Evans, S.Howson, S.A.Booth, A.E.C.Shahmohamadi, E.Lim, M.Bacchi, S.Jayakumar, M.Kamsani, S.Fitzgerald, J.Thiyagarajah, A.Emami, M.Elliott, A.D.Middeldorp, M.E.Sanders, P.2025-09-222025-09-222025Heart Rhythm, 2025; 22(9):e710-e7161547-52711556-3871https://hdl.handle.net/2440/147484Background: 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.en© 2025 Published by Elsevier Inc. on behalf of Heart Rhythm Society. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Deep learning; Machine learning; Convolutional neural network; Cardiology; PrognosticationHumansElectrocardiographyPrognosisSurvival RateRisk AssessmentRetrospective StudiesFollow-Up StudiesAgingArtificial IntelligenceAdultAgedAged, 80 and overMiddle AgedFemaleMaleYoung AdultJournal article10.1016/j.hrthm.2025.05.009740119Evans, S. [0000-0001-5067-2743]Booth, A.E.C. [0009-0005-0023-6552]Shahmohamadi, E. [0000-0003-1912-9923]Bacchi, S. [0000-0001-5130-8628]Jayakumar, M. [0000-0001-5589-1531]Kamsani, S. [0000-0003-3088-0275]Fitzgerald, J. [0000-0003-3995-3549]Emami, M. [0000-0003-2093-6909]Elliott, A.D. [0000-0002-5951-4239]Middeldorp, M.E. [0000-0002-4106-9771]Sanders, P. [0000-0003-3803-8429]