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TRUVACE RECORD VERSION
record: TRV-2026-1096
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-15T06:56:06.518345Z
status: published
lens: trace
sector: health
headline: Predicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study
dek: Background Major depressive disorder (MDD) is associated with high relapse rates, with around 20% of inpatients experiencing readmission within 90 days after discharge. Routinely collected clinical information may help identify individuals at higher risk of readmission. Machine learning (ML) models could complement more traditional statistical approaches by exploring complex relationships among multiple vulnerability factors. Methods The DEEP READ study is a prospective, multicentre cohort study conducted across…
gain_title: A Random Forest trained on 22 routine clinical variables predicted unplanned 90-day psychiatric readmission in adults with MDD with moderate discrimination.
problem_title: The model showed variable cross-validation performance down to 0.59 AUC and is not validated for clinical implementation without further calibration and utility assessment.
trace_subject: prediction of 90-day unplanned psychiatric readmission in adults with major depressive disorder using routinely collected clinical data
gain_reading: A Random Forest trained on 22 routine clinical variables predicted unplanned 90-day psychiatric readmission in adults with MDD with moderate discrimination.
gain_evidence: A ML model based on routinely collected data showed moderate discrimination for 90-day readmission in MDD
problem_reading: The model showed variable cross-validation performance down to 0.59 AUC and is not validated for clinical implementation without further calibration and utility assessment.
problem_evidence: However, external validation, assessment of model calibration, and clinical utility are needed prior to clinical implementation
quick_read: The DEEP READ prospective study across 13 Italian provinces followed 322 adults with major depressive disorder discharged from inpatient care and tested whether routinely collected clinical information could predict unplanned psychiatric readmission within 90 days. A Random Forest classifier trained on 22 predictors achieved a mean test AUC of 0.74 in internal cross-validation, with 50 patients (15.5%) readmitted.

Moderate discrimination from standard variables suggests potential for risk stratification, but wide fold variation and lack of external validation leave clinical usefulness unproven. The authors note that calibration, generalizability beyond the Italian cohort, and impact on care decisions remain to be established before any implementation.
limitation: Model was only internally evaluated with stratified 5-fold cross-validation and lacks external validation, calibration assessment, and demonstrated clinical utility.
tag: Dual reading
key_points: Prospective multicentre cohort across 13 Italian provinces enrolled 322 adults aged 18-65 with DSM-5-TR MDD between January 2024 and December 2025. | Primary outcome was unplanned psychiatric readmission within 90 days, observed in 15.5% (n = 50) of the sample. | Leading predictors included suicide attempts, prior hospitalisations, anticonvulsant prescription, cluster B personality disorder, female sex, and anxious distress.
rundown: DEEP READ enrolled 322 individuals (mean age 43.0 years; 38.5% men) with DSM-5-TR MDD and recorded sociodemographic features, Hamilton Depression Rating Scale scores, DSM-5 specifiers, comorbidities, and pharmacological treatment at discharge.

The Random Forest used 22 predictors; SHAP analysis highlighted suicide attempts and prior hospitalisations as top contributors, while lithium prescription showed a negative SHapley Additive exPlanations (SHAP) pattern described as exploratory.
sources:
- peer_reviewed | Journal of Affective Disorders | https://doi.org/10.1016/j.jad.2026.122502 | 2026-09-13
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