Abstract
AIMS: Despite the availability of clinical risk scores for atherosclerotic cardiovascular disease (ASCVD), their use is limited because the required predictor data are often missing. We developed and validated ECG-ASCVD, a scalable risk prediction paradigm that utilizes ECGs to target ASCVD risk factor assessment. METHODS: Adults aged 30-79 who had undergone a clinical ECG were identified in the Yale New Haven Health System (YNNHS) and a state death index. We developed ECG-ASCVD-12, ECG-ASCVD-IMAGE, and ECG-ASCVD-1 to predict time-to-ASCVD from 12-lead ECG signals, ECG images, and lead-1 signals, respectively. Model performance was assessed in held-out individuals without prior ASCVD and in two external prospective cohorts, ELSA-Brasil (ELSA) and the UK Biobank (UKB). We then simulated the deployment of ECG-ASCVD in a random sample of 100,000 adults at YNHHS. RESULTS: The development cohort included 363,788 individuals (median age, 57.1 [45.5-67.2] years; 48% Women). The YNHHS, ELSA, and UKB test cohorts included 83,917, 10,934, and 54,166 individuals, respectively. ECG-ASCVD-12 demonstrated generalizable discrimination (C-index: 0.684 to 0.746) and remained independently associated with ASCVD (adjusted hazard ratio: 1.23-1.34 per SD) after adjusting for PREVENT scores (C-index: 0.696-0.782) across the validation cohorts. ECG-ASCVD-IMAGE performed similarly (C-index: 0.673-0.748) while ECG-ASCVD-1 had modestly lower performance (C-index: 0.671-0.735). Simulated deployment suggested that ECG-ASCVD could enable the detection of high ASCVD risk patients who lack the data required for PREVENT. CONCLUSION: We developed an ECG-ASCVD toolkit and validated it across diverse multinational cohorts. These results highlight the potential utility of resting ECG information for predicting ASCVD risk, enabling targeted screening.