Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences

Abstract

Background

Biological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events.

Objective

We developed an AI model to predict age from an ECG and compared baseline characteristics to identify determinants of advanced biological age.

Methods

An AI model was trained on ECGs from cardiology inpatients aged 20–90 years. AI analysis used a convolutional neural network with data divided in an 80:20 ratio (development/internal validation), with external validation undertaken using data from the UK Biobank. Performance and subgroup comparison measures included correlation, difference, and mean absolute difference.

Results

A total of 63,246 patients with 353,704 total ECGs were included. In internal validation, the correlation coefficient was 0.72, with a mean absolute difference between chronological age and AI-predicted age of 9.1 years. The same model performed similarly in external validation. In patients aged 20–29 years, AI-ECG–predicted biological age was greater than chronological age by a mean of 14.3 ± 0.2 years. In patients aged 80–89 years, biological age was lower by a mean of 10.5 ± 0.1 years. Women were biologically younger than men by a mean of 10.7 months (P = .023), and patients with a single ECG were biologically 1.0 years younger than those with multiple ECGs (P < .0001).

Conclusion

There are significant between-group differences in AI-ECG–predicted biological age for patient subgroups. Biological age was greater than chronological age in young hospitalized patients and lower than chronological age in older hospitalized patients. Women and patients with a single ECG recorded were biologically younger than men and patients with multiple recorded ECGs.