A Multicohort Machine Learning Framework to Predict Mortality in Elderly Patients With Heart Disease: Insights From HARLS, SHARE, and HRS.

Background: Elderly patients with heart disease face elevated mortality risk, yet predictive models specifically tailored for this population across different global regions remain limited. Current mortality prediction tools often lack cross-cultural validation and interpretability, hindering their clinical application in diverse healthcare settings. Methods: We developed and validated machine learning models for predicting mortality in elderly heart disease patients using data from three major aging cohorts: the China Health and Retirement Longitudinal Study (CHARLS, Results: XGBoost demonstrated superior performance with the highest average AUC (0.798) across all datasets, showing excellent generalizability from the SHARE training set (AUC: 0.805) to internal validation (AUC: 0.799) and external validation in HRS (AUC: 0.821) and CHARLS (AUC: 0.770) cohorts. Age consistently emerged as the most influential predictor across all cohorts (SHAP values: 0.056-0.102), followed by gender, moderate physical activity, and self-rated health, though their relative importance varied by cohort. Feature dependence analysis revealed important nonlinear relationships, including U-shaped associations between grip strength and mortality risk. Conclusion: Our multicohort machine learning approach successfully developed a robust, interpretable model for predicting mortality in elderly heart disease patients across diverse global populations. The model's strong performance in external validation demonstrates its potential for cross-cultural clinical application, while SHAP analysis provides valuable insights into population-specific risk factors that could guide targeted interventions.