Machine Learning-Based Prognostic Models for Mortality in Patients Receiving Implantable Cardioverter Defibrillators.

Background: Accurately predicting the clinical trajectory of patients with implantable cardioverter-defibrillators (ICDs) is critical for guiding their care and management. Machine learning (ML) methods surpass traditional statistical approaches by addressing complex data patterns and variability, providing more precise and personalized risk estimates.

Methods: This retrospective study included patients from four major hospitals in China. Data from three hospitals were used for training and internal tests, while data from the remaining hospital were used for external tests. Six ML models were developed and validated. Model discrimination was measured using the area under the receiver operating characteristic curve (AUROC). Kaplan-Meier survival curves were generated by stratifying patients into high-risk and low-risk groups based on the optimal model's predictions. Interpretation analysis was performed to rank the importance of predictive features.

Results: A total of 3175 patients were studied. The multilayer perceptron (MLP) model demonstrated superior predictive accuracy, with the AUROC of 0.70 and 0.72 in internal and external test sets, respectively, outperforming other models. Kaplan-Meier curves show distinct survival trends over time between high-risk and low-risk groups, with stratification determined by the MLP model using a Youden's index cut-off value of 0.3443 (p < 0.001). Among the seven key predictors identified, glomerular filtration rate (GFR) was the most influential factor.

Conclusions: The MLP model effectively predicted 3-year survival for ICD or cardiac resynchronization therapy defibrillator (CRT-D) patients and accurately stratified them into distinct risk groups. The integration of MLP and SHapley Additive exPlanations (SHAP) provided explicit explanations for individualized risk predictions, facilitated clinical decision-making, and supported the optimization of treatment strategies.

Trial registration: ClinicalTrials.gov identifier: NCT05396313.