Trial reportJournal of affective disorders2025
Prediction of remission of pharmacologically treated psychotic depression: A machine learning approach.
Trial report in Journal of affective disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.Alzheimer's research & therapy · 2026Article
- Identifying Transdiagnostic Predictors of Depression across Psychoses: Informing Stratified Antidepressant Treatments.Psychotherapy and psychosomatics · 2026Article
- AI-based prediction of depression symptomatology in first-episode psychosis patients: insights from the EUFEST and RAISE-ETP clinical trials.Psychological medicine · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundThe combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic features ('psychotic depression'). The present study aims to identify sociodemographic and clinical predictors of remission of psychotic depression treated with combination pharmacotherapy and determine the accuracy of prediction models.
methodsTwo hundred and sixty-nine participants aged 18 to 85 years with psychotic depression were acutely treated with protocolized sertraline plus olanzapine for up to 12 weeks. Three cross-validated machine learning models were implemented to predict remission based on 74 sociodemographic and clinical variables measured at acute baseline. The optimal model for each method was selected by the average fold C-index. Based on the performance of each method, grouped elastic net (cox) regression was chosen to examine the association of each predictor with remission of psychotic depression.
resultsOf the 269 participants, 145 (53.9 %) experienced full remission of the depressive episode and psychotic features. Multivariable models had 65.1 % to 67.4 % accuracy in predicting remission. In the grouped elastic net (cox) regression model, longer duration of index episode, somatic or tactile hallucinations, higher burden of comorbid physical problems, and single or divorced marital status were independent predictors of longer time to remission. A higher number of lifetime depressive episodes and peripheral vascular or cardiovascular disease were predictors of shorter time to remission.
conclusionsFuture research needs to determine whether the addition of biomarkers to clinical and sociodemographic variables can improve model accuracy in predicting remission of pharmacologically-treated psychotic depression.
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