{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T06:45:42Z","timestamp":1785393942275,"version":"3.55.0"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T00:00:00Z","timestamp":1750636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Off-pump coronary artery bypass grafting (OPCABG) is an alternative to traditional coronary artery bypass grafting (CABG), which avoids cardiopulmonary bypass. However, acute kidney injury (AKI) is a common complication, with incidence rates ranging from 5% to 42%, significantly affecting postoperative outcomes. This study aimed to develop a robust risk prediction model for post-OPCABG AKI using machine learning (ML) techniques.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a multicenter, retrospective study involving 3,043 coronary artery disease (CAD) patients, with an overall AKI incidence of 15.28%. The cohort was divided into a training set (<jats:italic>n<\/jats:italic> = 2,130) and a validation set (<jats:italic>n<\/jats:italic> = 913). An external validation cohort of 878 patients was also included. Five ML methods -Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), AdaBoost, and XGBoost-were employed to predict the risk of AKI. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The XGBoost model demonstrated the highest performance, with an area under the curve (AUC) of 0.88, sensitivity of 82%, and specificity of 83% in the internal validation set. In the external validation cohort, the XGBoost model achieved an AUC of 0.84, sensitivity of 74%, and specificity of 90%. The model utilized 26 predictive features, including patient demographics and preoperative laboratory values.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The XGBoost model outperformed other ML methods (SVM, DT, RF, and AdaBoost) in both internal and external validations, demonstrating its robustness and generalizability. By integrating diverse patient data from multiple institutions, our model significantly improved AKI risk assessment and identified novel predictive factors. These findings highlight the potential of machine learning models in enhancing AKI risk prediction and supporting personalized management strategies to improve outcomes in OPCABG patients.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2025.1600012","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T05:21:09Z","timestamp":1750656069000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based prediction model of acute kidney injury following coronary artery bypass grafting in coronary heart disease patients: a multicenter clinical study from 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