To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%).
Patients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality.
A total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P<.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%.
A novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery.
Abbreviations and Acronyms:AI (artificial intelligence), CABG (coronary artery bypass grafting), ECG (electrocardiogram), HR (hazard ratio), LVEF (left ventricular ejection fraction)
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- Artificial intelligence in cardiology: present and future.Mayo Clin Proc. 2020; 95: 1015-1039
- Artificial intelligence–electrocardiography to predict incident atrial fibrillation: a population-based study.Circ Arrhythm Electrophysiol. 2020; 13: e009355
- Artificial Intelligence–enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea.Circ Arrhythm Electrophysiol. 2020; 13: e008437
- Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram.J Am Coll Cardiol. 2020; 75: 722-733
- An artificial intelligence–enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.Lancet. 2019; 394: 861-867
- Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram.Nat Med. 2019; 25: 70-74
- Machine learning improves mortality risk prediction after cardiac surgery: systematic review and meta-analysis.J Thorac Cardiovasc Surg. 2020; (S0022-5223(20)32357-6. https://doi.org/10.1016/j.jtcvs.2020.07.10.)
- Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.J Cardiovasc Electrophysiol. 2019; 30: 668-674
- Risk assessment methods for cardiac surgery and intervention.Nat Rev Cardiol. 2014; 11: 704-714
- A systematic review of risk prediction in adult cardiac surgery: considerations for future model development.Eur J Cardiothorac Surg. 2013; 43: e121-e129
- A simple-to-use nomogram to predict long term survival of patients undergoing coronary artery bypass grafting (CABG) using bilateral internal thoracic artery grafting technique.PLoS One. 2019; 14: e0224310
- Meta-analysis comparing established risk prediction models (EuroSCORE II, STS Score, and ACEF Score) for perioperative mortality during cardiac surgery.Am J Cardiol. 2016; 118: 1574-1582
- Reliability of modern scores to predict long-term mortality after isolated aortic valve operations.Ann Thorac Surg. 2016; 101: 599-605
- Risk score for predicting long-term mortality after coronary artery bypass graft surgery.Circulation. 2012; 125: 2423-2430
Potential Competing Interests: The authors report no competing interests.
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