Multitask Artificial Intelligence-Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs

BackgroundPreoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing costs without improving outcomes. Artificial intelligence (AI)-enabled electrocardiography (ECG) may enhance perioperative risk assessment by identifying patients at very low risk for adverse events. ObjectiveThis study aimed to evaluate whether AI-ECG-based risk stratification could help maintain safety and decrease potentially avoidable preoperative CV testing, while reducing the associated costs, in patients undergoing non-cardiac surgery. MethodsWe retrospectively analyzed 41,218 patients (46,135 ECG-surgery pairs) undergoing elective non-cardiac surgery at Seoul National University Bundang Hospital (2020-2021). An AI-ECG algorithm generated eight probability scores for cardiac conditions, classifying patients as low- or high-risk. Based on the performance and results of preoperative cardiovascular testing (transthoracic echocardiography, coronary computed tomography angiography, single-photon emission computed tomography, or coronary angiography), patients were classified as no test, negative test, or positive test. The primary endpoint was a 30-day composite of all-cause mortality, unplanned percutaneous coronary intervention, or prolonged mechanical ventilation ([≥]3 days). ResultsAI-ECG classified 92.4% of patients as low-risk, with an event rate of 0.62% versus 6.04% in high-risk patients. Preoperative CV testing was performed in 11.8% of cases, with only 16.3% yielding positive findings. In AI-ECG low-risk patients, event rates were uniformly low (0.6-2.7%) regardless of testing, whereas in high-risk patients, rates were consistently high (5.3-6.2%), suggesting no additional prognostic value of conventional testing. An AI-ECG guided approach could reduce potentially avoidable preoperative testing by 35.7% while preserving safety. Integrating AI-ECG with established risk tools may better delineate patients who truly require preoperative CV testing while conserving medical resources. ConclusionAI-enabled ECG reliably identified surgical candidates at low risk for postoperative complications, for whom additional CV testing may be potentially avoidable. Integrating AI-ECG with conventional risk tools may optimize resource use and minimize redundant testing without compromising outcomes. Prospective studies are needed to confirm clinical and economic benefits. Trial RegistrationN/A