JMIR Publications

ABSTRACT

Background:

Myocardial injury after noncardiac surgery (MINS) is associated with increased postoperative mortality, but the relevant 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 performing predictive model based on machine learning algorithms.

Methods:

Using clinical data for 7,629 patients with MINS from the Clinical Data Warehouse, we evaluated eight machine learning algorithms for accuracy, precision, recall, F1 score, AUROC (area under the receiver operating characteristic) curve, and area under the precision-recall curve to investigate the best model for predicting mortality. Feature importance and SHapley Additive exPlanations value 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 AUROC of 0.923 (95% confidence interval (CI): 0.916–0.930). The AUROC of the model was not decreased in the test dataset (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 mortality of patients with MINS was shown to be feasible using machine learning. By analyzing the impact of predictors, markers that should be cautiously monitored by clinicians may be identified.