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 ageing, 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 ageing rate was 0.7±4.1 years/year. Increasing biological age gap was associated with a non-linear mortality hazard increase, while 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=0.002). The improvement in predictive accuracy increased with more ECGs per-patient, plateauing at ≥10 ECGs.

Conclusion: Many patients demonstrate biological ageing which diverges from chronological ageing. 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.