Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators

Abstract

Background: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional statistical methods and provide more accurate, personalized risk estimates.

Objectives: The authors sought to develop and externally validate a novel machine learning algorithm for predicting all-cause mortality and/or heart failure (HF) hospitalization in ICD patients with and without cardiac resynchronization therapy (CRT) using variables that are readily available to treating clinicians. We also sought to identify key factors that separate patients along a continuum of risk.

Methods: Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict 3-month and 1-year risks for all-cause mortality and a composite outcome of death/HF hospitalization during the first 5 years of device implant. Models were trained using a nationwide cohort from the Veterans Health Administration. Three models were sequentially tested, and external validation was performed in a separate nonveteran clinical registry.

Results: The training and validation cohorts included 12,043 patients (age 67.5 ± 9.4 years) and 1,394 patients (age 66.3 ± 11.9 years), respectively. Median follow-up was 3.3 years for the training cohort and 3.6 years for validation cohort. The most accurate models for both outcomes included baseline demographics entered at the time of ICD implant (age, sex, CRT therapy) and time-varying ICD data with area under the receiver-operating characteristic curve for predicting death at 3 months (0.91; 95% CI: 0.87-0.94) and 1 year (0.80; 95% CI: 0.78-0.82); death/HF hospitalization at 3 months (0.81; 95% CI: 0.79-0.83) and 1 year (0.71; 95% CI: 0.70-0.72). Models demonstrated high discrimination and good calibration in the validation cohort. Additionally, time-varying physiologic data from ICDs, especially daily physical activity, had substantial importance in predicting outcomes.

Conclusions: The RF-SLAM algorithm accurately predicted all-cause mortality and death/HF hospitalization at 3 months and 1 year during the first 5 years of device implant, demonstrating good internal and external validity. Prospective studies and randomized trials are needed to evaluate model performance in other populations and settings and to determine its impact on patient outcomes.

Keywords: artificial intelligence; defibrillator; machine learning; remote monitoring; risk prediction.