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Machine Learning Based Prediction of Postoperative Acute Kidney Injury Risk in Coronary Artery Bypass Grafting
Yang Zhang1, Dabei Cai2, Ye Deng2
1Department of Cardiovascular Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, People's Republic of China.
Insights
Machine learning models accurately predict acute kidney injury (AKI) after coronary artery bypass grafting (CABG). Early identification of high-risk patients is now possible, improving outcomes for severe coronary artery disease patients.
Area of Science:
- Cardiology
- Nephrology
- Medical Informatics
Background:
- Postoperative acute kidney injury (AKI) is a significant complication following coronary artery bypass grafting (CABG).
- AKI after CABG is associated with increased mortality and prolonged hospital stays.
- Reliable predictive models for early AKI detection post-CABG are currently lacking.
Purpose of the Study:
- To develop and evaluate machine learning models for the early prediction of AKI in patients undergoing CABG.
- To identify key clinical variables that predict AKI development after CABG.
Main Methods:
- Data from 520 CABG patients were analyzed, split into training (70%) and validation (30%) sets.
- Six machine learning models (RF, XGBoost, LR, LightGBM, Softmax Regression, SVM) were constructed after variable screening using LASSO regression.
- SHapley Additive exPlanations (SHAP) was employed to determine feature importance.
Main Results:
- The incidence of post-CABG AKI was 25.96%.
- The XGBoost model demonstrated superior performance in the training group (AUC=0.89).
- Logistic Regression and Softmax Regression models showed high stability in the validation group (AUC=0.86).
- Estimated glomerular filtration rate (eGFR), intraoperative epinephrine, and calcium levels were identified as top predictors by SHAP analysis.
Conclusions:
- Machine learning models can effectively predict AKI following CABG.
- These models facilitate the early identification of patients at high risk for developing AKI.
- Early prediction allows for timely intervention to mitigate adverse outcomes.
Background:
Coronary artery bypass grafting (CABG) is key for severe coronary artery disease, but postoperative acute kidney injury (AKI) may increase mortality and prolong hospital stays. Reliable models for early prediction of post-CABG AKI remain lacking.
Methods:
Data of 520 CABG patients (September 2021-December 2024) from the Affiliated Hospital of Xuzhou Medical University were collected, and the patients were divided into a training group (70%, for model building) and a validation group (30%). Key variables were screened through Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by the construction of six machine learning models: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Softmax Regression, and Support Vector Machine (SVM). The SHapley Additive exPlanations (SHAP) was used to quantify feature importance.
Results:
The incidence of post-CABG AKI was 25.96%, and the median age of patients in the AKI group was significantly higher than that in the non-AKI group (66.09 ± 8.15 vs 64.32 ± 7.76, p = 0.025). In the training group, the XGBoost model using the top 5 important variables outperformed other models (Area Under the Curve [AUC] = 0.89, 95% Confidence Interval [CI]: 0.86-0.91), followed by the LightGBM model using the top 5 important variables and the RF model using the top 5 important variables (both had an AUC of 0.88; 95% CI: 0.85-0.90 and 0.85-0.91, respectively). In the validation group, the LR model using the top 15 important variables and the Softmax Regression model using the top 15 important variables maintained the highest stability (both had an AUC of 0.86, 95% CI: 0.79-0.92). SHAP analysis confirmed that estimated glomerular filtration rate (eGFR), intraoperative epinephrine use and calcium levels were the top three predictive factors.
Conclusion:
The machine learning models constructed in this study can effectively predict post-CABG AKI, facilitating early identification of high-risk patients.
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