An interpretable machine learning model to predict off-pump coronary artery bypass grafting-associated acute kidney injury.

Abstract

Off-pump coronary artery bypass grafting-associated acute kidney injury (OPCAB-AKI) is related to 30-day perioperative mortality. Existing mathematical models cannot be applied to help clinicians make early diagnosis and intervention decisions. This study used an interpretable machine learning method to establish and screen an optimized OPCAB-AKI prediction model. Clinical data of 1110 patients who underwent OPCAB in the Department of Cardiac Surgery of General Hospital of Northern Theater Command (Shenyang, China) from January 2018 to December 2020 were collected retrospectively. Four machine learning models were used, including logistic regression (LR), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The SHapley Additive exPlanation (SHAP) tool was used for explanatory analysis of the black-box model. The mean absolute value of the characteristic SHAP parameter was defined and sorted. The correlation between the characteristic parameters and OPCAB-AKI was determined based on the SHAP value. A quantitative analysis of a single characteristic and an interaction analysis of multiple characteristics were carried out for the main risk factors. The RF prediction model had the best performance, with an area under the curve (AUC) of 0.90, a precision rate of 0.80, an accuracy rate of 0.83, a recall rate of 0.74, and an F1 score of 0.78 for positive samples. The interpretation analysis of the SHAP model results showed that intraoperative urine volume contributed to the greatest extent to the RF model, and other parameters included intraoperative sufentanil dosage, intraoperative dexmedetomidine dosage, cyclic variation coefficient during the induction period, intraoperative hypotension duration, age, preoperative baseline serum creatinine, body mass index (BMI), and Acute Physiology, Age and Chronic Health Evaluation (APACHE) II score. The model constructed by the RF ensemble learning algorithm predicted OPCAB-AKI, and indicators such as intraoperative urine volume were closely related to OPCAB-AKI.