Risk Stratification of Coronary Artery Bypass Patients Using an Artificial Intelligence Electrocardiogram-Derived Age

In this article, the authors evaluated whether an artificial intelligence (AI) electrocardiogram (ECG)-derived age can improve risk stratification in patients undergoing isolated coronary artery bypass grafting (CABG). Using preoperative ECGs from 13,808 patients, they calculated an age gap defined as AI-derived age minus chronological age. A positive age gap greater than five years identified patients with a higher comorbidity burden and more advanced physiological aging. This group experienced higher rates of postoperative complications, including atrial fibrillation, prolonged ventilation, blood transfusion, renal dysfunction, and longer hospital stay. Importantly, an age gap greater than five years was independently associated with worse long-term survival. The study concludes that AI ECG-derived age is a simple, accessible biomarker of physiological reserve that adds prognostic value beyond chronological age in CABG patients.