Why the study?
Preoperative cardiovascular testing is frequently overused in noncardiac surgery, increasing costs without improving outcomes, and it was unknown whether AI-ECG risk stratification could decrease low-yield testing and costs without increasing adverse outcomes.
Does AI-ECG risk stratification effectively identify noncardiac surgery candidates at low risk for postoperative adverse events, potentially reducing redundant preoperative cardiovascular testing?
Cohort (n=46,135)
No
Does AI-ECG risk stratification effectively identify noncardiac surgery candidates at low risk for postoperative adverse events, potentially reducing redundant preoperative cardiovascular testing?
AI-ECG safely and accurately identifies surgical candidates at very low risk for postoperative cardiac complications, offering a pathway to reduce low-yield preoperative cardiovascular testing and optimize healthcare resource allocation.
High-risk AI-ECG classification was associated with higher postoperative events; leaves open utility for safely reducing preoperative testing.
Background Preoperative cardiovascular risk stratification is essential in noncardiac 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 surgical candidates at very low-risk for adverse events. Objective This study aimed to evaluate whether AI-ECG–based risk stratification could help decrease low-yield preoperative cardiovascular testing and reduce associated costs, without an observed increase in postoperative adverse outcomes, in candidates for noncardiac surgery. Methods We retrospectively analyzed 41,218 patients (46,135 ECG-surgery pairs) undergoing noncardiac surgery at Seoul National University Bundang Hospital (2020-2021). An AI-ECG algorithm generated eight probability scores for cardiac conditions, classifying cases 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), cases were classified as no advanced cardiovascular imaging, negative-test, or positive-test. The primary end point was a 30-day composite of all-cause mortality and unplanned percutaneous coronary intervention. Results AI-ECG classified 92.3% (42,599/46,135) of cases as low-risk, with a composite outcome rate of 0.2% (79/42,599) vs 2.9% (101/3536) in high-risk cases. Preoperative cardiovascular testing was performed in 11.8% (5458/46,135) of cases, with only 16.3% (892/5458) yielding positive findings. In AI-ECG low-risk cases, event rates were uniformly low (0.2%-0.4%) irrespective of whether advanced cardiovascular testing was performed, whereas in high-risk cases, rates were consistently high (2.6%-3.4%). The incidence of the composite outcome was consistently higher in AI-ECG–graded high-risk cases across all European Society of Cardiology surgical risk and Revised Cardiac Risk Index strata. Conclusions In this retrospective cohort, a multitask AI-ECG identified surgical candidates at low-risk for postoperative complications, for whom advanced cardiovascular testing demonstrated low diagnostic yield. Integrating AI-ECG with conventional risk tools may offer an exploratory strategy to optimize resource use and minimize redundant testing. Prospective studies are needed to confirm the clinical and economic benefits of AI-ECG as a screening tool.
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Choi et al. (2026) conducted a cohort in Candidates for noncardiac surgery (n=46,135). High-risk AI-ECG classification vs. Low-risk AI-ECG classification was evaluated on 30-day composite of all-cause mortality and unplanned percutaneous coronary intervention (RR 15.40, 95% CI 11.50-20.63, p=<0.001).
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