Key result
A gradient boosting decision tree (GBDT) machine learning model effectively predicted the risk of acute kidney injury development after off-pump CABG, achieving an area under the curve of 0.87.
Why the study?
Most established predictive models for CSA-AKI do not adequately capture nonlinear relationships or intraoperative and early postoperative variables, and refined predictive models specifically for off-pump CABG are lacking.
Can machine learning models using comprehensive perioperative data accurately predict the development of postoperative acute kidney injury after off-pump CABG?
Cohort
No
Can machine learning models using comprehensive perioperative data accurately predict the development of postoperative acute kidney injury after off-pump CABG?
Effect estimate: AUC 0.87
A gradient boosting decision tree machine learning model using perioperative variables can accurately predict the risk of acute kidney injury after off-pump CABG with an AUC of 0.87.
May aid perioperative AKI risk stratification after off-pump CABG; extends prior models but remains hypothesis-generating pending prospective validation.
Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that increases morbidity and mortality after cardiac surgery. Most established predictive models are limited to the analysis of nonlinear relationships and do not adequately consider intraoperative variables and early postoperative variables. Nonextracorporeal circulation coronary artery bypass grafting (off-pump CABG) remains the procedure of choice for most coronary surgeries, and refined CSA-AKI predictive models for off-pump CABG are notably lacking. Therefore, this study used an artificial intelligence-based machine learning approach to predict CSA-AKI from comprehensive perioperative data. Methods: In total, 293 variables were analysed in the clinical data of patients undergoing off-pump CABG in the Department of Cardiac Surgery at the First Affiliated Hospital of Guangxi Medical University between 2012 and 2021. According to the KDIGO criteria, postoperative AKI was defined by an elevation of at least 50% within 7 days, or 0.3 mg/dL within 48 hours, with respect to the reference serum creatinine level. Five machine learning algorithms—a simple decision tree, random forest, support vector machine, extreme gradient boosting and gradient boosting decision tree (GBDT)—were used to construct the CSA-AKI predictive model. The performance of these models was evaluated with the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) values were used to explain the predictive model. Results: The three most influential features in the importance matrix plot were 1-day postoperative serum potassium concentration, 1-day postoperative serum magnesium ion concentration, and 1-day postoperative serum creatine phosphokinase concentration. Conclusion: GBDT exhibited the largest AUC (0.87) and can be used to predict the risk of AKI development after surgery, thus enabling clinicians to optimise treatment strategies and minimise postoperative complications.
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Zheng et al. (2023) conducted a cohort in Cardiac surgery-associated acute kidney injury (CSA-AKI) after off-pump CABG. Machine learning predictive models (GBDT) vs. Other machine learning algorithms was evaluated on Development of postoperative acute kidney injury (CSA-AKI) (AUC 0.87). A gradient boosting decision tree (GBDT) machine learning model effectively predicted the risk of acute kidney injury development after off-pump CABG, achieving an area under the curve of 0.87.
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