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Electrocardiographic deep learning for predicting post- procedural mortality: a model development and validation study
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DOI：10.1016/S2589-7500(23)00220-0.png)
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Abstract
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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 center dot 83 (95% CI 0 center dot 79-0 center dot 87), surpassing the discrimination of the RCRI score with an AUC of 0 center dot 67 (0 center dot 61-0 center dot 72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0 center dot 79 (0 center dot 75-0 center dot 83) and 0 center dot 75 (0 center dot 74-0 center dot 76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8 center dot 83 (5 center dot 57-13 center dot 20) for postoperative mortality compared with an unadjusted OR of 2 center dot 08 (0 center dot 77-3 center dot 50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0 center dot 85 [0 center dot 77-0 center dot 92]), non-cardiac surgery (AUC 0 center dot 83 [0 center dot 79-0 center dot 88]), and catheterisation or endoscopy suite procedures (AUC 0 center dot 76 [0 center dot 72-0 center dot 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 the risk of future
Keywords:
POSTOPERATIVE TROPONIN LEVELS
CARDIAC RISK
CALCULATOR
DERIVATION
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