ECG Deep-Learning Algorithm Predicts Mortality Post Surgery

Researchers evaluated the performance of an AI algorithm (PreOpNet) trained on preoperative ECGs in 36,839 patients, mean age 65 years, undergoing procedures at Cedars-Sinai Medical Center (CSMC) from 2015 to 2019 who had at least one 12-lead ECG performed within 30 days before the procedure.
The main outcome was mortality after cardiac surgery, noncardiac surgery, and procedures performed in the catheterization laboratory or endoscopy suite, up to 30 days post-procedure.
Researchers compared the performance of PreOpNet with the Revised Cardiac Risk Index (RCRI), an established risk calculator that uses preoperative clinical characteristics from electronic medical records.
To assess the accuracy of PreOpNet in hospital settings with diverse patient populations, researchers applied the algorithm to cohorts from two separate external healthcare systems: Stanford Healthcare (SHC) and Columbia University Medical Center (CUMC).

The algorithm discriminated mortality with an area under the curve (AUC) of 0.83 (95% CI, 0.79-0.87) compared to conventional RCRI (AUC, 0.67; 95% CI, 0.61-0.72).
Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) for postoperative mortality of 9.17 (95% CI, 5.85-13.82) compared with an unadjusted OR of 2.08 (0.77-3.50) for RCRI scores of more than 2, an indicator of high risk.
PreOpNet performed similarly in discriminating mortality in patients undergoing cardiovascular surgery (AUC, 0.85; 95% CI, 0.77-0.92) and in those undergoing noncardiac surgery (AUC, 0.83; 95% CI, 0.79-0.88); however, for the RCRI score, the AUC was 0.62 (95% CI, 0.52-0.72) in patients undergoing cardiac surgery and 0.70 (95% CI, 0.63-0.77) in those undergoing noncardiac surgery.
The external validation analysis showed the algorithm discriminated postoperative mortality with AUCs of 0.75 (95% CI, 0.74-0.76) in the SHC and 0.79 (95% CI, 0.75-0.83) in the CUMC cohort, with similar specificity, sensitivity, and positive and negative predictive value as with the CSMC cohort.

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