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Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting
Yuchen Gao1, Xiaojie Liu2, Lijuan Wang1
1Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Frontiers in Cardiovascular Medicine
|August 15, 2022
Summary
Machine learning models accurately predict major bleeding after cardiac surgery, outperforming existing risk scores. These advanced methods can help identify patients at higher risk for bleeding complications.
Area of Science:
- Cardiovascular Surgery
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Postoperative major bleeding is a significant complication following cardiac surgery, associated with adverse patient outcomes.
- Accurate prediction of major bleeding is crucial for risk stratification and management in cardiac surgery patients.
Purpose of the Study:
- To evaluate the performance of various machine learning (ML) methods in predicting major bleeding after coronary artery bypass graft (CABG) surgery.
- To compare the predictive accuracy of ML models against established risk scores like TRUST and WILL-BLEED.
Main Methods:
- A cohort of 1,045 patients undergoing isolated CABG surgery was analyzed, with data randomly split into training (70%) and testing (30%) sets.
- Major bleeding was defined using the universal definition of perioperative bleeding (UDPB) classes 3-4.
- Several ML algorithms, including conditional inference random forest (CIRF), stochastic gradient boosting (SGBT), and random forest, were developed and compared to a logistic regression model and existing risk scores using Area Under the Curve (AUC).
Main Results:
- The prevalence of postoperative major bleeding was 7.1%.
- The CIRF model achieved the highest AUC (0.831), followed by SGBT (0.820) and random forest (0.810), indicating superior predictive performance.
- All evaluated ML models demonstrated significantly higher AUCs compared to the TRUST (0.629) and WILL-BLEED (0.557) risk scores.
Conclusions:
- Machine learning methods show high efficacy in predicting major bleeding after cardiac surgery.
- Modern ML models offer improved performance over traditional scoring systems for identifying patients at risk of major bleeding.
- These ML tools have the potential to enhance the identification of high-risk subpopulations for targeted interventions.
