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.

Abstract

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 established to help clinicians stratify the risk of PC-AKI (https://xydsbakigroup.streamlit.app/).

Conclusion: The explainable DNN models serve as promising tools for predicting PC-AKI in CKD patients undergoing coronary angiography and intervention, which is crucial for risk stratification and clinical descion-making.