Machine Learning for Predicting the Development of Postoperative Acute Kidney Injury After Coronary Artery Bypass Grafting Without Extracorporeal Circulation

Aug 5, 2026

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Cardiac surgery-associated acute kidney injury (CSA-AKI) remains a major complication that increases morbidity and mortality after surgery. Existing predictive models often capture nonlinear effects but insufficiently integrate intraoperative and early postoperative variables, while refined predictions for off-pump coronary artery bypass grafting (CABG) are limited. This study applies machine learning to comprehensive perioperative data to forecast CSA-AKI risk, evaluate multiple algorithms, and use SHAP to interpret influential clinical features.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusion

Introduction

Clinical significance of CSA-AKI

Off-pump vs on-pump CABG evidence

Cardiovascular Innovations and Applications Vol. 7 (2023) 20 ISSN 2009-8618 DOI 10. 15212/CVIA.2023.0006 RESEARCH ARTICLE Machine Learning for Predicting the Development of Postoperative Acute Kidney Injury After Coronary Artery Bypass Grafting Without Extracorporeal Circulation Sai Zheng1,a, Yugui Li1,a, Cheng Luo1, Fang Chen1, Guoxing Ling1 and Baoshi Zheng1 1The First Affiliated Hospital of Guangxi Medical University, Cardiac Surgery, Nanning, Guangxi, China Received: 24 November 2022; Revised: 4 January 2023; Accepted: 10 January 2023 Abstract 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. Keywords: Machine learning; CSA-AKI; off-pump CABG aSai Zheng and Yugui Li contributed equally to this work. Correspondence: Baoshi Zheng, The First Affiliated Hospital of Guangxi Medical University, Department of Cardiac Surgery, No 6 Shuangyong Road, Nanning, Guangxi, China, Tel.: +86-13977189015, E-mail: [zhengbs25@vip.sina.com](zhengbs25@vip.sina.com) Introduction Cardiac surgery-associated acute kidney injury (CSA-AKI) is a complication after cardiac surgery that is associated with increased morbidity and © 2023 Cardiovascular Innovations and Applications. Creative Commons Attribution-NonCommercial 4.0 International License 2 S. Zheng et al. , Machine Learning for Predicting the Development of Postoperative Acute Kidney Injury mortality, hospital stays and health care costs [1, 2] . A meta-analysis investigating the global incidence and prognosis of CSA-AKI over the period from 2004 to 2014 has indicated that the incidence of all stages of AKI is approximately 22%, and the combined short-term and long-term mortality rates are 10.7% and 30%, respectively [3, 4] . The two main types of surgical treatment for coronary artery disease are coronary artery bypass grafting with extracorporeal circulation and nonexternal circulation coronary artery bypass grafting (on-pump and offpump CABG) . Previously, off-pump CABG was believed to avoid the second strike of extracorpore

FAQs

Why is predicting CSA-AKI after off-pump CABG important?

CSA-AKI after cardiac surgery increases morbidity, mortality, hospital stays, and healthcare costs. Accurate prediction supports interventions to prevent or minimize consequences.

What data and criteria were used to define postoperative AKI?

Postoperative AKI followed KDIGO criteria, using at least 50% creatinine elevation within 7 days or 0.3 mg/dL within 48 hours. The study analyzed 293 perioperative variables for patients undergoing off-pump CABG.

Which machine learning models were evaluated and how was interpretability handled?

A decision tree, random forest, support vector machine, extreme gradient boosting, and gradient boosting decision tree (GBDT) were compared. SHAP values were used to explain the predictive model and identify key contributing features.

Aug 5, 2026

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