Predictability of mortality in patients with myocardial injury after noncardiac surgery based on perioperative factors via machine learning: Retrospective study

Abstract

Background: Myocardial injury after noncardiac surgery (MINS) is associated with increased postoperative mortality, but therelevant perioperative factors that contribute to the mortality of patients with MINS have not been fully evaluated.Objective: To establish a comprehensive body of knowledge relating to patients with MINS, we researched the best performingpredictive model based on machine learning algorithms.Methods: Using clinical data from 7629 patients with MINS from the clinical data warehouse, we evaluated 8 machine learningalgorithms for accuracy, precision, recall, F1 score, area under the receiver operating characteristic (AUROC) curve, and areaunder the precision-recall curve to investigate the best model for predicting mortality. Feature importance and Shapley AdditiveExplanations values were analyzed to explain the role of each clinical factor in patients with MINS.Results: Extreme gradient boosting outperformed the other models. The model showed an AUROC of 0.923 (95% CI 0.916-0.930).The AUROC of the model did not decrease in the test data set (0.894, 95% CI 0.86-0.922; P=.06). Antiplatelet drugs prescription,elevated C-reactive protein level, and beta blocker prescription were associated with reduced 30-day mortality.Conclusions: Predicting the mortality of patients with MINS was shown to be feasible using machine learning. By analyzingthe impact of predictors, markers that should be cautiously monitored by clinicians may be identified.