Mortality prediction in the elderly patients with coronary artery disease and atrial fibrillation: a retrospective machine learning approach.

Background: Elderly patients with coexisting coronary artery disease (CAD) and atrial fibrillation (AF) are at significantly increased risk of mortality. Accurate risk stratification is crucial for improving clinical management, yet a dedicated predictive tool for this specific population is lacking. The widely used CHA Methods: A cohort of elderly inpatients (≥65 years) diagnosed with CAD and AF were retrospectively enrolled from the Department of Cardiology at the Chinese PLA General Hospital between January 2010 and December 2017. Baseline clinical data during hospitalization were collected, and all patients were followed up for all-cause mortality. Patients were randomly divided into a training set (70%) and a validation set (30%). Variable selection was performed using LASSO-Cox regression. Predictive models were established through a Cox proportional hazards model (via LASSO-Cox) to predict all-cause mortality risk. To assess model robustness and address potential overfitting, we performed 10-fold cross-validation and bootstrap resampling (500 replicates) with optimism-corrected C-statistics. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), and compared against the CHA Results: A total of 1678 elderly patients with CAD and AF were randomly divided into a training set ( Conclusion: The LASSO-Cox machine learning model demonstrates superior predictive performance for all cause mortality relative to the traditional CHA