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Published on: December 11, 2019
Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences
Shaun Evans1, Sarah A Howson2, Andrew E C Booth1
1Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia.
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.
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