Machine Learning-Based Prediction of Long-Term Mortality in Patients with Implantable Cardioverter Defibrillators

Abstract

Aims: Accurate prediction of the clinical course in implantable cardioverter-defibrillator (ICD) and cardiac resynchronization therapy-defibrillator (CRT-D) patients is crucial, as their disease progression varies significantly due to comorbidities and individual characteristics. Methods: This retrospective study included 3321 patients from four hospitals in China, with 3207 for model development and internal validation and 114 for external testing. Five machine learning (ML) models were evaluated using the C-index, time-dependent AUROC, and Brier score. Decision curve analysis (DCA) assessed clinical utility, while Kaplan–Meier curves stratified patients into high-, intermediate-, and low-risk groups. SHAP analysis identified key predictors and explained model outputs. Results: The Random Survival Forest (RSF) model demonstrated favorable numerical performance across multiple evaluation metrics, achieving the highest time-dependent AUROC (0.74, 0.74) and C-index (0.71, 0.70) in internal and external test sets. It also had the lowest time-dependent Brier score (0.17) in the internal test and ranked second (0.13) in the external test. DCA demonstrated numerically higher net benefit for the RSF model across evaluated threshold probabilities. Kaplan–Meier curves revealed distinct survival trajectories, with high-risk patients showing significantly higher mortality than low-risk patients (HR: 4.50, 95% CI: 3.10–6.55, p < 0.0001), while intermediate-risk patients also had increased mortality (HR: 2.49, 95% CI: 1.69–3.68, p < 0.0001). LVEDD was the most influential predictor of mortality. Conclusions: The RSF survival model enables individualized, time-resolved prediction of all-cause mortality risk in ICD and CRT-D patients, supporting risk stratification and future evaluation of clinical utility, while requiring cautious interpretation of longer-term predictions due to limited numbers at risk.