Deep learning for the prediction of acute kidney injury after coronary angiography and intervention in patients with chronic kidney disease: a model development and validation study

作者：Ying Tang, Ting Wu, Xiufen Wang, Xi Wu, Anqun Chen, Guochun Chen, Chengyuan Tang, Liyu He, Yuting Liu, Meiyu Zeng, Xiaoqin Luo, Shao-Bin Duan · 发表于：Renal Failure · 年份：2025 · DOI：10.1080/0886022x.2025.2474206 · 被引用次数：9 · 研究领域：Acute Kidney Injury Research、Chronic Kidney Disease and Diabetes、Erythropoietin and Anemia Treatment

BACKGROUND: Patients with chronic kidney disease (CKD) are considered the primary population at risk for post-contrast acute kidney injury (PC-AKI), yet there are few predictive tools specifically designed for this vulnerable population. METHODS: Adult CKD patients undergoing coronary angiography or percutaneous coronary intervention at the Second Xiangya Hospital (2015-2021) were enrolled. The patients were divided into a derivation cohort and a validation cohort based on their admission dates. The primary outcome was the development of PC-AKI. The random forest algorithm was used to identify the most influential predictors of PC-AKI. Six machine learning algorithms were used to construct predictive models for PC-AKI. Model 1 included only preoperative variables, whereas Model 2 included both preoperative and intraoperative variables. The Mehran score was included in the comparison as a classic postoperative predictive model for PC-AKI. RESULTS: Among the 989 CKD patients enrolled, 125 (12.6%) developed PC-AKI. In the validation cohort, deep neural network (DNN) outperformed other machine learning models with the area under the receiver operating characteristic curve (AUROC) of 0.733 (95% CI 0.654-0.812) for Model 1 and 0.770 (95% CI 0.695-0.845) for Model 2. Furthermore, Model 2 showed better performance compared to the Mehran score (AUROC 0.631, 95% CI 0.538-0.724). The SHapley Additive exPlanations method provided interpretability for the DNN models. A web-based tool was ...