Artificial intelligence electrocardiogram-predicted biological age gap and mortality: Capturing dynamic risk with multiple electrocardiograms

Background: Artificial intelligence (AI) can predict biological age from electrocardiograms (ECGs), which is prognostic for mortality. Widely available and inexpensive, serial ECG measurements may enhance individual risk profiles. Objective: We investigated whether repeated measurement of AI-derived biological age identifies divergent biological and chronological aging and whether it significantly improves all-cause mortality hazard estimates. Methods: This single-center, retrospective cohort study included cardiology patients aged 20–90 years with ≥ 2 ECGs recorded. An AI model estimated the biological age from each ECG, and the biological age gap (difference from chronological age) was calculated. Survival was analyzed using Cox proportional-hazards models; a fixed-hazard model with a single ECG per patient and a time-varying hazards model for multiple ECGs. Models were evaluated with the log-likelihood ratio test, and overall mortality risk predictions were compared with the C-index. Results: Among 46,960 patients (337,415 ECGs; median follow-up, 4.5 years), the mean biological aging rate was 0.7 ± 4.1 years/y. Increasing biological age gap was associated with a nonlinear mortality hazard increase, whereas negative gaps had a small protective effect. The multiple-ECG model outperformed the single-ECG model with a higher log-likelihood ratio test value (6280 vs 5225) and improved C-index estimates (0.763 vs 0.747; P = .002). The improvement in predictive accuracy increased with more ECGs per patient, plateauing at ≥ 10 ECGs. Conclusion: Many patients demonstrate biological aging that diverges from chronological aging. AI-derived biological age from a single ECG predicted all-cause mortality, but multiple ECGs significantly increased predictive accuracy. Serial biological age estimates may enhance risk assessment and inform personalized care.