AI-based prediction of depression symptomatology in first-episode psychosis patients: insights from the EUFEST and RAISE-ETP clinical trials

Background: Depressive symptoms are highly prevalent in first-episode psychosis (FEP) and worsen clinical outcomes. It is currently difficult to determine which patients will have persistent depressive symptoms based on a clinical assessment. We aimed to determine whether depressive symptoms and post-psychotic depressive episodes can be predicted from baseline clinical data, quality of life, and blood-based biomarkers, and to assess the geographical generalizability of these models. Methods: Two FEP trials were analyzed: European First-Episode Schizophrenia Trial (EUFEST) (n = 498; 2002–2006) and Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) (n = 404; 2010–2012). Participants included those aged 15–40 years, meeting Diagnostic and Statistical Manual of Mental Disorders IV criteria for schizophrenia spectrum disorders. We developed support vector regressors and classifiers to predict changes in depressive symptoms at 6 and 12 months and depressive episodes within the first 6 months. These models were trained in one sample and externally validated in another for geographical generalizability. Results: A total of 320 EUFEST and 234 RAISE-ETP participants were included (mean [SD] age: 25.93 [5.60] years, 56.56% male; 23.90 [5.27] years, 73.50% male). Models predicted changes in depressive symptoms at 6 months with balanced accuracy (BAC) of 66.26% (RAISE-ETP) and 75.09% (EUFEST), and at 12 months with BAC of 67.88% (RAISE-ETP) and 77.61% (EUFEST). Depressive episodes were predicted with BAC of 66.67% (RAISE-ETP) and 69.01% (EUFEST), showing fair external predictive performance. Conclusions: Predictive models using clinical data, quality of life, and biomarkers accurately forecast depressive events in FEP, demonstrating generalization across populations

EPMA-World Congress 2015: Bonn, Germany. 3-5 September 2015

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Recently attention has begun to focus not only on assessing the effectiveness of interventions to tackle mental health problems, but also on measures to prevent physical co-morbidity. Individuals with mental health problems are at significantly increased risk of chronic physical health problems, such as cardiovascular disease or diabetes, as well as reduced life expectancy. The excess costs of co-morbid physical and mental health problems are substantial. Potentially, measures to reduce the risk of co-morbid physical health problems may represent excellent value for money

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Predisposing factors for early retirement in patients with Schizophrenia in Germany

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