ArticlePsychotherapy and psychosomatics2026
Identifying Transdiagnostic Predictors of Depression across Psychoses: Informing Stratified Antidepressant Treatments.
Article in Psychotherapy and psychosomatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDepression 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.
methodsSupport 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.
resultsModels 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.
conclusionML 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.