Journal of Cardiovascular Development and Disease
Machine Learning-Based Prediction of Long-Term Mortality in Patients with Implantable Cardioverter Defibrillators
By Lei Pan, Zilong Xiao, Ziqing Yu and 8 others.
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.