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

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

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 pe...

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Bibliographic Details
Main Authors: Ying Tang, Ting Wu, Xiufen Wang, Xi Wu, Anqun Chen, Guochun Chen, Chengyuan Tang, Liyu He, Yuting Liu, Meiyu Zeng, Xiaoqin Luo, Shaobin Duan
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Renal Failure
Subjects:
Post-contrast acute kidney injury
chronic kidney disease
coronary angiography and intervention
deep learning
predictive model
Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2025.2474206
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https://www.tandfonline.com/doi/10.1080/0886022X.2025.2474206

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