Journal
JMIR MEDICAL INFORMATICS
Volume 9, Issue 10, Pages -Publisher
JMIR PUBLICATIONS, INC
DOI: 10.2196/32771
Keywords
myocardial injury after noncardiac surgery; high-sensitivity cardiac troponin; machine learning; extreme gradient boosting
Categories
Funding
- Bio Industrial Strategic Technology Development Program by the Ministry of Trade, Industry Energy (Korea) [20003883, 20005021]
- Korea Health Technology R&D Project through the Korea Health Industry Development Institute - Ministry of Health & Welfare, Republic of Korea [HR16C0001]
- Healthcare AI Convergence Research & Development Program through the National IT Industry Promotion Agency of Korea - Ministry of Science and ICT [1711120339]
Ask authors/readers for more resources
Machine learning algorithms were used to establish a predictive model for patients with MINS, with extreme gradient boosting outperforming other models and showing high accuracy in predicting mortality.
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. (JMIR Med Inform 2021;9(10):e32771) doi: 10.2196/32771
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available