Abstract Aims Artificial Intelligence (AI) models applied to standard 12-lead ECGs enable estimation of biological age (AI-ECG age), which has shown prognostic value in general populations. However, its clinical utility in high-risk patients with cardiovascular disease (CVD) or acute medical conditions remains insufficiently explored. Methods and results We analysed the first ECG of 48 950 consecutive patients presenting to a tertiary care centre with CVD or acute illness between 2000 and 2021. AI-ECG age was derived using a validated deep learning model. Δ-age, defined as the difference between AI-ECG and chronological age, was analysed categorically (±8 years) and continuously using multivariable Cox models adjusted for clinical and ECG variables. Primary endpoint was long-term total mortality (up to 10 years). Saliency map analysis was performed to identify input regions that the model was most sensitive to. AI-ECG age correlated strongly with chronological age (r = 0.72, P 0.001), though this correlation weakened in patients with multiple comorbidities. Patients with a positive Δ-age (≥+8 years) had significantly higher 10 year mortality risk (HR: 1.45, P 0.001), while those with a negative Δ-age (≤−8 years) had lower risk (HR: 0.88, P 0.001). These associations were consistent across care settings and remained robust when Δ-age was analysed continuously. Saliency maps indicated that the AI model was most sensitive to the P-wave. Conclusion AI-ECG age is a strong and independent predictor of long-term mortality in cardiovascular and acute care patients.
Pavluk et al. (2025) studied this question.