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Risk stratification of coronary artery bypass patients using an artificial intelligence electrocardiogram-derived age
Tedy Sawma1, Arman Arghami1, Hartzell V Schaff1
1Department of Cardiovascular Surgery, Mayo Clinic, Rochester, Minn.
Objective:
The study objective was to assess the prognostic value of artificial intelligence electrocardiogram-derived age in predicting outcomes after isolated coronary artery bypass grafting.
Methods:
We used preoperative electrocardiograms (within 30 days from surgery) from 13,808 isolated patients undergoing coronary artery bypass surgery to calculate artificial intelligence electrocardiography-derived age using convolutional neural networks. The age gap was calculated as artificial intelligence electrocardiography-derived age minus chronological age. The associations of age gap with baseline comorbidities, operative outcomes, and long-term survival were analyzed using multivariable regression models.
Results:
The median chronological age was 68 (60-74) years, and the artificial intelligence electrocardiography-derived age was 67 (61-72) years. Mean age gap was -1 ± 8 years (range, -39 to +35 years). In 44% of patients, the artificial intelligence electrocardiography-derived age was older than the chronological age (positive age gap of 6 (±5) years), and in 21.4% this gap was more than 5 years. Patients with an age gap more than 5 years were more likely to have renal failure, congestive heart failure, a history of myocardial infarction, higher body mass index, and lower ejection fraction. Postoperatively, they were at higher risk of atrial fibrillation (odds ratio, 1.15; 95% CI, 1.05-1.30; P = .042), prolonged ventilation (odds ratio, 1.2; 95% CI, 1.1-1.5; P = .047), blood transfusion (odds ratio, 1.15; 95% CI, 1.1-1.30; P = .017), postoperative creatinine (B-coefficient +0.15 units, P < .001), and hospital stay (B-coefficient +0.7 days, P = .001). Long-term survival was lower in patients with an age gap more than 5 years compared with those with an age gap 5 years or less (hazard ratio, 1.4; 95% CI, 1.2-1.5; P < .001).
Conclusions:
Higher age gap identifies a cohort of patients undergoing coronary artery bypass grafting with increased comorbidities and advanced physiological age. Advanced physiological age identified by artificial intelligence electrocardiography is independently associated with worse operative outcomes and long-term mortality.
