Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

PubMed

Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

2017-07-01

Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p < .001). Elevated caudate activation was associated with robust improvement for methylphenidate and little improvement for atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p < .001). Enhanced caudate activation for response inhibition may be a candidate biomarker of superior response to methylphenidate over atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American

Performance of immunological response in predicting virological failure.

PubMed

Ingole, Nayana; Mehta, Preeti; Pazare, Amar; Paranjpe, Supriya; Sarkate, Purva

2013-03-01

In HIV-infected individuals on antiretroviral therapy (ART), the decision on when to switch from first-line to second-line therapy is dictated by treatment failure, and this can be measured in three ways: clinically, immunologically, and virologically. While viral load (VL) decreases and CD4 cell increases typically occur together after starting ART, discordant responses may be seen. Hence the current study was designed to determine the immunological and virological response to ART and to evaluate the utility of immunological response to predict virological failure. All treatment-naive HIV-positive individuals aged >18 years who were eligible for ART were enrolled and assessed at baseline, 6 months, and 12 months clinically and by CD4 cell count and viral load estimations. The patients were categorized as showing concordant favorable (CF), immunological only (IO), virological only (VO), and concordant unfavorable responses (CU). The efficiency of immunological failure to predict virological failure was analyzed across various levels of virological failure (VL>50, >500, and >5,000 copies/ml). At 6 months, 87(79.81%), 7(5.5%), 13 (11.92%), and 2 (1.83%) patients and at 12 months 61(69.3%), 9(10.2%), 16 (18.2%), and 2 (2.3%) patients had CF, IO, VO, and CU responses, respectively. Immunological failure criteria had a very low sensitivity (11.1-40%) and positive predictive value (8.3-25%) to predict virological failure. Immunological criteria do not accurately predict virological failure resulting in significant misclassification of therapeutic responses. There is an urgent need for inclusion of viral load testing in the initiation and monitoring of ART.

Empirically Derived Personality Subtyping for Predicting Clinical Symptoms and Treatment Response in Bulimia Nervosa

PubMed Central

Haynos, Ann F.; Pearson, Carolyn M.; Utzinger, Linsey M.; Wonderlich, Stephen A.; Crosby, Ross D.; Mitchell, James E.; Crow, Scott J.; Peterson, Carol B.

2016-01-01

Objective Evidence suggests that eating disorder subtypes reflecting under-controlled, over-controlled, and low psychopathology personality traits constitute reliable phenotypes that differentiate treatment response. This study is the first to use statistical analyses to identify these subtypes within treatment-seeking individuals with bulimia nervosa (BN) and to use these statistically derived clusters to predict clinical outcomes. Methods Using variables from the Dimensional Assessment of Personality Pathology–Basic Questionnaire, K-means cluster analyses identified under-controlled, over-controlled, and low psychopathology subtypes within BN patients (n = 80) enrolled in a treatment trial. Generalized linear models examined the impact of personality subtypes on Eating Disorder Examination global score, binge eating frequency, and purging frequency cross-sectionally at baseline and longitudinally at end of treatment (EOT) and follow-up. In the longitudinal models, secondary analyses were conducted to examine personality subtype as a potential moderator of response to Cognitive Behavioral Therapy-Enhanced (CBT-E) or Integrative Cognitive-Affective Therapy for BN (ICAT-BN). Results There were no baseline clinical differences between groups. In the longitudinal models, personality subtype predicted binge eating (p = .03) and purging (p = .01) frequency at EOT and binge eating frequency at follow-up (p = .045). The over-controlled group demonstrated the best outcomes on these variables. In secondary analyses, there was a treatment by subtype interaction for purging at follow-up (p = .04), which indicated a superiority of CBT-E over ICAT-BN for reducing purging among the over-controlled group. Discussion Empirically derived personality subtyping is appears to be a valid classification system with potential to guide eating disorder treatment decisions. PMID:27611235

Absolute number of new lesions on 18F-FDG PET/CT is more predictive of clinical response than SUV changes in metastatic melanoma patients receiving ipilimumab.

PubMed

Anwar, Hoda; Sachpekidis, Christos; Winkler, Julia; Kopp-Schneider, Annette; Haberkorn, Uwe; Hassel, Jessica C; Dimitrakopoulou-Strauss, Antonia

2018-03-01

Evaluation of response to immunotherapy is a matter of debate. The aim of the present study was to evaluate the response of metastatic melanoma to treatment with ipilimumab by means of 18 F-FDG PET/CT, using the patients' clinical response as reference. The final cohort included in the analyses consisted of 41 patients with metastatic melanoma who underwent 18 F-FDG PET/CT before and after administration of ipilimumab. After determination of the best clinical response, the PET/CT scans were reviewed and a separate independent analysis was performed, based on the number and functional size of newly emerged 18 F-FDG-avid lesions, as well as on the SUV changes after therapy. The median observation time of the patients after therapy was 21.4 months (range 6.3-41.9 months). Based on their clinical response, patients were dichotomized into those with clinical benefit (CB) and those without CB (No-CB). The CB group (31 patients) included those with stable disease, partial remission and complete remission, and the No-CB group (10 patients) included those with progressive disease. The application of a threshold of four newly emerged 18 F-FDG-avid lesions on the posttherapy PET/CT scan led to a sensitivity (correctly predicting CB) of 84% and a specificity (correctly predicting No-CB) of 100%. This cut-off was lower for lesions with larger functional diameters (three new lesions larger than 1.0 cm and two new lesions larger than 1.5 cm). SUV changes after therapy did not correlate with clinical response. Based on these findings, we developed criteria for predicting clinical response to immunotherapy by means of 18 F-FDG PET/CT (PET Response Evaluation Criteria for Immunotherapy, PERCIMT). Our results show that a cut-off of four newly emerged 18 F-FDG-avid lesions on posttherapy PET/CT gives a reliable indication of treatment failure in patients under ipilimumab treatment. Moreover, the functional size of the new lesions plays an important role in predicting the clinical

The role of clinical variables, neuropsychological performance and SLC6A4 and COMT gene polymorphisms on the prediction of early response to fluoxetine in major depressive disorder.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Cruz, David; Hernández, Sandra; Genis, Alma; Carrillo-Guerrero, Mariana Y; Avilés Reyes, Rubén; Guàrdia-Olmos, Joan

2010-12-01

Major depressive disorder (MDD) is treated with antidepressants, but only between 50% and 70% of the patients respond to the initial treatment. Several authors suggested different factors that could predict antidepressant response, including clinical, psychophysiological, neuropsychological, neuroimaging, and genetic variables. However, these different predictors present poor prognostic sensitivity and specificity by themselves. The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms in the prediction of the response to fluoxetine after 4weeks of treatment in a sample of patient with MDD. 64 patients with MDD were genotyped according to the above-mentioned polymorphisms, and were clinically and neuropsychologically assessed before a 4-week fluoxetine treatment. Fluoxetine response was assessed by using the Hamilton Depression Rating Scale. We carried out a binary logistic regression model for the potential predictive variables. Out of the clinical variables studied, only the number of anxiety disorders comorbid with MDD have predicted a poor response to the treatment. A combination of a good performance in variables of attention and low performance in planning could predict a good response to fluoxetine in patients with MDD. None of the genetic variables studied had predictive value in our model. The possible placebo effect has not been controlled. Our study is focused on response prediction but not in remission prediction. Our work suggests that the combination of the number of comorbid anxiety disorders, an attentional variable, and two planning variables makes it possible to correctly classify 82% of the depressed patients who responded to the treatment with fluoxetine, and 74% of the patients who did not respond to that treatment. Copyright © 2010 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-12-07

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

Ultrasonographic Changes at 12 Weeks of Anti-TNF Drugs Predict 1-year Sonographic Response and Clinical Outcome in Crohn's Disease: A Multicenter Study.

PubMed

Ripollés, Tomás; Paredes, José M; Martínez-Pérez, María J; Rimola, Jordi; Jauregui-Amezaga, Arantza; Bouzas, Rosa; Martin, Gregorio; Moreno-Osset, Eduardo

2016-10-01

The objective was to assess the long-term effect of biological treatment on transmural lesions of Crohn's disease evaluated with ultrasound, including contrast-enhanced ultrasound. Fifty-one patients with active Crohn's disease were included in a prospective multicenter longitudinal study. All patients underwent a clinical assessment and sonographic examination at baseline, 12 weeks after treatment initiation, and after 1-year of treatment. Patients were clinically followed at least 2 years from inclusion until the end of the study. Ultrasonographic evaluation included bowel wall thickness, color Doppler grade, parietal enhancement, and presence of transmural complications or stenosis. Sonographic changes after treatment were classified as normalization, improvement, or lack of response. Improvement at 52 weeks was more frequent in patients with improvement at final of induction (12 weeks) compared with patients who did not improve (85% versus 28%; P < 0.0001). One-year sonographic evolution correlated with clinical response; 28 of the 29 (96.5%) patients with sonographic improvement at 52 weeks showed clinical remission or response. Patients without sonographic improvement at 52 weeks of treatment were more likely to have a change or intensification in medication or surgery (13/20, 65%) during the next year of follow-up than patients with improvement on the sonography (3/28, 11%). Stricturing behavior was the only sonographic feature associated to a negative predictive value of response (P = 0.0001). Sonographic response after 12 weeks of therapy is more pronounced and predicts 1-year sonographic response. Sonographic response at 1-year examination correlates with 1-year clinical response and is a predictor of further treatment's efficacy, 1-year or longer period of follow-up.

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

PubMed

Kischkel, Frank Christian; Meyer, Carina; Eich, Julia; Nassir, Mani; Mentze, Monika; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid

2017-10-27

In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

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.

Identification of immune signatures predictive of clinical protection from malaria.

PubMed

Valletta, John Joseph; Recker, Mario

2017-10-01

Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite Plasmodium falciparum cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual's malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone.

Identification of immune signatures predictive of clinical protection from malaria

PubMed Central

2017-01-01

Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite Plasmodium falciparum cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual’s malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone. PMID:29065113

Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics.

PubMed

Geeleher, Paul; Loboda, Andrey; Lenkala, Divya; Wang, Fan; LaCroix, Bonnie; Karovic, Sanja; Wang, Jacqueline; Nebozhyn, Michael; Chisamore, Michael; Hardwick, James; Maitland, Michael L; Huang, R Stephanie

2015-11-01

Many disparate biomarkers have been proposed as predictors of response to histone deacetylase inhibitors (HDI); however, all have failed when applied clinically. Rather than this being entirely an issue of reproducibility, response to the HDI vorinostat may be determined by the additive effect of multiple molecular factors, many of which have previously been demonstrated. We conducted a large-scale gene expression analysis using the Cancer Genome Project for discovery and generated another large independent cancer cell line dataset across different cancers for validation. We compared different approaches in terms of how accurately vorinostat response can be predicted on an independent out-of-batch set of samples and applied the polygenic marker prediction principles in a clinical trial. Using machine learning, the small effects that aggregate, resulting in sensitivity or resistance, can be recovered from gene expression data in a large panel of cancer cell lines.This approach can predict vorinostat response accurately, whereas single gene or pathway markers cannot. Our analyses recapitulated and contextualized many previous findings and suggest an important role for processes such as chromatin remodeling, autophagy, and apoptosis. As a proof of concept, we also discovered a novel causative role for CHD4, a helicase involved in the histone deacetylase complex that is associated with poor clinical outcome. As a clinical validation, we demonstrated that a common dose-limiting toxicity of vorinostat, thrombocytopenia, can be predicted (r = 0.55, P = .004) several days before it is detected clinically. Our work suggests a paradigm shift from single-gene/pathway evaluation to simultaneously evaluating multiple independent high-throughput gene expression datasets, which can be easily extended to other investigational compounds where similar issues are hampering clinical adoption. © The Author 2015. Published by Oxford University Press. All rights reserved. For

Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy.

PubMed

Hector, Suzanne; Rehm, Markus; Schmid, Jasmin; Kehoe, Joan; McCawley, Niamh; Dicker, Patrick; Murray, Frank; McNamara, Deborah; Kay, Elaine W; Concannon, Caoimhin G; Huber, Heinrich J; Prehn, Jochen H M

2012-05-01

Key to the clinical management of colorectal cancer is identifying tools which aid in assessing patient prognosis and determining more effective and personalised treatment strategies. We evaluated whether an experimental systems biology strategy which analyses the susceptibility of cancer cells to undergo caspase activation can be exploited to predict patient responses to 5-fluorouracil-based chemotherapy and to case-specifically identify potential alternative targeted treatments to reactivate apoptosis. We quantified five essential apoptosis-regulating proteins (Pro-Caspases 3 and 9, APAF-1, SMAC and XIAP) in samples of Stage II (n = 13) and III (n=17) tumour and normal colonic (n = 8) tissue using absolute quantitative immunoblotting and employed systems simulations of apoptosis signalling to predict the susceptibility of tumour cells to execute apoptosis. Additional systems analyses assessed the efficacy of novel apoptosis-inducing therapeutics such as XIAP antagonists, proteasome inhibitors and Pro-Caspase-3-activating compounds in restoring apoptosis execution in apoptosis-incompetent tumours. Comparisons of caspase activity profiles demonstrated that the likelihood of colorectal tumours to undergo apoptosis decreases with advancing disease stage. Systems-level analysis correctly predicted positive or negative outcome in 85% (p=0.004) of colorectal cancer patients receiving 5-fluorouracil based chemotherapy and significantly outperformed common uni- and multi-variate statistical approaches. Modelling of individual patient responses to novel apoptosis-inducing therapeutics revealed markedly different inter-individual responses. Our study represents the first proof-of-concept example demonstrating the significant clinical potential of systems biology-based approaches for predicting patient outcome and responsiveness to novel targeted treatment paradigms.

Empirically derived personality subtyping for predicting clinical symptoms and treatment response in bulimia nervosa.

PubMed

Haynos, Ann F; Pearson, Carolyn M; Utzinger, Linsey M; Wonderlich, Stephen A; Crosby, Ross D; Mitchell, James E; Crow, Scott J; Peterson, Carol B

2017-05-01

Evidence suggests that eating disorder subtypes reflecting under-controlled, over-controlled, and low psychopathology personality traits constitute reliable phenotypes that differentiate treatment response. This study is the first to use statistical analyses to identify these subtypes within treatment-seeking individuals with bulimia nervosa (BN) and to use these statistically derived clusters to predict clinical outcomes. Using variables from the Dimensional Assessment of Personality Pathology-Basic Questionnaire, K-means cluster analyses identified under-controlled, over-controlled, and low psychopathology subtypes within BN patients (n = 80) enrolled in a treatment trial. Generalized linear models examined the impact of personality subtypes on Eating Disorder Examination global score, binge eating frequency, and purging frequency cross-sectionally at baseline and longitudinally at end of treatment (EOT) and follow-up. In the longitudinal models, secondary analyses were conducted to examine personality subtype as a potential moderator of response to Cognitive Behavioral Therapy-Enhanced (CBT-E) or Integrative Cognitive-Affective Therapy for BN (ICAT-BN). There were no baseline clinical differences between groups. In the longitudinal models, personality subtype predicted binge eating (p = 0.03) and purging (p = 0.01) frequency at EOT and binge eating frequency at follow-up (p = 0.045). The over-controlled group demonstrated the best outcomes on these variables. In secondary analyses, there was a treatment by subtype interaction for purging at follow-up (p = 0.04), which indicated a superiority of CBT-E over ICAT-BN for reducing purging among the over-controlled group. Empirically derived personality subtyping appears to be a valid classification system with potential to guide eating disorder treatment decisions. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:506-514). © 2016 Wiley Periodicals, Inc.

Circulating tumor DNA evaluated by Next-Generation Sequencing is predictive of tumor response and prolonged clinical benefit with nivolumab in advanced non-small cell lung cancer.

PubMed

Giroux Leprieur, Etienne; Herbretau, Guillaume; Dumenil, Coraline; Julie, Catherine; Giraud, Violaine; Labrune, Sylvie; Dumoulin, Jennifer; Tisserand, Julie; Emile, Jean-François; Blons, Hélène; Chinet, Thierry

2018-01-01

Nivolumab is an anti-PD1 antibody, given in second-line or later treatment in advanced non-small cell lung cancer (NSCLC). The objective of this study was to describe the predictive value of circulating tumor DNA (ctDNA) on the efficacy of nivolumab in advanced NSCLC. We prospectively included all consecutive patients with advanced NSCLC treated with nivolumab in our Department between June 2015 and October 2016. Plasma samples were obtained before the first injection of nivolumab and at the first tumor evaluation with nivolumab. ctDNA was analyzed by Next-Generation Sequencing (NGS), and the predominant somatic mutation was followed for each patient and correlated with tumor response, clinical benefit (administration of nivolumab for more than 6 months), and progression-free survival (PFS). Of 23 patients, 15 had evaluable NGS results at both times of analysis. ctDNA concentration at the first tumor evaluation and ctDNA change correlated with tumor response, clinical benefit and PFS. ROC curve analyses showed good diagnostic performances for tumor response and clinical benefit, both for ctDNA concentration at the first tumor evaluation (tumor response: positive predictive value (PPV) at 100.0% and negative predictive value (NPV) at 71.0%; clinical benefit: PPV at 83.3% and NPV 77.8%) and the ctDNA change (tumor response: PPV 100.0% and NPV 62.5%; clinical benefit: PPV 100.0% and NPV 80.0%). Patients without ctDNA concentration increase >9% at 2 months had a long-term benefit of nivolumab. In conclusion, NGS analysis of ctDNA allows the early detection of tumor response and long-term clinical benefit with nivolumab in NSCLC.

Early non-response to certolizumab pegol in rheumatoid arthritis predicts treatment failure at one year. Data from a randomised phase III clinical trial.

PubMed

Berenbaum, Francis; Pham, Thao; Claudepierre, Pascal; de Chalus, Thibault; Joubert, Jean-Michel; Saadoun, Carine; Riou França, Lionel; Fautrel, Bruno

2018-01-01

To compare different early clinical criteria of non-response determined at three months as predictors of clinical failure at one year in patients with rheumatoid arthritis starting therapy with certolizumab pegol. Data were derived from a randomised Phase III clinical trial in patients with rheumatoid arthritis who failed to respond to methotrexate monotherapy. Patients included in this post-hoc analysis were treated with certolizumab pegol (400mg qd reduced to 200mg qd after one month) and with methotrexate. The study duration was twelve months. Response at three months was determined with the American College of Rheumatology-50, Disease Assessment Score-28 ESR, Health Assessment Questionnaire and the Clinical Disease Activity Index. The performance of these measures at predicting treatment failure at twelve months defined by the American College of Rheumatology-50 criteria was determined, using the positive predictive values as the principal evaluation criterion. Three hundred and eighty two patients were available for analysis and 225 completed the twelve-month follow-up. At Week 52, 149 (38.1%) patients met the American College of Rheumatology-50 response criterion. Positive predictive values ranged from 81% for a decrease in Health Assessment Questionnaire- Disability index score since baseline >0.22 to 95% for a decrease in Disease Assessment Score-28 score since baseline≥1.2. Sensitivity was≤70% in all cases. Performance of these measures was similar irrespective of the definition of treatment failure at 12months. Simple clinical measures of disease activity can predict future treatment failure reliably and are appropriate for implementing treat-to-target treatment strategies in everyday practice. Copyright © 2017 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

Prediction of therapeutic response in steroid-treated pulmonary sarcoidosis. Evaluation of clinical parameters, bronchoalveolar lavage, gallium-67 lung scanning, and serum angiotensin-converting enzyme levels

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

Hollinger, W.M.; Staton, G.W. Jr.; Fajman, W.A.

1985-07-01

To find a pretreatment predictor of steroid responsiveness in pulmonary sarcoidosis the authors studied 21 patients before and after steroid treatment by clinical evaluation, pulmonary function tests, bronchoalveolar lavage (BAL), gallium-67 lung scan, and serum angiotensin-converting enzyme (SACE) level. Although clinical score, forced vital capacity (FVC), BAL percent lymphocytes (% lymphs), quantitated gallium-67 lung uptake, and SACE levels all improved with therapy, only the pretreatment BAL % lymphs correlated with the improvement in FVC (r = 0.47, p less than 0.05). Pretreatment BAL % lymphs of greater than or equal to 35% predicted improvement in FVC of 10/11 patients, whereasmore » among 10 patients with BAL % lymphs less than 35%, 5 patients improved and 5 deteriorated. Clinical score, pulmonary function parameters, quantitated gallium-67 lung uptake, and SACE level used alone, in combination with BAL % lymphs or in combination with each other, did not improve this predictive value. The authors conclude that steroid therapy improves a number of clinical and laboratory parameters in sarcoidosis, but only the pretreatment BAL % lymphs are useful in predicting therapeutic responsiveness.« less

Faecal calprotectin assay after induction with anti-Tumour Necrosis Factor α agents in inflammatory bowel disease: Prediction of clinical response and mucosal healing at one year.

PubMed

Guidi, Luisa; Marzo, Manuela; Andrisani, Gianluca; Felice, Carla; Pugliese, Daniela; Mocci, Giammarco; Nardone, Olga; De Vitis, Italo; Papa, Alfredo; Rapaccini, Gianlodovico; Forni, Franca; Armuzzi, Alessandro

2014-11-01

Faecal calprotectin levels correlate with inflammation in inflammatory bowel disease. We evaluated the role of faecal calprotectin after anti-Tumour Necrosis Factor α induction in inflammatory bowel disease patients to predict therapeutic effect at one year. Faecal calprotectin levels were measured in stools of 63 patients before and after induction of anti-Tumour Necrosis Factor α therapy. Clinical activity, measured by clinical indices, was assessed before and after biologic treatment. Clinical responders after induction were included in the study and colonoscopy was performed before and after one year of treatment to assess mucosal healing. 63 patients (44 Crohn's disease, 19 ulcerative colitis) were prospectively included (41.2% males, mean age at diagnosis 33 years). A sustained clinical response during the first year was observed in 57% of patients; median faecal calprotectin was 106 μg/g after induction versus 308 μg/g pre-induction (p<0.0001). Post-induction faecal calprotectin was significantly lower in responders versus non-responders (p=0.0002). Post-induction faecal calprotectin had 83% sensitivity and 74% specificity (cut-off ≤ 168 μg/g) for predicting a sustained clinical response at one year (p=0.0001); also, sensitivity was 79% and specificity 57% (cut-off ≤ 121 μg/g) for predicting mucosal healing (p=0.0001). In inflammatory bowel disease faecal calprotectin assay after anti-Tumour Necrosis Factor α induction can be used as a marker to predict sustained clinical response and mucosal healing at one year. Copyright © 2014 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

PubMed

Xia, Shu; Kohli, Manish; Du, Meijun; Dittmar, Rachel L; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L; Huang, Chiang-Ching; Wang, Liang

2015-06-30

Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer.

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

PubMed

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predicting response to EGFR inhibitors in metastatic colorectal cancer: current practice and future directions.

PubMed

Shankaran, Veena; Obel, Jennifer; Benson, Al B

2010-01-01

The identification of KRAS mutational status as a predictive marker of response to antibodies against the epidermal growth factor receptor (EGFR) has been one of the most significant and practice-changing recent advances in colorectal cancer research. Recently, data suggesting a potential role for other markers (including BRAF mutations, loss of phosphatase and tension homologue deleted on chromosome ten expression, and phosphatidylinositol-3-kinase-AKT pathway mutations) in predicting response to anti-EGFR therapy have emerged. Ongoing clinical trials and correlative analyses are essential to definitively identify predictive markers and develop therapeutic strategies for patients who may not derive benefit from anti-EGFR therapy. This article reviews recent clinical trials supporting the predictive role of KRAS, recent changes to clinical guidelines and pharmaceutical labeling, investigational predictive molecular markers, and newer clinical trials targeting patients with mutated KRAS.

Drug response in a genetically engineered mouse model of multiple myeloma is predictive of clinical efficacy

PubMed Central

Chesi, Marta; Matthews, Geoffrey M.; Garbitt, Victoria M.; Palmer, Stephen E.; Shortt, Jake; Lefebure, Marcus; Stewart, A. Keith; Johnstone, Ricky W.

2012-01-01

The attrition rate for anticancer drugs entering clinical trials is unacceptably high. For multiple myeloma (MM), we postulate that this is because of preclinical models that overemphasize the antiproliferative activity of drugs, and clinical trials performed in refractory end-stage patients. We validate the Vk*MYC transgenic mouse as a faithful model to predict single-agent drug activity in MM with a positive predictive value of 67% (4 of 6) for clinical activity, and a negative predictive value of 86% (6 of 7) for clinical inactivity. We identify 4 novel agents that should be prioritized for evaluation in clinical trials. Transplantation of Vk*MYC tumor cells into congenic mice selected for a more aggressive disease that models end-stage drug-resistant MM and responds only to combinations of drugs with single-agent activity in untreated Vk*MYC MM. We predict that combinations of standard agents, histone deacetylase inhibitors, bromodomain inhibitors, and hypoxia-activated prodrugs will demonstrate efficacy in the treatment of relapsed MM. PMID:22451422

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

PubMed Central

Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.

2013-01-01

Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized

Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab.

PubMed

Chaput, N; Lepage, P; Coutzac, C; Soularue, E; Le Roux, K; Monot, C; Boselli, L; Routier, E; Cassard, L; Collins, M; Vaysse, T; Marthey, L; Eggermont, A; Asvatourian, V; Lanoy, E; Mateus, C; Robert, C; Carbonnel, F

2017-06-01

Ipilimumab, an immune checkpoint inhibitor targeting CTLA-4, prolongs survival in a subset of patients with metastatic melanoma (MM) but can induce immune-related adverse events, including enterocolitis. We hypothesized that baseline gut microbiota could predict ipilimumab anti-tumor response and/or intestinal toxicity. Twenty-six patients with MM treated with ipilimumab were prospectively enrolled. Fecal microbiota composition was assessed using 16S rRNA gene sequencing at baseline and before each ipilimumab infusion. Patients were further clustered based on microbiota patterns. Peripheral blood lymphocytes immunophenotypes were studied in parallel. A distinct baseline gut microbiota composition was associated with both clinical response and colitis. Compared with patients whose baseline microbiota was driven by Bacteroides (cluster B, n = 10), patients whose baseline microbiota was enriched with Faecalibacterium genus and other Firmicutes (cluster A, n = 12) had longer progression-free survival (P = 0.0039) and overall survival (P = 0.051). Most of the baseline colitis-associated phylotypes were related to Firmicutes (e.g. relatives of Faecalibacterium prausnitzii and Gemmiger formicilis), whereas no colitis-related phylotypes were assigned to Bacteroidetes. A low proportion of peripheral blood regulatory T cells was associated with cluster A, long-term clinical benefit and colitis. Ipilimumab led to a higher inducible T-cell COStimulator induction on CD4+ T cells and to a higher increase in serum CD25 in patients who belonged to Faecalibacterium-driven cluster A. Baseline gut microbiota enriched with Faecalibacterium and other Firmicutes is associated with beneficial clinical response to ipilimumab and more frequent occurrence of ipilimumab-induced colitis. © 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.

Clinical value of serum anti-mullerian hormone and inhibin B in prediction of ovarian response in patients with polycystic ovary syndrome.

PubMed

Zhang, Fan; Liu, Xiao-Ling; Rong, Nan; Huang, Xiao-Wen

2017-02-01

The present study aimed to investigate the clinical value of serum anti-mullerian hormone (AMH) and inhibin B (INHB) in predicting the ovarian response of patients with polycystic ovary syndrome (PCOS). A total of 120 PCOS patients were enrolled and divided into three groups in terms of the ovarian response: a low-response group (n=36), a normal-response group (n=44), and a high-response group (n=40). The serum AMH and INHB levels were measured by enzyme-linked immunosorbent assay (ELISA). The follicle stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2) levels were determined by chemiluminescence microparticle immunoassay. The correlation of the serum AMH and INHB levels with other indicators was analyzed. A receiver operating characteristic (ROC) curve was established to analyze the prediction of ovarian response by AMH and INHB. The results showed that there were significant differences in age, body mass index (BMI), FSH, total gonadotropin-releasing hormone (GnRH), LH, E2, and antral follicle counts (AFCs) between the groups (P<0.05). The serum AMH and INHB levels were increased significantly with the ovarian response of PCOS patients increasing (P<0.05). The serum AMH and INHB levels were negatively correlated with the age, BMI, FSH level, Gn, and E2 levels (P<0.05). They were positively correlated with the LH levels and AFCs (P<0.05). ROC curve analysis of serum AMH and INHB in prediction of a low ovarian response showed that the area under the ROC curve (AUC) value of the serum AMH level was 0.817, with a cut-off value of 1.29 ng/mL. The sensitivity and specificity were 71.2% and 79.6%, respectively. The AUC value of serum INHB was 0.674, with a cut-off value of 38.65 ng/mL, and the sensitivity and specificity were 50.7% and 74.5%, respectively. ROC curve analysis showed when the serum AMH and INHB levels were used to predict a high ovarian response, the AUC value of the serum AMH level was 0.742, with a cut-off value of 2.84 ng/mL, and

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

PubMed Central

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

2009-01-01

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

Clinical relevance of total HCV core antigen testing for hepatitis C monitoring and for predicting patients' response to therapy.

PubMed

Maynard, M; Pradat, P; Berthillon, P; Picchio, G; Voirin, N; Martinot, M; Marcellin, P; Trepo, C

2003-07-01

To study the correlation between total Hepatitis C virus (HCV) Core antigen (Ag) and HCV-RNA, and to assess the proficiency of HCV Core Ag testing in monitoring and predicting virologic response during and after pegylated interferon (PEG-IFN) and ribavirin combination therapy. A total of 307 samples from treated and untreated patients were used to assess the correlation between the total HCV Core Ag test and quantitative HCV-RNA assays (Superquant, and Quantiplex branched DNA 2.0 assay). Twenty-four patients received combination therapy for 48 weeks. Blood samples were collected at day 0, and week 2, 4, 12, 24, 48 and 72 for virologic evaluation. A linear relation exists between total HCV Core Ag and HCV-RNA levels. At 3 months the positive predictive value (PPV) of response to therapy was 100% with either HCV Core Ag or HCV-RNA. For HCV Core Ag the negative predictive value (NPV) was 100% whereas for HCV-RNA the NPV was 80% (P > 0.05). At month 1, the PPV was 95% and 100% when determined by HCV Core Ag and HCV-RNA, respectively. The NPV value was 100% for HCV Core Ag and 33% for HCV-RNA (P = 0.005). HCV Core Ag quantification could be useful in clinical practice to predict a sustained virological response early during therapy (4 weeks), reaching an optimal performance at month 3. The determination of total HCV Core Ag levels in serum, constitutes an accurate and reliable alternative to HCV-RNA for monitoring and predicting treatment outcome in patients receiving PEG-IFN/Ribavirin combination therapy.

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study.

PubMed

Yu, Su Jong; Kim, Hyunsoo; Min, Hophil; Sohn, Areum; Cho, Young Youn; Yoo, Jeong-Ju; Lee, Dong Hyeon; Cho, Eun Ju; Lee, Jeong-Hoon; Gim, Jungsoo; Park, Taesung; Kim, Yoon Jun; Kim, Chung Yong; Yoon, Jung-Hwan; Kim, Youngsoo

2017-03-03

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

Predicting breast cancer using an expression values weighted clinical classifier.

PubMed

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

2014-12-31

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

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-03-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test.

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed Central

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-01-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test. PMID:8459240

Pharmacogenetic markers to predict the clinical response to methotrexate in south Indian Tamil patients with psoriasis.

PubMed

Indhumathi, S; Rajappa, Medha; Chandrashekar, Laxmisha; Ananthanarayanan, P H; Thappa, D M; Negi, V S

2017-08-01

Despite the advent of several new systemic therapies, methotrexate remains the gold standard for the treatment of moderate to severe psoriasis. However, there exists a significant heterogeneity in individual response to methotrexate. There are no consistently reliable markers to predict methotrexate treatment response till date. We aimed to demonstrate the association of certain genetic variants in the HLA (HLA-A2, HLA-B17, and HLA-Cw6) and the non-HLA genes including T-helper (Th)-1, Th-2, Th-17 cytokine genes (IFN-γ, IL-2, IL-4, IL-10, IL-12B, and IL-23R), and T-regulatory gene (FOXP3) with the methotrexate treatment response in South Indian Tamil patients with psoriasis. Of the 360 patients recruited, 189 patients with moderate to severe psoriasis were treated with methotrexate. Of the 189 patients, 132 patients responded to methotrexate and the remaining 57 patients were non-responders. We analyzed the association of aforesaid polymorphisms with the methotrexate treatment outcome using binary logistic regression. We observed that there were significant differences between genotype frequencies of HLA-Cw6 and FOXP3 (rs3761548) among the responders compared to non-responders, with conservative estimation. We observed that pro-inflammatory cytokines such as IFN-γ, IL-2, IL-12, and IL-23 were markedly reduced with the use of methotrexate, in comparison to the baseline levels, while the plasma IL-4 levels were increased posttreatment. Our results serve as preliminary evidence for the clinical use of genetic markers as predictors of response to methotrexate in psoriasis. This might aid in the future in the development of a point-of-care testing (POCT) gene chip, to predict optimal treatment response in patients with psoriasis, based on their individual genotypic profile.

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

PubMed

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

2016-07-05

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

Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study.

PubMed

Johnston, Blair A; Coghill, David; Matthews, Keith; Steele, J Douglas

2015-01-01

Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH. © The Author(s) 2014.

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

PubMed Central

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

2013-01-01

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

Empirically and Clinically Useful Decision Making in Psychotherapy: Differential Predictions with Treatment Response Models

ERIC Educational Resources Information Center

Lutz, Wolfgang; Saunders, Stephen M.; Leon, Scott C.; Martinovich, Zoran; Kosfelder, Joachim; Schulte, Dietmar; Grawe, Klaus; Tholen, Sven

2006-01-01

In the delivery of clinical services, outcomes monitoring (i.e., repeated assessments of a patient's response to treatment) can be used to support clinical decision making (i.e., recurrent revisions of outcome expectations on the basis of that response). Outcomes monitoring can be particularly useful in the context of established practice research…

Utility of nociceptive flexion reflex threshold and bispectral index to predict movement responses under propofol anaesthesia.

PubMed

Jakuscheit, Axel; Posch, Matthias J; Gkaitatzis, Stefanos; Neumark, Lisa; Hackbarth, Mark; Schneider, Martin; Lichtner, Gregor; Baars, Jan H; von Dincklage, Falk

2017-06-01

The nociceptive flexion reflex threshold (NFRT) is a promising tool to monitor analgesia during general anaesthesia. Clinical studies have shown that the NFRT allows to predict movement responses to painful stimuli under a combined anaesthetic regime of sedative and opioid agents. Experimental studies indicated that the NFRT is also able to predict such movement responses under an exclusively sedative regime like propofol mono-anaesthesia. Therefore, we performed this study to investigate the ability of the NFRT to predict movement responses to painful stimuli in patients during a clinical propofol mono-anaesthesia. We investigated 140 cardiac surgery patients during their postoperative phase under propofol mono-anaesthesia. NFRT and bispectral index (BIS) were determined in each patient right before endotracheal suctioning or painful electrical test stimulation. Prediction probabilities were calculated to quantify how accurate each measure is able to predict movement responses to the stimuli. The 124 patients included in the analysis received a median propofol dosage of 3.2 (2.5-3.9) [median (IQR)] mg/kg/h. The included patients showed 287 movement responses after a total of 725 investigated stimuli. The prediction probabilities for positive movement responses were 0.63 (95%CI: 0.59-0.67) for the NFRT and 0.69 (95%CI: 0.65-0.73) for the BIS. The NFRT allows the prediction of movement responses under propofol mono-anaesthesia, which confirms its utility as a monitor to predict movement responses under general anaesthesia. The BIS allows an even more accurate prediction, although it does not reflect the physiological structures of movement suppression, but correlates closely with the dose of propofol. German clinical trial register (DRKS00003062, Deutsches Register Klinischer Studien).

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

PubMed

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

2015-06-01

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

Genomic and Histopathological Tissue Biomarkers That Predict Radiotherapy Response in Localised Prostate Cancer

PubMed Central

Wilkins, Anna; Dearnaley, David; Somaiah, Navita

2015-01-01

Localised prostate cancer, in particular, intermediate risk disease, has varied survival outcomes that cannot be predicted accurately using current clinical risk factors. External beam radiotherapy (EBRT) is one of the standard curative treatment options for localised disease and its efficacy is related to wide ranging aspects of tumour biology. Histopathological techniques including immunohistochemistry and a variety of genomic assays have been used to identify biomarkers of tumour proliferation, cell cycle checkpoints, hypoxia, DNA repair, apoptosis, and androgen synthesis, which predict response to radiotherapy. Global measures of genomic instability also show exciting capacity to predict survival outcomes following EBRT. There is also an urgent clinical need for biomarkers to predict the radiotherapy fraction sensitivity of different prostate tumours and preclinical studies point to possible candidates. Finally, the increased resolution of next generation sequencing (NGS) is likely to enable yet more precise molecular predictions of radiotherapy response and fraction sensitivity. PMID:26504789

Serum Immune Responses Predict Rapid Disease Progression among Children with Crohn’s Disease: Immune Responses Predict Disease Progression

PubMed Central

Dubinsky, Marla C.; Lin, Ying-Chao; Dutridge, Debra; Picornell, Yoana; Landers, Carol J.; Farrior, Sharmayne; Wrobel, Iwona; Quiros, Antonio; Vasiliauskas, Eric A.; Grill, Bruce; Israel, David; Bahar, Ron; Christie, Dennis; Wahbeh, Ghassan; Silber, Gary; Dallazadeh, Saied; Shah, Praful; Thomas, Danny; Kelts, Drew; Hershberg, Robert M.; Elson, Charles O.; Targan, Stephan R.; Taylor, Kent D.; Rotter, Jerome I.; Yang, Huiying

2007-01-01

BACKGROUND AND AIM Crohn’s disease (CD) is a heterogeneous disorder characterized by diverse clinical phenotypes. Childhood-onset CD has been described as a more aggressive phenotype. Genetic and immune factors may influence disease phenotype and clinical course. We examined the association of immune responses to microbial antigens with disease behavior and prospectively determined the influence of immune reactivity on disease progression in pediatric CD patients. METHODS Sera were collected from 196 pediatric CD cases and tested for immune responses: anti-I2, anti-outer membrane protein C (anti-OmpC), anti-CBir1 flagellin (anti-CBir1), and anti-Saccharomyces-cerevisiae (ASCA) using ELISA. Associations between immune responses and clinical phenotype were evaluated. RESULTS Fifty-eight patients (28%) developed internal penetrating and/or stricturing (IP/S) disease after a median follow-up of 18 months. Both anti-OmpC (p < 0.0006) and anti-I2 (p < 0.003) were associated with IP/S disease. The frequency of IP/S disease increased with increasing number of immune responses (p trend = 0.002). The odds of developing IP/S disease were highest in patients positive for all four immune responses (OR (95% CI): 11 (1.5–80.4); p = 0.03). Pediatric CD patients positive for ≥1 immune response progressed to IP/S disease sooner after diagnosis as compared to those negative for all immune responses (p < 0.03). CONCLUSIONS The presence and magnitude of immune responses to microbial antigens are significantly associated with more aggressive disease phenotypes among children with CD. This is the first study to prospectively demonstrate that the time to develop a disease complication in children is significantly faster in the presence of immune reactivity, thereby predicting disease progression to more aggressive disease phenotypes among pediatric CD patients. PMID:16454844

Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

PubMed

Siegal, Tali

2016-01-01

Sorting and grading of glial tumors by the WHO classification provide clinicians with guidance as to the predicted course of the disease and choice of treatment. Nonetheless, histologically identical tumors may have very different outcome and response to treatment. Molecular markers that carry both diagnostic and prognostic information add useful tools to traditional classification by redefining tumor subtypes within each WHO category. Therefore, molecular markers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas. The routine assessment of IDH status improves histological diagnostic accuracy by differentiating diffuse glioma from reactive gliosis. It carries a favorable prognostic implication for all glial tumors and it is predictive for chemotherapeutic response in anaplastic oligodendrogliomas with codeletion of 1p/19q chromosomes. Glial tumors that contain chromosomal codeletion of 1p/19q are defined as tumors of oligodendroglial lineage and have favorable prognosis. MGMT promoter methylation is a favorable prognostic marker in astrocytic high-grade gliomas and it is predictive for chemotherapeutic response in anaplastic gliomas with wild-type IDH1/2 and in glioblastoma of the elderly. The clinical implication of other molecular markers of gliomas like mutations of EGFR and ATRX genes and BRAF fusion or point mutation is highlighted. The potential of molecular biomarker-based classification to guide future therapeutic approach is discussed and accentuated.

Clinical utility and validity of minoxidil response testing in androgenetic alopecia.

PubMed

Goren, Andy; Shapiro, Jerry; Roberts, Janet; McCoy, John; Desai, Nisha; Zarrab, Zoulikha; Pietrzak, Aldona; Lotti, Torello

2015-01-01

Clinical response to 5% topical minoxidil for the treatment of androgenetic alopecia (AGA) is typically observed after 3-6 months. Approximately 40% of patients will regrow hair. Given the prolonged treatment time required to elicit a response, a diagnostic test for ruling out nonresponders would have significant clinical utility. Two studies have previously reported that sulfotransferase enzyme activity in plucked hair follicles predicts a patient's response to topical minoxidil therapy. The aim of this study was to assess the clinical utility and validity of minoxidil response testing. In this communication, the present authors conducted an analysis of completed and ongoing studies of minoxidil response testing. The analysis confirmed the clinical utility of a sulfotransferase enzyme test in successfully ruling out 95.9% of nonresponders to topical minoxidil for the treatment of AGA. © 2014 Wiley Periodicals, Inc.

Prospective analysis of adoptive TIL therapy in patients with metastatic melanoma: response, impact of anti-CTLA4, and biomarkers to predict clinical outcome.

PubMed

Forget, Marie-Andrée; Haymaker, Cara; Hess, Kenneth R; Meng, Yuzhong Jeff; Creasy, Caitlin; Karpinets, Tatiana V; Fulbright, Orenthial J; Roszik, Jason; Woodman, Scott E; Kim, Young Uk; Sakellariou-Thompson, Donastas; Bhatta, Ankit; Wahl, Arely; Flores, Esteban; Thorsen, Shawne T; Tavera, Rene J; Ramachandran, Renjith; Gonzalez, Audrey M; Toth, Christopher; Wardell, Seth; Mansaray, Rahmatu; Patel, Vruti; Carpio, Destiny Joy; Vaughn, Carol S; Farinas, Chantell M; Velasquez, Portia G; Hwu, Wen-Jen; Patel, Sapna P; Davies, Michael A; Diab, Adi; Glitza, Isabella C; Tawbi, Hussein; Wong, Michael K K; Cain, Suzanne; Ross, Merrick I; Lee, Jeffrey E; Gershenwald, Jeffrey E; Lucci, Anthony; Royal, Richard; Cormier, J N; Wargo, Jennifer A; Radvanyi, Laszlo G; Torres Cabala, Carlos A; Beroukhim, Rameen; Hwu, Patrick; Amaria, Rodabe N; Bernatchez, Chantale

2018-05-30

Adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TIL) has consistently demonstrated clinical efficacy in metastatic melanoma. Recent widespread use of checkpoint blockade has shifted the treatment landscape, raising questions regarding impact of these therapies on response to TIL and appropriate immunotherapy sequence. Seventy-four metastatic melanoma patients were treated with autologous TIL and evaluated for clinical response according to irRC, overall survival and progression free survival. Immunologic factors associated with response were also evaluated. Best overall response for the entire cohort was 42%; 47% in 43 checkpoint naïve patients, 38% when patients were exposed to anti-CTLA4 alone (21 patients) and 33% if also exposed to anti-PD1 (9 patients) prior to TIL ACT. Median overall survival was 17.3 months; 24.6 months in CTLA4 naïve patients and 8.6 months in patients with prior CTLA4 blockade. The latter patients were infused with fewer TIL and experienced a shorter duration of response. Infusion of higher numbers of TIL with CD8 predominance and expression of BTLA correlated with improved response in anti-CTLA-4 naive patients, but not in anti-CTLA-4 refractory patients. Baseline serum levels of IL-9 predicted response to TIL ACT, while TIL persistence, tumor recognition and mutation burden did not correlate with outcome. This study demonstrates the deleterious effects of prior exposure to anti-CTLA4 on TIL ACT response and shows that baseline IL-9 levels can potentially serve as a predictive tool to appropriately select sequence for immunotherapies. Copyright ©2018, American Association for Cancer Research.

Accuracy of stroke volume variation in predicting fluid responsiveness: a systematic review and meta-analysis.

PubMed

Zhang, Zhongheng; Lu, Baolong; Sheng, Xiaoyan; Jin, Ni

2011-12-01

Stroke volume variation (SVV) appears to be a good predictor of fluid responsiveness in critically ill patients. However, a wide range of its predictive values has been reported in recent years. We therefore undertook a systematic review and meta-analysis of clinical trials that investigated the diagnostic value of SVV in predicting fluid responsiveness. Clinical investigations were identified from several sources, including MEDLINE, EMBASE, WANFANG, and CENTRAL. Original articles investigating the diagnostic value of SVV in predicting fluid responsiveness were considered to be eligible. Participants included critically ill patients in the intensive care unit (ICU) or operating room (OR) who require hemodynamic monitoring. A total of 568 patients from 23 studies were included in our final analysis. Baseline SVV was correlated to fluid responsiveness with a pooled correlation coefficient of 0.718. Across all settings, we found a diagnostic odds ratio of 18.4 for SVV to predict fluid responsiveness at a sensitivity of 0.81 and specificity of 0.80. The SVV was of diagnostic value for fluid responsiveness in OR or ICU patients monitored with the PiCCO or the FloTrac/Vigileo system, and in patients ventilated with tidal volume greater than 8 ml/kg. SVV is of diagnostic value in predicting fluid responsiveness in various settings.

Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy.

PubMed

Montero, Joan; Sarosiek, Kristopher A; DeAngelo, Joseph D; Maertens, Ophélia; Ryan, Jeremy; Ercan, Dalia; Piao, Huiying; Horowitz, Neil S; Berkowitz, Ross S; Matulonis, Ursula; Jänne, Pasi A; Amrein, Philip C; Cichowski, Karen; Drapkin, Ronny; Letai, Anthony

2015-02-26

There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo. Copyright © 2015 Elsevier Inc. All rights reserved.

Early prediction of blonanserin response in Japanese patients with schizophrenia.

PubMed

Kishi, Taro; Matsuda, Yuki; Fujita, Kiyoshi; Iwata, Nakao

2014-01-01

Blonanserin is a second-generation antipsychotic used for the treatment of schizophrenia in Japan and Korea. The present study aimed to examine early prediction of blonanserin in patients with schizophrenia. An 8-week, prospective, single-arm, flexible-dose clinical trial of blonanserin in patients with schizophrenia was conducted under real-world conditions. The inclusion criteria were antipsychotic naïve, and first-episode schizophrenia patients or schizophrenia patients with no consumption of any antipsychotic medication for more than 4 weeks before enrollment in this study. The positive predictive value, negative predictive value, sensitivity, specificity, and predictive power were calculated for the response status at week 4 to predict the subsequent response at week 8. Thirty-seven patients were recruited (56.8% of them had first-episode schizophrenia), and 28 (75.7%) completed the trial. At week 8, blonanserin was associated with a significant improvement in the Positive and Negative Syndrome Scale (PANSS) total score (P<0.0001) and in positive (P<0.0001), negative (P<0.0001), and general subscale scores (P<0.0001). In terms of percentage improvement of PANSS total scores from baseline to week 8, 64.9% of patients showed a ≥20% reduction in the PANSS total score and 48.6% showed a ≥30% reduction. However, 8.1% of patients experienced at least one adverse event. Using the 20% reduction in the PANSS total score at week 4 as a definition of an early response, the negative predictive values for later responses (ie, reductions of ≥30 and ≥40 in the PANSS total scores) were 88.9% and 94.1%, respectively. The specificities were 80.0% and 51.6%, respectively. Our results suggest that the blonanserin response at week 4 could predict the later response at week 8.

Early prediction of blonanserin response in Japanese patients with schizophrenia

PubMed Central

Kishi, Taro; Matsuda, Yuki; Fujita, Kiyoshi; Iwata, Nakao

2014-01-01

Background Blonanserin is a second-generation antipsychotic used for the treatment of schizophrenia in Japan and Korea. The present study aimed to examine early prediction of blonanserin in patients with schizophrenia. Methods An 8-week, prospective, single-arm, flexible-dose clinical trial of blonanserin in patients with schizophrenia was conducted under real-world conditions. The inclusion criteria were antipsychotic naïve, and first-episode schizophrenia patients or schizophrenia patients with no consumption of any antipsychotic medication for more than 4 weeks before enrollment in this study. The positive predictive value, negative predictive value, sensitivity, specificity, and predictive power were calculated for the response status at week 4 to predict the subsequent response at week 8. Results Thirty-seven patients were recruited (56.8% of them had first-episode schizophrenia), and 28 (75.7%) completed the trial. At week 8, blonanserin was associated with a significant improvement in the Positive and Negative Syndrome Scale (PANSS) total score (P<0.0001) and in positive (P<0.0001), negative (P<0.0001), and general subscale scores (P<0.0001). In terms of percentage improvement of PANSS total scores from baseline to week 8, 64.9% of patients showed a ≥20% reduction in the PANSS total score and 48.6% showed a ≥30% reduction. However, 8.1% of patients experienced at least one adverse event. Using the 20% reduction in the PANSS total score at week 4 as a definition of an early response, the negative predictive values for later responses (ie, reductions of ≥30 and ≥40 in the PANSS total scores) were 88.9% and 94.1%, respectively. The specificities were 80.0% and 51.6%, respectively. Conclusion Our results suggest that the blonanserin response at week 4 could predict the later response at week 8. PMID:25285009

Rostral anterior cingulate cortex morphology predicts treatment response to internet-based CBT for depression

PubMed Central

Webb, Christian A.; Olson, Elizabeth A.; Killgore, William D.S.; Pizzagalli, Diego A.; Rauch, Scott L.; Rosso, Isabelle M.

2018-01-01

Background Rostral and subgenual anterior cingulate cortex (rACC and sgACC) activity and, to a lesser extent, volume have been shown to predict depressive symptom improvement across different antidepressant treatments. This study extends prior work by examining whether rACC and/or sgACC morphology predicts treatment response to internet-based cognitive behavioral therapy (iCBT) for major depressive disorder (MDD). This is the first study to examine neural predictors of response to iCBT. Methods Hierarchical linear modeling tested whether pre-treatment rACC and sgACC volumes predicted depressive symptom improvement during a 6-session (10-week) randomized clinical trial of iCBT (n = 35) vs. a monitored attention control (MAC; n = 38). Analyses also tested whether pre-treatment rACC and sgACC volumes differed between patients who achieved depression remission versus those who did not remit. Results Larger pre-treatment right rACC volume was a significant predictor of greater depressive symptom improvement in iCBT, even when controlling for demographic (age, gender, race) and clinical (baseline depression, anhedonia and anxiety) variables previously linked to treatment response. In addition, pre-treatment right rACC volume was larger among iCBT patients whose depression eventually remitted relative to those who did not remit. Corresponding analyses in the MAC group and for the sgACC were not significant. Conclusions rACC volume prior to iCBT demonstrated incremental predictive validity beyond clinical and demographic variables previously found to predict symptom improvement. Such findings may help inform our understanding of the mediating anatomy of iCBT and, if replicated, may suggest neural targets to augment treatment response (e.g., via modulation of rACC function). ClinicalTrials.gov Identifier NCT01598922 PMID:29486867

[Application of three compartment model and response surface model to clinical anesthesia using Microsoft Excel].

PubMed

Abe, Eiji; Abe, Mari

2011-08-01

With the spread of total intravenous anesthesia, clinical pharmacology has become more important. We report Microsoft Excel file applying three compartment model and response surface model to clinical anesthesia. On the Microsoft Excel sheet, propofol, remifentanil and fentanyl effect-site concentrations are predicted (three compartment model), and probabilities of no response to prodding, shaking, surrogates of painful stimuli and laryngoscopy are calculated using predicted effect-site drug concentration. Time-dependent changes in these calculated values are shown graphically. Recent development in anesthetic drug interaction studies are remarkable, and its application to clinical anesthesia with this Excel file is simple and helpful for clinical anesthesia.

Physiotherapy clinical educators' perceptions and experiences of clinical prediction rules.

PubMed

Knox, Grahame M; Snodgrass, Suzanne J; Rivett, Darren A

2015-12-01

Clinical prediction rules (CPRs) are widely used in medicine, but their application to physiotherapy practice is more recent and less widespread, and their implementation in physiotherapy clinical education has not been investigated. This study aimed to determine the experiences and perceptions of physiotherapy clinical educators regarding CPRs, and whether they are teaching CPRs to students on clinical placement. Cross-sectional observational survey using a modified Dillman method. Clinical educators (n=211, response rate 81%) supervising physiotherapy students from 10 universities across 5 states and territories in Australia. Half (48%) of respondents had never heard of CPRs, and a further 25% had never used CPRs. Only 27% reported using CPRs, and of these half (51%) were rarely if ever teaching CPRs to students in the clinical setting. However most respondents (81%) believed CPRs assisted in the development of clinical reasoning skills and few (9%) were opposed to teaching CPRs to students. Users of CPRs were more likely to be male (p<0.001), have post-professional qualifications (p=0.020), work in private practice (p<0.001), and work in the area of musculoskeletal physiotherapy (p<0.001) compared with non-users. The CPRs most commonly known, used and taught were the Ottawa Ankle Rule, the Ottawa Knee Rule, and Wells' Rule for Deep Vein Thrombosis. Students are unlikely to be learning about CPRs on clinical placement, as few clinical educators use them. Clinical educators will require training in CPRs and assistance in teaching them if students are to better learn about implementing CPRs in physiotherapy clinical practice. Copyright © 2015 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety.

PubMed

Bunford, Nora; Kujawa, Autumn; Fitzgerald, Kate D; Swain, James E; Hanna, Gregory L; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S; Phan, K Luan

2017-02-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7-19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post- CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment.

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety

PubMed Central

Kujawa, Autumn; Fitzgerald, Kate D.; Swain, James E.; Hanna, Gregory L.; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S.; Phan, K. Luan

2018-01-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7–19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post-CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment. PMID:27255517

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

NASA Astrophysics Data System (ADS)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

Predicting Fluid Responsiveness by Passive Leg Raising: A Systematic Review and Meta-Analysis of 23 Clinical Trials.

PubMed

Cherpanath, Thomas G V; Hirsch, Alexander; Geerts, Bart F; Lagrand, Wim K; Leeflang, Mariska M; Schultz, Marcus J; Groeneveld, A B Johan

2016-05-01

Passive leg raising creates a reversible increase in venous return allowing for the prediction of fluid responsiveness. However, the amount of venous return may vary in various clinical settings potentially affecting the diagnostic performance of passive leg raising. Therefore we performed a systematic meta-analysis determining the diagnostic performance of passive leg raising in different clinical settings with exploration of patient characteristics, measurement techniques, and outcome variables. PubMed, EMBASE, the Cochrane Database of Systematic Reviews, and citation tracking of relevant articles. Clinical trials were selected when passive leg raising was performed in combination with a fluid challenge as gold standard to define fluid responders and non-responders. Trials were included if data were reported allowing the extraction of sensitivity, specificity, and area under the receiver operating characteristic curve. Twenty-three studies with a total of 1,013 patients and 1,034 fluid challenges were included. The analysis demonstrated a pooled sensitivity of 86% (95% CI, 79-92), pooled specificity of 92% (95% CI, 88-96), and a summary area under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.98). Mode of ventilation, type of fluid used, passive leg raising starting position, and measurement technique did not affect the diagnostic performance of passive leg raising. The use of changes in pulse pressure on passive leg raising showed a lower diagnostic performance when compared with passive leg raising-induced changes in flow variables, such as cardiac output or its direct derivatives (sensitivity of 58% [95% CI, 44-70] and specificity of 83% [95% CI, 68-92] vs sensitivity of 85% [95% CI, 78-90] and specificity of 92% [95% CI, 87-94], respectively; p < 0.001). Passive leg raising retains a high diagnostic performance in various clinical settings and patient groups. The predictive value of a change in pulse pressure on passive leg raising is

Prediction of individual response to anticancer therapy: historical and future perspectives.

PubMed

Unger, Florian T; Witte, Irene; David, Kerstin A

2015-02-01

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

PubMed

Kalincik, Tomas; Manouchehrinia, Ali; Sobisek, Lukas; Jokubaitis, Vilija; Spelman, Tim; Horakova, Dana; Havrdova, Eva; Trojano, Maria; Izquierdo, Guillermo; Lugaresi, Alessandra; Girard, Marc; Prat, Alexandre; Duquette, Pierre; Grammond, Pierre; Sola, Patrizia; Hupperts, Raymond; Grand'Maison, Francois; Pucci, Eugenio; Boz, Cavit; Alroughani, Raed; Van Pesch, Vincent; Lechner-Scott, Jeannette; Terzi, Murat; Bergamaschi, Roberto; Iuliano, Gerardo; Granella, Franco; Spitaleri, Daniele; Shaygannejad, Vahid; Oreja-Guevara, Celia; Slee, Mark; Ampapa, Radek; Verheul, Freek; McCombe, Pamela; Olascoaga, Javier; Amato, Maria Pia; Vucic, Steve; Hodgkinson, Suzanne; Ramo-Tello, Cristina; Flechter, Shlomo; Cristiano, Edgardo; Rozsa, Csilla; Moore, Fraser; Luis Sanchez-Menoyo, Jose; Laura Saladino, Maria; Barnett, Michael; Hillert, Jan; Butzkueven, Helmut

2017-09-01

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of

Genetic and clinical predictors of ovarian response in assisted reproductive technology

NASA Astrophysics Data System (ADS)

Wiweko, B.; Damayanti, I.; Suryandari, D.; Natadisastra, M.; Pratama, G.; Sumapraja, K.; Meutia, K.; Iffanolia, P.; Harzief, A. K.; Hestiantoro, A.

2017-08-01

Several factors are known to influence ovarian response to rFSH stimulation such as age, antral follicle count (AFC), and basal FSH level, Mutation of allele Ser680Asn in FSHR gene was responsible to ovarian resistance toward exogenous FSH. The aim of this study is to develop a prediction model of ovarian response to COS in IVF. This study was a prospective cohort study. One hundred and thirteen women undergoing their first cycle of IVF in Yasmin IVF Clinic Jakarta were recruited to this study. Clinical datas included were age, BMI, and AFC. Basal FSH and E2 as well as serum AMH was measured from peripheral blood taken at second day of cycle. Bsr-1 enzyme is used to identify the polymorphism in exon 10 position 680 with RFLP technique. Three genotype polymorphism, Asn/Asn (255 bp ribbon), Asn/Ser (97 bp and 158 bp), and Ser/Ser (97 bp, 158 bp, and 255 bp). AFC has the highest predictor for ovarian response with AUC 0.922 (CI 95% 0.833-1.000). AMH also showed high predicting value (AUC 0.843 CI 95% 0.663-1.000). The multivariate analysis revealed combination of AFC, AMH, age, and basal FSH is a good model for ovarian response prediction (AUC=0.97). No significant relation between Asn/Asn, Asn/Ser, or Ser/Ser genotype FSHR polymorphism with ovarian response (p = 0.866) and total dose of rRSH (p = 0.08). This study showed that model combination of AFC, AMH, patient’s age and basal FSH are very good to predict number of mature oocytes.

Depression and anxiety predict sex-specific cortisol responses to interpersonal stress.

PubMed

Powers, Sally I; Laurent, Heidemarie K; Gunlicks-Stoessel, Meredith; Balaban, Susan; Bent, Eileen

2016-07-01

Clinical theories posit interpersonal stress as an important factor in the emergence and exacerbation of depression and anxiety, while neuroendocrine research confirms the association of these syndromes with dysregulation in a major stress response system, the hypothalamic-pituitary-adrenal (HPA) axis. However, the proposal that depression and anxiety symptoms and diagnoses are associated with problematic HPA responses to close relationship stress has not been directly tested. We examined 196 heterosexual dating couples' depression and anxiety symptoms and diagnoses, assessed with questionnaires and diagnostic interviews, in relation to cortisol responses to discussion of an unresolved relationship conflict. Participants provided seven salivary samples in anticipation of and directly following the discussion, and throughout an hour-long recovery period, which were assayed for cortisol. Multilevel models of the HPA response predicted by symptoms or diagnoses showed that women's depressive symptoms predicted attenuated cortisol levels, with a flatter response curve. In contrast, men's depression symptoms and women's anxiety symptoms and diagnoses predicted higher cortisol levels. These findings highlight the importance of examining sex differences in responses to interpersonal stressors for understanding HPA dysregulation in internalizing psychopathology. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictive factors for the placebo effect in clinical trials for dry eye: a pooled analysis of three clinical trials.

PubMed

Imanaka, Takahiro; Sato, Izumi; Tanaka, Shiro; Kawakami, Koji

2017-11-01

Placebo effect is one of the methodological difficulties in dry eye clinical trials. If we could elucidate the tendencies of the placebo response and find predictors, we could reduce the placebo response in clinical trials for dry eye. In this study, we investigated the predictive factors for the placebo effect in dry eye clinical trials. A total of 205 patients with dry eye assigned to the placebo arms of three placebo-controlled randomised clinical trials were analysed by simple and multivariable regression analysis. The corneal fluorescein (FL) staining score and dry eye symptoms were studied at week 4. The variables of interest included gender, age, complications of Sjögren's syndrome, Schirmer's test I value, tear break-up time and conjunctival hyperaemia score. We also conducted a stratified analysis according to the patients' age. Among all the studied endpoints, the baseline scores were significantly related to the corresponding placebo response. In addition, for the FL score and the dryness score, age was a significant predictor of the placebo response (p=0.04 and p<0.0001, respectively). Stratified analysis by age showed that patients more than 40 years of age are more likely to have a stronger placebo response in the FL and dryness scores. The baseline scores and age were predictive factors of the placebo response in frequently used endpoints, such as FL score or dryness symptoms. These patient characteristics can be controlled by study design, and our findings enable the design of more efficient placebo-controlled studies with good statistical power. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Prefrontal mediation of the reading network predicts intervention response in dyslexia.

PubMed

Aboud, Katherine S; Barquero, Laura A; Cutting, Laurie E

2018-04-01

A primary challenge facing the development of interventions for dyslexia is identifying effective predictors of intervention response. While behavioral literature has identified core cognitive characteristics of response, the distinction of reading versus executive cognitive contributions to response profiles remains unclear, due in part to the difficulty of segregating these constructs using behavioral outputs. In the current study we used functional neuroimaging to piece apart the mechanisms of how/whether executive and reading network relationships are predictive of intervention response. We found that readers who are responsive to intervention have more typical pre-intervention functional interactions between executive and reading systems compared to nonresponsive readers. These findings suggest that intervention response in dyslexia is influenced not only by domain-specific reading regions, but also by contributions from intervening domain-general networks. Our results make a significant gain in identifying predictive bio-markers of outcomes in dyslexia, and have important implications for the development of personalized clinical interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions.

PubMed

Carr, Brendan M; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C; Zhu, Wei; Shroyer, A Laurie

2016-01-01

Clinical risk models are commonly used to predict short-term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long-term mortality. The added value of long-term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long-term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Long-term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c-index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Mortality rates were 3%, 9%, and 17% at one-, three-, and five years, respectively (median follow-up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long-term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Long-term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long-term mortality risk can be accurately assessed and subgroups of higher-risk patients can be identified for enhanced follow-up care. More research appears warranted to refine long-term CABG clinical risk models. © 2015 The Authors. Journal of Cardiac Surgery Published by Wiley Periodicals, Inc.

Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

PubMed Central

Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

2015-01-01

Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: the PALMS study protocol.

PubMed

Jerosch-Herold, Christina; Shepstone, Lee; Wilson, Edward C F; Dyer, Tony; Blake, Julian

2014-02-07

Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions.

Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: the PALMS study protocol

PubMed Central

2014-01-01

Background Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. Methods/Design In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. Discussion This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions. PMID:24507749

Corticosteroid therapy in ulcerative colitis: Clinical response and predictors

PubMed Central

Li, Jin; Wang, Fan; Zhang, Hong-Jie; Sheng, Jian-Qiu; Yan, Wen-Feng; Ma, Min-Xing; Fan, Ru-Ying; Gu, Fang; Li, Chuan-Feng; Chen, Da-Fan; Zheng, Ping; Gu, Yu-Pei; Cao, Qian; Yang, Hong; Qian, Jia-Ming; Hu, Pin-Jin; Xia, Bing

2015-01-01

AIM: To evaluate clinical response to initial corticosteroid (CS) treatment in Chinese ulcerative colitis patients (UC) and identify predictors of clinical response. METHODS: Four hundred and twenty-three UC patients who were initially treated with oral or intravenous CS from 2007 to 2011 were retrospectively reviewed at eight inflammatory bowel disease centers in China, and 101 consecutive cases with one-year follow-up were analyzed further for clinical response and predictors. Short-term outcomes within one month were classified as primary response and primary non-response. Long-term outcomes within one year were classified as prolonged CS response, CS dependence and secondary non-response. CS refractoriness included primary and secondary non-response. Multivariate analyses were performed to identify predictors associated with clinical response. RESULTS: Within one month, 95.0% and 5.0% of the cases were classified into primary response and non-response, respectively. Within one year, 41.6% of cases were assessed as prolonged CS response, while 49.5% as CS dependence and 4.0% as secondary non-response. The rate of CS refractoriness was 8.9%, while the cumulative rate of surgery was 6.9% within one year. After multivariate analysis of all the variables, tenesmus was found to be a negative predictor of CS dependence (OR = 0.336; 95%CI: 0.147-0.768; P = 0.013) and weight loss as a predictor of CS refractoriness (OR = 5.662; 95%CI: 1.111-28.857; P = 0.040). After one-month treatment, sustained high Sutherland score (≥ 6) also predicted CS dependence (OR = 2.347; 95%CI: 0.935-5.890; P = 0.014). CONCLUSION: Tenesmus was a negative predictor of CS dependence, while weight loss and sustained high Sutherland score were strongly associated with poor CS response. PMID:25780299

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

PubMed Central

Meng, Xing; Jiang, Rongtao; Lin, Dongdong; Bustillo, Juan; Jones, Thomas; Chen, Jiayu; Yu, Qingbao; Du, Yuhui; Zhang, Yu; Jiang, Tianzi; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making. PMID:27177764

Quantification of hazard prediction ability at hazard prediction training (Kiken-Yochi Training: KYT) by free-response receiver-operating characteristic (FROC) analysis.

PubMed

Hashida, Masahiro; Kamezaki, Ryousuke; Goto, Makoto; Shiraishi, Junji

2017-03-01

The ability to predict hazards in possible situations in a general X-ray examination room created for Kiken-Yochi training (KYT) is quantified by use of free-response receiver-operating characteristics (FROC) analysis for determining whether the total number of years of clinical experience, involvement in general X-ray examinations, occupation, and training each have an impact on the hazard prediction ability. Twenty-three radiological technologists (RTs) (years of experience: 2-28), four nurses (years of experience: 15-19), and six RT students observed 53 scenes of KYT: 26 scenes with hazardous points (hazardous points are those that might cause injury to patients) and 27 scenes without points. Based on the results of these observations, we calculated the alternative free-response receiver-operating characteristic (AFROC) curve and the figure of merit (FOM) to quantify the hazard prediction ability. The results showed that the total number of years of clinical experience did not have any impact on hazard prediction ability, whereas recent experience with general X-ray examinations greatly influenced this ability. In addition, the hazard prediction ability varied depending on the occupations of the observers while they were observing the same scenes in KYT. The hazard prediction ability of the radiologic technology students was improved after they had undergone patient safety training. This proposed method with FROC observer study enabled the quantification and evaluation of the hazard prediction capability, and the application of this approach to clinical practice may help to ensure the safety of examinations and treatment in the radiology department.

Prediction of remission of depression with clinical variables, neuropsychological performance, and serotonergic/dopaminergic gene polymorphisms.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Asbun-Bojalil, Juan; Lira-Islas, Yuridia; Reyes-Ponce, Celia; Guàrdia-Olmos, Joan

2012-11-01

The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms on the prediction of depression remission after 12 weeks' treatment with fluoxetine. These variables have been studied as potential predictors of depression remission, but they present poor prognostic sensitivity and specificity by themselves. Seventy-two depressed patients were genotyped according to the aforementioned polymorphisms and were clinically and neuropsychologically assessed before a 12-week fluxetine treatment. Only the La allele of rs25531 polymorphism and the GG and AA forms of the val 108/158 Met polymorphism predict major depressive disorder remission after 12 weeks' treatment with fluoxetine. None of the clinical and neuropsychological variables studied predicted remission. Our results suggest that clinical and neuropsychological variables can initially predict early response to fluoxetine and mask the predictive role of genetic variables; but in remission, where clinical and neuropsychological symptoms associated with depression tend to disappear thanks to the treatment administered, the polymorphisms studied are the only variables in our model capable of predicting remission. However, placebo effects that are difficult to control require cautious interpretation of the results.

Human Papillomavirus DNA Methylation Predicts Response to Treatment Using Cidofovir and Imiquimod in Vulval Intraepithelial Neoplasia 3.

PubMed

Jones, Sadie E F; Hibbitts, Samantha; Hurt, Christopher N; Bryant, Dean; Fiander, Alison N; Powell, Ned; Tristram, Amanda J

2017-09-15

Purpose: Response rates to treatment of vulval intraepithelial neoplasia (VIN) with imiquimod and cidofovir are approximately 57% and 61%, respectively. Treatment is associated with significant side effects and, if ineffective, risk of malignant progression. Treatment response is not predicted by clinical factors. Identification of a biomarker that could predict response is an attractive prospect. This work investigated HPV DNA methylation as a potential predictive biomarker in this setting. Experimental Design: DNA from 167 cases of VIN 3 from the RT3 VIN clinical trial was assessed. HPV-positive cases were identified using Greiner PapilloCheck and HPV 16 type-specific PCR. HPV DNA methylation status was assessed in three viral regions: E2, L1/L2, and the promoter, using pyrosequencing. Results: Methylation of the HPV E2 region was associated with response to treatment. For cidofovir ( n = 30), median E2 methylation was significantly higher in patients who responded ( P ≤ 0.0001); E2 methylation >4% predicted response with 88.2% sensitivity and 84.6% specificity. For imiquimod ( n = 33), median E2 methylation was lower in patients who responded to treatment ( P = 0.03; not significant after Bonferroni correction); E2 methylation <4% predicted response with 70.6% sensitivity and 62.5% specificity. Conclusions: These data indicate that cidofovir and imiquimod may be effective in two biologically defined groups. HPV E2 DNA methylation demonstrated potential as a predictive biomarker for the treatment of VIN with cidofovir and may warrant investigation in a biomarker-guided clinical trial. Clin Cancer Res; 23(18); 5460-8. ©2017 AACR . ©2017 American Association for Cancer Research.

Electroconvulsive therapy in treatment-resistant schizophrenia: prediction of response and the nature of symptomatic improvement.

PubMed

Chanpattana, Worrawat; Sackeim, Harold A

2010-12-01

The clinical features of patients with schizophrenia who respond to electroconvulsive therapy (ECT) are uncertain. There is a longstanding belief that the duration of illness and/or the presence of affective symptoms associate with good prognosis. There is also little information on the nature of symptomatic improvement with this treatment. We examined the demographic and clinical history features associated with response, the symptom profile predictive of response, and the profile of symptomatic improvement. Using a standardized protocol, 253 patients with treatment-resistant schizophrenia were prospectively treated with a combination of ECT and flupenthixol. Of this group, 138 patients (54.5%) met the response criteria. Independence of sex, longer duration of current episode, and greater severity of baseline negative symptoms were predictive of poorer outcome. Duration of illness had weak relations with outcome only among females. There were marked sex differences in other clinical features and symptoms associated with response. In contrast, no sex differences were observed in the nature of symptomatic improvement. Treatment resulted in marked improvement in specific positive symptoms, with an intermediate effect on affective symptoms and no effect or worsening of specific negative symptoms. The findings challenge recommendations that long duration of illness or absence of affective symptoms portends poor response to ECT in patients with treatment-resistant schizophrenia. Sex may play a critical role in determining the features of the illness that predict outcome.

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

PubMed

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

2017-05-01

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

DNA Mismatch Repair Deficiency in Rectal Cancer: Benchmarking Its Impact on Prognosis, Neoadjuvant Response Prediction, and Clinical Cancer Genetics.

PubMed

de Rosa, Nicole; Rodriguez-Bigas, Miguel A; Chang, George J; Veerapong, Jula; Borras, Ester; Krishnan, Sunil; Bednarski, Brian; Messick, Craig A; Skibber, John M; Feig, Barry W; Lynch, Patrick M; Vilar, Eduardo; You, Y Nancy

2016-09-01

DNA mismatch repair deficiency (dMMR) hallmarks consensus molecular subtype 1 of colorectal cancer. It is being routinely tested, but little is known about dMMR rectal cancers. The efficacy of novel treatment strategies cannot be established without benchmarking the outcomes of dMMR rectal cancer with current therapy. We aimed to delineate the impact of dMMR on prognosis, the predicted response to fluoropyrimidine-based neoadjuvant therapy, and implications of germline alterations in the MMR genes in rectal cancer. Between 1992 and 2012, 62 patients with dMMR rectal cancers underwent multimodality therapy. Oncologic treatment and outcomes as well as clinical genetics work-up were examined. Overall and rectal cancer-specific survival were calculated by the Kaplan-Meier method. The median age at diagnosis was 41 years. MMR deficiency was most commonly due to alterations in MSH2 (53%) or MSH6 (23%). After a median follow-up of 6.8 years, the 5-year rectal cancer-specific survival was 100% for stage I and II, 85.1% for stage III, and 60.0% for stage IV disease. Fluoropyrimidine-based neoadjuvant chemoradiation was associated with a complete pathologic response rate of 27.6%. The extent of surgical resection was influenced by synchronous colonic disease at presentation, tumor height, clinical stage, and pelvic radiation. An informed decision for a limited resection focusing on proctectomy did not compromise overall survival. Five of the 11 (45.5%) deaths during follow-up were due to extracolorectal malignancies. dMMR rectal cancer had excellent prognosis and pathologic response with current multimodality therapy including an individualized surgical treatment plan. Identification of a dMMR rectal cancer should trigger germline testing, followed by lifelong surveillance for both colorectal and extracolorectal malignancies. We herein provide genotype-specific outcome benchmarks for comparison with novel interventions. © 2016 by American Society of Clinical Oncology.

Healthcare provider perceptions of clinical prediction rules

PubMed Central

Richardson, Safiya; Khan, Sundas; McCullagh, Lauren; Kline, Myriam; Mann, Devin; McGinn, Thomas

2015-01-01

Objectives To examine internal medicine and emergency medicine healthcare provider perceptions of usefulness of specific clinical prediction rules. Setting The study took place in two academic medical centres. A web-based survey was distributed and completed by participants between 1 January and 31 May 2013. Participants Medical doctors, doctors of osteopathy or nurse practitioners employed in the internal medicine or emergency medicine departments at either institution. Primary and secondary outcome measures The primary outcome was to identify the clinical prediction rules perceived as most useful by healthcare providers specialising in internal medicine and emergency medicine. Secondary outcomes included comparing usefulness scores of specific clinical prediction rules based on provider specialty, and evaluating associations between usefulness scores and perceived characteristics of these clinical prediction rules. Results Of the 401 healthcare providers asked to participate, a total of 263 (66%), completed the survey. The CHADS2 score was chosen by most internal medicine providers (72%), and Pulmonary Embolism Rule-Out Criteria (PERC) score by most emergency medicine providers (45%), as one of the top three most useful from a list of 24 clinical prediction rules. Emergency medicine providers rated their top three significantly more positively, compared with internal medicine providers, as having a better fit into their workflow (p=0.004), helping more with decision-making (p=0.037), better fitting into their thought process when diagnosing patients (p=0.001) and overall, on a 10-point scale, more useful (p=0.009). For all providers, the perceived qualities of useful at point of care, helps with decision making, saves time diagnosing, fits into thought process, and should be the standard of clinical care correlated highly (≥0.65) with overall 10-point usefulness scores. Conclusions Healthcare providers describe clear preferences for certain clinical prediction

The role of interim 18F-FDG PET/CT in prediction of response to ipilimumab treatment in metastatic melanoma.

PubMed

Sachpekidis, Christos; Anwar, Hoda; Winkler, Julia; Kopp-Schneider, Annette; Larribere, Lionel; Haberkorn, Uwe; Hassel, Jessica C; Dimitrakopoulou-Strauss, Antonia

2018-07-01

The aim of the present study was to assess the value of interim 18 F-FDG PET/CT performed after the first two cycles of ipilimumab treatment in the prediction of the final clinical response to this type of immunotherapy. The study group comprised 41 patients with unresectable metastatic melanoma scheduled for ipilimumab therapy. Whole-body 18 F-FDG PET/CT was performed before the start of ipilimumab treatment (baseline PET/CT) and after the initial two cycles of ipilimumab treatment (interim PET/CT). Evaluation of patient response to treatment was based on the European Organization for Research and Treatment of Cancer (EORTC) 1999 criteria for PET as well as the recently proposed PET Response Evaluation Criteria for Immunotherapy (PERCIMT). The patients' best clinical response, assessed at a median of 21.4 months (range 6.3-41.9 months) was used as reference. According to their best clinical response, the patients were divided into two groups: those showing clinical benefit (CB) including stable disease, partial response and complete response (31 patients), and those showing no clinical benefit (no-CB including progressive disease (10 patients). According to the EORTC criteria, interim PET/CT demonstrated progressive metabolic disease (PMD) in 20 patients, stable metabolic disease (SMD) in 11 patients, partial metabolic response (PMR) in 8 patients, and complete metabolic response (CMR) in 2 patients. According to the PERCIMT, interim PET/CT demonstrated PMD in 9 patients, SMD in 24 patients, PMR in 6 patients and CMR in 2 patients. On the basis of the interim PET, the patients were divided in a similar manner to the division according to clinical response into those showing metabolic benefit (MB) including SMD, PMR and CMR, and those showing no metabolic benefit (no-MB) including PMD. According to this dichotomization, the EORTC criteria showed a sensitivity (correctly predicting CB) of 64.5%, a specificity (correctly predicting no-CB) of 90.0%, a positive

Prospective assessment of a gene signature potentially predictive of clinical benefit in metastatic melanoma patients following MAGE-A3 immunotherapeutic (PREDICT).

PubMed

Saiag, P; Gutzmer, R; Ascierto, P A; Maio, M; Grob, J-J; Murawa, P; Dreno, B; Ross, M; Weber, J; Hauschild, A; Rutkowski, P; Testori, A; Levchenko, E; Enk, A; Misery, L; Vanden Abeele, C; Vojtek, I; Peeters, O; Brichard, V G; Therasse, P

2016-10-01

Genomic profiling of tumor tissue may aid in identifying predictive or prognostic gene signatures (GS) in some cancers. Retrospective gene expression profiling of melanoma and non-small-cell lung cancer led to the characterization of a GS associated with clinical benefit, including improved overall survival (OS), following immunization with the MAGE-A3 immunotherapeutic. The goal of the present study was to prospectively evaluate the predictive value of the previously characterized GS. An open-label prospective phase II trial ('PREDICT') in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Of 123 subjects who received the MAGE-A3 immunotherapeutic, 71 (58.7%) displayed the predictive GS (GS+). The 1-year OS rate was 83.1%/83.3% in the GS+/GS- populations. The rate of progression-free survival at 12 months was 5.8%/4.1% in GS+/GS- patients. The median time-to-treatment failure was 2.7/2.4 months (GS+/GS-). There was one complete response (GS-) and two partial responses (GS+). The MAGE-A3 immunotherapeutic was similarly immunogenic in both populations and had a clinically acceptable safety profile. Treatment of patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma with the MAGE-A3 immunotherapeutic demonstrated an overall 1-year OS rate of 83.5%. GS- and GS+ patients had similar 1-year OS rates, indicating that in this study, GS was not predictive of outcome. Unexpectedly, the objective response rate was lower in this study than in other studies carried out in the same setting with the MAGE-A3 immunotherapeutic. Investigation of a GS to predict clinical benefit to adjuvant MAGE-A3 immunotherapeutic treatment is ongoing in another melanoma study.This study is registered at www.clinicatrials.gov NCT00942162. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Can we predict the blood pressure response to renal denervation?

PubMed Central

Fink, Gregory D.; Phelps, Jeremiah T.

2016-01-01

Renal denervation (RDN) is a new therapy used to treat drug-resistant hypertension in the clinical setting. Published human trials show substantial inter-individual variability in the blood pressure (BP) response to RDN, even when technical aspects of the treatment are standardized as much as possible between patients. Widespread acceptance of RDN for treating hypertension will require accurate identification of patients likely to respond to RDN with a fall in BP that is clinically significant in magnitude, well-maintained over time and does not cause adverse consequences. In this paper we review and evaluate clinical studies that address possible predictors of the BP response to RDN. We conclude that only one generally reliable predictor has been identified to date, namely pre-RDN BP level, although there is some evidence for a few other factors. Experimental interventions in laboratory animals provide the opportunity to explore potential predictors that are difficult to investigate in human patients. Therefore we also describe results (from our lab and others) with RDN in spontaneously hypertensive rats. Since virtually all patients receiving RDN are taking three or more antihypertensive drugs, a particular focus of our work was on how ongoing antihypertensive drug treatment might alter the BP response to RDN. We conclude that patient age (or duration of hypertension) and concomitant treatment with certain drugs can affect the blood pressure response to RDN and that this information could help predict a favorable clinical response. PMID:27530600

Responsiveness of clinical tests for people with neck pain.

PubMed

Jørgensen, René; Ris, Inge; Juhl, Carsten; Falla, Deborah; Juul-Kristensen, Birgit

2017-12-28

Responsiveness of a clinical test is highly relevant in order to evaluate the effect of a given intervention. However, the responsiveness of clinical tests for people with neck pain has not been adequately evaluated. The objective of the present study was to examine the responsiveness of four clinical tests which are low cost and easy to perform in a clinical setting, including the craniocervical flexion test, cervical active range of movement, test for the cervical extensors and pressure pain threshold testing. This study is a secondary analysis of data collected in a previously published randomised controlled trial. Participants were randomized to either physical training, exercises and pain education combined or pain education only. Participants were tested on the clinical tests at baseline and at 4-month follow-up. An anchor-based approach using Receiver Operator Characteristics (ROC) curves was used to evaluate responsiveness of the clinical tests. The Neck Disability Index was used to discriminate between those who had improved and those who were unchanged at the 4-month follow-up. Minimum Clinically Important Difference (MCID), together with sensitivity, specificity, positive and negative predictive values, in addition to positive and negative likelihood ratios were calculated. In total, 164 participants completed the 4 month follow up. One-hundred forty four participants were classified as unchanged whereas 20 patients were considered to be improved. Twenty-six participants didn't complete all of the clinical tests, leaving a total of 138 to be included for analyses. Area Under Curve (AUC) ranged from 0.50-0.62 for the clinical tests, and were all below an acceptable level. MCID was generally large, and the corresponding sensitivity and specificity was low with sensitivity ranging from 20 to 60%, and specificity from 54 to 86%. LR+ (0.8-2.07) and LR- (0.7-1.1) showed low diagnostic value for all variables, with PPV ranging from 12.1 to 26.1 and NPV ranging

Prediction of chemo-response in serous ovarian cancer.

PubMed

Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J

2016-10-19

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

A review of statistical updating methods for clinical prediction models.

PubMed

Su, Ting-Li; Jaki, Thomas; Hickey, Graeme L; Buchan, Iain; Sperrin, Matthew

2018-01-01

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.

A Bayesian predictive two-stage design for phase II clinical trials.

PubMed

Sambucini, Valeria

2008-04-15

In this paper, we propose a Bayesian two-stage design for phase II clinical trials, which represents a predictive version of the single threshold design (STD) recently introduced by Tan and Machin. The STD two-stage sample sizes are determined specifying a minimum threshold for the posterior probability that the true response rate exceeds a pre-specified target value and assuming that the observed response rate is slightly higher than the target. Unlike the STD, we do not refer to a fixed experimental outcome, but take into account the uncertainty about future data. In both stages, the design aims to control the probability of getting a large posterior probability that the true response rate exceeds the target value. Such a probability is expressed in terms of prior predictive distributions of the data. The performance of the design is based on the distinction between analysis and design priors, recently introduced in the literature. The properties of the method are studied when all the design parameters vary.

What predicts performance during clinical psychology training?

PubMed Central

Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

2014-01-01

Objectives While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Design Longitudinal cross-sectional study using prospective and retrospective data. Method Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Results Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Conclusions Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. Practitioner points While referee and selection interview ratings did not predict performance during training, they may be useful in screening out unsuitable candidates at the application stage High school final academic performance

Clinical and biological predictors of response to electroconvulsive therapy (ECT): a review.

PubMed

Pinna, Martina; Manchia, Mirko; Oppo, Rossana; Scano, Filomena; Pillai, Gianluca; Loche, Anna Paola; Salis, Piergiorgio; Minnai, Gian Paolo

2018-03-16

Electroconvulsive therapy (ECT), developed in the 30's by Bini and Cerletti, remains a key element of the therapeutic armamentarium in psychiatry, particularly for severe and life-threatening psychiatric symptoms. However, despite its well-established clinical efficacy, the prescription of ECT has declined constantly over the years due to concerns over its safety (cognitive side effects) and an increasingly negative public perception. As for other treatments in the field of psychiatry, ECT is well suited to a personalized approach that would increment its efficacy, as well as reducing the impact of side effects. This should be based on the priori identification of sub-populations of patients sharing common clinical and biological features that predict a good response to ECT. In this review we have selectively reviewed the evidence on clinical and biological predictors of ECT response. Clinical features such as an older age, presence of psychotic and melancholic depression, a high severity of suicide behavior, and speed of response, appear to be shared by ECT good responders with depressive symptoms. In mania, a greater severity of the index episode, and a reduction of whole brain cortical blood flow are associated with ECT good response. Biological determinants of ECT response in depressive patients are the presence of pre-treatment hyperconnectivity between key areas of brain circuitry of depression, as well as of reduced glutamine/glutamate levels, particularly in the anterior cingulated cortex (ACC). Furthermore, pre ECT high plasma homovanillic acid (HVA) levels, as well as of tumor necrosis factor (TNF)-α, and low pre-ECT levels of S-100B protein, appear to predict ECT response. Finally, polymorphisms within the genes encoding for the brain-derived neurotrophic factor (BDNF), the dopamine 2 receptor gene (DRD2), the dopamine receptor 3 gene (DRD3), the cathechol-o-methyltransferase (COMT), the serotonin-transporter (5-HTT), the 5-hydroxytryptamine 2A receptor

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

PubMed

Patel, Meenal J; Andreescu, Carmen; Price, Julie C; Edelman, Kathryn L; Reynolds, Charles F; Aizenstein, Howard J

2015-10-01

Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley

Response inhibition predicts poor antidepressant treatment response in very old depressed patients.

PubMed

Sneed, Joel R; Roose, Steven P; Keilp, John G; Krishnan, K Ranga Rama; Alexopoulos, George S; Sackeim, Harold A

2007-07-01

There have been mixed findings regarding the prognostic significance of age of onset, executive dysfunction, and hyperintensity burden on treatment outcome in late-life depression. Growth curve models were fit to data from the only 8-week, double-blind, placebo controlled trial of citalopram (20-40 mg/day) in patients aged 75 years and older with unipolar depression. Baseline assessment included Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (to determine age at onset), Stroop Color-Word Test (to assess the response inhibition component of execution dysfunction), and structural magnetic resonance imaging (to determine hyperintensity burden). In the citalopram condition, patients with response inhibition (most impaired quartile) scored higher at endpoint than those without response inhibition. There were no effects for age of onset or hyperintensity load on response in the citalopram condition. In the placebo condition, patients with early-onset depression had higher depression scores at endpoint than patients with late-onset depression. Only response inhibition, a fundamental executive function, predicted poor treatment response to antidepressant medication. Although patients with response inhibition also showed deficits in reaction time, adjusting for reaction time in our final response inhibition model did not substantively change the findings.

What predicts performance during clinical psychology training?

PubMed

Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

2014-06-01

While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Longitudinal cross-sectional study using prospective and retrospective data. Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. © 2013 The Authors. British Journal of Clinical Psychology published by John Wiley & Sons Ltd on behalf of the British Psychological Society.

Prediction of anti-cancer drug response by kernelized multi-task learning.

PubMed

Tan, Mehmet

2016-10-01

Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim. Data produced by using cancer cell lines provide a test bed for machine learning algorithms that try to predict the response of cancer cells to different agents. The potential use of these algorithms in drug discovery/repositioning and personalized treatments motivated us in this study to work on predicting drug response by exploiting the recent pharmacogenomic databases. We aim to improve the prediction of drug response of cancer cell lines. We propose to use a method that employs multi-task learning to improve learning by transfer, and kernels to extract non-linear relationships to predict drug response. The method outperforms three state-of-the-art algorithms on three anti-cancer drug screen datasets. We achieved a mean squared error of 3.305 and 0.501 on two different large scale screen data sets. On a recent challenge dataset, we obtained an error of 0.556. We report the methodological comparison results as well as the performance of the proposed algorithm on each single drug. The results show that the proposed method is a strong candidate to predict drug response of cancer cell lines in silico for pre-clinical studies. The source code of the algorithm and data used can be obtained from http://mtan.etu.edu.tr/Supplementary/kMTrace/. Copyright © 2016 Elsevier B.V. All rights reserved.

Early improvement with pregabalin predicts endpoint response in patients with generalized anxiety disorder: an integrated and predictive data analysis.

PubMed

Montgomery, Stuart A; Lyndon, Gavin; Almas, Mary; Whalen, Ed; Prieto, Rita

2017-01-01

Generalized anxiety disorder (GAD), a common mental disorder, has several treatment options including pregabalin. Not all patients respond to treatment; quickly determining which patients will respond is an important treatment goal. Patient-level data were pooled from nine phase II and III randomized, double-blind, short-term, placebo-controlled trials of pregabalin for the treatment of GAD. Efficacy outcomes included the change from baseline in the Hamilton Anxiety Scale (HAM-A) total score and psychic and somatic subscales. Predictive modelling assessed baseline characteristics and early clinical responses to determine those predictive of clinical improvement at endpoint. A total of 2155 patients were included in the analysis (1447 pregabalin, 708 placebo). Pregabalin significantly improved the HAM-A total score compared with the placebo at endpoint, treatment difference (95% confidence interval), -2.61 (-3.21 to -2.01), P<0.0001. Pregabalin significantly improved HAM-A psychic and somatic scores compared with placebo, -1.52 (-1.85 to -1.18), P<0.0001, and -1.10 (-1.41 to -0.80), P<0.0001, respectively. Response to pregabalin in the first 1-2 weeks (≥20 or ≥30% improvement in HAM-A total, psychic or somatic score) was predictive of an endpoint greater than or equal to 50% improvement in the HAM-A total score. Pregabalin is an effective treatment option for patients with GAD. Patients with early response to pregabalin are more likely to respond significantly at endpoint.

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Infants, mothers, and dyadic contributions to stability and prediction of social stress response at 6 months.

PubMed

Provenzi, Livio; Olson, Karen L; Montirosso, Rosario; Tronick, Ed

2016-01-01

The study of infants' interactive style and social stress response to repeated stress exposures is of great interest for developmental and clinical psychologists. Stable maternal and dyadic behavior is critical to sustain infants' development of an adaptive social stress response, but the association between infants' interactive style and social stress response has received scant attention in previous literature. In the present article, overtime stability of infant, maternal, and dyadic behaviors was measured across 2 social stress (i.e., Face-to-Face Still-Face, FFSF) exposures, separated by 15 days. Moreover, infant, maternal, and dyadic behaviors were simultaneously assessed as predictors of infants' social stress to both FFSF exposures. Eighty-one mother-infant dyads underwent the FFSF twice, at 6 months (Exposure 1: the first social stress) and at 6 months and 15 days (Exposure 2: repeated social stress). Infant and mother behavior and dyadic synchrony were microanalytically coded. Overall, individual behavioral stability emerged between FFSF exposures. Infants' response to the first stress was predicted by infant behavior during Exposure 1 Play. Infants' response to the repeated social stress was predicted by infants' response to the first exposure to the Still-Face and by infants' behavior and dyadic synchrony during Exposure 2 Play. Findings reveal stability for individual, but not for dyadic, behavior between 2 social stress exposures at 6 months. Infants' response to repeated social stress was predicted by infants' earlier stress response, infants' own behavior in play, and dyadic synchrony. No predictive effects of maternal behavior were found. Insights for research and clinical work are discussed. (c) 2015 APA, all rights reserved).

Neuropsychological impairments predict the clinical course in schizophrenia.

PubMed

Wölwer, Wolfgang; Brinkmeyer, Jürgen; Riesbeck, Mathias; Freimüller, Lena; Klimke, Ansgar; Wagner, Michael; Möller, Hans-Jürgen; Klingberg, Stefan; Gaebel, Wolfgang

2008-11-01

To add to the open question whether cognitive impairments predict clinical outcome in schizophrenia, a sample of 125 first episode patients was assessed at the onset and over one year of controlled long-term treatment within a study of the German Research Network on Schizophrenia. No relapse according to predefined criteria occurred within the first year, but a total of 29 patients fulfilled post-hoc criteria of "clinical deterioration". Impairments in cognitive functioning assessed by the Trail-Making Test B at the onset of long-term treatment differentiated between patients with vs. without later clinical deterioration and proved to be a significant predictor of the clinical course in a regression analysis outperforming initial clinical status as predictor. However, low sensitivity (72%) and specificity (51%) limit possibilities of a transfer to individual predictions. As a linear combination of neuropsychological and psychopathological variables obtained highest predictive validity, such a combination may improve the prediction of the course of schizophrenic disorders and may ultimately lead to a more efficient and comprehensive treatment planning.

Promoter hypermethylation of mismatch repair gene hMLH1 predicts the clinical response of malignant astrocytomas to nitrosourea.

PubMed

Fukushima, Takao; Katayama, Yoichi; Watanabe, Takao; Yoshino, Atsuo; Ogino, Akiyoshi; Ohta, Takashi; Komine, Chiaki

2005-02-15

In certain types of human cancers, transcriptional inactivation of hMLH1 by promoter hypermethylation plays a causal role in the loss of mismatch repair functions that modulate cytotoxic pathways in response to DNA-damaging agents. The aim of the present study was to investigate the role of promoter methylation of the hMLH1 gene in malignant astrocytomas. We examined the hMLH1 promoter methylation in a homogeneous cohort of patients with 41 malignant astrocytomas treated by 1-(4-amino-2-methyl-5-pyrimidinyl)methyl-3-2(2-chloroethyl)-3-nitrosourea chemotherapy in combination with radiation and interferon therapy, and assessed the correlation of such methylation with clinical outcome. hMLH1 promoter methylation was found in 6 (15%) of the 41 newly diagnosed malignant astrocytomas. Hypermethylation of the hMLH1 promoter corresponded closely with a loss of immunohistochemical staining for hMLH1 protein (P = 0.0013). Patients with hMLH1-methylated tumors displayed a greater chance of responding to adjuvant therapy as compared with those with hMLH1-unmethylated tumors (P = 0.0150). The presence of hMLH1 hypermethylation was significantly associated with a longer progression-free survival on both univariate analysis (P = 0.0340) and multivariate analysis (P = 0.0161). The present study identified hMLH1 methylation status as a predictor of the clinical response of malignant astrocytomas to chloroethylnitrosourea-based adjuvant therapy. The findings obtained suggest that determination of the methylation status of hMLH1 could provide a potential basis for designing rational chemotherapeutic strategies, as well as for predicting prognosis.

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

PubMed

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

2016-07-01

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

Prediction of HR/BP response to the spontaneous breathing trial by fluctuation dissipation theory

NASA Astrophysics Data System (ADS)

Chen, Man

2014-03-01

We applied the non-equilibrium fluctuation dissipation theorem to predict how critically-ill patients respond to treatment, based on both heart rate data and blood pressure data collected by standard hospital monitoring devices. The non-equilibrium fluctuation dissipation theorem relates the response of a system to a perturbation to the fluctuations in the stationary state of the system. It is shown that the response of patients to a standard procedure performed on patients, the spontaneous breathing trial (SBT), can be predicted by the non-equilibrium fluctuation dissipation approach. We classify patients into different groups according to the patients' characteristics. For each patient group, we extend the fluctuation dissipation theorem to predict interactions between blood pressure and beat-to-beat dynamics of heart rate in response to a perturbation (SBT), We also extract the form of the perturbation function directly from the physiological data, which may help to reduce the prediction error. We note this method is not limited to the analysis of the heart rate dynamics, but also can be applied to analyze the response of other physiological signals to other clinical interventions.

Prediction of clinical behaviour and treatment for cancers.

PubMed

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

2003-01-01

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

Distinct responses to predictable and unpredictable threat in anxiety pathologies: effect of panic attack.

PubMed

Grillon, Christian; O'Connell, Katherine; Lieberman, Lynne; Alvarez, Gabriella; Geraci, Marilla; Pine, Daniel S; Ernst, Monique

2017-10-01

Delineating specific clinical phenotypes of anxiety disorders is a crucial step toward better classification and understanding of these conditions. The present study sought to identify differential aversive responses to predictable and unpredictable threat of shock in healthy comparisons and in non-medicated anxiety patients with and without a history of panic attacks (PAs). 143 adults (72 healthy controls; 71 patients with generalized anxiety disorder (GAD) or/and social anxiety disorder (SAD), 24 with and 47 without PAs) were exposed to three conditions: 1) predictable shocks signaled by a cue, 2) unpredictable shocks, and 3) no shock. Startle magnitude was used to assess aversive responses. Across disorders, a PA history was specifically associated with hypersensitivity to unpredictable threat. By disorder, SAD was associated with hypersensitivity to predictable threat, whereas GAD was associated with exaggerated baseline startle. These results identified three physiological patterns. The first is hypersensitivity to unpredictable threat in individuals with PAs. The second is hypersensitivity to predictable threat, which characterizes SAD. The third is enhanced baseline startle in GAD, which may reflect propensity for self-generated anxious thoughts in the absence of imminent danger. These results inform current thinking by linking specific clinical features to particular physiology profiles.

Supraspinal Control Predicts Locomotor Function and Forecasts Responsiveness to Training after Spinal Cord Injury

PubMed Central

Field-Fote, Edelle C.; Yang, Jaynie F.; Basso, D. Michele; Gorassini, Monica A.

2017-01-01

Abstract Restoration of walking ability is an area of great interest in the rehabilitation of persons with spinal cord injury. Because many cortical, subcortical, and spinal neural centers contribute to locomotor function, it is important that intervention strategies be designed to target neural elements at all levels of the neuraxis that are important for walking ability. While to date most strategies have focused on activation of spinal circuits, more recent studies are investigating the value of engaging supraspinal circuits. Despite the apparent potential of pharmacological, biological, and genetic approaches, as yet none has proved more effective than physical therapeutic rehabilitation strategies. By making optimal use of the potential of the nervous system to respond to training, strategies can be developed that meet the unique needs of each person. To complement the development of optimal training interventions, it is valuable to have the ability to predict future walking function based on early clinical presentation, and to forecast responsiveness to training. A number of clinical prediction rules and association models based on common clinical measures have been developed with the intent, respectively, to predict future walking function based on early clinical presentation, and to delineate characteristics associated with responsiveness to training. Further, a number of variables that are correlated with walking function have been identified. Not surprisingly, most of these prediction rules, association models, and correlated variables incorporate measures of volitional lower extremity strength, illustrating the important influence of supraspinal centers in the production of walking behavior in humans. PMID:27673569

Predicting disease onset in clinically healthy people

PubMed Central

2016-01-01

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

Regulatory T cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to intravenous immunoglobulin therapy.

PubMed

Maddur, Mohan S; Stephen-Victor, Emmanuel; Das, Mrinmoy; Prakhar, Praveen; Sharma, Varun K; Singh, Vikas; Rabin, Magalie; Trinath, Jamma; Balaji, Kithiganahalli N; Bolgert, Francis; Vallat, Jean-Michel; Magy, Laurent; Kaveri, Srini V; Bayry, Jagadeesh

2017-03-20

Intravenous immunoglobulin (IVIG) is a polyspecific pooled immunoglobulin G preparation and one of the commonly used therapeutics for autoimmune diseases including those of neurological origin. A recent report in murine model proposed that IVIG expands regulatory T (T reg ) cells via induction of interleukin 33 (IL-33). However, translational insight on these observations is lacking. Ten newly diagnosed Guillain-Barré syndrome (GBS) patients were treated with IVIG at the rate of 0.4 g/kg for three to five consecutive days. Clinical evaluation for muscular weakness was performed by Medical Research Council (MRC) and modified Rankin scoring (MRS) system. Heparinized blood samples were collected before and 1, 2, and 4-5 weeks post-IVIG therapy. Peripheral blood mononuclear cells were stained for surface CD4 and intracellular Foxp3, IFN-γ, and tumor necrosis factor alpha (TNF-α) and were analyzed by flow cytometry. IL-33 and prostaglandin E2 in the plasma were measured by ELISA. The fold changes in plasma IL-33 at week 1 showed no correlation with the MRC and MRS scores at weeks 1, 2, and ≥4 post-IVIG therapy. Clinical recovery following IVIG therapy appears to be associated with T reg cell response. Contrary to murine study, there was no association between the fold changes in IL-33 at week 1 and T reg cell frequency at weeks 1, 2, and ≥4 post-IVIG therapy. T reg cell-mediated clinical response to IVIG therapy in GBS patients was associated with reciprocal regulation of effector T cells-expressing TNF-α. T reg cell expansion by IVIG in patients with autoimmune diseases lack correlation with IL-33. T reg cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to IVIG therapy.

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

PubMed

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

2018-05-19

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

Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study

PubMed Central

Erdman, Laura K.; D’Acremont, Valérie; Hayford, Kyla; Kilowoko, Mary; Kyungu, Esther; Hongoa, Philipina; Alamo, Leonor; Streiner, David L.; Genton, Blaise; Kain, Kevin C.

2015-01-01

Background Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO) has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance. Methods We studied children with WHO-defined clinical pneumonia (n = 155) within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30), other infiltrates (n = 31), or normal chest x-ray (n = 94). Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis. Results Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5–98.8), 80.8% specificity (72.6–87.1), positive likelihood ratio 4.9 (3.4–7

Early physiological response to intensive care as a clinically relevant approach to predicting the outcome in severe acute pancreatitis.

PubMed

Flint, Richard; Windsor, John A

2004-04-01

The physiological response to treatment is a better predictor of outcome in acute pancreatitis than are traditional static measures. Retrospective diagnostic test study. The criterion standard was Organ Failure Score (OFS) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score at the time of hospital admission. Intensive care unit of a tertiary referral center, Auckland City Hospital, Auckland, New Zealand. Consecutive sample of 92 patients (60 male, 32 female; median age, 61 years; range, 24-79 years) with severe acute pancreatitis. Twenty patients were not included because of incomplete data. The cause of pancreatitis was gallstones (42%), alcohol use (27%), or other (31%). At hospital admission, the mean +/- SD OFS was 8.1 +/- 6.1, and the mean +/- SD APACHE II score was 19.9 +/- 8.2. All cases were managed according to a standardized protocol. There was no randomization or testing of any individual interventions. Survival and death. There were 32 deaths (pretest probability of dying was 35%). The physiological response to treatment was more accurate in predicting the outcome than was OFS or APACHE II score at hospital admission. For example, 17 patients had an initial OFS of 7-8 (posttest probability of dying was 58%); after 48 hours, 7 had responded to treatment (posttest probability of dying was 28%), and 10 did not respond (posttest probability of dying was 82%). The effect of the change in OFS and APACHE II score was graphically depicted by using a series of logistic regression equations. The resultant sigmoid curve suggests that there is a midrange of scores (the steep portion of the graph) within which the probability of death is most affected by the response to intensive care treatment. Measuring the initial severity of pancreatitis combined with the physiological response to intensive care treatment is a practical and clinically relevant approach to predicting death in patients with severe acute pancreatitis.

Prediction of Response to Preoperative Chemoradiotherapy in Rectal Cancer by Multiplex Kinase Activity Profiling

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

Folkvord, Sigurd; Flatmark, Kjersti; Department of Cancer and Surgery, Norwegian Radium Hospital, Oslo University Hospital

2010-10-01

Purpose: Tumor response of rectal cancer to preoperative chemoradiotherapy (CRT) varies considerably. In experimental tumor models and clinical radiotherapy, activity of particular subsets of kinase signaling pathways seems to predict radiation response. This study aimed to determine whether tumor kinase activity profiles might predict tumor response to preoperative CRT in locally advanced rectal cancer (LARC). Methods and Materials: Sixty-seven LARC patients were treated with a CRT regimen consisting of radiotherapy, fluorouracil, and, where possible, oxaliplatin. Pretreatment tumor biopsy specimens were analyzed using microarrays with kinase substrates, and the resulting substrate phosphorylation patterns were correlated with tumor response to preoperative treatmentmore » as assessed by histomorphologic tumor regression grade (TRG). A predictive model for TRG scores from phosphosubstrate signatures was obtained by partial-least-squares discriminant analysis. Prediction performance was evaluated by leave-one-out cross-validation and use of an independent test set. Results: In the patient population, 73% and 15% were scored as good responders (TRG 1-2) or intermediate responders (TRG 3), whereas 12% were assessed as poor responders (TRG 4-5). In a subset of 7 poor responders and 12 good responders, treatment outcome was correctly predicted for 95%. Application of the prediction model on the remaining patient samples resulted in correct prediction for 85%. Phosphosubstrate signatures generated by poor-responding tumors indicated high kinase activity, which was inhibited by the kinase inhibitor sunitinib, and several discriminating phosphosubstrates represented proteins derived from signaling pathways implicated in radioresistance. Conclusions: Multiplex kinase activity profiling may identify functional biomarkers predictive of tumor response to preoperative CRT in LARC.« less

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.

New Paradigm for Translational Modeling to Predict Long‐term Tuberculosis Treatment Response

PubMed Central

Bartelink, IH; Zhang, N; Keizer, RJ; Strydom, N; Converse, PJ; Dooley, KE; Nuermberger, EL

2017-01-01

Abstract Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse‐to‐human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species‐specific protein binding, drug‐drug interactions, and patient‐specific pathology. Simulations of recent trials testing 4‐month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse‐free patients vs. 80% observed in the REMox‐TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6‐month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials. PMID:28561946

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.

DNA Mismatch Repair Deficiency in Rectal Cancer: Benchmarking Its Impact on Prognosis, Neoadjuvant Response Prediction, and Clinical Cancer Genetics

PubMed Central

de Rosa, Nicole; Rodriguez-Bigas, Miguel A.; Chang, George J.; Veerapong, Jula; Borras, Ester; Krishnan, Sunil; Bednarski, Brian; Messick, Craig A.; Skibber, John M.; Feig, Barry W.; Lynch, Patrick M.; Vilar, Eduardo

2016-01-01

Purpose DNA mismatch repair deficiency (dMMR) hallmarks consensus molecular subtype 1 of colorectal cancer. It is being routinely tested, but little is known about dMMR rectal cancers. The efficacy of novel treatment strategies cannot be established without benchmarking the outcomes of dMMR rectal cancer with current therapy. We aimed to delineate the impact of dMMR on prognosis, the predicted response to fluoropyrimidine-based neoadjuvant therapy, and implications of germline alterations in the MMR genes in rectal cancer. Methods Between 1992 and 2012, 62 patients with dMMR rectal cancers underwent multimodality therapy. Oncologic treatment and outcomes as well as clinical genetics work-up were examined. Overall and rectal cancer–specific survival were calculated by the Kaplan-Meier method. Results The median age at diagnosis was 41 years. MMR deficiency was most commonly due to alterations in MSH2 (53%) or MSH6 (23%). After a median follow-up of 6.8 years, the 5-year rectal cancer–specific survival was 100% for stage I and II, 85.1% for stage III, and 60.0% for stage IV disease. Fluoropyrimidine-based neoadjuvant chemoradiation was associated with a complete pathologic response rate of 27.6%. The extent of surgical resection was influenced by synchronous colonic disease at presentation, tumor height, clinical stage, and pelvic radiation. An informed decision for a limited resection focusing on proctectomy did not compromise overall survival. Five of the 11 (45.5%) deaths during follow-up were due to extracolorectal malignancies. Conclusion dMMR rectal cancer had excellent prognosis and pathologic response with current multimodality therapy including an individualized surgical treatment plan. Identification of a dMMR rectal cancer should trigger germline testing, followed by lifelong surveillance for both colorectal and extracolorectal malignancies. We herein provide genotype-specific outcome benchmarks for comparison with novel interventions. PMID:27432916

Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression.

PubMed

Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

2016-05-03

Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors-overall trauma 'load' and specific type of abuse-on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology-Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer outcomes

[Clinical and biological predictors of ketamine response in treatment-resistant major depression: Review].

PubMed

Romeo, B; Choucha, W; Fossati, P; Rotge, J-Y

2017-08-01

The aim of this review was to determine the clinical and biological predictors of the ketamine response. A systematic research on PubMed and PsycINFO database was performed without limits on year of publication. The main predictive factors of ketamine response, which were found in different studies, were (i) a family history of alcohol dependence, (ii) unipolar depressive disorder, and (iii) neurocognitive impairments, especially a slower processing speed. Many other predictive factors were identified, but not replicated, such as personal history of alcohol dependence, no antecedent of suicide attempt, anxiety symptoms. Some biological factors were also found such as markers of neural plasticity (slow wave activity, brain-derived neurotrophic factor Val66Met polymorphism, expression of Shank 3 protein), other neurologic factors (anterior cingulate activity, concentration of glutamine/glutamate), inflammatory factors (IL-6 concentration) or metabolic factors (concentration of B12 vitamin, D- and L-serine, alterations in the mitochondrial β-oxidation of fatty acids). This review had several limits: (i) patients had exclusively resistant major depressive episodes which represent a sub-type of depression and not all depression, (ii) response criteria were more frequently assessed than remission criteria, it was therefore difficult to conclude that these predictors were similar, and finally (iii) many studies used a very small number of patients. In conclusion, this review found that some predictors of ketamine response, like basal activity of anterior cingulate or vitamin B12 concentration, were identical to other therapeutics used in major depressive episode. These factors could be more specific to the major depressive episode and not to the ketamine response. Others, like family history of alcohol dependence, body mass index, or D- and L-serine were different from the other therapeutics. Neurocognitive impairments like slower speed processing or alterations in

Correlation of clinical predictions and surgical results in maxillary superior repositioning.

PubMed

Tabrizi, Reza; Zamiri, Barbad; Kazemi, Hamidreza

2014-05-01

This is a prospective study to evaluate the accuracy of clinical predictions related to surgical results in subjects who underwent maxillary superior repositioning without anterior-posterior movement. Surgeons' predictions according to clinical (tooth show at rest and at the maximum smile) and cephalometric evaluation were documented for the amount of maxillary superior repositioning. Overcorrection or undercorrection was documented for every subject 1 year after the operations. Receiver operating characteristic curve test was used to find a cutoff point in prediction errors and to determine positive predictive value (PPV) and negative predictive value. Forty subjects (14 males and 26 females) were studied. Results showed a significant difference between changes in the tooth show at rest and at the maximum smile line before and after surgery. Analysis of the data demonstrated no correlation between the predictive data and the surgical results. The incidence of undercorrection (25%) was more common than overcorrection (7.5%). The cutoff point for errors in predictions was 5 mm for tooth show at rest and 15 mm at the maximum smile. When the amount of the presurgical tooth show at rest was more than 5 mm, 50.5% of clinical predictions did not match the clinical results (PPV), and 75% of clinical predictions showed the same results when the tooth show was less than 5 mm (negative predictive value). When the amount of presurgical tooth shown in the maximum smile line was more than 15 mm, 75% of clinical predictions did not match with clinical results (PPV), and 25% of the predictions had the same results because the tooth show at the maximum smile was lower than 15 mm. Clinical predictions according to the tooth show at rest and at the maximum smile have a poor correlation with clinical results in maxillary superior repositioning for vertical maxillary excess. The risk of errors in predictions increased when the amount of superior repositioning of the maxilla increased

Does a family history of RA influence the clinical presentation and treatment response in RA?

PubMed

Frisell, Thomas; Saevarsdottir, Saedis; Askling, Johan

2016-06-01

To assess whether family history of rheumatoid arthritis (RA), among the strongest risk factors for developing RA, also carries information on the clinical presentation and treatment response. The prospective Swedish Rheumatology register was linked to family history of RA, defined as diagnosed RA in any first-degree relative, ascertained through the Swedish Multi-Generation and Patient registers. Clinical presentation was examined among patients with early RA 2000-2011 (symptom onset <12 months before inclusion, N=6869), and response to methotrexate (MTX) monotherapy in the subset starting this treatment (N=4630). Response to tumour necrosis factor inhibitors (TNFi) was examined among all patients with RA starting a TNFi as the first biological disease-modifying antirheumatic drug 2000-2011 (N=9249). Association of family history with clinical characteristics, drug survival, European League Against Rheumatism (EULAR) response and change in disease activity at 3 and 6 months was estimated using linear and generalised logistic regression models. Correlation in relatives' response measures was also assessed. Patients with early RA with family history of RA were more often rheumatoid factor positive, but with no other clinically meaningful differences in their clinical presentation. Family history of RA did not predict response to MTX or TNFi, with the possible exception of no versus good EULAR response to TNFi at 6 months (OR=1.4, 95% CI 1.1 to 1.7). Having a relative who discontinued TNFi within a year increased the odds of doing the same (OR=3.7, 95% CI 1.8 to 7.5), although we found no significant familial correlations in change in disease activity measures. Family history of RA did not modify the clinical presentation of RA or predict response to standard treatment with MTX or TNFi. Treatment response, particularly drug survival, may itself be familial. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

NASA Astrophysics Data System (ADS)

Ravichandran, Kavya; Braman, Nathaniel; Janowczyk, Andrew; Madabhushi, Anant

2018-02-01

Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

[Current Possibilities for Predicting Responses to EGFR Blockade in Metastatic Colorectal Cancer].

PubMed

Němeček, R; Svoboda, M; Slabý, O

2016-01-01

The combination of modern systemic chemotherapy and anti-EGFR monoclonal antibodies improves overall survival and quality of life for patients with metastatic colorecal cancer. By contrast, the addition of anti-EGFR therapy to the treatment regime of resistant patients may lead to worse progression-free survival and overall survival. Therefore, identifying sensitive and resistant patients prior to targeted therapy of metastatic colorecal cancer is a key point during the initial decision making process. Previous research shows that primary resistance to EGFR blockade is in most cases caused by constitutive activation of signaling pathways downstream of EGFR. Of all relevant factors (mutation of KRAS, NRAS, BRAF, and PIK3CA oncogenes, inactivation of tumor suppressors PTEN and TP53, amplification of EGFR and HER2, and expression of epiregulin and amphiregulin, mikroRNA miR-31-3p, and miR-31-5p), only evaluation of KRAS and NRAS mutations has entered routine clinical practice. The role of the other markers still needs to be validated. The ongoing benefit of anti-EGFR therapy could be indicated by specific clinical parameters measured after the initiation of targeted therapy, including early tumor shrinkage, the deepness of the response, or hypomagnesemia. The accuracy of predictive dia-gnostic tools could be also increased by examining a combination of predictive markers using next generation sequencing methods. However, unjustified investigation of many molecular markers should be resisted as this may complicate interpretation of the results, particularly in terms of their specific clinical relevance. The aim of this review is to describe current possibilities with respect to predicting responses to EGFR blockade in the context of the EGFR pathway, and the utilization of such results in routine clinical practice.

Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

PubMed

Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

2015-07-01

Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

Clinical Trials for Predictive Medicine—New Challenges and Paradigms*

PubMed Central

Simon, Richard

2014-01-01

Background Developments in biotechnology and genomics have increased the focus of biostatisticians on prediction problems. This has led to many exciting developments for predictive modeling where the number of variables is larger than the number of cases. Heterogeneity of human diseases and new technology for characterizing them presents new opportunities and challenges for the design and analysis of clinical trials. Purpose In oncology, treatment of broad populations with regimens that do not benefit most patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine[1, 2]. Results We have reviewed prospective designs for the development of new therapeutics with candidate predictive biomarkers. We have also outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from the new regimen. Conclusions Developing new treatments with predictive biomarkers for identifying the patients who are most likely or least likely to benefit makes drug development more complex. But for many new oncology drugs it is the only science based approach and should increase the chance of success. It may also lead to more consistency in results among trials and has obvious benefits for reducing the number of patients who ultimately receive expensive drugs which expose them risks of adverse events but no benefit. This approach also has great potential value for controlling societal expenditures on health care. Development of treatments with predictive biomarkers requires major changes in the standard paradigms for the design and analysis of clinical trials. Some of the key assumptions

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

PubMed

Abajian, Aaron; Murali, Nikitha; Savic, Lynn Jeanette; Laage-Gaupp, Fabian Max; Nezami, Nariman; Duncan, James S; Schlachter, Todd; Lin, MingDe; Geschwind, Jean-François; Chapiro, Julius

2018-06-01

To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques. This study included 36 patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization. The cohort (age 62 ± 8.9 years; 31 men; 13 white; 24 Eastern Cooperative Oncology Group performance status 0, 10 status 1, 2 status 2; 31 Child-Pugh stage A, 4 stage B, 1 stage C; 1 Barcelona Clinic Liver Cancer stage 0, 12 stage A, 10 stage B, 13 stage C; tumor size 5.2 ± 3.0 cm; number of tumors 2.6 ± 1.1; and 30 conventional transarterial chemoembolization, 6 with drug-eluting embolic agents). MR imaging was obtained before and 1 month after transarterial chemoembolization. Image-based tumor response to transarterial chemoembolization was assessed with the use of the 3D quantitative European Association for the Study of the Liver (qEASL) criterion. Clinical information, baseline imaging, and therapeutic features were used to train logistic regression (LR) and random forest (RF) models to predict patients as treatment responders or nonresponders under the qEASL response criterion. The performance of each model was validated using leave-one-out cross-validation. Both LR and RF models predicted transarterial chemoembolization treatment response with an overall accuracy of 78% (sensitivity 62.5%, specificity 82.1%, positive predictive value 50.0%, negative predictive value 88.5%). The strongest predictors of treatment response included a clinical variable (presence of cirrhosis) and an imaging variable (relative tumor signal intensity >27.0). Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques. Copyright © 2018 SIR. Published by Elsevier Inc. All rights reserved.

A novel neural-inspired learning algorithm with application to clinical risk prediction.

PubMed

Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

2015-04-01

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials. Copyright © 2015 Elsevier Inc. All rights reserved.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Neural responses to negative outcomes predict success in community-based substance use treatment

PubMed Central

Forster, Sarah E.; Finn, Peter R.; Brown, Joshua W.

2017-01-01

Background and aims Activation in some specific brain regions has demonstrated promise as prognostic indicators in substance dependent individuals (SDIs) but this issue has not yet been explored in SDIs attending typical of community-based treatment. We used a data-driven, exploratory approach to identify brain-based predictors of treatment outcome in a representative community sample of SDIs. The predictive utility of brain-based measures was evaluated against clinical indicators, cognitive-behavioral performance, and self-report assessments. Design Prospective clinical outcome design, evaluating baseline functional magnetic resonance imaging data from the Balloon Analogue Risk Task (BART) as a predictor of 3-month substance use treatment outcomes. Setting Community-based substance use programs in Bloomington, Indiana, USA. Participants Twenty-three SDIs (17 male, ages 18–43) in an intensive outpatient or residential treatment program; abstinent 1–4 weeks at baseline. Measurements Event-related brain response, BART performance, and self-report scores at treatment onset, substance use outcome measure (based on days of use) Findings Using voxel-level predictive modeling and leave-one-out cross-validation, an elevated response to unexpected negative feedback in bilateral amygdala and anterior hippocampus (Amyg/aHipp) at baseline successfully predicted greater substance use over the 3-month study interval (p ≤ 0.006, cluster-corrected). This effect was robust to inclusion of significant non-brain-based covariates. A larger response to negative feedback in bilateral Amyg/aHipp was also associated with faster reward-seeking responses after negative feedback (r(23) = −0.544, p = 0.007; r(23) = −0.588, p = 0.003). A model including Amyg/aHipp activation, faster reward-seeking after negative feedback, and significant self-report scores accounted for 45% of the variance in substance use outcomes in our sample. Conclusions An elevated response to unexpected

Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information.

PubMed

Hahn, Tim; Kircher, Tilo; Straube, Benjamin; Wittchen, Hans-Ulrich; Konrad, Carsten; Ströhle, Andreas; Wittmann, André; Pfleiderer, Bettina; Reif, Andreas; Arolt, Volker; Lueken, Ulrike

2015-01-01

Although neuroimaging research has made substantial progress in identifying the large-scale neural substrate of anxiety disorders, its value for clinical application lags behind expectations. Machine-learning approaches have predictive potential for individual-patient prognostic purposes and might thus aid translational efforts in psychiatric research. To predict treatment response to cognitive behavioral therapy (CBT) on an individual-patient level based on functional magnetic resonance imaging data in patients with panic disorder with agoraphobia (PD/AG). We included 49 patients free of medication for at least 4 weeks and with a primary diagnosis of PD/AG in a longitudinal study performed at 8 clinical research institutes and outpatient centers across Germany. The functional magnetic resonance imaging study was conducted between July 2007 and March 2010. Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure. Treatment response was defined as exceeding a 50% reduction in Hamilton Anxiety Rating Scale scores. Blood oxygenation level-dependent signal was measured during a differential fear-conditioning task. Regional and whole-brain gaussian process classifiers using a nested leave-one-out cross-validation were used to predict the treatment response from data acquired before CBT. Although no single brain region was predictive of treatment response, integrating regional classifiers based on data from the acquisition and the extinction phases of the fear-conditioning task for the whole brain yielded good predictive performance (accuracy, 82%; sensitivity, 92%; specificity, 72%; P < .001). Data from the acquisition phase enabled 73% correct individual-patient classifications (sensitivity, 80%; specificity, 67%; P < .001), whereas data from the extinction phase led to an accuracy of 74% (sensitivity, 64%; specificity, 83%; P < .001). Conservative reanalyses under consideration of potential confounders yielded nominally lower but comparable

Emotional Responses to Suicidal Patients: Factor Structure, Construct, and Predictive Validity of the Therapist Response Questionnaire-Suicide Form.

PubMed

Barzilay, Shira; Yaseen, Zimri S; Hawes, Mariah; Gorman, Bernard; Altman, Rachel; Foster, Adriana; Apter, Alan; Rosenfield, Paul; Galynker, Igor

2018-01-01

Mental health professionals have a pivotal role in suicide prevention. However, they also often have intense emotional responses, or countertransference, during encounters with suicidal patients. Previous studies of the Therapist Response Questionnaire-Suicide Form (TRQ-SF), a brief novel measure aimed at probing a distinct set of suicide-related emotional responses to patients found it to be predictive of near-term suicidal behavior among high suicide-risk inpatients. The purpose of this study was to validate the TRQ-SF in a general outpatient clinic setting. Adult psychiatric outpatients ( N  = 346) and their treating mental health professionals ( N  = 48) completed self-report assessments following their first clinic meeting. Clinician measures included the TRQ-SF, general emotional states and traits, therapeutic alliance, and assessment of patient suicide risk. Patient suicidal outcomes and symptom severity were assessed at intake and one-month follow-up. Following confirmatory factor analysis of the TRQ-SF, factor scores were examined for relationships with clinician and patient measures and suicidal outcomes. Factor analysis of the TRQ-SF confirmed three dimensions: (1) affiliation, (2) distress, and (3) hope. The three factors also loaded onto a single general factor of negative emotional response toward the patient that demonstrated good internal reliability. The TRQ-SF scores were associated with measures of clinician state anger and anxiety and therapeutic alliance, independently of clinician personality traits after controlling for the state- and patient-specific measures. The total score and three subscales were associated in both concurrent and predictive ways with patient suicidal outcomes, depression severity, and clinicians' judgment of patient suicide risk, but not with global symptom severity, thus indicating specifically suicide-related responses. The TRQ-SF is a brief and reliable measure with a 3-factor structure. It demonstrates

Is electromyography a predictive test of patient response to biofeedback in the treatment of fecal incontinence?

PubMed

Lacima, Gloria; Pera, Miguel; González-Argenté, Xavier; Torrents, Abiguei; Valls-Solé, Josep; Espuña-Pons, Montserrat

2016-03-01

Biofeedback is effective in more than 70% of patients with fecal incontinence. However, reliable predictors of successful treatment have not been identified. The aim was to identify clinical variables and diagnostic tests, particularly electromyography, that could predict a successful outcome. We included 135 consecutive women with fecal incontinence treated with biofeedback. Clinical evaluation, manometry, ultrasonography, electromyography, and pudendal nerve terminal motor latency were performed before therapy. Treatment outcome was assessed using a symptoms diary, Wexner incontinence score and the patient's subjective perception. According to the symptoms diaries, 106 (78.5%) women had a good clinical result and 29 (21.5%) had a poor result. There were no differences in age, severity and type of fecal incontinence. Maximum resting pressure (39.3 ± 19.1 mmHg vs. 33.7 ± 20.2 mmHg; P = 0.156) and maximum squeeze pressure (91.8 ± 33.2 mmHg vs. 79.8 ± 31.2 mmHg; P = 0.127) were higher in patients having good clinical outcome although the difference was not significant. There were no differences in the presence of sphincter defects or abnormalities in electromyographic recordings. Logistic regression analysis found no independent predictive factor for good clinical outcome. Biofeedback is effective in more than 75% of patients with fecal incontinence. Clinical characteristics of patients and results of baseline tests have no predictive value of response to therapy. Specifically, we found no association between severity of electromyographic deficit and clinical response. © 2015 Wiley Periodicals, Inc.

Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review.

PubMed

Ryan, J E; Warrier, S K; Lynch, A C; Ramsay, R G; Phillips, W A; Heriot, A G

2016-03-01

Approximately 20% of patients treated with neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer achieve a pathological complete response (pCR) while the remainder derive the benefit of improved local control and downstaging and a small proportion show a minimal response. The ability to predict which patients will benefit would allow for improved patient stratification directing therapy to those who are likely to achieve a good response, thereby avoiding ineffective treatment in those unlikely to benefit. A systematic review of the English language literature was conducted to identify pathological factors, imaging modalities and molecular factors that predict pCR following chemoradiotherapy. PubMed, MEDLINE and Cochrane Database searches were conducted with the following keywords and MeSH search terms: 'rectal neoplasm', 'response', 'neoadjuvant', 'preoperative chemoradiation', 'tumor response'. After review of title and abstracts, 85 articles addressing the prediction of pCR were selected. Clear methods to predict pCR before chemoradiotherapy have not been defined. Clinical and radiological features of the primary cancer have limited ability to predict response. Molecular profiling holds the greatest potential to predict pCR but adoption of this technology will require greater concordance between cohorts for the biomarkers currently under investigation. At present no robust markers of the prediction of pCR have been identified and the topic remains an area for future research. This review critically evaluates existing literature providing an overview of the methods currently available to predict pCR to nCRT for locally advanced rectal cancer. The review also provides a comprehensive comparison of the accuracy of each modality. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

Which ante mortem clinical features predict progressive supranuclear palsy pathology?

PubMed

Respondek, Gesine; Kurz, Carolin; Arzberger, Thomas; Compta, Yaroslau; Englund, Elisabet; Ferguson, Leslie W; Gelpi, Ellen; Giese, Armin; Irwin, David J; Meissner, Wassilios G; Nilsson, Christer; Pantelyat, Alexander; Rajput, Alex; van Swieten, John C; Troakes, Claire; Josephs, Keith A; Lang, Anthony E; Mollenhauer, Brit; Müller, Ulrich; Whitwell, Jennifer L; Antonini, Angelo; Bhatia, Kailash P; Bordelon, Yvette; Corvol, Jean-Christophe; Colosimo, Carlo; Dodel, Richard; Grossman, Murray; Kassubek, Jan; Krismer, Florian; Levin, Johannes; Lorenzl, Stefan; Morris, Huw; Nestor, Peter; Oertel, Wolfgang H; Rabinovici, Gil D; Rowe, James B; van Eimeren, Thilo; Wenning, Gregor K; Boxer, Adam; Golbe, Lawrence I; Litvan, Irene; Stamelou, Maria; Höglinger, Günter U

2017-07-01

Progressive supranuclear palsy (PSP) is a neuropathologically defined disease presenting with a broad spectrum of clinical phenotypes. To identify clinical features and investigations that predict or exclude PSP pathology during life, aiming at an optimization of the clinical diagnostic criteria for PSP. We performed a systematic review of the literature published since 1996 to identify clinical features and investigations that may predict or exclude PSP pathology. We then extracted standardized data from clinical charts of patients with pathologically diagnosed PSP and relevant disease controls and calculated the sensitivity, specificity, and positive predictive value of key clinical features for PSP in this cohort. Of 4166 articles identified by the database inquiry, 269 met predefined standards. The literature review identified clinical features predictive of PSP, including features of the following 4 functional domains: ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction. No biomarker or genetic feature was found reliably validated to predict definite PSP. High-quality original natural history data were available from 206 patients with pathologically diagnosed PSP and from 231 pathologically diagnosed disease controls (54 corticobasal degeneration, 51 multiple system atrophy with predominant parkinsonism, 53 Parkinson's disease, 73 behavioral variant frontotemporal dementia). We identified clinical features that predicted PSP pathology, including phenotypes other than Richardson's syndrome, with varying sensitivity and specificity. Our results highlight the clinical variability of PSP and the high prevalence of phenotypes other than Richardson's syndrome. The features of variant phenotypes with high specificity and sensitivity should serve to optimize clinical diagnosis of PSP. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study.

PubMed

Schmaal, Lianne; Marquand, Andre F; Rhebergen, Didi; van Tol, Marie-José; Ruhé, Henricus G; van der Wee, Nic J A; Veltman, Dick J; Penninx, Brenda W J H

2015-08-15

A chronic course of major depressive disorder (MDD) is associated with profound alterations in brain volumes and emotional and cognitive processing. However, no neurobiological markers have been identified that prospectively predict MDD course trajectories. This study evaluated the prognostic value of different neuroimaging modalities, clinical characteristics, and their combination to classify MDD course trajectories. One hundred eighteen MDD patients underwent structural and functional magnetic resonance imaging (MRI) (emotional facial expressions and executive functioning) and were clinically followed-up at 2 years. Three MDD trajectories (chronic n = 23, gradual improving n = 36, and fast remission n = 59) were identified based on Life Chart Interview measuring the presence of symptoms each month. Gaussian process classifiers were employed to evaluate prognostic value of neuroimaging data and clinical characteristics (including baseline severity, duration, and comorbidity). Chronic patients could be discriminated from patients with more favorable trajectories from neural responses to various emotional faces (up to 73% accuracy) but not from structural MRI and functional MRI related to executive functioning. Chronic patients could also be discriminated from remitted patients based on clinical characteristics (accuracy 69%) but not when age differences between the groups were taken into account. Combining different task contrasts or data sources increased prediction accuracies in some but not all cases. Our findings provide evidence that the prediction of naturalistic course of depression over 2 years is improved by considering neuroimaging data especially derived from neural responses to emotional facial expressions. Neural responses to emotional salient faces more accurately predicted outcome than clinical data. Copyright © 2015 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.

Dissociation predicts treatment response in eye-movement desensitization and reprocessing for posttraumatic stress disorder.

PubMed

Bae, Hwallip; Kim, Daeho; Park, Yong Chon

2016-01-01

Using clinical data from a specialized trauma clinic, this study investigated pretreatment clinical factors predicting response to eye-movement desensitization and reprocessing (EMDR) among adult patients diagnosed with posttraumatic stress disorder (PTSD). Participants were evaluated using the Clinician-Administered PTSD Scale (CAPS), the Symptom Checklist-90-Revised, the Beck Depression Inventory, and the Dissociative Experiences Scale before treatment and were reassessed using the CAPS after treatment and at 6-month follow-up. A total of 69 patients underwent an average of 4 sessions of EMDR, and 60 (87%) completed the posttreatment evaluation, including 8 participants who terminated treatment prematurely. Intent-to-treat analysis revealed that 39 (65%) of the 60 patients were classified as responders and 21 (35%) as nonresponders when response was defined as more than a 30% decrease in total CAPS score. The nonresponders had higher levels of dissociation (depersonalization and derealization) and numbing symptoms, but other PTSD symptoms, such as avoidance, hyperarousal, and intrusion, were not significantly different. The number of psychiatric comorbidities was also associated with treatment nonresponse. The final logistic regression model yielded 2 significant variables: dissociation (p < .001) and more than 2 comorbidities compared to none (p < .05). These results indicate that complex symptom patterns in PTSD may predict treatment response and support the inclusion of the dissociative subtype of PTSD in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Preclinical efficacy of immune-checkpoint monotherapy does not recapitulate corresponding biomarkers-based clinical predictions in glioblastoma

PubMed Central

Vandenberk, Lien; Van Woensel, Matthias; Schaaf, Marco; De Vleeschouwer, Steven; Agostinis, Patrizia

2017-01-01

ABSTRACT Glioblastoma (GBM) is resistant to most multimodal therapies. Clinical success of immune-checkpoint inhibitors (ICIs) has spurred interest in applying ICIs targeting CTLA4, PD1 or IDO1 against GBM. This amplifies the need to ascertain GBM's intrinsic susceptibility (or resistance) toward these ICIs, through clinical biomarkers that may also “guide and prioritize” preclinical testing. Here, we interrogated the TCGA and/or REMBRANDT human patient-cohorts to predict GBM's predisposition toward ICIs. We exploited various broad clinical biomarkers, including mutational or predicted-neoantigen burden, pre-existing or basal levels of tumor-infiltrating T lymphocytes (TILs), differential expression of immune-checkpoints within the tumor and their correlation with particular TILs/Treg-associated functional signature and prognostic impact of differential immune-checkpoint expression. Based on these analyses, we found that predictive biomarkers of ICI responsiveness exhibited inconsistent patterns in GBM patients, i.e., they either predicted ICI resistance (as compared with typical ICI-responsive cancer-types like melanoma, lung cancer or bladder cancer) or susceptibility to therapeutic targeting of CTLA4 or IDO1. On the other hand, our comprehensive literature meta-analysis and preclinical testing of ICIs using an orthotopic GL261-glioma mice model, indicated significant antitumor properties of anti-PD1 antibody, whereas blockade of IDO1 or CTLA4 either failed or provided very marginal advantage. These trends raise the need to better assess the applicability of ICIs and associated biomarkers for GBM. PMID:28507806

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I - pre-analytical and analytical validation.

PubMed

Masucci, Giuseppe V; Cesano, Alessandra; Hawtin, Rachael; Janetzki, Sylvia; Zhang, Jenny; Kirsch, Ilan; Dobbin, Kevin K; Alvarez, John; Robbins, Paul B; Selvan, Senthamil R; Streicher, Howard Z; Butterfield, Lisa H; Thurin, Magdalena

2016-01-01

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question. Although numerous candidate biomarkers have been described, there are currently three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated to identify patients who are more likely to benefit from a single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accurate prediction of clinical benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers

Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression

PubMed Central

Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

2016-01-01

Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors—overall trauma ‘load' and specific type of abuse—on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology—Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer

Mid-Treatment Sleep Duration Predicts Clinically Significant Knee Osteoarthritis Pain reduction at 6 months: Effects From a Behavioral Sleep Medicine Clinical Trial.

PubMed

Salwen, Jessica K; Smith, Michael T; Finan, Patrick H

2017-02-01

To determine the relative influence of sleep continuity (sleep efficiency, sleep onset latency, total sleep time [TST], and wake after sleep onset) on clinical pain outcomes within a trial of cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid knee osteoarthritis and insomnia. Secondary analyses were performed on data from 74 patients with comorbid insomnia and knee osteoarthritis who completed a randomized clinical trial of 8-session multicomponent CBT-I versus an active behavioral desensitization control condition (BD), including a 6-month follow-up assessment. Data used herein include daily diaries of sleep parameters, actigraphy data, and self-report questionnaires administered at specific time points. Patients who reported at least 30% improvement in self-reported pain from baseline to 6-month follow-up were considered responders (N = 31). Pain responders and nonresponders did not differ significantly at baseline across any sleep continuity measures. At mid-treatment, only TST predicted pain response via t tests and logistic regression, whereas other measures of sleep continuity were nonsignificant. Recursive partitioning analyses identified a minimum cut-point of 382 min of TST achieved at mid-treatment in order to best predict pain improvements 6-month posttreatment. Actigraphy results followed the same pattern as daily diary-based results. Clinically significant pain reductions in response to both CBT-I and BD were optimally predicted by achieving approximately 6.5 hr sleep duration by mid-treatment. Thus, tailoring interventions to increase TST early in treatment may be an effective strategy to promote long-term pain reductions. More comprehensive research on components of behavioral sleep medicine treatments that contribute to pain response is warranted. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results

NASA Astrophysics Data System (ADS)

Jarrett, Angela M.; Hormuth, David A.; Barnes, Stephanie L.; Feng, Xinzeng; Huang, Wei; Yankeelov, Thomas E.

2018-05-01

Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used—obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety–Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p  <  0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and

DNA Repair Biomarkers Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer

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

Alexander, Brian M., E-mail: bmalexander@lroc.harvard.edu; Wang Xiaozhe; Niemierko, Andrzej

2012-05-01

Purpose: The addition of neoadjuvant chemoradiotherapy prior to surgical resection for esophageal cancer has improved clinical outcomes in some trials. Pathologic complete response (pCR) following neoadjuvant therapy is associated with better clinical outcome in these patients, but only 22% to 40% of patients achieve pCR. Because both chemotherapy and radiotherapy act by inducing DNA damage, we analyzed proteins selected from multiple DNA repair pathways, using quantitative immunohistochemistry coupled with a digital pathology platform, as possible biomarkers of treatment response and clinical outcome. Methods and Materials: We identified 79 patients diagnosed with esophageal cancer between October 1994 and September 2002, withmore » biopsy tissue available, who underwent neoadjuvant chemoradiotherapy prior to surgery at the Massachusetts General Hospital and used their archived, formalin-fixed, paraffin-embedded biopsy samples to create tissue microarrays (TMA). TMA sections were stained using antibodies against proteins in various DNA repair pathways including XPF, FANCD2, PAR, MLH1, PARP1, and phosphorylated MAPKAP kinase 2 (pMK2). Stained TMA slides were evaluated using machine-based image analysis, and scoring incorporated both the intensity and the quantity of positive tumor nuclei. Biomarker scores and clinical data were assessed for correlations with clinical outcome. Results: Higher scores for MLH1 (p = 0.018) and lower scores for FANCD2 (p = 0.037) were associated with pathologic response to neoadjuvant chemoradiation on multivariable analysis. Staining of MLH1, PARP1, XPF, and PAR was associated with recurrence-free survival, and staining of PARP1 and FANCD2 was associated with overall survival on multivariable analysis. Conclusions: DNA repair proteins analyzed by immunohistochemistry may be useful as predictive markers for response to neoadjuvant chemoradiotherapy in patients with esophageal cancer. These results are hypothesis generating and need

Using short-term evidence to predict six-month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis.

PubMed

Nixon, Richard M; Bansback, Nick; Stevens, John W; Brennan, Alan; Madan, Jason

2009-01-01

A model is presented to generate a distribution for the probability of an ACR response at six months for a new treatment for rheumatoid arthritis given evidence from a one- or three-month clinical trial. The model is based on published evidence from 11 randomized controlled trials on existing treatments. A hierarchical logistic regression model is used to find the relationship between the proportion of patients achieving ACR20 and ACR50 at one and three months and the proportion at six months. The model is assessed by Bayesian predictive P-values that demonstrate that the model fits the data well. The model can be used to predict the number of patients with an ACR response for proposed six-month clinical trials given data from clinical trials of one or three months duration. Copyright 2008 John Wiley & Sons, Ltd.

The Clinical Prediction of Dangerousness.

DTIC Science & Technology

1985-05-01

1967). Psychopathy, mental deficiency, aggressiveness, and the XYY syndrome . Nature, 214, (5087), 500-501. Wexler, D. (1979). Patients, therapists...27 Improving Clinical Predictions: The Actuarial Approach ......... 30 Past Crime ................................................ 32...because of fears they will commit violent crimes to support their habit). 3. Decisions concerning the emergency and longer term in- voluntary commitment

Adaptation of clinical prediction models for application in local settings.

PubMed

Kappen, Teus H; Vergouwe, Yvonne; van Klei, Wilton A; van Wolfswinkel, Leo; Kalkman, Cor J; Moons, Karel G M

2012-01-01

When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.

Identifying a predictive model for response to atypical antipsychotic monotherapy treatment in south Indian schizophrenia patients.

PubMed

Gupta, Meenal; Moily, Nagaraj S; Kaur, Harpreet; Jajodia, Ajay; Jain, Sanjeev; Kukreti, Ritushree

2013-08-01

Atypical antipsychotic (AAP) drugs are the preferred choice of treatment for schizophrenia patients. Patients who do not show favorable response to AAP monotherapy are subjected to random prolonged therapeutic treatment with AAP multitherapy, typical antipsychotics or a combination of both. Therefore, prior identification of patients' response to drugs can be an important step in providing efficacious and safe therapeutic treatment. We thus attempted to elucidate a genetic signature which could predict patients' response to AAP monotherapy. Our logistic regression analyses indicated the probability that 76% patients carrying combination of four SNPs will not show favorable response to AAP therapy. The robustness of this prediction model was assessed using repeated 10-fold cross validation method, and the results across n-fold cross-validations (mean accuracy=71.91%; 95%CI=71.47-72.35) suggest high accuracy and reliability of the prediction model. Further validations of these results in large sample sets are likely to establish their clinical applicability. Copyright © 2013 Elsevier Inc. All rights reserved.

Do Resting Plasma β-Endorphin Levels Predict Responses to Opioid Analgesics?

PubMed

Bruehl, Stephen; Burns, John W; Gupta, Rajnish; Buvanendran, Asokumar; Chont, Melissa; Orlowska, Daria; Schuster, Erik; France, Christopher R

2017-01-01

Clinically feasible predictors of opioid analgesic responses for use in precision pain medicine protocols are needed. This study evaluated whether resting plasma β-endorphin (BE) levels predicted responses to an opioid analgesic, and whether chronic pain status or sex moderated these effects. Participants included 73 individuals with chronic low back pain (CLBP) and 88 pain-free controls, all using no daily opioid analgesics. Participants attended 2 identical laboratory sessions during which they received either intravenous morphine (0.08 mg/kg) or saline placebo, with blood samples obtained before drug administration to assay resting plasma BE levels. Once peak drug activity was achieved in each session, participants engaged in an ischemic forearm pain task (ISC) and a heat pain task. Morphine analgesic effects were derived reflecting the difference in pain outcomes between placebo and morphine conditions. In hierarchical regressions, significant Type (CLBP vs. control)×BE interactions (Ps<0.05) were noted for morphine effects on ISC tolerance, ISC intratask pain ratings, and thermal VAS unpleasantness ratings. These interactions derived primarily from associations between higher BE levels and smaller morphine effects restricted to the CLBP subgroup. All other BE-related effects, including sex interactions, for predicting morphine analgesia failed to reach statistical significance. BE was a predictor of morphine analgesia for only 3 out of 9 outcomes examined, with these effects moderated by chronic pain status but not sex. On the whole, results do not suggest that resting plasma BE levels are likely to be a clinically useful predictor of opioid analgesic responses.

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

PubMed

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

2018-03-01

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

Physiotherapy students' perceptions and experiences of clinical prediction rules.

PubMed

Knox, Grahame M; Snodgrass, Suzanne J; Stanton, Tasha R; Kelly, David H; Vicenzino, Bill; Wand, Benedict M; Rivett, Darren A

2017-09-01

Clinical reasoning can be difficult to teach to pre-professional physiotherapy students due to their lack of clinical experience. It may be that tools such as clinical prediction rules (CPRs) could aid the process, but there has been little investigation into their use in physiotherapy clinical education. This study aimed to determine the perceptions and experiences of physiotherapy students regarding CPRs, and whether they are learning about CPRs on clinical placement. Cross-sectional survey using a paper-based questionnaire. Final year pre-professional physiotherapy students (n=371, response rate 77%) from five universities across five states of Australia. Sixty percent of respondents had not heard of CPRs, and a further 19% had not clinically used CPRs. Only 21% reported using CPRs, and of these nearly three-quarters were rarely, if ever, learning about CPRs in the clinical setting. However most of those who used CPRs (78%) believed CPRs assisted in the development of clinical reasoning skills and none (0%) was opposed to the teaching of CPRs to students. The CPRs most commonly recognised and used by students were those for determining the need for an X-ray following injuries to the ankle and foot (67%), and for identifying deep venous thrombosis (63%). The large majority of students in this sample knew little, if anything, about CPRs and few had learned about, experienced or practiced them on clinical placement. However, students who were aware of CPRs found them helpful for their clinical reasoning and were in favour of learning more about them. Copyright © 2016 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Can early host responses to mycobacterial infection predict eventual disease outcomes?

PubMed

de Silva, Kumudika; Begg, Douglas J; Plain, Karren M; Purdie, Auriol C; Kawaji, Satoko; Dhand, Navneet K; Whittington, Richard J

2013-11-01

Diagnostic tests used for Johne's disease in sheep either have poor sensitivity and specificity or only detect disease in later stages of infection. Predicting which of the infected sheep are likely to become infectious later in life is currently not feasible and continues to be a major hindrance in disease control. We conducted this longitudinal study to investigate if a suite of diagnostic tests conducted in Mycobacterium avium subspecies paratuberculosis (MAP) exposed lambs at 4 months post infection can accurately predict their clinical status at 12 months post infection. We tracked cellular and humoral responses and quantity of MAP shedding for up to 12 months post challenge in 20 controls and 37 exposed sheep. Infection was defined at necropsy by tissue culture and disease spectrum by lesion type. Data were analysed using univariable and multivariable logistic regression models and a subset of variables from the earliest period post inoculation (4 months) was selected for predicting disease outcomes later on (12 months). Sensitivity and specificity of tests and their combinations in series and parallel were determined. Early elevation in faecal MAP DNA quantity and a lower interferon gamma (IFNγ) response were significantly associated with sheep becoming infectious as well as progressing to severe disease. Conversely, early low faecal MAP DNA and higher interleukin-10 responses were significantly associated with an exposed animal developing protective immunity. Combination of early elevated faecal MAP DNA or lower IFNγ response had the highest sensitivity (75%) and specificity (81%) for identifying sheep that would become infectious. Collectively, these results highlight the potential for combined test interpretation to aid in the early prediction of sheep susceptibility to MAP infection. Copyright © 2013 Elsevier B.V. All rights reserved.

Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer.

PubMed

Dayde, Delphine; Tanaka, Ichidai; Jain, Rekha; Tai, Mei Chee; Taguchi, Ayumu

2017-03-07

The standard of care in locally advanced rectal cancer is neoadjuvant chemoradiation (nCRT) followed by radical surgery. Response to nCRT varies among patients and pathological complete response is associated with better outcome. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. The utility of clinicopathological and radiological features are limited due to lack of adequate sensitivity and specificity. Molecular biomarkers have the potential to predict response to nCRT at an early time point, but none have currently reached the clinic. Integration of diverse types of biomarkers including clinicopathological and imaging features, identification of mechanistic link to tumor biology, and rigorous validation using samples which represent disease heterogeneity, will allow to develop a sensitive and cost-effective molecular biomarker panel for precision medicine in rectal cancer. Here, we aim to review the recent advance in tissue- and blood-based molecular biomarker research and illustrate their potential in predicting nCRT response in rectal cancer.

Imbalanced target prediction with pattern discovery on clinical data repositories.

PubMed

Chan, Tak-Ming; Li, Yuxi; Chiau, Choo-Chiap; Zhu, Jane; Jiang, Jie; Huo, Yong

2017-04-20

Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and

Prediction of response to PPI therapy and factors influencing treatment outcome in patients with GORD: a prospective pragmatic trial using pantoprazole

PubMed Central

2011-01-01

Background Management of patients with gastro-oesophageal reflux disease (GORD) can be assisted by information predicting the likely response to proton pump inhibitor (PPI) treatment. The aim was to undertake a study of GORD patients designed to approximate ordinary clinical practice that would identify patient characteristics predicting symptomatic response to pantoprazole treatment. Methods 1888 patients with symptoms of GORD were enrolled in a multicentre, multinational, prospective, open study of 8 weeks pantoprazole treatment, 40 mg daily. Response was assessed by using the ReQuest™ questionnaire, by the investigator making conventional clinical enquiry and by asking patients about their satisfaction with symptom control. Factors including pre-treatment oesophagitis, gender, age, body mass index (BMI), Helicobacter pylori status, anxiety and depression, and concurrent IBS symptoms were examined using logistic regression to determine if they were related to response, judged from the ReQuest™-GI score. Results Poorer treatment responses were associated with non-erosive reflux disease, female gender, lower BMI, anxiety and concurrent irritable bowel syndrome symptoms before treatment. No association was found with age, Helicobacter pylori status or oesophagitis grade. Some reflux-related symptoms were still present in 14% of patients who declared themselves 'well-satisfied' with their symptom control. Conclusions Some readily identifiable features help to predict symptomatic responses to a PPI and consequently may help in managing patient expectation. ClinicalTrial.gov identifier: NCT00312806. PMID:21569313

Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

PubMed

El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

2015-04-01

This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

Prediction of clinical outcome in patients treated with cardiac resynchronization therapy - the role of NT-ProBNP and a combined response score.

PubMed

Bakos, Z; Chatterjee, N C; Reitan, C; Singh, J P; Borgquist, R

2018-04-24

Cardiac resynchronization therapy (CRT) is an established therapy for appropriately selected patients with heart failure. Response to CRT has been heterogeneously defined using both clinical and echocardiographic measures, with poor correlation between the two. The study cohort was comprised of 202 CRT-treated patients and CRT response was defined at 6 months post-implant. Echocardiographic response (E+) was defined as a reduction in LVESV ≥ 15%, clinical response as an improvement of ≥ 1 NYHA class (C+), and biomarker response as a ≥ 25% reduction in NT-proBNP(B+). The association of response measures (E+, B+, C+; response score range 0-3) and clinical endpoints at 3 years was assessed in landmarked Cox models. Echo and clinical responders demonstrated greater declines in NT-proBNP than non-responders (median [E+/B+]: -52%, [E+]: -27%, [C+]: -39% and [E-/C-]: -13%; p = 0.01 for trend). Biomarker (HR 0.43 [95% CI: 0.22-0.86], p = 0.02) and clinical (HR 0.40 [0.23-0.70] p = 0.001) response were associated with a significantly reduced risk of the primary endpoint. When integrating each response measure into a composite score, each 1 point increase was associated with a 31% decreased risk for a composite endpoint of mortality, LVAD, transplant and HF hospitalization (HR 0.69 [95% CI: 0.50-0.96], p = 0.03), and a 52% decreased risk of all-cause mortality (HR 0.48 [95% CI: 0.26-0.89], p = 0.02). Serial changes in NT-proBNP are associated with clinical outcomes following CRT implant. Integration of biomarker, clinical, and echocardiographic response may discriminate CRT responders versus non-responders in a clinically meaningful way, and with higher accuracy. The cohort was combined from study NCT01949246 and the study based on local review board approval 2011/550 in Lund, Sweden.

Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study

NASA Astrophysics Data System (ADS)

Wu, Guolin; Fan, Ming; Zhang, Juan; Zheng, Bin; Li, Lihua

2017-03-01

Breast cancer is one of the most malignancies among women in worldwide. Neoadjuvant Chemotherapy (NACT) has gained interest and is increasingly used in treatment of breast cancer in recent years. Therefore, it is necessary to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NACT. Recent studies have highlighted the use of MRI for predicting response to NACT. In addition, molecular subtype could also effectively identify patients who are likely have better prognosis in breast cancer. In this study, a radiomic analysis were performed, by extracting features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunohistochemistry (IHC) to determine subtypes. A dataset with fifty-seven breast cancer patients were included, all of them received preoperative MRI examination. Among them, 47 patients had complete response (CR) or partial response (PR) and 10 had stable disease (SD) to chemotherapy based on the RECIST criterion. A total of 216 imaging features including statistical characteristics, morphology, texture and dynamic enhancement were extracted from DCE-MRI. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.923 (P = 0.0002) in leave-one-out crossvalidation. The performance of the classifier increased to 0.960, 0.950 and 0.936 when status of HER2, Luminal A and Luminal B subtypes were added into the statistic model, respectively. The results of this study demonstrated that IHC determined molecular status combined with radiomic features from DCE-MRI could be used as clinical marker that is associated with response to NACT.

Drain data to predict clinically relevant pancreatic fistula

PubMed Central

Moskovic, Daniel J; Hodges, Sally E; Wu, Meng-Fen; Brunicardi, F Charles; Hilsenbeck, Susan G; Fisher, William E

2010-01-01

Background Post-operative pancreatic fistula (POPF) is a common and potentially devastating complication of pancreas resection. Management of this complication is important to the pancreas surgeon. Objective The aim of the present study was to evaluate whether drain data accurately predicts clinically significant POPF. Methods A prospectively maintained database with daily drain amylase concentrations and output volumes from 177 consecutive pancreatic resections was analysed. Drain data, demographic and operative data were correlated with POPF (ISGPF Grade: A – clinically silent, B – clinically evident, C – severe) to determine predictive factors. Results Twenty-six (46.4%) out of 56 patients who underwent distal pancreatectomy and 52 (43.0%) out of 121 patients who underwent a Whipple procedure developed a POPF (Grade A-C). POPFs were classified as A (24, 42.9%) and C (2, 3.6%) after distal pancreatectomy whereas they were graded as A (35, 28.9%), B (15, 12.4%) and C (2, 1.7%) after Whipple procedures. Drain data analysis was limited to Whipple procedures because only two patients developed a clinically significant leak after distal pancreatectomy. The daily total drain output did not differ between patients with a clinical leak (Grades B/C) and patients without a clinical leak (no leak and Grade A) on post-operative day (POD) 1 to 7. Although the median amylase concentration was significantly higher in patients with a clinical leak on POD 1–6, there was no day that amylase concentration predicted a clinical leak better than simply classifying all patients as ‘no leak’ (maximum accuracy =86.1% on POD 1, expected accuracy by chance =85.6%, kappa =10.2%). Conclusion Drain amylase data in the early post-operative period are not a sensitive or specific predictor of which patients will develop clinically significant POPF after pancreas resection. PMID:20815856

Probabilistic prediction of barrier-island response to hurricanes

USGS Publications Warehouse

Plant, Nathaniel G.; Stockdon, Hilary F.

2012-01-01

Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

Participants' responsibilities in clinical research.

PubMed

Resnik, David B; Ness, Elizabeth

2012-12-01

Discussions on the ethics and regulation of clinical research have a great deal to say about the responsibilities of investigators, sponsors, research institutions and institutional review boards, but very little about the responsibilities of research participants. In this article, we discuss the responsibilities of participants in clinical research. We argue that competent adult participants are responsible for complying with study requirements and fulfilling other obligations they undertake when they make an informed choice to enroll in a study. These responsibilities are based on duties related to promise-keeping, avoiding harm to one's self or others, beneficence and reciprocity. Investigators and research staff should inform participants about their responsibilities during the consent process, and should stress the importance of fulfilling study requirements. They should address any impediments to compliance, and they may provide participants with financial incentives for meeting study requirements. In very rare cases, coercive measures may be justified to prevent immanent harm to others resulting from non-compliance with study requirements.

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study

PubMed Central

Stevens, Adam; Murray, Philip; Wojcik, Jerome; Raelson, John; Koledova, Ekaterina; Chatelain, Pierre

2016-01-01

Objective Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations. Design and methods Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification. Results The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes – SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR). Conclusions The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use. PMID:27651465

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study.

PubMed

Stevens, Adam; Murray, Philip; Wojcik, Jerome; Raelson, John; Koledova, Ekaterina; Chatelain, Pierre; Clayton, Peter

2016-12-01

Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations. Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification. The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes - SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR). The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use. © 2016 European Society of Endocrinology.

Inhaled corticosteroids do not influence the early inflammatory response and clinical presentation of hospitalized subjects with COPD exacerbation.

PubMed

Crisafulli, Ernesto; Guerrero, Mónica; Menéndez, Rosario; Huerta, Arturo; Martinez, Raquel; Gimeno, Alexandra; Soler, Néstor; Torres, Antoni

2014-10-01

Inhaled corticosteroids are anti-inflammatory medications that can down-regulate the immunologic response in patients with COPD; however, their role at onset of COPD exacerbation is still not understood. The aim of this study was to assess the early inflammatory response and clinical presentation of patients with COPD exacerbation mediated by inhaled corticosteroids. Prospective data were collected on 123 hospitalized subjects with COPD exacerbation over a 30-month period at 2 Spanish university hospitals. Based on domiciliary use, comparative analyses were performed between subjects who did not use inhaled corticosteroids (n = 58) and subjects who did (n = 65). Measurements of serum biomarkers were recorded on admission to the hospital (day 1) and on day 3; clinical, physiological, microbiological, and severity data and mortality/readmission rates were also recorded. At days 1 and 3, both groups showed a similar inflammatory response; fluticasone produced lower levels of interleukin-8 compared with budesonide (P < .01). All clinical features considered were similar in the 2 groups; multivariate analysis predicting clinical complications on hospitalization showed air-flow obstruction severity as the only predictive factor (odds ratio 3.13, 95% CI 1.13-8.63, P = .02). Our study demonstrates a lack of inhaled corticosteroid influence in the early systemic inflammatory response to and clinical presentation of COPD exacerbation. Copyright © 2014 by Daedalus Enterprises.

Baseline Factors Predicting Placebo Response to Treatment in Children and Adolescents With Autism Spectrum Disorders

PubMed Central

King, Bryan H.; Dukes, Kimberly; Donnelly, Craig L.; Sikich, Linmarie; McCracken, James T.; Scahill, Lawrence; Hollander, Eric; Bregman, Joel D.; Anagnostou, Evdokia; Robinson, Fay; Sullivan, Lisa; Hirtz, Deborah

2016-01-01

IMPORTANCE The finding of factors that differentially predict the likelihood of response to placebo over that of an active drug could have a significant impact on study design in this population. OBJECTIVE To identify possible nonspecific, baseline predictors of response to intervention in a large randomized clinical trial of children and adolescents with autism spectrum disorders. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial of citalopram hydrobromide for children and adolescents with autism spectrum disorders and prominent repetitive behavior. Baseline data at study entry were examined with respect to final outcome to determine if response predictors could be identified. A total of 149 children and adolescents 5 to 17 years of age (mean [SD] age, 9.4 [3.1] years) from 6 academic centers were randomly assigned to citalopram (n = 73) or placebo (n = 76). Participants had autistic disorder, Asperger syndrome, or pervasive developmental disorder, not otherwise specified; had illness severity ratings that were moderate or more than moderate on the Clinical Global Impression–Severity scale; and scored moderate or more than moderate on compulsive behaviors measured with the modified Children’s Yale-Brown Obsessive-Compulsive Scale. INTERVENTIONS Twelve weeks of treatment with citalopram (10 mg/5 mL) or placebo. The mean (SD) maximum dose of citalopram was 16.5 (6.5) mg by mouth daily (maximum dose, 20 mg/d). MAIN OUTCOMES AND MEASURES A positive response was defined as having a score of at least much improved on the Clinical Global Impression–Improvement scale at week 12. Baseline measures included demographic (sex, age, weight, and pubertal status), clinical, and family measures. Clinical variables included baseline illness severity ratings (the Aberrant Behavior Checklist, the Child and Adolescent Symptom Inventory, the Vineland Adaptive Behavior Scales, the Repetitive Behavior Scale–Revised, and the Children’s Yale-Brown Obsessive

Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in Rheumatoid Arthritis.

PubMed

Nguyen, Minh Vu Chuong; Baillet, Athan; Romand, Xavier; Trocmé, Candice; Courtier, Anaïs; Marotte, Hubert; Thomas, Thierry; Soubrier, Martin; Miossec, Pierre; Tébib, Jacques; Grange, Laurent; Toussaint, Bertrand; Lequerré, Thierry; Vittecoq, Olivier; Gaudin, Philippe

2018-06-06

Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients. Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi. A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR-) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually. A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment. Copyright © 2018. Published by Elsevier Masson SAS.

Post-Exercise Heart Rate Recovery Independently Predicts Clinical Outcome in Patients with Acute Decompensated Heart Failure.

PubMed

Youn, Jong-Chan; Lee, Hye Sun; Choi, Suk-Won; Han, Seong-Woo; Ryu, Kyu-Hyung; Shin, Eui-Cheol; Kang, Seok-Min

2016-01-01

Post-exercise heart rate recovery (HRR) is an index of parasympathetic function associated with clinical outcome in patients with chronic heart failure. However, its relationship with the pro-inflammatory response and prognostic value in consecutive patients with acute decompensated heart failure (ADHF) has not been investigated. We measured HRR and pro-inflammatory markers in 107 prospectively and consecutively enrolled, recovered ADHF patients (71 male, 59 ± 15 years, mean ejection fraction 28.9 ± 14.2%) during the pre-discharge period. The primary endpoint included cardiovascular (CV) events defined as CV mortality, cardiac transplantation, or rehospitalization due to HF aggravation. The CV events occurred in 30 (28.0%) patients (5 cardiovascular deaths and 7 cardiac transplantations) during the follow-up period (median 214 days, 11-812 days). When the patients with ADHF were grouped by HRR according to the Contal and O'Quigley's method, low HRR was shown to be associated with significantly higher levels of serum monokine-induced by gamma interferon (MIG) and poor clinical outcome. Multivariate Cox regression analysis revealed that low HRR was an independent predictor of CV events in both enter method and stepwise method. The addition of HRR to a model significantly increased predictability for CV events across the entire follow-up period. Impaired post-exercise HRR is associated with a pro-inflammatory response and independently predicts clinical outcome in patients with ADHF. These findings may explain the relationship between autonomic dysfunction and clinical outcome in terms of the inflammatory response in these patients.

Fourier and non-Fourier bio-heat transfer models to predict ex vivo temperature response to focused ultrasound heating

NASA Astrophysics Data System (ADS)

Li, Chenghai; Miao, Jiaming; Yang, Kexin; Guo, Xiasheng; Tu, Juan; Huang, Pintong; Zhang, Dong

2018-05-01

Although predicting temperature variation is important for designing treatment plans for thermal therapies, research in this area is yet to investigate the applicability of prevalent thermal conduction models, such as the Pennes equation, the thermal wave model of bio-heat transfer, and the dual phase lag (DPL) model. To address this shortcoming, we heated a tissue phantom and ex vivo bovine liver tissues with focused ultrasound (FU), measured the temperature response, and compared the results with those predicted by these models. The findings show that, for a homogeneous-tissue phantom, the initial temperature increase is accurately predicted by the Pennes equation at the onset of FU irradiation, although the prediction deviates from the measured temperature with increasing FU irradiation time. For heterogeneous liver tissues, the predicted response is closer to the measured temperature for the non-Fourier models, especially the DPL model. Furthermore, the DPL model accurately predicts the temperature response in biological tissues because it increases the phase lag, which characterizes microstructural thermal interactions. These findings should help to establish more precise clinical treatment plans for thermal therapies.

Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST.

PubMed

Fenerty, Kathleen E; Folio, Les R; Patronas, Nicholas J; Marté, Jennifer L; Gulley, James L; Heery, Christopher R

2016-08-23

The Response Evaluation Criteria in Solid Tumors (RECIST) are the current standard for evaluating disease progression or therapy response in patients with solid tumors. RECIST 1.1 calls for axial, longest-diameter (or perpendicular short axis of lymph nodes) measurements of a maximum of five tumors, which limits clinicians' ability to adequately measure disease burden, especially in patients with irregularly shaped tumors. This is especially problematic in chordoma, a disease for which RECIST does not always adequately capture disease burden because chordoma tumors are typically irregularly shaped and slow-growing. Furthermore, primary chordoma tumors tend to be adjacent to vital structures in the skull or sacrum that, when compressed, lead to significant clinical consequences. Volumetric segmentation is a newer technology that allows tumor burden to be measured in three dimensions on either MR or CT. Here, we compared the ability of RECIST measurements and tumor volumes to predict clinical outcomes in a cohort of 21 chordoma patients receiving immunotherapy. There was a significant difference in radiologic time to progression Kaplan-Meier curves between clinical outcome groups using volumetric segmentation (P = 0.012) but not RECIST (P = 0.38). In several cases, changes in volume were earlier and more sensitive reflections of clinical status. RECIST is a useful evaluation method when obvious changes are occurring in patients with chordoma. However, in many cases, RECIST does not detect small changes, and volumetric assessment was capable of detecting changes and predicting clinical outcome earlier than RECIST. Although this study was small and retrospective, we believe our results warrant further research in this area.

BIM expression in treatment naïve cancers predicts responsiveness to kinase inhibitors

PubMed Central

Faber, Anthony; Corcoran, Ryan B.; Ebi, Hiromichi; Sequist, Lecia V.; Waltman, Belinda A.; Chung, Euiheon; Incio, Joao; Digumarthy, Subba R.; Pollack, Sarah F.; Song, Youngchul; Muzikansky, Alona; Lifshits, Eugene; Roberge, Sylvie; Coffman, Erik J.; Benes, Cyril; Gómez, Henry; Baselga, Jose; Arteaga, Carlos L.; Rivera, Miguel N.; Dias-Santagata, Dora; Jain, Rakesh K.; Engelman, Jeffrey A.

2011-01-01

Cancers with specific genetic mutations are susceptible to selective kinase inhibitors. However, there is wide spectrum of benefit among cancers harboring the same sensitizing genetic mutations. Herein, we measured apoptotic rates among cell lines sharing the same driver oncogene following treatment with the corresponding kinase inhibitor. There was a wide range of kinase inhibitor-induced apoptosis despite comparable inhibition of the target and associated downstream signaling pathways. Surprisingly, pre-treatment RNA levels of the BH3-only pro-apoptotic BIM strongly predicted the capacity of EGFR, HER2, and PI3K inhibitors to induce apoptosis in EGFR mutant, HER2 amplified, and PIK3CA mutant cancers, respectively, but BIM levels did not predict responsiveness to standard chemotherapies. Furthermore, BIM RNA levels in EGFR mutant lung cancer specimens predicted response and duration of clinical benefit from EGFR inhibitors. These findings suggest assessment of BIM levels in treatment naïve tumor biopsies may indicate the degree of benefit from single-agent kinase inhibitors in multiple oncogene-addiction paradigms. PMID:22145099

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela; ...

2017-07-01

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Parent Prediction of Child Mood and Emotional Resilience: The Role of Parental Responsiveness and Psychological Control

PubMed Central

Boughton, Kristy L.; Lumley, Margaret N.

2011-01-01

Research consistently shows low to moderate agreement between parent and child reports of child mood, suggesting that parents are not always the best predictors of child emotional functioning. This study examines parental responsiveness and psychological control for improving prediction of early adolescent mood and emotional resilience beyond parent report of child emotional functioning. Participants were 268 early adolescents administered measures of depression symptoms, emotional resilience, and perceptions of parenting. Parents of participating youth completed measures of youth emotional functioning. Parental responsiveness and psychological control each emerged as family variables that may be of value for predicting child emotional functioning beyond parent reports. Specifically, responsiveness explained significant variance in child depression and resilience after accounting for parent reports, while parental psychological control increased prediction of child mood alone. Results generally suggest that parenting behaviours may be an important consideration when children and parents provide discrepant reports of child emotional well-being. Conceptual and clinical implications of these results are discussed. PMID:22110912

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

NASA Astrophysics Data System (ADS)

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

2005-08-01

The purpose of this work is to evaluate the predictive strength of the relative seriality, parallel and LKB normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis, in a large group of patients following breast cancer radiotherapy, and furthermore, to illustrate statistical methods for examining whether certain published radiobiological parameters are compatible with a clinical treatment methodology and patient group characteristics. The study is based on 150 consecutive patients who received radiation therapy for breast cancer. For each patient, the 3D dose distribution delivered to lung and the clinical treatment outcome were available. Clinical symptoms and radiological findings, along with a patient questionnaire, were used to assess the manifestation of radiation-induced complications. Using this material, different methods of estimating the likelihood of radiation effects were evaluated. This was attempted by analysing patient data based on their full dose distributions and associating the calculated complication rates with the clinical follow-up records. Additionally, the need for an update of the criteria that are being used in the current clinical practice was also examined. The patient material was selected without any conscious bias regarding the radiotherapy treatment technique used. The treatment data of each patient were applied to the relative seriality, LKB and parallel NTCP models, using published parameter sets. Of the 150 patients, 15 experienced radiation-induced pneumonitis (grade 2) according to the radiation pneumonitis scoring criteria used. Of the NTCP models examined, the relative seriality model was able to predict the incidence of radiation pneumonitis with acceptable accuracy, although radiation pneumonitis was developed by only a few patients. In the case of modern breast radiotherapy, radiobiological modelling appears to be very sensitive to model and parameter selection giving clinically

Evaluation of the tip-bending response in clinically used endoscopes.

PubMed

Rozeboom, Esther D; Reilink, Rob; Schwartz, Matthijs P; Fockens, Paul; Broeders, Ivo A M J

2016-04-01

Endoscopic interventions require accurate and precise control of the endoscope tip. The endoscope tip response depends on a cable pulling system, which is known to deliver a significantly nonlinear response that eventually reduces control. It is unknown whether the current technique of endoscope tip control is adequate for a future of high precision procedures, steerable accessories, and add-on robotics. The aim of this study was to determine the status of the tip response of endoscopes used in clinical practice. We evaluated 20 flexible colonoscopes and five gastroscopes, used in the endoscopy departments of a Dutch university hospital and two Dutch teaching hospitals, in a bench top setup. First, maximal tip bending was determined manually. Next, the endoscope navigation wheels were rotated individually in a motor setup. Tip angulation was recorded with a USB camera. Cable slackness was derived from the resulting hysteresis plot. Only two of the 20 colonoscopes (10 %) and none of the five gastroscopes reached the maximal tip angulation specified by the manufacturer. Four colonoscopes (20 %) and none of the gastroscopes demonstrated the recommended cable tension. Eight colonoscopes (40 %) had undergone a maintenance check 1 month before the measurements were made. The tip responses of these eight colonoscopies did not differ significantly from the tip responses of the other colonoscopes. This study suggests that the majority of clinically used endoscopes are not optimally tuned to reach maximal bending angles and demonstrate adequate tip responses. We suggest a brief check before procedures to predict difficulties with bending angles and tip responses.

CT contrast predicts pancreatic cancer treatment response to verteporfin-based photodynamic therapy

NASA Astrophysics Data System (ADS)

Jermyn, Michael; Davis, Scott C.; Dehghani, Hamid; Huggett, Matthew T.; Hasan, Tayyaba; Pereira, Stephen P.; Bown, Stephen G.; Pogue, Brian W.

2014-04-01

The goal of this study was to determine dominant factors affecting treatment response in pancreatic cancer photodynamic therapy (PDT), based on clinically available information in the VERTPAC-01 trial. This trial investigated the safety and efficacy of verteporfin PDT in 15 patients with locally advanced pancreatic adenocarcinoma. CT scans before and after contrast enhancement from the 15 patients in the VERTPAC-01 trial were used to determine venous-phase blood contrast enhancement and this was correlated with necrotic volume determined from post-treatment CT scans, along with estimation of optical absorption in the pancreas for use in light modeling of the PDT treatment. Energy threshold contours yielded estimates for necrotic volume based on this light modeling. Both contrast-derived venous blood content and necrotic volume from light modeling yielded strong correlations with observed necrotic volume (R2 = 0.85 and 0.91, respectively). These correlations were much stronger than those obtained by correlating energy delivered versus necrotic volume in the VERTPAC-01 study and in retrospective analysis from a prior clinical study. This demonstrates that contrast CT can provide key surrogate dosimetry information to assess treatment response. It also implies that light attenuation is likely the dominant factor in the VERTPAC treatment response, as opposed to other factors such as drug distribution. This study is the first to show that contrast CT provides needed surrogate dosimetry information to predict treatment response in a manner which uses standard-of-care clinical images, rather than invasive dosimetry methods.

Prediction Models for Dynamic Demand Response

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

Aman, Saima; Frincu, Marc; Chelmis, Charalampos

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2R, which we address inmore » this paper. Our first contribution is the formal definition of D 2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions

Measuring and predicting prostate cancer related quality of life changes using EPIC for clinical practice.

PubMed

Chipman, Jonathan J; Sanda, Martin G; Dunn, Rodney L; Wei, John T; Litwin, Mark S; Crociani, Catrina M; Regan, Meredith M; Chang, Peter

2014-03-01

We expanded the clinical usefulness of EPIC-CP (Expanded Prostate Cancer Index Composite for Clinical Practice) by evaluating its responsiveness to health related quality of life changes, defining the minimally important differences for an individual patient change in each domain and applying it to a sexual outcome prediction model. In 1,201 subjects from a previously described multicenter longitudinal cohort we modeled the EPIC-CP domain scores of each treatment group before treatment, and at short-term and long-term followup. We considered a posttreatment domain score change from pretreatment of 0.5 SD or greater clinically significant and p ≤ 0.01 statistically significant. We determined the domain minimally important differences using the pooled 0.5 SD of the 2, 6, 12 and 24-month posttreatment changes from pretreatment values. We then recalibrated an EPIC-CP based nomogram model predicting 2-year post-prostatectomy functional erection from that developed using EPIC-26. For each health related quality of life domain EPIC-CP was sensitive to similar posttreatment health related quality of life changes with time, as was observed using EPIC-26. The EPIC-CP minimally important differences in changes in the urinary incontinence, urinary irritation/obstruction, bowel, sexual and vitality/hormonal domains were 1.0, 1.3, 1.2, 1.6 and 1.0, respectively. The EPIC-CP based sexual prediction model performed well (AUC 0.76). It showed robust agreement with its EPIC-26 based counterpart with 10% or less predicted probability differences between models in 95% of individuals and a mean ± SD difference of 0.0 ± 0.05 across all individuals. EPIC-CP is responsive to health related quality of life changes during convalescence and it can be used to predict 2-year post-prostatectomy sexual outcomes. It can facilitate shared medical decision making and patient centered care. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc

A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion.

PubMed

Spiegelhalter, D J; Freedman, L S

1986-01-01

The 'textbook' approach to determining sample size in a clinical trial has some fundamental weaknesses which we discuss. We describe a new predictive method which takes account of prior clinical opinion about the treatment difference. The method adopts the point of clinical equivalence (determined by interviewing the clinical participants) as the null hypothesis. Decision rules at the end of the study are based on whether the interval estimate of the treatment difference (classical or Bayesian) includes the null hypothesis. The prior distribution is used to predict the probabilities of making the decisions to use one or other treatment or to reserve final judgement. It is recommended that sample size be chosen to control the predicted probability of the last of these decisions. An example is given from a multi-centre trial of superficial bladder cancer.

Auditory brainstem responses to stop consonants predict literacy.

PubMed

Neef, Nicole E; Schaadt, Gesa; Friederici, Angela D

2017-03-01

Precise temporal coding of speech plays a pivotal role in sound processing throughout the central auditory system, which, in turn, influences literacy acquisition. The current study tests whether an electrophysiological measure of this precision predicts literacy skills. Complex auditory brainstem responses were analysed from 62 native German-speaking children aged 11-13years. We employed the cross-phaseogram approach to compute the quality of the electrophysiological stimulus contrast [da] and [ba]. Phase shifts were expected to vary with literacy. Receiver operating curves demonstrated a feasible sensitivity and specificity of the electrophysiological measure. A multiple regression analysis resulted in a significant prediction of literacy by delta cross-phase as well as phonological awareness. A further commonality analysis separated a unique variance that was explained by the physiological measure, from a unique variance that was explained by the behavioral measure, and common effects of both. Despite multicollinearities between literacy, phonological awareness, and subcortical differentiation of stop consonants, a combined assessment of behavior and physiology strongly increases the ability to predict literacy skills. The strong link between the neurophysiological signature of sound encoding and literacy outcome suggests that the delta cross-phase could indicate the risk of dyslexia and thereby complement subjective psychometric measures for early diagnoses. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

Clinical time series prediction: towards a hierarchical dynamical system framework

PubMed Central

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Responsiveness and predictive validity of the tablet-based symbol digit modalities test in patients with stroke.

PubMed

Hsiao, Pei-Chi; Yu, Wan-Hui; Lee, Shih-Chieh; Chen, Mei-Hsiang; Hsieh, Ching-Lin

2018-06-14

The responsiveness and predictive validity of the Tablet-based Symbol Digit Modalities Test (T-SDMT) are unknown, which limits the utility of the T-SDMT in both clinical and research settings. The purpose of this study was to examine the responsiveness and predictive validity of the T-SDMT in inpatients with stroke. A follow-up, repeated-assessments design. One rehabilitation unit at a local medical center. A total of 50 inpatients receiving rehabilitation completed T-SDMT assessments at admission to and discharge from a rehabilitation ward. The median follow-up period was 14 days. The Barthel index (BI) was assessed at discharge and was used as the criterion of the predictive validity. The mean changes in the T-SDMT scores between admission and discharge were statistically significant (paired t-test = 3.46, p = 0.001). The T-SDMT scores showed a nearly moderate standardized response mean (0.49). A moderate association (Pearson's r = 0.47) was found between the scores of the T-SDMT at admission and those of the BI at discharge, indicating good predictive validity of the T-SDMT. Our results support the responsiveness and predictive validity of the T-SDMT in patients with stroke receiving rehabilitation in hospitals. This study provides empirical evidence supporting the use of the T-SDMT as an outcome measure for assessing processingspeed in inpatients with stroke. The scores of the T-SDMT could be used to predict basic activities of daily living function in inpatients with stroke.

Oxytocin receptor gene variations predict neural and behavioral response to oxytocin in autism

PubMed Central

Watanabe, Takamitsu; Otowa, Takeshi; Abe, Osamu; Kuwabara, Hitoshi; Aoki, Yuta; Natsubori, Tatsunobu; Takao, Hidemasa; Kakiuchi, Chihiro; Kondo, Kenji; Ikeda, Masashi; Iwata, Nakao; Kasai, Kiyoto; Sasaki, Tsukasa

2017-01-01

Abstract Oxytocin appears beneficial for autism spectrum disorder (ASD), and more than 20 single-nucleotide polymorphisms (SNPs) in oxytocin receptor (OXTR) are relevant to ASD. However, neither biological functions of OXTR SNPs in ASD nor critical OXTR SNPs that determine oxytocin’s effects on ASD remains known. Here, using a machine-learning algorithm that was designed to evaluate collective effects of multiple SNPs and automatically identify most informative SNPs, we examined relationships between 27 representative OXTR SNPs and six types of behavioral/neural response to oxytocin in ASD individuals. The oxytocin effects were extracted from our previous placebo-controlled within-participant clinical trial administering single-dose intranasal oxytocin to 38 high-functioning adult Japanese ASD males. Consequently, we identified six different SNP sets that could accurately predict the six different oxytocin efficacies, and confirmed the robustness of these SNP selections against variations of the datasets and analysis parameters. Moreover, major alleles of several prominent OXTR SNPs—including rs53576 and rs2254298—were found to have dissociable effects on the oxytocin efficacies. These findings suggest biological functions of the OXTR SNP variants on autistic oxytocin responses, and implied that clinical oxytocin efficacy may be genetically predicted before its actual administration, which would contribute to establishment of future precision medicines for ASD. PMID:27798253

Can non‐clinical repolarization assays predict the results of clinical thorough QT studies? Results from a research consortium

PubMed Central

Park, Eunjung; Gintant, Gary A; Bi, Daoqin; Kozeli, Devi; Pettit, Syril D; Skinner, Matthew; Willard, James; Wisialowski, Todd; Koerner, John; Valentin, Jean‐Pierre

2018-01-01

Background and Purpose Translation of non‐clinical markers of delayed ventricular repolarization to clinical prolongation of the QT interval corrected for heart rate (QTc) (a biomarker for torsades de pointes proarrhythmia) remains an issue in drug discovery and regulatory evaluations. We retrospectively analysed 150 drug applications in a US Food and Drug Administration database to determine the utility of established non‐clinical in vitro IKr current human ether‐à‐go‐go‐related gene (hERG), action potential duration (APD) and in vivo (QTc) repolarization assays to detect and predict clinical QTc prolongation. Experimental Approach The predictive performance of three non‐clinical assays was compared with clinical thorough QT study outcomes based on free clinical plasma drug concentrations using sensitivity and specificity, receiver operating characteristic (ROC) curves, positive (PPVs) and negative predictive values (NPVs) and likelihood ratios (LRs). Key Results Non‐clinical assays demonstrated robust specificity (high true negative rate) but poor sensitivity (low true positive rate) for clinical QTc prolongation at low‐intermediate (1×–30×) clinical exposure multiples. The QTc assay provided the most robust PPVs and NPVs (ability to predict clinical QTc prolongation). ROC curves (overall test accuracy) and LRs (ability to influence post‐test probabilities) demonstrated overall marginal performance for hERG and QTc assays (best at 30× exposures), while the APD assay demonstrated minimal value. Conclusions and Implications The predictive value of hERG, APD and QTc assays varies, with drug concentrations strongly affecting translational performance. While useful in guiding preclinical candidates without clinical QT prolongation, hERG and QTc repolarization assays provide greater value compared with the APD assay. PMID:29181850

Tips for Teachers of Evidence-based Medicine: Clinical Prediction Rules (CPRs) and Estimating Pretest Probability

PubMed Central

McGinn, Thomas; Jervis, Ramiro; Wisnivesky, Juan; Keitz, Sheri

2008-01-01

Background Clinical prediction rules (CPR) are tools that clinicians can use to predict the most likely diagnosis, prognosis, or response to treatment in a patient based on individual characteristics. CPRs attempt to standardize, simplify, and increase the accuracy of clinicians’ diagnostic and prognostic assessments. The teaching tips series is designed to give teachers advice and materials they can use to attain specific educational objectives. Educational Objectives In this article, we present 3 teaching tips aimed at helping clinical learners use clinical prediction rules and to more accurately assess pretest probability in every day practice. The first tip is designed to demonstrate variability in physician estimation of pretest probability. The second tip demonstrates how the estimate of pretest probability influences the interpretation of diagnostic tests and patient management. The third tip exposes learners to various examples and different types of Clinical Prediction Rules (CPR) and how to apply them in practice. Pilot Testing We field tested all 3 tips with 16 learners, a mix of interns and senior residents. Teacher preparatory time was approximately 2 hours. The field test utilized a board and a data projector; 3 handouts were prepared. The tips were felt to be clear and the educational objectives reached. Potential teaching pitfalls were identified. Conclusion Teaching with these tips will help physicians appreciate the importance of applying evidence to their every day decisions. In 2 or 3 short teaching sessions, clinicians can also become familiar with the use of CPRs in applying evidence consistently in everyday practice. PMID:18491194

International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol.

PubMed

Williams, Leanne M; Rush, A John; Koslow, Stephen H; Wisniewski, Stephen R; Cooper, Nicholas J; Nemeroff, Charles B; Schatzberg, Alan F; Gordon, Evian

2011-01-05

Clinically useful treatment moderators of Major Depressive Disorder (MDD) have not yet been identified, though some baseline predictors of treatment outcome have been proposed. The aim of iSPOT-D is to identify pretreatment measures that predict or moderate MDD treatment response or remission to escitalopram, sertraline or venlafaxine; and develop a model that incorporates multiple predictors and moderators. The International Study to Predict Optimized Treatment - in Depression (iSPOT-D) is a multi-centre, international, randomized, prospective, open-label trial. It is enrolling 2016 MDD outpatients (ages 18-65) from primary or specialty care practices (672 per treatment arm; 672 age-, sex- and education-matched healthy controls). Study-eligible patients are antidepressant medication (ADM) naïve or willing to undergo a one-week wash-out of any non-protocol ADM, and cannot have had an inadequate response to protocol ADM. Baseline assessments include symptoms; distress; daily function; cognitive performance; electroencephalogram and event-related potentials; heart rate and genetic measures. A subset of these baseline assessments are repeated after eight weeks of treatment. Outcomes include the 17-item Hamilton Rating Scale for Depression (primary) and self-reported depressive symptoms, social functioning, quality of life, emotional regulation, and side-effect burden (secondary). Participants may then enter a naturalistic telephone follow-up at weeks 12, 16, 24 and 52. The first half of the sample will be used to identify potential predictors and moderators, and the second half to replicate and confirm. First enrolment was in December 2008, and is ongoing. iSPOT-D evaluates clinical and biological predictors of treatment response in the largest known sample of MDD collected worldwide. International Study to Predict Optimised Treatment - in Depression (iSPOT-D) ClinicalTrials.gov Identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849?term=International+Study+to+Predict

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

PubMed

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

The utility of Bayesian predictive probabilities for interim monitoring of clinical trials

PubMed Central

Connor, Jason T.; Ayers, Gregory D; Alvarez, JoAnn

2014-01-01

Background Bayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. Purpose We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods. Results For interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. Limitations Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval. Conclusions The use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision making process. PMID:24872363

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; ...

2017-11-21

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Maternal Responsiveness Predicts Child Language at Ages 3 and 4 in a Community-Based Sample of Slow-to-Talk Toddlers

ERIC Educational Resources Information Center

Hudson, Sophie; Levickis, Penny; Down, Kate; Nicholls, Ruth; Wake, Melissa

2015-01-01

Background: Maternal responsiveness has been shown to predict child language outcomes in clinical samples of children with language delay and non-representative samples of typically developing children. An effective and timely measure of maternal responsiveness for use at the population level has not yet been established. Aims: To determine…

Prediction of Response to Treatment in a Randomized Clinical Trial of Couple Therapy: A 2-Year Follow-Up

ERIC Educational Resources Information Center

Baucom, Brian R.; Atkins, David C.; Simpson, Lorelei E.; Christensen, Andrew

2009-01-01

Many studies have examined pretreatment predictors of immediate posttreatment outcome, but few studies have examined prediction of long-term treatment response to couple therapies. Four groups of predictors (demographic, intrapersonal, communication, and other interpersonal) and 2 moderators (pretreatment severity and type of therapy) were…

Evaluation of the tip-bending response in clinically used endoscopes

PubMed Central

Rozeboom, Esther D.; Reilink, Rob; Schwartz, Matthijs P.; Fockens, Paul; Broeders, Ivo A. M. J.

2016-01-01

Background and study aims: Endoscopic interventions require accurate and precise control of the endoscope tip. The endoscope tip response depends on a cable pulling system, which is known to deliver a significantly nonlinear response that eventually reduces control. It is unknown whether the current technique of endoscope tip control is adequate for a future of high precision procedures, steerable accessories, and add-on robotics. The aim of this study was to determine the status of the tip response of endoscopes used in clinical practice. Materials and methods: We evaluated 20 flexible colonoscopes and five gastroscopes, used in the endoscopy departments of a Dutch university hospital and two Dutch teaching hospitals, in a bench top setup. First, maximal tip bending was determined manually. Next, the endoscope navigation wheels were rotated individually in a motor setup. Tip angulation was recorded with a USB camera. Cable slackness was derived from the resulting hysteresis plot. Results: Only two of the 20 colonoscopes (10 %) and none of the five gastroscopes reached the maximal tip angulation specified by the manufacturer. Four colonoscopes (20 %) and none of the gastroscopes demonstrated the recommended cable tension. Eight colonoscopes (40 %) had undergone a maintenance check 1 month before the measurements were made. The tip responses of these eight colonoscopies did not differ significantly from the tip responses of the other colonoscopes. Conclusion: This study suggests that the majority of clinically used endoscopes are not optimally tuned to reach maximal bending angles and demonstrate adequate tip responses. We suggest a brief check before procedures to predict difficulties with bending angles and tip responses. PMID:27092330

42 CFR 493.1457 - Standard; Clinical consultant responsibilities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 5 2010-10-01 2010-10-01 false Standard; Clinical consultant responsibilities. 493... Testing Laboratories Performing High Complexity Testing § 493.1457 Standard; Clinical consultant responsibilities. The clinical consultant provides consultation regarding the appropriateness of the testing...

42 CFR 493.1419 - Standard; Clinical consultant responsibilities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 5 2010-10-01 2010-10-01 false Standard; Clinical consultant responsibilities. 493... Testing Laboratories Performing Moderate Complexity Testing § 493.1419 Standard; Clinical consultant responsibilities. The clinical consultant provides consultation regarding the appropriateness of the testing...

Evaluation of stroke volume variation obtained by arterial pulse contour analysis to predict fluid responsiveness intraoperatively.

PubMed

Lahner, D; Kabon, B; Marschalek, C; Chiari, A; Pestel, G; Kaider, A; Fleischmann, E; Hetz, H

2009-09-01

Fluid management guided by oesophageal Doppler monitor has been reported to improve perioperative outcome. Stroke volume variation (SVV) is considered a reliable clinical predictor of fluid responsiveness. Consequently, the aim of the present trial was to evaluate the accuracy of SVV determined by arterial pulse contour (APCO) analysis, using the FloTrac/Vigileo system, to predict fluid responsiveness as measured by the oesophageal Doppler. Patients undergoing major abdominal surgery received intraoperative fluid management guided by oesophageal Doppler monitoring. Fluid boluses of 250 ml each were administered in case of a decrease in corrected flow time (FTc) to <350 ms. Patients were connected to a monitoring device, obtaining SVV by APCO. Haemodynamic variables were recorded before and after fluid bolus application. Fluid responsiveness was defined as an increase in stroke volume index >10%. The ability of SVV to predict fluid responsiveness was assessed by calculation of the area under the receiver operating characteristic (ROC) curve. Twenty patients received 67 fluid boluses. Fifty-two of the 67 fluid boluses administered resulted in fluid responsiveness. SVV achieved an area under the ROC curve of 0.512 [confidence interval (CI) 0.32-0.70]. A cut-off point for fluid responsiveness was found for SVV > or =8.5% (sensitivity: 77%; specificity: 43%; positive predictive value: 84%; and negative predictive value: 33%). This prospective, interventional observer-blinded study demonstrates that SVV obtained by APCO, using the FloTrac/Vigileo system, is not a reliable predictor of fluid responsiveness in the setting of major abdominal surgery.

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses.

PubMed

Roth, Christian J; Becher, Tobias; Frerichs, Inéz; Weiler, Norbert; Wall, Wolfgang A

2017-04-01

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results

The EXCITE Trial: Predicting a Clinically Meaningful Motor Activity Log Outcome

PubMed Central

Park, Si-Woon; Wolf, Steven L.; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S.

2013-01-01

Background and Objective This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. Methods A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Results Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Conclusions Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT. PMID:18780883

The EXCITE Trial: Predicting a clinically meaningful motor activity log outcome.

PubMed

Park, Si-Woon; Wolf, Steven L; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S

2008-01-01

This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT.

How accurate is our clinical prediction of "minimal prostate cancer"?

PubMed

Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

2013-07-01

Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

RNA sequencing to predict response to TNF-α inhibitors reveals possible mechanism for nonresponse in smokers.

PubMed

Cuppen, Bart V J; Rossato, Marzia; Fritsch-Stork, Ruth D E; Concepcion, Arno N; Linn-Rasker, Suzanne P; Bijlsma, Johannes W J; van Laar, Jacob M; Lafeber, Floris P J G; Radstake, Timothy R

2018-06-06

Several studies have employed microarray-based profiling to predict response to tumor necrosis factor-alpha inhibitors (TNFi) in rheumatoid arthritis (RA); yet efforts to validate these targets have failed to show predictive abilities acceptable for clinical practice. The eighty most extreme responders and nonresponders to TNFi therapy were selected from the observational BiOCURA cohort. RNA sequencing was performed on mRNA from peripheral blood mononuclear cells (PBMCs) collected before initiation of treatment. The expression of pathways as well as individual gene transcripts between responders and nonresponders was investigated. Promising targets were technically replicated and validated in n = 40 new patients using qPCR assays. Before therapy initiation, nonresponders had lower expression of pathways related to interferon and cytokine signaling, while also showing higher levels of two genes, GPR15 and SEMA6B (p = 0.02). The two targets could be validated, however, additional analyses revealed that GPR15 and SEMA6B did not independently predict response, but were rather dose-dependent markers of smoking (p < 0.0001). The study did not identify new transcripts ready to use in clinical practice, yet GPR15 and SEMA6B were recognized as candidate explanatory markers for the reduced treatment success in RA smokers.

The value of integrating pre-clinical data to predict nausea and vomiting risk in humans as illustrated by AZD3514, a novel androgen receptor modulator

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

Grant, Claire, E-mail: claire.grant@astrazeneca.com; Ewart, Lorna; Muthas, Daniel

Nausea and vomiting are components of a complex mechanism that signals food avoidance and protection of the body against the absorption of ingested toxins. This response can also be triggered by pharmaceuticals. Predicting clinical nausea and vomiting liability for pharmaceutical agents based on pre-clinical data can be problematic as no single animal model is a universal predictor. Moreover, efforts to improve models are hampered by the lack of translational animal and human data in the public domain. AZD3514 is a novel, orally-administered compound that inhibits androgen receptor signaling and down-regulates androgen receptor expression. Here we have explored the utility ofmore » integrating data from several pre-clinical models to predict nausea and vomiting in the clinic. Single and repeat doses of AZD3514 resulted in emesis, salivation and gastrointestinal disturbances in the dog, and inhibited gastric emptying in rats after a single dose. AZD3514, at clinically relevant exposures, induced dose-responsive “pica” behaviour in rats after single and multiple daily doses, and induced retching and vomiting behaviour in ferrets after a single dose. We compare these data with the clinical manifestation of nausea and vomiting encountered in patients with castration-resistant prostate cancer receiving AZD3514. Our data reveal a striking relationship between the pre-clinical observations described and the experience of nausea and vomiting in the clinic. In conclusion, the emetic nature of AZD3514 was predicted across a range of pre-clinical models, and the approach presented provides a valuable framework for predicition of clinical nausea and vomiting. - Highlights: • Integrated pre-clinical data can be used to predict clinical nausea and vomiting. • Data integrated from standard toxicology studies is sufficient to make a prediction. • The use of the nausea algorithm developed by Parkinson (2012) aids the prediction. • Additional pre-clinical studies can

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

PubMed

Vidyasagar, Mathukumalli

2015-01-01

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

New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs

PubMed Central

Beckman, Robert A.; Chen, Cong

2013-01-01

Predictive biomarkers are important to the future of oncology; they can be used to identify patient populations who will benefit from therapy, increase the value of cancer medicines, and decrease the size and cost of clinical trials while increasing their chance of success. But predictive biomarkers do not always work. When unsuccessful, they add cost, complexity, and time to drug development. This perspective describes phases 2 and 3 development methods that efficiently and adaptively check the ability of a biomarker to predict clinical outcomes. In the end, the biomarker is emphasized to the extent that it can actually predict. PMID:23489587

Insufficient sleep predicts clinical burnout.

PubMed

Söderström, Marie; Jeding, Kerstin; Ekstedt, Mirjam; Perski, Aleksander; Akerstedt, Torbjörn

2012-04-01

The present prospective study aimed to identify risk factors for subsequent clinical burnout. Three hundred eighty-eight working individuals completed a baseline questionnaire regarding work stress, sleep, mood, health, and so forth. During a 2-year period, 15 subjects (7 women and 8 men) of the total sample were identified as "burnout cases," as they were assessed and referred to treatment for clinical burnout. Questionnaire data from the baseline measurement were used as independent variables in a series of logistic regression analyses to predict clinical burnout. The results identified "too little sleep (< 6 h)" as the main risk factor for burnout development, with adjustment for "work demands," "thoughts of work during leisure time," and "sleep quality." The first two factors were significant predictors in earlier steps of the multivariate regression. The results indicate that insufficient sleep, preoccupation with thoughts of work during leisure time, and high work demands are risk factors for subsequent burnout. The results suggest a chain of causation. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Immunological tumor status may predict response to neoadjuvant chemotherapy and outcome after radical cystectomy in bladder cancer.

PubMed

Tervahartiala, Minna; Taimen, Pekka; Mirtti, Tuomas; Koskinen, Ilmari; Ecke, Thorsten; Jalkanen, Sirpa; Boström, Peter J

2017-10-04

Bladder cancer (BC) is the ninth most common cancer worldwide. Radical cystectomy (RC) with neoadjuvant chemotherapy (NAC) is recommended for muscle-invasive BC. The challenge of the neoadjuvant approach relates to challenges in selection of patients to chemotherapy that are likely to respond to the treatment. To date, there are no validated molecular markers or baseline clinical characteristics to identify these patients. Different inflammatory markers, including tumor associated macrophages with their plastic pro-tumorigenic and anti-tumorigenic functions, have extensively been under interests as potential prognostic and predictive biomarkers in different cancer types. In this immunohistochemical study we evaluated the predictive roles of three immunological markers, CD68, MAC387, and CLEVER-1, in response to NAC and outcome of BC. 41% of the patients had a complete response (pT0N0) to NAC. Basic clinicopathological variables did not predict response to NAC. In contrast, MAC387 + cells and CLEVER-1 + macrophages associated with poor NAC response, while CLEVER-1 + vessels associated with more favourable response to NAC. Higher counts of CLEVER-1 + macrophages associated with poorer overall survival and CD68 + macrophages seem to have an independent prognostic value in BC patients treated with NAC. Our findings point out that CD68, MAC387, and CLEVER-1 may be useful prognostic and predictive markers in BC.

Predicting the response of olfactory sensory neurons to odor mixtures from single odor response

NASA Astrophysics Data System (ADS)

Marasco, Addolorata; de Paris, Alessandro; Migliore, Michele

2016-04-01

The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific olfactory receptor responses by manipulating mixtures in a mathematically predictable manner. This result is general and applies, independently of the number of odor components, to any olfactory sensory neuron type with a response curve that can be represented as a sigmoidal function of the odor concentration.

Nurse-Administered, Gut-Directed Hypnotherapy in IBS: Efficacy and Factors Predicting a Positive Response.

PubMed

Lövdahl, Jenny; Ringström, Gisela; Agerforz, Pia; Törnblom, Hans; Simrén, Magnus

2015-07-01

Hypnotherapy is an effective treatment in irritable bowel syndrome (IBS). It is often delivered by a psychotherapist and is costly and time consuming. Nurse-administered hypnotherapy could increase availability and reduce costs. In this study the authors evaluate the effectiveness of nurse-administered, gut-directed hypnotherapy and identify factors predicting treatment outcome. Eighty-five patients were included in the study. Participants received hypnotherapy by a nurse once/week for 12 weeks. Patients reported marked improvement in gastrointestinal (GI) and extra-colonic symptoms after treatment, as well as a reduction in GI-specific anxiety, general anxiety, and depression. Fifty-eight percent were responders after the 12 weeks treatment period, and of these 82% had a favorable clinical response already at week 6. Women were more likely than men to respond favorably to the treatment. Nurse-administered hypnotherapy is an effective treatment for IBS. Being female and reporting a favorable response to treatment by week 6 predicted a positive treatment response at the end of the 12 weeks treatment period.

High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer

PubMed Central

Lluch, Ana; Ribelles, Nuria; Anton-Torres, Antonio; Sanchez-Rovira, Pedro; Albanell, Joan; Calvo, Lourdes; García-Asenjo, Jose Antonio Lopez; Palacios, Jose; Chacon, Jose Ignacio; Ruiz, Amparo; De la Haba-Rodriguez, Juan; Segui-Palmer, Miguel A.; Cirauqui, Beatriz; Margeli, Mireia; Plazaola, Arrate; Barnadas, Agusti; Casas, Maribel; Caballero, Rosalia; Carrasco, Eva; Rojo, Federico

2016-01-01

Background. In the neoadjuvant setting, changes in the proliferation marker Ki67 are associated with primary endocrine treatment efficacy, but its value as a predictor of response to chemotherapy is still controversial. Patients and Methods. We analyzed 262 patients with centralized basal Ki67 immunohistochemical evaluation derived from 4 GEICAM (Spanish Breast Cancer Group) clinical trials of neoadjuvant chemotherapy for breast cancer. The objective was to identify the optimal threshold for Ki67 using the receiver-operating characteristic curve method to maximize its predictive value for chemotherapy benefit. We also evaluated the predictive role of the defined Ki67 cutoffs for molecular subtypes defined by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Results. A basal Ki67 cutpoint of 50% predicted pathological complete response (pCR). Patients with Ki67 >50% achieved a pCR rate of 40% (36 of 91) versus a pCR rate of 19% in patients with Ki67 ≤50% (33 of 171) (p = .0004). Ki67 predictive value was especially relevant in ER-HER2− and ER-HER2+ patients (pCR rates of 42% and 64%, respectively, in patients with Ki67 >50% versus 15% and 45%, respectively, in patients with Ki67 ≤50%; p = .0337 and .3238, respectively). Both multivariate analyses confirmed the independent predictive value of the Ki67 cutpoint of 50%. Conclusion. Basal Ki67 proliferation index >50% should be considered an independent predictive factor for pCR reached after neoadjuvant chemotherapy, suggesting that cell proliferation is a phenomenon closely related to chemosensitivity. These findings could help to identify a group of patients with a potentially favorable long-term prognosis. Implications for Practice: The use of basal Ki67 status as a predictive factor of chemotherapy benefit could facilitate the identification of a patient subpopulation with high probability of achieving pathological complete response when treated with primary chemotherapy, and thus

Dopamine Reward Prediction Error Responses Reflect Marginal Utility

PubMed Central

Stauffer, William R.; Lak, Armin; Schultz, Wolfram

2014-01-01

Summary Background Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. Results In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions’ shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. Conclusions These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). PMID:25283778

Dopamine reward prediction error responses reflect marginal utility.

PubMed

Stauffer, William R; Lak, Armin; Schultz, Wolfram

2014-11-03

Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Blood DNA methylation biomarkers predict clinical reactivity in food-sensitized infants.

PubMed

Martino, David; Dang, Thanh; Sexton-Oates, Alexandra; Prescott, Susan; Tang, Mimi L K; Dharmage, Shyamali; Gurrin, Lyle; Koplin, Jennifer; Ponsonby, Anne-Louise; Allen, Katrina J; Saffery, Richard

2015-05-01

The diagnosis of food allergy (FA) can be challenging because approximately half of food-sensitized patients are asymptomatic. Current diagnostic tests are excellent makers of sensitization but poor predictors of clinical reactivity. Thus oral food challenges (OFCs) are required to determine a patient's risk of reactivity. We sought to discover genomic biomarkers of clinical FA with utility for predicting food challenge outcomes. Genome-wide DNA methylation (DNAm) profiling was performed on blood mononuclear cells from volunteers who had undergone objective OFCs, concurrent skin prick tests, and specific IgE tests. Fifty-eight food-sensitized patients (aged 11-15 months) were assessed, half of whom were clinically reactive. Thirteen nonallergic control subjects were also assessed. Reproducibility was assessed in an additional 48 samples by using methylation data from an independent population of patients with clinical FA. Using a supervised learning approach, we discovered a DNAm signature of 96 CpG sites that predict clinical outcomes. Diagnostic scores were derived from these 96 methylation sites, and cutoffs were determined in a sensitivity analysis. Methylation biomarkers outperformed allergen-specific IgE and skin prick tests for predicting OFC outcomes. FA status was correctly predicted in the replication cohort with an accuracy of 79.2%. DNAm biomarkers with clinical utility for predicting food challenge outcomes are readily detectable in blood. The development of this technology in detailed follow-up studies will yield highly innovative diagnostic assays. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by 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.

Use of reflectance spectrophotometry to predict the response of port wine stains to pulsed dye laser.

PubMed

Halachmi, Shlomit; Azaria, Ron; Inbar, Roy; Ad-El, Dean; Lapidoth, Moshe

2014-01-01

Reflectance spectroscopy can be used to quantitate subtle differences in color. We applied a portable reflectance spectrometer to determine its utility in the evaluation of pulsed dye laser treatment of port wine stains (PWS) and in prediction of clinical outcome, in a prospective study. Forty-eight patients with PWS underwent one to nine pulsed dye laser treatments. Patient age and skin color as well as PWS surface area, anatomic location, and color were recorded. Pretreatment spectrophotometric measurements were performed. The subjective clinical results of treatment and the quantitative spectrophotometry results were evaluated by two independent teams, and the findings were correlated. The impact of the clinical characteristics on the response to treatment was assessed as well. Patients with excellent to good clinical results of laser treatments had pretreatment spectrophotometric measurements which differed by more than 10%, whereas patients with fair to poor results had spectrophotometric measurements with a difference of of less than 10%. The correlation between the spectrophotometric results and the clinical outcome was 73% (p < 0.01). The impact of the other clinical variables on outcome agreed with the findings in the literature. Spectrophotometry has a higher correlation with clinical outcome and a better predictive value than other nonmeasurable, nonquantitative, dependent variables.

Predicting factors of fistula healing and clinical remission after infliximab-based combined therapy for perianal fistulizing Crohn's disease.

PubMed

Tougeron, David; Savoye, Guillaume; Savoye-Collet, Céline; Koning, Edith; Michot, Francis; Lerebours, Eric

2009-08-01

Perianal fistulizing Crohn's disease (PFCD) treatment is based on fistula drainage, antibiotics, immunosuppressant (IS) drugs, and infliximab. Our aim was to study the effectiveness of combination therapy on PFCD and to search for clinical or imaging features associated with the initial complete clinical response and its stability overtime. All patients with PFCD treated in our tertiary center between 2000 and 2005 by infliximab in combination with seton placement and/or IS and evaluated by MRI before treatment were included in the study. Basal clinical and MRI characteristics were recorded. Response to treatment was evaluated after the infliximab induction regiment and at the end of the follow-up. Twenty-six patients were included and followed-up for an average 4.9 years. A complex fistula was present in 69% (18/26 patients) of cases and eight (8/26 patients) had an ano-vaginal fistula. After infliximab induction therapy, 13 patients (50%) achieved a complete clinical response. The initial clinical response was significantly associated with the absence of both, active intestinal disease (54% vs. 8%, P = 0.03) and active proctitis (77% vs. 23%, P = 0.01). No initial MRI characteristics were linked to the initial response. In multivariate analysis, only the presence of active proctitis was associated with the lack of response (P = 0.047). At the end of the follow-up, 42% of the patients remained in clinical remission. No clinical characteristics were associated to sustained response when among long-standing responders two exhibited a normal post-treatment MRI. An initial complete response of PFCD was observed in half of the patients after combined therapy including infliximab that decreased to 42% later on. Complete healing of fistulas on MRI was possible but unusual. The initial response seemed related to the absence of active intestinal disease, especially in the rectum, when the long-term response could not be predicted by the basal characteristics of patients.

Predictive factors in the evaluation of treatment response to neoadjuvant chemoradiotherapy in patients with advanced esophageal squamous cell cancer

PubMed Central

Wong, Claudia

2017-01-01

Neoadjuvant therapy before esophagectomy is evidence-based, and is a standard-of-care for locally advanced and operable esophageal cancer. However response to such treatment varies in individual patients, from no clinical response to pathological complete response. It has been consistently shown that a good pathological responses is of prognostic value, but perhaps in the expense of those who do not. It is important to identify suitable predictive factors for response, so that patients are not exposed to potentially harmful chemotherapy and/or radiotherapy without benefits. Alternative management strategies can be devised. Various clinical, radiological, serological and potential molecular markers have been studied. None has been shown to be sufficiently reliable to be used in daily practice. Certainly more understanding of the molecular basis for response to chemotherapy/radiotherapy is needed, so that patient treatment can be tailored and individualized. PMID:28815073

[An Investigation of the Role Responsibilities of Clinical Research Nurses in Conducting Clinical Trials].

PubMed

Kao, Chi-Yin; Huang, Guey-Shiun; Dai, Yu-Tzu; Pai, Ya-Ying; Hu, Wen-Yu

2015-06-01

Clinical research nurses (CRNs) play an important role in improving the quality of clinical trials. In Taiwan, the increasing number of clinical trials has increased the number of practicing CRNs. Understanding the role responsibilities of CRNs is necessary to promote professionalism in this nursing category. This study investigates the role responsibilities of CRNs in conducting clinical trials / research. A questionnaire survey was conducted in a medical center in Taipei City, Taiwan. Eighty CRNs that were registered to facilitate and conduct clinical trials at this research site completed the survey. "Subject protection" was the CRN role responsibility most recognized by participants, followed by "research coordination and management", "subject clinical care", and "advanced professional nursing". Higher recognition scores were associated with higher importance scores and lower difficulty scores. Participants with trial training had significantly higher difficulty scores for "subject clinical care" and "research coordination and management" than their peers without this training (p < .05). Participants who had participated in a long-term trial-training course earned higher importance scores for "CRN four-subthemes role responsibilities" (p <.05) and lower difficulty scores for "subject protection", "research coordination and management" (p <.005) than their short-term course peers. "Recognition of overall responsibilities" and "receiving trial training" were the significant predictors of difficulty in performing CRN role responsibilities, explaining 21.9% of the total variance. To further promote CRN as a professional career in Taiwan, the findings of this study recommend identifying the core competences of CRNs and adding CRN-related study materials into the advanced nursing curriculum. Long-term and systematic educational training may help CRNs understand the importance of their role responsibilities, better recognize their professional role, and reflect these

Predicting switched-bias response from steady-state irradiations

NASA Astrophysics Data System (ADS)

Fleetwood, D. M.; Winokur, P. S.; Riewe, L. C.

1990-12-01

A novel semiempirical model of radiation-induced charge neutralization is presented. This model is combined with 12 heuristic guidelines derived from studies of oxide- and interface-trap charge (Delta Vot and Delta Vit) buildup and annealing to develop a method to predict MOS switched-bias response from steady-state irradiations, with no free parameters. For n-channel MOS devices, predictions of Delta Vot, Delta Vit, and mobility degradation differ from experimental values through irradiation by less than 30 percent in all cases considered. This is demonstrated for gate oxides with widely varying Delta Vot and Delta Vit and for parasitic field oxides. Preliminary results suggest that n-channel MOS Delta Vot annealing and Delta Vit buildup following switched-bias irradiation and through switched-bias annealing also may be predicted with less than 30 percent error. The p-channel MOS response at high frequencies is more difficult to predict.

Pulse pressure variation and prediction of fluid responsiveness in patients ventilated with low tidal volumes.

PubMed

Oliveira-Costa, Clarice Daniele Alves de; Friedman, Gilberto; Vieira, Sílvia Regina Rios; Fialkow, Léa

2012-07-01

To determine the utility of pulse pressure variation (ΔRESP PP) in predicting fluid responsiveness in patients ventilated with low tidal volumes (V T) and to investigate whether a lower ΔRESP PP cut-off value should be used when patients are ventilated with low tidal volumes. This cross-sectional observational study included 37 critically ill patients with acute circulatory failure who required fluid challenge. The patients were sedated and mechanically ventilated with a V T of 6-7 ml/kg ideal body weight, which was monitored with a pulmonary artery catheter and an arterial line. The mechanical ventilation and hemodynamic parameters, including ΔRESP PP, were measured before and after fluid challenge with 1,000 ml crystalloids or 500 ml colloids. Fluid responsiveness was defined as an increase in the cardiac index of at least 15%. ClinicalTrial.gov: NCT01569308. A total of 17 patients were classified as responders. Analysis of the area under the ROC curve (AUC) showed that the optimal cut-off point for ΔRESP PP to predict fluid responsiveness was 10% (AUC = 0.74). Adjustment of the ΔRESP PP to account for driving pressure did not improve the accuracy (AUC = 0.76). A ΔRESP PP ≥ 10% was a better predictor of fluid responsiveness than central venous pressure (AUC = 0.57) or pulmonary wedge pressure (AUC = 051). Of the 37 patients, 25 were in septic shock. The AUC for ΔRESP PP ≥ 10% to predict responsiveness in patients with septic shock was 0.484 (sensitivity, 78%; specificity, 93%). The parameter D RESP PP has limited value in predicting fluid responsiveness in patients who are ventilated with low tidal volumes, but a ΔRESP PP>10% is a significant improvement over static parameters. A ΔRESP PP ≥ 10% may be particularly useful for identifying responders in patients with septic shock.

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

PubMed

Creasy, John M; Midya, Abhishek; Chakraborty, Jayasree; Adams, Lauryn B; Gomes, Camilla; Gonen, Mithat; Seastedt, Kenneth P; Sutton, Elizabeth J; Cercek, Andrea; Kemeny, Nancy E; Shia, Jinru; Balachandran, Vinod P; Kingham, T Peter; Allen, Peter J; DeMatteo, Ronald P; Jarnagin, William R; D'Angelica, Michael I; Do, Richard K G; Simpson, Amber L

2018-06-19

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R 2 . Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R 2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be

Predicting the response of populations to environmental change

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

Ives, A.R.

1995-04-01

When subject to long-term directional environmental perturbations, changes in population densities depend on the positive and negative feedbacks operating through interactions within and among species in a community. This paper develops techniques to predict the long-term responses of population densities to environmental changes using data on short-term population fluctuations driven by short-term environmental variability. In addition to giving quantitative predictions, the techniques also reveal how different qualitative patterns of species interactions either buffer or accentuate population responses to environmental trends. All of the predictions are based on regression coefficients extracted from time series data, and they can therefore be appliedmore » with a minimum of mathematical and statistical gymnastics. 48 refs., 10 figs., 4 tabs.« less

TU-CD-BRB-09: Prediction of Chemo-Radiation Outcome for Rectal Cancer Based On Radiomics of Tumor Clinical Characteristics and Multi-Parametric MRI

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

Nie, K; Yue, N; Shi, L

2015-06-15

Purpose: To evaluate the tumor clinical characteristics and quantitative multi-parametric MR imaging features for prediction of response to chemo-radiation treatment (CRT) in locally advanced rectal cancer (LARC). Methods: Forty-three consecutive patients (59.7±6.9 years, from 09/2013 – 06/2014) receiving neoadjuvant CRT followed by surgery were enrolled. All underwent MRI including anatomical T1/T2, Dynamic Contrast Enhanced (DCE)-MRI and Diffusion-Weighted MRI (DWI) prior to the treatment. A total of 151 quantitative features, including morphology/Gray Level Co-occurrence Matrix (GLCM) texture from T1/T2, enhancement kinetics and the voxelized distribution from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, along with clinical information (carcinoembryonic antigen CEA level,more » TNM staging etc.), were extracted for each patient. Response groups were separated based on down-staging, good response and pathological complete response (pCR) status. Logistic regression analysis (LRA) was used to select the best predictors to classify different groups and the predictive performance were calculated using receiver operating characteristic (ROC) analysis. Results: Individual imaging category or clinical charateristics might yield certain level of power in assessing the response. However, the combined model outperformed than any category alone in prediction. With selected features as Volume, GLCM AutoCorrelation (T2), MaxEnhancementProbability (DCE-MRI), and MeanADC (DWI), the down-staging prediciton accuracy (area under the ROC curve, AUC) could be 0.95, better than individual tumor metrics with AUC from 0.53–0.85. While for the pCR prediction, the best set included CEA (clinical charateristics), Homogeneity (DCE-MRI) and MeanADC (DWI) with an AUC of 0.89, more favorable compared to conventional tumor metrics with an AUC ranging from 0.511–0.79. Conclusion: Through a systematic analysis of multi-parametric MR imaging features, we are able to build

Markers predicting response to bacillus Calmette-Guérin immunotherapy in high-risk bladder cancer patients: a systematic review.

PubMed

Zuiverloon, Tahlita C M; Nieuweboer, Annemieke J M; Vékony, Hedvig; Kirkels, Wim J; Bangma, Chris H; Zwarthoff, Ellen C

2012-01-01

Currently, bacillus Calmette-Guérin (BCG) intravesical instillations are standard treatment for patients with high-grade non-muscle-invasive bladder cancer; however, no markers are available to predict BCG response. To review the contemporary literature on markers predicting BCG response, to discuss the key issues concerning the identification of predictive markers, and to provide recommendations for further research studies. We performed a systematic review of the literature using PubMed and Embase databases in the period 1996-2010. The free-text search was extended by adding the following keywords: recurrence, progression, survival, molecular marker, prognosis, TP53, Ki-67, RB, fibronectin, immunotherapy, cytokine, interleukin, natural killer, macrophage, PMN, polymorphism, SNP, single nucleotide polymorphism, and gene signature. If thresholds for the detection of urinary interleukin (IL)-8, IL-18, and tumour necrosis factor apoptosis-inducing ligand levels are standardised, measurement of these cytokines holds promise in the assessment of BCG therapy outcome. Studies on immunohistochemical markers (ie, TP53, Ki-67, and retinoblastoma) display contradictory results, probably because of the small patient groups that were used and seem unsuitable to predict BCG response. Exploring combinations of protein levels might prove to be more helpful to establish the effect of BCG therapy. Single nucleotide polymorphisms, either in cytokines or in genes involved in DNA repair, need to be investigated in different ethnicities before their clinical relevance can be determined. Measurement of urinary IL-2 levels seems to be the most potent marker of all the clinical parameters reviewed. IL-2 levels are currently the most promising predictive markers of BCG response. For future studies focusing on new biomarkers, it is essential to make more use of new biomedical techniques such as microRNA profiling and genomewide sequencing. Copyright © 2011 European Association of Urology

The nature of placebo response in clinical studies of major depressive disorder.

PubMed

Papakostas, George I; Østergaard, Søren D; Iovieno, Nadia

2015-04-01

To review factors influencing placebo response and clinical trial outcome in depression, and suggest ways to optimize trial success in mood disorders. PubMed searches were conducted by cross-referencing the terms depression, depressive with placebo, clinical trial, and clinical trials for studies published in English between 1970 and September 2013. Relevant abstracts were identified in PubMed, including clinical trials, quantitative studies, and qualitative research. We obtained and reviewed relevant articles and utilized their information to synthesize the present review. Included articles were grouped in the following areas of relevance: (1) biological validity of illness, (2) baseline severity of illness, (3) chronicity of the index episode of depression, (4) age of participants, (5) medical and psychiatric comorbidity, (6) probability of receiving placebo, (7) use of prospective treatment phases (lead-in) (8) dosing schedule, (9) trial duration, (10) frequency of follow-up assessments, and (11) study outcome measure. Several key elements emerge as critical to the ultimate success of a clinical trial, including the probability of receiving placebo, study duration, dosing schedule, visit frequency, the use of blinded lead-in phases, the use of centralized raters, illness severity and duration, and comorbid anxiety. Our increasing understanding of the placebo response in clinical trials of major depressive disorder lends to a, gradually, more predictable phenomenon and, hopefully, to one that becomes lesser in magnitude and variability. Several elements have emerged that seem to play a critical role in trial success, gradually reshaping the design of clinical, translational, as well as mechanistic studies in depression. © Copyright 2015 Physicians Postgraduate Press, Inc.

Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data.

PubMed

Richardson, Alice; Signor, Ben M; Lidbury, Brett A; Badrick, Tony

2016-11-01

Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Sustained anterior cingulate cortex activation during reward processing predicts response to psychotherapy in major depressive disorder.

PubMed

Carl, Hannah; Walsh, Erin; Eisenlohr-Moul, Tory; Minkel, Jared; Crowther, Andrew; Moore, Tyler; Gibbs, Devin; Petty, Chris; Bizzell, Josh; Dichter, Gabriel S; Smoski, Moria J

2016-10-01

The purpose of the present investigation was to evaluate whether pre-treatment neural activation in response to rewards is a predictor of clinical response to Behavioral Activation Therapy for Depression (BATD), an empirically validated psychotherapy that decreases depressive symptoms by increasing engagement with rewarding stimuli and reducing avoidance behaviors. Participants were 33 outpatients with major depressive disorder (MDD) and 20 matched controls. We examined group differences in activation, and the capacity to sustain activation, across task runs using functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task. Hierarchical linear modeling was used to investigate whether pre-treatment neural responses predicted change in depressive symptoms over the course of BATD treatment. MDD and Control groups differed in sustained activation during reward outcomes in the right nucleus accumbens, such that the MDD group experienced a significant decrease in activation in this region from the first to second task run relative to controls. Pretreatment anhedonia severity and pretreatment task-related reaction times were predictive of response to treatment. Furthermore, sustained activation in the anterior cingulate cortex during reward outcomes predicted response to psychotherapy; patients with greater sustained activation in this region were more responsive to BATD treatment. The current study only included a single treatment condition, thus it unknown whether these predictors of treatment response are specific to BATD or psychotherapy in general. Findings add to the growing body of literature suggesting that the capacity to sustain neural responses to rewards may be a critical endophenotype of MDD. Copyright © 2016 Elsevier B.V. All rights reserved.

Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study.

PubMed

Ban, Jong-Wook; Emparanza, José Ignacio; Urreta, Iratxe; Burls, Amanda

2016-01-01

Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules' performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2-4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved.

On the prediction of the human response: a recycled mechanistic pharmacokinetic/pharmacodynamic approach.

PubMed

Meno-Tetang, Guy M L; Lowe, Philip J

2005-03-01

Although it is routine to predict the blood or plasma pharmacokinetics of compounds for man based upon preclinical studies, the real value of such predictions only comes when linked to drug effects. In the first example, the immunomodulator, FTY720, the first sphingosine-1-phosphate receptor agonist, stimulates the sequestration of lymphocytes into lymph nodes thus removing cells from blood circulation. A prior physiology-based pharmacokinetic model fitted the concentration-time course of FTY720 in rats. This was connected to an indirect response model of the lymphocyte system to characterise the cell trafficking effects. The IC(50) of FTY720 was different in the rat compared with the monkey; man was assumed to be similar to the monkey. The systemic lymphocyte half-lives were also different between species. To make predictions of the pharmacodynamic behaviour for man, two elements are required, i) systemic exposure, in this case from an upscaled physiology based model, and ii) an estimate of lymphocyte turnover in man, gained from the literature from other drug treatments. Predictions compared well with clinical results. The second example is the monoclonal antibody Xolair, designed to bind immunoglobulin E for atopic diseases. A mechanism based two-site binding model described the kinetics of both Xolair and endogenous IgE. This model has been reused for other monoclonal antibodies designed to bind fluid-phase ligands. Sensitivity analysis shows that if differences across species in the kinetics of the endogenous system are not accounted for, then pharmacokinetic/pharmacodynamic models may give misleading predictions of the time course and extent of the response.

Classification of childhood asthma phenotypes and long-term clinical responses to inhaled anti-inflammatory medications.

PubMed

Howrylak, Judie A; Fuhlbrigge, Anne L; Strunk, Robert C; Zeiger, Robert S; Weiss, Scott T; Raby, Benjamin A

2014-05-01

Although recent studies have identified the presence of phenotypic clusters in asthmatic patients, the clinical significance and temporal stability of these clusters have not been explored. Our aim was to examine the clinical relevance and temporal stability of phenotypic clusters in children with asthma. We applied spectral clustering to clinical data from 1041 children with asthma participating in the Childhood Asthma Management Program. Posttreatment randomization follow-up data collected over 48 months were used to determine the effect of these clusters on pulmonary function and treatment response to inhaled anti-inflammatory medication. We found 5 reproducible patient clusters that could be differentiated on the basis of 3 groups of features: atopic burden, degree of airway obstruction, and history of exacerbation. Cluster grouping predicted long-term asthma control, as measured by the need for oral prednisone (P < .0001) or additional controller medications (P = .001), as well as longitudinal differences in pulmonary function (P < .0001). We also found that the 2 clusters with the highest rates of exacerbation had different responses to inhaled corticosteroids when compared with the other clusters. One cluster demonstrated a positive response to both budesonide (P = .02) and nedocromil (P = .01) compared with placebo, whereas the other cluster demonstrated minimal responses to both budesonide (P = .12) and nedocromil (P = .56) compared with placebo. Phenotypic clustering can be used to identify longitudinally consistent and clinically relevant patient subgroups, with implications for targeted therapeutic strategies and clinical trials design.

Turbine blade forced response prediction using FREPS

NASA Technical Reports Server (NTRS)

Murthy, Durbha, V.; Morel, Michael R.

1993-01-01

This paper describes a software system called FREPS (Forced REsponse Prediction System) that integrates structural dynamic, steady and unsteady aerodynamic analyses to efficiently predict the forced response dynamic stresses in axial flow turbomachinery blades due to aerodynamic and mechanical excitations. A flutter analysis capability is also incorporated into the system. The FREPS system performs aeroelastic analysis by modeling the motion of the blade in terms of its normal modes. The structural dynamic analysis is performed by a finite element code such as MSC/NASTRAN. The steady aerodynamic analysis is based on nonlinear potential theory and the unsteady aerodynamic analyses is based on the linearization of the non-uniform potential flow mean. The program description and presentation of the capabilities are reported herein. The effectiveness of the FREPS package is demonstrated on the High Pressure Oxygen Turbopump turbine of the Space Shuttle Main Engine. Both flutter and forced response analyses are performed and typical results are illustrated.

The Glasgow Prognostic Score Predicts Response to Chemotherapy in Patients with Metastatic Breast Cancer.

PubMed

Wang, Dexing; Duan, Li; Tu, Zhiquan; Yan, Fei; Zhang, Cuicui; Li, Xu; Cao, Yuzhu; Wen, Hongsheng

2016-01-01

Breast cancer is one of the most common causes of cancer death in women worldwide. The Glasgow Prognostic Score (GPS), a cumulative prognostic score based on C-reactive protein and albumin, indicates the presence of a systemic inflammatory response. The GPS has been adopted as a powerful prognostic tool for patients with various types of malignant tumors, including breast cancer. The aim of this study was to assess the value of the GPS in predicting the response and toxicity in breast cancer patients treated with chemotherapy. Patients with metastatic breast cancers in a progressive stage for consideration of chemotherapy were eligible. The clinical characteristics and demographics were recorded. The GPS was calculated before the onset of chemotherapy. Data on the response to chemotherapy and progression-free survival (PFS) were also collected. Objective tumor responses were evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST). Toxicities were graded according to National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTC) version 3.0 throughout therapy. In total, 106 breast cancer patients were recruited. The GPS was associated with the response rate (p = 0.05), the clinical benefit rate (p = 0.03), and PFS (p = 0.005). The GPS was the only independent predictor of PFS (p = 0.005). The GPS was significantly associated with neutropenia, thrombocytopenia, anorexia, nausea and vomiting, fatigue, and mucositis (p = 0.05-0.001). Our data demonstrate that GPS assessment is associated with poor clinical outcomes and severe chemotherapy-related toxicities in patients with metastatic breast cancer who have undergone chemotherapy, without any specific indication regarding the type of chemotherapy applied. © 2016 S. Karger AG, Basel.

Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance.

PubMed

Brubaker, Douglas; Difeo, Analisa; Chen, Yanwen; Pearl, Taylor; Zhai, Kaide; Bebek, Gurkan; Chance, Mark; Barnholtz-Sloan, Jill

2014-01-01

The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

Do we have biomarkers to predict response to neoadjuvant and adjuvant chemotherapy and immunotherapy in bladder cancer?

PubMed Central

Wezel, Felix; Vallo, Stefan

2017-01-01

Radical cystectomy (RC) is the standard of care treatment of localized muscle-invasive bladder cancer (BC). However, about 50% of patients develop metastases within 2 years after cystectomy. Neoadjuvant cisplatin-based chemotherapy before cystectomy improves the overall survival (OS) in patients with muscle-invasive BC. Pathological response to neoadjuvant treatment is a strong predictor of better disease-specific survival. Nevertheless, some patients do not benefit from chemotherapy. The identification of reliable biomarkers enabling clinicians to identify patients who might benefit from chemotherapy is a very important clinical task. An identification tool could lead to individualized therapy, optimizing response rates. In addition, unnecessary treatment with chemotherapy which potentially leads to a loss of quality of life and which might also might cause a delay of cystectomy in a neoadjuvant setting could be avoided. The present review aims to summarize and discuss the current literature on biomarkers for the prediction of response to systemic therapy in muscle-invasive BC. Tremendous efforts in genetic and molecular characterization have led to the identification of predictive candidate biomarkers in urothelial carcinoma (UC), although prospective validation is pending. Ongoing clinical trials examining the benefit of individual therapies in UC of the bladder (UCB) by molecular patient selection hold promise to shed light on this question. PMID:29354494

Prediction and control of neural responses to pulsatile electrical stimulation

NASA Astrophysics Data System (ADS)

Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

2012-04-01

This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases.

PubMed

Ananthakrishnan, Ashwin N; Luo, Chengwei; Yajnik, Vijay; Khalili, Hamed; Garber, John J; Stevens, Betsy W; Cleland, Thomas; Xavier, Ramnik J

2017-05-10

The gut microbiome plays a central role in inflammatory bowel diseases (IBDs) pathogenesis and propagation. To determine whether the gut microbiome may predict responses to IBD therapy, we conducted a prospective study with Crohn's disease (CD) or ulcerative colitis (UC) patients initiating anti-integrin therapy (vedolizumab). Disease activity and stool metagenomes at baseline, and weeks 14, 30, and 54 after therapy initiation were assessed. Community α-diversity was significantly higher, and Roseburia inulinivorans and a Burkholderiales species were more abundant at baseline among CD patients achieving week 14 remission. Several significant associations were identified with microbial function; 13 pathways including branched chain amino acid synthesis were significantly enriched in baseline samples from CD patients achieving remission. A neural network algorithm, vedoNet, incorporating microbiome and clinical data, provided highest classifying power for clinical remission. We hypothesize that the trajectory of early microbiome changes may be a marker of response to IBD treatment. Copyright © 2017 Elsevier Inc. All rights reserved.

Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study

PubMed Central

Ban, Jong-Wook; Emparanza, José Ignacio; Urreta, Iratxe; Burls, Amanda

2016-01-01

Background Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules’ performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. Methods Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. Results A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2–4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. Conclusion Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved. PMID:26730980

Comparative prediction of nonepileptic events using MMPI-2 clinical scales, Harris Lingoes subscales, and restructured clinical scales.

PubMed

Yamout, Karim Z; Heinrichs, Robin J; Baade, Lyle E; Soetaert, Dana K; Liow, Kore K

2017-03-01

The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a psychological testing tool used to measure psychological and personality constructs. The MMPI-2 has proven helpful in identifying individuals with nonepileptic events/nonepileptic seizures. However, the MMPI-2 has had some updates that enhanced its original scales. The aim of this article was to test the utility of updated MMPI-2 scales in predicting the likelihood of non-epileptic seizures in individuals admitted to an EEG video monitoring unit. We compared sensitivity, specificity, and likelihood ratios of traditional MMPI-2 Clinical Scales against more homogenous MMPI-2 Harris-Lingoes subscales and the newer Restructured Clinical (RC) scales. Our results showed that the Restructured Scales did not show significant improvement over the original Clinical scales. However, one Harris-Lingoes subscale (HL4 of Clinical Scale 3) did show improved predictive utility over the original Clinical scales as well as over the newer Restructured Clinical scales. Our study suggests that the predictive utility of the MMPI-2 can be improved using already existing scales. This is particularly useful for those practitioners who are not invested in switching over to the newly developed MMPI-2 Restructured Form (MMPI-2 RF). Copyright © 2016 Elsevier Inc. All rights reserved.

Response to Infant Cry in Clinically Depressed and Non-Depressed Mothers

PubMed Central

Manian, Nanmathi; Truzzi, Anna; Bornstein, Marc H.

2017-01-01

Background Bowlby and Ainsworth hypothesized that maternal responsiveness is displayed in the context of infant distress. Depressed mothers are less responsive to infant distress vocalizations (cry) than non-depressed mothers. The present study focuses on acoustical components of infant cry that give rise to responsive caregiving in clinically depressed (n = 30) compared with non-depressed mothers (n = 30) in the natural setting of the home. Methods Analyses of infant and mother behaviors followed three paths: (1) tests of group differences in acoustic characteristics of infant cry, (2) tests of group differences of mothers’ behaviors during their infant’s crying, and (3) tree-based modeling to ascertain which variable(s) best predict maternal behaviors during infant cry. Results (1) Infants of depressed mothers cried as frequently and for equal durations as infants of non-depressed mothers; however, infants of depressed mothers cried with a higher fundamental frequency (f0) and in a more restricted range of f0. (2) Depressed mothers fed, rocked, and touched their crying infants less than non-depressed mothers, and depressed mothers were less responsive to their infants overall. (3) Novel tree-based analyses confirmed that depressed mothers engaged in less caregiving during their infants’ cry and indicated that depressed mothers responded only to cries at higher f0s and shorter durations. Older non-depressed mothers were the most interactive with infants. Conclusions Clinical depression affects maternal responsiveness during infant cry, leading to patterns of action that appear poorly attuned to infant needs. PMID:28046020

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

Clinical- and imaging-based prediction of stroke risk after transient ischemic attack: the CIP model.

PubMed

Ay, Hakan; Arsava, E Murat; Johnston, S Claiborne; Vangel, Mark; Schwamm, Lee H; Furie, Karen L; Koroshetz, Walter J; Sorensen, A Gregory

2009-01-01

Predictive instruments based on clinical features for early stroke risk after transient ischemic attack suffer from limited specificity. We sought to combine imaging and clinical features to improve predictions for 7-day stroke risk after transient ischemic attack. We studied 601 consecutive patients with transient ischemic attack who had MRI within 24 hours of symptom onset. A logistic regression model was developed using stroke within 7 days as the response criterion and diffusion-weighted imaging findings and dichotomized ABCD(2) score (ABCD(2) >/=4) as covariates. Subsequent stroke occurred in 25 patients (5.2%). Dichotomized ABCD(2) score and acute infarct on diffusion-weighted imaging were each independent predictors of stroke risk. The 7-day risk was 0.0% with no predictor, 2.0% with ABCD(2) score >/=4 alone, 4.9% with acute infarct on diffusion-weighted imaging alone, and 14.9% with both predictors (an automated calculator is available at http://cip.martinos.org). Adding imaging increased the area under the receiver operating characteristic curve from 0.66 (95% CI, 0.57 to 0.76) using the ABCD(2) score to 0.81 (95% CI, 0.74 to 0.88; P=0.003). The sensitivity of 80% on the receiver operating characteristic curve corresponded to a specificity of 73% for the CIP model and 47% for the ABCD(2) score. Combining acute imaging findings with clinical transient ischemic attack features causes a dramatic boost in the accuracy of predictions with clinical features alone for early risk of stroke after transient ischemic attack. If validated in relevant clinical settings, risk stratification by the CIP model may assist in early implementation of therapeutic measures and effective use of hospital resources.

Thermo-mechanical response predictions for metal matrix composite laminates

NASA Technical Reports Server (NTRS)

Aboudi, J.; Hidde, J. S.; Herakovich, C. T.

1991-01-01

An analytical micromechanical model is employed for prediction of the stress-strain response of metal matrix composite laminates subjected to thermomechanical loading. The predicted behavior of laminates is based upon knowledge of the thermomechanical response of the transversely isotropic, elastic fibers and the elastic-viscoplastic, work-hardening matrix. The method is applied to study the behavior of silicon carbide/titanium metal matrix composite laminates. The response of laminates is compared with that of unidirectional lamina. The results demonstrate the effect of cooling from a stress-free temperature and the mismatch of thermal and mechanical properties of the constituent phases on the laminate's subsequent mechanical response. Typical results are presented for a variety of laminates subjected to monotonic tension, monotonic shear and cyclic tensile/compressive loadings.

Time to clinical response: an outcome of antibiotic therapy of febrile neutropenia with implications for quality and cost of care.

PubMed

Elting, L S; Rubenstein, E B; Rolston, K; Cantor, S B; Martin, C G; Kurtin, D; Rodriguez, S; Lam, T; Kanesan, K; Bodey, G

2000-11-01

To determine whether antibiotic regimens with similar rates of response differ significantly in the speed of response and to estimate the impact of this difference on the cost of febrile neutropenia. The time point of clinical response was defined by comparing the sensitivity, specificity, and predictive values of alternative objective and subjective definitions. Data from 488 episodes of febrile neutropenia, treated with either of two commonly used antibiotics (coded A or B) during six clinical trials, were pooled to compare the median time to clinical response, days of antibiotic therapy and hospitalization, and estimated costs. Response rates were similar; however, the median time to clinical response was significantly shorter with A-based regimens (5 days) compared with B-based regimens (7 days; P =.003). After 72 hours of therapy, 33% of patients who received A but only 18% of those who received B had responded (P =.01). These differences resulted in fewer days of antibiotic therapy and hospitalization with A-based regimens (7 and 9 days) compared with B-based regimens (9 and 12 days, respectively; P <.04) and in significantly lower estimated median costs ($8,491 v $11,133 per episode; P =.03). Early discharge at the time of clinical response should reduce the median cost from $10,752 to $8,162 (P <.001). Despite virtually identical rates of response, time to clinical response and estimated cost of care varied significantly among regimens. An early discharge strategy based on our definition of the time point of clinical response may further reduce the cost of treating non-low-risk patients with febrile neutropenia.

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

PubMed

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

2018-02-06

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

Use of sequential endorectal US to predict the tumor response of preoperative chemoradiotherapy in rectal cancer.

PubMed

Li, Ning; Dou, Lizhou; Zhang, Yueming; Jin, Jing; Wang, Guiqi; Xiao, Qin; Li, Yexiong; Wang, Xin; Ren, Hua; Fang, Hui; Wang, Weihu; Wang, Shulian; Liu, Yueping; Song, Yongwen

2017-03-01

Accurate prediction of the response to preoperative chemoradiotherapy (CRT) potentially assists in the individualized selection of treatment. Endorectal US (ERUS) is widely used for the pretreatment staging of rectal cancer, but its use for preoperatively predicting the effects of CRT is not well evaluated because of the inflammation, necrosis, and fibrosis induced by CRT. This study assessed the value of sequential ERUS in predicting the efficacy of preoperative CRT for locally advanced rectal cancer. Forty-one patients with clinical stage II/III rectal adenocarcinoma were enrolled prospectively. Radiotherapy was delivered to the pelvis with concurrent chemotherapy of capecitabine and oxaliplatin. Total mesorectal excision was performed 6 to 8 weeks later. EUS measurements of primary tumor maximum diameter were performed before (ERUS1), during (ERUS2), and 6 to 8 weeks after (ERUS3) CRT, and the ratios of these were calculated. Correlations between ERUS values, tumor regression grade (TRG), T down-staging rate, and pathologic complete response (pCR) rate were assessed, and survival was analyzed. There was no significant correlation between ERUS2/ERUS1 and TRG. The value of ERUS3/ERUS1 correlated with pCR rate and TRG but not T down-staging rate. An ERUS3 value of 6.3 mm and ERUS3/ERUS1 of 52% were used as the cut-off for predicting pCR, and patients were divided into good and poor prognosis groups. Although not statistically significant, 3-year recurrence and survival rates of the good prognosis group were better than those of the poor prognosis group. Sequential ERUS may predict therapeutic efficacy of preoperative CRT for locally advanced rectal cancer. (Clinical trial registration number: NCT01582750.). Copyright © 2017 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

PubMed

Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Dopamine prediction error responses integrate subjective value from different reward dimensions

PubMed Central

Lak, Armin; Stauffer, William R.; Schultz, Wolfram

2014-01-01

Prediction error signals enable us to learn through experience. These experiences include economic choices between different rewards that vary along multiple dimensions. Therefore, an ideal way to reinforce economic choice is to encode a prediction error that reflects the subjective value integrated across these reward dimensions. Previous studies demonstrated that dopamine prediction error responses reflect the value of singular reward attributes that include magnitude, probability, and delay. Obviously, preferences between rewards that vary along one dimension are completely determined by the manipulated variable. However, it is unknown whether dopamine prediction error responses reflect the subjective value integrated from different reward dimensions. Here, we measured the preferences between rewards that varied along multiple dimensions, and as such could not be ranked according to objective metrics. Monkeys chose between rewards that differed in amount, risk, and type. Because their choices were complete and transitive, the monkeys chose “as if” they integrated different rewards and attributes into a common scale of value. The prediction error responses of single dopamine neurons reflected the integrated subjective value inferred from the choices, rather than the singular reward attributes. Specifically, amount, risk, and reward type modulated dopamine responses exactly to the extent that they influenced economic choices, even when rewards were vastly different, such as liquid and food. This prediction error response could provide a direct updating signal for economic values. PMID:24453218

The value of integrating pre-clinical data to predict nausea and vomiting risk in humans as illustrated by AZD3514, a novel androgen receptor modulator.

PubMed

Grant, Claire; Ewart, Lorna; Muthas, Daniel; Deavall, Damian; Smith, Simon A; Clack, Glen; Newham, Pete

2016-04-01

Nausea and vomiting are components of a complex mechanism that signals food avoidance and protection of the body against the absorption of ingested toxins. This response can also be triggered by pharmaceuticals. Predicting clinical nausea and vomiting liability for pharmaceutical agents based on pre-clinical data can be problematic as no single animal model is a universal predictor. Moreover, efforts to improve models are hampered by the lack of translational animal and human data in the public domain. AZD3514 is a novel, orally-administered compound that inhibits androgen receptor signaling and down-regulates androgen receptor expression. Here we have explored the utility of integrating data from several pre-clinical models to predict nausea and vomiting in the clinic. Single and repeat doses of AZD3514 resulted in emesis, salivation and gastrointestinal disturbances in the dog, and inhibited gastric emptying in rats after a single dose. AZD3514, at clinically relevant exposures, induced dose-responsive "pica" behaviour in rats after single and multiple daily doses, and induced retching and vomiting behaviour in ferrets after a single dose. We compare these data with the clinical manifestation of nausea and vomiting encountered in patients with castration-resistant prostate cancer receiving AZD3514. Our data reveal a striking relationship between the pre-clinical observations described and the experience of nausea and vomiting in the clinic. In conclusion, the emetic nature of AZD3514 was predicted across a range of pre-clinical models, and the approach presented provides a valuable framework for predicition of clinical nausea and vomiting. Copyright © 2016 Elsevier Inc. All rights reserved.

Discrimination of amygdala response predicts future separation anxiety in youth with early deprivation.

PubMed

Green, Shulamite A; Goff, Bonnie; Gee, Dylan G; Gabard-Durnam, Laurel; Flannery, Jessica; Telzer, Eva H; Humphreys, Kathryn L; Louie, Jennifer; Tottenham, Nim

2016-10-01

Significant disruption in caregiving is associated with increased internalizing symptoms, most notably heightened separation anxiety symptoms during childhood. It is also associated with altered functional development of the amygdala, a neurobiological correlate of anxious behavior. However, much less is known about how functional alterations of amygdala predict individual differences in anxiety. Here, we probed amygdala function following institutional caregiving using very subtle social-affective stimuli (trustworthy and untrustworthy faces), which typically result in large differences in amygdala signal, and change in separation anxiety behaviors over a 2-year period. We hypothesized that the degree of differentiation of amygdala signal to trustworthy versus untrustworthy face stimuli would predict separation anxiety symptoms. Seventy-four youths mean (SD) age = 9.7 years (2.64) with and without previous institutional care, who were all living in families at the time of testing, participated in an fMRI task designed to examine differential amygdala response to trustworthy versus untrustworthy faces. Parents reported on their children's separation anxiety symptoms at the time of scan and again 2 years later. Previous institutional care was associated with diminished amygdala signal differences and behavioral differences to the contrast of untrustworthy and trustworthy faces. Diminished differentiation of these stimuli types predicted more severe separation anxiety symptoms 2 years later. Older age at adoption was associated with diminished differentiation of amygdala responses. A history of institutional care is associated with reduced differential amygdala responses to social-affective cues of trustworthiness that are typically exhibited by comparison samples. Individual differences in the degree of amygdala differential responding to these cues predict the severity of separation anxiety symptoms over a 2-year period. These findings provide a biological

Dopamine reward prediction-error signalling: a two-component response

PubMed Central

Schultz, Wolfram

2017-01-01

Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy. PMID:26865020

Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.

PubMed

Beukinga, Roelof J; Hulshoff, Jan Binne; Mul, Véronique E M; Noordzij, Walter; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Plukker, John T M

2018-06-01

Purpose To assess the value of baseline and restaging fluorine 18 ( 18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017. Clinical variables and radiomic features from baseline and restaging 18 F-FDG PET were selected by univariable logistic regression and least absolute shrinkage and selection operator. The selected variables were used to fit a multivariable logistic regression model, which was internally validated by using bootstrap resampling with 20 000 replicates. The performance of this model was compared with reference prediction models composed of maximum standardized uptake value metrics, clinical variables, and maximum standardized uptake value at baseline NCRT radiomic features. Outcome was defined as complete versus incomplete pathologic response (tumor regression grade 1 vs 2-5 according to the Mandard classification). Results Pathologic response was complete in 16 patients (21.9%) and incomplete in 57 patients (78.1%). A prediction model combining clinical T-stage and restaging NCRT (post-NCRT) joint maximum (quantifying image orderliness) yielded an optimism-corrected area under the receiver operating characteristics curve of 0.81. Post-NCRT joint maximum was replaceable with five other redundant post-NCRT radiomic features that provided equal model performance. All reference prediction models exhibited substantially lower discriminatory accuracy. Conclusion The combination of clinical T-staging and quantitative assessment of post-NCRT 18 F-FDG PET orderliness (joint maximum

Attenuation of Frontostriatal Connectivity During Reward Processing Predicts Response to Psychotherapy in Major Depressive Disorder.

PubMed

Walsh, Erin; Carl, Hannah; Eisenlohr-Moul, Tory; Minkel, Jared; Crowther, Andrew; Moore, Tyler; Gibbs, Devin; Petty, Chris; Bizzell, Josh; Smoski, Moria J; Dichter, Gabriel S

2017-03-01

There are few reliable predictors of response to antidepressant treatments. In the present investigation, we examined pretreatment functional brain connectivity during reward processing as a potential predictor of response to Behavioral Activation Treatment for Depression (BATD), a validated psychotherapy that promotes engagement with rewarding stimuli and reduces avoidance behaviors. Thirty-three outpatients with major depressive disorder (MDD) and 20 matched controls completed two runs of the monetary incentive delay task during functional magnetic resonance imaging after which participants with MDD received up to 15 sessions of BATD. Seed-based generalized psychophysiological interaction analyses focused on task-based connectivity across task runs, as well as the attenuation of connectivity from the first to the second run of the task. The average change in Beck Depression Inventory-II scores due to treatment was 10.54 points, a clinically meaningful response. Groups differed in seed-based functional connectivity among multiple frontostriatal regions. Hierarchical linear modeling revealed that improved treatment response to BATD was predicted by greater connectivity between the left putamen and paracingulate gyrus during reward anticipation. In addition, MDD participants with greater attenuation of connectivity between several frontostriatal seeds, and midline subcallosal cortex and left paracingulate gyrus demonstrated improved response to BATD. These findings indicate that pretreatment frontostriatal functional connectivity during reward processing is predictive of response to a psychotherapy modality that promotes improving approach-related behaviors in MDD. Furthermore, connectivity attenuation among reward-processing regions may be a particularly powerful endophenotypic predictor of response to BATD in MDD.

Advanced Computational Modeling Approaches for Shock Response Prediction

NASA Technical Reports Server (NTRS)

Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

2015-01-01

Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.

A comparative study: classification vs. user-based collaborative filtering for clinical prediction.

PubMed

Hao, Fang; Blair, Rachael Hageman

2016-12-08

Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.). User-based collaborative filtering is a popular recommender system, which leverages an individuals' prior satisfaction with items, as well as the satisfaction of individuals that are "similar". Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the "Big Data" era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records). In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity), Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR) or Missing Completely At Random (MCAR) under various degrees of missingness and levels of class imbalance in the response variable. Our results demonstrate that user-based collaborative filtering is consistently inferior to logistic regression and random forests with different

Tumor response ratio predicts overall survival in breast cancer patients treated with neoadjuvant chemotherapy.

PubMed

Miller, Marian; Ottesen, Rebecca A; Niland, Joyce C; Kruper, Laura; Chen, Steven L; Vito, Courtney

2014-10-01

Neoadjuvant chemotherapy (NAC) is commonly used to treat locally advanced breast cancer. Pathologic complete response (pCR) predicts improved overall survival (OS); however, prognosis of patients with partial response remains unclear. We evaluated whether tumor response ratio (TRR) is a better predictor of OS than current staging methods. Using the National Comprehensive Cancer Network Breast Cancer Outcomes Database, we identified patients with stage I-III breast cancer who had NAC and pretreatment imaging at City of Hope (1997-2010). Patient demographics, tumor characteristics, and OS were analyzed. TRR was calculated as residual in-breast disease divided by size on pre-NAC imaging. Four TRR groups were stratified; TRR 0 (pCR), TRR > 0-0.4 (strong partial response, SPR), TRR > 0.4-1.0 (weak partial response, WPR), or TRR > 1.0 (tumor growth, TG). OS was estimated by the Kaplan-Meier method and tested by the log-rank test. Cox regression was performed to evaluate associations between OS and TRR in a multivariable analysis while controlling for potential confounders. There were 218 eligible patients identified; 59 (27 %) had pCR, 61 (28 %) SPR, 72 (33 %) WPR, and 26 (12 %) TG. Five-year OS decreased continuously with increasing TRR:pCR (90 %), SPR (79 %), WPR (66 %), and TG (60 %). TRR was the only measure that significantly predicted OS (p = 0.0035); pathologic stage (p = 0.23) and pre-NAC clinical tumor stage (cT) (p = 0.87) were not significant. TRR continued to be statistically significant by multivariable analysis (p = 0.016). TRR takes into account both pretreatment and residual disease and more accurately predicts OS than pathologic stage and pre-NAC cT. TRR may be useful to more accurately assess prognosis and OS in breast cancer patients undergoing NAC.

Timed non-transferrin bound iron determinations probe the origin of chelatable iron pools during deferiprone regimens and predict chelation response

PubMed Central

Aydinok, Yesim; Evans, Patricia; Manz, Chantal Y.; Porter, John B.

2012-01-01

Background Plasma non-transferrin bound iron refers to heterogeneous plasma iron species, not bound to transferrin, which appear in conditions of iron overload and ineffective erythropoiesis. The clinical utility of non-transferrin bound iron in predicting complications from iron overload, or response to chelation therapy remains unproven. We undertook carefully timed measurements of non-transferrin bound iron to explore the origin of chelatable iron and to predict clinical response to deferiprone. Design and Methods Non-transferrin bound iron levels were determined at baseline and after 1 week of chelation in 32 patients with thalassemia major receiving deferiprone alone, desferrioxamine alone, or a combination of the two chelators. Samples were taken at baseline, following a 2-week washout without chelation, and after 1 week of chelation, this last sample being taken 10 hours after the previous evening dose of deferiprone and, in those receiving desferrioxamine, 24 hours after cessation of the overnight subcutaneous infusion. Absolute or relative non-transferrin bound iron levels were related to transfusional iron loading rates, liver iron concentration, 24-hour urine iron and response to chelation therapy over the subsequent year. Results Changes in non-transferrin bound iron at week 1 were correlated positively with baseline liver iron, and inversely with transfusional iron loading rates, with deferiprone-containing regimens but not with desferrioxamine monotherapy. Changes in week 1 non-transferrin bound iron were also directly proportional to the plasma concentration of deferiprone-iron complexes and correlated significantly with urine iron excretion and with changes in liver iron concentration over the next 12 months. Conclusions The widely used assay chosen for this study detects both endogenous non-transferrin bound iron and the iron complexes of deferiprone. The week 1 increments reflect chelatable iron derived both from liver stores and from red cell

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

PubMed

Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

2016-08-23

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

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

PubMed

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

2017-01-01

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

The costs and benefits of temporal predictability: impaired inhibition of prepotent responses accompanies increased activation of task-relevant responses.

PubMed

Korolczuk, Inga; Burle, Boris; Coull, Jennifer T

2018-06-20

While the benefit of temporal predictability on sensorimotor processing is well established, it is still unknown whether this is due to efficient execution of an appropriate response and/or inhibition of an inappropriate one. To answer this question, we examined the effects of temporal predictability in tasks that required selective (Simon task) or global (Stop-signal task) inhibitory control of prepotent responses. We manipulated temporal expectation by presenting cues that either predicted (temporal cues) or not (neutral cues) when the target would appear. In the Simon task, performance was better when target location (left/right) was compatible with the hand of response and performance was improved further still if targets were temporally cued. However, Conditional Accuracy Functions revealed that temporal predictability selectively increased the number of fast, impulsive errors. Temporal cueing had no effect on selective response inhibition, as measured by the dynamics of the interference effect (delta plots) in the Simon task. By contrast, in the Stop-signal task, Stop-signal reaction time, a covert measure of a more global form of response inhibition, was significantly longer in temporally predictive trials. Therefore, when the time of target onset could be predicted in advance, it was harder to stop the impulse to respond to the target. Collectively, our results indicate that temporal cueing compounded the interfering effects of a prepotent response on task performance. We suggest that although temporal predictability enhances activation of task-relevant responses, it impairs inhibition of prepotent responses. Copyright © 2018 Elsevier B.V. All rights reserved.

An empirical propellant response function for combustion stability predictions

NASA Technical Reports Server (NTRS)

Hessler, R. O.

1980-01-01

An empirical response function model was developed for ammonium perchlorate propellants to supplant T-burner testing at the preliminary design stage. The model was developed by fitting a limited T-burner data base, in terms of oxidizer size and concentration, to an analytical two parameter response function expression. Multiple peaks are predicted, but the primary effect is of a single peak for most formulations, with notable bulges for the various AP size fractions. The model was extended to velocity coupling with the assumption that dynamic response was controlled primarily by the solid phase described by the two parameter model. The magnitude of velocity coupling was then scaled using an erosive burning law. Routine use of the model for stability predictions on a number of propulsion units indicates that the model tends to overpredict propellant response. It is concluded that the model represents a generally conservative prediction tool, suited especially for the preliminary design stage when T-burner data may not be readily available. The model work included development of a rigorous summation technique for pseudopropellant properties and of a concept for modeling ordered packing of particulates.

Predicting the shock compression response of heterogeneous powder mixtures

NASA Astrophysics Data System (ADS)

Fredenburg, D. A.; Thadhani, N. N.

2013-06-01

A model framework for predicting the dynamic shock-compression response of heterogeneous powder mixtures using readily obtained measurements from quasi-static tests is presented. Low-strain-rate compression data are first analyzed to determine the region of the bulk response over which particle rearrangement does not contribute to compaction. This region is then fit to determine the densification modulus of the mixture, σD, an newly defined parameter describing the resistance of the mixture to yielding. The measured densification modulus, reflective of the diverse yielding phenomena that occur at the meso-scale, is implemented into a rate-independent formulation of the P-α model, which is combined with an isobaric equation of state to predict the low and high stress dynamic compression response of heterogeneous powder mixtures. The framework is applied to two metal + metal-oxide (thermite) powder mixtures, and good agreement between the model and experiment is obtained for all mixtures at stresses near and above those required to reach full density. At lower stresses, rate-dependencies of the constituents, and specifically those of the matrix constituent, determine the ability of the model to predict the measured response in the incomplete compaction regime.

Predictive biomarkers for response of esophageal cancer to chemo(radio)therapy: A systematic review and meta-analysis.

PubMed

Li, Yang; Huang, He-Cheng; Chen, Long-Qi; Xu, Li-Yan; Li, En-Min; Zhang, Jian-Jun

2017-12-01

Esophageal cancer remains a major public health issue worldwide. In clinical practice, chemo(radio)therapy is an important approach to patients with esophageal cancer. Only the part of patients who respond to chemo(radio)therapy achieve better long-term outcome. In this case, predictive biomarkers for response of esophageal cancer patients treated with chemo(radio)therapy are of importance. Meta-analysis of P53 for predicting esophageal cancer response has been reported before and is not included in our study. We performed a systematic review and meta-analysis to summarize and evaluate the biomarkers for predicting response to chemo(radio)therapy. PubMed, Web of Science and the Ovid databases were searched to identify eligible studies published in English before March 2017. The risk ratio (or relative risk, RR) was retrieved in articles regarding biomarkers for predicting response of esophageal cancer patients treated with neoadjuvant therapy or chemo(radio)therapy. Fixed and random effects models were used to undertake the meta-analysis as appropriate. Forty-six articles reporting 56 biomarkers correlated with the response were finally included. Meta-analyses were carried out when there was more than one study related to the reported biomarker. Results indicated that low expression of (or IHC-negative) COX2, miR-200c, ERCC1 and TS was individually associated with prediction of response. The RR was 1.64 (n = 202, 95% CI 1.22-2.19, P < 0.001), 1.96 (n = 162, 95% CI 1.36-2.83, P < 0.001), 2.55 (n = 206, 95% CI 1.80-3.62, P < 0.001) and 1.69 (n = 144, 95% CI 1.10-2.61, P = 0.02), respectively. High expression of (or IHC-positive) CDC25B and p16 was individually related to prediction of response. The RR was 0.62 (n = 159, 95% CI 0.43-0.89, P = 0.01) and 0.62 (n = 142, 95% CI 0.43-0.91, P = 0.01), respectively. Low expression of (or IHC-negative) COX2, miR-200c, ERCC1 and TS, or high expression of (or IHC-positive) CDC25B and p16 are potential

Nomograms Predicting Response to Therapy and Outcomes After Bladder-Preserving Trimodality Therapy for Muscle-Invasive Bladder Cancer

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

Coen, John J., E-mail: jcoen@harthosp.org; Paly, Jonathan J.; Niemierko, Andrzej

2013-06-01

Purpose: Selective bladder preservation by use of trimodality therapy is an established management strategy for muscle-invasive bladder cancer. Individual disease features have been associated with response to therapy, likelihood of bladder preservation, and disease-free survival. We developed prognostic nomograms to predict the complete response rate, disease-specific survival, and likelihood of remaining free of recurrent bladder cancer or cystectomy. Methods and Materials: From 1986 to 2009, 325 patients were managed with selective bladder preservation at Massachusetts General Hospital (MGH) and had complete data adequate for nomogram development. Treatment consisted of a transurethral resection of bladder tumor followed by split-course chemoradiation. Patientsmore » with a complete response at midtreatment cystoscopic assessment completed radiation, whereas those with a lesser response underwent a prompt cystectomy. Prognostic nomograms were constructed predicting complete response (CR), disease-specific survival (DSS), and bladder-intact disease-free survival (BI-DFS). BI-DFS was defined as the absence of local invasive or regional recurrence, distant metastasis, bladder cancer-related death, or radical cystectomy. Results: The final nomograms included information on clinical T stage, presence of hydronephrosis, whether a visibly complete transurethral resection of bladder tumor was performed, age, sex, and tumor grade. The predictive accuracy of these nomograms was assessed. For complete response, the area under the receiving operating characteristic curve was 0.69. The Harrell concordance index was 0.61 for both DSS and BI-DFS. Conclusions: Our nomograms allow individualized estimates of complete response, DSS, and BI-DFS. They may assist patients and clinicians making important treatment decisions.« less

A quick behavioral dichotic word test is prognostic for clinical response to cognitive therapy for depression: A replication study.

PubMed

Bruder, Gerard E; Haggerty, Agnes; Siegle, Greg J

2017-02-01

There are no commonly used clinical indicators of whether an individual will benefit from cognitive therapy (CT) for depression. A prior study found right ear (left hemisphere) advantage for perceiving dichotic words predicted CT response. This study replicates this finding at a different research center in clinical trials that included clinically representative samples and community therapists. Right-handed individuals with unipolar major depressive disorder who subsequently received 12-14 weeks of CT at the University of Pittsburgh were tested on dichotic fused words and complex tones tests. Responders to CT showed twice the mean right ear advantage in dichotic fused words performance than non-responders. Patients with a right ear advantage greater than the mean for healthy controls had an 81% response rate to CT, whereas those with performance lower than the mean for controls had a 46% response rate. Individuals with a right ear advantage, indicative of strong left hemisphere language dominance, may be better at utilizing cognitive processes and left frontotemporal cortical regions critical for success of CT for depression. Findings at two clinical research centers suggest that verbal dichotic listening may be a clinically disseminative brief, inexpensive and easily automated test prognostic for response to CT across diverse clinical settings. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Methods and kits for predicting a response to an erythropoietic agent

DOEpatents

Merchant, Michael L.; Klein, Jon B.; Brier, Michael E.; Gaweda, Adam E.

2015-06-16

Methods for predicting a response to an erythropoietic agent in a subject include providing a biological sample from the subject, and determining an amount in the sample of at least one peptide selected from the group consisting of SEQ ID NOS: 1-17. If there is a measurable difference in the amount of the at least one peptide in the sample, when compared to a control level of the same peptide, the subject is then predicted to have a good response or a poor response to the erythropoietic agent. Kits for predicting a response to an erythropoietic agent are further provided and include one or more antibodies, or fragments thereof, that specifically recognize a peptide of SEQ ID NOS: 1-17.

Development of clinical decision rules to predict recurrent shock in dengue

PubMed Central

2013-01-01

Introduction Mortality from dengue infection is mostly due to shock. Among dengue patients with shock, approximately 30% have recurrent shock that requires a treatment change. Here, we report development of a clinical rule for use during a patient’s first shock episode to predict a recurrent shock episode. Methods The study was conducted in Center for Preventive Medicine in Vinh Long province and the Children’s Hospital No. 2 in Ho Chi Minh City, Vietnam. We included 444 dengue patients with shock, 126 of whom had recurrent shock (28%). Univariate and multivariate analyses and a preprocessing method were used to evaluate and select 14 clinical and laboratory signs recorded at shock onset. Five variables (admission day, purpura/ecchymosis, ascites/pleural effusion, blood platelet count and pulse pressure) were finally trained and validated by a 10-fold validation strategy with 10 times of repetition, using a logistic regression model. Results The results showed that shorter admission day (fewer days prior to admission), purpura/ecchymosis, ascites/pleural effusion, low platelet count and narrow pulse pressure were independently associated with recurrent shock. Our logistic prediction model was capable of predicting recurrent shock when compared to the null method (P < 0.05) and was not outperformed by other prediction models. Our final scoring rule provided relatively good accuracy (AUC, 0.73; sensitivity and specificity, 68%). Score points derived from the logistic prediction model revealed identical accuracy with AUCs at 0.73. Using a cutoff value greater than −154.5, our simple scoring rule showed a sensitivity of 68.3% and a specificity of 68.2%. Conclusions Our simple clinical rule is not to replace clinical judgment, but to help clinicians predict recurrent shock during a patient’s first dengue shock episode. PMID:24295509

Clinical prediction of Gardnerella vaginalis in general practice

PubMed Central

O'Dowd, T.C.; West, R.R.

1987-01-01

In a study of 162 women with vaginal symptoms the clinical features of increased discharge, yellow discharge, 'high cheese' odour and pH greater than 5 were statistically strongly associated with the presence of Gardnerella vaginalis, confirmed by microbiological culture. The sensitivities and specificities of these clinical tests, although not as high as those of previously described sideroom tests using the amine test and microscopy for 'clue cells' nevertheless allow the clinician to predict G. vaginalis reliably and initiate treatment at first consultation. PMID:3499508

A Clinical Score to Predict Appendicitis in Older Male Children.

PubMed

Kharbanda, Anupam B; Monuteaux, Michael C; Bachur, Richard G; Dudley, Nanette C; Bajaj, Lalit; Stevenson, Michelle D; Macias, Charles G; Mittal, Manoj K; Bennett, Jonathan E; Sinclair, Kelly; Dayan, Peter S

2017-04-01

To develop a clinical score to predict appendicitis among older, male children who present to the emergency department with suspected appendicitis. Patients with suspected appendicitis were prospectively enrolled at 9 pediatric emergency departments. A total of 2625 patients enrolled; a subset of 961 male patients, age 8-18 were analyzed in this secondary analysis. Outcomes were determined using pathology, operative reports, and follow-up calls. Clinical and laboratory predictors with <10% missing data and kappa > 0.4 were entered into a multivariable model. Resultant β-coefficients were used to develop a clinical score. Test performance was assessed by calculating the sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios. The mean age was 12.2 years; 49.9% (480) had appendicitis, 22.3% (107) had perforation, and the negative appendectomy rate was 3%. In patients with and without appendicitis, overall imaging rates were 68.6% (329) and 84.4% (406), respectively. Variables retained in the model included maximum tenderness in the right lower quadrant, pain with walking/coughing or hopping, and the absolute neutrophil count. A score ≥8.1 had a sensitivity of 25% (95% confidence interval [CI], 20%-29%), specificity of 98% (95% CI, 96%-99%), and positive predictive value of 93% (95% CI, 86%-97%) for ruling in appendicitis. We developed an accurate scoring system for predicting appendicitis in older boys. If validated, the score might allow clinicians to manage a proportion of male patients without diagnostic imaging. Copyright © 2016 Academic Pediatric Association. Published by Elsevier 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.

Serial MR diffusion to predict treatment response in high-grade pediatric brain tumors: a comparison of regional and voxel-based diffusion change metrics

PubMed Central

Rodriguez Gutierrez, Daniel; Manita, Muftah; Jaspan, Tim; Dineen, Robert A.; Grundy, Richard G.; Auer, Dorothee P.

2013-01-01

Background Assessment of treatment response by measuring tumor size is known to be a late and potentially confounded response index. Serial diffusion MRI has shown potential for allowing earlier and possibly more reliable response assessment in adult patients, with limited experience in clinical settings and in pediatric brain cancer. We present a retrospective study of clinical MRI data in children with high-grade brain tumors to assess and compare the values of several diffusion change metrics to predict treatment response. Methods Eighteen patients (age range, 1.9–20.6 years) with high-grade brain tumors and serial diffusion MRI (pre- and posttreatment interval range, 1–16 weeks posttreatment) were identified after obtaining parental consent. The following diffusion change metrics were compared with the clinical response status assessed at 6 months: (1) regional change in absolute and normalized apparent diffusivity coefficient (ADC), (2) voxel-based fractional volume of increased (fiADC) and decreased ADC (fdADC), and (3) a new metric based on the slope of the first principal component of functional diffusion maps (fDM). Results Responders (n = 12) differed significantly from nonresponders (n = 6) in all 3 diffusional change metrics demonstrating higher regional ADC increase, larger fiADC, and steeper slopes (P < .05). The slope method allowed the best response prediction (P < .01, η2 = 0.78) with a classification accuracy of 83% for a slope of 58° using receiver operating characteristic (ROC) analysis. Conclusions We demonstrate that diffusion change metrics are suitable response predictors for high-grade pediatric tumors, even in the presence of variable clinical diffusion imaging protocols. PMID:23585630

Comprehensive analysis of MGMT promoter methylation: correlation with MGMT expression and clinical response in GBM.

PubMed

Shah, Nameeta; Lin, Biaoyang; Sibenaller, Zita; Ryken, Timothy; Lee, Hwahyung; Yoon, Jae-Geun; Rostad, Steven; Foltz, Greg

2011-01-07

O⁶-methylguanine DNA-methyltransferase (MGMT) promoter methylation has been identified as a potential prognostic marker for glioblastoma patients. The relationship between the exact site of promoter methylation and its effect on gene silencing, and the patient's subsequent response to therapy, is still being defined. The aim of this study was to comprehensively characterize cytosine-guanine (CpG) dinucleotide methylation across the entire MGMT promoter and to correlate individual CpG site methylation patterns to mRNA expression, protein expression, and progression-free survival. To best identify the specific MGMT promoter region most predictive of gene silencing and response to therapy, we determined the methylation status of all 97 CpG sites in the MGMT promoter in tumor samples from 70 GBM patients using quantitative bisulfite sequencing. We next identified the CpG site specific and regional methylation patterns most predictive of gene silencing and improved progression-free survival. Using this data, we propose a new classification scheme utilizing methylation data from across the entire promoter and show that an analysis based on this approach, which we call 3R classification, is predictive of progression-free survival (HR  = 5.23, 95% CI [2.089-13.097], p<0.0001). To adapt this approach to the clinical setting, we used a methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) test based on the 3R classification and show that this test is both feasible in the clinical setting and predictive of progression free survival (HR  = 3.076, 95% CI [1.301-7.27], p = 0.007). We discuss the potential advantages of a test based on this promoter-wide analysis and compare it to the commonly used methylation-specific PCR test. Further prospective validation of these two methods in a large independent patient cohort will be needed to confirm the added value of promoter wide analysis of MGMT methylation in the clinical setting.

Comprehensive Analysis of MGMT Promoter Methylation: Correlation with MGMT Expression and Clinical Response in GBM

PubMed Central

Shah, Nameeta; Lin, Biaoyang; Sibenaller, Zita; Ryken, Timothy; Lee, Hwahyung; Yoon, Jae-Geun; Rostad, Steven; Foltz, Greg

2011-01-01

O6-methylguanine DNA-methyltransferase (MGMT) promoter methylation has been identified as a potential prognostic marker for glioblastoma patients. The relationship between the exact site of promoter methylation and its effect on gene silencing, and the patient's subsequent response to therapy, is still being defined. The aim of this study was to comprehensively characterize cytosine-guanine (CpG) dinucleotide methylation across the entire MGMT promoter and to correlate individual CpG site methylation patterns to mRNA expression, protein expression, and progression-free survival. To best identify the specific MGMT promoter region most predictive of gene silencing and response to therapy, we determined the methylation status of all 97 CpG sites in the MGMT promoter in tumor samples from 70 GBM patients using quantitative bisulfite sequencing. We next identified the CpG site specific and regional methylation patterns most predictive of gene silencing and improved progression-free survival. Using this data, we propose a new classification scheme utilizing methylation data from across the entire promoter and show that an analysis based on this approach, which we call 3R classification, is predictive of progression-free survival (HR  = 5.23, 95% CI [2.089–13.097], p<0.0001). To adapt this approach to the clinical setting, we used a methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) test based on the 3R classification and show that this test is both feasible in the clinical setting and predictive of progression free survival (HR  = 3.076, 95% CI [1.301–7.27], p = 0.007). We discuss the potential advantages of a test based on this promoter-wide analysis and compare it to the commonly used methylation-specific PCR test. Further prospective validation of these two methods in a large independent patient cohort will be needed to confirm the added value of promoter wide analysis of MGMT methylation in the clinical setting. PMID

Comparison of methods of predicting community response to impulsive and nonimpulsive noise

NASA Technical Reports Server (NTRS)

Fidell, Sanford; Pearsons, Karl S.

1994-01-01

Several scientific, regulatory, and policy-coordinating bodies have developed methods for predicting community response to sonic booms. The best known of these is the dosage-response relationship of Working Group 84 of the National Academy of Science's Committee on Hearing, Bioacoustics and Biomechanics. This dosage-response relationship between C-weighted DayNight Average Sound Level and the prevalence of annoyance with high energy impulsive sounds was derived from limited amounts of information about community response to regular, prolonged, and expected exposure to artillery and sonic booms. U.S. Army Regulation 201 adapts this approach to predictions of the acceptability of impulsive noise exposure in communities. This regulation infers equivalent degrees of effect with respect to a well known dosage-response relationship for general (nonimpulsive) transportation noise. Differences in prevalence of annoyance predicted by various relationships lead to different predictions of the compatibility of land uses with sonic boom exposure. An examination of these differences makes apparent several unresolved issues in current practice for predicting and interpreting the prevalence of annoyance due to sonic boom exposure.

Dopamine neurons share common response function for reward prediction error

PubMed Central

Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

2016-01-01

Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically-identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found striking homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we could describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal. PMID:26854803

Prediction of accommodative optical response in prepresbyopic patients using ultrasound biomicroscopy

PubMed Central

Ramasubramanian, Viswanathan; Glasser, Adrian

2015-01-01

PURPOSE To determine whether relatively low-resolution ultrasound biomicroscopy (UBM) can predict the accommodative optical response in prepresbyopic eyes as well as in a previous study of young phakic subjects, despite lower accommodative amplitudes. SETTING College of Optometry, University of Houston, Houston, USA. DESIGN Observational cross-sectional study. METHODS Static accommodative optical response was measured with infrared photorefraction and an autorefractor (WR-5100K) in subjects aged 36 to 46 years. A 35 MHz UBM device (Vumax, Sonomed Escalon) was used to image the left eye, while the right eye viewed accommodative stimuli. Custom-developed Matlab image-analysis software was used to perform automated analysis of UBM images to measure the ocular biometry parameters. The accommodative optical response was predicted from biometry parameters using linear regression, 95% confidence intervals (CIs), and 95% prediction intervals. RESULTS The study evaluated 25 subjects. Per-diopter (D) accommodative changes in anterior chamber depth (ACD), lens thickness, anterior and posterior lens radii of curvature, and anterior segment length were similar to previous values from young subjects. The standard deviations (SDs) of accommodative optical response predicted from linear regressions for UBM-measured biometry parameters were ACD, 0.15 D; lens thickness, 0.25 D; anterior lens radii of curvature, 0.09 D; posterior lens radii of curvature, 0.37 D; and anterior segment length, 0.42 D. CONCLUSIONS Ultrasound biomicroscopy parameters can, on average, predict accommodative optical response with SDs of less than 0.55 D using linear regressions and 95% CIs. Ultrasound biomicroscopy can be used to visualize and quantify accommodative biometric changes and predict accommodative optical response in prepresbyopic eyes. PMID:26049831

Clinical Gestalt and the Prediction of Massive Transfusion after Trauma

PubMed Central

Pommerening, Matthew J.; Goodman, Michael D.; Holcomb, John B.; Wade, Charles E.; Fox, Erin E.; del Junco, Deborah J.; Brasel, Karen J.; Bulger, Eileen M.; Cohen, Mitch J.; Alarcon, Louis H.; Schreiber, Martin A.; Myers, John G.; Phelan, Herb A.; Muskat, Peter; Rahbar, Mohammad; Cotton, Bryan A.

2016-01-01

Introduction Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesized that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Methods Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centers in patients who survived ≥30 minutes after admission and received ≥1 unit of RBC within 6 hours of arrival. Subjects who received ≥ 10 units within 24 hours of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question “Is the patient likely to be massively transfused?” ten minutes after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Results Of the 1,245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all p < 0.05). Gestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9% and 86.2% respectively. Conclusion Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier. Level of Evidence II; Diagnostic study - Development of diagnostic criteria on basis of consecutive patients (with universally applied reference standard) PMID:25682314

Anger responses to psychosocial stress predict heart rate and cortisol stress responses in men but not women.

PubMed

Lupis, Sarah B; Lerman, Michelle; Wolf, Jutta M

2014-11-01

While previous research has suggested that anger and fear responses to stress are linked to distinct sympathetic nervous system (SNS) stress responses, little is known about how these emotions predict hypothalamus-pituitary-adrenal (HPA) axis reactivity. Further, earlier research primarily relied on retrospective self-report of emotion. The current study aimed at addressing both issues in male and female individuals by assessing the role of anger and fear in predicting heart rate and cortisol stress responses using both self-report and facial coding analysis to assess emotion responses. We exposed 32 healthy students (18 female; 19.6±1.7 yr) to an acute psychosocial stress paradigm (TSST) and measured heart rate and salivary cortisol levels throughout the protocol. Anger and fear before and after stress exposure was assessed by self-report, and video recordings of the TSST were assessed by a certified facial coder to determine emotion expression (FACS). Self-reported emotions and emotion expressions did not correlate (all p>.23). Increases in self-reported fear predicted blunted cortisol responses in men (β=0.41, p=.04). Also for men, longer durations of anger expression predicted exaggerated cortisol responses (β=0.67 p=.004), and more anger incidences predicted exaggerated cortisol and heart rate responses (β=0.51, p=.033; β=0.46, p=.066, resp.). Anger and fear did not predict SNS or HPA activity for females (all p>.23). The current differential self-report and facial coding findings support the use of multiple modes of emotion assessment. Particularly, FACS but not self-report revealed a robust anger-stress association that could have important downstream health effects for men. For women, future research may clarify the role of other emotions, such as self-conscious expressions of shame, for physiological stress responses. A better understanding of the emotion-stress link may contribute to behavioral interventions targeting health-promoting ways of

Anger responses to psychosocial stress predict heart rate and cortisol stress responses in men but not women

PubMed Central

Lupis, Sarah B.; Lerman, Michelle; Wolf, Jutta M.

2014-01-01

While previous research has suggested that anger and fear responses to stress are linked to distinct sympathetic nervous system (SNS) stress responses, little is known about how these emotions predict hypothalamus-pituitary-adrenal (HPA) axis reactivity. Further, earlier research primarily relied on retrospective self-report of emotion. The current study aimed at addressing both issues in male and female individuals by assessing the role of anger and fear in predicting heart rate and cortisol stress responses using both self-report and facial coding analysis to assess emotion responses. We exposed 32 healthy students (18 female; 19.6+/−1.7 yrs.) to an acute psychosocial stress paradigm (TSST) and measured heart rate and salivary cortisol levels throughout the protocol. Anger and fear before and after stress exposure was assessed by self-report, and video recordings of the TSST were assessed by a certified facial coder to determine emotion expression (FACS). Self-reported emotions and emotion expressions did not correlate (all p > .23). Increases in self-reported fear predicted blunted cortisol responses in men (β = 0.41, p = .04). Also for men, longer durations of anger expression predicted exaggerated cortisol responses (β = 0.67 p = .004), and more anger incidences predicted exaggerated cortisol and heart rate responses (β = 0.51, p = .033; β = 0.46, p = .066, resp.). Anger and fear did not predict SNS or HPA activity for females (all p > .23). The current differential self-report and facial coding findings support the use of multiple modes of emotion assessment. Particularly, FACS but not self-report revealed a robust anger-stress association that could have important downstream health effects for men. For women, future research may clarify the role of other emotions, such as self-conscious expressions of shame, for physiological stress responses. A better understanding of the emotion-stress link may contribute to behavioral interventions targeting health

Clinical implications of omics and systems medicine: focus on predictive and individualized treatment.

PubMed

Benson, M

2016-03-01

Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide

Recommendations for imaging tumor response in neurofibromatosis clinical trials

PubMed Central

Ardern-Holmes, Simone L.; Babovic-Vuksanovic, Dusica; Barker, Fred G.; Connor, Steve; Evans, D. Gareth; Fisher, Michael J.; Goutagny, Stephane; Harris, Gordon J.; Jaramillo, Diego; Karajannis, Matthias A.; Korf, Bruce R.; Mautner, Victor; Plotkin, Scott R.; Poussaint, Tina Y.; Robertson, Kent; Shih, Chie-Schin; Widemann, Brigitte C.

2013-01-01

Objective: Neurofibromatosis (NF)-related benign tumors such as plexiform neurofibromas (PN) and vestibular schwannomas (VS) can cause substantial morbidity. Clinical trials directed at these tumors have become available. Due to differences in disease manifestations and the natural history of NF-related tumors, response criteria used for solid cancers (1-dimensional/RECIST [Response Evaluation Criteria in Solid Tumors] and bidimensional/World Health Organization) have limited applicability. No standardized response criteria for benign NF tumors exist. The goal of the Tumor Measurement Working Group of the REiNS (Response Evaluation in Neurofibromatosis and Schwannomatosis) committee is to propose consensus guidelines for the evaluation of imaging response in clinical trials for NF tumors. Methods: Currently used imaging endpoints, designs of NF clinical trials, and knowledge of the natural history of NF-related tumors, in particular PN and VS, were reviewed. Consensus recommendations for response evaluation for future studies were developed based on this review and the expertise of group members. Results: MRI with volumetric analysis is recommended to sensitively and reproducibly evaluate changes in tumor size in clinical trials. Volumetric analysis requires adherence to specific imaging recommendations. A 20% volume change was chosen to indicate a decrease or increase in tumor size. Use of these criteria in future trials will enable meaningful comparison of results across studies. Conclusions: The proposed imaging response evaluation guidelines, along with validated clinical outcome measures, will maximize the ability to identify potentially active agents for patients with NF and benign tumors. PMID:24249804

Recommendations for imaging tumor response in neurofibromatosis clinical trials.

PubMed

Dombi, Eva; Ardern-Holmes, Simone L; Babovic-Vuksanovic, Dusica; Barker, Fred G; Connor, Steve; Evans, D Gareth; Fisher, Michael J; Goutagny, Stephane; Harris, Gordon J; Jaramillo, Diego; Karajannis, Matthias A; Korf, Bruce R; Mautner, Victor; Plotkin, Scott R; Poussaint, Tina Y; Robertson, Kent; Shih, Chie-Schin; Widemann, Brigitte C

2013-11-19

Neurofibromatosis (NF)-related benign tumors such as plexiform neurofibromas (PN) and vestibular schwannomas (VS) can cause substantial morbidity. Clinical trials directed at these tumors have become available. Due to differences in disease manifestations and the natural history of NF-related tumors, response criteria used for solid cancers (1-dimensional/RECIST [Response Evaluation Criteria in Solid Tumors] and bidimensional/World Health Organization) have limited applicability. No standardized response criteria for benign NF tumors exist. The goal of the Tumor Measurement Working Group of the REiNS (Response Evaluation in Neurofibromatosis and Schwannomatosis) committee is to propose consensus guidelines for the evaluation of imaging response in clinical trials for NF tumors. Currently used imaging endpoints, designs of NF clinical trials, and knowledge of the natural history of NF-related tumors, in particular PN and VS, were reviewed. Consensus recommendations for response evaluation for future studies were developed based on this review and the expertise of group members. MRI with volumetric analysis is recommended to sensitively and reproducibly evaluate changes in tumor size in clinical trials. Volumetric analysis requires adherence to specific imaging recommendations. A 20% volume change was chosen to indicate a decrease or increase in tumor size. Use of these criteria in future trials will enable meaningful comparison of results across studies. The proposed imaging response evaluation guidelines, along with validated clinical outcome measures, will maximize the ability to identify potentially active agents for patients with NF and benign tumors.

Evaluating predictive modeling's potential to improve teleretinal screening participation in urban safety net clinics.

PubMed

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. The predictive models were modestly predictive with the best model having an AUC of 0.71. Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics.

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

PubMed Central

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

PubMed

Storchi, Riccardo; Zippo, Antonio G; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

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.

Baseline clinical predictors of antitumor response to the PARP inhibitor olaparib in germline BRCA1/2 mutated patients with advanced ovarian cancer.

PubMed

Rafii, Saeed; Gourley, Charlie; Kumar, Rajiv; Geuna, Elena; Ern Ang, Joo; Rye, Tzyvia; Chen, Lee-May; Shapira-Frommer, Ronnie; Friedlander, Michael; Matulonis, Ursula; De Greve, Jacques; Oza, Amit M; Banerjee, Susana; Molife, L Rhoda; Gore, Martin E; Kaye, Stan B; Yap, Timothy A

2017-07-18

The PARP inhibitor olaparib was recently granted Food and Drug Administration (FDA) accelerated approval in patients with advanced BRCA1/2 mutation ovarian cancer. However, antitumor responses are observed in only approximately 40% of patients and the impact of baseline clinical factors on response to treatment remains unclear. Although platinum sensitivity has been suggested as a marker of response to PARP inhibitors, patients with platinum-resistant disease still respond to olaparib. 108 patients with advanced BRCA1/2 mutation ovarian cancers were included. The interval between the end of the most recent platinum chemotherapy and PARPi (PTPI) was used to predict response to olaparib independent of conventional definition of platinum sensitivity. RECIST complete response (CR) and partial response (PR) rates were 35% in patients with platinum-sensitive versus 13% in platinum-resistant (p<0.005). Independent of platinum sensitivity status, the RECIST CR/PR rates were 42% in patients with PTPI greater than 52 weeks and 18% in patients with PTPI less than 52 weeks (p=0.016). No association was found between baseline clinical factors such as FIGO staging, debulking surgery, BRCA1 versus BRCA2 mutations, prior history of breast cancer and prior chemotherapy for breast cancer, and the response to olaparib. We conducted an international multicenter retrospective study to investigate the association between baseline clinical characteristics of patients with advanced BRCA1/2 mutation ovarian cancers from eight different cancer centers and their antitumor response to olaparib. PTPI may be used to refine the prediction of response to PARP inhibition based on the conventional categorization of platinum sensitivity.

Predictive value of dorso-lateral prefrontal connectivity for rTMS response in treatment-resistant depression: A brain perfusion SPECT study.

PubMed

Richieri, Raphaëlle; Verger, Antoine; Boyer, Laurent; Boucekine, Mohamed; David, Anthony; Lançon, Christophe; Cermolacce, Michel; Guedj, Eric

2018-05-18

Previous clinical trials have suggested that repetitive transcranial magnetic stimulation (rTMS) has a significant antidepressant effect in patients with treatment resistant depression (TRD). However, results remain heterogeneous with many patients without effective response. The aim of this SPECT study was to determine before treatment the predictive value of the connectivity of the stimulated area on further rTMS response in patients with TRD. Fifty-eight TRD patients performed a brain perfusion SPECT before high frequency rTMS of the left dorsolateral prefrontal cortex (DLPFC). A voxel based-analysis was achieved to compare connectivity of the left DLPFC in responders and non-responders using inter-regional correlations (p < 0.005, corrected for cluster volume). A multiple logistic regression model was thereafter used with the goal of establishing a predictive score. Before rTMS, responders exhibited increased SPECT connectivity between the left DLPFC and the right cerebellum in comparison to non-responders, independently of age, gender, severity of depression, and severity of treatment resistance. The area under the curve for the combination of these two SPECT clusters to predict rTMS response was 0.756 (p < 0.005). SPECT connectivity of the left DLPFC predicts rTMS response before treatment. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

Predicting the sensitivity to ion therapy based on the response to photon irradiation--experimental evidence and mathematical modelling.

PubMed

Mohanty, Chitralekha; Zielinska-Chomej, Katarzyna; Edgren, Margareta; Hirayama, Ryoichi; Murakami, Takeshi; Lind, Bengt; Toma-Dasu, Iuliana

2014-06-01

The use of ion radiation therapy is growing due to the continuously increasing positive clinical experience obtained. Therefore, there is a high interest in radio-biological experiments comparing the relative efficiency in cell killing of ions and photons as photons are currently the main radiation modality used for cancer treatment. This comparison is particularly important since the treatment planning systems (TPSs) used at the main ion therapy Centers make use of parameters describing the cellular response to photons, respectively ions, determined in vitro. It was, therefore, the aim of this article to compare the effects of high linear energy transfer (LET) ion radiation with low LET photons and determine whether the cellular response to low LET could predict the response to high LET irradiation. Clonogenic cell survival data of five tumor cell lines irradiated with different ion beams of similar, clinically-relevant, LET were studied in relation to response to low LET photons. Two mathematical models were used to fit the data, the repairable-conditionally repairable damage (RCR) model and the linear quadratic (LQ) model. The results indicate that the relative biological efficiency of the high LET radiation assessed with the RCR model could be predicted based only on the response to the low LET irradiation. The particular features of the RCR model indicate that tumor cells showing a large capacity for repairing the damage will have the larger benefit from radiation therapy with ion beams. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Sleep-stage sequencing of sleep-onset REM periods in MSLT predicts treatment response in patients with narcolepsy.

PubMed

Drakatos, Panagis; Patel, Kishankumar; Thakrar, Chiraag; Williams, Adrian J; Kent, Brian D; Leschziner, Guy D

2016-04-01

Current treatment recommendations for narcolepsy suggest that modafinil should be used as a first-line treatment ahead of conventional stimulants or sodium oxybate. In this study, performed in a tertiary sleep disorders centre, treatment responses were examined following these recommendations, and the ability of sleep-stage sequencing of sleep-onset rapid eye movement periods in the multiple sleep latency test to predict treatment response. Over a 3.5-year period, 255 patients were retrospectively identified in the authors' database as patients diagnosed with narcolepsy, type 1 (with cataplexy) or type 2 (without) using clinical and polysomnographic criteria. Eligible patients were examined in detail, sleep study data were abstracted and sleep-stage sequencing of sleep-onset rapid eye movement periods were analysed. Response to treatment was graded utilizing an internally developed scale. Seventy-five patients were included (39% males). Forty (53%) were diagnosed with type 1 narcolepsy with a mean follow-up of 2.37 ± 1.35 years. Ninety-seven percent of the patients were initially started on modafinil, and overall 59% reported complete response on the last follow-up. Twenty-nine patients (39%) had the sequence of sleep stage 1 or wake to rapid eye movement in all of their sleep-onset rapid eye movement periods, with most of these diagnosed as narcolepsy type 1 (72%). The presence of this specific sleep-stage sequence in all sleep-onset rapid eye movement periods was associated with worse treatment response (P = 0.0023). Sleep-stage sequence analysis of sleep-onset rapid eye movement periods in the multiple sleep latency test may aid the prediction of treatment response in narcoleptics and provide a useful prognostic tool in clinical practice, above and beyond their classification as narcolepsy type 1 or 2. © 2015 European Sleep Research Society.

Loop Gain Predicts the Response to Upper Airway Surgery in Patients With Obstructive Sleep Apnea.

PubMed

Joosten, Simon A; Leong, Paul; Landry, Shane A; Sands, Scott A; Terrill, Philip I; Mann, Dwayne; Turton, Anthony; Rangaswamy, Jhanavi; Andara, Christopher; Burgess, Glen; Mansfield, Darren; Hamilton, Garun S; Edwards, Bradley A

2017-07-01

Upper airway surgery is often recommended to treat patients with obstructive sleep apnea (OSA) who cannot tolerate continuous positive airways pressure. However, the response to surgery is variable, potentially because it does not improve the nonanatomical factors (ie, loop gain [LG] and arousal threshold) causing OSA. Measuring these traits clinically might predict responses to surgery. Our primary objective was to test the value of LG and arousal threshold to predict surgical success defined as 50% reduction in apnea-hypopnea index (AHI) and AHI <10 events/hour post surgery. We retrospectively analyzed data from patients who underwent upper airway surgery for OSA (n = 46). Clinical estimates of LG and arousal threshold were calculated from routine polysomnographic recordings presurgery and postsurgery (median of 124 [91-170] days follow-up). Surgery reduced both the AHI (39.1 ± 4.2 vs. 26.5 ± 3.6 events/hour; p < .005) and estimated arousal threshold (-14.8 [-22.9 to -10.2] vs. -9.4 [-14.5 to -6.0] cmH2O) but did not alter LG (0.45 ± 0.08 vs. 0.45 ± 0.12; p = .278). Responders to surgery had a lower baseline LG (0.38 ± 0.02 vs. 0.48 ± 0.01, p < .05) and were younger (31.0 [27.3-42.5] vs. 43.0 [33.0-55.3] years, p < .05) than nonresponders. Lower LG remained a significant predictor of surgical success after controlling for covariates (logistic regression p = .018; receiver operating characteristic area under curve = 0.80). Our study provides proof-of-principle that upper airway surgery most effectively resolves OSA in patients with lower LG. Predicting the failure of surgical treatment, consequent to less stable ventilatory control (elevated LG), can be achieved in the clinic and may facilitate avoidance of surgical failures. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials.

PubMed

Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

2012-01-01

The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy's law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

Clinical prediction and the idea of a population.

PubMed

Armstrong, David

2017-04-01

Using an analysis of the British Medical Journal over the past 170 years, this article describes how changes in the idea of a population have informed new technologies of medical prediction. These approaches have largely replaced older ideas of clinical prognosis based on understanding the natural histories of the underlying pathologies. The 19 th -century idea of a population, which provided a denominator for medical events such as births and deaths, was constrained in its predictive power by its method of enumerating individual bodies. During the 20 th century, populations were increasingly constructed through inferential techniques based on patient groups and samples seen to possess variable characteristics. The emergence of these new virtual populations created the conditions for the emergence of predictive algorithms that are used to foretell our medical futures.

Crohn Disease: FDG PET/CT Before and After Initial Dose of Anti-Tumor Necrosis Factor Therapy to Predict Long-term Response.

PubMed

Epelboym, Yan; Shyn, Paul B; Chick, Jeffrey Forris Beecham; Hamilton, Matthew J; OʼConnor, Stacy D; Silverman, Stuart G; Kim, Chun K

2017-11-01

Clinical assessments of Crohn disease activity are limited in their capacity to assess treatment response to biologic therapy. The purpose of this study was to determine if changes in FDG activity between baseline PET and repeat PET performed prior to the second dose of induction anti-tumor necrosis factor (TNF) therapy correlate with clinical response. In this prospective, institutional review board-approved, Health Insurance Portability and Accountability Act-compliant pilot study of 8 adult patients with active Crohn disease, FDG activity before and 2 weeks after initiation of anti-TNF therapy was assessed using low-dose PET/CT. FDG activity was measured in the most inflamed bowel loop using an SUVratio (SUVmax/liver SUVmean). Changes in SUVratio were compared with a blinded gastroenterologist assessment of clinical response and steroid-free remission, as well as C-reactive protein (CRP), during a 12-month follow-up period. Of 8 patients, 7 showed FDG activity decline at 2 weeks, 5 of whom achieved a clinical response and steroid-free remission at 8, 26, and 52 weeks. The remaining 2 patients with FDG activity decline did not achieve a clinical response or steroid-free remission at these time points, but there were reductions in CRP. The 1 patient without FDG activity decline was a clinical nonresponder, had no reduction in CRP, and did not achieve steroid-free remission. A change in FDG activity at FDG PET/CT performed prior to the second induction dose of anti-TNF therapy has the potential to predict clinical response and steroid-free remission in patients with Crohn disease.

Clinical and cytological features predictive of malignancy in thyroid follicular neoplasms.

PubMed

Lubitz, Carrie C; Faquin, William C; Yang, Jingyun; Mekel, Michal; Gaz, Randall D; Parangi, Sareh; Randolph, Gregory W; Hodin, Richard A; Stephen, Antonia E

2010-01-01

The preoperative diagnosis of malignancy in nodules suspicious for a follicular neoplasm remains challenging. A number of clinical and cytological parameters have been previously studied; however, none have significantly impacted clinical practice. The aim of this study was to determine predictive characteristics of follicular neoplasms useful for clinical application. Four clinical (age, sex, nodule size, solitary nodule) and 17 cytological variables were retrospectively reviewed for 144 patients with a nodule suspicious for follicular neoplasm, diagnosed preoperatively by fine-needle aspiration (FNA), from a single institution over a 2-year period (January 2006 to December 2007). The FNAs were examined by a single, blinded pathologist and compared with final surgical pathology. Significance of clinical and cytological variables was determined by univariate analysis and backward stepwise logistic regression. Odds ratios (ORs) for malignancy, a receiver operating characteristic curve, and predicted probabilities of combined features were determined. There was an 11% incidence of malignancy (16/144). On univariate analysis, nodule size >OR=4.0 cm nears significance (p = 0.054) and 9 of 17 cytological features examined were significantly associated with malignancy. Three variables stay in the final model after performing backward stepwise selection in logistic regression: nodule size (OR = 0.25, p = 0.05), presence of a transgressing vessel (OR = 23, p < 0.0001), and nuclear grooves (OR = 4.3, p = 0.03). The predicted probability of malignancy was 88.4% with the presence of all three variables on preoperative FNA. When the two papillary carcinomas were excluded from the analysis, the presence of nuclear grooves was no longer significant, and anisokaryosis (OR = 12.74, p = 0.005) and presence of nucleolus (OR = 0.11, p = 0.04) were significantly associated with malignancy. Excluding the two papillary thyroid carcinomas, a nodule size >or=4 cm, with a transgressing

Idiopathic normal pressure hydrocephalus: the CSF tap-test may predict the clinical response to shunting.

PubMed

Sand, T; Bovim, G; Grimse, R; Myhr, G; Helde, G; Cappelen, J

1994-05-01

A follow-up study was performed in nine patients with idiopathic normal pressure hydrocephalus (NPH) 37 months (mean) after shunting and 10 non-operated controls with comparable degrees of ventricular enlargement, gait disorder, and dementia. Five operated patients vs. no controls reported sustained general improvement (p < 0.02). Objectively improved gait at follow-up (compared with preoperative status) was found in five of the six tested NPH-patients vs. none of the controls (p < 0.005). Improved gait and/or psychometric function was found in four of six NPH vs. none of eight control patients (p < 0.02) after drainage of 40 ml cerebrospinal fluid (CSF tap-test). Improved gait during the CSF tap-test predicted continued improvement at follow-up. Temporal horn size was the only radiological variable which showed a (moderate) positive correlation with resistance to CSF absorption and rate of pressure increase. The size of the third ventricle diminished in parallel with clinical improvement.

The role of ovarian reserve markers in prediction of clinical pregnancy.

PubMed

Zebitay, Ali G; Cetin, Orkun; Verit, Fatma F; Keskin, Seda; Sakar, M Nafi; Karahuseyinoglu, Sercin; Ilhan, Gulsah; Sahmay, Sezai

2017-05-01

To evaluate the role of ovarian reserve markers in the prediction of clinical pregnancy and embryo transfer accomplishment among poor responder IVF applicants. 304 female poor responder IVF applicants were included in this prospective cohort study conducted at the IVF-unit. Antral follicle count, FSH, LH, E2, AMH and IVF outcomes were compared in pregnant and non-pregnant groups as well as in ET vs. non-ET groups. The number of retrieved oocytes was significantly correlated positively with AMH and AFC, and negatively with FSH and age. Quartiles of FSH and AFC were similar to the rate of pregnancy. Quartiles of AMH (<25%/25-75% and <25%/>75%) were statistically significant. Mean serum levels for AMH were significantly lower in the non-ET group. Our findings seem to indicate that day 3 AMH values can predict ET accomplishment with a sensitivity of 96% and a specificity of 35%. Quartiles of AMH <25% (< 0.21 ng/mL) can predict the IVF results among poor responder IVF applicants. Impact statement Various cut-off values have been determined for day 3 serum AMH values. These values help to determine the groups that are expected to give normal, high or low response to stimulation and decide the treatment options. In contrast to other groups of patients, poor responders cannot reach the embryo transfer stage for several reasons. These are; absence of a mature oocyte after oocyte pick-up, fertilisation failure without male factor or poor embryo quality. In the present study; a cut-off value of 0.33 ng/mL for the prediction of ET accomplishment in poor responder patients was determined with a sensitivity of 96%. Additionally, clinical pregnancy could not be achieved under the value of 0.21 ng/mL day 3 AMH values. It is important to clarify the embryo transfer success of poor responder patients prior to expected treatment success. Pre-treatment counselling for these patients would lessen the disappointment that may develop after treatment. The cost-effectiveness of

Outcome Prediction in Mathematical Models of Immune Response to Infection.

PubMed

Mai, Manuel; Wang, Kun; Huber, Greg; Kirby, Michael; Shattuck, Mark D; O'Hern, Corey S

2015-01-01

Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs) that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of 'virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.

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

PubMed

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

2012-01-01

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

Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation.

PubMed

Liou, Jing-Yang; Ting, Chien-Kun; Mandell, M Susan; Chang, Kuang-Yi; Teng, Wei-Nung; Huang, Yu-Yin; Tsou, Mei-Yung

2016-08-01

Selecting an effective dose of sedative drugs in combined upper and lower gastrointestinal endoscopy is complicated by varying degrees of pain stimulation. We tested the ability of 5 response surface models to predict depth of sedation after administration of midazolam and alfentanil in this complex model. The procedure was divided into 3 phases: esophagogastroduodenoscopy (EGD), colonoscopy, and the time interval between the 2 (intersession). The depth of sedation in 33 adult patients was monitored by Observer Assessment of Alertness/Scores. A total of 218 combinations of midazolam and alfentanil effect-site concentrations derived from pharmacokinetic models were used to test 5 response surface models in each of the 3 phases of endoscopy. Model fit was evaluated with objective function value, corrected Akaike Information Criterion (AICc), and Spearman ranked correlation. A model was arbitrarily defined as accurate if the predicted probability is <0.5 from the observed response. The effect-site concentrations tested ranged from 1 to 76 ng/mL and from 5 to 80 ng/mL for midazolam and alfentanil, respectively. Midazolam and alfentanil had synergistic effects in colonoscopy and EGD, but additivity was observed in the intersession group. Adequate prediction rates were 84% to 85% in the intersession group, 84% to 88% during colonoscopy, and 82% to 87% during EGD. The reduced Greco and Fixed alfentanil concentration required for 50% of the patients to achieve targeted response Hierarchy models performed better with comparable predictive strength. The reduced Greco model had the lowest AICc with strong correlation in all 3 phases of endoscopy. Dynamic, rather than fixed, γ and γalf in the Hierarchy model improved model fit. The reduced Greco model had the lowest objective function value and AICc and thus the best fit. This model was reliable with acceptable predictive ability based on adequate clinical correlation. We suggest that this model has practical clinical value

(99m)Tc-Annexin A5 quantification of apoptotic tumor response: a systematic review and meta-analysis of clinical imaging trials.

PubMed

Belhocine, Tarik Z; Blankenberg, Francis G; Kartachova, Marina S; Stitt, Larry W; Vanderheyden, Jean-Luc; Hoebers, Frank J P; Van de Wiele, Christophe

2015-12-01

(99m)Tc-Annexin A5 has been used as a molecular imaging probe for the visualization, characterization and measurement of apoptosis. In an effort to define the quantitative (99m)Tc-annexin A5 uptake criteria that best predict tumor response to treatment, we performed a systematic review and meta-analysis of the results of all clinical imaging trials found in the literature or publicly available databases. Included in this review were 17 clinical trials investigating quantitative (99m)Tc-annexin A5 (qAnx5) imaging using different parameters in cancer patients before and after the first course of chemotherapy and/or radiation therapy. Qualitative assessment of the clinical studies for diagnostic accuracy was performed using the QUADAS-2 criteria. Of these studies, five prospective single-center clinical trials (92 patients in total) were included in the meta-analysis after exclusion of one multicenter clinical trial due to heterogeneity. Pooled positive predictive values (PPV) and pooled negative predictive values (NPV) (with 95% CI) were calculated using Meta-Disc software version 1.4. Absolute quantification and/or relative quantification of (99m)Tc-annexin A5 uptake were performed at baseline and after the start of treatment. Various quantitative parameters have been used for the calculation of (99m)Tc-annexin A5 tumor uptake and delta (Δ) tumor changes post-treatment compared to baseline including: tumor-to-background ratio (TBR), ΔTBR, tumor-to-noise ratio, relative tumor ratio (TR), ΔTR, standardized tumor uptake ratio (STU), ΔSTU, maximum count per pixel within the tumor volume (Cmax), Cmax%, absolute ΔU and percentage (ΔU%), maximum ΔU counts, semiquantitative visual scoring, percent injected dose (%ID) and %ID/cm(3). Clinical trials investigating qAnx5 imaging have included patients with lung cancer, lymphoma, breast cancer, head and neck cancer and other less common tumor types. In two phase I/II single-center clinical trials, an increase of ≥25% in

Clinical Predictors of Response to Cognitive-Behavioral Therapy in Pediatric Anxiety Disorders: The Genes for Treatment (GxT) Study

PubMed Central

Hudson, Jennifer L.; Keers, Robert; Roberts, Susanna; Coleman, Jonathan R.I.; Breen, Gerome; Arendt, Kristian; Bögels, Susan; Cooper, Peter; Creswell, Cathy; Hartman, Catharina; Heiervang, Einar R.; Hötzel, Katrin; In-Albon, Tina; Lavallee, Kristen; Lyneham, Heidi J.; Marin, Carla E.; McKinnon, Anna; Meiser-Stedman, Richard; Morris, Talia; Nauta, Maaike; Rapee, Ronald M.; Schneider, Silvia; Schneider, Sophie C.; Silverman, Wendy K.; Thastum, Mikael; Thirlwall, Kerstin; Waite, Polly; Wergeland, Gro Janne; Lester, Kathryn J.; Eley, Thalia C.

2015-01-01

Objective The Genes for Treatment study is an international, multisite collaboration exploring the role of genetic, demographic, and clinical predictors in response to cognitive-behavioral therapy (CBT) in pediatric anxiety disorders. The current article, the first from the study, examined demographic and clinical predictors of response to CBT. We hypothesized that the child’s gender, type of anxiety disorder, initial severity and comorbidity, and parents’ psychopathology would significantly predict outcome. Method A sample of 1,519 children 5 to 18 years of age with a primary anxiety diagnosis received CBT across 11 sites. Outcome was defined as response (change in diagnostic severity) and remission (absence of the primary diagnosis) at each time point (posttreatment, 3-, 6-, and/or 12-month follow-up) and analyzed using linear and logistic mixed models. Separate analyses were conducted using data from posttreatment and follow-up assessments to explore the relative importance of predictors at these time points. Results Individuals with social anxiety disorder (SoAD) had significantly poorer outcomes (poorer response and lower rates of remission) than those with generalized anxiety disorder (GAD). Although individuals with specific phobia (SP) also had poorer outcomes than those with GAD at posttreatment, these differences were not maintained at follow-up. Both comorbid mood and externalizing disorders significantly predicted poorer outcomes at posttreatment and follow-up, whereas self-reported parental psychopathology had little effect on posttreatment outcomes but significantly predicted response (although not remission) at follow-up. Conclusion SoAD, nonanxiety comorbidity, and parental psychopathology were associated with poorer outcomes after CBT. The results highlight the need for enhanced treatments for children at risk for poorer outcomes. PMID:26004660

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

PubMed

Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-05-01

Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

PubMed Central

Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-01-01

Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. Materials and Methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% (P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. PMID:27655861

Predictive assessment in pharmacogenetics of XRCC1 gene on clinical outcomes of advanced lung cancer patients treated with platinum-based chemotherapy

PubMed Central

Yuan, Zhengrong; Li, Jiao; Hu, Ruiqi; Jiao, Yang; Han, Yingying; Weng, Qiang

2015-01-01

Published data have shown inconsistent results about the pharmacogenetics of XRCC1 gene on clinical outcomes of advanced lung cancer patients treated with platinum-based chemotherapy. This meta-analysis aimed to summarize published findings and provide more reliable association. A total of 53 eligible studies including 7433 patients were included. Patients bearing the favorable TrpTrp and TrpArg genotypes of Arg194Trp were more likely to better response rates to platinum-based chemotherapy compared to those with the unfavorable ArgArg genotype (TrpTrp+TrpArg vs. ArgArg: odds ratio (OR) = 2.02, 95% CI, 1.66–2.45). The GlnGln and GlnArg genotypes of Arg399Gln were significantly associated with the poorer response rates compared to those with the ArgArg genotype (GlnGln +GlnArg vs. ArgArg: OR = 0.68, 95% CI, 0.54–0.86). The GlnGln genotype might be more closely associated with shorter survival time and higher risks of death for patients (GlnGln vs. ArgArg: hazard ratio (HR) = 1.14, 95% CI, 0.75–1.75). Our cumulative meta-analyses indicated a distinct apparent trend toward a better response rate for Arg194Trp, but a poorer response rate in Arg399Gln. These findings indicate a predictive role of XRCC1 polymorphisms in clinical outcomes. The use of XRCC1 polymorphisms as predictive factor of clinical outcomes in personalized chemotherapy treatment requires further verification from large well-designed pharmacogenetics studies. PMID:26585370

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

PubMed

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

2004-03-01

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

Clinical relevance of pulse pressure variations for predicting fluid responsiveness in mechanically ventilated intensive care unit patients: the grey zone approach.

PubMed

Biais, Matthieu; Ehrmann, Stephan; Mari, Arnaud; Conte, Benjamin; Mahjoub, Yazine; Desebbe, Olivier; Pottecher, Julien; Lakhal, Karim; Benzekri-Lefevre, Dalila; Molinari, Nicolas; Boulain, Thierry; Lefrant, Jean-Yves; Muller, Laurent

2014-11-04

Pulse pressure variation (PPV) has been shown to predict fluid responsiveness in ventilated intensive care unit (ICU) patients. The present study was aimed at assessing the diagnostic accuracy of PPV for prediction of fluid responsiveness by using the grey zone approach in a large population. The study pooled data of 556 patients from nine French ICUs. Hemodynamic (PPV, central venous pressure (CVP) and cardiac output) and ventilator variables were recorded. Responders were defined as patients increasing their stroke volume more than or equal to 15% after fluid challenge. The receiver operating characteristic (ROC) curve and grey zone were defined for PPV. The grey zone was evaluated according to the risk of fluid infusion in hypoxemic patients. Fluid challenge led to increased stroke volume more than or equal to 15% in 267 patients (48%). The areas under the ROC curve of PPV and CVP were 0.73 (95% confidence interval (CI): 0.68 to 0.77) and 0.64 (95% CI 0.59 to 0.70), respectively (P<0.001). A grey zone of 4 to 17% (62% of patients) was found for PPV. A tidal volume more than or equal to 8 ml.kg(-1) and a driving pressure (plateau pressure - PEEP) more than 20 cmH2O significantly improved the area under the ROC curve for PPV. When taking into account the risk of fluid infusion, the grey zone for PPV was 2 to 13%. In ventilated ICU patients, PPV values between 4 and 17%, encountered in 62% patients exhibiting validity prerequisites, did not predict fluid responsiveness.

Clinical trial designs incorporating predictive biomarkers☆

PubMed Central

Renfro, Lindsay A.; Mallick, Himel; An, Ming-Wen; Sargent, Daniel J.; Mandrekar, Sumithra J.

2016-01-01

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer “targeted” drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research. PMID:26827695

Identification of predictive markers of cytarabine response in AML by integrative analysis of gene-expression profiles with multiple phenotypes

PubMed Central

Lamba, Jatinder K; Crews, Kristine R; Pounds, Stanley B; Cao, Xueyuan; Gandhi, Varsha; Plunkett, William; Razzouk, Bassem I; Lamba, Vishal; Baker, Sharyn D; Raimondi, Susana C; Campana, Dario; Pui, Ching-Hon; Downing, James R; Rubnitz, Jeffrey E; Ribeiro, Raul C

2011-01-01

Aim To identify gene-expression signatures predicting cytarabine response by an integrative analysis of multiple clinical and pharmacological end points in acute myeloid leukemia (AML) patients. Materials & methods We performed an integrated analysis to associate the gene expression of diagnostic bone marrow blasts from acute myeloid leukemia (AML) patients treated in the discovery set (AML97; n = 42) and in the independent validation set (AML02; n = 46) with multiple clinical and pharmacological end points. Based on prior biological knowledge, we defined a gene to show a therapeutically beneficial (detrimental) pattern of association of its expression positively (negatively) correlated with favorable phenotypes such as intracellular cytarabine 5´-triphosphate levels, morphological response and event-free survival, and negatively (positively) correlated with unfavorable end points such as post-cytarabine DNA synthesis levels, minimal residual disease and cytarabine LC50. Results We identified 240 probe sets predicting a therapeutically beneficial pattern and 97 predicting detrimental pattern (p ≤ 0.005) in the discovery set. Of these, 60 were confirmed in the independent validation set. The validated probe sets correspond to genes involved in PIK3/PTEN/AKT/mTOR signaling, G-protein-coupled receptor signaling and leukemogenesis. This suggests that targeting these pathways as potential pharmacogenomic and therapeutic candidates could be useful for improving treatment outcomes in AML. Conclusion This study illustrates the power of integrated data analysis of genomic data as well as multiple clinical and pharmacologic end points in the identification of genes and pathways of biological relevance. PMID:21449673

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

PubMed Central

Sieberts, Solveig K.; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O.; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S. K.; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-ud-din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R.; Marttinen, Pekka; Mezlini, Aziz M.; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E.; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Calaza, Manuel; Elmarakeby, Haitham; Heath, Lenwood S.; Long, Quan; Moore, Jonathan D.; Opiyo, Stephen Obol; Savage, Richard S.; Zhu, Jun; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P.; Gerlag, Danielle; Huizinga, Tom W. J.; Kurreeman, Fina; Allaart, Cornelia F.; Louis Bridges Jr., S.; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K.; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M.

2016-01-01

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data. PMID:27549343

A Bayesian prediction model between a biomarker and the clinical endpoint for dichotomous variables.

PubMed

Jiang, Zhiwei; Song, Yang; Shou, Qiong; Xia, Jielai; Wang, William

2014-12-20

Early biomarkers are helpful for predicting clinical endpoints and for evaluating efficacy in clinical trials even if the biomarker cannot replace clinical outcome as a surrogate. The building and evaluation of an association model between biomarkers and clinical outcomes are two equally important concerns regarding the prediction of clinical outcome. This paper is to address both issues in a Bayesian framework. A Bayesian meta-analytic approach is proposed to build a prediction model between the biomarker and clinical endpoint for dichotomous variables. Compared with other Bayesian methods, the proposed model only requires trial-level summary data of historical trials in model building. By using extensive simulations, we evaluate the link function and the application condition of the proposed Bayesian model under scenario (i) equal positive predictive value (PPV) and negative predictive value (NPV) and (ii) higher NPV and lower PPV. In the simulations, the patient-level data is generated to evaluate the meta-analytic model. PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome. The minimum number of historical trials to be included in building the model is also considered. It is seen from the simulations that the logit link function performs better than the odds and cloglog functions under both scenarios. PPV/NPV ≥0.5 for equal PPV and NPV, and PPV + NPV ≥1 for higher NPV and lower PPV are proposed in order to predict clinical outcome accurately and precisely when the proposed model is considered. Twenty historical trials are required to be included in model building when PPV and NPV are equal. For unequal PPV and NPV, the minimum number of historical trials for model building is proposed to be five. A hypothetical example shows an application of the proposed model in global drug development. The proposed Bayesian model is able to predict well the clinical endpoint from the observed biomarker

Nottingham Clinico-Pathological Response Index (NPRI) after neoadjuvant chemotherapy (Neo-ACT) accurately predicts clinical outcome in locally advanced breast cancer.

PubMed

Abdel-Fatah, Tarek M; Ball, Graham; Lee, Andrew H S; Pinder, Sarah; MacMilan, R Douglas; Cornford, Eleanor; Moseley, Paul M; Silverman, Rafael; Price, James; Latham, Bruce; Palmer, David; Chan, Arlene; Ellis, Ian O; Chan, Stephen Y T

2015-03-01

There is a need to identify more sensitive clinicopathologic criteria to assess the response to neoadjuvant chemotherapy (Neo-ACT) and guide subsequent adjuvant therapy. We performed a clinicopathologic assessment of 426 patients who had completed Neo-ACT for locally advanced breast cancer (LABC) with a median follow-up of 70 months. Patients were divided into a training set treated with anthracycline combination chemotherapy (n = 172); an internal validation set treated with anthracycline and taxane (n = 129); and an external validation set treated with anthracycline with or without taxane (n = 125). A multivariate Cox regression model demonstrated the absence of fibrosis, presence of lymphovascular invasion, increasing number of lymph node metastases, and administration of hormone therapy were significantly associated with short breast cancer-specific survival (BCSS) and disease-free survival (DFS); Ps < 0.01, while reduction of tumor size was associated with DFS (P = 0.022). Nottingham Clinico-Pathological Response Indexes (NPRI) were calculated, and four prognostic groups (NPRI-PG) were identified. Patients in prognostic group 2 (NPRI-PG2) for BCSS (66 of 172; 38.4%) have the same prognosis as those who achieved pathologic complete response (pCR; NPRI-PG1; 15%). Receiver-operating characteristic (ROC) curves indicated that the NPRI outperformed the currently used prognostic factors and adding the NPRI improved their performance as a predictor for both BCSS (area under the curve [AUC], 0.88) and DFS (AUC, 0.87). The NPRI predicts BCSS and DFS, with a higher sensitivity than pCR. The NPRI can also improve the sensitivity and specificity of clinicopathologic response as a study endpoint, for assessing response to Neo-ACT, and can serve as a valuable tool for the discovery of future predictive molecular markers. ©2014 American Association for Cancer Research.

Usefulness of elevated red cell distribution width for predicting systemic inflammatory response syndrome after extracorporeal circulation.

PubMed

Özeren, M; Aytaçoğlu, B; Vezir, Ö; Karaca, K; Akın, R; Sucu, N

2015-10-01

Cardiac surgical operations performed by using extracorporeal circulation (ECC) lead to a systemic inflammatory response (SIR). Sometimes SIR may turn into a severe state, the systemic inflammatory response syndrome (SIRS) that usually has a poor outcome with no specific clinical tools described for its prediction. Red cell distribution width (RDW) is a routine hematological parameter. It has been proposed as a marker of morbidity and mortality in various clinical conditions. We aimed to investigate the relationship between high RDW and SIRS which is triggered by ECC. Eleven hundred consecutive patients who underwent elective heart surgery with the use of ECC were retrospectively analyzed. A total of 19 patients fulfilled the described SIRS criteria and 20 consecutive patients were selected as the control group. RDW and other laboratory parameters, preoperative clinical status, operative data and postoperative data were compared between the SIRS and the control groups. Baseline characteristics of the patient groups were similar. Significant mortality was found in the SIRS group; 18 (94.73%) patients and 2 (10%) patients in the control group (p < 0.002). RDW was found to be significantly higher in the SIRS group vs the control group (15.02 ± 2.03 vs 13.01 ± 1.93, respectively, p < 0.003). Multiple logistic regression analyses showed an association between high RDW levels and SIRS development (OR for RDW levels exceeding 13.5%; 95% confidence limits of 1.0-1.3; p < 0.04). Total operation time and the need for inotropic support were also found to be significant against the SIRS group (p = 0.049). Increased RDW was significantly associated with increased risk of SIRS after ECC. The results of this study suggest that paying attention to RDW might provide valuable clinical information for predicting SIRS development among patients who are candidates for open heart surgery, without incurring additional costs. © The Author(s) 2015.

Etiology of Cellulitis and Clinical Prediction of Streptococcal Disease: A Prospective Study

PubMed Central

Bruun, Trond; Oppegaard, Oddvar; Kittang, Bård R.; Mylvaganam, Haima; Langeland, Nina; Skrede, Steinar

2016-01-01

Background. The importance of bacteria other than group A streptococci (GAS) in different clinical presentations of cellulitis is unclear, commonly leading to treatment with broad-spectrum antibiotics. The aim of this study was to describe the etiological and clinical spectrum of cellulitis and identify clinical features predicting streptococcal etiology. Methods. We prospectively enrolled 216 patients hospitalized with cellulitis. Clinical details were registered. Bacterial culture was performed from blood, cutaneous or subcutaneous tissue, and/or swabs from skin lesions. Paired serum samples were analyzed for anti-streptolysin O and anti-deoxyribonuclease B antibodies. Results. Serology or blood or tissue culture confirmed β-hemolytic streptococcal (BHS) etiology in 72% (146 of 203) of cases. An additional 13% (27 of 203) of cases had probable BHS infection, indicated by penicillin response or BHS cultured from skin swabs. β-hemolytic streptococcal etiology was predominant in all clinical subgroups, including patients without sharply demarcated erythema. β-hemolytic group C or G streptococci (GCS/GGS) were more commonly isolated than GAS (36 vs 22 cases). This predominance was found in the lower extremity infections. Group C or G streptococci in swabs were associated with seropositivity just as often as GAS. Staphylococcus aureus was cultured from swabs as a single pathogen in 24 cases, 14 (64%) of which had confirmed BHS etiology. Individual BHS-associated clinical characteristics increased the likelihood of confirmed BHS disease only slightly; positive likelihood ratios did not exceed 2.1. Conclusions. β-hemolytic streptococci were the dominating cause of cellulitis in all clinical subgroups and among cases with S aureus in cutaneous swabs. Group C or G streptococci were more frequently detected than GAS. No single clinical feature substantially increased the probability of confirmed BHS etiology. PMID:26734653

Predicting DNAPL Source Zone and Plume Response Using Site-Measured Characteristics

DTIC Science & Technology

2017-05-19

FINAL REPORT Predicting DNAPL Source Zone and Plume Response Using Site- Measured Characteristics SERDP Project ER-1613 MAY 2017...Final Report 3. DATES COVERED (From - To) 2007 - 2017 4. TITLE AND SUBTITLE PREDICTING DNAPL SOURCE ZONE AND PLUME RESPONSE USING SITE- MEASURED ...historical record of concentration and head measurements , particularly in the near-source region. For each site considered, currently available data

Pressure Ulcers in Adults: Prediction and Prevention. Clinical Practice Guideline Number 3.

ERIC Educational Resources Information Center

Agency for Health Care Policy and Research (DHHS/PHS), Rockville, MD.

This package includes a clinical practice guideline, quick reference guide for clinicians, and patient's guide to predicting and preventing pressure ulcers in adults. The clinical practice guideline includes the following: overview of the incidence and prevalence of pressure ulcers; clinical practice guideline (introduction, risk assessment tools…

Implementations of clinical functional magnetic resonance imaging using character-based paradigms for the prediction of Chinese language dominance.

PubMed

Liu, Ho-Ling; Wu, Chien-Te; Chen, Jian-Chuan; Hsu, Yuan-Yu; Wai, Yau-Yau; Wan, Yung-Liang

2003-01-01

Recently, functional MRI (fMRI) using word generation (WG) tasks has been shown to be effective for mapping the Chinese language-related brain areas. In clinical applications, however, patients' performance cannot be easily monitored during WG tasks. In this study, we evaluated the feasibility of a word choice (WC) paradigm in the clinical setting and compared the results with those from WG tasks. Intrasubject comparisons of fMRI with both WG and WC paradigms were performed on six normal human subjects and two tumor patients. Subject responses in the WC paradigm, based on semantic judgments, were recorded. Activation strength, extent, and laterality were evaluated and compared. Our results showed that fMRI with the WC paradigm evoked weaker neuronal activation than that with the WG paradigm in Chinese language-related brain areas. It was sufficient to reveal language laterality for clinical use, however. In addition, it resulted in less nonlanguage-specific brain activation. Results from the patient data demonstrated strong evidence for the necessity of incorporating response monitoring during fMRI studies, which suggested that fMRI with the WC paradigm is more appropriate to be implemented for the prediction of Chinese language dominance in clinical environments.

Nonlinear random response prediction using MSC/NASTRAN

NASA Technical Reports Server (NTRS)

Robinson, J. H.; Chiang, C. K.; Rizzi, S. A.

1993-01-01

An equivalent linearization technique was incorporated into MSC/NASTRAN to predict the nonlinear random response of structures by means of Direct Matrix Abstract Programming (DMAP) modifications and inclusion of the nonlinear differential stiffness module inside the iteration loop. An iterative process was used to determine the rms displacements. Numerical results obtained for validation on simple plates and beams are in good agreement with existing solutions in both the linear and linearized regions. The versatility of the implementation will enable the analyst to determine the nonlinear random responses for complex structures under combined loads. The thermo-acoustic response of a hexagonal thermal protection system panel is used to highlight some of the features of the program.

Molecular biomarkers predictive of sertraline treatment response in young children with fragile X syndrome

PubMed Central

AlOlaby, Reem Rafik; Sweha, Stefan R; Silva, Marisol; Durbin-Johnson, Blythe; Yrigollen, Carolyn M; Pretto, Dalyir; Hagerman, Randi J; Tassone, Flora

2017-01-01

Objectives Several neurotransmitters involved in brain development are altered in fragile X syndrome (FXS), the most common monogenic cause of autism spectrum disorder (ASD). Serotonin plays a vital role in synaptogenesis and postnatal brain development. Deficits in serotonin synthesis and abnormal neurogenesis were shown in young children with autism, suggesting that treating within the first years of life with a selective serotonin reuptake inhibitor might be the most effective time. In this study we aimed to identify molecular biomarkers involved in the serotonergic pathway that could predict the response to sertraline treatment in young children with FXS. Methods Genotypes were determined for several genes involved in serotonergic pathway in 51 children with FXS, ages 24 to 68 months. Correlations between genotypes and deviations from baseline in primary and secondary outcome measures were modeled using linear regression models. Results A significant association was observed between a BDNF polymorphism and improvements for several clinical measures, including the Clinical Global Impression scale (P= 0.008) and the Cognitive T Score (P= 0.017) in those treated with sertraline compared to those in the placebo group. Additionally, polymorphisms in the MAOA, Cytochrome P450 2C19 and 2D6, and in the 5-HTTLPR gene showed a significant correlation with some of the secondary measures included in this study. Conclusion This study shows that polymorphisms of genes involved in the serotonergic pathway could play a potential role in predicting response to sertraline treatment in young children with FXS. Larger studies are warranted to confirm these initial findings. PMID:28242040

OPTIC NERVE INFILTRATION BY RETINOBLASTOMA: Predictive Clinical Features and Outcome.

PubMed

Kaliki, Swathi; Tahiliani, Prerana; Mishra, Dilip K; Srinivasan, Visweswaran; Ali, Mohammed Hasnat; Reddy, Vijay Anand P

2016-06-01

To identify the clinical features predictive of any optic nerve infiltration and postlaminar optic nerve infiltration by retinoblastoma on histopathology and to report the outcome (metastasis and death) in these patients. Retrospective study. Of the 403 patients who underwent primary enucleation for retinoblastoma, 196 patients had optic nerve tumor infiltration (Group 1) and 207 patients had no evidence of optic nerve tumor infiltration (Group 2). Group 1 included patients with prelaminar (n = 47; 24%), laminar (n = 74; 38%), and postlaminar tumor infiltration with or without involving optic nerve transection (n = 74; 38%). Comparing Group 1 and Group 2, the patients in Group 1 had prolonged duration of symptoms (>6 months) (16% vs. 8%; P = 0.02) and were associated with no vision at presentation (23% vs. 10%; P = 0.01), higher rates of secondary glaucoma (42% vs. 12%; P < 0.0001), iris neovascularization (39% vs. 23%; P < 0.001), and larger tumors (mean tumor thickness, 12.8 mm vs. 12 mm; P = 0.0001). There was a higher prevalence of metastasis in Group 1 than in Group 2 (4% vs. 0%; P = 0.006). On multivariate analysis, clinical features predictive of any optic nerve tumor infiltration secondary glaucoma (hazard ratio = 5.38; P < 0.001) and those predictive of postlaminar optic nerve tumor infiltration included iris neovascularization (hazard ratio = 2.66; P = 0.001) and secondary glaucoma (hazard ratio = 3.13; P < 0.001). In this study, clinical features predictive of any optic nerve tumor infiltration included secondary glaucoma and those predictive of postlaminar optic nerve tumor infiltration included iris neovascularization and secondary glaucoma. Despite adjuvant treatment in those with postlaminar optic nerve tumor infiltration, metastasis occurred in 8% of patients.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials

PubMed Central

Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

2012-01-01

Purpose The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Patients and methods Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Results Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. Conclusion It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones. PMID:23226004

Recent ecological responses to climate change support predictions of high extinction risk

PubMed Central

Maclean, Ilya M. D.; Wilson, Robert J.

2011-01-01

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity. PMID:21746924

Recent ecological responses to climate change support predictions of high extinction risk.

PubMed

Maclean, Ilya M D; Wilson, Robert J

2011-07-26

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity.

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer.

PubMed

Wang, Haiying; Molina, Julian; Jiang, John; Ferber, Matthew; Pruthi, Sandhya; Jatkoe, Timothy; Derecho, Carlo; Rajpurohit, Yashoda; Zheng, Jian; Wang, Yixin

2013-11-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

Factors affecting interactome-based prediction of human genes associated with clinical signs.

PubMed

González-Pérez, Sara; Pazos, Florencio; Chagoyen, Mónica

2017-07-17

Clinical signs are a fundamental aspect of human pathologies. While disease diagnosis is problematic or impossible in many cases, signs are easier to perceive and categorize. Clinical signs are increasingly used, together with molecular networks, to prioritize detected variants in clinical genomics pipelines, even if the patient is still undiagnosed. Here we analyze the ability of these network-based methods to predict genes that underlie clinical signs from the human interactome. Our analysis reveals that these approaches can locate genes associated with clinical signs with variable performance that depends on the sign and associated disease. We analyzed several clinical and biological factors that explain these variable results, including number of genes involved (mono- vs. oligogenic diseases), mode of inheritance, type of clinical sign and gene product function. Our results indicate that the characteristics of the clinical signs and their related diseases should be considered for interpreting the results of network-prediction methods, such as those aimed at discovering disease-related genes and variants. These results are important due the increasing use of clinical signs as an alternative to diseases for studying the molecular basis of human pathologies.

Thymidine phosphorylase and hypoxia-inducible factor 1-α expression in clinical stage II/III rectal cancer: association with response to neoadjuvant chemoradiation therapy and prognosis.

PubMed

Lin, Shuhan; Lai, Hao; Qin, Yuzhou; Chen, Jiansi; Lin, Yuan

2015-01-01

The aim of this study was to determine whether pretreatment status of thymidine phosphorylase (TP), and hypoxia-inducible factor alpha (HIF-1α) could predict pathologic response to neoadjuvant chemoradiation therapy with oxaliplatin and capecitabine (XELOXART) and outcomes for clinical stage II/III rectal cancer patients. A total of 180 patients diagnosed with clinical stage II/III rectal cancer received XELOXART. The status of TP, and HIF-1α were determined in pretreatment biopsies by immunohistochemistry (IHC). Tumor response was assessed in resected regimens using the tumor regression grade system and TNM staging system. 5-year disease free survival (DFS) and 5-year overall survival (OS) were evaluated with the Kaplan-Meier method and were compared by the log-rank test. Over expression of TP and low expression of HIF-1α were associated with pathologic response to XELOXART and better outcomes (DFS and OS) in clinical stage II/III rectal cancer patients (P < 0.05). Our result suggested that pretreatment status of TP and HIF-1α were found to predict pathologic response and outcomes in clinical stage II/III rectal cancer received XELOXART. Additional well-designed, large sample, multicenter, prospective studies are needed to confirm the result of this study.

Physical examination variables predict response to conservative treatment of non-chronic plantar fasciitis: Secondary analysis of a randomized placebo controlled footwear study

PubMed Central

Matzkin-Bridger, Jonathon; Fascione, Jeanna; Crews, Ryan; Bruning, Nicholas; Jarrett, Beth

2016-01-01

Background Plantar fasciitis is a common disabling condition and the prognosis of conservative treatment is difficult to predict. Objective To determine whether initial clinical findings could help predict patient response to conservative treatment primarily consisting of supportive footwear and stretching. Setting Patients were recruited and seen at two outpatient podiatric clinics in the Chicago, Illinois metropolitan area. Patients Seventy-seven patients with non-chronic plantar fasciitis were recruited. Patients were excluded if they had a heel injection in the previous six months or were currently utilizing custom foot orthoses at the time of screening. Sixty-nine patients completed the final follow-up visit three months after receiving the footwear intervention. Methods Treatment failure was considered a <50% reduction in heel pain at three month follow23 Logistic regression models evaluated the possible association between over thirty clinical and physical exam findings prospectively assessed at enrollment, and treatment response. Results Inability to dorsiflex the ankle past −5° (OR 27 3.9, p=.024), non-severe (≤ 7 on ordinal scale) first-step pain (OR 3.8, p=.021), and heel valgus in relaxed stance (OR 4.0, p=.014) each predicted treatment failure in multivariable analysis (Receiver operating characteristic area under the curve=.769). Limited ankle dorsiflexion also correlated with higher heel pain severity at initial presentation (r = −.312, p =.006). Conclusions Patients with severe ankle equinus were nearly four times more likely to experience a favorable response to treatment centered on home Achilles tendon stretching and supportive therapy. Thus earlier use of more advanced therapies may be most appropriate in those presenting without severe ankle equinus or without severe first step pain. The findings from our study may not be clinically intuitive as patients with less severe equinus and less severe pain at presentation did worse with

S100B Serum Levels Predict Treatment Response in Patients with Melancholic Depression

PubMed Central

Bergink, Veerle; Grosse, Laura; Alferink, Judith; Drexhage, Hemmo A.; Rothermundt, Matthias; Arolt, Volker; Birkenhäger, Tom K.

2016-01-01

Background: There is an ongoing search for biomarkers in psychiatry, for example, as diagnostic tools or predictors of treatment response. The neurotrophic factor S100 calcium binding protein B (S100B) has been discussed as a possible predictor of antidepressant response in patients with major depression, but also as a possible biomarker of an acute depressive state. The aim of the present study was to study the association of serum S100B levels with antidepressant treatment response and depression severity in melancholically depressed inpatients. Methods: After a wash-out period of 1 week, 40 inpatients with melancholic depression were treated with either venlafaxine or imipramine. S100B levels and Hamilton Depression Rating Scale (HAM-D) scores were assessed at baseline, after 7 weeks of treatment, and after 6 months. Results: Patients with high S100B levels at baseline showed a markedly better treatment response defined as relative reduction in HAM-D scores than those with low baseline S100B levels after 7 weeks (P=.002) and 6 months (P=.003). In linear regression models, S100B was a significant predictor for treatment response at both time points. It is of interest to note that nonresponders were detected with a predictive value of 85% and a false negative rate of 7.5%. S100B levels were not associated with depression severity and did not change with clinical improvement. Conclusions: Low S100B levels predict nonresponse to venlafaxine and imipramine with high precision. Future studies have to show which treatments are effective in patients with low levels of S100B so that this biomarker will help to reduce patients’ burden of nonresponding to frequently used antidepressants. PMID:26364276

Solitary Alcohol Use in Teens Is Associated With Drinking in Response to Negative Affect and Predicts Alcohol Problems in Young Adulthood

PubMed Central