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 Mode

SUBMITTER: Tang Y 

PROVIDER: S-EPMC11912247 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature