Abstract
Abstract
Objective: Cardiovascular disease remains one of the leading causes of morbidity and mortality worldwide, highlighting the need for accurate risk prediction. Atrial fibrillation burden (AFB), premature ventricular contraction burden (PVCB), and T-wave alternans (TWA) have been individually associated with adverse outcomes. We hypothesized that these three digital ECG biomarkers provide complementary prognostic information for major cardiovascular endpoints and all-cause mortality, and developed a machine learning approach combining these biomarkers for risk prediction. Approach: We analyzed 81,362 Holter recordings from 54,395 individuals across 20 primary care centers in Israel. A random forest model using AFB, PVCB, TWA, and age was trained to predict 5-year risk of heart failure (HF), ischemic stroke (IS), and all-cause mortality (ACM). Main results: On the test set, best AUROCs were for HF 0.75 [95% CI: 0.74-0.77] (n_{pos}=622), for IS 0.69 [0.66-0.71] (n_{pos}=365), and for ACM 0.79 [0.77-0.80] (n_{pos}=1060). Combining the three ECG-derived biomarkers showed complementary predictive value and improved discrimination by up to 10% over age alone in individuals aged <75 years.Significance: AFB, PVCB, and TWA contribute complementary prognostic information for risk stratification. When combined with age, these biomarkers improve predictive performance compared with age alone.