which are based on microarray chemiluminescence immunoassay and can simultaneously detected 11 biomarkers, and then to evaluate their clinical diagnostic value by comparing with the traditional methods. Protein chips were evaluated for limit of detection, specificity, common interferences, linearity, precision and accuracy. 11 biomarkers were simultaneously detected by traditional methods and protein chips in 3683 samples, which included 1723 cancer patients, 1798 benign diseases patients and 162 healthy controls. After assay validation, protein chips demonstrated high sensitivity, high specificity, good linearity, low imprecision and were free of common interferences. Compared with the traditional methods, protein chips have good correlation in the detection of all the 13 kinds of biomarkers (r≥0.935, P<0.001). For specific cancer detection, there were no statistically significant differences between the traditional method and novel protein chips, except that male protein chip showed significantly better diagnostic value on NSE detection (P=0.004) but significantly worse value on pro-GRP detection (P=0.012), female chip showed significantly better diagnostic value on pro-GRP detection (P=0.005). Furthermore, both male and female multivariate diagnostic models had significantly better diagnostic value than single detection of PGI, PG II, pro-GRP, NSE and CA125 (P<0.05). In addition, male models had significantly better diagnostic value than single CA199 and free-PSA (P<0.05), while female models observed significantly better diagnostic value than single CA724 and β-HCG (P<0.05). For total disease or cancer detection, the AUC of multivariate logistic regression for the male and female disease detection was 0.981 (95% CI: 0.975-0.987) and 0.836 (95% CI: 0.798-0.874), respectively. While, that for total cancer detection was 0.691 (95% CI: 0.666-0.717) and 0.753 (95% CI: 0.731-0.775), respectively. The new designed protein chips are simple, multiplex and reliable clinical assays and the multi-parameter diagnostic models based on them could significantly improve their clinical performance. PMID:26884957

SU-F-R-07: Radiomics of CT Features and Associations and Correlation with Outcomes Following Lung SBRT

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

Schreibmann, E; Iwinski Sutter, A; Whitaker, D

Objective: To investigate the prognostic significance of image gradients and in predicting clinical outcomes in a patients with non-small cell lung cancer treated with stereotactic body radiotherapy (SBRT) on 71 patients with 83 treated lesions. Methods: The records of patients treated with lung SBRT were retrospectively reviewed. When applicable, SBRT target volumes were modified to exclude any overlap with pleura, chestwall, or mediastinum. The ITK software package was utilized to generate quantitative measures of image intensity, inhomogeneity, shape morphology and first and second-order CT textures. Multivariate and univariate models containing CT features were generated to assess associations with clinicopathologic factors.more » Results: On univariate analysis, tumor size (HR 0.54, p=0.045) sumHU (HR 0.31, p=0.044) and short run grey level emphasis STD (HR 0.22, p=0.019) were associated with regional failure-free survival; meanHU (HR 0.30, p=0.035), long run emphasis (HR 0.21, p=0.011) and long run low grey level emphasis (HR 0.14, p=0.005) was associated with distant failure-free survival (DFFS). No features were significant on multivariate modeling however long run low grey level emphasis had a hazard ratio of 0.12 (p=0.061) for DFFS. Adenocarcinoma and squamous cell carcinoma differed with respect to long run emphasis STD (p=0.024), short run low grey level emphasis STD (p<0.001), and long run low grey level emphasis STD (p=0.024). Multivariate modeling of texture features associated with tumor histology was used to estimate histologies of 18 lesions treated without histologic confirmation. Of these, MVA suggested the same histology as a prior metachronous lung malignancy in 3/7 patients. Conclusion: Extracting radiomics features on clinical datasets was feasible with the ITK package with minimal effort to identify pre-treatment quantitative CT features with prognostic factors for distant control after lung SBRT.« less

PTEN Loss as Determined by Clinical-grade Immunohistochemistry Assay Is Associated with Worse Recurrence-free Survival in Prostate Cancer

PubMed Central

Lotan, Tamara L.; Wei, Wei; Morais, Carlos L.; Hawley, Sarah T.; Fazli, Ladan; Hurtado-Coll, Antonio; Troyer, Dean; McKenney, Jesse K.; Simko, Jeffrey; Carroll, Peter R.; Gleave, Martin; Lance, Raymond; Lin, Daniel W.; Nelson, Peter S.; Thompson, Ian M.; True, Lawrence D.; Feng, Ziding; Brooks, James D.

2015-01-01

Background PTEN is the most commonly deleted tumor suppressor gene in primary prostate cancer (PCa) and its loss is associated with poor clinical outcomes and ERG gene rearrangement. Objective We tested whether PTEN loss is associated with shorter recurrence-free survival (RFS) in surgically treated PCa patients with known ERG status. Design, setting, and participants A genetically validated, automated PTEN immunohistochemistry (IHC) protocol was used for 1275 primary prostate tumors from the Canary Foundation retrospective PCa tissue microarray cohort to assess homogeneous (in all tumor tissue sampled) or heterogeneous (in a subset of tumor tissue sampled) PTEN loss. ERG status as determined by a genetically validated IHC assay was available for a subset of 938 tumors. Outcome measurements and statistical analysis Associations between PTEN and ERG status were assessed using Fisher’s exact test. Kaplan-Meier and multivariate weighted Cox proportional models for RFS were constructed. Results and limitations When compared to intact PTEN, homogeneous (hazard ratio [HR] 1.66, p = 0.001) but not heterogeneous (HR 1.24, p = 0.14) PTEN loss was significantly associated with shorter RFS in multivariate models. Among ERG-positive tumors, homogeneous (HR 3.07, p < 0.0001) but not heterogeneous (HR 1.46, p = 0.10) PTEN loss was significantly associated with shorter RFS. Among ERG-negative tumors, PTEN did not reach significance for inclusion in the final multivariate models. The interaction term for PTEN and ERG status with respect to RFS did not reach statistical significance (p = 0.11) for the current sample size. Conclusions These data suggest that PTEN is a useful prognostic biomarker and that there is no statistically significant interaction between PTEN and ERG status for RFS. Patient summary We found that loss of the PTEN tumor suppressor gene in prostate tumors as assessed by tissue staining is correlated with shorter time to prostate cancer recurrence after radical prostatectomy. PMID:27617307

Detectable end of radiation prostate specific antigen assists in identifying men with unfavorable intermediate-risk prostate cancer at high risk of distant recurrence and cancer-specific mortality.

PubMed

Hayman, Jonathan; Phillips, Ryan; Chen, Di; Perin, Jamie; Narang, Amol K; Trieu, Janson; Radwan, Noura; Greco, Stephen; Deville, Curtiland; McNutt, Todd; Song, Daniel Y; DeWeese, Theodore L; Tran, Phuoc T

2018-06-01

Undetectable End of Radiation PSA (EOR-PSA) has been shown to predict improved survival in prostate cancer (PCa). While validating the unfavorable intermediate-risk (UIR) and favorable intermediate-risk (FIR) stratifications among Johns Hopkins PCa patients treated with radiotherapy, we examined whether EOR-PSA could further risk stratify UIR men for survival. A total of 302 IR patients were identified in the Johns Hopkins PCa database (178 UIR, 124 FIR). Kaplan-Meier curves and multivariable analysis was performed via Cox regression for biochemical recurrence free survival (bRFS), distant metastasis free survival (DMFS), and overall survival (OS), while a competing risks model was used for PCa specific survival (PCSS). Among the 235 patients with known EOR-PSA values, we then stratified by EOR-PSA and performed the aforementioned analysis. The median follow-up time was 11.5 years (138 months). UIR was predictive of worse DMFS and PCSS (P = 0.008 and P = 0.023) on multivariable analysis (MVA). Increased radiation dose was significant for improved DMFS (P = 0.016) on MVA. EOR-PSA was excluded from the models because it did not trend towards significance as a continuous or binary variable due to interaction with UIR, and we were unable to converge a multivariable model with a variable to control for this interaction. However, when stratifying by detectable versus undetectable EOR-PSA, UIR had worse DMFS and PCSS among detectable EOR-PSA patients, but not undetectable patients. UIR was significant on MVA among detectable EOR-PSA patients for DMFS (P = 0.021) and PCSS (P = 0.033), while RT dose also predicted PCSS (P = 0.013). EOR-PSA can assist in predicting DMFS and PCSS among UIR patients, suggesting a clinically meaningful time point for considering intensification of treatment in clinical trials of intermediate-risk men. © 2018 Wiley Periodicals, Inc.

The role of loss of control eating in purging disorder.

PubMed

Forney, K Jean; Haedt-Matt, Alissa A; Keel, Pamela K

2014-04-01

Purging Disorder (PD), an Other Specified Feeding or Eating Disorder (APA, 2013), is characterized by recurrent purging in the absence of binge eating. Though objectively large binge episodes are not present, individuals with PD may experience a loss of control (LOC) while eating a normal or small amounts of food. The present study sought to examine the role of LOC eating in PD using archival data from 101 women with PD. Participants completed diagnostic interviews and self-report questionnaires. Analyses examined the relationship between LOC eating and eating disorder features, psychopathology, personality traits, and impairment in bivariate models and then in multivariate models controlling for purging frequency, age, and body mass index. Across bivariate and multivariate models, LOC eating frequency was associated with greater disinhibition around food, hunger, depressive symptoms, negative urgency, distress, and impairment. LOC eating is a clinically significant feature of PD and should be considered in future definitions of PD. Future research should examine whether LOC eating better represents a dimension of severity in PD or a specifier that may impact treatment response or course. Copyright © 2013 Wiley Periodicals, Inc.

Health-related quality-of-life parameters as independent prognostic factors in advanced or metastatic bladder cancer.

PubMed

Roychowdhury, D F; Hayden, A; Liepa, A M

2003-02-15

This retrospective analysis examined prognostic significance of health-related quality-of-life (HRQoL) parameters combined with baseline clinical factors on outcomes (overall survival, time to progressive disease, and time to treatment failure) in bladder cancer. Outcome and HRQoL (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30) data were collected prospectively in a phase III study assessing gemcitabine and cisplatin versus methotrexate, vinblastine, doxorubicin, and cisplatin in locally advanced or metastatic bladder cancer. Prespecified baseline clinical factors (performance status, tumor-node-metastasis staging, visceral metastases [VM], alkaline phosphatase [AP] level, number of metastatic sites, prior radiotherapy, disease measurability, sex, time from diagnosis, and sites of disease) and selected HRQoL parameters (global QoL; all functional scales; symptoms: pain, fatigue, insomnia, dyspnea, anorexia) were evaluated using Cox's proportional hazards model. Factors with individual prognostic value (P <.05) on outcomes in univariate models were assessed for joint prognostic value in a multivariate model. A final model was developed using a backward selection strategy. Patients with baseline HRQoL were included (364 of 405, 90%). The final model predicted longer survival with low/normal AP levels, no VM, high physical functioning, low role functioning, and no anorexia. Positive prognostic factors for time to progressive disease were good performance status, low/normal AP levels, no VM, and minimal fatigue; for time to treatment failure, they were low/normal AP levels, minimal fatigue, and no anorexia. Global QoL was a significant predictor of outcome in univariate analyses but was not retained in the multivariate model. HRQoL parameters are independent prognostic factors for outcome in advanced bladder cancer; their prognostic importance needs further evaluation.

A model and nomogram to predict tumor site origin for squamous cell cancer confined to cervical lymph nodes.

PubMed

Ali, Arif N; Switchenko, Jeffrey M; Kim, Sungjin; Kowalski, Jeanne; El-Deiry, Mark W; Beitler, Jonathan J

2014-11-15

The current study was conducted to develop a multifactorial statistical model to predict the specific head and neck (H&N) tumor site origin in cases of squamous cell carcinoma confined to the cervical lymph nodes ("unknown primaries"). The Surveillance, Epidemiology, and End Results (SEER) database was analyzed for patients with an H&N tumor site who were diagnosed between 2004 and 2011. The SEER patients were identified according to their H&N primary tumor site and clinically positive cervical lymph node levels at the time of presentation. The SEER patient data set was randomly divided into 2 data sets for the purposes of internal split-sample validation. The effects of cervical lymph node levels, age, race, and sex on H&N primary tumor site were examined using univariate and multivariate analyses. Multivariate logistic regression models and an associated set of nomograms were developed based on relevant factors to provide probabilities of tumor site origin. Analysis of the SEER database identified 20,011 patients with H&N disease with both site-level and lymph node-level data. Sex, race, age, and lymph node levels were associated with primary H&N tumor site (nasopharynx, hypopharynx, oropharynx, and larynx) in the multivariate models. Internal validation techniques affirmed the accuracy of these models on separate data. The incorporation of epidemiologic and lymph node data into a predictive model has the potential to provide valuable guidance to clinicians in the treatment of patients with squamous cell carcinoma confined to the cervical lymph nodes. © 2014 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

Human Papillomavirus Vaccination Among Young Men Who Have Sex With Men and Transgender Women in 2 US Cities, 2012-2014.

PubMed

Gorbach, Pamina M; Cook, Ryan; Gratzer, Beau; Collins, Thomas; Parrish, Adam; Moore, Janell; Kerndt, Peter R; Crosby, Richard A; Markowitz, Lauri E; Meites, Elissa

2017-07-01

Since 2011, in the United States, quadrivalent human papillomavirus (HPV) vaccine has been recommended for boys aged 11 to 12 years, men through age 21, and men who have sex with men (MSM) through age 26. We assessed HPV vaccination coverage and factors associated with vaccination among young MSM (YMSM) and transgender women (TGW) in 2 cities. During 2012-2014, 808 YMSM and TGW aged 18 to 26 years reported vaccination status in a self-administered computerized questionnaire at 3 sexually transmitted disease (STD) clinics in Los Angeles and Chicago. Associations with HPV vaccination were assessed using bivariate and multivariable models to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Few of the diverse participants (Hispanic/Latino, 38.0%; white, 27.0%; and black/African American, 17.9%) reported receiving 1 or more HPV vaccine doses (n = 111 [13.7%]) and even fewer reported 3 doses (n = 37 [4.6%]). A multivariable model found associations between vaccination and having a 4-year college degree or higher (aOR, 2.83; CI, 1.55-5.17) and self-reported STDs (aOR, 1.21; CI, 1.03-1.42). In a model including recommendation variables, the strongest predictor of vaccination was a health care provider recommendation (aOR, 11.85; CI, 6.70-20.98). Human papillomavirus vaccination coverage was low among YMSM and TGW in this 2-US city study. Our findings suggest further efforts are needed to reach YMSM seeking care in STD clinics, increase strong recommendations from health care providers, and integrate HPV vaccination with other clinical services such as STD testing.

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Clostridium Difficile Infection Due to Pneumonia Treatment: Mortality Risk Models.

PubMed

Chmielewska, M; Zycinska, K; Lenartowicz, B; Hadzik-Błaszczyk, M; Cieplak, M; Kur, Z; Wardyn, K A

2017-01-01

One of the most common gastrointestinal infection after the antibiotic treatment of community or nosocomial pneumonia is caused by the anaerobic spore Clostridium difficile (C. difficile). The aim of this study was to retrospectively assess mortality due to C. difficile infection (CDI) in patients treated for pneumonia. We identified 94 cases of post-pneumonia CDI out of the 217 patients with CDI. The mortality issue was addressed by creating a mortality risk models using logistic regression and multivariate fractional polynomial analysis. The patients' demographics, clinical features, and laboratory results were taken into consideration. To estimate the influence of the preceding respiratory infection, a pneumonia severity scale was included in the analysis. The analysis showed two statistically significant and clinically relevant mortality models. The model with the highest prognostic strength entailed age, leukocyte count, serum creatinine and urea concentration, hematocrit, coexisting neoplasia or chronic obstructive pulmonary disease. In conclusion, we report on two prognostic models, based on clinically relevant factors, which can be of help in predicting mortality risk in C. difficile infection, secondary to the antibiotic treatment of pneumonia. These models could be useful in preventive tailoring of individual therapy.

DSM-based problem gambling: increasing the odds of heavy drinking in a national sample of U.S. college athletes?

PubMed

Huang, Jiun-Hau; Jacobs, Durand F; Derevensky, Jeffrey L

2011-03-01

Despite previously found co-occurrence of youth gambling and alcohol use, their relationship has not been systematically explored in a national sample using DSM-based gambling measures and multivariate modeling, adjusted for potential confounders. This study aimed to empirically examine the prevalence patterns and odds of at-least-weekly alcohol use and heavy episodic drinking (HED) in relation to various levels of gambling severity in college athletes. Multivariate logistic regression analyses were performed on data from a national sample of 20,739 U.S. college athletes from the first National Collegiate Athletic Association national survey of gambling and health-risk behaviors. Prevalence of at-least-weekly alcohol use significantly increased as DSM-IV-based gambling severity increased, from non-gambling (24.5%) to non-problem gambling (43.7%) to sub-clinical gambling (58.5%) to problem gambling (67.6%). Multivariate results indicated that all levels of gambling were associated with significantly elevated risk of at-least-weekly HED, from non-problem (OR = 1.25) to sub-clinical (OR = 1.75) to problem gambling (OR = 3.22); the steep increase in the relative risk also suggested a possible quadratic relationship between gambling level and HED risk. Notably, adjusted odds ratios showed problem gambling had the strongest association with at-least-weekly HED, followed by marijuana (OR = 3.08) and cigarette use (OR = 2.64). Gender interactions and differences were also identified and assessed. In conclusion, attention should be paid to college athletes exhibiting gambling problems, especially considering their empirical multivariate associations with high-risk drinking; accordingly, screening for problem gambling is recommended. More research is warranted to elucidate the etiologic mechanisms of these associations. Copyright © 2010 Elsevier Ltd. All rights reserved.

Outcomes and Pharmacoeconomic Analysis of a Home Intravenous Antibiotic Infusion Program in Veterans.

PubMed

Ruh, Christine A; Parameswaran, Ganapathi I; Wojciechowski, Amy L; Mergenhagen, Kari A

2015-11-01

The use of outpatient parenteral antibiotic therapy (OPAT) programs has become more frequent because of benefits in costs with equivalent clinical outcomes compared with inpatient care. The purpose of this study was to evaluate the outcomes of our program. A modified pharmacoeconomic analysis was performed to compare costs of our program with hospital or rehabilitation facility care. This was a retrospective chart review of 96 courses of OPAT between April 1, 2011, and July 31, 2013. Clinical failures were defined as readmission or death due to worsening infection or readmission secondary to adverse drug event (ADE) to antibiotic therapy. This does not include those patients readmitted for reasons not associated with OPAT therapy, including comorbidities or elective procedures. Baseline characteristics and program-specific data were analyzed. Statistically significant variables were built into a multivariate logistic regression model to determine predictors of failure. A pharmacoeconomic analysis was performed with the use of billing records. Of the total episodes evaluated, 17 (17.71%) clinically failed therapy, and 79 (82.29%) were considered a success. In the multivariate analysis, number of laboratory draws (P = 0.02) and occurrence of drug reaction were significant in the final model, P = 0.02 and P = 0.001, respectively. The presence an adverse drug reaction increases the odds of failure (OR = 10.10; 95% CI, 2.69-44.90). Compared with inpatient or rehabilitation care, the cost savings was $6,932,552.03 or $2,649,870.68, respectively. In our study, patients tolerated OPAT well, with a low number of failures due to ADE. The clinical outcomes and cost savings of our program indicate that OPAT can be a viable alternative to long-term inpatient antimicrobial therapy. Published by Elsevier Inc.

Factors associated with reduction in use of neoadjuvant androgen suppression therapy before radical prostatectomy.

PubMed

O'Shaughnessy, Matthew J; Jarosek, Stephanie L; Virnig, Beth A; Konety, Badrinath R; Elliott, Sean P

2013-04-01

To determine whether the prescribing patterns for nonindicated androgen suppression therapy (AST), using neoadjuvant AST as the model, changed according to the prevailing clinical evidence, changes in reimbursement, or evidence of increased harm from treatment. We identified 34,976 men with prostate cancer who had undergone radical prostatectomy within 12 months of diagnosis from the Surveillance, Epidemiology, and End Results-Medicare data set (1992-2007), and their clinical and demographic parameters were assessed. We measured the Medicare claims for receipt of AST before radical prostatectomy and calculated the annual rates of neoadjuvant AST, which were adjusted for confounding variables using multivariate logistic regression analysis, and compared them with the prevailing published clinical data on the outcomes of neoadjuvant AST, changes in reimbursement, or published data on clinical harm from treatment. The use of neoadjuvant AST increased from 7.8% in 1992 to a peak of 17.6% in 1996 and then decreased steadily to 4.6% in 2007. This rate change was significant on multivariate regression analysis, with a single join point in 1996 (P <.001), and corresponded to published data showing improved surgical margin rates and pathologic downstaging in the early 1990s and data showing no improvement in disease recurrence or overall survival beginning in 1997. Changes in reimbursement and evidence of harm from AST were not associated with the decreased use of neoadjuvant AST. Using neoadjuvant AST as the model for the nonindicated use of AST, physicians reduced AST use in response to high-level evidence showing a lack of benefit, despite the high reimbursement. This suggests that physicians adapt to emerging evidence and use evidence-based practice. Copyright © 2013 Elsevier Inc. All rights reserved.

Use of Clinical and Neuroimaging Characteristics to Distinguish Temporal Lobe Herpes Simplex Encephalitis From Its Mimics

PubMed Central

Chow, Felicia C.; Glaser, Carol A.; Sheriff, Heather; Xia, Dongxiang; Messenger, Sharon; Whitley, Richard; Venkatesan, Arun

2015-01-01

Background. We describe the spectrum of etiologies associated with temporal lobe (TL) encephalitis and identify clinical and radiologic features that distinguish herpes simplex encephalitis (HSE) from its mimics. Methods. We reviewed all adult cases of encephalitis with TL abnormalities on magnetic resonance imaging (MRI) from the California Encephalitis Project. We evaluated the association between specific clinical and MRI characteristics and HSE compared with other causes of TL encephalitis and used multivariate logistic modeling to identify radiologic predictors of HSE. Results. Of 251 cases of TL encephalitis, 43% had an infectious etiology compared with 16% with a noninfectious etiology. Of infectious etiologies, herpes simplex virus was the most commonly identified agent (n = 60), followed by tuberculosis (n = 8) and varicella zoster virus (n = 7). Of noninfectious etiologies, more than half (n = 21) were due to autoimmune disease. Patients with HSE were older (56.8 vs 50.2 years; P = .012), more likely to be white (53% vs 35%; P = .013), more likely to present acutely (88% vs 64%; P = .001) and with a fever (80% vs 49%; P < .001), and less likely to present with a rash (2% vs 15%; P = .010). In a multivariate model, bilateral TL involvement (odds ratio [OR], 0.38; 95% confidence interval [CI], .18–.79; P = .010) and lesions outside the TL, insula, or cingulate (OR, 0.37; 95% CI, .18–.74; P = .005) were associated with lower odds of HSE. Conclusions. In addition to HSE, other infectious and noninfectious etiologies should be considered in the differential diagnosis for TL encephalitis, depending on the presentation. Specific clinical and imaging features may aid in distinguishing HSE from non-HSE causes of TL encephalitis. PMID:25637586

Clinically Identified Postpartum Depression in Asian American Mothers

PubMed Central

Goyal, Deepika; Wang, Elsie J.; Shen, Jeremy; Wong, Eric C.; Palaniappan, Latha P.

2015-01-01

Objective To identify the clinical diagnosis rate of postpartum depression (PPD) in Asian American subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) compared to non-Hispanic Whites. Design Cross-sectional study using electronic health records (EHR). Setting A large, outpatient, multiservice clinic in Northern California. Participants A diverse clinical population of non-Hispanic White (N = 4582), Asian Indian (N = 1264), Chinese (N = 1160), Filipino (N = 347), Japanese (N = 124), Korean (N = 183), and Vietnamese (N = 147) mothers. Methods Cases of PPD were identified from EHRs using physician diagnosis codes, medication usage, and age standardized for comparison. The relationship between PPD and other demographic variables (race/ethnicity, maternal age, delivery type, marital status, and infant gender) were examined in a multivariate logistic regression model. Results The PPD diagnosis rate for all Asian American mothers in aggregate was significantly lower than the diagnosis rate in non-Hispanic White mothers. Moreover, of the six Asian American subgroups, PPD diagnosis rates for Asian Indian, Chinese, and Filipino mothers were significantly lower than non-Hispanic White mothers. In multivariate analyses, race/ethnicity, age, and cesarean were significant predictors of PPD. Conclusion In this insured population, PPD diagnosis rates were lower among Asian Americans, with variability in rates across the individual Asian American subgroups. It is unclear whether these lower rates are due to underreporting, underdiagnosis, or underutilization of mental health care in this setting. PMID:22536783

Clinically identified postpartum depression in Asian American mothers.

PubMed

Goyal, Deepika; Wang, Elsie J; Shen, Jeremy; Wong, Eric C; Palaniappan, Latha P

2012-01-01

To identify the clinical diagnosis rate of postpartum depression (PPD) in Asian American subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) compared to non-Hispanic Whites. Cross-sectional study using electronic health records (EHR). A large, outpatient, multiservice clinic in Northern California. A diverse clinical population of non-Hispanic White (N = 4582), Asian Indian (N = 1264), Chinese (N = 1160), Filipino (N = 347), Japanese (N = 124), Korean (N = 183), and Vietnamese (N = 147) mothers. Cases of PPD were identified from EHRs using physician diagnosis codes, medication usage, and age standardized for comparison. The relationship between PPD and other demographic variables (race/ethnicity, maternal age, delivery type, marital status, and infant gender) were examined in a multivariate logistic regression model. The PPD diagnosis rate for all Asian American mothers in aggregate was significantly lower than the diagnosis rate in non-Hispanic White mothers. Moreover, of the six Asian American subgroups, PPD diagnosis rates for Asian Indian, Chinese, and Filipino mothers were significantly lower than non-Hispanic White mothers. In multivariate analyses, race/ethnicity, age, and cesarean were significant predictors of PPD. In this insured population, PPD diagnosis rates were lower among Asian Americans, with variability in rates across the individual Asian American subgroups. It is unclear whether these lower rates are due to underreporting, underdiagnosis, or underutilization of mental health care in this setting. © 2012 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.

Health-state utilities in a prisoner population: a cross-sectional survey

PubMed Central

Chong, Christopher AKY; Li, Sicong; Nguyen, Geoffrey C; Sutton, Andrew; Levy, Michael H; Butler, Tony; Krahn, Murray D; Thein, Hla-Hla

2009-01-01

Background Health-state utilities for prisoners have not been described. Methods We used data from a 1996 cross-sectional survey of Australian prisoners (n = 734). Respondent-level SF-36 data was transformed into utility scores by both the SF-6D and Nichol's method. Socio-demographic and clinical predictors of SF-6D utility were assessed in univariate analyses and a multivariate general linear model. Results The overall mean SF-6D utility was 0.725 (SD 0.119). When subdivided by various medical conditions, prisoner SF-6D utilities ranged from 0.620 for angina to 0.764 for those with none/mild depressive symptoms. Utilities derived by the Nichol's method were higher than SF-6D scores, often by more than 0.1. In multivariate analysis, significant independent predictors of worse utility included female gender, increasing age, increasing number of comorbidities and more severe depressive symptoms. Conclusion The utilities presented may prove useful for future economic and decision models evaluating prison-based health programs. PMID:19715571

Risk factors of flare in rheumatoid arthritis patients with both clinical and ultrasonographic remission: a retrospective study from China.

PubMed

Han, Jingjing; Geng, Yan; Deng, Xuerong; Zhang, Zhuoli

2017-08-01

Ultrasonographic remission in addition to clinical remission is probably becoming a new target in the treatment of rheumatoid arthritis. The current study aimed to investigate the risk factors of flare in RA patients who achieved both clinical and ultrasonographic remission. RA patients fulfilled both clinical remission and ultrasonographic remissions were retrospectively enrolled in this study. Baseline clinical, laboratory, and ultrasonographic data were collected. Durations of clinical remission before enrollment and medication strategy during follow-up were recorded. Differences between the flare and the non-flare group were analyzed. Risk factors of flare were assessed with univariate and multivariate Cox proportional hazards models. One hundred and twenty-one RA patients were included. Forty-eight patients relapsed during a median follow-up period of 12.3 months. The flare group had higher percentage of females, shorter duration of clinical remission before enrollment, higher baseline ESR and DAS28 (ESR), and lower baseline gray scale score. Univariate Cox regression revealed female, short duration of remission, high DAS28 (ESR), and failure to achieve 2010 ACR/EULAR remission criteria were risk factors of flare. Furthermore, multivariate analysis showed short duration of remission was the only independent risk factor of flare (HR 0.93, 95% CI 0.88-0.98, P = 0.007). One more month in duration of remission led to a reduction in flare of 7.3%. Short duration of remission at baseline could be an independent risk factor of flare in RA patients who achieved both clinical and ultrasonographic remission, which implicates the significance of sustained remission in the prognosis of RA patients.

Only One Third of Tehran's Physicians are Familiar with 'Evidence-Based Clinical Guidelines'.

PubMed

Mounesan, Leila; Nedjat, Saharnaz; Majdzadeh, Reza; Rashidian, Arash; Gholami, Jaleh

2013-03-01

Clinical guidelines have increasingly been used as tools for applying new knowledge and research findings. Although, efforts have been made to produce clinical guidelines in Iran, it is not clear whether they have been used by physicians and what factors are associated with them?. Four hundred and forty three practicing physicians in Tehran were selected from private clinics through weighted random sampling. The data collection tool was a questionnaire on familiarity and attitude toward clinical guidelines. The descriptive and analytical findings were analyzed with t-tests, Chi(2), logistic and linear multivariate regression by SPSS, version 16. 31.8% of physicians were familiar with clinical guidelines. Based on the logistic regression model physicians' familiarity with clinical guidelines was positively and significantly associated with 'working experience in a health service delivery point' OR = 2.13 (95% CI, 1.17-3.90), 'familiarity with therapeutic protocols' OR = 2.09 (95% CI, 1.22-3.57) and 'holding a specialty degree' OR = 2.51 (95% CI, 1.24-5.07). The mean overall attitude scores in the 'usefulness', 'reliability', and 'problems and barriers' domains were, respectively, 78.9 (SD = 16.5), 78.9 (SD = 19.7) and 50.4 (SD = 15.9) out of a total of 100 scores in each domain. No significant association was observed between attitude domains and other independent variables using multivariate linear regression. Little familiarity with clinical guidelines may represent weakness in of production and distribution of domestic evidence. Although, physicians considered guidelines as useful and reliable tools, but problems such as difficult access to guidelines and lack of facilities to apply them were stated as well.

Concurrent Anemia and Elevated C-Reactive Protein Predicts HIV Clinical Treatment Failure, Including Tuberculosis, After Antiretroviral Therapy Initiation.

PubMed

Shivakoti, Rupak; Yang, Wei-Teng; Gupte, Nikhil; Berendes, Sima; Rosa, Alberto La; Cardoso, Sandra W; Mwelase, Noluthando; Kanyama, Cecilia; Pillay, Sandy; Samaneka, Wadzanai; Riviere, Cynthia; Sugandhavesa, Patcharaphan; Santos, Brento; Poongulali, Selvamuthu; Tripathy, Srikanth; Bollinger, Robert C; Currier, Judith S; Tang, Alice M; Semba, Richard D; Christian, Parul; Campbell, Thomas B; Gupta, Amita

2015-07-01

Anemia is a known risk factor for clinical failure following antiretroviral therapy (ART). Notably, anemia and inflammation are interrelated, and recent studies have associated elevated C-reactive protein (CRP), an inflammation marker, with adverse human immunodeficiency virus (HIV) treatment outcomes, yet their joint effect is not known. The objective of this study was to assess prevalence and risk factors of anemia in HIV infection and to determine whether anemia and elevated CRP jointly predict clinical failure post-ART. A case-cohort study (N = 470 [236 cases, 234 controls]) was nested within a multinational randomized trial of ART efficacy (Prospective Evaluation of Antiretrovirals in Resource Limited Settings [PEARLS]). Cases were incident World Health Organization stage 3, 4, or death by 96 weeks of ART treatment (clinical failure). Multivariable logistic regression was used to determine risk factors for pre-ART (baseline) anemia (females: hemoglobin <12.0 g/dL; males: hemoglobin <13.0 g/dL). Association of anemia as well as concurrent baseline anemia and inflammation (CRP ≥ 10 mg/L) with clinical failure were assessed using multivariable Cox models. Baseline anemia prevalence was 51% with 15% prevalence of concurrent anemia and inflammation. In analysis of clinical failure, multivariate-adjusted hazard ratios were 6.41 (95% confidence interval [CI], 2.82-14.57) for concurrent anemia and inflammation, 0.77 (95% CI, .37-1.58) for anemia without inflammation, and 0.45 (95% CI, .11-1.80) for inflammation without anemia compared to those without anemia and inflammation. ART-naive, HIV-infected individuals with concurrent anemia and inflammation are at particularly high risk of failing treatment, and understanding the pathogenesis could lead to new interventions. Reducing inflammation and anemia will likely improve HIV disease outcomes. Alternatively, concurrent anemia and inflammation could represent individuals with occult opportunistic infections in need of additional screening. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Associations Between Body Anthropometric Measures and Severity of Carpal Tunnel Syndrome.

PubMed

Mondelli, Mauro; Curti, Stefania; Mattioli, Stefano; Aretini, Alessandro; Ginanneschi, Federica; Greco, Giuseppe; Farioli, Andrea

2016-09-01

To assess the associations between carpal tunnel syndrome (CTS) severity and selected anthropometric and obesity indexes. We performed a case-control study. Clinical and electrophysiological severity of CTS was classified as mild, moderate, or severe based on validated scales. Body and hand anthropometric characteristics were measured at the time of the electrodiagnostic study. We estimated the relative risk ratios (RRRs) of CTS severity by fitting multinomial logistic regression models adjusted by age and sex. In addition, we fitted multivariable models, including age, sex, wrist ratio, hand ratio, body mass index (BMI), and waist/stature ratio. Electromyography laboratories. Consecutive patients (N=1087), those with CTS (n=340) and those without CTS (n=747), were enrolled. Not applicable. Associations between CTS severity and selected anthropometric and obesity indexes. We observed associations between many anthropometric indexes and CTS severity. Among obesity indexes, the waist/stature ratio, and among hand anthropometric indexes, the wrist/palm ratio, showed the highest RRRs for the clinical and electrophysiological severity scales. The RRRs of severe CTS (adjusted for age and sex) for the wrist/palm ratio were 3.5 for the clinical scale and 2.4 for the electrophysiological scale. The RRRs of severe CTS for the waist/stature ratio were 2.3 for the clinical scale and 2.0 for the electrophysiological scale. In the multivariable models, both BMI and the waist/stature ratio were associated with the outcomes. Different configurations of the body and, in particular, the hand and wrist system may influence the occurrence and severity of CTS. Multiple obesity indexes, possibly including the waist/stature ratio, should be considered when investigating the association between body composition and CTS. Future studies should determine whether in obese subjects with CTS the weight and waist circumference loss produces an improvement in CTS symptoms and recovery of distal conduction velocity of the median nerve. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

PubMed

Zinszer, Kate; Kigozi, Ruth; Charland, Katia; Dorsey, Grant; Brewer, Timothy F; Brownstein, John S; Kamya, Moses R; Buckeridge, David L

2015-06-18

Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda. Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period. The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%. Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens.

PubMed

Frantzidis, Christos A; Gilou, Sotiria; Billis, Antonis; Karagianni, Maria; Bratsas, Charalampos D; Bamidis, Panagiotis

2016-03-01

Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.

Can Patient Comorbidities Be Included in Clinical Performance Measures for Radiation Oncology?

PubMed Central

Owen, Jean B.; Khalid, Najma; Ho, Alex; Kachnic, Lisa A.; Komaki, Ritsuko; Tao, May Lin; Currey, Adam; Wilson, J. Frank

2014-01-01

Purpose: Patient comorbidities may affect the applicability of performance measures that are inherent in multidisciplinary cancer treatment guidelines. This article describes the distribution of common comorbid conditions by disease site and by patient and facility characteristics in patients who received radiation therapy as part of treatment for cancer of the breast, cervix, lung, prostate, and stomach, and investigates the association of comorbidities with treatment decisions. Materials and Methods: Stratified two-stage cluster sampling provided a random sample of radiation oncology facilities. Eligible patients were randomly sampled from each participating facility for each disease site, and data were abstracted from medical records. The Adult Comorbidity Evaluation Index (ACE-27) was used to measure comorbid conditions and their severity. National estimates were calculated using SUDAAN statistical software. Results: Multivariable logistic regression models predicted the dependent variable “treatment changed or contraindicated due to comorbidities.” The final model showed that ACE-27 was highly associated with change in treatment for patients with severe or moderate index values compared to those with none or mild (P < .001). Two other covariates, age and medical coverage, had no (age) or little (medical coverage) significant contribution to predicting treatment change in the multivariable model. Disease site was associated with treatment change after adjusting for other covariates in the model. Conclusions: ACE-27 is highly predictive of treatment modifications for patients treated for these cancers who receive radiation as part of their care. A standardized tool identifying patients who should be excluded from clinical performance measures allows more accurate use of these measures. PMID:24643573

Immunosuppressive Drug Discontinuation in Noninfectious Uveitis From Real-Life Clinical Practice: A Survival Analysis.

PubMed

Abásolo, Lydia; Rosales, Zulema; Díaz-Valle, David; Gómez-Gómez, Alejandro; Peña-Blanco, Rayma C; Prieto-García, Ángela; Benítez-Del-Castillo, José Manuel; Pato, Esperanza; García-Feijoo, Julián; Fernández-Gutiérrez, Benjamín; Rodriguez-Rodriguez, Luis

2016-09-01

To assess in uveitis patients the rate of immunosuppressive drug (ISD) discontinuation in real-life clinical practice, comparing this rate among ISDs. Longitudinal retrospective cohort study. We included uveitis patients attending a tertiary eye referral center from Madrid (Spain) between 1989 and 2015, prescribed any ISDs (cyclosporine, methotrexate, azathioprine, anti-TNF drugs, or others). Our main outcome was discontinuation of all ISDs owing to clinical efficacy, inefficacy, adverse drug reaction (ADR), and other medical causes. Discontinuation rates (DRs) per 100 patient-years were estimated. Variables associated with specific-cause discontinuations were analyzed using Cox bivariate and multivariate models. We analyzed 110 patients with 263 treatment courses and 665.2 patient-years of observation. Cyclosporine (66.4%), methotrexate (47.3%), azathioprine (30.9%), and anti-TNFs (30.9%) were the most frequently used ISDs. Treatment was suspended in 136 cases (mostly owing to clinical efficacy [38.2%], inefficacy [26.5%], and ADRs [22.8%]). All-cause DR with 95% confidence interval was 20.4 [17.3-24.2]. Retention rates at 1 and 10 years were 74% and 16%, respectively. In the multivariate analysis, combined treatment exhibited higher DRs owing to clinical efficacy than other ISDs in monotherapy. Conversely, nonbiologic combination therapy with azathioprine exhibited the highest DR owing to ADRs. Clinical efficacy was the most frequent cause for ISD discontinuation, followed by inefficacy and ADRs. DR owing to efficacy was higher for combination therapy. Furthermore, nonbiologic combination therapy with azathioprine was associated with a higher DR owing to ADRs. Copyright © 2016 Elsevier Inc. All rights reserved.

Hemoglobin concentration does not impact 3-month outcome following acute ischemic stroke.

PubMed

Sharma, Kartavya; Johnson, Daniel J; Johnson, Brenda; Frank, Steven M; Stevens, Robert D

2018-06-02

There is uncertainty regarding the effect of anemia and red blood cell transfusion on functional outcome following acute ischemic stroke. We studied the relationship of hemoglobin parameters and red cell transfusion with post stroke functional outcome after adjustment for neurological severity and medical comorbidities. Retrospective cohort study of 536 patients discharged with a diagnosis of ischemic stroke from a tertiary care hospital between January 2012 and April 2015. Hemoglobin level at hospital admission, lowest recorded value during hospitalization (nadir), delta hemoglobin (admission minus nadir), red cell transfusion during hospitalization were noted. Charlson Comorbidity Index (CCI) was computed as a summary measure of medical comorbidities. A multivariable logistic regression model was used to determine risk-adjusted odds of unfavorable outcome, defined as a modified Rankin Score of > 2. Anemia was present on hospital admission in 31% of patients. Forty five percent of patients had unfavorable outcome. In the univariable analysis increasing age, admission National Institutes of Health Stroke Scale (NIHSS), CCI, nadir hemoglobin, delta hemoglobin and blood transfusion were associated with unfavorable outcome. In the multivariable model, only increasing age, CCI and NIHSS remained associated with unfavorable outcome. No quadratic association was found on repeating the model to identify a possible U-shaped relationship of hemoglobin with outcome. Our findings contradict prior observational studies and highlight an area of clinical equipoise regarding the optimal management of anemia in patients hospitalized for ischemic stroke. This uncertainty could be addressed with appropriately designed clinical trials.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Validation of Single-Item Screening Measures for Provider Burnout in a Rural Health Care Network.

PubMed

Waddimba, Anthony C; Scribani, Melissa; Nieves, Melinda A; Krupa, Nicole; May, John J; Jenkins, Paul

2016-06-01

We validated three single-item measures for emotional exhaustion (EE) and depersonalization (DP) among rural physician/nonphysician practitioners. We linked cross-sectional survey data (on provider demographics, satisfaction, resilience, and burnout) with administrative information from an integrated health care network (1 academic medical center, 6 community hospitals, 31 clinics, and 19 school-based health centers) in an eight-county underserved area of upstate New York. In total, 308 physicians and advanced-practice clinicians completed a self-administered, multi-instrument questionnaire (65.1% response rate). Significant proportions of respondents reported high EE (36.1%) and DP (9.9%). In multivariable linear mixed models, scores on EE/DP subscales of the Maslach Burnout Inventory were regressed on each single-item measure. The Physician Work-Life Study's single-item measure (classifying 32.8% of respondents as burning out/completely burned out) was correlated with EE and DP (Spearman's ρ = .72 and .41, p < .0001; Kruskal-Wallis χ(2) = 149.9 and 56.5, p < .0001, respectively). In multivariable models, it predicted high EE (but neither low EE nor low/high DP). EE/DP single items were correlated with parent subscales (Spearman's ρ = .89 and .81, p < .0001; Kruskal-Wallis χ(2) = 230.98 and 197.84, p < .0001, respectively). In multivariable models, the EE item predicted high/low EE, whereas the DP item predicted only low DP. Therefore, the three single-item measures tested varied in effectiveness as screeners for EE/DP dimensions of burnout. © The Author(s) 2015.

Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial.

PubMed

Peikert, Tobias; Duan, Fenghai; Rajagopalan, Srinivasan; Karwoski, Ronald A; Clay, Ryan; Robb, Richard A; Qin, Ziling; Sicks, JoRean; Bartholmai, Brian J; Maldonado, Fabien

2018-01-01

Optimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules. Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model. Eight of the originally considered 57 quantitative radiologic features were selected by LASSO multivariate modeling. These 8 features include variables capturing Location: vertical location (Offset carina centroid z), Size: volume estimate (Minimum enclosing brick), Shape: flatness, Density: texture analysis (Score Indicative of Lesion/Lung Aggression/Abnormality (SILA) texture), and surface characteristics: surface complexity (Maximum shape index and Average shape index), and estimates of surface curvature (Average positive mean curvature and Minimum mean curvature), all with P<0.01. The optimism-corrected AUC for these 8 features is 0.939. Our novel radiomic LDCT-based approach for indeterminate screen-detected nodule characterization appears extremely promising however independent external validation is needed.

Risk factors for the development of heterotopic ossification in seriously burned adults: A National Institute on Disability, Independent Living and Rehabilitation Research burn model system database analysis.

PubMed

Levi, Benjamin; Jayakumar, Prakash; Giladi, Avi; Jupiter, Jesse B; Ring, David C; Kowalske, Karen; Gibran, Nicole S; Herndon, David; Schneider, Jeffrey C; Ryan, Colleen M

2015-11-01

Heterotopic ossification (HO) is a debilitating complication of burn injury; however, incidence and risk factors are poorly understood. In this study, we use a multicenter database of adults with burn injuries to identify and analyze clinical factors that predict HO formation. Data from six high-volume burn centers, in the Burn Injury Model System Database, were analyzed. Univariate logistic regression models were used for model selection. Cluster-adjusted multivariate logistic regression was then used to evaluate the relationship between clinical and demographic data and the development of HO. Of 2,979 patients in the database with information on HO that addressed risk factors for development of HO, 98 (3.5%) developed HO. Of these 98 patients, 97 had arm burns, and 96 had arm grafts. When controlling for age and sex in a multivariate model, patients with greater than 30% total body surface area burn had 11.5 times higher odds of developing HO (p < 0.001), and those with arm burns that required skin grafting had 96.4 times higher odds of developing HO (p = 0.04). For each additional time a patient went to the operating room, odds of HO increased by 30% (odds ratio, 1.32; p < 0.001), and each additional ventilator day increased odds by 3.5% (odds ratio, 1.035; p < 0.001). Joint contracture, inhalation injury, and bone exposure did not significantly increase odds of HO. Risk factors for HO development include greater than 30% total body surface area burn, arm burns, arm grafts, ventilator days, and number of trips to the operating room. Future studies can use these results to identify highest-risk patients to guide deployment of prophylactic and experimental treatments. Prognostic study, level III.

Risk Factors for the Development of Heterotopic Ossification in Seriously Burned Adults: A NIDRR Burn Model System Database Analysis

PubMed Central

Levi, Benjamin; Jayakumar, Prakash; Giladi, Avi; Jupiter, Jesse B.; Ring, David C.; Kowalske, Karen; Gibran, Nicole S.; Herndon, David; Schneider, Jeffrey C.; Ryan, Colleen M.

2015-01-01

Purpose Heterotopic ossification (HO) is a debilitating complication of burn injury; however, incidence and risk factors are poorly understood. In this study we utilize a multicenter database of adults with burn injuries to identify and analyze clinical factors that predict HO formation. Methods Data from 6 high-volume burn centers, in the Burn Injury Model System Database, were analyzed. Univariate logistic regression models were used for model selection. Cluster-adjusted multivariate logistic regression was then used to evaluate the relationship between clinical and demographic data and the development of HO. Results Of 2,979 patients in the database with information on HO that addressed risk factors for development of HO, 98 (3.5%) developed HO. Of these 98 patients, 97 had arm burns, and 96 had arm grafts. Controlling for age and sex in a multivariate model, patients with >30% total body surface area (TBSA) burn had 11.5x higher odds of developing HO (p<0.001), and those with arm burns that required skin grafting had 96.4x higher odds of developing HO (p=0.04). For each additional time a patient went to the operating room, odds of HO increased 30% (OR 1.32, p<0.001), and each additional ventilator day increase odds 3.5% (OR 1.035, p<0.001). Joint contracture, inhalation injury, and bone exposure did not significantly increase odds of HO. Conclusion Risk factors for HO development include >30% TBSA burn, arm burns, arm grafts, ventilator days, and number of trips to the operating room. Future studies can use these results to identify highest-risk patients to guide deployment of prophylactic and experimental treatments. PMID:26496115

A clinical scoring system for predicting nonalcoholic steatohepatitis in morbidly obese patients.

PubMed

Campos, Guilherme M; Bambha, Kiran; Vittinghoff, Eric; Rabl, Charlotte; Posselt, Andrew M; Ciovica, Ruxandra; Tiwari, Umesh; Ferrel, Linda; Pabst, Mark; Bass, Nathan M; Merriman, Raphael B

2008-06-01

Nonalcoholic steatohepatitis (NASH) is common in morbidly obese persons. Liver biopsy is diagnostic but technically challenging in such individuals. This study was undertaken to develop a clinically useful scoring system to predict the probability of NASH in morbidly obese persons, thus assisting in the decision to perform liver biopsy. Consecutive subjects undergoing bariatric surgery without evidence of other liver disease underwent intraoperative liver biopsy. The outcome was pathologic diagnosis of NASH. Predictors evaluated were demographic, clinical, and laboratory variables. A clinical scoring system was constructed by rounding the estimated regression coefficients for the independent predictors in a multivariate logistic model for the diagnosis of NASH. Of 200 subjects studied, 64 (32%) had NASH. Median body mass index was 48 kg/m(2) (interquartile range, 43-55). Multivariate analysis identified six predictive factors for NASH: the diagnosis of hypertension (odds ratio [OR], 2.4; 95% confidence interval [CI], 1-5.6), type 2 diabetes (OR, 2.6; 95% CI, 1.1-6.3), sleep apnea (OR, 4.0; 95% CI, 1.3-12.2), AST > 27 IU/L (OR, 2.9; 95% CI, 1.2-7.0), alanine aminotransferase (ALT) > 27 IU/L (OR, 3.3; 95% CI, 1.4-8.0), and non-Black race (OR, 8.4; 95% CI, 1.9-37.1). A NASH Clinical Scoring System for Morbid Obesity was derived to predict the probability of NASH in four categories (low, intermediate, high, and very high). The proposed clinical scoring can predict NASH in morbidly obese persons with sufficient accuracy to be considered for clinical use, identifying a very high-risk group in whom liver biopsy would be very likely to detect NASH, as well as a low-risk group in whom biopsy can be safely delayed or avoided.

Increasing Complexity of Clinical Research in Gastroenterology: Implications for Training Clinician-Scientists

PubMed Central

Scott, Frank I.; McConnell, Ryan A.; Lewis, Matthew E.; Lewis, James D.

2014-01-01

Background Significant advances have been made in clinical and epidemiologic research methods over the past 30 years. We sought to demonstrate the impact of these advances on published research in gastroenterology from 1980 to 2010. Methods Three journals (Gastroenterology, Gut, and American Journal of Gastroenterology) were selected for evaluation given their continuous publication during the study period. Twenty original clinical articles were randomly selected from each journal from 1980, 1990, 2000, and 2010. Each article was assessed for topic studied, whether the outcome was clinical or physiologic, study design, sample size, number of authors and centers collaborating, and reporting of statistical methods such as sample size calculations, p-values, confidence intervals, and advanced techniques such as bioinformatics or multivariate modeling. Research support with external funding was also recorded. Results A total of 240 articles were included in the study. From 1980 to 2010, there was a significant increase in analytic studies (p<0.001), clinical outcomes (p=0.003), median number of authors per article (p<0.001), multicenter collaboration (p<0.001), sample size (p<0.001), and external funding (p<0.001)). There was significantly increased reporting of p-values (p=0.01), confidence intervals (p<0.001), and power calculations (p<0.001). There was also increased utilization of large multicenter databases (p=0.001), multivariate analyses (p<0.001), and bioinformatics techniques (p=0.001). Conclusions There has been a dramatic increase in complexity in clinical research related to gastroenterology and hepatology over the last three decades. This increase highlights the need for advanced training of clinical investigators to conduct future research. PMID:22475957

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

Cycling Empirical Antibiotic Therapy in Hospitals: Meta-Analysis and Models

PubMed Central

Abel, Sören; Viechtbauer, Wolfgang; Bonhoeffer, Sebastian

2014-01-01

The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden. Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance. However, theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy (cycling). Here, we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice. We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections, of which 11 met our selection criteria. We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4.95 [9.43–0.48] and resistant infections by 7.2 [14.00–0.44]. This positive effect was observed in most pathogens despite a large variance between individual species. Our findings remain robust in uni- and multivariate metaregressions. We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs (mixing). We make the realistic assumption that therapy is changed when first line treatment is ineffective, which we call “adjustable cycling/mixing”. In concordance with earlier theoretical studies, we find that in strict regimens, cycling is detrimental. However, in adjustable regimens single resistance is suppressed and cycling is successful in most settings. Both a meta-regression and our theoretical model indicate that “adjustable cycling” is especially useful to suppress emergence of multiple resistance. While our model predicts that cycling periods of one month perform well, we expect that too long cycling periods are detrimental. Our results suggest that “adjustable cycling” suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings. PMID:24968123

Comparison of information content of structured and narrative text data sources on the example of medication intensification.

PubMed

Turchin, Alexander; Shubina, Maria; Breydo, Eugene; Pendergrass, Merri L; Einbinder, Jonathan S

2009-01-01

OBJECTIVE To compare information obtained from narrative and structured electronic sources using anti-hypertensive medication intensification as an example clinical issue of interest. DESIGN A retrospective cohort study of 5,634 hypertensive patients with diabetes from 2000 to 2005. MEASUREMENTS The authors determined the fraction of medication intensification events documented in both narrative and structured data in the electronic medical record. The authors analyzed the relationship between provider characteristics and concordance between intensifications in narrative and structured data. As there is no gold standard data source for medication information, the authors clinically validated medication intensification information by assessing the relationship between documented medication intensification and the patients' blood pressure in univariate and multivariate models. RESULTS Overall, 5,627 (30.9%) of 18,185 medication intensification events were documented in both sources. For a medication intensification event documented in narrative notes the probability of a concordant entry in structured records increased by 11% for each study year (p < 0.0001) and decreased by 19% for each decade of provider age (p = 0.035). In a multivariate model that adjusted for patient demographics and intraphysician correlations, an increase of one medication intensification per month documented in either narrative or structured data were associated with a 5-8 mm Hg monthly decrease in systolic and 1.5-4 mm Hg decrease in diastolic blood pressure (p < 0.0001 for all). CONCLUSION Narrative and structured electronic data sources provide complementary information on anti-hypertensive medication intensification. Clinical validity of information in both sources was demonstrated by correlation with changes in blood pressure.

Thrombomodulin gene variants are associated with increased mortality after coronary artery bypass surgery in replicated analyses.

PubMed

Lobato, Robert L; White, William D; Mathew, Joseph P; Newman, Mark F; Smith, Peter K; McCants, Charles B; Alexander, John H; Podgoreanu, Mihai V

2011-09-13

We tested the hypothesis that genetic variation in thrombotic and inflammatory pathways is independently associated with long-term mortality after coronary artery bypass graft (CABG) surgery. Two separate cohorts of patients undergoing CABG surgery at a single institution were examined, and all-cause mortality between 30 days and 5 years after the index CABG was ascertained from the National Death Index. In a discovery cohort of 1018 patients, a panel of 90 single-nucleotide polymorphisms (SNPs) in 49 candidate genes was tested with Cox proportional hazard models to identify clinical and genomic multivariate predictors of incident death. After adjustment for multiple comparisons and clinical predictors of mortality, the homozygote minor allele of a common variant in the thrombomodulin (THBD) gene (rs1042579) was independently associated with significantly increased risk of all-cause mortality (hazard ratio, 2.26; 95% CI, 1.31 to 3.92; P=0.003). Six tag SNPs in the THBD gene, 1 of which (rs3176123) in complete linkage disequilibrium with rs1042579, were then assessed in an independent validation cohort of 930 patients. After multivariate adjustment for the clinical predictors identified in the discovery cohort and multiple testing, the homozygote minor allele of rs3176123 independently predicted all-cause mortality (hazard ratio, 3.6; 95% CI, 1.67 to 7.78; P=0.001). In 2 independent cardiac surgery cohorts, linked common allelic variants in the THBD gene are independently associated with increased long-term mortality risk after CABG and significantly improve the classification ability of traditional postoperative mortality prediction models.

Clinical predictors of advanced sellar masses.

PubMed

Rambaldini, Gloria M; Butalia, Sonia; Ezzat, Shereen; Kucharczyk, Walter; Sawka, Anna M

2007-10-01

To identify clinical variables associated with the presence of a structurally advanced sellar mass (ASM). We performed a retrospective study of patients referred for evaluation of suspected new pituitary disease or sellar mass to the Endocrine Oncology Unit of Mount Sinai Hospital in Toronto, Ontario, Canada. By multivariate analysis, we examined predictors of a structurally ASM (a sellar lesion with any of the following characteristics: diameter of >or=1 cm on magnetic resonance imaging [MRI], optic chiasmal compression on MRI, or clinical or biochemical evidence of hypopituitarism). Data from 152 patients were analyzed. Of the 152 sellar masses, 142 (93%) were pituitary adenomas. An ASM was noted in 85 of the 152 patients (56%). In the final multivariate model, male sex (odds ratio [OR], 6.23; 95% confidence interval [CI], 2.84 to 13.56; P<0.001) and self-reported visual field defect (OR, 3.62; 95% CI, 1.07 to 12.25; P = 0.039) were significantly independently associated with the presence of an ASM. The presence of new or changed headaches also tended to be associated with an ASM (OR, 2.11; 95% CI, 0.96 to 4.64; P = 0.063). Age and self-reported galactorrhea were not independently associated with the presence of an ASM and were conditionally removed from the final model. In patients with suspected sellar or pituitary disease, male sex and self-reported visual field defects independently predict the presence of an ASM. New or changed headaches also tend to be related to the presence of an ASM. The presence of predictors of an ASM should prompt expedited sellar MRI and biochemical evaluation.

Comparison between alcohol- and hepatitis C virus-related hepatocellular carcinoma: clinical presentation, treatment and outcome.

PubMed

Bucci, L; Garuti, F; Camelli, V; Lenzi, B; Farinati, F; Giannini, E G; Ciccarese, F; Piscaglia, F; Rapaccini, G L; Di Marco, M; Caturelli, E; Zoli, M; Borzio, F; Sacco, R; Maida, M; Felder, M; Morisco, F; Gasbarrini, A; Gemini, S; Foschi, F G; Missale, G; Masotto, A; Affronti, A; Bernardi, M; Trevisani, F

2016-02-01

Hepatitis C virus (HCV) and alcohol abuse are the main risk factors for hepatocellular carcinoma (HCC) in Western countries. To investigate the role of alcoholic aetiology on clinical presentation, treatment and outcome of HCC as well as on each Barcelona Clinic Liver Cancer (BCLC) stage, as compared to HCV-related HCCs. A total of 1642 HCV and 573 alcoholic patients from the Italian Liver Cancer (ITA.LI.CA) database, diagnosed with HCC between January 2000 and December 2012 were compared for age, gender, type of diagnosis, tumour burden, portal vein thrombosis (PVT), oesophageal varices, liver function tests, alpha-fetoprotein, BCLC, treatment and survival. Aetiology was tested as predictor of survival in multivariate Cox regression models and according to HCC stages. Cirrhosis was present in 96% of cases in both groups. Alcoholic patients were younger, more likely male, with HCC diagnosed outside surveillance, in intermediate/terminal BCLC stage and had worse liver function. After adjustment for the lead-time, median (95% CI) overall survival (OS) was 27.4 months (21.5-33.2) in alcoholic and 33.6 months (30.7-36.5) in HCV patients (P = 0.021). The prognostic role of aetiology disappeared when survival was assessed in each BCLC stage and in the Cox regression multivariate models. Alcoholic aetiology affects survival of HCC patients through its negative effects on secondary prevention and cancer presentation but not through a greater cancer aggressiveness or worse treatment result. In fact, survival adjusted for confounding factors was similar in alcoholic and HCV patients. © 2015 John Wiley & Sons Ltd.

Development of the Complex General Linear Model in the Fourier Domain: Application to fMRI Multiple Input-Output Evoked Responses for Single Subjects

PubMed Central

Rio, Daniel E.; Rawlings, Robert R.; Woltz, Lawrence A.; Gilman, Jodi; Hommer, Daniel W.

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function. PMID:23840281

Development of the complex general linear model in the Fourier domain: application to fMRI multiple input-output evoked responses for single subjects.

PubMed

Rio, Daniel E; Rawlings, Robert R; Woltz, Lawrence A; Gilman, Jodi; Hommer, Daniel W

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function.

Compliance to follow up and adherence to medication in hypertensive patients in an urban informal settlement in Kenya: comparison of three models of care.

PubMed

Kuria, Ng'endo; Reid, Anthony; Owiti, Philip; Tweya, Hannock; Kibet, Caleb Kipkurui; Mbau, Lilian; Manzi, Marcel; Murunga, Victor; Namusonge, Tecla; Kibachio, Joseph

2018-05-19

To determine and compare, among three models of care, compliance to scheduled clinic appointments and adherence to anti-hypertensive medication of patients in an informal settlement of Kibera, Kenya. Routinely collected patient data were used from three health facilities, six walkway clinics and one weekend/church clinic. Patients were eligible if they had received hypertension care for more than six months. Compliance with clinic appointments and self-reported adherence to medication were determined from clinic records and compared using the Chi-square test. Univariate and multivariate logistic regression models estimated the odds of overall adherence to medication. 785 patients received hypertension treatment eligible for analysis, of whom two-thirds were women. Between them, there were 5879 clinic visits with an overall compliance to appointments of 63%. Compliance was high in the health facilities and walkway clinics but men were more likely to attend the weekend/church clinics. Self-reported adherence to medication by those complying with scheduled clinic visits was 94%. Patients in the walkway clinics were two times more likely to adhere to anti-hypertensive medication than patients at the health facility (OR 1.97, 95%CI 1.25-3.10). Walkway clinics outperformed health facilities and weekend clinics. Use of multiple sites for the management of hypertensive patients led to good compliance with scheduled clinic visits and very good self-reported adherence to medication in a low-resource setting. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Risk factors for displaced abomasum or ketosis in Swedish dairy herds.

PubMed

Stengärde, L; Hultgren, J; Tråvén, M; Holtenius, K; Emanuelson, U

2012-03-01

Risk factors associated with high or low long-term incidence of displaced abomasum (DA) or clinical ketosis were studied in 60 Swedish dairy herds, using multivariable logistic regression modelling. Forty high-incidence herds were included as cases and 20 low-incidence herds as controls. Incidence rates were calculated based on veterinary records of clinical diagnoses. During the 3-year period preceding the herd classification, herds with a high incidence had a disease incidence of DA or clinical ketosis above the 3rd quartile in a national database for disease recordings. Control herds had no cows with DA or clinical ketosis. All herds were visited during the housing period and herdsmen were interviewed about management routines, housing, feeding, milk yield, and herd health. Target groups were heifers in late gestation, dry cows, and cows in early lactation. Univariable logistic regression was used to screen for factors associated with being a high-incidence herd. A multivariable logistic regression model was built using stepwise regression. A higher maximum daily milk yield in multiparous cows and a large herd size (p=0.054 and p=0.066, respectively) tended to be associated with being a high-incidence herd. Not cleaning the heifer feeding platform daily increased the odds of having a high-incidence herd twelvefold (p<0.01). Keeping cows in only one group in the dry period increased the odds of having a high incidence herd eightfold (p=0.03). Herd size was confounded with housing system. Housing system was therefore added to the final logistic regression model. In conclusion, a large herd size, a high maximum daily milk yield, keeping dry cows in one group, and not cleaning the feeding platform daily appear to be important risk factors for a high incidence of DA or clinical ketosis in Swedish dairy herds. These results confirm the importance of housing, management and feeding in the prevention of metabolic disorders in dairy cows around parturition and in early lactation. Copyright © 2011 Elsevier B.V. All rights reserved.

Predicting the clinical manifestations in necrotizing acute pancreatitis patients with splanchnic vein thrombosis.

PubMed

Zhou, Jing; Ke, Lu; Yang, Dongliang; Chen, Yizhe; Li, Gang; Tong, Zhihui; Li, Weiqin; Li, Jieshou

Splanchnic venous thrombosis (SVT) is a relatively rare but important complication of necrotizing acute pancreatitis (NAP). Clinical manifestations and severity of this complication in different patients vary greatly, ranging from mild abdominal discomfort even asymptomatic to lethal gastrorrhagia or hepatic failure. The aim of the present study was to develop a model to predict the clinical manifestations of SVT in NAP patients. This retrospective cohort study was conducted in the surgical intensive care unit (SICU) of Jinling Hospital. Patients with the presence of both pancreatic necrosis and SVT were selected for possible inclusion. Both univariate and multivariate logistic regression analyses were applied using 12 indices including age, gender, Acute Physiology and Chronic Health Evaluation II scores (APACHE II), CRP(C - reactive protein) levels, etc to assess potential predictors for symptomatic pancreatic splanchnic venous thrombosis (PSVT) in this cohort. A prognostic nomogram was also applied to develop an easy-to-use prediction model. A total of 104 patients with necrotizing acute pancreatitis (NAP) and splanchnic vein thrombosis (SVT) from January 2012 to December 2013 were enrolled for analysis. A quarter of study subjects (26 of 104, 25%) developed variable symptomatic manifestations including variceal bleeding, persistent ascites and enteral nutrition (EN) intolerance during the disease course. In the multivariable regression model, the following factors were found to be associated with the occurrence of symptomatic SVT: Balthazar's computed tomography (CT) score (OR = 1.818; 95% CI: 1.251-2.641; P = 0.002), intra-abdominal pressure (IAP) (OR = 1.172; 95% CI: 1.001-1.251; P = 0.043 and presence of SMVT (OR = 6.946; 95% CI: 2.290-21.074; P = 0.001). A prediction model incorporating these factors demonstrated an area under the receiver operating characteristic curve of 0.842. Balthazar's CT score, IAP and SMVT are predictors of symptomatic SVT in NAP patients. The nomogram we conducted can be used as an easy-to-use risk stratification tool in either clinical practice or future studies. Copyright © 2016 IAP and EPC. Published by Elsevier B.V. All rights reserved.

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Predictive 5-Year Survivorship Model of Cystic Fibrosis

PubMed Central

Liou, Theodore G.; Adler, Frederick R.; FitzSimmons, Stacey C.; Cahill, Barbara C.; Hibbs, Jonathan R.; Marshall, Bruce C.

2007-01-01

The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. The authors used information from the Cystic Fibrosis Foundation Patient Registry (CFFPR), which has collected longitudinal data on approximately 90% of cystic fibrosis patients diagnosed in the United States since 1986. They developed multivariate logistic regression models by using data on 5,820 patients randomly selected from 11,630 in the CFFPR in 1993. Models were tested for goodness of fit and were validated for the remaining 5,810 patients for 1993. The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. The model provides insights into the complex nature of cystic fibrosis and supplies a rigorous tool for clinical practice and research. PMID:11207152

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.

Clinical correlates of verbal aggression, physical aggression and inappropriate sexual behaviour after brain injury.

PubMed

James, Andrew I W; Young, Andrew W

2013-01-01

To explore the relationships between verbal aggression, physical aggression and inappropriate sexual behaviour following acquired brain injury. Multivariate statistical modelling of observed verbal aggression, physical aggression and inappropriate sexual behaviour utilizing demographic, pre-morbid, injury-related and neurocognitive predictors. Clinical records of 152 participants with acquired brain injury were reviewed, providing an important data set as disordered behaviours had been recorded at the time of occurrence with the Brain Injury Rehabilitation Trust (BIRT) Aggression Rating Scale and complementary measures of inappropriate sexual behaviour. Three behavioural components (verbal aggression, physical aggression and inappropriate sexual behaviour) were identified and subjected to separate logistical regression modelling in a sub-set of 77 participants. Successful modelling was achieved for both verbal and physical aggression (correctly classifying 74% and 65% of participants, respectively), with use of psychotropic medication and poorer verbal function increasing the odds of aggression occurring. Pre-morbid history of aggression predicted verbal but not physical aggression. No variables predicted inappropriate sexual behaviour. Verbal aggression, physical aggression and inappropriate sexual behaviour following acquired brain injury appear to reflect separate clinical phenomena rather than general behavioural dysregulation. Clinical markers that indicate an increased risk of post-injury aggression were not related to inappropriate sexual behaviour.

A Competing Risk Model of First Failure Site after Definitive Chemoradiation Therapy for Locally Advanced Non-Small Cell Lung Cancer.

PubMed

Nygård, Lotte; Vogelius, Ivan R; Fischer, Barbara M; Kjær, Andreas; Langer, Seppo W; Aznar, Marianne C; Persson, Gitte F; Bentzen, Søren M

2018-04-01

The aim of the study was to build a model of first failure site- and lesion-specific failure probability after definitive chemoradiotherapy for inoperable NSCLC. We retrospectively analyzed 251 patients receiving definitive chemoradiotherapy for NSCLC at a single institution between 2009 and 2015. All patients were scanned by fludeoxyglucose positron emission tomography/computed tomography for radiotherapy planning. Clinical patient data and fludeoxyglucose positron emission tomography standardized uptake values from primary tumor and nodal lesions were analyzed by using multivariate cause-specific Cox regression. In patients experiencing locoregional failure, multivariable logistic regression was applied to assess risk of each lesion being the first site of failure. The two models were used in combination to predict probability of lesion failure accounting for competing events. Adenocarcinoma had a lower hazard ratio (HR) of locoregional failure than squamous cell carcinoma (HR = 0.45, 95% confidence interval [CI]: 0.26-0.76, p = 0.003). Distant failures were more common in the adenocarcinoma group (HR = 2.21, 95% CI: 1.41-3.48, p < 0.001). Multivariable logistic regression of individual lesions at the time of first failure showed that primary tumors were more likely to fail than lymph nodes (OR = 12.8, 95% CI: 5.10-32.17, p < 0.001). Increasing peak standardized uptake value was significantly associated with lesion failure (OR = 1.26 per unit increase, 95% CI: 1.12-1.40, p < 0.001). The electronic model is available at http://bit.ly/LungModelFDG. We developed a failure site-specific competing risk model based on patient- and lesion-level characteristics. Failure patterns differed between adenocarcinoma and squamous cell carcinoma, illustrating the limitation of aggregating them into NSCLC. Failure site-specific models add complementary information to conventional prognostic models. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose volume outcome relationships

NASA Astrophysics Data System (ADS)

El Naqa, I.; Suneja, G.; Lindsay, P. E.; Hope, A. J.; Alaly, J. R.; Vicic, M.; Bradley, J. D.; Apte, A.; Deasy, J. O.

2006-11-01

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

Factors for short-term outcomes in patients with a minor stroke: results from China National Stroke Registry.

PubMed

Wu, Lingyun; Wang, Anxin; Wang, Xianwei; Zhao, Xingquan; Wang, Chunxue; Liu, Liping; Zheng, Huaguang; Wang, Yongjun; Cao, Yibin; Wang, Yilong

2015-12-09

Stroke recurrence and disability in patients with a minor stroke is one of the most depressing medical situations. In this study, we aimed to identify which factors were associated with adverse outcomes of a minor stroke. The China National Stroke Registry (CNSR) is a nationwide prospective registry for patients presented to hospitals with acute cerebrovascular events between September 2007 and August 2008. The 3-month follow-up was completed in 4669 patients with a minor stroke defined as the initial neurological severity lower than 4 in the National Institutes of Health Stroke Scale (NIHSS). Multivariate model was used to determine the association between risk factors and clinical outcomes. Of 4669 patients with a minor stroke during 3-month follow-up, 459 (9.8 %) patients experienced recurrent stroke, 679 (14.5 %) had stroke disability and 168 (3.6 %) died. Multivariate model identified hypertension, diabetes mellitus, atrial fibrillation, coronary heart disease and previous stroke as independent predictors for the recurrent stroke. Age, diabetes mellitus, atrial fibrillation, previous stroke and time from onset to admission < 24 h were independent predictors for stroke disability. The independent predictors for the all-caused death were age, atrial fibrillation, and coronary heart disease. The short-term risk of poor clinical outcome in Chinese patients with a minor stroke was substantial. Therefore, patients with a minor stroke should be given expeditious assessment and urgent aggressive intervention.

Clinical and demographic covariates of chronic opioid and non-opioid analgesic use in rural-dwelling older adults: the MoVIES project.

PubMed

Karp, Jordan F; Lee, Ching-Wen; McGovern, Jonathan; Stoehr, Gary; Chang, Chung-Chou H; Ganguli, Mary

2013-11-01

To describe covariates and patterns of late-life analgesic use in the rural, population-based MoVIES cohort from 1989 to 2002. Secondary analysis of epidemiologic survey of elderly people conducted over six biennial assessment waves. Potential covariates of analgesic use included age, gender, depression, sleep, arthritis, smoking, alcohol, and general health status. Of the original cohort of 1,681, this sample comprised 1,109 individuals with complete data on all assessments. Using trajectory analysis, participants were characterized as chronic or non-chronic users of opioid and non-opioid analgesics. Multivariable regression was used to model predictors of chronic analgesic use. The cohort was followed for mean (SD) 7.3 (2.7) years. Chronic use of opioid analgesics was reported by 7.2%, while non-opioid use was reported by 46.1%. In the multivariable model, predictors of chronic use of both opioid and non-opioid analgesics included female sex, taking ≥2 prescription medications, and "arthritis" diagnoses. Chronic opioid use was also associated with age 75-84 years; chronic non-opioid use was also associated with sleep continuity disturbance. These epidemiological data confirm clinical observations and generate hypotheses for further testing. Future studies should investigate whether addressing sleep problems might lead to decreased use of non-opioid analgesics and possibly enhanced pain management.

Linear Combinations of Multiple Outcome Measures to Improve the Power of Efficacy Analysis ---Application to Clinical Trials on Early Stage Alzheimer Disease

PubMed Central

Xiong, Chengjie; Luo, Jingqin; Morris, John C; Bateman, Randall

2018-01-01

Modern clinical trials on Alzheimer disease (AD) focus on the early symptomatic stage or even the preclinical stage. Subtle disease progression at the early stages, however, poses a major challenge in designing such clinical trials. We propose a multivariate mixed model on repeated measures to model the disease progression over time on multiple efficacy outcomes, and derive the optimum weights to combine multiple outcome measures by minimizing the sample sizes to adequately power the clinical trials. A cross-validation simulation study is conducted to assess the accuracy for the estimated weights as well as the improvement in reducing the sample sizes for such trials. The proposed methodology is applied to the multiple cognitive tests from the ongoing observational study of the Dominantly Inherited Alzheimer Network (DIAN) to power future clinical trials in the DIAN with a cognitive endpoint. Our results show that the optimum weights to combine multiple outcome measures can be accurately estimated, and that compared to the individual outcomes, the combined efficacy outcome with these weights significantly reduces the sample size required to adequately power clinical trials. When applied to the clinical trial in the DIAN, the estimated linear combination of six cognitive tests can adequately power the clinical trial. PMID:29546251

Noninvasive assessment of mitral inertness [correction of inertance]: clinical results with numerical model validation.

PubMed

Firstenberg, M S; Greenberg, N L; Smedira, N G; McCarthy, P M; Garcia, M J; Thomas, J D

2001-01-01

Inertial forces (Mdv/dt) are a significant component of transmitral flow, but cannot be measured with Doppler echo. We validated a method of estimating Mdv/dt. Ten patients had a dual sensor transmitral (TM) catheter placed during cardiac surgery. Doppler and 2D echo was performed while acquiring LA and LV pressures. Mdv/dt was determined from the Bernoulli equation using Doppler velocities and TM gradients. Results were compared with numerical modeling. TM gradients (range: 1.04-14.24 mmHg) consisted of 74.0 +/- 11.0% inertial forcers (range: 0.6-12.9 mmHg). Multivariate analysis predicted Mdv/dt = -4.171(S/D (RATIO)) + 0.063(LAvolume-max) + 5. Using this equation, a strong relationship was obtained for the clinical dataset (y=0.98x - 0.045, r=0.90) and the results of numerical modeling (y=0.96x - 0.16, r=0.84). TM gradients are mainly inertial and, as validated by modeling, can be estimated with echocardiography.

Noninvasive assessment of mitral inertness: clinical results with numerical model validation

NASA Technical Reports Server (NTRS)

Firstenberg, M. S.; Greenberg, N. L.; Smedira, N. G.; McCarthy, P. M.; Garcia, M. J.; Thomas, J. D.

2001-01-01

Inertial forces (Mdv/dt) are a significant component of transmitral flow, but cannot be measured with Doppler echo. We validated a method of estimating Mdv/dt. Ten patients had a dual sensor transmitral (TM) catheter placed during cardiac surgery. Doppler and 2D echo was performed while acquiring LA and LV pressures. Mdv/dt was determined from the Bernoulli equation using Doppler velocities and TM gradients. Results were compared with numerical modeling. TM gradients (range: 1.04-14.24 mmHg) consisted of 74.0 +/- 11.0% inertial forcers (range: 0.6-12.9 mmHg). Multivariate analysis predicted Mdv/dt = -4.171(S/D (RATIO)) + 0.063(LAvolume-max) + 5. Using this equation, a strong relationship was obtained for the clinical dataset (y=0.98x - 0.045, r=0.90) and the results of numerical modeling (y=0.96x - 0.16, r=0.84). TM gradients are mainly inertial and, as validated by modeling, can be estimated with echocardiography.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Transition from a multiport technique to a single-port technique for lung cancer surgery: is lymph node dissection inferior using the single-port technique?†.

PubMed

Liu, Chia-Chuan; Shih, Chih-Shiun; Pennarun, Nicolas; Cheng, Chih-Tao

2016-01-01

The feasibility and radicalism of lymph node dissection for lung cancer surgery by a single-port technique has frequently been challenged. We performed a retrospective cohort study to investigate this issue. Two chest surgeons initiated multiple-port thoracoscopic surgery in a 180-bed cancer centre in 2005 and shifted to a single-port technique gradually after 2010. Data, including demographic and clinical information, from 389 patients receiving multiport thoracoscopic lobectomy or segmentectomy and 149 consecutive patients undergoing either single-port lobectomy or segmentectomy for primary non-small-cell lung cancer were retrieved and entered for statistical analysis by multivariable linear regression models and Box-Cox transformed multivariable analysis. The mean number of total dissected lymph nodes in the lobectomy group was 28.5 ± 11.7 for the single-port group versus 25.2 ± 11.3 for the multiport group; the mean number of total dissected lymph nodes in the segmentectomy group was 19.5 ± 10.8 for the single-port group versus 17.9 ± 10.3 for the multiport group. In linear multivariable and after Box-Cox transformed multivariable analyses, the single-port approach was still associated with a higher total number of dissected lymph nodes. The total number of dissected lymph nodes for primary lung cancer surgery by single-port video-assisted thoracoscopic surgery (VATS) was higher than by multiport VATS in univariable, multivariable linear regression and Box-Cox transformed multivariable analyses. This study confirmed that highly effective lymph node dissection could be achieved through single-port VATS in our setting. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Fast-track services for all? The preferences of Chinese-, Korean-, and Thai-speaking women attending a sexual health service.

PubMed

Martin, Lynne; Knight, Vickie; Read, Phillip J; McNulty, Anna

2013-12-01

Sydney Sexual Health Centre (SSHC) Xpress clinic has significantly reduced the length of stay and waiting time for clients at SSHC but is currently only available to clients who can read and understand a high level of English. This reduces access for culturally and linguistically diverse (CALD) clients. This study sought to determine the acceptability of 4 proposed components of an express clinic model among CALD clients: computer-assisted self-interview (CASI), self-collection of swabs/urine specimens, not having a physical examination, and consultation with a health promotion officer rather than with a clinician. Differences in acceptability based on language group, new or return client status, sex worker status, clinic visited status, and age were analyzed. A cross-sectional, anonymous questionnaire was offered to all female Chinese, Thai, and Korean clients attending SSHC between March and November 2012. Multivariate regression and Pearson χ statistical analyses were conducted using STATA 12 software. A total of 366 questionnaires were returned from 149 Thai, 145 Chinese, and 72 Korean participants. After multivariate analysis, the only predictor of willingness to use an express model of service provision was language group: overall, 67% Thai (odds ratio, 3.74: confidence interval [CI], 2.03-6.89; P < 0.01) and 64% Korean (odds ratio, 3.58; CI, 1.77-7.25, P < 0.01) said that they would use it compared with 35% Chinese. Age, history of sex work, new or returning clients, and general or language clinic attendance did not impact on choices. Within the preference for individual components of the model, more Thai women were happy with using a health promotion officer (43.2%) than Chinese (14.1%) or Korean (8.5%) (P < 0.001); no groups were happy with forfeiting a physical examination; Thai (48.6%) and Korean (40.9%) were happier with self-swabbing than Chinese women (23.9%, P < 0.001); and more Thai were happy to use a CASI (44.2%) than Chinese (12%) or Korean (11.1%; P < 0.001). This research shows that the components of an express model used at SSHC are not favorable to our CALD client base. Despite a CALD express clinic having the potential to reduce waiting times, most clients did not favor reduced waiting time over being physically examined or using a CASI.

Reporting and Methodology of Multivariable Analyses in Prognostic Observational Studies Published in 4 Anesthesiology Journals: A Methodological Descriptive Review.

PubMed

Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric

2015-10-01

Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate <36% (i.e., the 25th percentile), with some of them not identified in the STROBE checklist: blinded evaluation of the outcome (11.9%), reason for sample size (15.1%), handling of missing data (36.0%), assessment of colinearity (17.4%), assessment of interactions (13.9%), and calibration (34.9%). When reported, a few methodological shortcomings were observed, both in explanatory and predictive studies, such as an insufficient number of events of the outcome (44.6%), exclusion of cases with missing data (93.6%), or categorization of continuous variables (65.1%.). The reporting of multivariable analysis was fairly good and could be further improved by checking reporting guidelines and EQUATOR Network website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.

Signal intensity of the pancreas on magnetic resonance imaging: Prediction of postoperative pancreatic fistula after a distal pancreatectomy using a triple-row stapler.

PubMed

Arai, Takuma; Kobayashi, Akira; Yokoyama, Takahide; Ohya, Ayumi; Fujinaga, Yasunari; Shimizu, Akira; Motoyama, Hiroaki; Furusawa, Norihiko; Sakai, Hiroshi; Uehara, Takeshi; Kadoya, Masumi; Miyagawa, Shin-Ichi

2015-01-01

The aim of this study was to evaluate the impact of the pancreatic signal intensity (SI) on magnetic resonance imaging (MRI) findings for predicting the development of pancreatic fistula (PF) after a distal pancreatectomy (DP) involving a triple-row stapler closure. A multivariate logistic regression analysis was used to identify risk factors for clinical PF, as defined by the International Study Group on Pancreatic Fistula grade B or C. The pancreas-to-muscle SI ratio was evaluated using fat-suppressed T1-weighted MRI. Of the 41 enrolled patients, 8 (19.5%) developed clinical PF. The pancreatic thickness (≥15 mm) and SI ratio (≥1.3) were identified as independent predictors of clinical PF in a multivariate analysis. Clinical PF was observed in one patient with a thick pancreas and a low SI ratio (14.3%), whereas it was observed in 60% of the patients with a thick pancreas and a high SI ratio. The area under the receiver operating characteristic curve for a predictive model consisting of the two factors was 0.87 (95% confidence interval, 0.75 to 0.99), the level of which tended to be greater than that for pancreatic thickness alone (0.81, p = 0.09). The SI ratio as evaluated using MRI might be useful for predicting clinical PF in patients with the pancreatic thickness ≥15 mm after DP involving a stapler closure. Copyright © 2015 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Automated pre-processing and multivariate vibrational spectra analysis software for rapid results in clinical settings

NASA Astrophysics Data System (ADS)

Bhattacharjee, T.; Kumar, P.; Fillipe, L.

2018-02-01

Vibrational spectroscopy, especially FTIR and Raman, has shown enormous potential in disease diagnosis, especially in cancers. Their potential for detecting varied pathological conditions are regularly reported. However, to prove their applicability in clinics, large multi-center multi-national studies need to be undertaken; and these will result in enormous amount of data. A parallel effort to develop analytical methods, including user-friendly software that can quickly pre-process data and subject them to required multivariate analysis is warranted in order to obtain results in real time. This study reports a MATLAB based script that can automatically import data, preprocess spectra— interpolation, derivatives, normalization, and then carry out Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) of the first 10 PCs; all with a single click. The software has been verified on data obtained from cell lines, animal models, and in vivo patient datasets, and gives results comparable to Minitab 16 software. The software can be used to import variety of file extensions, asc, .txt., .xls, and many others. Options to ignore noisy data, plot all possible graphs with PCA factors 1 to 5, and save loading factors, confusion matrices and other parameters are also present. The software can provide results for a dataset of 300 spectra within 0.01 s. We believe that the software will be vital not only in clinical trials using vibrational spectroscopic data, but also to obtain rapid results when these tools get translated into clinics.

Shared clinical decision making

PubMed Central

AlHaqwi, Ali I.; AlDrees, Turki M.; AlRumayyan, Ahmad; AlFarhan, Ali I.; Alotaibi, Sultan S.; AlKhashan, Hesham I.; Badri, Motasim

2015-01-01

Objectives: To determine preferences of patients regarding their involvement in the clinical decision making process and the related factors in Saudi Arabia. Methods: This cross-sectional study was conducted in a major family practice center in King Abdulaziz Medical City, Riyadh, Saudi Arabia, between March and May 2012. Multivariate multinomial regression models were fitted to identify factors associated with patients preferences. Results: The study included 236 participants. The most preferred decision-making style was shared decision-making (57%), followed by paternalistic (28%), and informed consumerism (14%). The preference for shared clinical decision making was significantly higher among male patients and those with higher level of education, whereas paternalism was significantly higher among older patients and those with chronic health conditions, and consumerism was significantly higher in younger age groups. In multivariate multinomial regression analysis, compared with the shared group, the consumerism group were more likely to be female [adjusted odds ratio (AOR) =2.87, 95% confidence interval [CI] 1.31-6.27, p=0.008] and non-dyslipidemic (AOR=2.90, 95% CI: 1.03-8.09, p=0.04), and the paternalism group were more likely to be older (AOR=1.03, 95% CI: 1.01-1.05, p=0.04), and female (AOR=2.47, 95% CI: 1.32-4.06, p=0.008). Conclusion: Preferences of patients for involvement in the clinical decision-making varied considerably. In our setting, underlying factors that influence these preferences identified in this study should be considered and tailored individually to achieve optimal treatment outcomes. PMID:26620990

Dose-surface analysis for prediction of severe acute radio-induced skin toxicity in breast cancer patients.

PubMed

Pastore, Francesco; Conson, Manuel; D'Avino, Vittoria; Palma, Giuseppe; Liuzzi, Raffaele; Solla, Raffaele; Farella, Antonio; Salvatore, Marco; Cella, Laura; Pacelli, Roberto

2016-01-01

Severe acute radiation-induced skin toxicity (RIST) after breast irradiation is a side effect impacting the quality of life in breast cancer (BC) patients. The aim of the present study was to develop normal tissue complication probability (NTCP) models of severe acute RIST in BC patients. We evaluated 140 consecutive BC patients undergoing conventional three-dimensional conformal radiotherapy (3D-CRT) after breast conserving surgery in a prospective study assessing acute RIST. The acute RIST was classified according to the RTOG scoring system. Dose-surface histograms (DSHs) of the body structure in the breast region were extracted as representative of skin irradiation. Patient, disease, and treatment-related characteristics were analyzed along with DSHs. NTCP modeling by Lyman-Kutcher-Burman (LKB) and by multivariate logistic regression using bootstrap resampling techniques was performed. Models were evaluated by Spearman's Rs coefficient and ROC area. By the end of radiotherapy, 139 (99%) patients developed any degree of acute RIST. G3 RIST was found in 11 of 140 (8%) patients. Mild-moderate (G1-G2) RIST was still present at 40 days after treatment in six (4%) patients. Using DSHs for LKB modeling of acute RIST severity (RTOG G3 vs. G0-2), parameter estimates were TD50=39 Gy, n=0.38 and m=0.14 [Rs = 0.25, area under the curve (AUC) = 0.77, p = 0.003]. On multivariate analysis, the most predictive model of acute RIST severity was a two-variable model including the skin receiving ≥30 Gy (S30) and psoriasis [Rs = 0.32, AUC = 0.84, p < 0.001]. Using body DSH as representative of skin dose, the LKB n parameter was consistent with a surface effect for the skin. A good prediction performance was obtained using a data-driven multivariate model including S30 and a pre-existing skin disease (psoriasis) as a clinical factor.

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Factors associated with Chlamydia trachomatis testing in a high school based screening and previously in clinical practice: a cross-sectional study in Norway

PubMed Central

2013-01-01

Background High school based chlamydia screening has been shown to increase uptake and detect hidden infections among sexually active adolescents. Our study aimed to: i) examine the proportions of 15–20 year-olds tested in a high school based screening and previously in clinical practice, ii) determine chlamydia prevalence according to testing pattern, and iii) examine factors associated with testing in the two settings. Methods A population based cross-sectional study was conducted in 5 high schools in Norway in 2009, using web-questionnaires and Chlamydia trachomatis PCR in first-void urine (800 girls/818 boys, mean age 17.2 years). Only sexually active participants at risk for chlamydia infections were included in the analyses. Crude and multivariable logistic regression models were applied with ‘clinic based testing’ and ‘school based screening’ as outcome variables. Results 56% of girls and 21% of boys reported previous clinic based testing. In the school based screening, 93% were tested with no gender difference. 42% of girls and 74% of boys were tested for the first time at school (‘school-only test’). Both girls with clinic based testing and girls with school-only test had high chlamydia prevalence (7.3% vs 7.2%). Boys with clinic based testing had twice the prevalence of those with school-only test (6.2% vs 3.0%, p = 0.01). Half of infections were detected in participants with school-only test. One-fifth were repeat infections. In multivariable analysis of girls and boys combined, female gender, older age, early sexual debut, no condom use at first and last intercourse, steady relationship, and higher number of lifetime partners increased the odds of clinic based testing. The odds of school based screening increased with male gender, academic affiliation, later sexual debut, condom use at first intercourse, and current urogenital symptoms in multivariable analysis. Conclusions More than half the girls had been tested prior to the school based screening and had high prevalence independent of previous clinic based testing. School screening was mostly associated with factors unknown to increase chlamydia infection risk, while clinic based testing was associated with traditional risk factors. The unusually high and equal participation between genders and the detection of a large chlamydia reservoir confirms the value of school based screening suggesting this approach to be further explored in Norway. PMID:23915415

Reliability Stress-Strength Models for Dependent Observations with Applications in Clinical Trials

NASA Technical Reports Server (NTRS)

Kushary, Debashis; Kulkarni, Pandurang M.

1995-01-01

We consider the applications of stress-strength models in studies involving clinical trials. When studying the effects and side effects of certain procedures (treatments), it is often the case that observations are correlated due to subject effect, repeated measurements and observing many characteristics simultaneously. We develop maximum likelihood estimator (MLE) and uniform minimum variance unbiased estimator (UMVUE) of the reliability which in clinical trial studies could be considered as the chances of increased side effects due to a particular procedure compared to another. The results developed apply to both univariate and multivariate situations. Also, for the univariate situations we develop simple to use lower confidence bounds for the reliability. Further, we consider the cases when both stress and strength constitute time dependent processes. We define the future reliability and obtain methods of constructing lower confidence bounds for this reliability. Finally, we conduct simulation studies to evaluate all the procedures developed and also to compare the MLE and the UMVUE.

[The role of supply-side characteristics of services in AIDS mortality in Mexico].

PubMed

Bautista-Arredondo, Sergio; Serván-Mori, Edson; Silverman-Retana, Omar; Contreras-Loya, David; Romero-Martínez, Martín; Magis-Rodríguez, Carlos; Uribe-Zúñiga, Patricia; Lozano, Rafael

2015-01-01

To document the association between supply-side determinants and AIDS mortality in Mexico between 2008 and 2013. We analyzed the SALVAR database (system for antiretroviral management, logistics and surveillance) as well as data collected through a nationally representative survey in health facilities. We used multivariate logit regression models to estimate the association between supply-side characteristics, namely management, training and experience of health care providers, and AIDS mortality, distinguishing early and non-early mortality and controlling for clinical indicators of the patients. Clinic status of the patients (initial CD4 and viral load) explain 44.4% of the variability of early mortality across clinics and 13.8% of the variability in non-early mortality. Supply-side characteristics increase explanatory power of the models by 16% in the case of early mortality, and 96% in the case of non-early mortality. Aspects of management and implementation of services contribute significantly to explain AIDS mortality in Mexico. Improving these aspects of the national program, can similarly improve its results.

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

NASA Astrophysics Data System (ADS)

Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

2009-09-01

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

PubMed

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Complementary nonparametric analysis of covariance for logistic regression in a randomized clinical trial setting.

PubMed

Tangen, C M; Koch, G G

1999-03-01

In the randomized clinical trial setting, controlling for covariates is expected to produce variance reduction for the treatment parameter estimate and to adjust for random imbalances of covariates between the treatment groups. However, for the logistic regression model, variance reduction is not obviously obtained. This can lead to concerns about the assumptions of the logistic model. We introduce a complementary nonparametric method for covariate adjustment. It provides results that are usually compatible with expectations for analysis of covariance. The only assumptions required are based on randomization and sampling arguments. The resulting treatment parameter is a (unconditional) population average log-odds ratio that has been adjusted for random imbalance of covariates. Data from a randomized clinical trial are used to compare results from the traditional maximum likelihood logistic method with those from the nonparametric logistic method. We examine treatment parameter estimates, corresponding standard errors, and significance levels in models with and without covariate adjustment. In addition, we discuss differences between unconditional population average treatment parameters and conditional subpopulation average treatment parameters. Additional features of the nonparametric method, including stratified (multicenter) and multivariate (multivisit) analyses, are illustrated. Extensions of this methodology to the proportional odds model are also made.

Severity of iron overload of proband determines serum ferritin levels in families with HFE-related hemochromatosis: the HEmochromatosis FAmily Study.

PubMed

Jacobs, Esther M G; Hendriks, Jan C M; van Deursen, Cees Th B M; Kreeftenberg, Herman G; de Vries, Richard A; Marx, Joannes J M; Stalenhoef, Anton F H; Verbeek, André L M; Swinkels, Dorine W

2009-01-01

In families of patients with clinically detected hereditary hemochromatosis (HH) early screening has been suggested to prevent morbidity and mortality. Here, we aim to identify determinants for iron overload in first-degree family members of C282Y homozygous probands with clinically detected HH. Data on HFE-genotype, iron parameters, demographics, lifestyle factors and health, were collected from 224 Dutch C282Y homozygous patients with clinically diagnosed HH and 735 of their first-degree family members (FDFM), all participating in the HEmochromatosis FAmily Study (HEFAS). The best predictive multivariable model forecasted 45% of variation of the serum ferritin levels. In this model severity of iron overload in the proband significantly predicted serum ferritin levels in FDFM. Other significant determinants in this model consisted of C282Y homozygosity, compound heterozygosity, age at testing for serum ferritin and supplemental iron intake, whereas a low body mass index showed a protective effect. This study provides a model to assess the risk of development of iron overload for relatives of probands with HH. These results might be instrumental in the development of an optimal strategy for future family screening programs.

Incidence and correlates of major bleeding after percutaneous coronary intervention across different clinical presentations.

PubMed

Loh, Joshua P; Pendyala, Lakshmana K; Torguson, Rebecca; Chen, Fang; Satler, Lowell F; Pichard, Augusto A; Waksman, Ron

2014-09-01

Bleeding after percutaneous coronary intervention (PCI) is identified as a strong predictor for adverse events, including mortality. This study aims to compare the incidence and correlates of post-PCI bleeding across different clinical presentations. The study included 23,943 consecutive PCI patients categorized according to their clinical presentation: stable angina pectoris (n = 6,741), unstable angina pectoris (UAP) (n = 5,215), non-ST-segment elevation myocardial infarction (NSTEMI) (n = 8,418), ST-segment elevation myocardial infarction (STEMI) (n = 2,721), and cardiogenic shock (CGS) (n = 848). Severity of clinical presentation was associated with a greater use of preprocedural anticoagulation, glycoprotein IIb/IIIa inhibitors, and intraaortic balloon pump (IABP). TIMI-defined major bleeding increased with increasing severity of clinical presentation: stable angina pectoris, 0.7%; UAP, 1.0%; NSTEMI, 1.6%; STEMI, 4.6%; and CGS, 13.5% (P < .001). On multivariable analysis, CGS (odds ratio [OR], 4.67; 95% CI [2.62-8.34]), STEMI (OR, 3.39; 95% CI [2.07-5.55]), and NSTEMI (OR, 2.00; 95% CI [1.29-3.10]) remained correlated with major bleeding even after adjusting for baseline and procedural differences, whereas UAP did not. The multivariable model also identified the use of IABP, female gender, congestive heart failure, no prior PCI, increased baseline hematocrit, and increased procedure time as correlates for major bleeding. In patients undergoing PCI, the worsening severity of clinical presentation corresponds to an increase in incidence of post-PCI major bleeding. The increased risk with CGS, STEMI, and NSTEMI persisted despite adjusting for more aggressive pharmacotherapy and use of IABP. Careful attention to antithrombotic pharmacotherapy is warranted in this high-risk population. Copyright © 2014 Mosby, Inc. All rights reserved.

Validation and Development of a Modified Breast Graded Prognostic Assessment As a Tool for Survival in Patients With Breast Cancer and Brain Metastases.

PubMed

Subbiah, Ishwaria M; Lei, Xiudong; Weinberg, Jeffrey S; Sulman, Erik P; Chavez-MacGregor, Mariana; Tripathy, Debu; Gupta, Rohan; Varma, Ankur; Chouhan, Jay; Guevarra, Richard P; Valero, Vicente; Gilbert, Mark R; Gonzalez-Angulo, Ana M

2015-07-10

Several indices have been developed to predict overall survival (OS) in patients with breast cancer with brain metastases, including the breast graded prognostic assessment (breast-GPA), comprising age, tumor subtype, and Karnofsky performance score. However, number of brain metastases-a highly relevant clinical variable-is less often incorporated into the final model. We sought to validate the existing breast-GPA in an independent larger cohort and refine it integrating number of brain metastases. Data were retrospectively gathered from a prospectively maintained institutional database. Patients with newly diagnosed brain metastases from 1996 to 2013 were identified. After validating the breast-GPA, multivariable Cox regression and recursive partitioning analysis led to the development of the modified breast-GPA. The performances of the breast-GPA and modified breast-GPA were compared using the concordance index. In our cohort of 1,552 patients, the breast-GPA was validated as a prognostic tool for OS (P < .001). In multivariable analysis of the breast-GPA and number of brain metastases (> three v ≤ three), both were independent predictors of OS. We therefore developed the modified breast-GPA integrating a fourth clinical parameter. Recursive partitioning analysis reinforced the prognostic significance of these four factors. Concordance indices were 0.78 (95% CI, 0.77 to 0.80) and 0.84 (95% CI, 0.83 to 0.85) for the breast-GPA and modified breast-GPA, respectively (P < .001). The modified breast-GPA incorporates four simple clinical parameters of high prognostic significance. This index has an immediate role in the clinic as a formative part of the clinician's discussion of prognosis and direction of care and as a potential patient selection tool for clinical trials. © 2015 by American Society of Clinical Oncology.

Remodeling characteristics and collagen distribution in synthetic mesh materials explanted from human subjects after abdominal wall reconstruction: an analysis of remodeling characteristics by patient risk factors and surgical site classifications

PubMed Central

Cavallo, Jaime A.; Roma, Andres A.; Jasielec, Mateusz S.; Ousley, Jenny; Creamer, Jennifer; Pichert, Matthew D.; Baalman, Sara; Frisella, Margaret M.; Matthews, Brent D.

2014-01-01

Background The purpose of this study was to evaluate the associations between patient characteristics or surgical site classifications and the histologic remodeling scores of synthetic meshes biopsied from their abdominal wall repair sites in the first attempt to generate a multivariable risk prediction model of non-constructive remodeling. Methods Biopsies of the synthetic meshes were obtained from the abdominal wall repair sites of 51 patients during a subsequent abdominal re-exploration. Biopsies were stained with hematoxylin and eosin, and evaluated according to a semi-quantitative scoring system for remodeling characteristics (cell infiltration, cell types, extracellular matrix deposition, inflammation, fibrous encapsulation, and neovascularization) and a mean composite score (CR). Biopsies were also stained with Sirius Red and Fast Green, and analyzed to determine the collagen I:III ratio. Based on univariate analyses between subject clinical characteristics or surgical site classification and the histologic remodeling scores, cohort variables were selected for multivariable regression models using a threshold p value of ≤0.200. Results The model selection process for the extracellular matrix score yielded two variables: subject age at time of mesh implantation, and mesh classification (c-statistic = 0.842). For CR score, the model selection process yielded two variables: subject age at time of mesh implantation and mesh classification (r2 = 0.464). The model selection process for the collagen III area yielded a model with two variables: subject body mass index at time of mesh explantation and pack-year history (r2 = 0.244). Conclusion Host characteristics and surgical site assessments may predict degree of remodeling for synthetic meshes used to reinforce abdominal wall repair sites. These preliminary results constitute the first steps in generating a risk prediction model that predicts the patients and clinical circumstances for which non-constructive remodeling of an abdominal wall repair site with synthetic mesh reinforcement is most likely to occur. PMID:24442681

Impact of cannabis and other drugs on age at onset of psychosis.

PubMed

González-Pinto, Ana; Vega, Patricia; Ibáñez, Berta; Mosquera, Fernando; Barbeito, Sara; Gutiérrez, Miguel; Ruiz de Azúa, Sonia; Ruiz, Iván; Vieta, Eduard

2008-08-01

The aim of this study was to investigate the relationship between age and cannabis use in patients with a first psychotic episode, and to analyze the mediating effect of comorbid use of other drugs and sex on age at onset of psychosis. All consenting patients (aged 15 to 65 years) with a first psychotic episode needing inpatient psychiatric treatment during a 2-year period between February 1997 and January 1999 were considered, confirming a total of 131 patients. Subjects were interviewed using the Structured Clinical Interview for DSM-IV Axis I Disorders, and clinical and demographic data were collected. We used general linear models with age at onset as the response variable and survival Cox models to confirm the results. Both a multivariate linear model and the corresponding Cox model were fitted with a covariate that summarizes the most significant contributors that seemed to decrease age at onset. Regarding the effect of cannabis use, a significant gradual reduction on age at onset was found as dependence on cannabis increased, consisting in a decrement of 7, 8.5, and 12 years for users, abusers, and dependents, respectively, with respect to nonusers (p = .004, p < .001, and p < .001, respectively). Multivariate analysis showed a clear effect of cannabis use on age at onset, which was not explained by the use of other drugs or by gender. The finding was similar in the youngest patients, suggesting that this effect was not due to chance. The major contribution of this investigation is the independent and strong link between cannabis use and early age at onset of psychosis, and the slight or nonexistent effect of sex and comorbid substance abuse in this variable. These results point to cannabis as a dangerous drug in young people at risk of developing psychosis.

DEPRESSION AND INCIDENT ALZHEIMER’S DISEASE: THE IMPACT OF DEPRESSION SEVERITY

PubMed Central

Gracia-García, Patricia; de-la-Cámara, Concepción; Santabárbara, Javier; Lopez-Anton, Raúl; Quintanilla, Miguel Angel; Ventura, Tirso; Marcos, Guillermo; Campayo, Antonio; Saz, Pedro; Lyketsos, Constantine; Lobo, Antonio

2014-01-01

Objective We test the hypothesis that clinically significant depression, severe depression in particular, increases the risk of Alzheimer’s Disease (AD). Design A longitudinal, three-wave epidemiological enquiry was implemented in a sample of individuals aged ≥55 years (n = 4,803) followed-up at 2.5 years and 4.5 years. Setting Population-based cohort drawn from the ZARADEMP Project, in Zaragoza, Spain. Participants Cognitively intact individuals at baseline (n = 3,864). Main outcome measures Depression was assessed by a standardized diagnostic interview (Geriatric Mental State, GMS-AGECAT). A panel of research psychiatrist diagnosed AD according to DSM-IV criteria. Fine and Gray multivariate regression model was used in the analysis, accounting for mortality. Results At baseline, clinically significant depression was diagnosed in 452 participants (11.7%). Among the depressed, 16.4% had severe depression. Seventy incident cases of AD were found at follow-up. Compared with non-depressed individuals, the incidence rate of AD was significantly higher in the depressed (incidence rate ratio, IRR = 1.91 (95%CI: 1.04–3.51) and particularly in the severely depressed (IRR = 3.59 (95%CI: 1.30–9.94). A consistent, significant association was observed between severe depression at baseline and incident AD in the multivariate model (hazard ratio, HR = 4.30 (95%CI: 1.39–13.33). Untreated depression was associated with incident AD in the unadjusted model, although in the final model this association was attenuated and non-significant. Conclusions Severe depression increases the risk of AD, even after controlling for the competing risk of death. This finding may stimulate studies about the effect of treating depression in relation to the risk of AD. PMID:23791538

Predictors of Mortality in the Critically Ill Cirrhotic Patient: Is the Model for End-Stage Liver Disease Enough?

PubMed

Annamalai, Alagappan; Harada, Megan Y; Chen, Melissa; Tran, Tram; Ko, Ara; Ley, Eric J; Nuno, Miriam; Klein, Andrew; Nissen, Nicholas; Noureddin, Mazen

2017-03-01

Critically ill cirrhotics require liver transplantation urgently, but are at high risk for perioperative mortality. The Model for End-stage Liver Disease (MELD) score, recently updated to incorporate serum sodium, estimates survival probability in patients with cirrhosis, but needs additional evaluation in the critically ill. The purpose of this study was to evaluate the predictive power of ICU admission MELD scores and identify clinical risk factors associated with increased mortality. This was a retrospective review of cirrhotic patients admitted to the ICU between January 2011 and December 2014. Patients who were discharged or underwent transplantation (survivors) were compared with those who died (nonsurvivors). Demographic characteristics, admission MELD scores, and clinical risk factors were recorded. Multivariate regression was used to identify independent predictors of mortality, and measures of model performance were assessed to determine predictive accuracy. Of 276 patients who met inclusion criteria, 153 were considered survivors and 123 were nonsurvivors. Survivor and nonsurvivor cohorts had similar demographic characteristics. Nonsurvivors had increased MELD, gastrointestinal bleeding, infection, mechanical ventilation, encephalopathy, vasopressors, dialysis, renal replacement therapy, requirement of blood products, and ICU length of stay. The MELD demonstrated low predictive power (c-statistic 0.73). Multivariate analysis identified MELD score (adjusted odds ratio [AOR] = 1.05), mechanical ventilation (AOR = 4.55), vasopressors (AOR = 3.87), and continuous renal replacement therapy (AOR = 2.43) as independent predictors of mortality, with stronger predictive accuracy (c-statistic 0.87). The MELD demonstrated relatively poor predictive accuracy in critically ill patients with cirrhosis and might not be the best indicator for prognosis in the ICU population. Prognostic accuracy is significantly improved when variables indicating organ support (mechanical ventilation, vasopressors, and continuous renal replacement therapy) are included in the model. Copyright © 2016. Published by Elsevier Inc.

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques.

PubMed

Teutonico, D; Musuamba, F; Maas, H J; Facius, A; Yang, S; Danhof, M; Della Pasqua, O

2015-10-01

Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates. COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods. Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols. Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.

An Exploratory Study of Fatigue and Physical Activity in Canadian Thyroid Cancer Patients.

PubMed

Alhashemi, Ahmad; Jones, Jennifer M; Goldstein, David P; Mina, Daniel Santa; Thabane, Lehana; Sabiston, Catherine M; Chang, Eugene K; Brierley, James D; Sawka, Anna M

2017-09-01

Fatigue is common among cancer survivors, but fatigue in thyroid cancer (TC) survivors may be under-appreciated. This study investigated the severity and prevalence of moderate and severe fatigue in TC survivors. Potential predictive factors, including physical activity, were explored. A cross-sectional, written, self-administered TC patient survey and retrospective chart review were performed in an outpatient academic Endocrinology clinic in Toronto, Canada. The primary outcome measure was the global fatigue score measured by the Brief Fatigue Inventory (BFI). Physical activity was evaluated using the International Physical Activity Questionnaire-7 day (IPAQ-7). Predictors of BFI global fatigue score were explored in univariate analyses and a multivariable linear regression model. The response rate was 63.1% (205/325). Three-quarters of the respondents were women (152/205). The mean age was 52.5 years, and the mean time since first TC surgery was 6.8 years. The mean global BFI score was 3.5 (standard deviation 2.4) out of 10 (10 is worst). The prevalence of moderate-severe fatigue (global BFI score 4.1-10 out of 10) was 41.4% (84/203). Individuals who were unemployed or unable to work due to disability reported significantly higher levels of fatigue compared to the rest of the study population, in uni-and multivariable analyses. Furthermore, increased physical activity was associated with reduced fatigue in uni- and multivariable analyses. Other socio-demographic, disease, or biochemical variables were not significantly associated with fatigue in the multivariable model. Moderate or severe fatigue was reported in about 4/10 TC survivors. Independent predictors of worse fatigue included unemployment and reduced physical activity.

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

PubMed

Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

2015-07-01

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Influence of the Rh (D) blood group system on graft survival in renal transplantation.

PubMed

Bryan, C F; Mitchell, S I; Lin, H M; Nelson, P W; Shield, C F; Luger, A M; Pierce, G E; Ross, G; Warady, B A; Aeder, M I; Helling, T S; Landreneau, M D; Harrell, K M

1998-02-27

The Rh (D) blood group system has not traditionally been considered to be a clinically relevant histocompatibility barrier in transplantation since conflicting results of its clinical importance have been reported. We analyzed 786 consecutive primary cadaveric renal transplants performed by transplant centers in our Organ Procurement Organization (OPO) between 1990 and 1997. We also analyzed United Network for Organ Sharing (UNOS) data on 26,469 kidney transplants done from April 1994 to June 1996. Multivariate analysis revealed that Rh identity between the recipient and donor was significantly related to better graft outcome (risk ratio, 0.43; 95% confidence interval, 0.30 to 0.61; P=0.0001). Multivariate analysis of the UNOS data revealed that the Rh -/- group may have a positive influence on graft survival with a risk ratio of 0.43 (P=0.14). Multivariate analysis of primary cadaveric renal allografts performed within the Midwest Organ Bank OPO indicates that Rh (D) is a clinically relevant histocompatibility barrier that influences 7-year graft survival.

Fiber-optic evanescent-wave spectroscopy for fast multicomponent analysis of human blood

NASA Astrophysics Data System (ADS)

Simhi, Ronit; Gotshal, Yaron; Bunimovich, David; Katzir, Abraham; Sela, Ben-Ami

1996-07-01

A spectral analysis of human blood serum was undertaken by fiber-optic evanescent-wave spectroscopy (FEWS) by the use of a Fourier-transform infrared spectrometer. A special cell for the FEWS measurements was designed and built that incorporates an IR-transmitting silver halide fiber and a means for introducing the blood-serum sample. Further improvements in analysis were obtained by the adoption of multivariate calibration techniques that are already used in clinical chemistry. The partial least-squares algorithm was used to calculate the concentrations of cholesterol, total protein, urea, and uric acid in human blood serum. The estimated prediction errors obtained (in percent from the average value) were 6% for total protein, 15% for cholesterol, 30% for urea, and 30% for uric acid. These results were compared with another independent prediction method that used a neural-network model. This model yielded estimated prediction errors of 8.8% for total protein, 25% for cholesterol, and 21% for uric acid. spectroscopy, fiber-optic evanescent-wave spectroscopy, Fourier-transform infrared spectrometer, blood, multivariate calibration, neural networks.

Factors Associated With Antidepressant Dosing in Asia: Findings From the Research on Asian Psychotropic Prescription Study.

PubMed

Rajaratnam, Kamini; Xiang, Yu-Tao; Tripathi, Adarsh; Chiu, Helen Fung Kum; Si, Tian-Mei; Chee, Kok-Yoon; Avasthi, Ajit; Grover, Sandeep; Chong, Mian-Yoon; Kuga, Hironori; Kanba, Shigenobu; He, Yan-Ling; Lee, Min-Soo; Yang, Shu-Yu; Udomratn, Pichet; Kallivayalil, Roy Abraham; Tanra, Andi J; Maramis, Margarita; Shen, Winston Wu-Dien; Sartorius, Norman; Kua, Ee-Heok; Tan, Chay-Hoon; Mahendran, Rathi; Shinfuku, Naotaka; Sum, Min Yi; Baldessarini, Ross J; Sim, Kang

2016-12-01

In this study, we sought to examine factors associated with dosing of antidepressants (ADs) in Asia. Based on reported data and clinical experience, we hypothesized that doses of ADs would be associated with demographic and clinical factors and would increase over time. This cross-sectional, pharmacoepidemiological study analyzed data collected within the Research Study on Asian Psychotropic Prescription Pattern for Antidepressants from 4164 participants in 10 Asian countries, using univariate and multivariate methods. The AD doses varied by twofold among countries (highest in PR China and RO Korea, lowest in Singapore and Indonesia), and averaged 124 (120-129) mg/d imipramine-equivalents. Average daily doses increased by 12% between 2004 and 2013. Doses were significantly higher among hospitalized patients and ranked by diagnosis: major depression > anxiety disorders > bipolar disorder, but were not associated with private/public or psychiatric/general-medical settings, nor with age, sex, or cotreatment with a mood stabilizer. In multivariate modeling, AD-dose remained significantly associated with major depressive disorder and being hospitalized. Doses of ADs have increased somewhat in Asia and were higher when used for major depression or anxiety disorders than for bipolar depression and for hospitalized psychiatric patients.

Serum Iron Level Is Associated with Time to Antibiotics in Cystic Fibrosis.

PubMed

Gifford, Alex H; Dorman, Dana B; Moulton, Lisa A; Helm, Jennifer E; Griffin, Mary M; MacKenzie, Todd A

2015-12-01

Serum levels of hepcidin-25, a peptide hormone that reduces blood iron content, are elevated when patients with cystic fibrosis (CF) develop pulmonary exacerbation (PEx). Because hepcidin-25 is unavailable as a clinical laboratory test, we questioned whether a one-time serum iron level was associated with the subsequent number of days until PEx, as defined by the need to receive systemic antibiotics (ABX) for health deterioration. Clinical, biochemical, and microbiological parameters were simultaneously checked in 54 adults with CF. Charts were reviewed to determine when they first experienced a PEx after these parameters were assessed. Time to ABX was compared in subgroups with and without specific attributes. Multivariate linear regression was used to identify parameters that significantly explained variation in time to ABX. In univariate analyses, time to ABX was significantly shorter in subjects with Aspergillus-positive sputum cultures and CF-related diabetes. Multivariate linear regression models demonstrated that shorter time to ABX was associated with younger age, lower serum iron level, and Aspergillus sputum culture positivity. Serum iron, age, and Aspergillus sputum culture positivity are factors associated with shorter time to subsequent PEx in CF adults. © 2015 Wiley Periodicals, Inc.

Musculoskeletal ultrasonography delineates ankle symptoms in rheumatoid arthritis.

PubMed

Toyota, Yukihiro; Tamura, Maasa; Kirino, Yohei; Sugiyama, Yumiko; Tsuchida, Naomi; Kunishita, Yosuke; Kishimoto, Daiga; Kamiyama, Reikou; Miura, Yasushi; Minegishi, Kaoru; Yoshimi, Ryusuke; Ueda, Atsuhisa; Nakajima, Hideaki

2017-05-01

To clarify the use of musculoskeletal ultrasonography (US) of ankle joints in rheumatoid arthritis (RA). Consecutive RA patients with or without ankle symptoms participated in the study. The US, clinical examination (CE), and patients' visual analog scale for pain (pVAS) for ankles were assessed. Prevalence of tibiotalar joint synovitis and tenosynovitis were assessed by grayscale (GS) and power Doppler (PD) US using a semi-quantitative grading (0-3). The positive US and CE findings were defined as GS score ≥2 and/or PD score ≥1, and joint swelling and/or tenderness, respectively. Multivariate analysis with the generalized linear mixed model was performed by assigning ankle pVAS as a dependent variable. Among a total of 120 ankles from 60 RA patients, positive ankle US findings were found in 21 (35.0%) patients. The concordance rate of CE and US was moderate (kappa 0.57). Of the 88 CE negative ankles, US detected positive findings in 9 (10.2%) joints. Multivariate analysis revealed that ankle US, clinical disease activity index, and foot Health Assessment Questionnaire, but not CE, was independently associated with ankle pVAS. US examination is useful to illustrate RA ankle involvement, especially for patients who complain ankle pain but lack CE findings.

A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

PubMed

Perlis, Roy H

2013-07-01

Early identification of depressed individuals at high risk for treatment resistance could be helpful in selecting optimal setting and intensity of care. At present, validated tools to facilitate this risk stratification are rarely used in psychiatric practice. Data were drawn from the first two treatment levels of a multicenter antidepressant effectiveness study in major depressive disorder, the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) cohort. This cohort was divided into training, testing, and validation subsets. Only clinical or sociodemographic variables available by or readily amenable to self-report were considered. Multivariate models were developed to discriminate individuals reaching remission with a first or second pharmacological treatment trial from those not reaching remission despite two trials. A logistic regression model achieved an area under the receiver operating characteristic curve exceeding .71 in training, testing, and validation cohorts and maintained good calibration across cohorts. Performance of three alternative models with machine learning approaches--a naïve Bayes classifier and a support vector machine, and a random forest model--was less consistent. Similar performance was observed between more and less severe depression, men and women, and primary versus specialty care sites. A web-based calculator was developed that implements this tool and provides graphical estimates of risk. Risk for treatment resistance among outpatients with major depressive disorder can be estimated with a simple model incorporating baseline sociodemographic and clinical features. Future studies should examine the performance of this model in other clinical populations and its utility in treatment selection or clinical trial design. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. 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.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

An Individualized Risk Calculator for Research in Prodromal Psychosis.

PubMed

Cannon, Tyrone D; Yu, Changhong; Addington, Jean; Bearden, Carrie E; Cadenhead, Kristin S; Cornblatt, Barbara A; Heinssen, Robert; Jeffries, Clark D; Mathalon, Daniel H; McGlashan, Thomas H; Perkins, Diana O; Seidman, Larry J; Tsuang, Ming T; Walker, Elaine F; Woods, Scott W; Kattan, Michael W

2016-10-01

Approximately 20%-35% of individuals 12-35 years old who meet criteria for a prodromal risk syndrome convert to psychosis within 2 years. However, this estimate ignores the fact that clinical high-risk cases vary considerably in risk. The authors sought to create a risk calculator, based on profiles of risk indicators, that can ascertain the probability of conversion to psychosis in individual patients. The study subjects were 596 clinical high-risk participants from the second phase of the North American Prodrome Longitudinal Study who were followed up to the time of conversion to psychosis or last contact (up to 2 years). The predictors examined were limited to those that are supported by previous studies and are readily obtainable in general clinical settings. Time-to-event regression was used to build a multivariate model predicting conversion, with internal validation using 1,000 bootstrap resamples. The 2-year probability of conversion to psychosis was 16%. Higher levels of unusual thought content and suspiciousness, greater decline in social functioning, lower verbal learning and memory performance, slower speed of processing, and younger age at baseline each contributed to individual risk for psychosis. Stressful life events, trauma, and family history of schizophrenia were not significant predictors. The multivariate model achieved a concordance index of 0.71 and, as reported in an article by Carrión et al., published concurrently with this one, was validated in an independent external data set. The results are instantiated in a web-based risk prediction tool envisioned to be most useful in research protocols involving the psychosis prodrome. A risk calculator comparable in accuracy to those for cardiovascular disease and cancer is available to predict individualized conversion risks in newly ascertained clinical high-risk cases. Given that the risk calculator can be validly applied only for patients who screen positive on the Structured Clinical Interview for Psychosis Risk Syndromes, which requires training to administer, its most immediate uses will be in research on psychosis risk factors and in research-driven clinical (prevention) trials.

The proliferation marker Ki67, but not neuroendocrine expression, is an independent factor in the prediction of prognosis of primary prostate cancer patients

PubMed Central

Pascale, Mariarosa; Aversa, Cinzia; Barbazza, Renzo; Marongiu, Barbara; Siracusano, Salvatore; Stoffel, Flavio; Sulfaro, Sando; Roggero, Enrico; Stanta, Giorgio

2016-01-01

Abstract Background Neuroendocrine markers, which could indicate for aggressive variants of prostate cancer and Ki67 (a well-known marker in oncology for defining tumor proliferation), have already been associated with clinical outcome in prostate cancer. The aim of this study was to investigate the prognostic value of those markers in primary prostate cancer patients. Patients and methods NSE (neuron specific enolase), ChrA (chromogranin A), Syp (Synaptophysin) and Ki67 staining were performed by immunohistochemistry. Then, the prognostic impact of their expression on overall survival was investigated in 166 primary prostate cancer patients by univariate and multivariate analyses. Results NSE, ChrA, Syp and Ki67 were positive in 50, 45, 54 and 146 out of 166 patients, respectively. In Kaplan-Meier analysis only diffuse NSE staining (negative vs diffuse, p = 0.004) and Ki67 (≤ 10% vs > 10%, p < 0.0001) were significantly associated with overall survival. Ki67 expression, but not NSE, resulted as an independent prognostic factor for overall survival in multivariate analysis. Conclusions A prognostic model incorporating Ki67 expression with clinical-pathological covariates could provide additional prognostic information. Ki67 may thus improve prediction of prostate cancer outcome based on standard clinical-pathological parameters improving prognosis and management of prostate cancer patients. PMID:27679548

Development and Validation of a Practical Two-Step Prediction Model and Clinical Risk Score for Post-Thrombotic Syndrome.

PubMed

Amin, Elham E; van Kuijk, Sander M J; Joore, Manuela A; Prandoni, Paolo; Cate, Hugo Ten; Cate-Hoek, Arina J Ten

2018-06-04

 Post-thrombotic syndrome (PTS) is a common chronic consequence of deep vein thrombosis that affects the quality of life and is associated with substantial costs. In clinical practice, it is not possible to predict the individual patient risk. We develop and validate a practical two-step prediction tool for PTS in the acute and sub-acute phase of deep vein thrombosis.  Multivariable regression modelling with data from two prospective cohorts in which 479 (derivation) and 1,107 (validation) consecutive patients with objectively confirmed deep vein thrombosis of the leg, from thrombosis outpatient clinic of Maastricht University Medical Centre, the Netherlands (derivation) and Padua University hospital in Italy (validation), were included. PTS was defined as a Villalta score of ≥ 5 at least 6 months after acute thrombosis.  Variables in the baseline model in the acute phase were: age, body mass index, sex, varicose veins, history of venous thrombosis, smoking status, provoked thrombosis and thrombus location. For the secondary model, the additional variable was residual vein obstruction. Optimism-corrected area under the receiver operating characteristic curves (AUCs) were 0.71 for the baseline model and 0.60 for the secondary model. Calibration plots showed well-calibrated predictions. External validation of the derived clinical risk scores was successful: AUC, 0.66 (95% confidence interval [CI], 0.63-0.70) and 0.64 (95% CI, 0.60-0.69).  Individual risk for PTS in the acute phase of deep vein thrombosis can be predicted based on readily accessible baseline clinical and demographic characteristics. The individual risk in the sub-acute phase can be predicted with limited additional clinical characteristics. Schattauer GmbH Stuttgart.

OS090. Performance of candidate clinical and biochemical markers in screening early in pregnancy to detect women at high risk to develop preeclampsia.

PubMed

Forest, J-C; Massé, J; Bujold, E; Rousseau, F; Charland, M; Thériault, S; Lafond, J; Giguère, Y

2012-07-01

The advent of early preventive measures, such as low-dose aspirin targeting women at high risk of preeclampsia (PE), emphasizes the need for better detection. Despite the emergence of promising biochemical markers linked to the pathophysiological processes, systematic reviews have shown that, until now, no single tests fulfill the criteria set by WHO for biomarkers to screen for a disease. However, recent literature reveals that by combining various clinical, biophysical and biochemical markers into multivariate algorithms, one can envisage to estimate the risk of PE with a performance that would reach clinical utility and cost-effectiveness, but this remains to be demonstrated in various environments and health care settings. To investigate, in a prospective study, the clinical utility of candidate biomarkers and clinical data to detect, early in pregnancy, women at risk to develop PE and to propose a multivariate prediction algorithm combining clinical parameters to biochemical markers. 7929 pregnant women prospectively recruited at the first prenatal visit, provided blood samples, clinical and sociodemographic information. 214 pregnant women developed hypertensive disorders of pregnancy (HDP) of which 88 had PE (1.2%), including 44 with severe PE (0.6%). A nested case-control study was performed including for each case of HDP two normal pregnancies matched for maternal age, gestational age at recruitment, ethnicity, parity, and smoking status. Based on the literature we selected the most promising markers in a multivariate logistic regression model: mean arterial pressure (MAP), BMI, placental growth factor (PlGF), soluble Flt-1, inhibin A and PAPP-A. Biomarker results measured between 10-18 weeks gestation were expressed as multiples of the median. Medians were determined for each gestational week. When combined with MAP at the time of blood sampling and BMI at the beginning of pregnancy, the four biochemical markers discriminate normal pregnancies from those with HDP. At a 5% false positive rate, 37% of the affected pregnancies would have been detected. However, considering the prevalence of HDP in our population, the positive predictive value would have been only 15%. If all the predicted positive women would have been proposed a preventive intervention, only one out 6.7 women could have potentially benefited. In the case of severe PE, performance was not improved, sensitivity was the same, but the positive predictive value decreased to 3% (lower prevalence of severe PE). In our low-risk Caucasian population, neither individual candidate markers nor multivariate risk algorithm using an a priori combination of selected markers reached a performance justifying implementation. This also emphasizes the necessity to take into consideration characteristics of the population and environment influencing prevalence before promoting wide implementation of such screening strategies. In a perspective of personalized medicine, it appears more than ever mandatory to tailor recommendations for HDP screening according not only to individual but also to population characteristics. Copyright © 2012. Published by Elsevier B.V.

Prefrontal gray matter volume mediates genetic risks for obesity.

PubMed

Opel, N; Redlich, R; Kaehler, C; Grotegerd, D; Dohm, K; Heindel, W; Kugel, H; Thalamuthu, A; Koutsouleris, N; Arolt, V; Teuber, A; Wersching, H; Baune, B T; Berger, K; Dannlowski, U

2017-05-01

Genetic and neuroimaging research has identified neurobiological correlates of obesity. However, evidence for an integrated model of genetic risk and brain structural alterations in the pathophysiology of obesity is still absent. Here we investigated the relationship between polygenic risk for obesity, gray matter structure and body mass index (BMI) by the use of univariate and multivariate analyses in two large, independent cohorts (n=330 and n=347). Higher BMI and higher polygenic risk for obesity were significantly associated with medial prefrontal gray matter decrease, and prefrontal gray matter was further shown to significantly mediate the effect of polygenic risk for obesity on BMI in both samples. Building on this, the successful individualized prediction of BMI by means of multivariate pattern classification algorithms trained on whole-brain imaging data and external validations in the second cohort points to potential clinical applications of this imaging trait marker.

Narcissistic Personality Disorder and the Structure of Common Mental Disorders.

PubMed

Eaton, Nicholas R; Rodriguez-Seijas, Craig; Krueger, Robert F; Campbell, W Keith; Grant, Bridget F; Hasin, Deborah S

2017-08-01

Narcissistic personality disorder (NPD) shows high rates of comorbidity with mood, anxiety, substance use, and other personality disorders. Previous bivariate comorbidity investigations have left NPD multivariate comorbidity patterns poorly understood. Structural psychopathology research suggests that two transdiagnostic factors, internalizing (with distress and fear subfactors) and externalizing, account for comorbidity among common mental disorders. NPD has rarely been evaluated within this framework, with studies producing equivocal results. We investigated how NPD related to other mental disorders in the internalizing-externalizing model using diagnoses from a nationally representative sample (N = 34,653). NPD was best conceptualized as a distress disorder. NPD variance accounted for by transdiagnostic factors was modest, suggesting its variance is largely unique in the context of other common mental disorders. Results clarify NPD multivariate comorbidity, suggest avenues for classification and clinical endeavors, and highlight the need to understand vulnerable and grandiose narcissism subtypes' comorbidity patterns and structural relations.

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.

New simple radiological criteria proposed for multiple primary lung cancers.

PubMed

Matsunaga, Takeshi; Suzuki, Kenji; Takamochi, Kazuya; Oh, Shiaki

2017-11-01

Controversies remain as to the differential diagnosis between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IM) in lung cancers. We have investigated the clinical criteria for MPLC and here propose a set of new and simple criteria from the stand point of prognosis. A retrospective study was conducted on 588 consecutive patients with resected lung cancer of clinical Stage IA between 2009 and 2012. Multiple lung cancers (MLCs) were observed in 103 (17.5%) of the 588 patients. All main and other tumors were divided into solid tumor (ST) and non-solid tumor (non-ST). We defined Group A as MLCs having at least one non-ST and Group B as all tumors being ST. Cox's proportional hazard model was used for the multivariate analyses to investigate the preoperative prognostic factors. We divided the MLCs into MPLC and IM based on the preoperative prognostic factors, and survival was estimated by the Kaplan-Meier method. A multivariate analysis with Cox's proportional hazards model revealed that Group A independently predicted good overall survival (HR = 0.165, 95% CI: 0.041-0.672).Differences in the 3- and 5-year overall survivals between Groups A and B were statistically significant (96.3%/92.2% vs. 70.0%/60.0%, Pvalue = 0.0002). We suggest that Group A, defined as the presence of at least one tumor with a ground glass opacity component and clinical N0, should be excluded from the conventional concept of multiple lung cancers based on the criteria of Martini and Melamed as it has a very good prognosis. This group would be considered to be radiological MPLC. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Fall Frequency among Men and Women with or at Risk for HIV Infection

PubMed Central

Erlandson, Kristine M.; Plankey, Michael W.; Springer, Gayle; Cohen, Helen S.; Cox, Christopher; Hoffman, Howard J.; Yin, Michael T.; Brown, Todd T.

2016-01-01

Background Falls and fall-related injuries are a major public health concern. HIV-infected adults have been shown to have a high incidence of falls. Identification of major risk factors for falls that are unique to HIV or similar to the general population will inform development of future interventions for fall prevention. Methods HIV-infected and uninfected men and women participating in a Hearing and Balance Sub-study of the Multicenter AIDS Cohort Study and Women’s Interagency HIV Study were asked about balance symptoms and falls during the prior 12 months. Falls were categorized as 0, 1, or ≥ 2; proportional odds logistic regression models were used to investigate relationships between falls and demographic and clinical variables and multivariable models were created. Results 24% of 303 HIV-infected participants reported ≥1 fall compared to 18% of 233 HIV-uninfected participants (p=0.27). HIV-infected participants were demographically different from HIV-uninfected participants, and were more likely to report clinical imbalance symptoms (p≤0.035). In univariate analyses, more falls were associated with hepatitis C, female sex, obesity, smoking, and clinical imbalance symptoms, but not age, HIV serostatus, or other comorbidities. In multivariable analyses, female sex and imbalance symptoms were independently associated with more falls. Among HIV-infected participants, smoking, number of medications, and imbalance symptoms remained independent fall predictors while current protease inhibitor use was protective. Discussion Similar rates of falls among HIV-infected and uninfected participants were largely explained by a high prevalence of imbalance symptoms. Routine assessment of falls and dizziness/imbalance symptoms should be considered, with interventions targeted at reducing symptomatology. PMID:27028463

Clinical outcomes of patient mobility in a neuroscience intensive care unit.

PubMed

Mulkey, Malissa; Bena, James F; Albert, Nancy M

2014-06-01

Patients treated in a neuroscience intensive care unit (NICU) are often viewed as too sick to tolerate physical activity. In this study, mobility status in NICU was assessed, and factors and outcomes associated with mobility were examined. Using a prospective design, daily mobility status, medical history, demographics, Acute Physiology and Chronic Health Evaluation (APACHE) III score, and clinical outcomes were collected by medical records and database review. Depression, anxiety, and hostility were assessed before NICU discharge. Analyses included comparative statistics and multivariable modeling. In 228 unique patients, median (minimum, maximum) age was 64.0 (20, 95) years, 66.4% were Caucasian, and 53.6% were men. Of 246 admissions, median NICU stay was 4 (1, 61) days; APACHE III score was 56 (16, 145). Turning, range of motion, and head of bed of >30° were uniformly applied (n = 241), but 94 patients (39%) never progressed; 94 (39%) progressed to head of bed of >45° or dangling legs, 29 (12%) progressed to standing or pivoting to chair, and 24 (10%) progressed to walking. Female gender (p = .019), mechanical ventilation (p < .001), higher APACHE score (p = .004), and 30-day mortality (p = .001) were associated with less mobility. In multivariable modeling, greater mobility was associated with longer unit stay (p < .001) and discharge to home (p < .001). Psychological profile characteristics were not associated with mobility level. Nearly 40% of patients never progressed beyond bed movement, and only 10% walked. Although limited mobility progression was not associated with many patient factors, it was associated with poorer clinical outcomes. Implementation and evaluation of a progressive mobility protocol are needed in NICU patients. For more insights from the authors, see Supplemental Digital Content 1, at http://link.lww.com/JNN/A10.

The Epidemiology and Clinical Features of Clostridium difficile Infection in Liver Transplant Recipients.

PubMed

Sullivan, Timothy; Weinberg, Alan; Rana, Meenakshi; Patel, Gopi; Huprikar, Shirish

2016-09-01

Clostridium difficile infection (CDI) is common after liver transplantation (LT); however, few studies have examined the risk factors, clinical manifestations, and outcomes of CDI in this population. A retrospective study of adults who underwent LT between January 1, 2011, and April 4, 2013, at The Mount Sinai Hospital was conducted. Potential risk factors were evaluated via univariate and multivariable analysis to determine predictors of CDI in this population. The clinical manifestations of CDI and patient outcomes were also reviewed. Clostridium difficile infection occurred in 27 (14%) of 192 patients after LT. In multivariable analysis, CDI was associated with having a model for end-stage liver disease score of 20 or greater (hazards ratio, 2.90; 95% confidence interval, 1.29-6.52; P = 0.010), and receiving a LT from a living donor (hazards ratio, 3.77; 95% confidence interval, 1.47-9.67; P = 0.006). Forty-one percent of CDI cases occurred within 1 week of LT. Seven percent of patients with CDI had a serum white blood cell count greater than 12 000 cells per μL, and 26% had a temperature greater than 38.0°C. After treatment 6 (22%) patients developed CDI relapse, and all were successfully treated. No patients died of CDI after a mean follow-up time of 1.8 years; however, overall survival was significantly lower among those with CDI (78% vs 92%; P = 0.033). Clostridium difficile infection after LT was associated with higher model for end-stage liver disease scores and receiving a LT from a living donor. Clostridium difficile infection often occurred soon after LT and was infrequently associated with leukocytosis or fever. Clostridium difficile infection in LT recipients was associated with lower overall survival.

Sociodemographic, clinical and childhood correlates of adult violent victimisation in a large, national survey sample of people with psychotic disorders.

PubMed

Morgan, Vera A; Morgan, Frank; Galletly, Cherrie; Valuri, Giulietta; Shah, Sonal; Jablensky, Assen

2016-02-01

Our aim was to establish the 12-month prevalence of violent victimisation in a large sample of adults with psychotic disorders (N = 1825), compare this to population estimates, and examine correlates of violent victimisation. The Australian national psychosis survey used a two-phase design to draw a representative sample of adults aged 18-64 years with psychotic disorders. Interview questions included psychopathology, cognition, sociodemographics, substance use, criminality, and childhood and adult victimisation. Multivariable logistic regression models were used to examine the independent contributions of known risk factors, clinical profile and childhood abuse, on risk of violent victimisation. Differences between men and women were examined. Among adults with psychotic disorders, 12-month prevalence of any victimisation was 38.6% (males 37.4%, females 40.5%), and of violent victimisation was 16.4% (males 15.2%; females 18.3%). Violent victimisation was 4.8 times higher than the population rate of 3.4% (6.5 times higher for women; 3.7 times higher for men). Significant correlates of violent victimisation were established sociodemographic and behavioural risk factors predicting victimisation in the general community: younger age, residence in the most disadvantaged neighbourhoods, homelessness, lifetime alcohol abuse/dependence, and prior criminal offending. Among clinical variables, only mania and self-harm remained significant in the multivariable model. Childhood abuse was independently associated with violent victimisation. Rates of violent victimisation are high for people with psychotic disorders, especially women, compared to population rates. Greater exposure to sociodemographic and behavioural risks may render them particularly vulnerable to victimisation. Social cognition as a valuable treatment target is discussed.

Development and validation of a prognostic nomogram for terminally ill cancer patients.

PubMed

Feliu, Jaime; Jiménez-Gordo, Ana María; Madero, Rosario; Rodríguez-Aizcorbe, José Ramón; Espinosa, Enrique; Castro, Javier; Acedo, Jesús Domingo; Martínez, Beatriz; Alonso-Babarro, Alberto; Molina, Raquel; Cámara, Juan Carlos; García-Paredes, María Luisa; González-Barón, Manuel

2011-11-02

Determining life expectancy in terminally ill cancer patients is a difficult task. We aimed to develop and validate a nomogram to predict the length of survival in patients with terminal disease. From February 1, 2003, to December 31, 2005, 406 consecutive terminally ill patients were entered into the study. We analyzed 38 features prognostic of life expectancy among terminally ill patients by multivariable Cox regression and identified the most accurate and parsimonious model by backward variable elimination according to the Akaike information criterion. Five clinical and laboratory variables were built into a nomogram to estimate the probability of patient survival at 15, 30, and 60 days. We validated and calibrated the nomogram with an external validation cohort of 474 patients who were treated from June 1, 2006, through December 31, 2007. The median overall survival was 29.1 days for the training set and 18.3 days for the validation set. Eastern Cooperative Oncology Group performance status, lactate dehydrogenase levels, lymphocyte levels, albumin levels, and time from initial diagnosis to diagnosis of terminal disease were retained in the multivariable Cox proportional hazards model as independent prognostic factors of survival and formed the basis of the nomogram. The nomogram had high predictive performance, with a bootstrapped corrected concordance index of 0.70, and it showed good calibration. External independent validation revealed 68% predictive accuracy. We developed a highly accurate tool that uses basic clinical and analytical information to predict the probability of survival at 15, 30, and 60 days in terminally ill cancer patients. This tool can help physicians making decisions on clinical care at the end of life.

Hospital-level Variation in Utilization of Surgery for Clinical Stage I-II Pancreatic Adenocarcinoma.

PubMed

Swords, Douglas S; Mulvihill, Sean J; Skarda, David E; Finlayson, Samuel R G; Stoddard, Gregory J; Ott, Mark J; Firpo, Matthew A; Scaife, Courtney L

2017-07-11

To (1) evaluate rates of surgery for clinical stage I-II pancreatic ductal adenocarcinoma (PDAC), (2) identify predictors of not undergoing surgery, (3) quantify the degree to which patient- and hospital-level factors explain differences in hospital surgery rates, and (4) evaluate the association between adjusted hospital-specific surgery rates and overall survival (OS) of patients treated at different hospitals. Curative-intent surgery for potentially resectable PDAC is underutilized in the United States. Retrospective cohort study of patients ≤85 years with clinical stage I-II PDAC in the 2004 to 2014 National Cancer Database. Mixed effects multivariable models were used to characterize hospital-level variation across quintiles of hospital surgery rates. Multivariable Cox proportional hazards models were used to estimate the effect of adjusted hospital surgery rates on OS. Of 58,553 patients without contraindications or refusal of surgery, 63.8% underwent surgery, and the rate decreased from 2299/3528 (65.2%) in 2004 to 4412/7092 (62.2%) in 2014 (P < 0.001). Adjusted hospital rates of surgery varied 6-fold (11.4%-70.9%). Patients treated at hospitals with higher rates of surgery had better unadjusted OS (median OS 10.2, 13.3, 14.2, 16.5, and 18.4 months in quintiles 1-5, respectively, P < 0.001, log-rank). Treatment at hospitals in lower surgery rate quintiles 1-3 was independently associated with mortality [Hazard ratio (HR) 1.10 (1.01, 1.21), HR 1.08 (1.02, 1.15), and HR 1.09 (1.04, 1.14) for quintiles 1-3, respectively, compared with quintile 5] after adjusting for patient factors, hospital type, and hospital volume. Quality improvement efforts are needed to help hospitals with low rates of surgery ensure that their patients have access to appropriate surgery.

Prognostic value of Ki-67 index in adult medulloblastoma after accounting for molecular subgroup: a retrospective clinical and molecular analysis.

PubMed

Zhao, Fu; Zhang, Jing; Li, Peng; Zhou, Qiangyi; Zhang, Shun; Zhao, Chi; Wang, Bo; Yang, Zhijun; Li, Chunde; Liu, Pinan

2018-04-23

Medulloblastoma (MB) is a rare primary brain tumor in adults. We previously evaluated that combining both clinical and molecular classification could improve current risk stratification for adult MB. In this study, we aimed to identify the prognostic value of Ki-67 index in adult MB. Ki-67 index of 51 primary adult MBs was reassessed using a computer-based image analysis (Image-Pro Plus). All patients were followed up ranging from 12 months up to 15 years. Gene expression profiling and immunochemistry were used to establish the molecular subgroups in adult MB. Combined risk stratification models were designed based on clinical characteristics, molecular classification and Ki-67 index, and identified by multivariable Cox proportional hazards analysis. In our cohort, the mean Ki-67 value was 30.0 ± 11.3% (range 6.56-63.55%). The average Ki-67 value was significantly higher in LC/AMB than in CMB and DNMB (P = .001). Among three molecular subgroups, Group 4-tumors had the highest average Ki-67 value compared with WNT- and SHH-tumors (P = .004). Patients with Ki-67 index large than 30% displayed poorer overall survival (OS) and progression free survival (PFS) than those with Ki-67 less than 30% (OS: P = .001; PFS: P = .006). Ki-67 index (i.e. > 30%, < 30%) was identified as an independent significant prognostic factor (OS: P = .017; PFS: P = .024) by using multivariate Cox proportional hazards model. In conclusion, Ki-67 index can be considered as a valuable independent prognostic biomarker for adult patients with MB.

Clinical correlates of hypoglycaemia over 4 years in people with type 2 diabetes starting insulin: An analysis from the CREDIT study

PubMed Central

Calvi‐Gries, Francoise; Blonde, Lawrence; Pilorget, Valerie; Berlingieri, Joseph; Freemantle, Nick

2018-01-01

Aim To identify factors associated with documented symptomatic and severe hypoglycaemia over 4 years in people with type 2 diabetes starting insulin therapy. Materials and methods CREDIT, a prospective international observational study, collected data over 4 years on people starting any insulin in 314 centres; 2729 and 2271 people had hypoglycaemia data during the last 6 months of years 1 and 4, respectively. Multivariable logistic regression was used to select the characteristics associated with documented symptomatic hypoglycaemia, and the model was tested against severe hypoglycaemia. Results The proportions of participants reporting ≥1 non‐severe event were 18.5% and 16.6% in years 1 and 4; the corresponding proportions of those achieving a glycated haemoglobin (HbA1c) concentration <7.0% (<53 mmol/mol) were 24.6% and 18.3%, and 16.5% and 16.2% of those who did not. For severe hypoglycaemia, the proportions were 3.0% and 4.6% of people reaching target vs 1.5% and 1.1% of those not reaching target. Multivariable analysis showed that, for documented symptomatic hypoglycaemia at both years 1 and 4, baseline lower body mass index and more physical activity were predictors, and lower HbA1c was an explanatory variable in the respective year. Models for documented symptomatic hypoglycaemia predicted severe hypoglycaemia. Insulin regimen was a univariate explanatory variable, and was not retained in the multivariable analysis. Conclusions Hypoglycaemia occurred at significant rates, but was stable over 4 years despite increased insulin doses. The association with insulin regimen and with oral agent use declined over that time. Associated predictors and explanatory variables for documented symptomatic hypoglycaemia conformed to clinical impressions and could be extended to severe hypoglycaemia. Better achieved HbA1c was associated with a higher risk of hypoglycaemia. PMID:29205734

Clinical correlates of hypoglycaemia over 4 years in people with type 2 diabetes starting insulin: An analysis from the CREDIT study.

PubMed

Home, Philip; Calvi-Gries, Francoise; Blonde, Lawrence; Pilorget, Valerie; Berlingieri, Joseph; Freemantle, Nick

2018-04-01

To identify factors associated with documented symptomatic and severe hypoglycaemia over 4 years in people with type 2 diabetes starting insulin therapy. CREDIT, a prospective international observational study, collected data over 4 years on people starting any insulin in 314 centres; 2729 and 2271 people had hypoglycaemia data during the last 6 months of years 1 and 4, respectively. Multivariable logistic regression was used to select the characteristics associated with documented symptomatic hypoglycaemia, and the model was tested against severe hypoglycaemia. The proportions of participants reporting ≥1 non-severe event were 18.5% and 16.6% in years 1 and 4; the corresponding proportions of those achieving a glycated haemoglobin (HbA1c) concentration <7.0% (<53 mmol/mol) were 24.6% and 18.3%, and 16.5% and 16.2% of those who did not. For severe hypoglycaemia, the proportions were 3.0% and 4.6% of people reaching target vs 1.5% and 1.1% of those not reaching target. Multivariable analysis showed that, for documented symptomatic hypoglycaemia at both years 1 and 4, baseline lower body mass index and more physical activity were predictors, and lower HbA1c was an explanatory variable in the respective year. Models for documented symptomatic hypoglycaemia predicted severe hypoglycaemia. Insulin regimen was a univariate explanatory variable, and was not retained in the multivariable analysis. Hypoglycaemia occurred at significant rates, but was stable over 4 years despite increased insulin doses. The association with insulin regimen and with oral agent use declined over that time. Associated predictors and explanatory variables for documented symptomatic hypoglycaemia conformed to clinical impressions and could be extended to severe hypoglycaemia. Better achieved HbA1c was associated with a higher risk of hypoglycaemia. © 2017 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Biomarker-Based Risk Model to Predict Cardiovascular Mortality in Patients With Stable Coronary Disease.

PubMed

Lindholm, Daniel; Lindbäck, Johan; Armstrong, Paul W; Budaj, Andrzej; Cannon, Christopher P; Granger, Christopher B; Hagström, Emil; Held, Claes; Koenig, Wolfgang; Östlund, Ollie; Stewart, Ralph A H; Soffer, Joseph; White, Harvey D; de Winter, Robbert J; Steg, Philippe Gabriel; Siegbahn, Agneta; Kleber, Marcus E; Dressel, Alexander; Grammer, Tanja B; März, Winfried; Wallentin, Lars

2017-08-15

Currently, there is no generally accepted model to predict outcomes in stable coronary heart disease (CHD). This study evaluated and compared the prognostic value of biomarkers and clinical variables to develop a biomarker-based prediction model in patients with stable CHD. In a prospective, randomized trial cohort of 13,164 patients with stable CHD, we analyzed several candidate biomarkers and clinical variables and used multivariable Cox regression to develop a clinical prediction model based on the most important markers. The primary outcome was cardiovascular (CV) death, but model performance was also explored for other key outcomes. It was internally bootstrap validated, and externally validated in 1,547 patients in another study. During a median follow-up of 3.7 years, there were 591 cases of CV death. The 3 most important biomarkers were N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), and low-density lipoprotein cholesterol, where NT-proBNP and hs-cTnT had greater prognostic value than any other biomarker or clinical variable. The final prediction model included age (A), biomarkers (B) (NT-proBNP, hs-cTnT, and low-density lipoprotein cholesterol), and clinical variables (C) (smoking, diabetes mellitus, and peripheral arterial disease). This "ABC-CHD" model had high discriminatory ability for CV death (c-index 0.81 in derivation cohort, 0.78 in validation cohort), with adequate calibration in both cohorts. This model provided a robust tool for the prediction of CV death in patients with stable CHD. As it is based on a small number of readily available biomarkers and clinical factors, it can be widely employed to complement clinical assessment and guide management based on CV risk. (The Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy Trial [STABILITY]; NCT00799903). Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

Derivation and Validation of a Serum Biomarker Panel to Identify Infants With Acute Intracranial Hemorrhage.

PubMed

Berger, Rachel Pardes; Pak, Brian J; Kolesnikova, Mariya D; Fromkin, Janet; Saladino, Richard; Herman, Bruce E; Pierce, Mary Clyde; Englert, David; Smith, Paul T; Kochanek, Patrick M

2017-06-05

Abusive head trauma is the leading cause of death from physical abuse. Missing the diagnosis of abusive head trauma, particularly in its mild form, is common and contributes to increased morbidity and mortality. Serum biomarkers may have potential as quantitative point-of-care screening tools to alert physicians to the possibility of intracranial hemorrhage. To identify and validate a set of biomarkers that could be the basis of a multivariable model to identify intracranial hemorrhage in well-appearing infants using the Ziplex System. Binary logistic regression was used to develop a multivariable model incorporating 3 serum biomarkers (matrix metallopeptidase-9, neuron-specific enolase, and vascular cellular adhesion molecule-1) and 1 clinical variable (total hemoglobin). The model was then prospectively validated. Multiplex biomarker measurements were performed using Flow-Thru microarray technology on the Ziplex System, which has potential as a point-of-care system. The model was tested at 3 pediatric emergency departments in level I pediatric trauma centers (Children's Hospital of Pittsburgh of University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Primary Children's Hospital, Salt Lake City, Utah; and Lurie Children's Hospital, Chicago, Illinois) among well-appearing infants who presented for care owing to symptoms that placed them at increased risk of abusive head trauma. The study took place from November 2006 to April 2014 at Children's Hospital of Pittsburgh, June 2010 to August 2013 at Primary Children's Hospital, and January 2011 to August 2013 at Lurie Children's Hospital. A mathematical model that can predict acute intracranial hemorrhage in infants at increased risk of abusive head trauma. The multivariable model, Biomarkers for Infant Brain Injury Score, was applied prospectively to 599 patients. The mean (SD) age was 4.7 (3.1) months. Fifty-two percent were boys, 78% were white, and 8% were Hispanic. At a cutoff of 0.182, the model was 89.3% sensitive (95% CI, 87.7-90.4) and 48.0% specific (95% CI, 47.3-48.9) for acute intracranial hemorrhage. Positive and negative predictive values were 21.3% and 95.6%, respectively. The model was neither sensitive nor specific for atraumatic brain abnormalities, isolated skull fractures, or chronic intracranial hemorrhage. The Biomarkers for Infant Brain Injury Score, a multivariable model using 3 serum biomarker concentrations and serum hemoglobin, can identify infants with acute intracranial hemorrhage. Accurate and timely identification of intracranial hemorrhage in infants without a history of trauma in whom trauma may not be part of the differential diagnosis has the potential to decrease morbidity and mortality from abusive head trauma.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Pre-cART Elevation of CRP and CD4+ T-Cell Immune Activation Associated With HIV Clinical Progression in a Multinational Case-Cohort Study.

PubMed

Balagopal, Ashwin; Asmuth, David M; Yang, Wei-Teng; Campbell, Thomas B; Gupte, Nikhil; Smeaton, Laura; Kanyama, Cecilia; Grinsztejn, Beatriz; Santos, Breno; Supparatpinyo, Khuanchai; Badal-Faesen, Sharlaa; Lama, Javier R; Lalloo, Umesh G; Zulu, Fatima; Pawar, Jyoti S; Riviere, Cynthia; Kumarasamy, Nagalingeswaran; Hakim, James; Li, Xiao-Dong; Pollard, Richard B; Semba, Richard D; Thomas, David L; Bollinger, Robert C; Gupta, Amita

2015-10-01

Despite the success of combination antiretroviral therapy (cART), a subset of HIV-infected patients who initiate cART develop early clinical progression to AIDS; therefore, some cART initiators are not fully benefitted by cART. Immune activation pre-cART may predict clinical progression in cART initiators. A case-cohort study (n = 470) within the multinational Prospective Evaluation of Antiretrovirals in Resource-Limited Settings clinical trial (1571 HIV treatment-naive adults who initiated cART; CD4 T-cell count <300 cells/mm; 9 countries) was conducted. A subcohort of 30 participants per country was randomly selected; additional cases were added from the main cohort. Cases [n = 236 (random subcohort 36; main cohort 200)] had clinical progression (incident WHO stage 3/4 event or death) within 96 weeks after cART initiation. Immune activation biomarkers were quantified pre-cART. Associations between biomarkers and clinical progression were examined using weighted multivariable Cox-proportional hazards models. Median age was 35 years, 45% were women, 49% black, 31% Asian, and 9% white. Median CD4 T-cell count was 167 cells per cubic millimeter. In multivariate analysis, highest quartile C-reactive protein concentration [adjusted hazard ratio (aHR), 2.53; 95% confidence interval (CI): 1.02 to 6.28] and CD4 T-cell activation (aHR, 5.18; 95% CI: 1.09 to 24.47) were associated with primary outcomes, compared with lowest quartiles. sCD14 had a trend toward association with clinical failure (aHR, 2.24; 95% CI: 0.96 to 5.21). Measuring C-reactive protein and CD4 T-cell activation may identify patients with CD4 T-cell counts <300 cells per cubic millimeter at risk for early clinical progression when initiating cART. Additional vigilance and symptom-based screening may be required in this subset of patients even after beginning cART.

Pre-cART Elevation of CRP and CD4+ T-cell Immune Activation Associated with HIV Clinical Progression in a Multinational Case-Cohort Study

PubMed Central

Balagopal, Ashwin; Asmuth, David M.; Yang, Wei-Teng; Campbell, Thomas B.; Gupte, Nikhil; Smeaton, Laura; Kanyama, Cecilia; Grinsztejn, Beatriz; Santos, Breno; Supparatpinyo, Khuanchai; Badal-Faesen, Sharlaa; Lama, Javier R.; Lalloo, Umesh G.; Zulu, Fatima; Pawar, Jyoti S; Riviere, Cynthia; Kumarasamy, Nagalingeswaran; Hakim, James; Li, Xiao-Dong; Pollard, Richard B.; Semba, Richard D.; Thomas, David L.; Bollinger, Robert C.; Gupta, Amita

2015-01-01

Background Despite the success of combination antiretroviral therapy (cART), a subset of HIV-infected patients who initiate cART develop early clinical progression to AIDS; therefore some cART initiators are not fully benefitted by cART. Immune activation pre-cART may predict clinical progression in cART initiators. Methods A case-cohort study (n=470) within the multinational Prospective Evaluation of Antiretrovirals in Resource-Limited Settings (PEARLS) clinical trial (1571 HIV treatment-naïve adults who initiated cART; CD4+ T cell count <300 cells/mm3; nine countries) was conducted. A subcohort of 30 participants/country was randomly selected; additional cases were added from the main cohort. Cases (n=236 [random subcohort–36; main cohort–200]) had clinical progression (incident WHO Stage 3/4 event or death) within 96 weeks following cART initiation. Immune activation biomarkers were quantified pre-cART. Associations between biomarkers and clinical progression were examined using weighted multivariable Cox-proportional hazards models. Results Median age was 35 years, 45% were women, 49% black, 31% Asian, and 9% white. Median CD4+ T-cell count was 167 cells/mm3. In multivariate analysis, highest quartile CRP concentration (adjusted hazards ratio [aHR] 2.53, 95%CI 1.02-6.28) and CD4+ T-cell activation (aHR 5.18, 95CI 1.09-24.47) were associated with primary outcomes, compared to lowest quartiles. sCD14 had a trend towards association with clinical failure (aHR 2.24, 95%CI 0.96–5.21). Conclusions Measuring CRP and CD4+ T-cell activation may identify patients with CD4+ T cell counts < 300 cells/mm3 at risk for early clinical progression when initiating cART. Additional vigilance and symptom-based screening may be required in this subset of patients even after beginning cART. PMID:26017661

When a gold standard isn't so golden: Lack of prediction of subjective sleep quality from sleep polysomnography.

PubMed

Kaplan, Katherine A; Hirshman, Jason; Hernandez, Beatriz; Stefanick, Marcia L; Hoffman, Andrew R; Redline, Susan; Ancoli-Israel, Sonia; Stone, Katie; Friedman, Leah; Zeitzer, Jamie M

2017-02-01

Reports of subjective sleep quality are frequently collected in research and clinical practice. It is unclear, however, how well polysomnographic measures of sleep correlate with subjective reports of prior-night sleep quality in elderly men and women. Furthermore, the relative importance of various polysomnographic, demographic and clinical characteristics in predicting subjective sleep quality is not known. We sought to determine the correlates of subjective sleep quality in older adults using more recently developed machine learning algorithms that are suitable for selecting and ranking important variables. Community-dwelling older men (n=1024) and women (n=459), a subset of those participating in the Osteoporotic Fractures in Men study and the Study of Osteoporotic Fractures study, respectively, completed a single night of at-home polysomnographic recording of sleep followed by a set of morning questions concerning the prior night's sleep quality. Questionnaires concerning demographics and psychological characteristics were also collected prior to the overnight recording and entered into multivariable models. Two machine learning algorithms, lasso penalized regression and random forests, determined variable selection and the ordering of variable importance separately for men and women. Thirty-eight sleep, demographic and clinical correlates of sleep quality were considered. Together, these multivariable models explained only 11-17% of the variance in predicting subjective sleep quality. Objective sleep efficiency emerged as the strongest correlate of subjective sleep quality across all models, and across both sexes. Greater total sleep time and sleep stage transitions were also significant objective correlates of subjective sleep quality. The amount of slow wave sleep obtained was not determined to be important. Overall, the commonly obtained measures of polysomnographically-defined sleep contributed little to subjective ratings of prior-night sleep quality. Though they explained relatively little of the variance, sleep efficiency, total sleep time and sleep stage transitions were among the most important objective correlates. Published by Elsevier B.V.

When a gold standard isn't so golden: Lack of prediction of subjective sleep quality from sleep polysomnography

PubMed Central

Kaplan, Katherine A.; Hirshman, Jason; Hernandez, Beatriz; Stefanick, Marcia L.; Hoffman, Andrew R.; Redline, Susan; Ancoli-Israel, Sonia; Stone, Katie; Friedman, Leah; Zeitzer, Jamie M.

2016-01-01

Background Reports of subjective sleep quality are frequently collected in research and clinical practice. It is unclear, however, how well polysomnographic measures of sleep correlate with subjective reports of prior-night sleep quality in elderly men and women. Furthermore, the relative importance of various polysomnographic, demographic and clinical characteristics in predicting subjective sleep quality is not known. We sought to determine the correlates of subjective sleep quality in in older adults using more recently developed machine learning algorithms that are suitable for selecting and ranking important variables. Methods Community-dwelling older men (n=1024) and women (n=459), a subset of those participating in the Osteoporotic Fractures in Men study and the Study of Osteoporotic Fractures study, respectively, completed a single night of at-home polysomnographic recording of sleep followed by a set of morning questions concerning the prior night's sleep quality. Questionnaires concerning demographics and psychological characteristics were also collected prior to the overnight recording and entered into multivariable models. Two machine learning algorithms, lasso penalized regression and random forests, determined variable selection and the ordering of variable importance separately for men and women. Results Thirty-eight sleep, demographic and clinical correlates of sleep quality were considered. Together, these multivariable models explained only 11-17% of the variance in predicting subjective sleep quality. Objective sleep efficiency emerged as the strongest correlate of subjective sleep quality across all models, and across both sexes. Greater total sleep time and sleep stage transitions were also significant objective correlates of subjective sleep quality. The amount of slow wave sleep obtained was not determined to be important. Conclusions Overall, the commonly obtained measures of polysomnographically-defined sleep contributed little to subjective ratings of prior-night sleep quality. Though they explained relatively little of the variance, sleep efficiency, total sleep time and sleep stage transitions were among the most important objective correlates. PMID:27889439

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

NASA Technical Reports Server (NTRS)

Koslovsky, Matthew; Arellano, John; Schaefer, Caroline; Feiveson, Alan; Young, Millennia; Lee, Stuart

2017-01-01

The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

EPA Science Inventory

Abstract

Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

Percentage of Positive Biopsy Cores: A Better Risk Stratification Model for Prostate Cancer?

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

Huang Jiayi; Vicini, Frank A.; Williams, Scott G.

2012-07-15

Purpose: To assess the prognostic value of the percentage of positive biopsy cores (PPC) and perineural invasion in predicting the clinical outcomes after radiotherapy (RT) for prostate cancer and to explore the possibilities to improve on existing risk-stratification models. Methods and Materials: Between 1993 and 2004, 1,056 patients with clinical Stage T1c-T3N0M0 prostate cancer, who had four or more biopsy cores sampled and complete biopsy core data available, were treated with external beam RT, with or without a high-dose-rate brachytherapy boost at William Beaumont Hospital. The median follow-up was 7.6 years. Multivariate Cox regression analysis was performed with PPC, Gleasonmore » score, pretreatment prostate-specific antigen, T stage, PNI, radiation dose, androgen deprivation, age, prostate-specific antigen frequency, and follow-up duration. A new risk stratification (PPC classification) was empirically devised to incorporate PPC and replace the T stage. Results: On multivariate Cox regression analysis, the PPC was an independent predictor of distant metastasis, cause-specific survival, and overall survival (all p < .05). A PPC >50% was associated with significantly greater distant metastasis (hazard ratio, 4.01; 95% confidence interval, 1.86-8.61), and its independent predictive value remained significant with or without androgen deprivation therapy (all p < .05). In contrast, PNI and T stage were only predictive for locoregional recurrence. Combining the PPC ({<=}50% vs. >50%) with National Comprehensive Cancer Network risk stratification demonstrated added prognostic value of distant metastasis for the intermediate-risk (hazard ratio, 5.44; 95% confidence interval, 1.78-16.6) and high-risk (hazard ratio, 4.39; 95% confidence interval, 1.70-11.3) groups, regardless of the use of androgen deprivation and high-dose RT (all p < .05). The proposed PPC classification appears to provide improved stratification of the clinical outcomes relative to the National Comprehensive Cancer Network classification. Conclusions: The PPC is an independent and powerful predictor of clinical outcomes of prostate cancer after RT. A risk model replacing T stage with the PPC to reduce subjectivity demonstrated potentially improved stratification.« less

Determinants of quality of life of patients with heart failure and iron deficiency treated with ferric carboxymaltose: FAIR-HF sub-analysis.

PubMed

Gutzwiller, Florian S; Pfeil, Alena M; Comin-Colet, Josep; Ponikowski, Piotr; Filippatos, Gerasimos; Mori, Claudio; Braunhofer, Peter G; Szucs, Thomas D; Schwenkglenks, Matthias; Anker, Stefan D

2013-10-09

Heart failure (HF) is a burden to patients and health care systems. The objectives of HF treatment are to improve health related quality of life (HRQoL) and reduce mortality and morbidity. We aimed to evaluate determinants of health-related quality of life (HRQoL) in patients with iron deficiency and HF treated with intravenous (i.v.) iron substitution or placebo. A randomised, double-blind, placebo-controlled trial (n = 459) in iron-deficient chronic heart failure (CHF) patients with or without anaemia studied clinical and HRQoL benefits of i.v. iron substitution using ferric carboxymaltose (FCM) over a 24-week trial period. Multivariate analysis was carried out with various clinical variables as independent variables and HRQoL measures as dependent variables. Mean change from baseline of European Quality of Life - 5 Dimensions (EQ-5D) (value set-based) utilities (on a 0 to 100 scale) at week 24 was 8.91 (i.v. iron) and 0.68 (placebo; p < 0.01). In a multivariate analysis excluding baseline HRQoL, a higher exercise tolerance and i.v. iron substitution positively influenced HRQoL, whereas impaired renal function and a history of stroke had a negative effect. The level of HRQoL was also influenced by country of residence. When baseline HRQoL was factored in, the multivariate model remained stable. In this study, i.v. iron substitution, exercise tolerance, stroke, country of residence and renal function influenced measures of HRQoL in patients with heart failure and iron deficiency. © 2013.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Predictive value of clinical scoring and simplified gait analysis for acetabulum fractures.

PubMed

Braun, Benedikt J; Wrona, Julian; Veith, Nils T; Rollman, Mika; Orth, Marcel; Herath, Steven C; Holstein, Jörg H; Pohlemann, Tim

2016-12-01

Fractures of the acetabulum show a high, long-term complication rate. The aim of the present study was to determine the predictive value of clinical scoring and standardized, simplified gait analysis on the outcome after these fractures. Forty-one patients with acetabular fractures treated between 2008 and 2013 and available, standardized video recorded aftercare were identified from a prospective database. A visual gait score was used to determine the patients walking abilities 6-m postoperatively. Clinical (Merle d'Aubigne and Postel score, visual analogue scale pain, EQ5d) and radiological scoring (Kellgren-Lawrence score, postoperative computed tomography, and Matta classification) were used to perform correlation and multivariate regression analysis. The average patient age was 48 y (range, 15-82 y), six female patients were included in the study. Mean follow-up was 1.6 y (range, 1-2 y). Moderate correlation between the gait score and outcome (versus EQ5d: r s  = 0.477; versus Merle d'Aubigne: r s  = 0.444; versus Kellgren-Lawrence: r s  = -0.533), as well as high correlation between the Merle d'Aubigne score and outcome were seen (versus EQ5d: r s  = 0.575; versus Merle d'Aubigne: r s  = 0.776; versus Kellgren-Lawrence: r s  = -0.419). Using a multivariate regression model, the 6 m gait score (B = -0.299; P < 0.05) and early osteoarthritis development (B = 1.026; P < 0.05) were determined as predictors of final osteoarthritis. A good fit of the regression model was seen (R 2  = 904). Easy and available clinical scoring (gait score/Merle d'Aubigne) can predict short-term radiological and functional outcome after acetabular fractures with sufficient accuracy. Decisions on further treatment and interventions could be based on simplified gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.

PubMed

O'Muircheartaigh, Jonathan; Marquand, Andre; Hodkinson, Duncan J; Krause, Kristina; Khawaja, Nadine; Renton, Tara F; Huggins, John P; Vennart, William; Williams, Steven C R; Howard, Matthew A

2015-02-01

Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather than clinically relevant pain states. These reports have coincided with others describing the application of arterial spin labeling (ASL) to detect changes in regional cerebral blood flow (rCBF) in patients with on-going clinical pain. We combined these acquisition and analysis methodologies in a well-characterized postsurgical pain model. The principal aims were (1) to assess the classification accuracy of rCBF indices acquired prior to and following surgical intervention and (2) to optimise the amount of data required to maintain accurate classification. Twenty male volunteers, requiring bilateral, lower jaw third molar extraction (TME), underwent ASL examination prior to and following individual left and right TME, representing presurgical and postsurgical states, respectively. Six ASL time points were acquired at each exam. Each ASL image was preceded by visual analogue scale assessments of alertness and subjective pain experiences. Using all data from all sessions, an independent Gaussian Process binary classifier successfully discriminated postsurgical from presurgical states with 94.73% accuracy; over 80% accuracy could be achieved using half of the data (equivalent to 15 min scan time). This work demonstrates the concept and feasibility of time-efficient, probabilistic prediction of clinically relevant pain at the individual level. We discuss the potential of ML techniques to impact on the search for novel approaches to diagnosis, management, and treatment to complement conventional patient self-reporting. © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cognitive versus Software-Assisted Registration: Development of a New Nomogram Predicting Prostate Cancer at MRI-Targeted Biopsies.

PubMed

Kaufmann, Sascha; Russo, Giorgio I; Thaiss, Wolfgang; Notohamiprodjo, Mike; Bamberg, Fabian; Bedke, Jens; Morgia, Giuseppe; Nikolaou, Konstantin; Stenzl, Arnulf; Kruck, Stephan

2018-04-03

Multiparametric magnetic resonance imaging (mpMRI) is gaining acceptance to guide targeted biopsy (TB) in prostate cancer (PC) diagnosis. We aimed to compare the detection rate of software-assisted fusion TB (SA-TB) versus cognitive fusion TB (COG-TB) for PC and to evaluate potential clinical features in detecting PC and clinically significant PC (csPC) at TB. This was a retrospective cohort study of patients with rising and/or persistently elevated prostate-specific antigen (PSA) undergoing mpMRI followed by either transperineal SA-TB or transrectal COG-TB. The analysis showed a matched-paired analysis between SA-TB versus COG-TB without differences in clinical or radiological characteristics. Differences among detection of PC/csPC among groups were analyzed. A multivariable logistic regression model predicting PC at TB was fitted. The model was evaluated using the receiver operating characteristic-derived area under the curve, goodness of fit test, and decision-curve analyses. One hundred ninety-one and 87 patients underwent SA-TB or COG-TB, respectively. The multivariate logistic analysis showed that SA-TB was associated with overall PC (odds ratio [OR], 5.70; P < .01) and PC at TB (OR, 3.00; P < .01) but not with overall csPC (P = .40) and csPC at TB (P = .40). A nomogram predicting PC at TB was constructed using the Prostate Imaging Reporting and Data System version 2.0, age, PSA density and biopsy technique, showing improved clinical risk prediction against a threshold probability of 10% with a c-index of 0.83. In patients with suspected PC, software-assisted biopsy detects most cancers and outperforms the cognitive approach in targeting magnetic resonance imaging-visible lesions. Furthermore, we introduced a prebiopsy nomogram for the probability of PC in TB. Copyright © 2018 Elsevier Inc. All rights reserved.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

PubMed Central

Winzer, Klaus-Jürgen; Buchholz, Anika; Schumacher, Martin; Sauerbrei, Willi

2016-01-01

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. PMID:26938061

Use of clinical and neuroimaging characteristics to distinguish temporal lobe herpes simplex encephalitis from its mimics.

PubMed

Chow, Felicia C; Glaser, Carol A; Sheriff, Heather; Xia, Dongxiang; Messenger, Sharon; Whitley, Richard; Venkatesan, Arun

2015-05-01

We describe the spectrum of etiologies associated with temporal lobe (TL) encephalitis and identify clinical and radiologic features that distinguish herpes simplex encephalitis (HSE) from its mimics. We reviewed all adult cases of encephalitis with TL abnormalities on magnetic resonance imaging (MRI) from the California Encephalitis Project. We evaluated the association between specific clinical and MRI characteristics and HSE compared with other causes of TL encephalitis and used multivariate logistic modeling to identify radiologic predictors of HSE. Of 251 cases of TL encephalitis, 43% had an infectious etiology compared with 16% with a noninfectious etiology. Of infectious etiologies, herpes simplex virus was the most commonly identified agent (n = 60), followed by tuberculosis (n = 8) and varicella zoster virus (n = 7). Of noninfectious etiologies, more than half (n = 21) were due to autoimmune disease. Patients with HSE were older (56.8 vs 50.2 years; P = .012), more likely to be white (53% vs 35%; P = .013), more likely to present acutely (88% vs 64%; P = .001) and with a fever (80% vs 49%; P < .001), and less likely to present with a rash (2% vs 15%; P = .010). In a multivariate model, bilateral TL involvement (odds ratio [OR], 0.38; 95% confidence interval [CI], .18-.79; P = .010) and lesions outside the TL, insula, or cingulate (OR, 0.37; 95% CI, .18-.74; P = .005) were associated with lower odds of HSE. In addition to HSE, other infectious and noninfectious etiologies should be considered in the differential diagnosis for TL encephalitis, depending on the presentation. Specific clinical and imaging features may aid in distinguishing HSE from non-HSE causes of TL encephalitis. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

Simple Predictive Model of Early Failure among Patients Undergoing First-Time Arteriovenous Fistula Creation.

PubMed

Eslami, Mohammad H; Zhu, Clara K; Rybin, Denis; Doros, Gheorghe; Siracuse, Jeffrey J; Farber, Alik

2016-08-01

Native arteriovenous fistulas (AVFs) have a high 1 year failure rate leading to a need for secondary procedures. We set out to create a predictive model of early failure in patients undergoing first-time AVF creation, to identify failure-associated factors and stratify initial failure risk. The Vascular Study Group of New England (VSGNE) (2010-2014) was queried to identify patients undergoing first-time AVF creation. Patients with early (within 3 months postoperation) AVF failure (EF) or no failure (NF) were compared, failure being defined as any AVF that could not be used for dialysis. A multivariate logistic regression predictive model of EF based on perioperative clinical variables was created. Backward elimination with alpha level of 0.2 was used to create a parsimonious model. We identified 376 first-time AVF patients with follow-up data available in VSGNE. EF rate was 17.5%. Patients in the EF group had lower rates of hypertension (80.3% vs. 93.2%, P = 0.003) and diabetes (47.0% vs. 61.3%, P = 0.039). EF patients were also more likely to have radial artery inflow (57.6% vs. 38.4%, P = 0.011) and have forearm cephalic vein outflow (57.6% vs. 36.5%, P = 0.008). Additionally, the EF group was noted to have significantly smaller mean diameters of target artery (3.1 ± 0.9 vs. 3.6 ± 1.1, P = 0.002) and vein (3.1 ± 0.7 vs. 3.6 ± 0.9, P < 0.001). Multivariate analyses revealed that hypertension, diabetes, and vein larger than 3 mm were protective of EF (P < 0.05). The discriminating ability of this model was good (C-statistic = 0.731) and the model fits the data well (Hosmer-Lemeshow P = 0.149). β-estimates of significant factors were used to create a point system and assign probabilities of EF. We developed a simple model that robustly predicts first-time AVF EF and suggests that anatomical and clinical factors directly affect early AVF outcomes. The risk score has the potential to be used in clinical settings to stratify risk and make informed follow-up plans for AVF patients. Copyright © 2016 Elsevier Inc. All rights reserved.

An error bound for a discrete reduced order model of a linear multivariable system

NASA Technical Reports Server (NTRS)

Al-Saggaf, Ubaid M.; Franklin, Gene F.

1987-01-01

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.

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

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

A longitudinal study of clinical peer review's impact on quality and safety in U.S. hospitals.

PubMed

Edwards, Marc T

2013-01-01

Clinical peer review is the dominant method of event analysis in U.S. hospitals. It is pivotal to medical staff efforts to improve quality and safety, yet the quality assurance process model that has prevailed for the past 30 years evokes fear and is fundamentally antithetical to a culture of safety. Two prior national studies characterized a quality improvement model that corrects this dysfunction but failed to demonstrate progress toward its adoption despite a high rate of program change between 2007 and 2009. This study's online survey of 470 organizations participating in either of the prior studies further assessed relationships between clinical peer review program factors, including the degree of conformance to the quality improvement model (the QI model score), and subjectively measured program impact variables. Among the 300 hospitals (64%) that responded, the median QI model score was only 60 on a 100-point scale. Scores increased somewhat for the 2007 cohort (mean pair-wise difference of 5.9 [2-10]), but not for the 2009 cohort. The QI model is expanded as the result of the finding that self-reporting of adverse events, near misses, and hazardous conditions--an essential practice in high-reliability organizations--is no longer rare in hospitals. Self-reporting and the quality of case review are additional multivariate predictors of the perceived ongoing impact of clinical peer review on quality and safety, medical staff perceptions of the program, and medical staff engagement in quality and safety initiatives. Hospital leaders and trustees who seek to improve patient outcomes should facilitate the adoption of this best practice model for clinical peer review.

A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis.

PubMed

Ji, Jun; Ling, Xuefeng B; Zhao, Yingzhen; Hu, Zhongkai; Zheng, Xiaolin; Xu, Zhening; Wen, Qiaojun; Kastenberg, Zachary J; Li, Ping; Abdullah, Fizan; Brandt, Mary L; Ehrenkranz, Richard A; Harris, Mary Catherine; Lee, Timothy C; Simpson, B Joyce; Bowers, Corinna; Moss, R Lawrence; Sylvester, Karl G

2014-01-01

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

PubMed Central

Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

2017-01-01

Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

Factors Impacting Growth in Infants with Single Ventricle Physiology: A Report from Pediatric Heart Network Infant Single Ventricle Trial

PubMed Central

Williams, Richard V.; Zak, Victor; Ravishankar, Chitra; Altmann, Karen; Anderson, Jeffrey; Atz, Andrew M.; Dunbar-Masterson, Carolyn; Ghanayem, Nancy; Lambert, Linda; Lurito, Karen; Medoff-Cooper, Barbara; Margossian, Renee; Pemberton, Victoria L.; Russell, Jennifer; Stylianou, Mario; Hsu, Daphne

2011-01-01

Objectives To describe growth patterns in infants with single ventricle physiology and determine factors influencing growth. Study design Data from 230 subjects enrolled in the Pediatric Heart Network Infant Single Ventricle Enalapril Trial were used to assess factors influencing change in weight-for-age z-score (Δz) from study enrollment (0.7 ± 0.4 months) to pre-superior cavopulmonary connection (SCPC) (5.1 ± 1.8 months, period 1), and pre-SCPC to final study visit (14.1 ± 0.9 months, period 2). Predictor variables included patient characteristics, feeding regimen, clinical center, and medical factors during neonatal (period 1) and SCPC hospitalizations (period 2). Univariate regression analysis was performed, followed by backward stepwise regression and bootstrapping reliability to inform a final multivariable model. Results Weights were available for 197/230 subjects for period 1 and 173/197 for period 2. For period 1, greater gestational age, younger age at study enrollment, tube feeding at neonatal discharge, and clinical center were associated with a greater negative Δz (poorer growth) in multivariable modeling (adjusted R2 = 0.39, p < 0.001). For period 2, younger age at SCPC and greater daily caloric intake were associated with greater positive Δz (better growth) (R2 = 0.10, p = 0.002). Conclusions Aggressive nutritional support and earlier SCPC are modifiable factors associated with a favorable change in weight-for-age z-score. PMID:21784436

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Preoperative Prediction of Node-Negative Disease After Neoadjuvant Chemotherapy in Patients Presenting with Node-Negative or Node-Positive Breast Cancer.

PubMed

Murphy, Brittany L; L Hoskin, Tanya; Heins, Courtney Day N; Habermann, Elizabeth B; Boughey, Judy C

2017-09-01

Axillary node status after neoadjuvant chemotherapy (NAC) influences the axillary surgical staging procedure as well as recommendations regarding reconstruction and radiation. Our aim was to construct a clinical preoperative prediction model to identify the likelihood of patients being node negative after NAC. Using the National Cancer Database (NCDB) from January 2010 to December 2012, we identified cT1-T4c, N0-N3 breast cancer patients treated with NAC. The effects of patient and tumor factors on pathologic node status were assessed by multivariable logistic regression separately for clinically node negative (cN0) and clinically node positive (cN+) disease, and two models were constructed. Model performance was validated in a cohort of NAC patients treated at our institution (January 2013-July 2016), and model discrimination was assessed by estimating the area under the curve (AUC). Of 16,153 NCDB patients, 6659 (41%) were cN0 and 9494 (59%) were cN+. Factors associated with pathologic nodal status and included in the models were patient age, tumor grade, biologic subtype, histology, clinical tumor category, and, in cN+ patients only, clinical nodal category. The validation dataset included 194 cN0 and 180 cN+ patients. The cN0 model demonstrated good discrimination, with an AUC of 0.73 (95% confidence interval [CI] 0.72-0.74) in the NCDB and 0.77 (95% CI 0.68-0.85) in the external validation, while the cN+ patient model AUC was 0.71 (95% CI 0.70-0.72) in the NCDB and 0.74 (95% CI 0.67-0.82) in the external validation. We constructed two models that showed good discrimination for predicting ypN0 status following NAC in cN0 and cN+ patients. These clinically useful models can guide surgical planning after NAC.

[Neutrophil to lymphocyte ratio in peripheral blood: a novel independent prognostic factor in patients with head and neck squamous cell carcinoma].

PubMed

Wu, F; Wu, L L; Zhu, L X

2017-01-23

Objective: To investigate whether neutrophil to lymphocyte ratio (NLR) in peripheral blood can be an independent prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Clinical data of 97 HNSCC patients who received surgical treatment in our department between January 2008 and January 2012 were analyzed retrospectively. The 97 patients were divided into low NLR group (NLR≤5, n =69) and high NLR group (NLR>5, n =28) according to the NLR in preoperative peripheral blood. The relationships of NLR and clinicopathological features were analyzed. Kaplan-Meier method was used for univariate survival analysis and Cox proportional hazard model for multivariate survival analysis. Results: The clinical stages were significantly different between high NLR group and low NLR group ( P <0.05), however, the age, gender, location, lymph node metastasis, smoking and alcohol of the two groups showed no significant differences ( P > 0.05 of all). Univariate survival analysis showed that smoking, lymph node metastasis, clinical stage and NLR value were risk factors for 3-year overall survival (OS) rate and relapse-free survival (RFS) rate of HNSCC patients ( P <0.05). The OS rate of high NLR and low NLR groups was 42.9% and 91.3%, and the RFS rate was 44.2% and 80.1%, respectively, with a statistically significant difference ( P <0.05 for both). Cox multivariate survival analysis showed that clinical stage and NLR were independent factors for prognostic evaluation of HNSCC patients ( P <0.05 for both). Conclusions: NLR level is significantly associated with clinical stage of HNSCC. High NLR is an independent prognostic rick factor and plays an important role in prognostic evaluation of HNSCC patients.

Clinically Non-Metastatic Renal Cell Carcinoma With Sarcomatoid Dedifferentiation: Natural History and Outcomes after Surgical Resection with Curative Intent

PubMed Central

Merrill, Megan M.; Wood, Christopher G.; Tannir, Nizar M.; Slack, Rebecca S.; Babaian, Kara N.; Jonasch, Eric; Pagliaro, Lance C.; Compton, Zachary; Tamboli, Pheroze; Sircar, Kanishka; Pisters, Louis L.; Matin, Surena F.; Karam, Jose A.

2015-01-01

Purpose Renal cell carcinoma with sarcomatoid dedifferentiation (sRCC) is an aggressive malignancy associated with a poor prognosis. While existing literature focuses on patients presenting with metastatic disease, characteristics and outcomes for patients with localized disease are not well described. We aimed to evaluate post-nephrectomy characteristics, outcomes, and predictors of survival in patients with sRCC who presented with clinically localized disease. Patients and Methods An IRB-approved review from 1986–2011 identified 77 patients who presented with clinically localized disease, underwent nephrectomy and had sRCC in their primary kidney tumor. Clinical and pathologic variables were captured for each patient. Overall survival (OS) and recurrence-free survival (RFS) were calculated for all patients and those who had no evidence of disease (NED) following nephrectomy, respectively. Comparisons were made with categorical groupings in proportional hazards regression models for univariable and multivariable analyses. Results OS for the entire cohort (N=77) at 2 years was 50%. A total of 56 (77%) patients of the 73 who were NED following nephrectomy experienced a recurrence, with a median time to recurrence of 26.2 months. On multivariable analysis, tumor stage, pathologically positive lymph nodes, and year of nephrectomy were significant predictors of both OS and RFS. Limitations include the retrospective nature of this study and relatively small sample size. Conclusions Long-term survival for patients with sRCC, even in clinically localized disease is poor. Aggressive surveillance of those who are NED following nephrectomy is essential and further prospective studies evaluating the benefit of adjuvant systemic therapies in this cohort are warranted. PMID:25700975

Rupture during coiling of intracranial aneurysms: Predictors and clinical outcome.

PubMed

Kocur, Damian; Przybyłko, Nikodem; Bażowski, Piotr; Baron, Jan

2018-02-01

The intraprocedural aneurysm rupture (IPR) is one of the most feared adverse effect associated with the coil embolization therapy. The aim of the study was to identify predisposing factors for IPR, as well as to define patient groups with worse clinical outcome following IPR. From February 2008 to March 2015, 273 consecutive patients were treated at our institution via endovascular coil embolization. Patient medical records were reviewed with emphasis on procedure description, potential risk factors and clinical outcomes related to IPR. The IPR occurred in 14 (5.13%) cases. Multivariate logistic regression models were used to determine independent predictors of IPR. Clinical outcome was analyzed using the Glasgow Outcome Scale (GOS). Multivariate analysis showed that aneurysm location at posterior communicating artery is an independent risk factor for IPR (p = 0.035; OR 3.5; 95%CI 1.09-11.26). The frequencies of favorable disability (GOS 4-5), severe disability (GOS 2-3), and mortality (GOS 1) between patients with IPR and without IPR were significantly different in the general study population (p < 0.001, p < 0.001 and p = 0.023, respectively) and in patients with previously unruptured aneurysms (p < 0.001, p = 0.006 and p = 0.003, respectively) but not in patients with previously ruptured aneurysms (p = 0.187, p = 0.089 and p = 1.0, respectively). Posterior communicating artery aneurysm location is an independent predictor for IPR. IPR is associated with a significant clinical deterioration in a subgroup of patients with previously unruptured aneurysms, but not in patients with ruptured aneurysms. Copyright © 2018 Elsevier B.V. All rights reserved.

Implementation of Evidence-Based Practice in Relation to a Clinical Nursing Ladder System: A National Survey in Taiwan

PubMed Central

Weng, Yi-Hao; Chen, Chiehfeng; Kuo, Ken N; Yang, Chun-Yuh; Lo, Heng-Lien; Chen, Kee-Hsin; Chiu, Ya-Wen

2015-01-01

Background Although evidence-based practice (EBP) has been widely investigated, few studies have investigated its correlation with a clinical nursing ladder system. The current national study evaluates whether EBP implementation has been incorporated into the clinical ladder system. Methods A cross-sectional questionnaire survey was conducted nationwide of registered nurses among regional hospitals of Taiwan in January to April 2011. Subjects were categorized into beginning nurses (N1 and N2) and advanced nurses (N3 and N4) by the clinical ladder system. Multivariate logistic regression model was used to adjust for possible confounding demographic factors. Results Valid postal questionnaires were collected from 4,206 nurses, including 2,028 N1, 1,595 N2, 412 N3, and 171 N4 nurses. Advanced nurses were more aware of EBP than beginning nurses (p < 0.001; 90.7% vs. 78.0%). In addition, advanced nurses were more likely to hold positive beliefs about and attitudes toward EBP (p < 0.001) and possessed more sufficient knowledge of and skills in EBP (p < 0.001). Furthermore, they more often implemented EBP principles (p < 0.001) and accessed online evidence-based retrieval databases (p < 0.001). The most common motivation for using online databases was self-learning for advanced nurses and positional promotion for beginning nurses. Multivariate logistic regression analyses showed advanced nurses were more aware of EBP, had higher knowledge and skills of EBP, and more often implemented EBP than beginning nurses. Linking Evidence to Action The awareness of, beliefs in, attitudes toward, knowledge of, skills in, and behaviors of EBP among advanced nurses were better than those among beginning nurses. The data indicate that a clinical ladder system can serve as a useful means to enhance EBP implementation. PMID:25588625

Nasal flaring as a clinical sign of respiratory acidosis in patients with dyspnea.

PubMed

Zorrilla-Riveiro, José Gregorio; Arnau-Bartés, Anna; Rafat-Sellarés, Ramón; García-Pérez, Dolors; Mas-Serra, Arantxa; Fernández-Fernández, Rafael

2017-04-01

To determine whether the presence of nasal flaring is a clinical sign of respiratory acidosis in patients attending emergency departments for acute dyspnea. Single-center, prospective, observational study of patients aged over 15 requiring urgent attention for dyspnea, classified as level II or III according to the Andorran Triage Program and who underwent arterial blood gas test on arrival at the emergency department. The presence of nasal flaring was evaluated by two observers. Demographic and clinical variables, signs of respiratory difficulty, vital signs, arterial blood gases and clinical outcome (hospitalization and mortality) were recorded. Bivariate and multivariate analyses were performed using logistic regression models. The sample comprised 212 patients, mean age 78years (SD=12.8), of whom 49.5% were women. Acidosis was recorded in 21.2%. Factors significantly associated with the presence of acidosis in the bivariate analysis were the need for pre-hospital medical care, triage level II, signs of respiratory distress, presence of nasal flaring, poor oxygenation, hypercapnia, low bicarbonates and greater need for noninvasive ventilation. Nasal flaring had a positive likelihood ratio for acidosis of 4.6 (95% CI 2.9-7.4). In the multivariate analysis, triage level II (aOR 5.16; 95% CI: 1.91 to 13.98), the need for oxygen therapy (aOR 2.60; 95% CI: 1.13-5.96) and presence of nasal flaring (aOR 6.32; 95% CI: 2.78-14.41) were maintained as factors independently associated with acidosis. Nasal flaring is a clinical sign of severity in patients requiring urgent care for acute dyspnea, which has a strong association with acidosis and hypercapnia. Copyright © 2016 Elsevier Inc. All rights reserved.

Are Characteristics of the Medical Home Associated with Diabetes Care Costs?

PubMed Central

Flottemesch, Thomas J.; Scholle, Sarah Hudson; O’Connor, Patrick J.; Solberg, Leif I.; Asche, Steve; Pawlson, L. Gregory

2015-01-01

Objective To examine the relationship between primary care medical home clinical practice systems (PCMH clinical practice systems) corresponding to the domains of the Chronic Care Model and diabetes-related healthcare costs incurred by members of a health plan who have diagnosed Type 2 diabetes and received care at one of 27 Minnesota-based medical groups over a 12-month period. Study Design Cross-sectional analysis of patient-level cost data in relation to the presence of PCMH clinical practice systems by Chronic Care Model domain using the Physician Practice Connections Readiness Survey (PPC-RS). Methods Multivariate regressions adjusting for patient demographics, health status and comorbidities estimated the relationship between the presence of PCMH clinical practice systems as measured by the PPC-RS and three outcomes: total diabetes-related healthcare costs, ambulatory care management costs, and potentially avoidable costs (e.g. unscheduled inpatient and emergency care). Results Two domains of PCMH clinical practice systems as measured by the PPC-RS were significantly associated with reductions in potentially avoidable costs. These were Health Care Organization (p=.04) and clinical reminder systems in the Decision Support domain (p=.01). Compared to medical groups with only quality improvement, those with improved Health Care Organization defined as performance measurement and individual provider feedback averaged $245/patient less. Similarly, medical groups with clinical reminders for counseling averaged $338/patient less. Conclusions PCMH clinical practice systems that correspond to some domains of the Chronic Care Model are related to reduced inpatient and emergency care costs. Further research is needed about how these systems impact costs over time. PMID:22710277

Predicting performance: relative importance of students' background and past performance.

PubMed

Stegers-Jager, Karen M; Themmen, Axel P N; Cohen-Schotanus, Janke; Steyerberg, Ewout W

2015-09-01

Despite evidence for the predictive value of both pre-admission characteristics and past performance at medical school, their relative contribution to predicting medical school performance has not been thoroughly investigated. This study was designed to determine the relative importance of pre-admission characteristics and past performance in medical school in predicting student performance in pre-clinical and clinical training. This longitudinal prospective study followed six cohorts of students admitted to a Dutch, 6-year, undergraduate medical course during 2002-2007 (n = 2357). Four prediction models were developed using multivariate logistic regression analysis. Main outcome measures were 'Year 1 course completion within 1 year' (models 1a, 1b), 'Pre-clinical course completion within 4 years' (model 2) and 'Achievement of at least three of five clerkship grades of ≥ 8.0' (model 3). Pre-admission characteristics (models 1a, 1b, 2, 3) and past performance at medical school (models 1b, 2, 3) were included as predictor variables. In model 1a - including pre-admission characteristics only - the strongest predictor for Year 1 course completion was pre-university grade point average (GPA). Success factors were 'selected by admission testing' and 'age > 21 years'; risk factors were 'Surinamese/Antillean background', 'foreign pre-university degree', 'doctor parent' and male gender. In model 1b, number of attempts and GPA at 4 months were the strongest predictors for Year 1 course completion, and male gender remained a risk factor. Year 1 GPA was the strongest predictor for pre-clinical course completion, whereas being male or aged 19-21 years were risk factors. Pre-clinical course GPA positively predicted clinical performance, whereas being non-Dutch or a first-generation university student were important risk factors for lower clinical grades. Nagelkerke's R(2) ranged from 0.16 to 0.62. This study not only confirms the importance of past performance as a predictor of future performance in pre-clinical training, but also reveals the importance of a student's background as a predictor in clinical training. These findings have important practical implications for selection and support during medical school. © 2015 John Wiley & Sons Ltd.

Physician control of needle and syringe during aspiration-injection procedures with the new reciprocating syringe.

PubMed

Sibbitt, Wilmer; Sibbitt, Randy R; Michael, Adrian A; Fu, Druce I; Draeger, Hilda T; Twining, Jon M; Bankhurst, Arthur D

2006-04-01

To evaluate physician control of needle and syringe during aspiration-injection syringe procedures by comparing the new reciprocating procedure syringe to a traditional conventional syringe. Twenty-six physicians were tested for their individual ability to control the reciprocating and conventional syringes in typical aspiration-injection procedures using a novel quantitative needle-based displacement procedure model. Subsequently, the physicians performed 48 clinical aspiration-injection (arthrocentesis) procedures on 32 subjects randomized to the reciprocating or conventional syringes. Clinical outcomes included procedure time, patient pain, and operator satisfaction. Multivariate modeling methods were used to determine the experimental variables in the syringe control model most predictive of clinical outcome measures. In the model system, the reciprocating syringe significantly improved physician control of the syringe and needle, with a 66% reduction in unintended forward penetration (p < 0.001) and a 68% reduction in unintended retraction (p < 0.001). In clinical arthrocentesis, improvements were also noted: 30% reduction in procedure time (p < 0.03), 57% reduction in patient pain (p < 0.001), and a 79% increase in physician satisfaction (p < 0.001). The variables in the experimental system--unintended forward penetration, unintended retraction, and operator satisfaction--independently predicted the outcomes of procedure time, patient pain, and physician satisfaction in the clinical study (p < or = 0.001). The reciprocating syringe reduces procedure time and patient pain and improves operator satisfaction with the procedure syringe. The reciprocating syringe improves physician performance in both the validated quantitative needle-based displacement model and in real aspiration-injection syringe procedures, including arthrocentesis.

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

The current contribution of molecular factors to risk estimation in neuroblastoma patients.

PubMed

Berthold, F; Sahin, K; Hero, B; Christiansen, H; Gehring, M; Harms, D; Horz, S; Lampert, F; Schwab, M; Terpe, J

1997-10-01

The association of molecular characteristics with prognosis has been reported, but not their relationship with each other and their impact in the context of known clinical risk factors. In this study, data of 1249 consecutive intent-to-treat-neuroblastoma patients with more than 1 year follow-up were examined by multivariate analysis using loglinear and Cox proportional hazard regression models on a stage-related basis (stages 1-3: 600, 4S: 116, 4: 533). In a first step, risk factors were identified from 18 selected clinical variables, and risk groups defined. The second step investigated whether molecular characteristics (MYCN, LOH 1p, del 1p, CD44, N-ras, NGF-R, bcl-2, APO-1 (CD95)) contributed additional prognostic information to the model. The loglinear model demonstrated several interactions between clinical factors. By the Cox regression model, seven independent clinical risk factors were found for stages 1-3, seven for stage 4 and two for stage 4S. By subsequent introduction of all molecular variables, MYCN amplification only added significant prognostic information to the clinical factors in localised and stage 4 neuroblastoma. The models allowed the definition of risk groups for stages 1-3 patients by age (e beta = 5.09) and MYCN (e beta = 4.26), for stage 4 by MYCN (e beta = 2.78) and number of symptoms (e beta = 2.44) and for stage 4S by platelet count (e beta = 3.91) and general condition (e beta = 2.99). Molecular factors and in particular MYCN contribute significantly to risk estimation. In conjunction with clinical factors, they are powerful tools to define risk groups in neuroblastoma.

One thing leads to another: the cascade of obligations when researchers report genetic research results to study participants.

PubMed

Miller, Fiona Alice; Hayeems, Robin Zoe; Li, Li; Bytautas, Jessica Peace

2012-08-01

Even as debate continues about the putative obligation to proactively report genetic research results to study participants, there is an increasing need to attend to the obligations that might cascade from any initial report. We conducted an international, quasi-experimental survey of researchers involved in autism spectrum disorders (ASD) and cystic fibrosis (CF) genetics to explore perceived obligations to ensure updated information or relevant clinical care subsequent to any initial communication of research results, and factors influencing these attitudes. 5-point Likert scales of dis/agreement were analyzed using descriptive and multivariate statistics. Of the 343 respondents (44% response rate), large majorities agreed that in general and in a variety of hypothetical research contexts, research teams that report results should ensure that participants gain subsequent access to updated information (74-83%) and implicated clinical services (79-87%). At the same time, researchers perceived barriers restricting access to relevant clinical care, though this was significantly more pronounced (P<0.001) for ASD (64%) than CF (34%). In the multivariate model, endorsement of cascading obligations was positively associated with researcher characteristics (eg, clinical role/training) and attitudes (eg, perceived initial reporting obligation), and negatively associated with the initial report of less scientifically robust hypothetical results, but unaffected by perceived or hypothetical barriers to care. These results suggest that researchers strongly endorse information and care-based obligations that cascade from the initial report of research results to study participants. In addition, they raise challenging questions about how any cascading obligations are to be met, especially where access challenges are already prevalent.

Clinical Usefulness of Measuring Red Blood Cell Distribution Width in Patients with Hepatitis B Virus-Related Acute-On-Chronic Liver Failure.

PubMed

Jin, Lei; Gao, Yufeng; Ye, Jun; Zou, Guizhou; Li, Xu

2017-09-01

The red blood cell distribution width (RDW) is increased in chronic liver disease, but its clinical significance in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is still unclear. The aim of the present study was to investigate the clinical significance of RDW in HBV-ACLF patients. The medical records of HBV-ACLF patients who were admitted to The Second Affiliated Hospital of Anhui Medical University between April 2012 and December 2015 were retrospectively reviewed. Correlations between RDW, neutrophil lymphocyte ratio (NLR), and the model for end-stage liver disease (MELD) scores were analyzed using the Spearman's approach. Multivariable stepwise logistic regression test was used to evaluate independent clinical parameters predicting 3-month mortality of HBV-ACLF patients. The association between RDW and hospitalization outcome was estimated by receiver operating curve (ROC) analysis. Patient survival was estimated by Kaplan-Meier analysis and subsequently compared by log-rank test. Sixty-two HBV-ACLF patients and sixty CHB patients were enrolled. RDW were increased in HBVACLF patients and positively correlated with the NLR as well as MELD scores. Multivariate analysis demonstrated that RDW value was an independent predictor for mortality. RDW had an area under the ROC of 0.799 in predicting 3-month mortality of HBV-ACLF patients. Patients with HBV-ACLF who had RDW > 17% showed significantly poorer survival than those who had RDW ≤ 17%. RDW values are significantly increased in patients with HBV-ACLF. Moreover, RDW values are an independent predicting factor for an in-hospital mortality in patients with HBV-ACLF.

Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules.

PubMed

Zhang, Man; Zhuo, Na; Guo, Zhanlin; Zhang, Xingguang; Liang, Wenhua; Zhao, Sheng; He, Jianxing

2015-10-01

The aim of this study was to establish a model for predicting the probability of malignancy in solitary pulmonary nodules (SPNs) and provide guidance for the diagnosis and follow-up intervention of SPNs. We retrospectively analyzed the clinical data and computed tomography (CT) images of 294 patients with a clear pathological diagnosis of SPN. Multivariate logistic regression analysis was used to screen independent predictors of the probability of malignancy in the SPN and to establish a model for predicting malignancy in SPNs. Then, another 120 SPN patients who did not participate in the model establishment were chosen as group B and used to verify the accuracy of the prediction model. Multivariate logistic regression analysis showed that there were significant differences in age, smoking history, maximum diameter of nodules, spiculation, clear borders, and Cyfra21-1 levels between subgroups with benign and malignant SPNs (P<0.05). These factors were identified as independent predictors of malignancy in SPNs. The area under the curve (AUC) was 0.910 [95% confidence interval (CI), 0.857-0.963] in model with Cyfra21-1 significantly better than 0.812 (95% CI, 0.763-0.861) in model without Cyfra21-1 (P=0.008). The area under receiver operating characteristic (ROC) curve of our model is significantly higher than the Mayo model, VA model and Peking University People's (PKUPH) model. Our model (AUC =0.910) compared with Brock model (AUC =0.878, P=0.350), the difference was not statistically significant. The model added Cyfra21-1 could improve prediction. The prediction model established in this study can be used to assess the probability of malignancy in SPNs, thereby providing help for the diagnosis of SPNs and the selection of follow-up interventions.

Performance of 21 HPV vaccination programs implemented in low and middle-income countries, 2009–2013

PubMed Central

2014-01-01

Background Cervical cancer is the third most common cancer in women worldwide, with high incidence in lowest income countries. Vaccination against Human Papilloma Virus (HPV) may help to reduce the incidence of cervical cancer. The aim of the study was to analyze HPV vaccination programs performance implemented in low and middle-income countries. Methods The Gardasil Access Program provides HPV vaccine at no cost to help national institutions gain experience implementing HPV vaccination. Data on vaccine delivery model, number of girls vaccinated, number of girls completing the three-dose campaign, duration of vaccination program, community involvement and sensitization strategies were collected from each program upon completion. Vaccine Uptake Rate (VUR) and Vaccine Adherence between the first and third doses (VA) rate were calculated. Multivariate linear regressions analyses were fitted. Results Twenty-one programs were included in 14 low and middle-income countries. Managing institutions were non-governmental organizations (NGOs) (n = 8) or Ministries of Health (n = 13). Twelve programs were school-based, five were health clinic-based and four utilized a mixed model. A total of 217,786 girls received a full course of vaccination. Mean VUR was 88.7% (SD = 10.5) and VA was 90.8% (SD = 7.3). The mean total number of girls vaccinated per program-month was 2,426.8 (SD = 2,826.6) in school model, 335.1 (SD = 202.5) in the health clinic and 544.7 (SD = 369.2) in the mixed models (p = 0.15). Community involvement in the follow-up of girls participating in the vaccination campaign was significantly associated with VUR. Multivariate analyses identified school-based (β = 13.35, p = 0.001) and health clinic (β = 13.51, p = 0.03) models, NGO management (β = 14.58, p < 10-3) and duration of program vaccination (β = -1.37, p = 0.03) as significant factors associated with VUR. Conclusion School and health clinic-based models appeared as predictive factors for vaccination coverage, as was management by an NGO; program duration could play a role in the program’s effectiveness. Results suggest that HPV vaccine campaigns tailored to meet the needs of communities can be effective. These results may be useful in the development of national HPV vaccination policies in low and middle-income countries. PMID:24981818

Multivariable model development and internal validation for prostate cancer specific survival and overall survival after whole-gland salvage Iodine-125 prostate brachytherapy.

PubMed

Peters, Max; van der Voort van Zyp, Jochem R N; Moerland, Marinus A; Hoekstra, Carel J; van de Pol, Sandrine; Westendorp, Hendrik; Maenhout, Metha; Kattevilder, Rob; Verkooijen, Helena M; van Rossum, Peter S N; Ahmed, Hashim U; Shah, Taimur T; Emberton, Mark; van Vulpen, Marco

2016-04-01

Whole-gland salvage Iodine-125-brachytherapy is a potentially curative treatment strategy for localised prostate cancer (PCa) recurrences after radiotherapy. Prognostic factors influencing PCa-specific and overall survival (PCaSS & OS) are not known. The objective of this study was to develop a multivariable, internally validated prognostic model for survival after whole-gland salvage I-125-brachytherapy. Whole-gland salvage I-125-brachytherapy patients treated in the Netherlands from 1993-2010 were included. Eligible patients had a transrectal ultrasound-guided biopsy-confirmed localised recurrence after biochemical failure (clinical judgement, ASTRO or Phoenix-definition). Recurrences were assessed clinically and with CT and/or MRI. Metastases were excluded using CT/MRI and technetium-99m scintigraphy. Multivariable Cox-regression was used to assess the predictive value of clinical characteristics in relation to PCa-specific and overall mortality. PCa-specific mortality was defined as patients dying with distant metastases present. Missing data were handled using multiple imputation (20 imputed sets). Internal validation was performed and the C-statistic calculated. Calibration plots were created to visually assess the goodness-of-fit of the final model. Optimism-corrected survival proportions were calculated. All analyses were performed according to the TRIPOD statement. Median total follow-up was 78months (range 5-139). A total of 62 patients were treated, of which 28 (45%) died from PCa after mean (±SD) 82 (±36) months. Overall, 36 patients (58%) patients died after mean 84 (±40) months. PSA doubling time (PSADT) remained a predictive factor for both types of mortality (PCa-specific and overall): corrected hazard ratio's (HR's) 0.92 (95% CI: 0.86-0.98, p=0.02) and 0.94 (95% CI: 0.90-0.99, p=0.01), respectively (C-statistics 0.71 and 0.69, respectively). Calibration was accurate up to 96month follow-up. Over 80% of patients can survive 8years if PSADT>24months (PCaSS) and >33months (OS). Only approximately 50% survival is achieved with a PSADT of 12months. A PSADT of respectively >24months and >33months can result in >80% probability of PCa- specific and overall survival 8years after whole-gland salvage I-125-brachytherapy. Survival should be weighed against toxicity from a salvage procedure. Larger series and external validation are necessary. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

TIA, stroke and orthostatic hypotension: a disease spectrum related to ageing vasculature?

PubMed

Kwok, C S; Ong, A C L; Potter, J F; Metcalf, A K; Myint, P K

2014-06-01

We sought to identify the determinants of orthostatic hypotension (OH) among patients referred to the transient ischaemic attack (TIA) clinic. We conducted a retrospective analysis of prospectively collected data on patients who attended the TIA clinic in a UK hospital between January 2006 and September 2009. Each patient had their supine and standing or sitting blood pressure measured. Logistic regression was used to estimate the univariate and multivariate odds of OH for the subgroups of patients based on their diagnosis. A 10% significance level for the univariate analysis was used to identify variables in the multivariate model. A total of 3222 patients were studied of whom 1131 had a TIA, 665 a stroke and 1426 had other diagnoses. The prevalence of either systolic or diastolic OH in the TIA, stroke and patients with other diagnoses was similar being 22% (n = 251), 24% (n = 162) and 20% (n = 292), respectively. Multivariate analyses showed age, prior history of TIA, and diabetes were independently significantly associated with systolic OH alone or diastolic OH alone or either systolic or diastolic OH [ORs 1.03 (1.02-1.05); 1.56 (1.05-2.31); 1.65 (1.10-2.47), respectively]. Among the patients with the diagnosis of stroke, peripheral vascular disease (PVD) was significantly associated with increased odds of OH (3.56, 1.53-8.31), whereas male gender had a significantly lower odds of OH (0.61, 0.42-0.88). In patients with other diagnoses, age (1.04, 1.02-1.05) and diabetes (1.47, 1.04-2.09) were associated with OH, whereas male gender was (0.76, 0.58-1.00) not associated with OH. Orthostatic hypotension is prevalent among patients presenting to TIA clinic. Previous history of vascular disease (prior TIA/stroke/PVD) appears to be a significant associate of OH in this patient population. © 2014 John Wiley & Sons Ltd.

Illicit drug use is a significant risk factor for loss to follow up in patients with HIV-1 infection at a large urban HIV clinic in Tokyo.

PubMed

Nishijima, Takeshi; Gatanaga, Hiroyuki; Komatsu, Hirokazu; Takano, Misao; Ogane, Miwa; Ikeda, Kazuko; Oka, Shinichi

2013-01-01

Loss to follow up (LTFU) is an important prognostic factor in patients with HIV-1 infection. The impact of illicit drug use on LTFU of patients with HIV-1 infection is unknown in Japan. A single center observational study was conducted to elucidate the impact of illicit drug use on LTFU at a large HIV clinic in Tokyo. LTFU was defined as those who discontinued their visits to the clinic for at least 12 months and were not known to be under the care of other facilities or have died within 12 months of their last visit. Patients who first visited the clinic between January 2005 and August 2010 were enrolled. Information on illicit drug use was collected in a structured interview and medical charts. Comparison of the effects of illicit drug use and no use on LTFU was conducted by uni- and multi-variate Cox hazards models as the primary exposure. The study subjects were 1,208 patients, mostly Japanese men, of relatively young age, and infected through homosexual contact. A total of 111 patients (9.2%) were LTFU (incidence: 24.9 per 1,000 person-years). Among illicit drug users and non users, 55 (13.3%) and 56 (7.1%) patients, respectively, were LTFU, with incidence of 35.7 and 19.2 per 1,000 person-years, respectively. Uni- and multi-variate analyses showed that illicit drug use was a significant risk for LTFU (HR=1.860; 95% CI, 1.282-2.699; p=0.001) (adjusted HR=1.544; 95% CI, 1.028-2.318; p=0.036). Multivariate analysis also identified young age, high CD4 count, no antiretroviral therapy, and no health insurance as risk factors for LTFU. The incidence of LTFU among illicit drug users was almost twice higher than that among non users. Effective intervention for illicit drug use in this population is warranted to ensure proper treatment and prevent the spread of HIV.

Creation of mortality risk charts using 123I meta-iodobenzylguanidine heart-to-mediastinum ratio in patients with heart failure: 2- and 5-year risk models.

PubMed

Nakajima, Kenichi; Nakata, Tomoaki; Matsuo, Shinro; Jacobson, Arnold F

2016-10-01

(123)I meta-iodobenzylguanidine (MIBG) imaging has been extensively used for prognostication in patients with chronic heart failure (CHF). The purpose of this study was to create mortality risk charts for short-term (2 years) and long-term (5 years) prediction of cardiac mortality. Using a pooled database of 1322 CHF patients, multivariate analysis, including (123)I-MIBG late heart-to-mediastinum ratio (HMR), left ventricular ejection fraction (LVEF), and clinical factors, was performed to determine optimal variables for the prediction of 2- and 5-year mortality risk using subsets of the patients (n = 1280 and 933, respectively). Multivariate logistic regression analysis was performed to create risk charts. Cardiac mortality was 10 and 22% for the sub-population of 2- and 5-year analyses. A four-parameter multivariate logistic regression model including age, New York Heart Association (NYHA) functional class, LVEF, and HMR was used. Annualized mortality rate was <1% in patients with NYHA Class I-II and HMR ≥ 2.0, irrespective of age and LVEF. In patients with NYHA Class III-IV, mortality rate was 4-6 times higher for HMR < 1.40 compared with HMR ≥ 2.0 in all LVEF classes. Among the subset of patients with b-type natriuretic peptide (BNP) results (n = 491 and 359 for 2- and 5-year models, respectively), the 5-year model showed incremental value of HMR in addition to BNP. Both 2- and 5-year risk prediction models with (123)I-MIBG HMR can be used to identify low-risk as well as high-risk patients, which can be effective for further risk stratification of CHF patients even when BNP is available. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

Association between Daily Hospital Outpatient Visits for Accidents and Daily Ambient Air Temperatures in an Industrial City.

PubMed

Chau, Tang-Tat; Wang, Kuo-Ying

2016-01-01

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007-2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0-15 years old). Middle-aged people (16-65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8-1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place.

Association between Daily Hospital Outpatient Visits for Accidents and Daily Ambient Air Temperatures in an Industrial City

PubMed Central

Chau, Tang-Tat; Wang, Kuo-Ying

2016-01-01

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007–2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0–15 years old). Middle-aged people (16–65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8–1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place. PMID:26815039

Predictors of psychiatric readmission among patients with bipolar disorder at an academic safety-net hospital.

PubMed

Hamilton, Jane E; Passos, Ives C; de Azevedo Cardoso, Taiane; Jansen, Karen; Allen, Melissa; Begley, Charles E; Soares, Jair C; Kapczinski, Flavio

2016-06-01

Even with treatment, approximately one-third of patients with bipolar disorder relapse into depression or mania within 1 year. Unfavorable clinical outcomes for patients with bipolar disorder include increased rates of psychiatric hospitalization and functional impairment. However, only a few studies have examined predictors of psychiatric hospital readmission in a sample of patients with bipolar disorder. The purpose of this study was to examine predictors of psychiatric readmission within 30 days, 90 days and 1 year of discharge among patients with bipolar disorder using a conceptual model adapted from Andersen's Behavioral Model of Health Service Use. In this retrospective study, univariate and multivariate logistic regression analyses were conducted in a sample of 2443 adult patients with bipolar disorder who were consecutively admitted to a public psychiatric hospital in the United States from 1 January to 31 December 2013. In the multivariate models, several enabling and need factors were significantly associated with an increased risk of readmission across all time periods examined, including being uninsured, having ⩾3 psychiatric hospitalizations and having a lower Global Assessment of Functioning score. Additional factors associated with psychiatric readmission within 30 and 90 days of discharge included patient homelessness. Patient race/ethnicity, bipolar disorder type or a current manic episode did not significantly predict readmission across all time periods examined; however, patients who were male were more likely to readmit within 1 year. The 30-day and 1-year multivariate models showed the best model fit. Our study found enabling and need factors to be the strongest predictors of psychiatric readmission, suggesting that the prevention of psychiatric readmission for patients with bipolar disorder at safety-net hospitals may be best achieved by developing and implementing innovative transitional care initiatives that address the issues of multiple psychiatric hospitalizations, housing instability, insurance coverage and functional impairment. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Evaluation of a Pharmacokinetic-Pharmacodynamic Model for Hypouricemic Effects of Febuxostat Using Datasets Obtained from Real-world Patients.

PubMed

Hirai, Toshinori; Itoh, Toshimasa; Kimura, Toshimi; Echizen, Hirotoshi

2018-06-06

Febuxostat is an active xanthine oxidase (XO) inhibitor that is widely used in the hyperuricemia treatment. We aimed to evaluate the predictive performance of a pharmacokinetic-pharmacodynamic (PK-PD) model for hypouricemic effects of febuxostat. Previously, we have formulated a PK--PD model for predicting hypouricemic effects of febuxostat as a function of baseline serum urate levels, body weight, renal function, and drug dose using datasets reported in preapproval studies (Hirai T et al., Biol Pharm Bull 2016; 39: 1013-21). Using an updated model with sensitivity analysis, we examined the predictive performance of the PK-PD model using datasets obtained from the medical records of patients who received febuxostat from March 2011 to December 2015 at Tokyo Women's Medical University Hospital. Multivariate regression analysis was performed to explore clinical variables to improve the predictive performance of the model. A total of 1,199 serum urate data were retrieved from 168 patients (age: 60.5 ±17.7 years, 71.4% males) who received febuxostat as hyperuricemia treatment. There was a significant correlation (r=0.68, p<0.01) between serum urate levels observed and those predicted by the modified PK-PD model. A multivariate regression analysis revealed that the predictive performance of the model may be improved further by considering comorbidities, such as diabetes mellitus, estimated glomerular filtration rate (eGFR), and co-administration of loop diuretics (r = 0.77, p<0.01). The PK-PD model may be useful for predicting individualized maintenance doses of febuxostat in real-world patients. This article is protected by copyright. All rights reserved.

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

The influence of individual socioeconomic status on the clinical outcomes in ischemic stroke patients with different neighborhood status in Shanghai, China

PubMed Central

Yan, Han; Liu, Baoxin; Meng, Guilin; Shang, Bo; Jie, Qiqiang; Wei, Yidong; Liu, Xueyuan

2017-01-01

Objective: Socioeconomic status (SES) is being recognized as an important factor in both social and medical problems. The aim of present study is to examine the relationship between SES and ischemic stroke and investigate whether SES is a predictor of clinical outcomes among patients with different neighborhood status from Shanghai, China. Methods: A total of 471 first-ever ischemic stroke patients aged 18-80 years were enrolled in this retrospective study. The personal SES of each patient was evaluated using a summed score derived from his or her educational level, household income, occupation, and medical reimbursement rate. Clinical adverse events and all-cause mortality were analyzed to determine whether SES was a prognostic factor, its prognostic impact was then assessed based on different neighborhood status using multivariable Cox proportional hazard models after adjusting for other covariates. Results: The individual SES showed a significant positive correlation with neighborhood status (r = 0.370; P < 0.001). The incidence of clinical adverse events and mortality were significantly higher in low SES patients compared with middle and high SES patients (P = 0.001 and P = 0.037, respectively). After adjusting other risk factors and neighborhood status, Kaplan-Meier analysis showed clinical adverse events and deaths were still higher in the low SES patients (all P < 0.05). Multivariate Cox regression analysis demonstrated that both personal SES and neighborhood status are independent prognostic factors for ischemic stroke (all P < 0.05). Besides, among patients with low and middle neighborhood status, lower individual SES was significantly associated with clinical adverse events and mortality (all P < 0.05). Conclusion: Both individual SES and neighborhood status are significantly associated with the prognosis after ischemic stroke. A lower personal SES as well as poorer neighborhood status may significantly increase risk for adverse clinical outcomes among ischemic stroke patients. PMID:28138313

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Risk factors for pressure ulcer development in critically Ill patients: a conceptual model to guide research.

PubMed

Benoit, Richard; Mion, Lorraine

2012-08-01

This paper presents a proposed conceptual model to guide research on pressure ulcer risk in critically ill patients, who are at high risk for pressure ulcer development. However, no conceptual model exists that guides risk assessment in this population. Results from a review of prospective studies were evaluated for design quality and level of statistical reporting. Multivariate findings from studies having high or medium design quality by the National Institute of Health and Clinical Excellence standards were conceptually grouped. The conceptual groupings were integrated into Braden and Bergstrom's (Braden and Bergstrom [1987] Rehabilitation Nursing, 12, 8-12, 16) conceptual model, retaining their original constructs and augmenting their concept of intrinsic factors for tissue tolerance. The model could enhance consistency in research on pressure ulcer risk factors. Copyright © 2012 Wiley Periodicals, Inc.

Partial Least Squares Calibration Modeling Towards the Multivariate Limit of Detection for Enriched Isotopic Mixtures via Laser Ablation Molecular Isotopic Spectroscopy

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

Harris, Candace; Profeta, Luisa; Akpovo, Codjo

The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

[PROGNOSTIC MODELS IN MODERN MANAGEMENT OF VULVAR CANCER].

PubMed

Tsvetkov, Ch; Gorchev, G; Tomov, S; Nikolova, M; Genchev, G

2016-01-01

The aim of the research was to evaluate and analyse prognosis and prognostic factors in patients with squamous cell vulvar carcinoma after primary surgery with individual approach applied during the course of treatment. In the period between January 2000 and July 2010, 113 patients with squamous cell carcinoma of the vulva were diagnosed and operated on at Gynecologic Oncology Clinic of Medical University, Pleven. All the patients were monitored at the same clinic. Individual approach was applied to each patient and whenever it was possible, more conservative operative techniques were applied. The probable clinicopathological characteristics influencing the overall survival and recurrence free survival were analyzed. Univariate statistical analysis and Cox regression analysis were made in order to evaluate the characteristics, which were statistically significant for overall survival and survival without recurrence. A multivariate logistic regression analysis (Forward Wald procedure) was applied to evaluate the combined influence of the significant factors. While performing the multivariate analysis, the synergic effect of the independent prognostic factors of both kinds of survivals was also evaluated. Approaching individually each patient, we applied the following operative techniques: 1. Deep total radical vulvectomy with separate incisions for lymph dissection (LD) or without dissection--68 (60.18 %) patients. 2. En-bloc vulvectomy with bilateral LD without vulva reconstruction--10 (8.85%) 3. Modified radical vulvactomy (hemivulvectomy, patial vulvactomy)--25 (22.02%). 4. wide-local excision--3 (2.65%). 5. Simple (total /partial) vulvectomy--5 (4.43%) patients. 6. En-bloc resection with reconstruction--2 (1.77%) After a thorough analysis of the overall survival and recurrence free survival, we made the conclusion that the relapse occurrence and clinical stage of FIGO were independent prognostic factors for overall survival and the independent prognostic factors for recurrence free survival were: metastatic inguinal nodes (unilateral or bilateral), tumor size (above or below 3 cm) and lymphovascular space invasion. On the basis of these results we created two prognostic models: 1. A prognostic model of overall survival 2. A prognostic model for survival without recurrence. Following the surgical staging of the disease, were able to gather and analyse important clinicopathological indexes, which gave us the opportunity to form prognostic groups for overall survival and recurrence-free survival.

Treatment Adherence and Its Impact on Disease-Free Survival in the Breast International Group 1-98 Trial of Tamoxifen and Letrozole, Alone and in Sequence.

PubMed

Chirgwin, Jacquie H; Giobbie-Hurder, Anita; Coates, Alan S; Price, Karen N; Ejlertsen, Bent; Debled, Marc; Gelber, Richard D; Goldhirsch, Aron; Smith, Ian; Rabaglio, Manuela; Forbes, John F; Neven, Patrick; Láng, István; Colleoni, Marco; Thürlimann, Beat

2016-07-20

To investigate adherence to endocrine treatment and its relationship with disease-free survival (DFS) in the Breast International Group (BIG) 1-98 clinical trial. The BIG 1-98 trial is a double-blind trial that randomly assigned 6,193 postmenopausal women with hormone receptor-positive early breast cancer in the four-arm option to 5 years of tamoxifen (Tam), letrozole (Let), or the agents in sequence (Let-Tam, Tam-Let). This analysis included 6,144 women who received at least one dose of study treatment. Conditional landmark analyses and marginal structural Cox proportional hazards models were used to evaluate the relationship between DFS and treatment adherence (persistence [duration] and compliance with dosage). Competing risks regression was used to assess demographic, disease, and treatment characteristics of the women who stopped treatment early because of adverse events. Both aspects of low adherence (early cessation of letrozole and a compliance score of < 90%) were associated with reduced DFS (multivariable model hazard ratio, 1.45; 95% CI, 1.09 to 1.93; P = .01; and multivariable model hazard ratio, 1.61; 95% CI, 1.08 to 2.38; P = .02, respectively). Sequential treatments were associated with higher rates of nonpersistence (Tam-Let, 20.8%; Let-Tam, 20.3%; Tam 16.9%; Let 17.6%). Adverse events were the reason for most trial treatment early discontinuations (82.7%). Apart from sequential treatment assignment, reduced adherence was associated with older age, smoking, node negativity, or prior thromboembolic event. Both persistence and compliance are associated with DFS. Toxicity management and, for sequential treatments, patient and physician awareness, may improve adherence. © 2016 by American Society of Clinical Oncology.

Factors associated with anti-tuberculosis medication adverse effects: a case-control study in Lima, Peru.

PubMed

Chung-Delgado, Kocfa; Revilla-Montag, Alejandro; Guillen-Bravo, Sonia; Velez-Segovia, Eduardo; Soria-Montoya, Andrea; Nuñez-Garbin, Alexandra; Silva-Caso, Wilmer; Bernabe-Ortiz, Antonio

2011-01-01

Long-term exposure to anti-tuberculosis medication increases risk of adverse drug reactions and toxicity. The objective of this investigation was to determine factors associated with anti-tuberculosis adverse drug reactions in Lima, Peru, with special emphasis on MDR-TB medication, HIV infection, diabetes, age and tobacco use. A case-control study was performed using information from Peruvian TB Programme. A case was defined as having reported an anti-TB adverse drug reaction during 2005-2010 with appropriate notification on clinical records. Controls were defined as not having reported a side effect, receiving anti-TB therapy during the same time that the case had appeared. Crude, and age- and sex-adjusted models were calculated using odds ratios (OR) and 95% confidence intervals (95%CI). A multivariable model was created to look for independent factors associated with side effect from anti-TB therapy. A total of 720 patients (144 cases and 576 controls) were analyzed. In our multivariable model, age, especially those over 40 years (OR = 3.93; 95%CI: 1.65-9.35), overweight/obesity (OR = 2.13; 95%CI: 1.17-3.89), anemia (OR = 2.10; IC95%: 1.13-3.92), MDR-TB medication (OR = 11.1; 95%CI: 6.29-19.6), and smoking (OR = 2.00; 95%CI: 1.03-3.87) were independently associated with adverse drug reactions. Old age, anemia, MDR-TB medication, overweight/obesity status, and smoking history are independent risk factors associated with anti-tuberculosis adverse drug reactions. Patients with these risk factors should be monitored during the anti-TB therapy. A comprehensive clinical history and additional medical exams, including hematocrit and HIV-ELISA, might be useful to identify these patients.

Retinal nerve fibre layer thinning is associated with drug resistance in epilepsy

PubMed Central

Balestrini, Simona; Clayton, Lisa M S; Bartmann, Ana P; Chinthapalli, Krishna; Novy, Jan; Coppola, Antonietta; Wandschneider, Britta; Stern, William M; Acheson, James; Bell, Gail S; Sander, Josemir W; Sisodiya, Sanjay M

2016-01-01

Objective Retinal nerve fibre layer (RNFL) thickness is related to the axonal anterior visual pathway and is considered a marker of overall white matter ‘integrity’. We hypothesised that RNFL changes would occur in people with epilepsy, independently of vigabatrin exposure, and be related to clinical characteristics of epilepsy. Methods Three hundred people with epilepsy attending specialist clinics and 90 healthy controls were included in this cross-sectional cohort study. RNFL imaging was performed using spectral-domain optical coherence tomography (OCT). Drug resistance was defined as failure of adequate trials of two antiepileptic drugs to achieve sustained seizure freedom. Results The average RNFL thickness and the thickness of each of the 90° quadrants were significantly thinner in people with epilepsy than healthy controls (p<0.001, t test). In a multivariate logistic regression model, drug resistance was the only significant predictor of abnormal RNFL thinning (OR=2.09, 95% CI 1.09 to 4.01, p=0.03). Duration of epilepsy (coefficient −0.16, p=0.004) and presence of intellectual disability (coefficient −4.0, p=0.044) also showed a significant relationship with RNFL thinning in a multivariate linear regression model. Conclusions Our results suggest that people with epilepsy with no previous exposure to vigabatrin have a significantly thinner RNFL than healthy participants. Drug resistance emerged as a significant independent predictor of RNFL borderline attenuation or abnormal thinning in a logistic regression model. As this is easily assessed by OCT, RNFL thickness might be used to better understand the mechanisms underlying drug resistance, and possibly severity. Longitudinal studies are needed to confirm our findings. PMID:25886782

MRI criteria differentiating asymptomatic PML from new MS lesions during natalizumab pharmacovigilance.

PubMed

Wijburg, Martijn T; Witte, Birgit I; Vennegoor, Anke; Roosendaal, Stefan D; Sanchez, Esther; Liu, Yaou; Martins Jarnalo, Carine O; Uitdehaag, Bernard Mj; Barkhof, Frederik; Killestein, Joep; Wattjes, Mike P

2016-10-01

Differentiation between progressive multifocal leukoencephalopathy (PML) and new multiple sclerosis (MS) lesions on brain MRI during natalizumab pharmacovigilance in the absence of clinical signs and symptoms is challenging but is of substantial clinical relevance. We aim to define MRI characteristics that can aid in this differentiation. Reference and follow-up brain MRIs of natalizumab-treated patients with MS with asymptomatic PML (n=21), or asymptomatic new MS lesions (n=20) were evaluated with respect to characteristics of newly detected lesions by four blinded raters. We tested the association with PML for each characteristic and constructed a multivariable prediction model which we analysed using a receiver operating characteristic (ROC) curve. Presence of punctate T2 lesions, cortical grey matter involvement, juxtacortical white matter involvement, ill-defined and mixed lesion borders towards both grey and white matter, lesion size of >3 cm, and contrast enhancement were all associated with PML. Focal lesion appearance and periventricular localisation were associated with new MS lesions. In the multivariable model, punctate T2 lesions and cortical grey matter involvement predict for PML, while focal lesion appearance and periventricular localisation predict for new MS lesions (area under the curve: 0.988, 95% CI 0.977 to 1.0, sensitivity: 100%, specificity: 80.6%). The MRI characteristics of asymptomatic natalizumab-associated PML lesions proved to differ from new MS lesions. This led to a prediction model with a high discriminating power. Careful assessment of the presence of punctate T2 lesions, cortical grey matter involvement, focal lesion appearance and periventricular localisation allows for an early diagnosis of PML. 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/

The Impact of Shame on Health-Related Quality of Life Among HIV-Positive Adults with a History of Childhood Sexual Abuse

PubMed Central

Persons, Elizabeth; Kershaw, Trace; Sikkema, Kathleen J.

2010-01-01

Abstract Childhood sexual abuse is prevalent among people living with HIV, and the experience of shame is a common consequence of childhood sexual abuse and HIV infection. This study examined the role of shame in health-related quality of life among HIV-positive adults who have experienced childhood sexual abuse. Data from 247 HIV-infected adults with a history of childhood sexual abuse were analyzed. Hierarchical linear regression was conducted to assess the impact of shame regarding both sexual abuse and HIV infection, while controlling for demographic, clinical, and psychosocial factors. In bivariate analyses, shame regarding sexual abuse and HIV infection were each negatively associated with health-related quality of life and its components (physical well-being, function and global well-being, emotional and social well-being, and cognitive functioning). After controlling for demographic, clinical, and psychosocial factors, HIV-related, but not sexual abuse-related, shame remained a significant predictor of reduced health-related quality of life, explaining up to 10% of the variance in multivariable models for overall health-related quality of life, emotional, function and global, and social well-being and cognitive functioning over and above that of other variables entered into the model. Additionally, HIV symptoms, perceived stress, and perceived availability of social support were associated with health-related quality of life in multivariable models. Shame is an important and modifiable predictor of health-related quality of life in HIV-positive populations, and medical and mental health providers serving HIV-infected populations should be aware of the importance of shame and its impact on the well-being of their patients. PMID:20718687

Quality of life after lacunar stroke: the Secondary Prevention of Small Subcortical Strokes study.

PubMed

Dhamoon, Mandip S; McClure, Leslie A; White, Carole L; Lau, Helena; Benavente, Oscar; Elkind, Mitchell S V

2014-01-01

We sought to describe the course and predictors of quality of life (QOL) after lacunar stroke. We hypothesized that there is a decline in QOL after recovery from lacunar stroke. The Secondary Prevention of Small Subcortical Strokes is a clinical trial in lacunar stroke patients with annual assessments of QOL with the stroke-specific QOL score. The overall score was used and analyzed as a continuous variable (range 0-5). We fit linear mixed models to assess the trend in QOL over time, assuming linearity of time, and adjusted for demographics, medical risk factors, cognitive factors, and functional status in univariable and multivariable models. Among 2870 participants, mean age was 63.4 years (SD 10.7), 63% were men, 51% White, 32% Hispanic, 36% had college education, 36% had diabetes, 89% had hypertension, and 10% had prior stroke. Mean poststroke Barthel Index (BI) score was 95.4 (assessed on average 6 months after stroke). In the final multivariable model, there was an average increase in QOL of .6% per year, and factors associated with decline in QOL over time included age (-.0003 per year, P < .0001), any college education (-.0013 per year, .01), prior stroke (-.004 per year, P < .0001), and BI (-.0002 per year, P < .0001). In this clinical trial of lacunar stroke patients, there was a slight annual increase in QOL overall, and age, level of education, and prior stroke were associated with changes in QOL over time. Multiple strokes may cause decline in QOL over time in the absence of recurrent events. Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Determinants of calcium and oxalate excretion in subjects with calcium nephrolithiasis: the role of metabolic syndrome traits.

PubMed

Ticinesi, Andrea; Guerra, Angela; Allegri, Franca; Nouvenne, Antonio; Cervellin, Gianfranco; Maggio, Marcello; Lauretani, Fulvio; Borghi, Loris; Meschi, Tiziana

2018-06-01

The association of metabolic syndrome (MetS) traits with urinary calcium (UCE) or oxalate excretion (UOE) is uncertain in calcium stone formers (CSFs). Our aim was to investigate this association in a large group of Caucasian CSFs. We retrospectively reviewed data of CSFs evaluated at our Kidney Stone Clinic from 1984 to 2015. Data on body mass index (BMI), MetS traits defined according to international consensus, family history of urolithiasis, anti-hypertensive treatments, calcemia, renal function, and 24-h urinary profile of lithogenic risk were collected. The association between MetS traits and UCE or UOE was tested with multivariate linear regression models accounting for a long list of potential confounders. We included 3003 CSFs, aged 44 ± 14 years. The prevalence of hypertension, diabetes, overweight (BMI ≥ 25 kg/m 2 ) and dyslipidemia was 17, 2, 42 and 38%, respectively. Median values of UCE and UOE were 211 mg/24 h (IQR 143-296) and 28 mg/24 h (IQR 22-34), respectively. At a multivariate model, including age, sex, date of examination, drug treatments, family history, renal function, blood calcium and urinary factors as covariates, hypertension was a significant positive determinant of UCE (β ± SE 0.23 ± 0.07, p = 0.003), but overweight, dyslipidemia and diabetes were not. No MetS trait was significantly associated with UOE in multivariate models. In a large group of Caucasian CSFs, hypertension was the only MetS trait significantly associated with UCE, while no MetS trait was associated with oxalate excretion.

Modeling the predictive value of pain intensity on costs and resources utilization in patients with peripheral neuropathic pain.

PubMed

Pérez, Concepción; Navarro, Ana; Saldaña, María T; Wilson, Koo; Rejas, Javier

2015-03-01

The aim of the present analysis was to model the association and predictive value of pain intensity on cost and resource utilization in patients with chronic peripheral neuropathic pain (PNP) treated in routine clinical practice settings in Spain. We performed a secondary economic analysis based on data from a multicenter, observational, and prospective cost-of-illness study in patients with chronic PNP that is refractory to prior treatment. Pain intensity was measured using the Short-Form McGill Pain Questionnaire. Univariate and multivariate linear regression models were fitted to identify independent predictors of cost and health care/non-health care resource utilization. A total of 1703 patients were included in the current analysis. Pain intensity was an independent predictor of total costs ([total costs]=35.6 [pain intensity]+214.5; coefficient of determination [R(2)]=0.19, P<0.001), direct costs ([direct costs]=10.8 [pain intensity]+257.7; R=0.06, P<0.001), and indirect costs ([indirect costs]=24.8 [pain intensity]-43.4; R(2)=0.20, P<0.001) related to chronic PNP in the univariate analysis. Pain intensity remains significantly associated with total costs, direct costs, and indirect costs after adjustment by other covariates in the multivariate analysis (P<0.001). None of the other variables considered in the multivariate analysis were predictors of resource utilization. Pain intensity predicts the health care and non-health care resource utilization, and costs related to chronic PNP. Management of patients with drugs associated with a higher reduction of pain intensity may have a greater impact on the economic burden of that condition.

Preventing Unintended Pregnancy Among Young Sexually Active Women: Recognizing the Role of Violence, Self-Esteem, and Depressive Symptoms on Use of Contraception.

PubMed

Nelson, Deborah B; Zhao, Huaqing; Corrado, Rachel; Mastrogiannnis, Dimitrios M; Lepore, Stephen J

2017-04-01

Ineffective contraceptive use among young sexually active women is extremely prevalent and poses a significant risk for unintended pregnancy (UP). Ineffective contraception involves the use of the withdrawal method or the inconsistent use of other types of contraception (i.e., condoms and birth control pills). This investigation examined violence exposure and psychological factors related to ineffective contraceptive use among young sexually active women. Young, nonpregnant sexually active women (n = 315) were recruited from an urban family planning clinic in 2013 to participate in a longitudinal study. Tablet-based surveys measured childhood violence, community-level violence, intimate partner violence, depressive symptoms, and self-esteem. Follow-up surveys measured type and consistency of contraception used 9 months later. Multivariate logistic regression models assessed violence and psychological risk factors as main effects and moderators related to ineffective compared with effective use of contraception. The multivariate logistic regression model showed that childhood sexual violence and low self-esteem were significantly related to ineffective use of contraception (adjusted odds ratio [aOR] = 2.69, confidence interval [95% CI]: 1.18-6.17, and aOR = 0.51, 95% CI: 0.28-0.93; respectively), although self-esteem did not moderate the relationship between childhood sexual violence and ineffective use of contraception (aOR = 0.38, 95% CI: 0.08-1.84). Depressive symptoms were not related to ineffective use of contraception in the multivariate model. Interventions to reduce UP should recognize the long-term effects of childhood sexual violence and address the role of low self-esteem on the ability of young sexually active women to effectively and consistently use contraception to prevent UP.

Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis.

PubMed

Lewis, G N; Rice, D A; McNair, P J; Kluger, M

2015-04-01

Several studies have identified clinical, psychosocial, patient characteristic, and perioperative variables that are associated with persistent postsurgical pain; however, the relative effect of these variables has yet to be quantified. The aim of the study was to provide a systematic review and meta-analysis of predictor variables associated with persistent pain after total knee arthroplasty (TKA). Included studies were required to measure predictor variables prior to or at the time of surgery, include a pain outcome measure at least 3 months post-TKA, and include a statistical analysis of the effect of the predictor variable(s) on the outcome measure. Counts were undertaken of the number of times each predictor was analysed and the number of times it was found to have a significant relationship with persistent pain. Separate meta-analyses were performed to determine the effect size of each predictor on persistent pain. Outcomes from studies implementing uni- and multivariable statistical models were analysed separately. Thirty-two studies involving almost 30 000 patients were included in the review. Preoperative pain was the predictor that most commonly demonstrated a significant relationship with persistent pain across uni- and multivariable analyses. In the meta-analyses of data from univariate models, the largest effect sizes were found for: other pain sites, catastrophizing, and depression. For data from multivariate models, significant effects were evident for: catastrophizing, preoperative pain, mental health, and comorbidities. Catastrophizing, mental health, preoperative knee pain, and pain at other sites are the strongest independent predictors of persistent pain after TKA. © The Author 2014. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Preventing Unintended Pregnancy Among Young Sexually Active Women: Recognizing the Role of Violence, Self-Esteem, and Depressive Symptoms on Use of Contraception

PubMed Central

Zhao, Huaqing; Corrado, Rachel; Mastrogiannnis, Dimitrios M.; Lepore, Stephen J.

2017-01-01

Abstract Objectives: Ineffective contraceptive use among young sexually active women is extremely prevalent and poses a significant risk for unintended pregnancy (UP). Ineffective contraception involves the use of the withdrawal method or the inconsistent use of other types of contraception (i.e., condoms and birth control pills). This investigation examined violence exposure and psychological factors related to ineffective contraceptive use among young sexually active women. Materials and Methods: Young, nonpregnant sexually active women (n = 315) were recruited from an urban family planning clinic in 2013 to participate in a longitudinal study. Tablet-based surveys measured childhood violence, community-level violence, intimate partner violence, depressive symptoms, and self-esteem. Follow-up surveys measured type and consistency of contraception used 9 months later. Multivariate logistic regression models assessed violence and psychological risk factors as main effects and moderators related to ineffective compared with effective use of contraception. Results: The multivariate logistic regression model showed that childhood sexual violence and low self-esteem were significantly related to ineffective use of contraception (adjusted odds ratio [aOR] = 2.69, confidence interval [95% CI]: 1.18–6.17, and aOR = 0.51, 95% CI: 0.28–0.93; respectively), although self-esteem did not moderate the relationship between childhood sexual violence and ineffective use of contraception (aOR = 0.38, 95% CI: 0.08–1.84). Depressive symptoms were not related to ineffective use of contraception in the multivariate model. Conclusions: Interventions to reduce UP should recognize the long-term effects of childhood sexual violence and address the role of low self-esteem on the ability of young sexually active women to effectively and consistently use contraception to prevent UP. PMID:28045570

Psychosocial Characteristics and Social Networks of Suicidal Prisoners: Towards a Model of Suicidal Behaviour in Detention

PubMed Central

Rivlin, Adrienne; Hawton, Keith; Marzano, Lisa; Fazel, Seena

2013-01-01

Prisoners are at increased risk of suicide. Investigation of both individual and environmental risk factors may assist in developing suicide prevention policies for prisoners and other high-risk populations. We conducted a matched case-control interview study with 60 male prisoners who had made near-lethal suicide attempts in prison (cases) and 60 male prisoners who had not (controls). We compared levels of depression, hopelessness, self-esteem, impulsivity, aggression, hostility, childhood abuse, life events (including events occurring in prison), social support, and social networks in univariate and multivariate models. A range of psychosocial factors was associated with near-lethal self-harm in prisoners. Compared with controls, cases reported higher levels of depression, hopelessness, impulsivity, and aggression, and lower levels of self-esteem and social support (all p values <0.001). Adverse life events and criminal history factors were also associated with near-lethal self-harm, especially having a prior prison spell and having been bullied in prison, both of which remained significant in multivariate analyses. The findings support a model of suicidal behaviour in prisoners that incorporates imported vulnerability factors, clinical factors, and prison experiences, and underscores their interaction. Strategies to reduce self-harm and suicide in prisoners should include attention to such factors. PMID:23922671

Racial and ethnic variations in one-year clinical and patient-reported outcomes following breast reconstruction.

PubMed

Berlin, Nicholas L; Momoh, Adeyiza O; Qi, Ji; Hamill, Jennifer B; Kim, Hyungjin M; Pusic, Andrea L; Wilkins, Edwin G

2017-08-01

Existing studies evaluating racial and ethnic disparities focus on describing differences in procedure type and the proportion of women who undergo reconstruction following mastectomy. This study seeks to examine racial and ethnic variations in clinical and patient-reported outcomes (PROs) following breast reconstruction. The Mastectomy Reconstruction Outcomes Consortium is an 11 center, prospective cohort study collecting clinical and PROs following autologous and implant-based breast reconstruction. Mixed-effects regression models, weighted to adjust for non-response, were performed to evaluate outcomes at one-year postoperatively. The cohort included 2703 women who underwent breast reconstruction. In multivariable models, Hispanic or Latina patients were less likely to experience any complications and major complications. Black or African-American women reported greater improvements in psychosocial and sexual well-being. Despite differences in pertinent clinical and socioeconomic variables, racial and ethnic minorities experienced equivalent or better outcomes. These findings provide reassurance in the context of numerous racial and ethnic health disparities and build upon our understanding of the delivery of surgical care to women with or at risk for developing breast cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Accuracy, calibration and clinical performance of the EuroSCORE: can we reduce the number of variables?

PubMed

Ranucci, Marco; Castelvecchio, Serenella; Menicanti, Lorenzo; Frigiola, Alessandro; Pelissero, Gabriele

2010-03-01

The European system for cardiac operative risk evaluation (EuroSCORE) is currently used in many institutions and is considered a reference tool in many countries. We hypothesised that too many variables were included in the EuroSCORE using limited patient series. We tested different models using a limited number of variables. A total of 11150 adult patients undergoing cardiac operations at our institution (2001-2007) were retrospectively analysed. The 17 risk factors composing the EuroSCORE were separately analysed and ranked for accuracy of prediction of hospital mortality. Seventeen models were created by progressively including one factor at a time. The models were compared for accuracy with a receiver operating characteristics (ROC) analysis and area under the curve (AUC) evaluation. Calibration was tested with Hosmer-Lemeshow statistics. Clinical performance was assessed by comparing the predicted with the observed mortality rates. The best accuracy (AUC 0.76) was obtained using a model including only age, left ventricular ejection fraction, serum creatinine, emergency operation and non-isolated coronary operation. The EuroSCORE AUC (0.75) was not significantly different. Calibration and clinical performance were better in the five-factor model than in the EuroSCORE. Only in high-risk patients were 12 factors needed to achieve a good performance. Including many factors in multivariable logistic models increases the risk for overfitting, multicollinearity and human error. A five-factor model offers the same level of accuracy but demonstrated better calibration and clinical performance. Models with a limited number of factors may work better than complex models when applied to a limited number of patients. Copyright (c) 2009 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

Comparison of the clinical efficacy between single-agent and dual-agent concurrent chemoradiotherapy in the treatment of unresectable esophageal squamous cell carcinoma: a multicenter retrospective analysis.

PubMed

Li, Jie; Gong, Youling; Diao, Peng; Huang, Qingmei; Wen, Yixue; Lin, Binwei; Cai, Hongwei; Tian, Honggang; He, Bing; Ji, Lanlan; Guo, Ping; Miao, Jidong; Du, Xiaobo

2018-01-22

Some Chinese patients with esophageal squamous cell carcinomaare often treated with single-agent concurrent chemoradiotherapy. However, no results have been reported from randomized controlled clinical trials comparing single-agent with double-agent concurrent chemoradiotherapy. It therefore remains unclear whether these regimens are equally clinically effective. In this study, we retrospectively analyzed and compared the therapeutic effects of single-agent and double-agent concurrent chemoradiotherapy in patients with unresectable esophageal squamous cell carcinoma. This study enrolled 168 patients who received definitive concurrent chemoradiotherapy for locally advanced unresectable esophageal squamous carcinoma at 10 hospitals between 2010 and 2015. We evaluated survival time and toxicity. The Kaplan-Meier method was used to estimate survival data. The log-rank test was used in univariate analysis A Cox proportional hazards regression model was used to conduct a multivariate analysis of the effects of prognostic factors on survival. In this study, 100 (59.5%) and 68 patients (40.5%) received single-agent and dual-agent combination chemoradiotherapy, respectively. The estimate 5-year progression-free survival (PFS) rate and overall survival (OS) rate of dual-agent therapy was higher than that of single-agent therapy (52.5% and 40.9%, 78.2% and 60.7%, respectively), but there were no significant differences (P = 0.367 and 0.161, respectively). Multivariate analysis showed that sex, age,and radiotherapy dose had no significant effects on OS or PFS. Only disease stage was associated with OS and PFS in the multivariable analysis (P = 0.006 and 0.003, respectively). In dual-agent group, the incidence of acute toxicity and the incidence of 3 and4 grade toxicity were higher than single-agent group. The 5-year PFS and OS rates of dual-agent therapy were higher than those of single-agent concurrent chemoradiotherapy for patients with unresectable esophageal squamous cell carcinoma; however, there were no significant differences in univariate analysis and multivariable analysis. Single-agent concurrent chemotherapy had less toxicity than a double-drug regimen. Therefore, we suggest that single therapis not inferior to dual therapy y. In the future, we aim to confirm our hypothesis through a prospective randomized study.

MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

EPA Science Inventory

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Prescription Patterns for Tigecycline in Severely Ill Patients for Non-FDA Approved Indications in a Developing Country: A Compromised Outcome

PubMed Central

Moghnieh, Rima A.; Abdallah, Dania I.; Fawaz, Ismail A.; Hamandi, Tarek; Kassem, Mohammad; El-Rajab, Nabila; Jisr, Tamima; Mugharbil, Anas; Droubi, Nabila; Al Tabah, Samaa; Sinno, Loubna; Ziade, Fouad; Daoud, Ziad; Ibrahim, Ahmad

2017-01-01

Introduction: With the rise in antibiotic resistance, tigecycline has been used frequently in off-label indications, based on its in-vitro activity against multidrug-resistant organisms. In this study, our aim was to assess its use in approved and unapproved indications. Materials and Methods: This is a retrospective chart review evaluating a 2-year experience of tigecycline use for > 72 h in 153 adult patients inside and outside critical care unit from January 2012 to December 2013 in a Lebanese tertiary-care hospital. Results: Tigecycline was mostly used in off-label indications (81%) and prescribed inside the critical care area, where the number of tigecycline cycles was 16/1,000 patient days. Clinical success was achieved in 43.4% of the patients. In the critically ill group, it was significantly higher in patients with a SOFA score <7 using multivariate analysis (Odds Ratio (OR) = 12.51 [4.29–36.51], P < 0.0001). Microbiological success was achieved in 43.3% of patients. Yet, the univariate and adjusted multivariate models failed to show a significant difference in this outcome between patients inside vs. outside critical care area, those with SOFA score <7 vs. ≥ 7, and in FDA-approved vs. off-label indications. Total mortality reached ~45%. It was significantly higher in critically ill patients with SOFA score ≥7 (OR = 5.17 [2.43–11.01], P < 0.0001) and in off-label indications (OR = 4.00 [1.30–12.31], P = 0.01) using an adjusted multivariate model. Gram-negative bacteria represented the majority of the clinical isolates (81%) and Acinetobacter baumannii predominated (28%). Carbapenem resistance was present in 85% of the recovered Acinetobacter, yet, more than two third of the carbapenem-resistant Acinetobacter species were still susceptible to tigecycline. Conclusion: In our series, tigecycline has been mostly used in off-label indications, specifically in severely ill patients. The outcome of such infections was not inferior to that of FDA-approved indications, especially inside critical care area. The use of this last resort antibiotic in complicated clinical scenarios with baseline microbiological epidemiology predominated by extensively-drug resistant pathogens ought to be organized. PMID:28396656

Features predictive of brain arteriovenous malformation hemorrhage: extrapolation to a physiologic model.

PubMed

Sahlein, Daniel H; Mora, Paloma; Becske, Tibor; Huang, Paul; Jafar, Jafar J; Connolly, E Sander; Nelson, Peter K

2014-07-01

Although there is generally thought to be a 2% to 4% per annum rupture risk for brain arteriovenous malformations (bAVMs), there is no way to estimate risk for an individual patient. In this retrospective study, patients were eligible who had nidiform bAVMs and underwent detailed pretreatment diagnostic cerebral angiography at our medical center from 1996 to 2006. All patients had superselective microcatheter angiography, and films were reviewed for the purpose of this project. Patient demographics, clinical presentation, and angioarchitectural characteristics were analyzed. A univariate analysis was performed, and angioarchitectural features with potential physiological significance that showed at least a trend toward significance were added to a multivariate logistic regression model. One hundred twenty-two bAVMs met criteria for study entry. bAVMs with single venous drainage anatomy were more likely to present with hemorrhage. In addition, patients with multiple draining veins and a venous stenosis reverted to a risk similar to those with 1 draining vein, whereas those with multiple draining veins and without stenosis had diminished association with hemorrhage presentation. Those bAVMs with associated aneurysms were more likely to present with hemorrhage. These findings were robust in both univariate and multivariate models. The results of this article lead to the first physiological, internally consistent model of individual bAVM hemorrhage risk, where 1 draining vein, venous stenosis, and associated aneurysms increase risk. © 2014 American Heart Association, Inc.

Comparing Within-Person Effects from Multivariate Longitudinal Models

ERIC Educational Resources Information Center

Bainter, Sierra A.; Howard, Andrea L.

2016-01-01

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model.…

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

PubMed

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Novel risk score of contrast-induced nephropathy after percutaneous coronary intervention.

PubMed

Ji, Ling; Su, XiaoFeng; Qin, Wei; Mi, XuHua; Liu, Fei; Tang, XiaoHong; Li, Zi; Yang, LiChuan

2015-08-01

Contrast-induced nephropathy (CIN) post-percutaneous coronary intervention (PCI) is a major cause of acute kidney injury. In this study, we established a comprehensive risk score model to assess risk of CIN after PCI procedure, which could be easily used in a clinical environment. A total of 805 PCI patients, divided into analysis cohort (70%) and validation cohort (30%), were enrolled retrospectively in this study. Risk factors for CIN were identified using univariate analysis and multivariate logistic regression in the analysis cohort. Risk score model was developed based on multiple regression coefficients. Sensitivity and specificity of the new risk score system was validated in the validation cohort. Comparisons between the new risk score model and previous reported models were applied. The incidence of post-PCI CIN in the analysis cohort (n = 565) was 12%. Considerably high CIN incidence (50%) was observed in patients with chronic kidney disease (CKD). Age >75, body mass index (BMI) >25, myoglobin level, cardiac function level, hypoalbuminaemia, history of chronic kidney disease (CKD), Intra-aortic balloon pump (IABP) and peripheral vascular disease (PVD) were identified as independent risk factors of post-PCI CIN. A novel risk score model was established using multivariate regression coefficients, which showed highest sensitivity and specificity (0.917, 95%CI 0.877-0.957) compared with previous models. A new post-PCI CIN risk score model was developed based on a retrospective study of 805 patients. Application of this model might be helpful to predict CIN in patients undergoing PCI procedure. © 2015 Asian Pacific Society of Nephrology.

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Fractional Brownian motion and multivariate-t models for longitudinal biomedical data, with application to CD4 counts in HIV-positive patients.

PubMed

Stirrup, Oliver T; Babiker, Abdel G; Carpenter, James R; Copas, Andrew J

2016-04-30

Longitudinal data are widely analysed using linear mixed models, with 'random slopes' models particularly common. However, when modelling, for example, longitudinal pre-treatment CD4 cell counts in HIV-positive patients, the incorporation of non-stationary stochastic processes such as Brownian motion has been shown to lead to a more biologically plausible model and a substantial improvement in model fit. In this article, we propose two further extensions. Firstly, we propose the addition of a fractional Brownian motion component, and secondly, we generalise the model to follow a multivariate-t distribution. These extensions are biologically plausible, and each demonstrated substantially improved fit on application to example data from the Concerted Action on SeroConversion to AIDS and Death in Europe study. We also propose novel procedures for residual diagnostic plots that allow such models to be assessed. Cohorts of patients were simulated from the previously reported and newly developed models in order to evaluate differences in predictions made for the timing of treatment initiation under different clinical management strategies. A further simulation study was performed to demonstrate the substantial biases in parameter estimates of the mean slope of CD4 decline with time that can occur when random slopes models are applied in the presence of censoring because of treatment initiation, with the degree of bias found to depend strongly on the treatment initiation rule applied. Our findings indicate that researchers should consider more complex and flexible models for the analysis of longitudinal biomarker data, particularly when there are substantial missing data, and that the parameter estimates from random slopes models must be interpreted with caution. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)

EPA Science Inventory

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...

An investigation of the relationship between ethnicity and success in a BSc (Hons) Physiotherapy degree programme in the UK.

PubMed

Williams, Annabel; Norris, Meriel; Cassidy, Elizabeth; Naylor, Sandra; Marston, Louise; Shiers, Pam

2015-06-01

To explore the potential relationship between ethnicity and achievement within undergraduate physiotherapy education. A retrospective analysis of assessment marks awarded for academic and clinical modules. A London University offering undergraduate physiotherapy education. Four hundred forty-eight undergraduate students enrolled onto the Physiotherapy honours degree programme between 2005 and 2009. Marks awarded following academic or clinical assessment. These were modelled through multivariable regression analysis to evaluate the relationship between marks awarded and ethnicity. Differences were noted between ethnic categories in final programme success and across academic and clinical modules. Our multivariable analysis demonstrated students from Asian backgrounds had decreased odds of succeeding compared with white British students (adjusted OR 0.43 95%CI 0.24, 0.79 P=0.006), as had Black students (adjusted OR 0.42 95%CI 0.19, 0.95 P=0.036) and students from Other ethnic backgrounds (adjusted OR 0.41 95%CI 0.20, 0.87 P=0.020). This analysis of undergraduate physiotherapy students illustrated a persistent difference in attainment between students from white British and those from BME backgrounds. Heterogeneity in academic outcomes both within and between minority ethnic groups was illustrated. This study not only reinforces the need to consider ethnicity within physiotherapy education but also raises further questions about why physiotherapy students from BME groups perform less well than their white British peers. Copyright © 2014. Published by Elsevier Ltd.

The relationship between hypophosphataemia and outcomes during low-intensity and high-intensity continuous renal replacement therapy.

PubMed

Bellomo, Rinaldo; Cass, Alan; Cole, Louise; Finfer, Simon; Gallagher, Martin; Kim, Inbyung; Lee, Joanne; Lo, Serigne; McArthur, Colin; McGuinness, Shay; McGuiness, Shay; Norton, Robyn; Myburgh, John; Scheinkestel, Carlos

2014-03-01

To identify risk factors for development of hypophosphataemia in patients treated with two different intensities of continuous renal replacement therapy (CRRT) and to assess the independent association of hypophosphataemia with major clinical outcomes. We performed secondary analysis of data collected from 1441 patients during a large, multicentre randomised controlled trial of CRRT intensity. We allocated patients to two different intensities of CRRT (25mL/kg/hour vs 40 mL/kg/hour of effluent generation) and obtained daily measurement of serum phosphate levels. We obtained 14 115 phosphate measurements and identified 462 patients (32.1%) with hypophosphataemia, with peak incidence on Day 2 and Day 3. With lower intensity CRRT, there were 58 episodes of hypophosphataemia/1000 patient days, compared with 112 episodes/1000 patient days with higher intensity CRRT (P < 0.001). On multivariable logistic regression analysis, higher intensity CRRT, female sex, higher Acute Physiology and Chronic Health Evaluation score and hypokalaemia were independently associated with an increased odds ratio (OR) for hypophosphataemia. On multivariable models, hypophosphataemia was associated with better clinical outcomes, but when analysis was confined to patients alive at 96 hours, hypophosphataemia was not independently associated with clinical outcomes. Hypophosphataemia is common during CRRT and its incidence increases with greater CRRT intensity. Hypophosphataemia is not a robust independent predictor of mortality. Its greater incidence in the higher intensity CRRT arm of the Randomised Evaluation of Normal vs Augmented Level trial does not explain the lack of improved outcomes with such treatment.

Trabecular Meshwork Height in Primary Open-Angle Glaucoma Versus Primary Angle-Closure Glaucoma.

PubMed

Masis, Marisse; Chen, Rebecca; Porco, Travis; Lin, Shan C

2017-11-01

To determine if trabecular meshwork (TM) height differs between primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) eyes. Prospective, cross-sectional clinical study. Adult patients were consecutively recruited from glaucoma clinics at the University of California, San Francisco, from January 2012 to July 2015. Images were obtained from spectral-domain optical coherence tomography (Cirrus OCT; Carl Zeiss Meditec, Inc, Dublin, California, USA). Univariate and multivariate linear mixed models comparing TM height and glaucoma type were performed to assess the relationship between TM height and glaucoma subtype. Mixed-effects regression was used to adjust for the use of both eyes in some subjects. The study included 260 eyes from 161 subjects, composed of 61 men and 100 women. Mean age was 70 years (SD 11.77). There were 199 eyes (123 patients) in the POAG group and 61 eyes (38 patients) in the PACG group. Mean TM heights in the POAG and PACG groups were 812 ± 13 μm and 732 ± 27 μm, respectively, and the difference was significant in univariate analysis (P = .004) and in multivariate analysis (β = -88.7 [24.05-153.5]; P = .008). In this clinic-based population, trabecular meshwork height is shorter in PACG patients compared to POAG patients. This finding may provide insight into the pathophysiology of angle closure and provide assistance in future diagnosis, prevention, and management of the angle-closure spectrum of disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

What is the best predictor of mortality in perforated peptic ulcer disease? A population-based, multivariable regression analysis including three clinical scoring systems.

PubMed

Thorsen, Kenneth; Søreide, Jon Arne; Søreide, Kjetil

2014-07-01

Mortality rates in perforated peptic ulcer (PPU) have remained unchanged. The aim of this study was to compare known clinical factors and three scoring systems (American Society of Anesthesiologists (ASA), Boey and peptic ulcer perforation (PULP)) in the ability to predict mortality in PPU. This is a consecutive, observational cohort study of patients surgically treated for perforated peptic ulcer over a decade (January 2001 through December 2010). Primary outcome was 30-day mortality. A total of 172 patients were included, of whom 28 (16 %) died within 30 days. Among the factors associated with mortality, the PULP score had an odds ratio (OR) of 18.6 and the ASA score had an OR of 11.6, both with an area under the curve (AUC) of 0.79. The Boey score had an OR of 5.0 and an AUC of 0.75. Hypoalbuminaemia alone (≤37 g/l) achieved an OR of 8.7 and an AUC of 0.78. In multivariable regression, mortality was best predicted by a combination of increasing age, presence of active cancer and delay from admission to surgery of >24 h, together with hypoalbuminaemia, hyperbilirubinaemia and increased creatinine values, for a model AUC of 0.89. Six clinical factors predicted 30-day mortality better than available risk scores. Hypoalbuminaemia was the strongest single predictor of mortality and may be included for improved risk estimation.

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Decision curve analysis assessing the clinical benefit of NMP22 in the detection of bladder cancer: secondary analysis of a prospective trial.

PubMed

Barbieri, Christopher E; Cha, Eugene K; Chromecki, Thomas F; Dunning, Allison; Lotan, Yair; Svatek, Robert S; Scherr, Douglas S; Karakiewicz, Pierre I; Sun, Maxine; Mazumdar, Madhu; Shariat, Shahrokh F

2012-03-01

• To employ decision curve analysis to determine the impact of nuclear matrix protein 22 (NMP22) on clinical decision making in the detection of bladder cancer using data from a prospective trial. • The study included 1303 patients at risk for bladder cancer who underwent cystoscopy, urine cytology and measurement of urinary NMP22 levels. • We constructed several prediction models to estimate risk of bladder cancer. The base model was generated using patient characteristics (age, gender, race, smoking and haematuria); cytology and NMP22 were added to the base model to determine effects on predictive accuracy. • Clinical net benefit was calculated by summing the benefits and subtracting the harms and weighting these by the threshold probability at which a patient or clinician would opt for cystoscopy. • In all, 72 patients were found to have bladder cancer (5.5%). In univariate analyses, NMP22 was the strongest predictor of bladder cancer presence (predictive accuracy 71.3%), followed by age (67.5%) and cytology (64.3%). • In multivariable prediction models, NMP22 improved the predictive accuracy of the base model by 8.2% (area under the curve 70.2-78.4%) and of the base model plus cytology by 4.2% (area under the curve 75.9-80.1%). • Decision curve analysis revealed that adding NMP22 to other models increased clinical benefit, particularly at higher threshold probabilities. • NMP22 is a strong, independent predictor of bladder cancer. • Addition of NMP22 improves the accuracy of standard predictors by a statistically and clinically significant margin. • Decision curve analysis suggests that integration of NMP22 into clinical decision making helps avoid unnecessary cystoscopies, with minimal increased risk of missing a cancer. © 2011 THE AUTHORS. BJU INTERNATIONAL © 2011 BJU INTERNATIONAL.

Preoperative predictive model of recovery of urinary continence after radical prostatectomy.

PubMed

Matsushita, Kazuhito; Kent, Matthew T; Vickers, Andrew J; von Bodman, Christian; Bernstein, Melanie; Touijer, Karim A; Coleman, Jonathan A; Laudone, Vincent T; Scardino, Peter T; Eastham, James A; Akin, Oguz; Sandhu, Jaspreet S

2015-10-01

To build a predictive model of urinary continence recovery after radical prostatectomy (RP) that incorporates magnetic resonance imaging (MRI) parameters and clinical data. We conducted a retrospective review of data from 2,849 patients who underwent pelvic staging MRI before RP from November 2001 to June 2010. We used logistic regression to evaluate the association between each MRI variable and continence at 6 or 12 months, adjusting for age, body mass index (BMI) and American Society of Anesthesiologists (ASA) score, and then used multivariable logistic regression to create our model. A nomogram was constructed using the multivariable logistic regression models. In all, 68% (1,742/2,559) and 82% (2,205/2,689) regained function at 6 and 12 months, respectively. In the base model, age, BMI and ASA score were significant predictors of continence at 6 or 12 months on univariate analysis (P < 0.005). Among the preoperative MRI measurements, membranous urethral length, which showed great significance, was incorporated into the base model to create the full model. For continence recovery at 6 months, the addition of membranous urethral length increased the area under the curve (AUC) to 0.664 for the validation set, an increase of 0.064 over the base model. For continence recovery at 12 months, the AUC was 0.674, an increase of 0.085 over the base model. Using our model, the likelihood of continence recovery increases with membranous urethral length and decreases with age, BMI and ASA score. This model could be used for patient counselling and for the identification of patients at high risk for urinary incontinence in whom to study changes in operative technique that improve urinary function after RP. © 2015 The Authors BJU International © 2015 BJU International Published by John Wiley & Sons Ltd.

Economic impact of enoxaparin after acute ischemic stroke based on PREVAIL.

PubMed

Pineo, Graham; Lin, Jay; Stern, Lee; Subrahmanian, Tarun; Annemans, Lieven

2011-04-01

The efficacy and safety of low-molecular-weight heparins (LMWHs) versus unfractionated heparin (UFH) has been demonstrated for the prevention of venous thromboembolism (VTE) after acute ischemic stroke. Few data exist regarding the economic impact of LMWHs versus UFH in this population. A decision-analytic model was constructed using clinical information from the Prevention of VTE after Acute Ischemic stroke with LMWH Enoxaparin (PREVAIL) study, and drug costs and mean Centers for Medicare & Medicaid Services event costs. When considering the total cost of events and drugs, enoxaparin was associated with cost-savings of $895 per patient compared with UFH ($2018 vs $2913). Findings were retained within the univariate and multivariate analyses. From a payer perspective, enoxaparin was cost-effective compared with UFH in patients with acute ischemic stroke. The difference was driven by the lower clinical event rates with enoxaparin. Use of enoxaparin may help to reduce the clinical and economic burden of VTE.

Patients' Positive and Negative Responses to Reading Mental Health Clinical Notes Online.

PubMed

Denneson, Lauren M; Chen, Jason I; Pisciotta, Maura; Tuepker, Anais; Dobscha, Steven K

2018-05-01

This study describes responses to OpenNotes, clinical notes available online, among patients receiving mental health care and explores whether responses vary by patient demographic or clinical characteristics. Survey data from 178 veterans receiving mental health treatment at a large Veterans Affairs medical center included patient-reported health self-efficacy, health knowledge, alliance with clinicians, and negative emotional responses after reading OpenNotes. Health care data were extracted from the patient care database. Reading OpenNotes helped many participants feel in control of their health care (49%) and have more trust in clinicians (45%), although a few (8%) frequently felt upset after reading their notes. In multivariate models, posttraumatic stress disorder was associated with increased patient-clinician alliance (p=.046) but also with negative emotional responses (p<.01). Patients receiving mental health care frequently reported benefits from reading OpenNotes, yet some experienced negative responses.

Predictors of retention in community-based methadone maintenance treatment program in Pearl River Delta, China

PubMed Central

2013-01-01

Background The aims were to identify predictors of treatment retention in methadone maintenance treatment (MMT) clinics in Pearl River Delta, China. Methods Retrospective longitudinal study. Participants: 6 MMT clinics in rural and urban area were selected. Statistical analysis: Stratified random sampling was employed, and the data were analyzed using Kaplan-Meier survival curves and life table method. Protective or risk factors were explored using Cox’s proportional hazards model. Independent variables were enrolled in univariate analysis and among which significant variables were analyzed by multivariate analysis. Results A total of 2728 patients were enrolled. The median of the retention duration was 13.63 months, and the cumulative retention rates at 1,2,3 years were 53.0%, 35.0%, 20.0%, respectively. Multivariate Cox analysis showed: age, relationship with family, live on support from family or friends, income, considering treatment cost suitable, considering treatment open time suitable, addiction severity (daily expense for drug), communication with former drug taking peer, living in rural area, daily treatment dosage, sharing needles, re-admission and history of being arrested were predictors for MMT retention. Conclusions MMT retention rate in Guangdong was low and treatment skills and quality should be improved. Meanwhile, participation of family and society should be encouraged. PMID:23497263

Analysis of threats to research validity introduced by audio recording clinic visits: Selection bias, Hawthorne effect, both, or neither?

PubMed Central

Henry, Stephen G.; Jerant, Anthony; Iosif, Ana-Maria; Feldman, Mitchell D.; Cipri, Camille; Kravitz, Richard L.

2015-01-01

Objective To identify factors associated with participant consent to record visits; to estimate effects of recording on patient-clinician interactions Methods Secondary analysis of data from a randomized trial studying communication about depression; participants were asked for optional consent to audio record study visits. Multiple logistic regression was used to model likelihood of patient and clinician consent. Multivariable regression and propensity score analyses were used to estimate effects of audio recording on 6 dependent variables: discussion of depressive symptoms, preventive health, and depression diagnosis; depression treatment recommendations; visit length; visit difficulty. Results Of 867 visits involving 135 primary care clinicians, 39% were recorded. For clinicians, only working in academic settings (P=0.003) and having worked longer at their current practice (P=0.02) were associated with increased likelihood of consent. For patients, white race (P=0.002) and diabetes (P=0.03) were associated with increased likelihood of consent. Neither multivariable regression nor propensity score analyses revealed any significant effects of recording on the variables examined. Conclusion Few clinician or patient characteristics were significantly associated with consent. Audio recording had no significant effect on any dependent variables. Practice Implications Benefits of recording clinic visits likely outweigh the risks of bias in this setting. PMID:25837372

Balance ability measured with the Berg balance scale: a determinant of fall history in community-dwelling adults with leg amputation.

PubMed

Wong, Christopher Kevin; Chen, Christine C; Blackwell, Wren M; Rahal, Rana T; Benoy, Stephany A

2015-01-01

Falls are common among adults with leg amputations and associated with balance confidence. But subjective confidence is not equivalent with physical ability. This multivariate analyses of community-dwelling adults with leg amputations examined relationships among individual characteristics, falls, balance ability and balance confidence. Cross-sectional study. Community-dwelling adults with leg amputations recruited from a support group and prosthetic clinic. Subjects provided self-reported medical/fall history, prosthetic functional use, and Activities-specific Balance Confidence (ABC) questionnaire data. Balance ability was assessed with the Berg Balance Scale (BBS). Fall incidence was categorized as any fall (one or more) and recurrent falls (more than one). Multivariate logistic regression analyzed relationships within the two fall categories. Cross tabulations and ANOVA analyzed differences among subcategories. Fifty-four subjects (mean age 56.8) with various etiologies, amputation levels, and balance abilities participated. 53.7% had any fall; 25.9% had recurrent falls. Models for both fall categories correctly classified fall history in > 70% of subjects with combinations of the variables ABC, BBS, body-mass-index, and amputation level. Falls occurred regardless of clinical characteristics. Total BBS and select item scores were independent determinants of fall history. Unlike other balance-impaired populations, adults with leg amputation and better balance ability had greater odds of falling.

Prognostic molecular markers with no impact on decision-making: the paradox of gliomas based on a prospective study.

PubMed

Wager, M; Menei, P; Guilhot, J; Levillain, P; Michalak, S; Bataille, B; Blanc, J-L; Lapierre, F; Rigoard, P; Milin, S; Duthe, F; Bonneau, D; Larsen, C-J; Karayan-Tapon, L

2008-06-03

This study assessed the prognostic value of several markers involved in gliomagenesis, and compared it with that of other clinical and imaging markers already used. Four-hundred and sixteen adult patients with newly diagnosed glioma were included over a 3-year period and tumour suppressor genes, oncogenes, MGMT and hTERT expressions, losses of heterozygosity, as well as relevant clinical and imaging information were recorded. This prospective study was based on all adult gliomas. Analyses were performed on patient groups selected according to World Health Organization histoprognostic criteria and on the entire cohort. The endpoint was overall survival, estimated by the Kaplan-Meier method. Univariate analysis was followed by multivariate analysis according to a Cox model. p14(ARF), p16(INK4A) and PTEN expressions, and 10p 10q23, 10q26 and 13q LOH for the entire cohort, hTERT expression for high-grade tumours, EGFR for glioblastomas, 10q26 LOH for grade III tumours and anaplastic oligodendrogliomas were found to be correlated with overall survival on univariate analysis and age and grade on multivariate analysis only. This study confirms the prognostic value of several markers. However, the scattering of the values explained by tumour heterogeneity prevents their use in individual decision-making.

[Violence and post-traumatic stress disorder in childhood].

PubMed

Ximenes, Liana Furtado; de Oliveira, Raquel de Vasconcelos Carvalhães; de Assis, Simone Gonçalves

2009-01-01

This study presents the prevalence of symptoms of Posttraumatic Stress Disorder (PTSD) in 500 schoolchildren (6-13 years old) in São Gonçalo, Rio de Janeiro. It also investigates the association between PTSD, violence and other adverse events in the lives of these children. The multi-stage cluster sampling strategy involved three selection stages. Parents were interviewed about their children's behavior. The instrument used to screen symptoms of PTSD was the Child Behavior Checklist-Posttraumatic Stress Disorder Scale (CBCL-PTSD). Conflict Tactics Scales (CTS) were applied to evaluate family violence and other scales to investigate the socioeconomic profile, familiar relationship, characteristics and adverse events in the lives of the children. Multivariate analysis was performed using a hierarchical model with a significance level of 5%. The prevalence of clinical symptoms of PTSD was of 6.5%. The multivariate analysis suggested an explanation model of PTSD characterized by 18 variables, such as the child's characteristics; specific life events; family violence; and other family factors. The results reveal that it is necessary to work with the child in particularly difficult moments of his/her life in order to prevent or minimize the impact of adverse events on their mental and social functioning.

Multivariate prediction of upper limb prosthesis acceptance or rejection.

PubMed

Biddiss, Elaine A; Chau, Tom T

2008-07-01

To develop a model for prediction of upper limb prosthesis use or rejection. A questionnaire exploring factors in prosthesis acceptance was distributed internationally to individuals with upper limb absence through community-based support groups and rehabilitation hospitals. A total of 191 participants (59 prosthesis rejecters and 132 prosthesis wearers) were included in this study. A logistic regression model, a C5.0 decision tree, and a radial basis function neural network were developed and compared in terms of sensitivity (prediction of prosthesis rejecters), specificity (prediction of prosthesis wearers), and overall cross-validation accuracy. The logistic regression and neural network provided comparable overall accuracies of approximately 84 +/- 3%, specificity of 93%, and sensitivity of 61%. Fitting time-frame emerged as the predominant predictor. Individuals fitted within two years of birth (congenital) or six months of amputation (acquired) were 16 times more likely to continue prosthesis use. To increase rates of prosthesis acceptance, clinical directives should focus on timely, client-centred fitting strategies and the development of improved prostheses and healthcare for individuals with high-level or bilateral limb absence. Multivariate analyses are useful in determining the relative importance of the many factors involved in prosthesis acceptance and rejection.

Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.

PubMed

Hinton, David J; Vázquez, Marely Santiago; Geske, Jennifer R; Hitschfeld, Mario J; Ho, Ada M C; Karpyak, Victor M; Biernacka, Joanna M; Choi, Doo-Sup

2017-05-31

Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.

A "Model" Multivariable Calculus Course.

ERIC Educational Resources Information Center

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

PubMed

Li, Haocheng; Zhang, Yukun; Carroll, Raymond J; Keadle, Sarah Kozey; Sampson, Joshua N; Matthews, Charles E

2017-11-10

A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus.

PubMed

Sweeting, Arianne N; Wong, Jencia; Appelblom, Heidi; Ross, Glynis P; Kouru, Heikki; Williams, Paul F; Sairanen, Mikko; Hyett, Jon A

2018-06-13

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model. A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM. © 2018 S. Karger AG, Basel.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. PMID:29312134

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Novel, Objective, Multivariate Biomarkers Composed of Plasma Amino Acid Profiles for the Diagnosis and Assessment of Inflammatory Bowel Disease

PubMed Central

Hisamatsu, Tadakazu; Okamoto, Susumu; Hashimoto, Masaki; Muramatsu, Takahiko; Andou, Ayatoshi; Uo, Michihide; Kitazume, Mina T.; Matsuoka, Katsuyoshi; Yajima, Tomoharu; Inoue, Nagamu; Kanai, Takanori; Ogata, Haruhiko; Iwao, Yasushi; Yamakado, Minoru; Sakai, Ryosei; Ono, Nobukazu; Ando, Toshihiko; Suzuki, Manabu; Hibi, Toshifumi

2012-01-01

Background Inflammatory bowel disease (IBD) is a chronic intestinal disorder that is associated with a limited number of clinical biomarkers. In order to facilitate the diagnosis of IBD and assess its disease activity, we investigated the potential of novel multivariate indexes using statistical modeling of plasma amino acid concentrations (aminogram). Methodology and Principal Findings We measured fasting plasma aminograms in 387 IBD patients (Crohn's disease (CD), n = 165; ulcerative colitis (UC), n = 222) and 210 healthy controls. Based on Fisher linear classifiers, multivariate indexes were developed from the aminogram in discovery samples (CD, n = 102; UC, n = 102; age and sex-matched healthy controls, n = 102) and internally validated. The indexes were used to discriminate between CD or UC patients and healthy controls, as well as between patients with active disease and those in remission. We assessed index performances using the area under the curve of the receiver operating characteristic (ROC AUC). We observed significant alterations to the plasma aminogram, including histidine and tryptophan. The multivariate indexes established from plasma aminograms were able to distinguish CD or UC patients from healthy controls with ROC AUCs of 0.940 (95% confidence interval (CI): 0.898–0.983) and 0.894 (95%CI: 0.853–0.935), respectively in validation samples (CD, n = 63; UC, n = 120; healthy controls, n = 108). In addition, other indexes appeared to be a measure of disease activity. These indexes distinguished active CD or UC patients from each remission patients with ROC AUCs of 0.894 (95%CI: 0.853–0.935) and 0.849 (95%CI: 0.770–0.928), and correlated with clinical disease activity indexes for CD (rs = 0.592, 95%CI: 0.385–0.742, p<0.001) or UC (rs = 0.598, 95%CI: 0.452–0.713, p<0.001), respectively. Conclusions and Significance In this study, we demonstrated that established multivariate indexes composed of plasma amino acid profiles can serve as novel, non-invasive, objective biomarkers for the diagnosis and monitoring of IBD, providing us with new insights into the pathophysiology of the disease. PMID:22303484

Morphological parameters associated with ruptured posterior communicating aneurysms.

PubMed

Ho, Allen; Lin, Ning; Charoenvimolphan, Nareerat; Stanley, Mary; Frerichs, Kai U; Day, Arthur L; Du, Rose

2014-01-01

The rupture risk of unruptured intracranial aneurysms is known to be dependent on the size of the aneurysm. However, the association of morphological characteristics with ruptured aneurysms has not been established in a systematic and location specific manner for the most common aneurysm locations. We evaluated posterior communicating artery (PCoA) aneurysms for morphological parameters associated with aneurysm rupture in that location. CT angiograms were evaluated to generate 3-D models of the aneurysms and surrounding vasculature. Univariate and multivariate analyses were performed to evaluate morphological parameters including aneurysm volume, aspect ratio, size ratio, distance to ICA bifurcation, aneurysm angle, vessel angles, flow angles, and vessel-to-vessel angles. From 2005-2012, 148 PCoA aneurysms were treated in a single institution. Preoperative CTAs from 63 patients (40 ruptured, 23 unruptured) were available and analyzed. Multivariate logistic regression revealed that smaller volume (p = 0.011), larger aneurysm neck diameter (0.048), and shorter ICA bifurcation to aneurysm distance (p = 0.005) were the most strongly associated with aneurysm rupture after adjusting for all other clinical and morphological variables. Multivariate subgroup analysis for patients with visualized PCoA demonstrated that larger neck diameter (p = 0.018) and shorter ICA bifurcation to aneurysm distance (p = 0.011) were significantly associated with rupture. Intracerebral hemorrhage was associated with smaller volume, larger maximum height, and smaller aneurysm angle, in addition to lateral projection, male sex, and lack of hypertension. We found that shorter ICA bifurcation to aneurysm distance is significantly associated with PCoA aneurysm rupture. This is a new physically intuitive parameter that can be measured easily and therefore be readily applied in clinical practice to aid in the evaluation of patients with PCoA aneurysms.

Morphological Parameters Associated with Ruptured Posterior Communicating Aneurysms

PubMed Central

Ho, Allen; Lin, Ning; Charoenvimolphan, Nareerat; Stanley, Mary; Frerichs, Kai U.; Day, Arthur L.; Du, Rose

2014-01-01

The rupture risk of unruptured intracranial aneurysms is known to be dependent on the size of the aneurysm. However, the association of morphological characteristics with ruptured aneurysms has not been established in a systematic and location specific manner for the most common aneurysm locations. We evaluated posterior communicating artery (PCoA) aneurysms for morphological parameters associated with aneurysm rupture in that location. CT angiograms were evaluated to generate 3-D models of the aneurysms and surrounding vasculature. Univariate and multivariate analyses were performed to evaluate morphological parameters including aneurysm volume, aspect ratio, size ratio, distance to ICA bifurcation, aneurysm angle, vessel angles, flow angles, and vessel-to-vessel angles. From 2005–2012, 148 PCoA aneurysms were treated in a single institution. Preoperative CTAs from 63 patients (40 ruptured, 23 unruptured) were available and analyzed. Multivariate logistic regression revealed that smaller volume (p = 0.011), larger aneurysm neck diameter (0.048), and shorter ICA bifurcation to aneurysm distance (p = 0.005) were the most strongly associated with aneurysm rupture after adjusting for all other clinical and morphological variables. Multivariate subgroup analysis for patients with visualized PCoA demonstrated that larger neck diameter (p = 0.018) and shorter ICA bifurcation to aneurysm distance (p = 0.011) were significantly associated with rupture. Intracerebral hemorrhage was associated with smaller volume, larger maximum height, and smaller aneurysm angle, in addition to lateral projection, male sex, and lack of hypertension. We found that shorter ICA bifurcation to aneurysm distance is significantly associated with PCoA aneurysm rupture. This is a new physically intuitive parameter that can be measured easily and therefore be readily applied in clinical practice to aid in the evaluation of patients with PCoA aneurysms. PMID:24733151

Association of ITPA polymorphisms rs6051702/rs1127354 instead of rs7270101/rs1127354 as predictor of ribavirin-associated anemia in chronic hepatitis C treated patients.

PubMed

D'Avolio, Antonio; De Nicolò, Amedeo; Cusato, Jessica; Ciancio, Alessia; Boglione, Lucio; Strona, Silvia; Cariti, Giuseppe; Troshina, Giulia; Caviglia, Gian Paolo; Smedile, Antonina; Rizzetto, Mario; Di Perri, Giovanni

2013-10-01

Functional variants rs7270101 and rs1127354 of inosine triphosphatase (ITPA) were recently found to protect against ribavirin (RBV)-induced hemolytic anemia. However, no definitive data are yet available on the role of no functional rs6051702 polymorphism. Since a simultaneous evaluation of the three ITPA SNPs for hemolytic anemia has not yet been investigated, we aimed to understand the contribution of each SNPs and its potential clinical use to predict anemia in HCV treated patients. A retrospective analysis included 379 HCV treated patients. The ITPA variants rs6051702, rs7270101 and rs1127354 were genotyped and tested for association with achieving anemia at week 4. We also investigated, using multivariate logistic regression, the impact of each single and paired associated polymorphism on anemia onset. All SNPs were associated with Hb decrease. The carrier of at least one variant allele in the functional ITPA SNPs was associated with a lower decrement of Hb, as compared to patients without a variant allele. In multivariate logistic regression analyses the carrier of a variant allele in the rs6051702/rs1127354 association (OR=0.11, p=1.75×10(-5)) and Hb at baseline (OR=1.51, p=1.21×10(-4)) were independently associated with protection against clinically significant anemia at week 4. All ITPA polymorphisms considered were shown to be significantly associated with anemia onset. A multivariate regression model based on ITPA genetic polymorphisms was developed for predicting the risk of anemia. Considering the characterization of pre-therapy anemia predictors, rs6051702 SNP in association to rs1127354 is more informative in order to avoid this relevant adverse event. Copyright © 2013 Elsevier B.V. All rights reserved.

Concentration-Dependent Antagonism and Culture Conversion in Pulmonary Tuberculosis

PubMed Central

Pasipanodya, Jotam G.; Denti, Paolo; Sirgel, Frederick; Lesosky, Maia; Gumbo, Tawanda; Meintjes, Graeme; McIlleron, Helen; Wilkinson, Robert J.

2017-01-01

Abstract Background. There is scant evidence to support target drug exposures for optimal tuberculosis outcomes. We therefore assessed whether pharmacokinetic/pharmacodynamic (PK/PD) parameters could predict 2-month culture conversion. Methods. One hundred patients with pulmonary tuberculosis (65% human immunodeficiency virus coinfected) were intensively sampled to determine rifampicin, isoniazid, and pyrazinamide plasma concentrations after 7–8 weeks of therapy, and PK parameters determined using nonlinear mixed-effects models. Detailed clinical data and sputum for culture were collected at baseline, 2 months, and 5–6 months. Minimum inhibitory concentrations (MICs) were determined on baseline isolates. Multivariate logistic regression and the assumption-free multivariate adaptive regression splines (MARS) were used to identify clinical and PK/PD predictors of 2-month culture conversion. Potential PK/PD predictors included 0- to 24-hour area under the curve (AUC0-24), maximum concentration (Cmax), AUC0-24/MIC, Cmax/MIC, and percentage of time that concentrations persisted above the MIC (%TMIC). Results. Twenty-six percent of patients had Cmax of rifampicin <8 mg/L, pyrazinamide <35 mg/L, and isoniazid <3 mg/L. No relationship was found between PK exposures and 2-month culture conversion using multivariate logistic regression after adjusting for MIC. However, MARS identified negative interactions between isoniazid Cmax and rifampicin Cmax/MIC ratio on 2-month culture conversion. If isoniazid Cmax was <4.6 mg/L and rifampicin Cmax/MIC <28, the isoniazid concentration had an antagonistic effect on culture conversion. For patients with isoniazid Cmax >4.6 mg/L, higher isoniazid exposures were associated with improved rates of culture conversion. Conclusions. PK/PD analyses using MARS identified isoniazid Cmax and rifampicin Cmax/MIC thresholds below which there is concentration-dependent antagonism that reduces 2-month sputum culture conversion. PMID:28205671

A predictive risk model for medical intractability in epilepsy.

PubMed

Huang, Lisu; Li, Shi; He, Dake; Bao, Weiqun; Li, Ling

2014-08-01

This study aimed to investigate early predictors (6 months after diagnosis) of medical intractability in epilepsy. All children <12 years of age having two or more unprovoked seizures 24 h apart at Xinhua Hospital between 1992 and 2006 were included. Medical intractability was defined as failure, due to lack of seizure control, of more than 2 antiepileptic drugs at maximum tolerated doses, with an average of more than 1 seizure per month for 24 months and no more than 3 consecutive months of seizure freedom during this interval. Univariate and multivariate logistic regression models were performed to determine the risk factors for developing medical intractability. Receiver operating characteristic curve was applied to fit the best compounded predictive model. A total of 649 patients were identified, out of which 119 (18%) met the study definition of intractable epilepsy at 2 years after diagnosis, and the rate of intractable epilepsy in patients with idiopathic syndromes was 12%. Multivariate logistic regression analysis revealed that neurodevelopmental delay, symptomatic etiology, partial seizures, and more than 10 seizures before diagnosis were significant and independent risk factors for intractable epilepsy. The best model to predict medical intractability in epilepsy comprised neurological physical abnormality, age at onset of epilepsy under 1 year, more than 10 seizures before diagnosis, and partial epilepsy, and the area under receiver operating characteristic curve was 0.7797. This model also fitted best in patients with idiopathic syndromes. A predictive model of medically intractable epilepsy composed of only four characteristics is established. This model is comparatively accurate and simple to apply clinically. Copyright © 2014 Elsevier Inc. All rights reserved.

Provider-related barriers to rapid HIV testing in U.S. urban non-profit community clinics, community-based organizations (CBOs) and hospitals.

PubMed

Bogart, Laura M; Howerton, Devery; Lange, James; Setodji, Claude Messan; Becker, Kirsten; Klein, David J; Asch, Steven M

2010-06-01

We examined provider-reported barriers to rapid HIV testing in U.S. urban non-profit community clinics, community-based organizations (CBOs), and hospitals. 12 primary metropolitan statistical areas (PMSAs; three per region) were sampled randomly, with sampling weights proportional to AIDS case reports. Across PMSAs, all 671 hospitals and a random sample of 738 clinics/CBOs were telephoned for a survey on rapid HIV test availability. Of the 671 hospitals, 172 hospitals were randomly selected for barriers questions, for which 158 laboratory and 136 department staff were eligible and interviewed in 2005. Of the 738 clinics/CBOs, 276 were randomly selected for barriers questions, 206 were reached, and 118 were eligible and interviewed in 2005-2006. In multivariate models, barriers regarding translation of administrative/quality assurance policies into practice were significantly associated with rapid HIV testing availability. For greater rapid testing diffusion, policies are needed to reduce administrative barriers and provide quality assurance training to non-laboratory staff.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Clinical management provided by board-certificated physiatrists in early rehabilitation is a significant determinant of functional improvement in acute stroke patients: a retrospective analysis of Japan rehabilitation database.

PubMed

Kinoshita, Shoji; Kakuda, Wataru; Momosaki, Ryo; Yamada, Naoki; Sugawara, Hidekazu; Watanabe, Shu; Abo, Masahiro

2015-05-01

Early rehabilitation for acute stroke patients is widely recommended. We tested the hypothesis that clinical outcome of stroke patients who receive early rehabilitation managed by board-certificated physiatrists (BCP) is generally better than that provided by other medical specialties. Data of stroke patients who underwent early rehabilitation in 19 acute hospitals between January 2005 and December 2013 were collected from the Japan Rehabilitation Database and analyzed retrospectively. Multivariate linear regression analysis using generalized estimating equations method was performed to assess the association between Functional Independence Measure (FIM) effectiveness and management provided by BCP in early rehabilitation. In addition, multivariate logistic regression analysis was also performed to assess the impact of management provided by BCP in acute phase on discharge destination. After setting the inclusion criteria, data of 3838 stroke patients were eligible for analysis. BCP provided early rehabilitation in 814 patients (21.2%). Both the duration of daily exercise time and the frequency of regular conferencing were significantly higher for patients managed by BCP than by other specialties. Although the mortality rate was not different, multivariate regression analysis showed that FIM effectiveness correlated significantly and positively with the management provided by BCP (coefficient, .35; 95% confidence interval [CI], .012-.059; P < .005). In addition, multivariate logistic analysis identified clinical management by BCP as a significant determinant of home discharge (odds ratio, 1.24; 95% CI, 1.08-1.44; P < .005). Our retrospective cohort study demonstrated that clinical management provided by BCP in early rehabilitation can lead to functional recovery of acute stroke. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Comparison of patient perceived primary care quality in public clinics, public hospitals and private clinics in rural China.

PubMed

Wang, Wenhua; Maitland, Elizabeth; Nicholas, Stephen; Loban, Ekaterina; Haggerty, Jeannie

2017-10-03

In rural China, patients have free choice of health facilities for outpatient services. Comparison studies exploring the attributes of different health facilities can help identify optimal primary care service models. Using a representative sample of Chinese provinces, this study aimed to compare patients' rating of three primary care service models used by rural residents (public clinics, public hospitals and private clinics) on a range of health care attributes related to responsiveness. This was a secondary analysis using the household survey data from World Health Organization (WHO) Study on global AGEing and adult health (SAGE). Using a multistage cluster sampling strategy, eight provinces were selected and finally 3435 overall respondents reporting they had visited public clinics, public hospitals or private clinics during the last year, were included in our analysis. Five items were used to measure patient perceived quality in five domains including prompt attention, communication and autonomy, dignity and confidentiality. ANOVA and Turkey's post hoc tests were used to conduct comparative analysis of five domains. Separate multivariate linear regression models were estimated to examine the association of primary care service models with each domain after controlling for patient characteristics. The distribution of last health facilities visited was: 29.5% public clinics; 31.2% public hospitals and; 39.3% private clinics. Public clinics perform best in all five domains: prompt attention (4.15), dignity (4.17), communication (4.07), autonomy (4.05) and confidentiality (4.02). Public hospitals perform better than private clinics in dignity (4.03 vs 3.94), communication (3.97 vs 3.82), autonomy (3.92 vs 3.74) and confidentiality (3.94 vs 3.73), but equivalently in prompt attention (3.92 vs 3.93). Rural residents who are older, wealthier, and with higher self-rated health status have significantly higher patient perceived quality of care in all domains. Rural public clinics, which share many characteristics with the optimal primary care delivery model, should be strongly strengthened to respond to patients' needs. Better doctor-patient interaction training would improve respect, confidentiality, autonomy and, most importantly, health care quality for rural patients.

Utilization of information and communication technology (ICT) among sexually transmitted disease clinics attendees with coexisting drinking problems

PubMed Central

2014-01-01

Background Alcohol misuse remains a major risk factor for contracting sexually transmitted diseases (STDs) not typically addressed in STD clinic settings. Information and communication technology (ICT) can offer new avenues to deliver evidence-based screening and treatment for problematic drinking, however, few data exists regarding the utilization of ICT among STD clinic attendees with coexisting drinking problems. The objectives of this study are to identify STD clinics attendees with hazardous drinking, to examine socio-demographic factors associated with ICT use, and to explore individuals’ interests in engaging in ICT-based health interventions. Methods Cross-sectional questionnaires examining alcohol consumption and ICT use were administered to 396 persons attending two non-urban STD clinics. Descriptive statistics for ICT use were calculated for both hazardous drinkers and the entire sample. Multivariable logistic regression models among hazardous drinkers identified factors significantly associated with use of each kind of ICT. Results The mean age of the 396 participants was 25 years, 66% were females and 60% were African-Americans. One third of the sample met the criteria of hazardous drinking. ICT use in hazardous drinkers included 94% reporting having internet access at least monthly, 82% reporting having an email account, 85% reporting currently owning a cell phone, and 91% reporting use of any cell phone application. More than two thirds (73%) of hazardous drinkers were willing to play health-related video games during clinic waiting time, slightly higher than the entire sample (69%). Multivariable analyses indicated that younger age were significantly related to monthly internet use, and multifunction cell phone use, while being males and younger age were significantly associated with monthly video game playing. Conclusions Our study demonstrates commonality of ICT use among STD clinic attendees with hazardous drinking, indicating the viability of using ICT to assist screening and behavioural intervention for alcohol misuse in STD clinic settings. Future research is needed to demonstrate the effectiveness of ICT-based health behavioural interventions in the STD clinic settings or other venues that serve populations at high risk for substance abuse, HIV or other STDs. PMID:24670037

Utilization of information and communication technology (ICT) among sexually transmitted disease clinics attendees with coexisting drinking problems.

PubMed

Hu, Xingdi; Dodd, Virginia J; Oliverio, James C; Cook, Robert L

2014-03-26

Alcohol misuse remains a major risk factor for contracting sexually transmitted diseases (STDs) not typically addressed in STD clinic settings. Information and communication technology (ICT) can offer new avenues to deliver evidence-based screening and treatment for problematic drinking, however, few data exists regarding the utilization of ICT among STD clinic attendees with coexisting drinking problems. The objectives of this study are to identify STD clinics attendees with hazardous drinking, to examine socio-demographic factors associated with ICT use, and to explore individuals' interests in engaging in ICT-based health interventions. Cross-sectional questionnaires examining alcohol consumption and ICT use were administered to 396 persons attending two non-urban STD clinics. Descriptive statistics for ICT use were calculated for both hazardous drinkers and the entire sample. Multivariable logistic regression models among hazardous drinkers identified factors significantly associated with use of each kind of ICT. The mean age of the 396 participants was 25 years, 66% were females and 60% were African-Americans. One third of the sample met the criteria of hazardous drinking. ICT use in hazardous drinkers included 94% reporting having internet access at least monthly, 82% reporting having an email account, 85% reporting currently owning a cell phone, and 91% reporting use of any cell phone application. More than two thirds (73%) of hazardous drinkers were willing to play health-related video games during clinic waiting time, slightly higher than the entire sample (69%). Multivariable analyses indicated that younger age were significantly related to monthly internet use, and multifunction cell phone use, while being males and younger age were significantly associated with monthly video game playing. Our study demonstrates commonality of ICT use among STD clinic attendees with hazardous drinking, indicating the viability of using ICT to assist screening and behavioural intervention for alcohol misuse in STD clinic settings. Future research is needed to demonstrate the effectiveness of ICT-based health behavioural interventions in the STD clinic settings or other venues that serve populations at high risk for substance abuse, HIV or other STDs.

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.

Risk factors for keratinocyte skin cancer in patients diagnosed with melanoma, a large retrospective study.

PubMed

Espinosa, Pablo; Pfeiffer, Ruth M; García-Casado, Zaida; Requena, Celia; Landi, Maria Teresa; Kumar, Rajiv; Nagore, Eduardo

2016-01-01

Melanoma survivors are at an increased risk of developing other malignancies, including keratinocyte skin cancer (KSC). While it is known that many risk factors for melanoma also impact risk of KSC in the general population, no previous study has investigated risk factors for KSC development in melanoma patients. We assessed associations of personal and clinical characteristics, including skin phenotype and variations in the melanocortin 1 receptor (MC1R) gene, with KSC risk in melanoma patients. We used prospective follow-up information on 1200 patients treated for melanoma at the Instituto Valenciano de Oncología, Spain, between 2000 and 2011. We computed hazard ratios and 95% confidence intervals (CIs) for the association of clinical, personal and genetic characteristics with risk of KSC, squamous cell carcinoma (SCC), or basal cell carcinoma (BCC) from Cox proportional hazard models. Five-year cumulative incidence based on competing risk models of SCC, BCC or KSC overall was computed using multivariate subdistribution hazard models. To assess predictive performance of the models, we computed areas under the receiver-operating characteristic curves (AUCs, discriminatory power) using cross-validation. Median follow-up was 57.2 months; a KSC was detected in 163 patients (13.6%). In multivariable Cox models, age, sex, sunburns, chronic sun exposure, past personal history of non-melanoma skin cancer or other non-cutaneous neoplasia, and the MC1R variants p.D294H and p.R163Q were significantly associated with KSC risk. A cumulative incidence model including age, sex, personal history of KSC, and of other non-cutaneous neoplasia had an AUC of 0.76 (95% CI: 0.71-0.80). When p.D294H and p.R163Q variants were added to the model, the AUC increased to 0.81 (95% CI: 0.77-0.84) (p-value for difference <0.0001). In addition to age, sex, skin characteristics, and sun exposure, p.R163Q and p.D294H MC1R variants significantly increased KSC risk among melanoma patients. Our findings may help identify patients who could benefit most from preventive measures. Copyright © 2015 Elsevier Ltd. All rights reserved.

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

The impact of training and working conditions on junior doctors’ intention to leave clinical practice

PubMed Central

2014-01-01

Background The shortage of physicians is an evolving problem throughout the world. In this study we aimed to identify to what extent junior doctors’ training and working conditions determine their intention to leave clinical practice after residency training. Methods A prospective cohort study was conducted in 557 junior doctors undergoing residency training in German hospitals. Self-reported specialty training conditions, working conditions and intention to leave clinical practice were measured over three time points. Scales covering training conditions were assessed by structured residency training, professional support, and dealing with lack of knowledge; working conditions were evaluated by work overload, job autonomy and social support, based on the Demand–Control–Support model. Multivariate ordinal logistic regression analyses with random intercept for longitudinal data were applied to determine the odds ratio of having a higher level of intention to leave clinical practice. Results In the models that considered training and working conditions separately to predict intention to leave clinical practice we found significant baseline effects and change effects. After modelling training and working conditions simultaneously, we found evidence that the change effect of job autonomy (OR 0.77, p = .005) was associated with intention to leave clinical practice, whereas for the training conditions, only the baseline effects of structured residency training (OR 0.74, p = .017) and dealing with lack of knowledge (OR 0.74, p = .026) predicted intention to leave clinical practice. Conclusions Junior doctors undergoing specialty training experience high workload in hospital practice and intense requirements in terms of specialty training. Our study indicates that simultaneously improving working conditions over time and establishing a high standard of specialty training conditions may prevent junior doctors from considering leaving clinical practice after residency training. PMID:24942360

Risk stratification for death and all-cause hospitalization in heart failure clinic outpatients.

PubMed

Hummel, Scott L; Ghalib, Hussam H; Ratz, David; Koelling, Todd M

2013-11-01

Most heart failure (HF) risk stratification models were developed for inpatient use, and available outpatient models use a complex set of variables. We hypothesized that routinely collected clinical data could predict the 6-month risk of death and all-cause medical hospitalization in HF clinic outpatients. Using a quality improvement database and multivariable Cox modeling, we derived the Heart Failure Patient Severity Index (HFPSI) in the University of Michigan HF clinic (UM cohort, n = 1,536; 314 reached primary outcome). We externally validated the HFPSI in the Ann Arbor Veterans' Affairs HF clinic (VA cohort, n = 445; 106 outcomes) and explored "real-time" HFPSI use (VA-RT cohort, n = 486; 141 outcomes) by tracking VA patients for 6 months from their most recently calculated HFPSI, rather than using an arbitrary start date for the cohort. The HFPSI model included blood urea nitrogen, B-type natriuretic peptide, New York Heart Association class, diabetes status, history of atrial fibrillation/flutter, and all-cause hospitalization within the prior 1 and 2 to 6 months. The concordance c statistics in the UM/VA/VA-RT cohorts were 0.71/0.68/0.74. Kaplan-Meier curves and log-rank testing demonstrated excellent risk stratification, particularly between a large, low-risk group (40% of patients, 6-month event rates in the UM/VA/VA-RT cohorts 8%/12%/12%) and a small, high-risk group (10% of patients, 6-month event rates in the UM/VA/VA-RT cohorts 57%/58%/79%). The HFPSI uses readily available data to predict the 6-month risk of death and/or all-cause medical hospitalization in HF clinic outpatients and could potentially help allocate specialized HF resources within health systems. © 2013.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Comparison of satisfaction with care between two different models of HIV care delivery in St. Petersburg, Russia.

PubMed

Suvorova, Alena; Belyakov, Andrey; Makhamatova, Aliia; Ustinov, Andrey; Levina, Olga; Tulupyev, Alexander; Niccolai, Linda; Rassokhin, Vadim; Heimer, Robert

2015-01-01

Prior to 2010, medical care for people living with HIV/AIDS was provided at an outpatient facility near the center of St. Petersburg. Since then, HIV specialty clinics have been established in more outlying regions of the city. The study examined the effect of this decentralization of HIV care on patients' satisfaction with care in clinics of St. Petersburg, Russia. We conducted a cross-sectional study with 418 HIV-positive patients receiving care at the St. Petersburg AIDS Center or at District Infectious Disease Departments (centralized and decentralized models, respectively). Face-to-face interviews included questions about psychosocial characteristics, patient's satisfaction with care, and clinic-related patient experience. Abstraction of medical records provided information on patients' viral load. To compare centralized and decentralized models of care delivery, we performed bivariate and multivariate analysis. Clients of District Infectious Disease Departments spent less time in lines and traveling to reach the clinic, and they had stronger relationships with their doctor. The overall satisfaction with care was high, with 86% of the sample reporting high level of satisfaction. Nevertheless, satisfaction with care was strongly and positively associated with the decentralized model of care and Patient-Doctor Relationship Score. Patient experience elements such as waiting time, travel time, and number of services used were not significant factors related to satisfaction. Given the positive association of satisfaction with decentralized service delivery, it is worth exploring decentralization as one way of improving healthcare services for people living with HIV/AIDS.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

[Psychosocial aspects associated with excessive attendance in primary care paediatric clinics].

PubMed

Martín Martín, Raquel; Sánchez Bayle, Marciano; Teruel de Francisco, Carmen

2018-04-20

Hyper-attendance is a significant problem in paediatric Primary Care clinics. The aim of our study was to analyse the level of attendance in these clinics and its relationship with certain psychosocial aspects of the families attending them. Observational descriptive study was conducted using questionnaires collected during a period of 6months, as well as recording the frequency of attendance in the previous 6months. A total of 346 questionnaires of children between 6months and 13years of age belonging to 2 urban Primary Care clinics in Madrid were completed. The raw data was analysed, and comparisons between groups and multivariate analysis were performed. The mean number of consultations in the last 6months, of the total included in the study, was 3.06 in the Primary Care centre, and 0.77 in the emergency services. It was considered over-frequent for those who had attended the Primary Care health centre 6 or more times in this period (>p90), of which there were 33 children (9.53%). In the multivariate analysis, the variables related to being frequent users of Primary Care clinics were: the presence of high level of anxiety in the parents (OR=5.50; 95%CI: 2.49-12.17, P<.0001), and the age of the children (OR=0.73; 95%CI: 0.58-0.91, P=.005). The model presented an area under the curve of 0.761 (95%CI: 0.678-0.945, P<.0001). The frequency of visits in paediatric Primary Care clinics is directly related to the high level of anxiety of the parents, and inversely to the age of the children. It would be advisable to detect and, if possible, intervene in cases of high parental anxiety in order to try to reduce the over-frequency in the paediatric primary health care. Copyright © 2018. Publicado por Elsevier España, S.L.U.

Use of diagnosis codes for detection of clinically significant opioid poisoning in the emergency department: A retrospective analysis of a surveillance case definition.

PubMed

Reardon, Joseph M; Harmon, Katherine J; Schult, Genevieve C; Staton, Catherine A; Waller, Anna E

2016-02-08

Although fatal opioid poisonings tripled from 1999 to 2008, data describing nonfatal poisonings are rare. Public health authorities are in need of tools to track opioid poisonings in near real time. We determined the utility of ICD-9-CM diagnosis codes for identifying clinically significant opioid poisonings in a state-wide emergency department (ED) surveillance system. We sampled visits from four hospitals from July 2009 to June 2012 with diagnosis codes of 965.00, 965.01, 965.02 and 965.09 (poisoning by opiates and related narcotics) and/or an external cause of injury code of E850.0-E850.2 (accidental poisoning by opiates and related narcotics), and developed a novel case definition to determine in which cases opioid poisoning prompted the ED visit. We calculated the percentage of visits coded for opioid poisoning that were clinically significant and compared it to the percentage of visits coded for poisoning by non-opioid agents in which there was actually poisoning by an opioid agent. We created a multivariate regression model to determine if other collected triage data can improve the positive predictive value of diagnosis codes alone for detecting clinically significant opioid poisoning. 70.1 % of visits (Standard Error 2.4 %) coded for opioid poisoning were primarily prompted by opioid poisoning. The remainder of visits represented opioid exposure in the setting of other primary diseases. Among non-opioid poisoning codes reviewed, up to 36 % were reclassified as an opioid poisoning. In multivariate analysis, only naloxone use improved the positive predictive value of ICD-9-CM codes for identifying clinically significant opioid poisoning, but was associated with a high false negative rate. This surveillance mechanism identifies many clinically significant opioid overdoses with a high positive predictive value. With further validation, it may help target control measures such as prescriber education and pharmacy monitoring.

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

The result of adjuvant chemotherapy for localized pT3 upper urinary tract carcinoma in a multi-institutional study.

PubMed

Kawashima, Atsunari; Nakai, Yasutomo; Nakayama, Masashi; Ujike, Takeshi; Tanigawa, Go; Ono, Yutaka; Kamoto, Akihito; Takada, Tsuyosi; Yamaguchi, Yuichiro; Takayama, Hitoshi; Nishimura, Kazuo; Nonomura, Norio; Tsujimura, Akira

2012-10-01

To determine through the analysis of our multi-institutional database whether postoperative adjuvant chemotherapy for upper urinary tract carcinoma with localized invasive upper urinary tract carcinoma (UUTC) is beneficial. A study population of 93 patients with pT3N0/xM0 UUTC was eligible for this study. Clinical features evaluated were sex, tumor location, adjuvant chemotherapy status, tumor pathology (histology, grade, infiltrating growth, lymphovascular invasion (LVI)), and cause of death. Cancer-specific survival (CSS) was estimated by Kaplan-Meier method. Prognostic factors related to CSS were analyzed by Cox proportional hazards regression model for multivariate analysis. In pT3 patients, overall 5-year CSS rate was 68.4% and median CSS time was 31 months (range 3-114 months). In the adjuvant chemotherapy group, 5-year CSS rate was 80.8%, whereas 5-year CSS rate was 64.4% in the non-adjuvant chemotherapy group. By multivariate analysis, adjuvant chemotherapy status was significantly associated with CSS (P = 0.008) were sex, tumor grade, tumor histology, and LVI presence. This study, although it was retrospective study, revealed that adjuvant chemotherapy after RNU may be beneficial in pT3N0/X patients by multivariate analysis. Prospective studies evaluating adjuvant therapy regimens for UTTC are required.

Multivariate analysis of fears in dental phobic patients according to a reduced FSS-II scale.

PubMed

Hakeberg, M; Gustafsson, J E; Berggren, U; Carlsson, S G

1995-10-01

This study analyzed and assessed dimensions of a questionnaire developed to measure general fears and phobias. A previous factor analysis among 109 dental phobics had revealed a five-factor structure with 22 items and an explained total variance of 54%. The present study analyzed the same material using a multivariate statistical procedure (LISREL) to reveal structural latent variables. The LISREL analysis, based on the correlation matrix, yielded a chi-square of 216.6 with 195 degrees of freedom (P = 0.138) and showed a model with seven latent variables. One was a general fear factor correlated to all 22 items. The other six factors concerned "Illness & Death" (5 items), "Failures & Embarrassment" (5 items), "Social situations" (5 items), "Physical injuries" (4 items), "Animals & Natural phenomena" (4 items). One item (opposite sex) was included in both "Failures & Embarrassment" and "Social situations". The last factor, "Social interaction", combined all the items in "Failures & Embarrassment" and "Social situations" (9 items). In conclusion, this multivariate statistical analysis (LISREL) revealed and confirmed a factor structure similar to our previous study, but added two important dimensions not shown with a traditional factor analysis. This reduced FSS-II version measures general fears and phobias and may be used on a routine clinical basis as well as in dental phobia research.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-07

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying explanation and elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article. © BMJ Publishing Group Ltd 2014.

Sources of meaning in family caregivers of terminally ill patients supported by a palliative nursing care team: A naturalistic three-month cohort study.

PubMed

Valdés-Stauber, Juan; Lemaczyk, Rafael; Kilian, Reinhold

2018-06-01

ABSTRACTObjective:Our aim was to identify possible patterns of change or durability in sources of meaning for family caregivers of terminally ill patients after the onset of support at home by an outreach palliative nursing team during a three-month survey period. The Sources of Meaning and Meaning in Life Questionnaire (SoMe) was administered to 100 caregivers of terminally ill patients at four measurement timepoints: immediately before the onset of the palliative care (t0), and at 1 week, 1 month, and 3 months after t0. Time-dependent changes were assessed for the completed subsample (n = 24) by means of bivariate linear as well as quadratic regression models. Multivariate regressions with dimensions of meaning in life as dependent variables were performed for the whole sample by means of random-effects models: dependent variables changed over time (four timepoints), whereas regressors remained constant. No significant differences were found for psychosocial and clinical variables or for sources of meaning between the uncompleted and completed subsamples. Growth curve analyses revealed no statistically significant but tendentiously parabolic changes for any dimensions or for single sources of meaning. In multivariate models, a negative association was found between patient age, psychological burden of family caregivers, and changes in total SoMe score, as well as for the superordinate dimensions. According to our hypothesis, sources of meaning and meaning in life seem to remain robust in relatives caring for terminally ill family members during the three-month survey period. A parabolic development pattern of single sources of meaning indicates an adjustment process. An important limitation of our study is the small number of participants compared with larger multivariate models because of high dropout rates, primarily due to the death of three-quarters of the participants during the survey period.

Who will have Sustainable Employment After a Back Injury? The Development of a Clinical Prediction Model in a Cohort of Injured Workers.

PubMed

Shearer, Heather M; Côté, Pierre; Boyle, Eleanor; Hayden, Jill A; Frank, John; Johnson, William G

2017-09-01

Purpose Our objective was to develop a clinical prediction model to identify workers with sustainable employment following an episode of work-related low back pain (LBP). Methods We used data from a cohort study of injured workers with incident LBP claims in the USA to predict employment patterns 1 and 6 months following a workers' compensation claim. We developed three sequential models to determine the contribution of three domains of variables: (1) basic demographic/clinical variables; (2) health-related variables; and (3) work-related factors. Multivariable logistic regression was used to develop the predictive models. We constructed receiver operator curves and used the c-index to measure predictive accuracy. Results Seventy-nine percent and 77 % of workers had sustainable employment at 1 and 6 months, respectively. Sustainable employment at 1 month was predicted by initial back pain intensity, mental health-related quality of life, claim litigation and employer type (c-index = 0.77). At 6 months, sustainable employment was predicted by physical and mental health-related quality of life, claim litigation and employer type (c-index = 0.77). Adding health-related and work-related variables to models improved predictive accuracy by 8.5 and 10 % at 1 and 6 months respectively. Conclusion We developed clinically-relevant models to predict sustainable employment in injured workers who made a workers' compensation claim for LBP. Inquiring about back pain intensity, physical and mental health-related quality of life, claim litigation and employer type may be beneficial in developing programs of care. Our models need to be validated in other populations.

Validation of the inverse pulse wave transit time series as surrogate of systolic blood pressure in MVAR modeling.

PubMed

Giassi, Pedro; Okida, Sergio; Oliveira, Maurício G; Moraes, Raimes

2013-11-01

Short-term cardiovascular regulation mediated by the sympathetic and parasympathetic branches of the autonomic nervous system has been investigated by multivariate autoregressive (MVAR) modeling, providing insightful analysis. MVAR models employ, as inputs, heart rate (HR), systolic blood pressure (SBP) and respiratory waveforms. ECG (from which HR series is obtained) and respiratory flow waveform (RFW) can be easily sampled from the patients. Nevertheless, the available methods for acquisition of beat-to-beat SBP measurements during exams hamper the wider use of MVAR models in clinical research. Recent studies show an inverse correlation between pulse wave transit time (PWTT) series and SBP fluctuations. PWTT is the time interval between the ECG R-wave peak and photoplethysmography waveform (PPG) base point within the same cardiac cycle. This study investigates the feasibility of using inverse PWTT (IPWTT) series as an alternative input to SBP for MVAR modeling of the cardiovascular regulation. For that, HR, RFW, and IPWTT series acquired from volunteers during postural changes and autonomic blockade were used as input of MVAR models. Obtained results show that IPWTT series can be used as input of MVAR models, replacing SBP measurements in order to overcome practical difficulties related to the continuous sampling of the SBP during clinical exams.

Serum CA125 predicts extrauterine disease and survival in uterine carcinosarcoma

PubMed Central

Huang, Gloria S.; Chiu, Lydia G.; Gebb, Juliana S.; Gunter, Marc J.; Sukumvanich, Paniti; Goldberg, Gary L.; Einstein, Mark H.

2009-01-01

Objective The purpose of this study was to determine the clinical utility of CA125 measurement in patients with uterine carcinosarcoma (CS). Methods Ninety-five consecutive patients treated for CS at a single institution were identified. All 54 patients who underwent preoperative CA125 measurement were included in the study. Data were abstracted from the medical records. Tests of association between preoperative CA125 and previously identified clinicopathologic prognostic factors were performed using Fisher’s exact test and Pearson chi-square test. To evaluate relationship of CA125 elevation and survival, a Cox proportional hazard model was used for multivariate analysis, incorporating all of prognostic factors identified by univariate analysis. Results Preoperative CA125 was significantly associated with the presence of extrauterine disease (P<0.001), deep myometrial invasion (P<0.001), and serous histology of the epithelial component (P=0.005). Using univariate survival analysis, stage (HR=1.808, P=0.004), postoperative CA125 level (HR=9.855, P<0.001), and estrogen receptor positivity (HR=0.314, P=0.029) were significantly associated with survival. In the multivariate model, only postoperative CA125 level remained significantly associated with poor survival (HR=5.725, P=0.009). Conclusion Preoperative CA125 elevation is a marker of extrauterine disease and deep myometrial invasion in patients with uterine CS. Postoperative CA125 elevation is an independent prognostic factor for poor survival. These findings indicate that CA125 may be a clinically useful serum marker in the management of patients with CS. PMID:17935762

Risk Factors for Methicillin Resistant Staphylococcus aureus: A Multi-Laboratory Study

PubMed Central

Catry, Boudewijn; Latour, Katrien; Jans, Béatrice; Vandendriessche, Stien; Preal, Ragna; Mertens, Karl; Denis, Olivier

2014-01-01

Background The present study aimed to investigate the dose response relationship between the prescriptions of antimicrobial agents and infection/colonization with methicillin resistant Staphylococcus aureus (MRSA) taking additional factors like stay in a health care facility into account. Methods Multi-centre retrospective study on a cohort of patients that underwent microbiological diagnostics in Belgium during 2005. The bacteriological results retrieved from 17 voluntary participating clinical laboratories were coupled with the individual antimicrobial consumption patterns (July 2004-December 2005) and other variables as provided by pooled data of health insurance funds. Multivariate analysis was used to identify risk factors for MRSA colonization/infection. Results A total of 6844 patients of which 17.5% died in the year 2005, were included in a logistic regression model. More than 97% of MRSA was associated with infection (clinical samples), and only a minority with screening/colonization (1.59%). Factors (95% CI) significantly (p≤<0.01) associated with MRSA in the final multivariate model were: admission to a long term care settings (2.79–4.46); prescription of antibiotics via a hospital pharmacy (1.30–2.01); age 55+ years (3.32–5.63); age 15–54 years (1.23–2.16); and consumption of antimicrobial agent per DDD (defined daily dose) (1.25–1.40). Conclusions The data demonstrated a direct dose-response relationship between MRSA and consumption of antimicrobial agents at the individual patient level of 25–40% increased risk per every single day. In addition the study indicated an involvement of specific healthcare settings and age in MRSA status. PMID:24586887

Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours.

PubMed

Wilson, Martin; Cummins, Carole L; Macpherson, Lesley; Sun, Yu; Natarajan, Kal; Grundy, Richard G; Arvanitis, Theodoros N; Kauppinen, Risto A; Peet, Andrew C

2013-01-01

Brain tumours cause the highest mortality and morbidity rate of all childhood tumour groups and new methods are required to improve clinical management. (1)H magnetic resonance spectroscopy (MRS) allows non-invasive concentration measurements of small molecules present in tumour tissue, providing clinically useful imaging biomarkers. The primary aim of this study was to investigate whether MRS detectable molecules can predict the survival of paediatric brain tumour patients. Short echo time (30ms) single voxel (1)H MRS was performed on children attending Birmingham Children's Hospital with a suspected brain tumour and 115 patients were included in the survival analysis. Patients were followed-up for a median period of 35 months and Cox-Regression was used to establish the prognostic value of individual MRS detectable molecules. A multivariate model of survival was also investigated to improve prognostic power. Lipids and scyllo-inositol predicted poor survival whilst glutamine and N-acetyl aspartate predicted improved survival (p<0.05). A multivariate model of survival based on three MRS biomarkers predicted survival with a similar accuracy to histologic grading (p<5e-5). A negative correlation between lipids and glutamine was found, suggesting a functional link between these molecules. MRS detectable biomolecules have been identified that predict survival of paediatric brain tumour patients across a range of tumour types. The evaluation of these biomarkers in large prospective studies of specific tumour types should be undertaken. The correlation between lipids and glutamine provides new insight into paediatric brain tumour metabolism that may present novel targets for therapy. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dental students' perceived sources of stress: a multi-country study.

PubMed

Polychronopoulou, Argy; Divaris, Kimon

2009-05-01

The aim of this study was to identify dental students' self-reported sources of stress and to explore the role of specific curricular and institutional differences in the variation of perceived stressors among dental students in Greece, Ireland, Slovenia, Sweden, Spain, and Croatia. A thirty-item modified version of the Dental Environment Stress (DES) questionnaire was administered to all undergraduate students enrolled at six European dental schools selected to reflect geographical, curricular, and professional environment diversity: Athens, Greece; Dublin, Ireland; Ljubljana, Slovenia; Malmö, Sweden; Santiago de Compostela, Spain; and Zagreb, Croatia. Participation varied from 93 percent in Athens to 65 percent in Dublin. A total of 1,492 questionnaires were available for analysis. Univariate analysis and multivariate modelling were used for data analysis. Performance pressure, workload, and self-efficacy beliefs constituted the students' main concerns. In the univariate analysis, student responses differed by country: Swedish students provided the lowestst scores in five out of six DES factors, Spanish students were the most concerned about "clinical training" and "performance pressure," whereas Greek students were the most concerned about "patient treatment." Multivariate modelling revealed that problem-based learning (PBL) was inversely associated with perceived stress for "self-efficacy beliefs" OR (95% CI): 0.66 (0.52, 0.84), "workload" OR (95% CI): 0.58 (0.41, 0.80); and "clinical training" OR (95% CI): 0.69 (0.50, 0.95) when compared to traditional curricula. Students' perceived stressors differed greatly among the six institutions and were associated with both individual (gender, study level) and educational/institutional (curriculum type, class size, educational costs) parameters.

Inactivation of Hippo Pathway Is Significantly Associated with Poor Prognosis in Hepatocellular Carcinoma.

PubMed

Sohn, Bo Hwa; Shim, Jae-Jun; Kim, Sang-Bae; Jang, Kyu Yun; Kim, Soo Mi; Kim, Ji Hoon; Hwang, Jun Eul; Jang, Hee-Jin; Lee, Hyun-Sung; Kim, Sang-Cheol; Jeong, Woojin; Kim, Sung Soo; Park, Eun Sung; Heo, Jeonghoon; Kim, Yoon Jun; Kim, Dae-Ghon; Leem, Sun-Hee; Kaseb, Ahmed; Hassan, Manal M; Cha, Minse; Chu, In-Sun; Johnson, Randy L; Park, Yun-Yong; Lee, Ju-Seog

2016-03-01

The Hippo pathway is a tumor suppressor in the liver. However, the clinical significance of Hippo pathway inactivation in HCC is not clearly defined. We analyzed genomic data from human and mouse tissues to determine clinical relevance of Hippo pathway inactivation in HCC. We analyzed gene expression data from Mst1/2(-/-) and Sav1(-/-) mice and identified a 610-gene expression signature reflecting Hippo pathway inactivation in the liver [silence of Hippo (SOH) signature]. By integrating gene expression data from mouse models with those from human HCC tissues, we developed a prediction model that could identify HCC patients with an inactivated Hippo pathway and used it to test its significance in HCC patients, via univariate and multivariate Cox analyses. HCC patients (National Cancer Institute cohort, n = 113) with the SOH signature had a significantly poorer prognosis than those without the SOH signature [P < 0.001 for overall survival (OS)]. The significant association of the signature with poor prognosis was further validated in the Korean (n = 100, P = 0.006 for OS) and Fudan University cohorts (n = 242, P = 0.001 for OS). On multivariate analysis, the signature was an independent predictor of recurrence-free survival (HR, 1.6; 95% confidence interval, 1.12-2.28: P = 0.008). We also demonstrated significant concordance between the SOH HCC subtype and the hepatic stem cell HCC subtype that had been identified in a previous study (P < 0.001). Inactivation of the Hippo pathway in HCC is significantly associated with poor prognosis. ©2015 American Association for Cancer Research.

Race/Ethnic Differences in the Associations of the Framingham Risk Factors with Carotid IMT and Cardiovascular Events

PubMed Central

Hoefer, Imo E.; Eijkemans, Marinus J. C.; Asselbergs, Folkert W.; Anderson, Todd J.; Britton, Annie R.; Dekker, Jacqueline M.; Engström, Gunnar; Evans, Greg W.; de Graaf, Jacqueline; Grobbee, Diederick E.; Hedblad, Bo; Holewijn, Suzanne; Ikeda, Ai; Kitagawa, Kazuo; Kitamura, Akihiko; de Kleijn, Dominique P. V.; Lonn, Eva M.; Lorenz, Matthias W.; Mathiesen, Ellisiv B.; Nijpels, Giel; Okazaki, Shuhei; O’Leary, Daniel H.; Pasterkamp, Gerard; Peters, Sanne A. E.; Polak, Joseph F.; Price, Jacqueline F.; Robertson, Christine; Rembold, Christopher M.; Rosvall, Maria; Rundek, Tatjana; Salonen, Jukka T.; Sitzer, Matthias; Stehouwer, Coen D. A.; Bots, Michiel L.; den Ruijter, Hester M.

2015-01-01

Background Clinical manifestations and outcomes of atherosclerotic disease differ between ethnic groups. In addition, the prevalence of risk factors is substantially different. Primary prevention programs are based on data derived from almost exclusively White people. We investigated how race/ethnic differences modify the associations of established risk factors with atherosclerosis and cardiovascular events. Methods We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity. Results Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites. Conclusion The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention. PMID:26134404

Race/Ethnic Differences in the Associations of the Framingham Risk Factors with Carotid IMT and Cardiovascular Events.

PubMed

Gijsberts, Crystel M; Groenewegen, Karlijn A; Hoefer, Imo E; Eijkemans, Marinus J C; Asselbergs, Folkert W; Anderson, Todd J; Britton, Annie R; Dekker, Jacqueline M; Engström, Gunnar; Evans, Greg W; de Graaf, Jacqueline; Grobbee, Diederick E; Hedblad, Bo; Holewijn, Suzanne; Ikeda, Ai; Kitagawa, Kazuo; Kitamura, Akihiko; de Kleijn, Dominique P V; Lonn, Eva M; Lorenz, Matthias W; Mathiesen, Ellisiv B; Nijpels, Giel; Okazaki, Shuhei; O'Leary, Daniel H; Pasterkamp, Gerard; Peters, Sanne A E; Polak, Joseph F; Price, Jacqueline F; Robertson, Christine; Rembold, Christopher M; Rosvall, Maria; Rundek, Tatjana; Salonen, Jukka T; Sitzer, Matthias; Stehouwer, Coen D A; Bots, Michiel L; den Ruijter, Hester M

2015-01-01

Clinical manifestations and outcomes of atherosclerotic disease differ between ethnic groups. In addition, the prevalence of risk factors is substantially different. Primary prevention programs are based on data derived from almost exclusively White people. We investigated how race/ethnic differences modify the associations of established risk factors with atherosclerosis and cardiovascular events. We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity. Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites. The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention.

Exhaled breath condensate adenosine tracks lung function changes in cystic fibrosis

PubMed Central

Olsen, Bonnie M.; Lin, Feng-Chang; Fine, Jason; Boucher, Richard C.

2013-01-01

Measurement of exhaled breath condensate (EBC) biomarkers offers a noninvasive means to assess airway disease, but the ability of EBC biomarkers to track longitudinal changes in disease severity remains unproven. EBC was collected from pediatric patients with cystic fibrosis (CF) during regular clinic visits over 1 yr. EBC biomarkers urea, adenosine (Ado), and phenylalanine (Phe) were measured by mass spectrometry, and biomarker ratios were used to control for variable dilution of airway secretions. EBC biomarker ratios were assessed relative to lung function in longitudinal, multivariate models and compared with sputum inflammatory markers and quality of life assessment (CFQ-R). EBC was successfully analyzed from 51 subjects during 184 visits (3.6 ± 0.9 visits per subject). EBC Ado/urea ratio was reproducible in duplicate samples (r = 0.62, P < 0.01, n = 20) and correlated with sputum neutrophil elastase (β = 2.5, P < 0.05). EBC Ado/urea correlated with the percentage predicted of forced expiratory volume in 1 s in longitudinal, multivariate models (β = −2.9, P < 0.01); EBC Ado/Phe performed similarly (β = −2.1, P < 0.05). In contrast, IL-8 and elastase measured in spontaneously expectorated sputum (n = 57 samples from 25 subjects) and the CFQ-R respiratory scale (n = 90 tests from 47 subjects) were not significantly correlated with lung function. EBC was readily collected in a clinic setting from a wide range of subjects. EBC Ado tracked longitudinal changes in lung function in CF, with results similar to or better than established measures. PMID:23355385

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Risk factors for early failure after peripheral endovascular intervention: application of a reliability engineering approach.

PubMed

Meltzer, Andrew J; Graham, Ashley; Connolly, Peter H; Karwowski, John K; Bush, Harry L; Frazier, Peter I; Schneider, Darren B

2013-01-01

We apply an innovative and novel analytic approach, based on reliability engineering (RE) principles frequently used to characterize the behavior of manufactured products, to examine outcomes after peripheral endovascular intervention. We hypothesized that this would allow for improved prediction of outcome after peripheral endovascular intervention, specifically with regard to identification of risk factors for early failure. Patients undergoing infrainguinal endovascular intervention for chronic lower-extremity ischemia from 2005 to 2010 were identified in a prospectively maintained database. The primary outcome of failure was defined as patency loss detected by duplex ultrasonography, with or without clinical failure. Analysis included univariate and multivariate Cox regression models, as well as RE-based analysis including product life-cycle models and Weibull failure plots. Early failures were distinguished using the RE principle of "basic rating life," and multivariate models identified independent risk factors for early failure. From 2005 to 2010, 434 primary endovascular peripheral interventions were performed for claudication (51.8%), rest pain (16.8%), or tissue loss (31.3%). Fifty-five percent of patients were aged ≥75 years; 57% were men. Failure was noted after 159 (36.6%) interventions during a mean follow-up of 18 months (range, 0-71 months). Using multivariate (Cox) regression analysis, rest pain and tissue loss were independent predictors of patency loss, with hazard ratios of 2.5 (95% confidence interval, 1.6-4.1; P < 0.001) and 3.2 (95% confidence interval, 2.0-5.2, P < 0.001), respectively. The distribution of failure times for both claudication and critical limb ischemia fit distinct Weibull plots, with different characteristics: interventions for claudication demonstrated an increasing failure rate (β = 1.22, θ = 13.46, mean time to failure = 12.603 months, index of fit = 0.99037, R(2) = 0.98084), whereas interventions for critical limb ischemia demonstrated a decreasing failure rate, suggesting the predominance of early failures (β = 0.7395, θ = 6.8, mean time to failure = 8.2, index of fit = 0.99391, R(2) = 0.98786). By 3.1 months, 10% of interventions failed. This point (90% reliability) was identified as the basic rating life. Using multivariate analysis of failure data, independent predictors of early failure (before 3.1 months) included tissue loss, long lesion length, chronic total occlusions, heart failure, and end-stage renal disease. Application of a RE framework to the assessment of clinical outcomes after peripheral interventions is feasible, and potentially more informative than traditional techniques. Conceptualization of interventions as "products" permits application of product life-cycle models that allow for empiric definition of "early failure" may facilitate comparative effectiveness analysis and enable the development of individualized surveillance programs after endovascular interventions. Copyright © 2013 Annals of Vascular Surgery Inc. Published by Elsevier Inc. All rights reserved.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Signatures of subacute potentially catastrophic illness in the intensive care unit: model development and validation

PubMed Central

Moss, Travis J.; Lake, Douglas E.; Forrest Calland, J; Enfield, Kyle B; Delos, John B.; Fairchild, Karen D.; Randall Moorman, J.

2016-01-01

Objective Patients in intensive care units are susceptible to subacute, potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early, subclinical signatures of incipient life-threatening illness. Design We report a study of model development and validation of a retrospective observational cohort using resampling (TRIPOD Type 1b internal validation), and a study of model validation using separate data (Type 2b internal/external validation). Setting University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. Patients Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical intensive care units with available physiologic monitoring data. Interventions None. Measurements and Main Results We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent, unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic, individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally-validated C-statistics of 0.61 to 0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. Conclusions Subacute, potentially catastrophic illnesses in 3 diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes. PMID:27452809

Evaluation of costs associated with tolvaptan-mediated length-of-stay reduction among heart failure patients with hyponatremia in the US, based on the EVEREST trial.

PubMed

Chiong, Jun R; Kim, Sonnie; Lin, Jay; Christian, Rudell; Dasta, Joseph F

2012-01-01

The Efficacy of Vasopressin Antagonism in Heart Failure Outcome Study with Tolvaptan (EVEREST) trial showed that tolvaptan use improved heart failure (HF) signs and symptoms without serious adverse events. To evaluate the potential cost savings associated with tolvaptan usage among hospitalized hyponatremic HF patients. The Healthcare Cost and Utilization Project (HCUP) 2008 Nationwide Inpatient Sample (NIS) database was used to estimate hospital cost and length of stay (LOS), for diagnosis-related group (DRG) hospitalizations of adult (age ≥18 years) HF patients with complications and comorbidities or major complications and comorbidities. EVEREST trial data for patients with hyponatremia were used to estimate tolvaptan-associated LOS reductions. A cost offset model was constructed to evaluate the impact of tolvaptan on hospital cost and LOS, with univariate and multivariate Monte Carlo sensitivity analyses. Tolvaptan use among hyponatremic EVEREST trial HF patients was associated with shorter hospital LOS than placebo patients (9.72 vs 11.44 days, respectively); 688,336 hospitalizations for HF DRGs were identified from the HCUP NIS database, with a mean LOS of 5.4 days and mean total hospital costs of $8415. Using an inpatient tolvaptan treatment duration of 4 days with a wholesale acquisition cost of $250 per day, the cost offset model estimated a LOS reduction among HF hospitalizations of 0.81 days and an estimated total cost saving of $265 per admission. Univariate and multivariate sensitivity analysis demonstrated that cost reduction associated with tolvaptan usage is consistent among variations of model variables. The estimated LOS reduction and cost savings projected by the cost offset model suggest a clinical and economic benefit to tolvaptan use in hyponatremic HF patients. The EVEREST trial data may not generalize well to the US population. Clinical trial patient profiles and relative LOS reductions may not be applicable to real-world patient populations.

Clinical Trial Participation and Time to Treatment Among Adolescents and Young Adults With Cancer: Does Age at Diagnosis or Insurance Make a Difference?

PubMed Central

Parsons, Helen M.; Harlan, Linda C.; Seibel, Nita L.; Stevens, Jennifer L.; Keegan, Theresa H.M.

2011-01-01

Purpose Because adolescent and young adult (AYA) patients with cancer have experienced variable improvement in survival over the past two decades, enhancing the quality and timeliness of cancer care in this population has emerged as a priority area. To identify current trends in AYA care, we examined patterns of clinical trial participation, time to treatment, and provider characteristics in a population-based sample of AYA patients with cancer. Methods Using the National Cancer Institute Patterns of Care Study, we used multivariate logistic regression to evaluate demographic and provider characteristics associated with clinical trial enrollment and time to treatment among 1,358 AYA patients with cancer (age 15 to 39 years) identified through the Surveillance, Epidemiology, and End Results Program. Results In our study, 14% of patients age 15 to 39 years had enrolled onto a clinical trial; participation varied by type of cancer, with the highest participation in those diagnosed with acute lymphoblastic leukemia (37%) and sarcoma (32%). Multivariate analyses demonstrated that uninsured, older patients and those treated by nonpediatric oncologists were less likely to enroll onto clinical trials. Median time from pathologic confirmation to first treatment was 3 days, but this varied by race/ethnicity and cancer site. In multivariate analyses, advanced cancer stage and outpatient treatment alone were associated with longer time from pathologic confirmation to treatment. Conclusion Our study identified factors associated with low clinical trial participation in AYA patients with cancer. These findings support the continued need to improve access to clinical trials and innovative treatments for this population, which may ultimately translate into improved survival. PMID:21931022

Expression of p53, p21 and cyclin D1 in penile cancer: p53 predicts poor prognosis.

PubMed

Gunia, Sven; Kakies, Christoph; Erbersdobler, Andreas; Hakenberg, Oliver W; Koch, Stefan; May, Matthias

2012-03-01

To evaluate the role of p53, p21 and cyclin D1 expression in patients with penile cancer (PC). Paraffin-embedded tissues from PC specimens from six pathology departments were subjected to a central histopathological review performed by one pathologist. The tissue microarray technique was used for immunostaining which was evaluated by two independent pathologists and correlated with cancer-specific survival (CSS). κ-statistics were used to assess interobserver variability. Uni- and multivariable Cox proportional hazards analysis was applied to assess the independent effects of several prognostic factors on CSS over a median of 32 months (IQR 6-66 months). Specimens and clinical data from 110 men treated surgically for primary PC were collected. p53 staining was positive in 30 and negative in 62 specimens. κ-statistics showed substantial interobserver reproducibility of p53 staining evaluation (κ=0.73; p<0.001). The 5-year CSS rate for the entire study cohort was 74%. Five-year CSS was 84% in p53-negative and 51% in p53-positive PC patients (p=0.003). Multivariable analysis showed p53 (HR=3.20; p=0.041) and pT-stage (HR=4.29; p<0.001) as independent significant prognostic factors for CSS. Cyclin D1 and p21 expression were not correlated with survival. However, incorporating p21 into a multivariable Cox model did contribute to improved model quality for predicting CSS. In patients with PC, the expression of p53 in the primary tumour specimen can be reproducibly assessed and is negatively associated with cancer specific survival.

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

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.

[Nasal flaring as a predictor of mortality in patients with severe dyspnea].

PubMed

Zorrilla Riveiro, José Gregorio; Arnau Bartés, Anna; García Pérez, Dolors; Rafat Sellarés, Ramón; Mas Serra, Arantxa; Fernández Fernández, Rafael

2015-02-01

To determine whether the presence of nasal flaring is a clinical sign of severity and a predictor of hospital mortality in emergency patients with dyspnea. Prospective, observational, single-center study. We enrolled patients older than 15 years of age who required attention for dyspnea categorized as level II or III emergencies according to the Andorran Medical Triage system. Two observers evaluated the presence of nasal flaring. We recorded demographic and clinical variables, including respiratory effort, vital signs, arterial blood gases, and clinical course (hospital admission and mortality). Bivariable analysis was performed and multivariable logistic regression models were constructed. We enrolled 246 patients with a mean (SD) age of 77 (13) years; 52% were female. Nasal flaring was present in 19.5%. Patients with nasal flaring had triage levels indicating greater severity and they had more severe tachypnea, worse oxygenation, and greater acidosis and hypercapnia. Bivariable analysis detected that the following variables were associated with mortality: age (odds ratio [OR], 1.05; 95% CI, 1.01-1.10), prehospital care from the emergency medical service (OR, 3.97; 95% CI, 1.39-11.39), triage level II (OR, 4.19; 95% CI, 1.63-10.78), signs of respiratory effort such as nasal flaring (OR, 3.79; 95% CI, 1.65-8.69), presence of acidosis (OR, 7.09; 95% CI, 2.97-16.94), and hypercapnia (OR, 2.67; 95% CI, 1,11-6,45). The factors that remained independent predictors of mortality in the multivariable analysis were age, severity (triage level), and nasal flaring. In patients requiring emergency care for dyspnea, nasal flaring is a clinical sign of severity and a predictor of mortality.

Prognosticators and risk grouping in patients with lung metastasis from nasopharyngeal carcinoma: a more accurate and appropriate assessment of prognosis.

PubMed

Cao, Xun; Luo, Rong-Zhen; He, Li-Ru; Li, Yong; Lin, Wen-Qian; Chen, You-Fang; Wen, Zhe-Sheng

2011-08-26

Lung metastases arising from nasopharyngeal carcinomas (NPC) have a relatively favourable prognosis. The purpose of this study was to identify the prognostic factors and to establish a risk grouping in patients with lung metastases from NPC. A total of 198 patients who developed lung metastases from NPC after primary therapy were retrospectively recruited from January 1982 to December 2000. Univariate and multivariate analyses of clinical variables were performed using Cox proportional hazards regression models. Actuarial survival rates were plotted against time using the Kaplan-Meier method, and log-rank testing was used to compare the differences between the curves. The median overall survival (OS) period and the lung metastasis survival (LMS) period were 51.5 and 20.9 months, respectively. After univariate and multivariate analyses of the clinical variables, age, T classification, N classification, site of metastases, secondary metastases and disease-free interval (DFI) correlated with OS, whereas age, VCA-IgA titre, number of metastases and secondary metastases were related to LMS. The prognoses of the low- (score 0-1), intermediate- (score 2-3) and high-risk (score 4-8) subsets based on these factors were significantly different. The 3-, 5- and 10-year survival rates of the low-, intermediate- and high-risk subsets, respectively (P < 0.001) were as follows: 77.3%, 60% and 59%; 52.3%, 30% and 27.8%; and 20.5%, 7% and 0%. In this study, clinical variables provided prognostic indicators of survival in NPC patients with lung metastases. Risk subsets would help in a more accurate assessment of a patient's prognosis in the clinical setting and could facilitate the establishment of patient-tailored medical strategies and supports.

The prognostic value of standardized reference values for speckle-tracking global longitudinal strain in hypertrophic cardiomyopathy.

PubMed

Hartlage, Gregory R; Kim, Jonathan H; Strickland, Patrick T; Cheng, Alan C; Ghasemzadeh, Nima; Pernetz, Maria A; Clements, Stephen D; Williams, B Robinson

2015-03-01

Speckle-tracking left ventricular global longitudinal strain (GLS) assessment may provide substantial prognostic information for hypertrophic cardiomyopathy (HCM) patients. Reference values for GLS have been recently published. We aimed to evaluate the prognostic value of standardized reference values for GLS in HCM patients. An analysis of HCM clinic patients who underwent GLS was performed. GLS was defined as normal (more negative or equal to -16%) and abnormal (less negative than -16%) based on recently published reference values. Patients were followed for a composite of events including heart failure hospitalization, sustained ventricular arrhythmia, and all-cause death. The power of GLS to predict outcomes was assessed relative to traditional clinical and echocardiographic variables present in HCM. 79 HCM patients were followed for a median of 22 months (interquartile range 9-30 months) after imaging. During follow-up, 15 patients (19%) met the primary outcome. Abnormal GLS was the only echocardiographic variable independently predictive of the primary outcome [multivariate Hazard ratio 5.05 (95% confidence interval 1.09-23.4, p = 0.038)]. When combined with traditional clinical variables, abnormal GLS remained independently predictive of the primary outcome [multivariate Hazard ratio 5.31 (95 % confidence interval 1.18-24, p = 0.030)]. In a model including the strongest clinical and echocardiographic predictors of the primary outcome, abnormal GLS demonstrated significant incremental benefit for risk stratification [net reclassification improvement 0.75 (95 % confidence interval 0.21-1.23, p < 0.0001)]. Abnormal GLS is an independent predictor of adverse outcomes in HCM patients. Standardized use of GLS may provide significant incremental value over traditional variables for risk stratification.

One thing leads to another: the cascade of obligations when researchers report genetic research results to study participants

PubMed Central

Miller, Fiona Alice; Hayeems, Robin Zoe; Li, Li; Bytautas, Jessica Peace

2012-01-01

Even as debate continues about the putative obligation to proactively report genetic research results to study participants, there is an increasing need to attend to the obligations that might cascade from any initial report. We conducted an international, quasi-experimental survey of researchers involved in autism spectrum disorders (ASD) and cystic fibrosis (CF) genetics to explore perceived obligations to ensure updated information or relevant clinical care subsequent to any initial communication of research results, and factors influencing these attitudes. 5-point Likert scales of dis/agreement were analyzed using descriptive and multivariate statistics. Of the 343 respondents (44% response rate), large majorities agreed that in general and in a variety of hypothetical research contexts, research teams that report results should ensure that participants gain subsequent access to updated information (74–83%) and implicated clinical services (79–87%). At the same time, researchers perceived barriers restricting access to relevant clinical care, though this was significantly more pronounced (P<0.001) for ASD (64%) than CF (34%). In the multivariate model, endorsement of cascading obligations was positively associated with researcher characteristics (eg, clinical role/training) and attitudes (eg, perceived initial reporting obligation), and negatively associated with the initial report of less scientifically robust hypothetical results, but unaffected by perceived or hypothetical barriers to care. These results suggest that researchers strongly endorse information and care-based obligations that cascade from the initial report of research results to study participants. In addition, they raise challenging questions about how any cascading obligations are to be met, especially where access challenges are already prevalent. PMID:22333903

An appraisal of drug development timelines in the Era of precision oncology

PubMed Central

Jardim, Denis Leonardo; Schwaederle, Maria; Hong, David S.; Kurzrock, Razelle

2016-01-01

The effects of incorporating a biomarker-based (personalized or precision) selection strategy on drug development timelines for new oncology drugs merit investigation. Here we accessed documents from the Food and Drug Administration (FDA) database for anticancer agents approved between 09/1998 and 07/2014 to compare drugs developed with and without a personalized strategy. Sixty-three drugs were included (28 [44%] personalized and 35 [56%] non-personalized). No differences in access to FDA-expedited programs were observed between personalized and non-personalized drugs. A personalized approach for drug development was associated with faster clinical development (Investigational New Drug [IND] to New Drug Application [NDA] submission; median = 58.8 months [95% CI 53.8–81.8] vs. 93.5 months [95% CI 73.9–112.9], P =.001), but a similar approval time (NDA submission to approval; median=6.0 months [95% CI 5.5–8.4] vs. 6.1 months [95% CI 5.9–8.3], P = .756) compared to a non-personalized strategy. In the multivariate model, class of drug stratified by personalized status (targeted personalized vs. targeted non-personalized vs. cytotoxic) was the only independent factor associated with faster total time of clinical drug development (clinical plus approval phase, median = 64.6 vs 87.1 vs. 112.7 months [cytotoxic], P = .038). Response rates (RR) in early trials were positively correlated with RR in registration trials (r = 0.63, P = <.001), and inversely associated with total time of drug development (r = −0.29, P = .049). In conclusion, targeted agents were developed faster than cytotoxic agents. Shorter times to approval were associated, in multivariate analysis, with a biomarker-based clinical development strategy. PMID:27419632

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

The contribution of antiphospholipid antibodies to organ damage in systemic lupus erythematosus.

PubMed

Taraborelli, M; Leuenberger, L; Lazzaroni, M G; Martinazzi, N; Zhang, W; Franceschini, F; Salmon, J; Tincani, A; Erkan, D

2016-10-01

The objective of this study was to assess the contribution of clinically significant antiphospholipid antibodies (aPL) to organ damage in systemic lupus erythematosus (SLE). Patients with disease duration of less than 10 years and at least 5 years of follow-up were identified from two SLE registries. A clinically significant antiphospholipid antibody (aPL) profile was defined as: positive lupus anticoagulant, anticardiolipin IgG/M ≥ 40 G phospholipid units (GPL)/M phospholipid units (MPL), and/or anti-β2-glycoprotein-I IgG/M ≥ 99th percentile on two or more occasions, at least 12 weeks apart. Organ damage was assessed by the Systemic Lupus International Collaborating Clinics Damage Index (SDI). Univariate and multivariate analysis compared SLE patients with and without SDI increase during a 15-year follow-up. Among 262 SLE patients, 33% had a clinically significant aPL profile, which was associated with an increased risk of organ damage accrual during a 5-year follow-up in univariate analysis, and during a 15-year follow-up in the multivariate analysis adjusting for age, gender, race, disease duration at registry entry, and time. In the multivariate analysis, older age at diagnosis and male gender were also associated with SDI increase at each time point. A clinically significant aPL profile is associated with an increased risk of organ damage accrual during a 15-year follow-up in SLE patients. © The Author(s) 2016.

Outcome Trajectories in Extremely Preterm Infants

PubMed Central

Carlo, Waldemar A.; Tyson, Jon E.; Langer, John C.; Walsh, Michele C.; Parikh, Nehal A.; Das, Abhik; Van Meurs, Krisa P.; Shankaran, Seetha; Stoll, Barbara J.; Higgins, Rosemary D.

2012-01-01

OBJECTIVE: Methods are required to predict prognosis with changes in clinical course. Death or neurodevelopmental impairment in extremely premature neonates can be predicted at birth/admission to the ICU by considering gender, antenatal steroids, multiple birth, birth weight, and gestational age. Predictions may be improved by using additional information available later during the clinical course. Our objective was to develop serial predictions of outcome by using prognostic factors available over the course of NICU hospitalization. METHODS: Data on infants with birth weight ≤1.0 kg admitted to 18 large academic tertiary NICUs during 1998–2005 were used to develop multivariable regression models following stepwise variable selection. Models were developed by using all survivors at specific times during hospitalization (in delivery room [n = 8713], 7-day [n = 6996], 28-day [n = 6241], and 36-week postmenstrual age [n = 5118]) to predict death or death/neurodevelopmental impairment at 18 to 22 months. RESULTS: Prediction of death or neurodevelopmental impairment in extremely premature infants is improved by using information available later during the clinical course. The importance of birth weight declines, whereas the importance of respiratory illness severity increases with advancing postnatal age. The c-statistic in validation models ranged from 0.74 to 0.80 with misclassification rates ranging from 0.28 to 0.30. CONCLUSIONS: Dynamic models of the changing probability of individual outcome can improve outcome predictions in preterm infants. Various current and future scenarios can be modeled by input of different clinical possibilities to develop individual “outcome trajectories” and evaluate impact of possible morbidities on outcome. PMID:22689874

A three-gene expression signature model for risk stratification of patients with neuroblastoma.

PubMed

Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V; Oberthuer, André; Fischer, Matthias; Maris, John M; Brodeur, Garrett M; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2012-04-01

Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P < 0.001; set 2 OS: 0.97 ± 0.02 vs. 0.61 ± 0.1, P = 0.005, EFS: 0.91 ± 0.8 vs. 0.56 ± 0.1, P = 0.005; and set 3 OS: 0.99 ± 0.01 vs. 0.56 ± 0.06, EFS: 0.96 ± 0.02 vs. 0.43 ± 0.05, both P < 0.001]. Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. ©2012 AACR.

A Three-Gene Expression Signature Model for Risk Stratification of Patients with Neuroblastoma

PubMed Central

Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V.; Oberthuer, André; Fischer, Matthias; Maris, John M.; Brodeur, Garrett M.; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2014-01-01

Purpose Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. Experimental Design The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. Results On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P < 0.001; set 2 OS: 0.97 ± 0.02 vs. 0.61 ± 0.1, P = 0.005, EFS: 0.91 ± 0.8 vs. 0.56 ± 0.1, P = 0.005; and set 3 OS: 0.99 ± 0.01 vs. 0.56 ± 0.06, EFS: 0.96 ± 0.02 vs. 0.43 ± 0.05, both P < 0.001]. Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. Conclusion We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. PMID:22328561

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

A Novel MiRNA-Based Predictive Model for Biochemical Failure Following Post-Prostatectomy Salvage Radiation Therapy

PubMed Central

Stegmaier, Petra; Drendel, Vanessa; Mo, Xiaokui; Ling, Stella; Fabian, Denise; Manring, Isabel; Jilg, Cordula A.; Schultze-Seemann, Wolfgang; McNulty, Maureen; Zynger, Debra L.; Martin, Douglas; White, Julia; Werner, Martin; Grosu, Anca L.; Chakravarti, Arnab

2015-01-01

Purpose To develop a microRNA (miRNA)-based predictive model for prostate cancer patients of 1) time to biochemical recurrence after radical prostatectomy and 2) biochemical recurrence after salvage radiation therapy following documented biochemical disease progression post-radical prostatectomy. Methods Forty three patients who had undergone salvage radiation therapy following biochemical failure after radical prostatectomy with greater than 4 years of follow-up data were identified. Formalin-fixed, paraffin-embedded tissue blocks were collected for all patients and total RNA was isolated from 1mm cores enriched for tumor (>70%). Eight hundred miRNAs were analyzed simultaneously using the nCounter human miRNA v2 assay (NanoString Technologies; Seattle, WA). Univariate and multivariate Cox proportion hazards regression models as well as receiver operating characteristics were used to identify statistically significant miRNAs that were predictive of biochemical recurrence. Results Eighty eight miRNAs were identified to be significantly (p<0.05) associated with biochemical failure post-prostatectomy by multivariate analysis and clustered into two groups that correlated with early (≤ 36 months) versus late recurrence (>36 months). Nine miRNAs were identified to be significantly (p<0.05) associated by multivariate analysis with biochemical failure after salvage radiation therapy. A new predictive model for biochemical recurrence after salvage radiation therapy was developed; this model consisted of miR-4516 and miR-601 together with, Gleason score, and lymph node status. The area under the ROC curve (AUC) was improved to 0.83 compared to that of 0.66 for Gleason score and lymph node status alone. Conclusion miRNA signatures can distinguish patients who fail soon after radical prostatectomy versus late failures, giving insight into which patients may need adjuvant therapy. Notably, two novel miRNAs (miR-4516 and miR-601) were identified that significantly improve prediction of biochemical failure post-salvage radiation therapy compared to clinico-histopathological factors, supporting the use of miRNAs within clinically used predictive models. Both findings warrant further validation studies. PMID:25760964

A multivariate analysis of clinical and morphological prognostic factors in squamous cell carcinoma of the vulva.

PubMed

Smyczek-Gargya, B; Volz, B; Geppert, M; Dietl, J

1997-01-01

Clinical and histological data of 168 patients with squamous cell carcinoma of the vulva were analyzed with respect to survival. 151 patients underwent surgery, 12 patients were treated with primary radiation and in 5 patients no treatment was performed. Follow-up lasted from at least 2 up to 22 years' posttreatment. In univariate analysis, the following factors were highly significant: presurgery lymph node status, tumor infiltration beyond the vulva, tumor grading, histological inguinal lymph node status, pre- and postsurgery tumor stage, depth of invasion and tumor diameter. In the multivariate analysis (Cox regression), the most powerful factors were shown to be histological inguinal lymph node status, tumor diameter and tumor grading. The multivariate logistic regression analysis worked out as main prognostic factors for metastases of inguinal lymph nodes: presurgery inguinal lymph node status, tumor size, depth of invasion and tumor grading. Based on these results, tumor biology seems to be the decisive factor concerning recurrence and survival. Therefore, we suggest a more conservative treatment of vulvar carcinoma. Patients with confined carcinoma to the vulva, with a tumor diameter up to 3 cm and without clinical suspected lymph nodes, should be treated by wide excision/partial vulvectomy with ipsilateral lymphadenectomy.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Reproductive health preventive screening among clinic vs. over-the-counter oral contraceptive users

PubMed Central

Hopkins, Kristine; Grossman, Daniel; White, Kari; Amastae, Jon; Potter, Joseph E.

2015-01-01

Background Interest is growing in moving oral contraceptives over-the-counter (OTC), although concerns exist about whether women would continue to get preventive health screening. Study Design We recruited cohorts of US-resident women who obtained oral contraceptives from US family planning clinics (n=532) and OTC from pharmacies in Mexico (n=514) and interviewed them four times over 9 months. Based on self-reports of having a Pap smear within 3 years or ever having had a pelvic exam, clinical breast exam and testing for sexually transmitted infections (STIs), we assessed the prevalence of preventive screening using Poisson regression models. Results The prevalence of screening was high for both groups (>88% for Pap smear, pelvic exam and clinical breast exam and >71% for STI screening), while the prevalence ratios for screening were higher for clinic users, even after multivariable adjustment. Conclusions Results suggest that most women would obtain reproductive health preventive screening if oral contraceptives were available OTC, and also highlight the need to improve access to preventive screening for all low-income women. PMID:22520645

Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

Efficient inference for genetic association studies with multiple outcomes.

PubMed

Ruffieux, Helene; Davison, Anthony C; Hager, Jorg; Irincheeva, Irina

2017-10-01

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Risk factors and algorithms for chlamydial and gonococcal cervical infections in women attending family planning clinics in Thailand.

PubMed

Rugpao, Sungwal; Rungruengthanakit, Kittipong; Werawatanakul, Yuthapong; Sinchai, Wanida; Ruengkris, Tosaporn; Lamlertkittikul, Surachai; Pinjareon, Sutham; Koonlertkit, Sompong; Limtrakul, Aram; Sriplienchan, Somchai; Wongthanee, Antika; Sirirojn, Bangorn; Morrison, Charles S; Celentano, David D

2010-02-01

To identify risk factors associated with and evaluate algorithms for predicting Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) cervical infections in women attending family planning clinics in Thailand. Eligible women were recruited from family planning clinics from all regions in Thailand. The women were followed at 3-month intervals for 15-24 months. At each visit, the women were interviewed for interval sexually transmitted infection (STI) history in the past 3 months, recent sexual behavior, and contraceptive use. Pelvic examinations were performed and endocervical specimens were collected to test for CT and NG using polymerase chain reaction. Factors associated with incident CT/NG cervical infections in multivariate analyses included region of country other than the north, age

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Comparative incidence and health care costs of medically attended adverse effects among U.S. Medicaid HIV patients on atazanavir- or darunavir-based antiretroviral therapy.

PubMed

Johnston, Stephen S; Juday, Timothy; Esker, Stephen; Espindle, Derek; Chu, Bong-Chul; Hebden, Tony; Uy, Jonathan

2013-01-01

This is the first study to compare the incidence and health care costs of medically attended adverse effects in atazanavir- and darunavir-based antiretroviral therapy (ART) among U.S. Medicaid patients receiving routine HIV care. This was a retrospective study using Medicaid administrative health care claims from 15 states. Subjects were HIV patients aged 18 to 64 years initiating atazanavir- or darunavir-based ART from January 1, 2003, to July 1, 2010, with continuous enrollment for 6 months before (baseline) and 6 months after (evaluation period) ART initiation and 1 or more evaluation period medical claim. Outcomes were incidence and health care costs of the following medically attended (International Classification of Diseases, Ninth Revision, Clinical Modification-coded or treated) adverse effects during the evaluation period: gastrointestinal, lipid abnormalities, diabetes/hyperglycemia, rash, and jaundice. All-cause health care costs were also determined. Patients treated with atazanavir and darunavir were propensity score matched (ratio = 3:1) by using demographic and clinical covariates. Multivariable models adjusted for covariates lacking postmatch statistical balance. Propensity-matched study sample included 1848 atazanavir- and 616 darunavir-treated patients (mean age 41 years, 50% women, 69% black). Multivariable-adjusted hazard ratios (HRs) (for darunavir, reference = atazanavir) and per-patient-per-month health care cost differences (darunavir minus atazanavir) were as follows: gastrointestinal, HR = 1.25 (P = 0.04), $43 (P = 0.13); lipid abnormalities, HR = 1.38 (P = 0.07), $3 (P = 0.88); diabetes/hyperglycemia, HR = 0.84 (P = 0.55), $13 (P = 0.69); and rash, HR = 1.11 (P = 0.23), $0 (P = 0.76); all-cause health care costs were $1086 (P<0.001). Too few instances of jaundice (11 in atazanavir and 1 in darunavir) occurred to support multivariable modeling. Medication tolerability can be critical to the success or failure of ART. Compared with darunavir-treated patients, atazanavir-treated patients had significantly fewer instances of medically attended gastrointestinal issues and more instances of jaundice and incurred significantly lower health care costs. Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Timing of Adjuvant Surgical Oophorectomy in the Menstrual Cycle and Disease-Free and Overall Survival in Premenopausal Women With Operable Breast Cancer

PubMed Central

Laudico, Adriano V.; Van Dinh, Nguyen; Allred, D. Craig; Uy, Gemma B.; Quang, Le Hong; Salvador, Jonathan Disraeli S.; Siguan, Stephen Sixto S.; Mirasol-Lumague, Maria Rica; Tung, Nguyen Dinh; Benjaafar, Noureddine; Navarro, Narciso S.; Quy, Tran Tu; De La Peña, Arturo S.; Dofitas, Rodney B.; Bisquera, Orlino C.; Linh, Nguyen Dieu; To, Ta Van; Young, Gregory S.; Hade, Erinn M.; Jarjoura, David

2015-01-01

Background: For women with hormone receptor–positive, operable breast cancer, surgical oophorectomy plus tamoxifen is an effective adjuvant therapy. We conducted a phase III randomized clinical trial to test the hypothesis that oophorectomy surgery performed during the luteal phase of the menstrual cycle was associated with better outcomes. Methods: Seven hundred forty premenopausal women entered a clinical trial in which those women estimated not to be in the luteal phase of their menstrual cycle for the next one to six days (n = 509) were randomly assigned to receive treatment with surgical oophorectomy either delayed to be during a five-day window in the history-estimated midluteal phase of the menstrual cycles, or in the next one to six days. Women who were estimated to be in the luteal phase of the menstrual cycle for the next one to six days (n = 231) were excluded from random assignment and received immediate surgical treatments. All patients began tamoxifen within 6 days of surgery and continued this for 5 years. Kaplan-Meier methods, the log-rank test, and multivariable Cox regression models were used to assess differences in five-year disease-free survival (DFS) between the groups. All statistical tests were two-sided. Results: The randomized midluteal phase surgery group had a five-year DFS of 64%, compared with 71% for the immediate surgery random assignment group (hazard ratio [HR] = 1.24, 95% confidence interval [CI] = 0.91 to 1.68, P = .18). Multivariable Cox regression models, which included important prognostic variables, gave similar results (aHR = 1.28, 95% CI = 0.94 to 1.76, P = .12). For overall survival, the univariate hazard ratio was 1.33 (95% CI = 0.94 to 1.89, P = .11) and the multivariable aHR was 1.43 (95% CI = 1.00 to 2.06, P = .05). Better DFS for follicular phase surgery, which was unanticipated, proved consistent across multiple exploratory analyses. Conclusions: The hypothesized benefit of adjuvant luteal phase oophorectomy was not shown in this large trial. PMID:25794890

Whole Body Magnetic Resonance Imaging Features in Diffuse Idiopathic Skeletal Hyperostosis in Conjunction with Clinical Variables to Whole Body MRI and Clinical Variables in Ankylosing Spondylitis.

PubMed

Weiss, Bettina G; Bachmann, Lucas M; Pfirrmann, Christian W A; Kissling, Rudolf O; Zubler, Veronika

2016-02-01

Discrimination of diffuse idiopathic skeletal hyperostosis (DISH) and ankylosing spondylitis (AS) can be challenging. Usefulness of whole-body magnetic resonance imaging (WB-MRI) in diagnosing spondyloarthritis has been recently proved. We assessed the value of clinical variables alone and in combination with WB-MRI to distinguish between DISH and AS. Diagnostic case-control study: 33 patients with AS and 15 patients with DISH were included. All patients underwent 1.5 Tesla WB-MRI scanning. MR scans were read by a blinded radiologist using the Canadian-Danish Working Group's recommendation. Imaging and clinical variables were identified using the bootstrap. The most important variables from MR and clinical history were assessed in a multivariate fashion resulting in 3 diagnostic models (MRI, clinical, and combined). The discriminative capacity was quantified using the area under the receiver-operating characteristic (ROC) curve. The strength of diagnostic variables was quantified with OR. Forty-eight patients provided 1545 positive findings (193 DISH/1352 AS). The final MR model contained upper anterior corner fat infiltration (32 DISH/181 AS), ankylosis on the vertebral endplate (4 DISH/60 AS), facet joint ankylosis (4 DISH/49 AS), sacroiliac joint edema (11 DISH/91 AS), sacroiliac joint fat infiltration (2 DISH/114 AS), sacroiliac joint ankylosis (2 DISH/119 AS); area under the ROC curve was 0.71, 95% CI 0.64-0.78. The final clinical model contained patient's age and body mass index (area under the ROC curve 0.90, 95% CI 0.89-0.91). The full diagnostic model containing clinical and MR information had an area under the ROC curve of 0.93 (95% CI 0.92-0.95). WB-MRI features can contribute to the correct diagnosis after a thorough conventional workup of patients with DISH and AS.