ArticleJournal of the American Heart Association2015
Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.
Article in Journal of the American Heart Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 58 citations in OpenAlex.
- Artificial intelligence in contrast-induced nephropathy after coronary interventions: A meta-analysis.Medicine · 2026Pooled it
- Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Guideline on the use of iodinated contrast media in patients with kidney disease 2018.Clinical and experimental nephrology · 2020Guideline
- Sustained Improvements After Intervention to Prevent Contrast-Associated Acute Kidney Injury: A Randomized Controlled Trial.Journal of the American Heart Association · 2025Trial
- Contrast-Induced Nephropathy.Annals of vascular diseases · 2026Review
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Atherosclerotic plaque composition and specific endovascular considerations in the end stage renal disease patients: a narrative review.Cardiovascular diagnosis and therapy · 2023Review
- Change Management Accompanying Implementation of Decision Support for Prevention of Acute Kidney Injury in Cardiac Catheterization Units: Program Report.Canadian journal of kidney health and disease · 2023Article
- Maintaining a National Acute Kidney Injury Risk Prediction Model to Support Local Quality Benchmarking.Circulation. Cardiovascular quality and outcomes · 2022Article
- Article
- Contrast-induced acute kidney injury and its contemporary prevention.Frontiers in cardiovascular medicine · 2022Review
- Racial Differences in AKI Incidence Following Percutaneous Coronary Intervention.Journal of the American Society of Nephrology : JASN · 2021Article
- A prediction model of contrast-associated acute kidney injury in patients with hypoalbuminemia undergoing coronary angiography.BMC cardiovascular disorders · 2020Article
- Coronary artery bypass graft markers: history, usage, and effects.General thoracic and cardiovascular surgery · 2020Review
- Guideline on the use of iodinated contrast media in patients with kidney disease 2018.Japanese journal of radiology · 2020Article
- Comparative Performance of Prediction Models for Contrast-Associated Acute Kidney Injury After Percutaneous Coronary Intervention.Circulation. Cardiovascular quality and outcomes · 2019Article
- Predicting reamputation risk in patients undergoing lower extremity amputation due to the complications of peripheral artery disease and/or diabetes.The British journal of surgery · 2019Article
- Maximum allowable contrast dose and prevention of acute kidney injury following cardiovascular procedures.Current opinion in nephrology and hypertension · 2018Review
- Calibration drift in regression and machine learning models for acute kidney injury.Journal of the American Medical Informatics Association : JAMIA · 2017Article
Corrections and comments
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Authors and funding
12 authors at 10 institutions in 1 country.
Funding
Abstract
backgroundAcute kidney injury (AKI) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration. METHODS AND
resultsWe collected data on all patients undergoing cardiac catheterization or percutaneous coronary intervention in the Veterans Health Administration from January 01, 2009 to September 30, 2013, excluding patients with chronic dialysis, end-stage renal disease, renal transplant, and missing pre- and postprocedural creatinine measurement. We used 4 AKI definitions in model development and included risk factors from up to 1 year prior to the procedure and at presentation. We developed our prediction models for postprocedural AKI using the least absolute shrinkage and selection operator (LASSO) and internally validated using bootstrapping. We developed models using 115 633 angiogram procedures and externally validated using 27 905 procedures from a New England cohort. Models had cross-validated C-statistics of 0.74 (95% CI: 0.74-0.75) for AKI, 0.83 (95% CI: 0.82-0.84) for AKIN2, 0.74 (95% CI: 0.74-0.75) for contrast-induced nephropathy, and 0.89 (95% CI: 0.87-0.90) for dialysis.
conclusionsWe developed a robust, externally validated clinical prediction model for AKI following cardiac catheterization or percutaneous coronary intervention to automatically identify high-risk patients before and immediately after a procedure in the Veterans Health Administration. Work is ongoing to incorporate these models into routine clinical practice.
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