A deep-learning algorithm interpreting preoperative ECGs discriminated postoperative mortality with an AUC of 0.83 (95% CI 0.79-0.87), surpassing the Revised Cardiac Risk Index score.
Cohort (n=45,969)
Yes
Does a deep-learning algorithm interpreting preoperative ECGs improve the prediction of post-procedural mortality compared to the RCRI score in preoperative patients?
A deep-learning algorithm applied to preoperative ECGs significantly improves the prediction of post-procedural mortality compared to the standard Revised Cardiac Risk Index across various procedure types.
Effect estimate: AUC 0.83 (95% CI 0.79-0.87)
BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 0·77-0·92), non-cardiac surgery (AUC 0·83 0·79-0·88), and catheterisation or endoscopy suite procedures (AUC 0·76 0·72-0·81). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.
Ouyang et al. (Thu,) conducted a cohort in Preoperative patients undergoing medical procedures (n=45,969). Deep-learning algorithm interpreting preoperative ECGs vs. Revised Cardiac Risk Index (RCRI) score was evaluated on post-procedural mortality (AUC 0.83, 95% CI 0.79-0.87). A deep-learning algorithm interpreting preoperative ECGs discriminated postoperative mortality with an AUC of 0.83 (95% CI 0.79-0.87), surpassing the Revised Cardiac Risk Index score.