Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients.

OBJECTIVES

This study aimed to develop machine learning models for risk prediction of continuous renal replacement therapy (CRRT) following coronary artery bypass grafting (CABG) surgery in intensive care unit (ICU) patients.

METHODS

We extracted CABG patients from the electronic medical record system of the hospital. The endpoint of this study was the requirement for CRRT after CABG surgery. The Boruta method was used for feature selection. Seven machine learning algorithms were developed to train models and validated using 10 fold cross-validation (CV). Model discrimination and calibration were estimated using the area under the receiver operating characteristic curve (AUC) and calibration plot, respectively. We used the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model and analyze the effects of individual features on the output of the mode.

RESULTS

In this study, 72 (37.89%) patients underwent CRRT, with a higher mortality compared to those patients without CRRT. The Gaussian Naïve Bayes (GNB) model with the highest AUC were considered as the final predictive model and performed best in predicting postoperative CRRT. The analysis of importance revealed that cardiac troponin T, creatine kinase isoenzyme, albumin, low-density lipoprotein cholesterol, NYHA, serum creatinine, and age were the top seven features of the GNB model. The SHAP force analysis illustrated how created model visualized individualized prediction of CRRT.

CONCLUSIONS

Machine learning models were developed to predict CRRT. This contributes to the identification of risk variables for CRRT following CABG surgery in ICU patients and enables the optimization of perioperative managements for patients.