Predicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study.

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 13 Italian provinces, enrolling adults aged 18-65 years with a DSM-5-TR MDD diagnosis between January 2024 and December 2025. The primary outcome was unplanned psychiatric readmission within 90 days. Sociodemographic and clinical features (including Hamilton Depression Rating Scale scores and DSM-5 specifiers), comorbidities, and pharmacological treatment at discharge were recorded. A Random Forest classifier was trained on 22 predictors and internally evaluated using stratified 5-fold cross-validation.

Results

The sample included 322 individuals (mean age 43.0 years; 38.5% men), of whom 15.5% (n = 50) were readmitted within 90 days. The model achieved a mean test AUC of 0.74 (range: 0.59-0.87). Suicide attempts, prior hospitalisations, anticonvulsant prescription, cluster B personality disorder, female sex, and anxious distress ranked among the leading predictors. Although exploratory, lithium prescription showed a negative SHapley Additive exPlanations (SHAP) pattern.

Conclusions

A ML model based on routinely collected data showed moderate discrimination for 90-day readmission in MDD. These findings suggest that standard clinical variables may hold predictive value for early readmission. However, external validation, assessment of model calibration, and clinical utility are needed prior to clinical implementation.