Key result
XGBoost model predicts post-OPCABG acute kidney injury with an AUC of ~0.98.
Why the study?
Off-pump coronary artery bypass grafting is a major contributor to cardiac surgery-associated acute kidney injury, making early detection and prompt intervention for high-risk patients crucial.
Does an XGBoost machine learning model integrating baseline and intraoperative time-series data accurately predict postoperative acute kidney injury in patients undergoing off-pump coronary artery bypass grafting?
Observational (n=1,569)
Yes
Does an XGBoost machine learning model integrating baseline and intraoperative time-series data accurately predict postoperative acute kidney injury in patients undergoing off-pump coronary artery bypass grafting?
Effect estimate: AUC 0.982
An XGBoost machine learning model integrating baseline clinical data and intraoperative time-series hemodynamics accurately predicts acute kidney injury following off-pump coronary artery bypass grafting, with Cystatin C identified as the strongest predictor.
Background The incidence of cardiac surgery-associated acute kidney injury (CSA-AKI) is 26.0%–28.5%. Among cardiac procedures, off-pump coronary artery bypass grafting (OPCABG) is a major contributor to CSA-AKI. Early detection and prompt intervention for high-risk patients are crucial. We therefore developed a machine learning model to predict OPCABG-associated acute kidney injury (AKI), aiming to inform perioperative clinical decision-making. Methods This retrospective study analyzed 938 patients who underwent OPCABG from June 2023 to January 2025. We preprocessed baseline and intraoperative time-series data separately. Using Tsfresh in Python, we extracted time-series intraoperative features, which were then integrated with selected features for model retraining. The final OPCABG-AKI risk prediction model was established by selecting the optimal model based on accuracy and AUC metrics. SHAP analysis was employed to rank feature importance and identify key risk factors. Results Of the 938 patients, 210 (22.39%) developed OPCABG-AKI. The XGBoost model outperformed others in predicting OPCABG-AKI, achieving an AUC of 0.982, accuracy of 0.916, and precision of 0.983 in external validation. SHAP analysis identified Cystatin C (Cys-C) as the top predictor, with NT-proBNP levels ranking second. Conclusions The XGBoost model showed outstanding performance in predicting OPCABG-AKI. Selecting features from preoperative data and integrating them with intraoperative data significantly improved prediction accuracy. This model enables clinicians to stratify patient risks and make informed decisions regarding OPCABG-AKI, promoting early patient recovery.
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Wang et al. (2026) conducted an observational in Off-pump coronary artery bypass grafting-associated acute kidney injury (OPCABG-AKI) (n=1,569). XGBoost machine learning prediction model vs. Other machine learning models was evaluated on Prediction of postoperative acute kidney injury (OPCABG-AKI) (AUC 0.982). The XGBoost machine learning model, integrating baseline and intraoperative time-series data, demonstrated outstanding performance in predicting OPCABG-associated acute kidney injury with an AUC of 0.982 in external validation.
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