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Predictive models to estimate utility from clinical questionnaires in schizophrenia: findings from EuroSC.

ABSTRACT: The clinical symptoms of schizophrenia are associated with serious social, quality of life and functioning alterations. Typically, data on health utilities are not available in clinical studies in schizophrenia. This makes the economic evaluation of schizophrenia treatments challenging. The purpose of this article was to provide a mapping function to predict unobserved utility values in patients with schizophrenia from the available clinical and socio-demographic information.The analysis was performed using data from EuroSC, a 2-year, multi-centre, cohort study conducted in France (N = 288), Germany (N = 618), and the UK (N = 302), totalling 1208 patients. Utility was calculated based on the EQ-5D questionnaire. The relationships between the utility values and the patients' socio-demographic and clinical characteristics (Positive and Negative Syndrome Scale--PANSS, Calgary Depression Scale for Schizophrenia--CDSS, Global Assessment of Functioning--GAF, extra-pyramidal symptoms measured by Barnes Akathisia Scale-BAS, age, sex, country, antipsychotic type) were modelled using a random and a fixed individual effects panel linear model.The analysis demonstrated the prediction ability of the used parameters for estimating utility measures in patients with schizophrenia. Although there are small variations between countries, the same variables appear to be the key predictors. From a clinical perspective, age, gender, psychopathology, and depression were the most important predictors associated with the EQ-5D.This paper proposed a reliable, robust and easy-to-apply mapping method to estimate EQ-5D utilities based on demographic and clinical measures in schizophrenia.

SUBMITTER: Siani C

PROVIDER: S-EPMC4830865 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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Predictive models to estimate utility from clinical questionnaires in schizophrenia: findings from EuroSC.

Siani Carole C de Peretti Christian C Millier Aurélie A Boyer Laurent L Toumi Mondher M

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation 20150918 4

<h4>Objective</h4>The clinical symptoms of schizophrenia are associated with serious social, quality of life and functioning alterations. Typically, data on health utilities are not available in clinical studies in schizophrenia. This makes the economic evaluation of schizophrenia treatments challenging. The purpose of this article was to provide a mapping function to predict unobserved utility values in patients with schizophrenia from the available clinical and socio-demographic information.<h ...[more]

PMID: 26385367

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Project description:BACKGROUND:COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. OBJECTIVE:The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. METHODS:Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. RESULTS:Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). CONCLUSIONS:Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

Project description:BackgroundResearch suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing.ObjectivesWe sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals.MethodsUsing a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells.ResultsThe literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model.DiscussionUsing the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779.

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Predictive models to estimate utility from clinical questionnaires in schizophrenia: findings from EuroSC.

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Predictive models to estimate utility from clinical questionnaires in schizophrenia: findings from EuroSC.

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