Machine learning for predicting subsequent same-hospital admission after alcohol-related admissions: Diagnostic phenotyping and temporal validation using 16 Years of routine hospital data
Alicia Merayo-Corcoba, Arturo Crespo-Álvaro, Sergio Rubio-Martín, Pablo Pascual-Navarro, Antonio Serrano-García, José Alberto Benítez-AndradesObjectives
Routinely collected data could support risk stratification after alcohol-related hospitalisation, but inconsistent diagnostic phenotyping and temporally invalid designs threaten model validity. We aimed to develop and temporally evaluate interpretable models for subsequent same-hospital admission recorded in the available extract after alcohol-related admissions.
Methods
We analysed anonymised hospital episodes from 2009–2024. Adult alcohol-related admissions were identified by the union of local text labels and ICD-9/10-compatible alcohol codes. At each discharge, models predicted the next same-hospital admission recorded in the extract within 30, 90, and 365 days using only contemporaneous and prior information. Logistic regression, random forest, and histogram gradient boosting were developed using admissions through 2020 and evaluated on later admissions. Discrimination, calibration, Brier scores, and highest-risk-decile performance were estimated with 95% confidence intervals from patient-clustered bootstrap samples.
Results
After excluding 58 records without usable admission or discharge dates and 36 records with missing age or age below 18 years, 18,335 alcohol-related index admissions from 10,478 patients were analysed. The text-only rule identified 6,652 patients, compared with 10,478 using text or ICD codes; source use changed completely from text labels in 2009–2019 to ICD codes in 2020–2024. In temporal evaluation, random-forest ROC-AUC was 0.659 (95% CI 0.631–0.683) at 30 days and 0.659 (0.637–0.681) at 90 days. At 365 days, logistic regression and random forest performed similarly (both ROC-AUC 0.659); logistic regression was well calibrated (intercept −0.044, slope 0.906). Its highest-risk decile had 57.4% positive predictive value and captured 18.3% of recorded subsequent admissions.
Conclusions
The operational cohort-definition rule materially changed the study population. Temporally evaluated models provided modest risk stratification but no complex algorithm clearly outperformed logistic regression. The observed discrimination and decision-concentration metrics do not support direct clinical deployment. External evaluation and richer clinical and social predictors are required before clinical use.