The 12-lead electrocardiogram as a biomarker of biological age.

ABSTRACT:

Background

We have demonstrated that a neural network is able to predict a person's age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes.

Methods and results

We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether

SUBMITTER: Ladejobi AO 

PROVIDER: S-EPMC9707884 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature