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已发表论文
基于机器学习的冠状动脉旁路移植术患者术后急性肾损伤风险预测
Authors Zhang Y, Cai D , Deng Y, Wang Z, Zhang Z, Zhang H, Wang Q , Feng S, Sun L , Wei J
Received 11 June 2025
Accepted for publication 6 November 2025
Published 15 November 2025 Volume 2025:20 Pages 2033—2048
DOI https://doi.org/10.2147/CIA.S543303
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Maddalena Illario
Yang Zhang,1,* Dabei Cai,2,* Ye Deng,2,* Zhu Wang,1 Zhihan Zhang,1 Hu Zhang,1 Qingjie Wang,2 Shoujie Feng,1 Ling Sun,3 Jun Wei1
1Department of Cardiovascular Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, People’s Republic of China; 2Department of Cardiology, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, 213000, People’s Republic of China; 3Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, 214023, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ling Sun, Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, 214023, People’s Republic of China, Email sunling85125@hotmail.com Jun Wei, Department of Cardiovascular Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, People’s Republic of China, Email weijunseu@outlook.com
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.
Keywords: coronary artery bypass grafting, acute kidney injury, machine learning, prediction model, area under the receiver operating characteristic curve
1Department of Cardiovascular Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, People’s Republic of China; 2Department of Cardiology, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, 213000, People’s Republic of China; 3Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, 214023, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ling Sun, Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, 214023, People’s Republic of China, Email sunling85125@hotmail.com Jun Wei, Department of Cardiovascular Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, People’s Republic of China, Email weijunseu@outlook.com
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.
Keywords: coronary artery bypass grafting, acute kidney injury, machine learning, prediction model, area under the receiver operating characteristic curve