Predicting Intentional Self-Harm Following Psychiatric Discharge in Catalonia, Spain: Machine Learning Models from Linked Registry Data

ABSTRACT Introduction Patients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked population-based registry data from Catalonia, Spain, we developed machine learning-based prediction models for post-discharge intentional self-harm across different follow-up horizons, sex, and age groups, and evaluated their generalizability and robustness with multiple validation strategies. Methods Retrospective cohort study including 41,827 individuals accounting for 71,865 psychiatric hospitalizations with discharge at age ≥10 years, between January 1, 2015, and December 31, 2018, in Catalonia, Spain, with follow-up until December 31, 2019. Primary outcome was intentional self-harm (fatal or non-fatal) within 7, 30, 90, 180, and 365 days post-discharge. Models incorporated 247 predictors from electronic health records, including sociodemographic characteristics, mental and physical disorder categories, categories of dispensed psychotropic medication, and history of self-harm and psychiatric hospitalization. Model performance was evaluated using the area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR). Predictor importance was assessed using Shapley Additive Explanations (SHAP). Results Within 365 days, 4,901 hospitalizations (6.8%) were followed by intentional self-harm. The 365-day model trained on the full cohort achieved a AUCROC of 0.819, in the test sample with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence. This model generalized well across event horizons and sex–age strata, outperforming subgroup-specific models when data sparsity limited performance. Separate models trained by event horizons, and stratified by sex, and sex–age groups achieved a median AUCROC of 0.775 (IQR 0.764–0.808), with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence (IQR 4.5–6.2). Key predictors included the recency of the last registered diagnosis of depressive episodes, recurrent depression, adjustment disorders, and schizophrenia, as well as recent SSRI dispensation and the number of childhood-onset disorder and musculoskeletal disease diagnoses in the previous five years. Predictor importance varied considerably across sex–age strata, with smaller differences across horizons. Subject-level and temporal split validation strategies reduced performance (AUCROC 0.711–0.746), though estimates remained clinically informative (2.8–3.1-fold improvement over baseline prevalence). Conclusions Machine learning models using routinely collected health records predicted intentional self-harm after psychiatric hospitalization with good discrimination and clinically meaningful precision–recall performance. A single 365-day model generalized well across horizons and demographic groups, suggesting that one broadly trained model may provide a pragmatic and scalable approach for clinical implementation.