Machine Learning Risk Prediction for Prolonged Hospitalization in Frail Older Adults with Multimorbidity

BackgroundFrailty and multimorbidity are common in older adults and contribute substantially to prolonged hospitalizations, readmissions, and mortality. Yet, existing prediction models often fail to integrate frailty-specific biomarkers and lack interpretability for routine clinical use. ObjectivesTo develop and internally validate an interpretable, machine learning-enhanced logistic regression model to predict prolonged hospital length of stay (LOS) among frail older adults with multimorbidity, and to identify key predictors to guide individualized inpatient care. MethodsWe conducted a retrospective study of 440 hospitalized adults aged [≥]65 years with multimorbidity ([≥]2 chronic conditions) and frailty (Frailty Index [≥]0.25) at a tertiary geriatric department between January 2022 and December 2023. Fourteen demographic, clinical, and biochemical variables were analysed. Feature selection employed Elastic Net regularization, Extreme Gradient Boosting with SHAP value analysis, and the Boruta algorithm to ensure robust predictor identification. A multivariable logistic regression model was trained and internally validated using stratified 10-fold cross-validation and 1,000 bootstrap iterations. Discrimination (AUC-ROC), calibration, and clinical utility (decision curve analysis) were assessed. ResultsEight predictors age, diabetes, hypertension, prior stroke, serum albumin, HDL cholesterol, systolic blood pressure, and neutrophil-to-lymphocyte ratio--were retained in the final model. The model achieved good discrimination (AUC = 0.770, 95% CI 0.688-0.853) and acceptable calibration (Hosmer-Lemeshow {chi}{superscript 2} = 14.86, p = 0.062). Cross-validation (mean AUC 0.687 {+/-} 0.072) and bootstrap correction (AUC 0.672) confirmed internal stability. Serum albumin was the strongest protective factor, while elevated neutrophil-to-lymphocyte ratio and prior stroke were significant risk factors. ConclusionsThis interpretable model accurately predicts prolonged hospital stay in frail older adults with multimorbidity using routinely available clinical data. Its transparent design supports integration into electronic health records for real-time risk stratification, facilitating targeted discharge planning and personalized geriatric care.