Machine learning for the prediction of acute kidney injury in patients after cardiac surgery

Abstract

Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792–0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.