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. 2021 Oct 14;9(10):e32771.
doi: 10.2196/32771.

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Seo Jeong Shin #  1 , Jungchan Park #  1   2 , Seung-Hwa Lee  3   4 , Kwangmo Yang #  1   5 , Rae Woong Park  1   6
Affiliations

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Seo Jeong Shin et al. JMIR Med Inform. .

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 from 7629 patients with MINS from the clinical data warehouse, we evaluated 8 machine learning algorithms for accuracy, precision, recall, F1 score, area under the receiver operating characteristic (AUROC) curve, and area under the precision-recall curve to investigate the best model for predicting mortality. Feature importance and Shapley Additive Explanations 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 analyzing the impact of predictors, markers that should be cautiously monitored by clinicians may be identified.

Keywords: extreme gradient boosting; high-sensitivity cardiac troponin; machine learning; myocardial injury after noncardiac surgery.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
A flowchart of our retrospective study design. peri-op hs cTn I: perioperative high-sensitivity cTn-I; peri-op hs cTn T: perioperative high-sensitivity cTn-T; Pts: patients; SMN-TINCO: Samsung Medical Center Troponin in Noncardiac Operation.
Figure 2
Figure 2
Performance comparison of each 30-day mortality prediction model with the range of (A) AUROC and (B) F1 score. AUROC: area under the receiver operating characteristic; CART: classification and regression trees; GBM: generalized boosted regression model; GLMNET: lasso/ridge/elastic net; kNN: k-nearest neighbors; LDA: linear discriminant analysis; RF: random forests; SVM: support vector machines; XGB: extreme gradient boosting.
Figure 3
Figure 3
SHAP summary plot of 30-day mortality prediction extreme gradient boosting model. According to the SHAP values of each feature, antiplatelet prescription at discharge (ie, purple dots) was associated with a lower probability of death (ie, the left side of the vertical dotted line). Higher C-reactive protein levels at discharge (ie, purple dots) were associated with a higher probability of death (ie, the right side of the vertical dotted line). Insulin prescription at discharge (ie, purple dots) was associated with a higher probability of death (ie, the right side of the vertical dotted line). CABG: coronary artery bypass graft; COPD: chronic obstructive pulmonary disease; ESA: European Society of Anaesthesiology; ESC: European Society of Cardiology; SHAP: Shapley Additive Explanations.
Figure 4
Figure 4
Internet app for predicting 30-day mortality of patients with MINS. CCB: calcium channel blockers; MINS: myocardial injury after noncardiac surgery.
See this image and copyright information in PMC

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