Development and External Validation of a Machine Learning Model Based on Preoperative Nutritional Status for Predicting Acute Kidney Injury After Coronary Artery Bypass Grafting

By:
Zhaodi Wang, Jinghao Song, Yang Gao, Jiankang Zheng, Yuxia Qi, Jie Li
Date:
2026
Resource type:
Peer review
Link:
Development and External Validation of a Machine Learning Model Based on Preope…

This original research article investigates whether preoperative nutritional status can predict acute kidney injury (AKI) after coronary artery bypass grafting (CABG). The study developed and externally validated several machine learning models using nutritional indicators and clinical variables, ultimately identifying a Gradient Boosting Machine (GBM) model as the best predictor of postoperative AKI.