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.

Authors

Institutions

Publication Details

Journal
Journal of Affective Disorders
Published
2026-09-14
DOI
https://doi.org/10.1016/j.jad.2026.122502
Primary Topic
Treatment of Major Depression
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

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

Daniele De Francesco, G. Cucchi, Francesco Piarulli, Angela Chiara Cecere et al.
Journal of Affective Disorders
Treatment of Major Depression
article

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

Daniele De Francesco, G. Cucchi, Francesco Piarulli, Angela Chiara Cecere, Salvatore Cipolla, Giulia Lucca, Alessandro Rodolico, Annalisa Maraone, Daniele Cavaleri, Lorenzo Tarsitani, Nicola Poloni, Cecilia Quitadamo, Mario Luciano, Cristina Crocamo, Eleonora Gambaro, F. Marcolini, Maria Luigina Pignataro, Denise Erbuto, Carla Gramaglia, Giulio Longo, Andrea Aguglia, Stefano Santo, Alessia Scordo, Maria Salvina Signorelli, Valeria Latorre, Monica Migliorati, Carlo Bassetti, Vincenzo Borrelli, Anna-Rita Atti, Beatrice Defilippi, Flavia Boccardi, Elisa Briasco, Gaspare Liparota, Martina Citton, Silvia Paparesta, Patrizia Zeppegno, Massimo Pasquini, Ruggero Cavallaro, Giacomo De Curtis, Renato de Filippis, Umberto Volpe, Isabella Berardelli, Francesco Bartoli, Antonio Ventriglio, Gianluca Serafini, Pasquale De Fazio, Carlo Bommartini, Gianluca Rosso, Luigi Gadaleta, Maurizio Pompili, Andrea Fiorillo, Giuseppe Carrà, Laura Orsolini, Andrea Amerio
article en

Abstract

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.

Journal of Affective DisordersVol. 415
University of Foggia (IT), Marche Polytechnic University (IT), Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT), University of Campania "Luigi Vanvitelli" (IT), Magna Graecia University (IT), University of Catania (IT), Azienda Socio Sanitaria Territoriale Lariana (IT), University of Turin (IT), University of Bari Aldo Moro (IT), University of Milano-Bicocca (IT), University of Genoa (IT), Sapienza University of Rome (IT), University of Bologna (IT)
Gender equality
Openalex Percentile: Top 13%
Treatment of Major Depression
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.