AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients.

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 tot

SUBMITTER: Pavluk D 

PROVIDER: S-EPMC12629650 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature