Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury

Abstract

Background: The cardiac surgery-associated acute kidney injury (CSA-AKI) occurs in up to 1 out of 3 patients. Off-pump coronary artery bypass grafting (OPCABG) is one of the major cardiac surgeries leading to CSA-AKI. Early identification and timely intervention are of clinical significance for CSA-AKI. In this study, we aimed to establish a prediction model of off-pump coronary artery bypass grafting-associated acute kidney injury (OPCABG-AKI) after surgery based on machine learning methods.

Methods: The preoperative and intraoperative data of 1,041 patients who underwent OPCABG in Chest Hospital, Tianjin University from June 1, 2021 to April 30, 2023 were retrospectively collected. The definition of OPCABG-AKI was based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The baseline data and intraoperative time series data were included in the dataset, which were preprocessed separately. A total of eight machine learning models were constructed based on the baseline data: logistic regression (LR), gradient-boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT). The intraoperative time series data were extracted using a long short-term memory (LSTM) deep learning model. The baseline data and intraoperative features were then integrated through transfer learning and fused into each of the eight machine learning models for training. Based on the calculation of accuracy and area under the curve (AUC) of the prediction model, the best model was selected to establish the final OPCABG-AKI risk prediction model. The importance of features was calculated and ranked by DT model, to identify the main risk factors.

Results: Among 701 patients included in the study, 73 patients (10.4%) developed OPCABG-AKI. The GBDT model was shown to have the best predictions, both based on baseline data only (AUC =0.739, accuracy: 0.943) as well as based on baseline and intraoperative datasets (AUC =0.861, accuracy: 0.936). The ranking of importance of features of the GBDT model showed that use of insulin aspart was the most important predictor of OPCABG-AKI, followed by use of acarbose, spironolactone, alfentanil, dezocine, levosimendan, clindamycin, history of myocardial infarction, and gender.

Conclusions: A GBDT-based model showed excellent performance for the prediction of OPCABG-AKI. The fusion of preoperative and intraoperative data can improve the accuracy of predicting OPCABG-AKI.

Keywords: Off-pump coronary artery bypass grafting (OPCABG); acute kidney injury (AKI); deep learning; machine learning.

Figure 1

The statistical analysis plan diagram. OPCABG, off-pump coronary artery bypass grafting; LSTM, long short-term memory; AdaBoost, adaptive boosting; XGBoost, eXtreme gradient boosting; SVM, support vector machine; KNN, k-nearest neighbor.

Figure 2

The patients inclusion flowchart. OPCABG, off-pump coronary artery bypass grafting; SCr, serum creatinine; AKI, acute kidney injury.

Figure 3

GBDT model predicts the feature importance ranking of OPCABG-AKI. MI, myocardial infarction; L, left; R, right; CK, creatine kinase; HR, heart rate; SVR, systemic vascular resistance; GBDT, gradient-boosting decision tree; OPCABG-AKI, off-pump coronary artery bypass grafting-associated acute kidney injury.