Psychotherapy and psychosomatics · 2026
Identifying Transdiagnostic Predictors of Depression across Psychoses: Informing Stratified Antidepressant Treatments.
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Mena Sergio, Coutts Fiona, von Trott Jana, Jacobs Grace R, Ucur Esin, Sirbu Nicoleta, Moles Louise, Bryant Linda, Vetter Clara, Wiegand Ariane, Kahn Rene R, Fleischhacker W Wolfgang, Kane John M, Flint Alastair, Voineskos Aristotle, Lalousis Paris A, Koutsouleris Nikolaos
Аннотация
INTRODUCTION: Depression frequently co-occurs with psychosis and is associated with poor outcomes. Early identification of patients at risk of persistent depression remains challenging, limiting opportunities for stratified treatment planning. This study aimed to evaluate the transdiagnostic generalizability of machine learning (ML) models predicting depressive episodes across the affective-psychotic spectrum and whether model-informed predictions could identify patients who may benefit from antidepressant treatment.
METHODS: Support vector machine models were trained to predict depressive episodes within 6 months using clinical and physiological data from two large, multisite first-episode psychosis (FEP) trials: EUFEST (n = 447) and RAISE-ETP (n = 288), totalling 735 participants. A nested cross-validation framework was used to evaluate model performance. Generalizability was tested in psychotic depression (PD) patients from the STOP-PD trial (n = 142), which compared olanzapine plus sertraline versus olanzapine plus placebo.
RESULTS: Models predicted depressive episodes in the FEP sample with a balanced accuracy (BAC) of 69% (sensitivity: 65.7%, specificity: 72.4%). When applied to STOP-PD patients treated with olanzapine plus placebo, FEP-trained models achieved a BAC of 65.2% (sensitivity: 58.3%, specificity: 72.0%) in predicting 3-month non-remission. In the olanzapine plus sertraline group, predictions were at chance levels (BAC: 47.2%, sensitivity: 48.4%, specificity: 46.0%), reflecting sertraline's therapeutic effects.
CONCLUSION: ML models can identify shared risk signatures for depression across the psychosis-affective spectrum. Patterns of depressive episodes in FEP patients share predictive features with PD patients not receiving antidepressants, while adjunctive antidepressant treatment improves remission outcomes beyond model expectations. These findings support ML-informed treatment stratification to identify patients unlikely to benefit from antipsychotic monotherapy.
METHODS: Support vector machine models were trained to predict depressive episodes within 6 months using clinical and physiological data from two large, multisite first-episode psychosis (FEP) trials: EUFEST (n = 447) and RAISE-ETP (n = 288), totalling 735 participants. A nested cross-validation framework was used to evaluate model performance. Generalizability was tested in psychotic depression (PD) patients from the STOP-PD trial (n = 142), which compared olanzapine plus sertraline versus olanzapine plus placebo.
RESULTS: Models predicted depressive episodes in the FEP sample with a balanced accuracy (BAC) of 69% (sensitivity: 65.7%, specificity: 72.4%). When applied to STOP-PD patients treated with olanzapine plus placebo, FEP-trained models achieved a BAC of 65.2% (sensitivity: 58.3%, specificity: 72.0%) in predicting 3-month non-remission. In the olanzapine plus sertraline group, predictions were at chance levels (BAC: 47.2%, sensitivity: 48.4%, specificity: 46.0%), reflecting sertraline's therapeutic effects.
CONCLUSION: ML models can identify shared risk signatures for depression across the psychosis-affective spectrum. Patterns of depressive episodes in FEP patients share predictive features with PD patients not receiving antidepressants, while adjunctive antidepressant treatment improves remission outcomes beyond model expectations. These findings support ML-informed treatment stratification to identify patients unlikely to benefit from antipsychotic monotherapy.