ArticleRenal failure2025
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.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- In-hospital electronic monitoring system approaches to epidemiologic investigation and predictive modeling of contrast-induced acute kidney injury.Renal failure · 2026Article
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Development and external validation of an interpretable early prediction model for acute kidney injury using TabPFN and routine admission data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- Machine Learning-Based Prediction and Feature Attribution Analysis of Contrast-Associated Acute Kidney Injury in Patients with Acute Myocardial Infarction.Medicina (Kaunas, Lithuania) · 2026Article
- Development and validation of a clinical prediction model for postcontrast acute kidney injury in patients with postoperative acute kidney injury of acute Stanford type A aortic dissection.Frontiers in cardiovascular medicine · 2026Article
- CIN-RiskNet: a dynamic feature-enhanced TabTransformer with hybrid SMOTE-noise augmentation for contrast-induced nephropathy prediction.Frontiers in cardiovascular medicine · 2026Article
- An interpretable XGBoost model for contrast-associated acute kidney injury in patients with acute coronary syndrome: development and geographical external validation.Frontiers in digital health · 2026Article
- Integration of continuous renal replacement therapy in patients on extracorporeal life support: timing, modality, and future directions.Renal failure · 2025Article
- Enhanced early chronic kidney disease prediction using hybrid waterwheel plant algorithm for deep neural network optimization.Scientific reports · 2025Article
- Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning.Sensors (Basel, Switzerland) · 2025Article
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12 authors.
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Abstract
backgroundPatients 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.
methodsAdult 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.
resultsAmong 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/).
conclusionThe 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.
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