ArticleCirculation. Cardiovascular quality and outcomes2019
Comparative Performance of Prediction Models for Contrast-Associated Acute Kidney Injury After Percutaneous Coronary Intervention.
Article in Circulation. Cardiovascular quality and outcomes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Comparative Evaluation of Clinical and Immunonutritional Risk Scores for Predicting Contrast-Associated Acute Kidney Injury in Emergency Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Risk-Stratified Prognostic Implications of Contrast-Associated Acute Kidney Injury After Percutaneous Coronary Intervention.JACC. Advances · 2025Article
- Association of Serum Cholinesterase with Contrast-Associated Acute Kidney Injury and Adverse Outcomes in Percutaneous Coronary Intervention Patients.Therapeutics and clinical risk management · 2025Article
- 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
- Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.JAMA network open · 2022Article
- Article
- Machine learning prediction model of acute kidney injury after percutaneous coronary intervention.Scientific reports · 2022Article
- Contrast-induced acute kidney injury and its contemporary prevention.Frontiers in cardiovascular medicine · 2022Review
- Patient Experience After Risk Stratification and Follow-up for Acute Kidney Injury After Cardiac Catheterization: Patient Survey.CJC open · 2021Article
- Mehran 2 Contrast-Associated Acute Kidney Injury Risk Score: Is it Applicable to the Asian Percutaneous Coronary Intervention Population?Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundIdentifying patients at increased risk of contrast-associated acute kidney injury (CA-AKI) can help target risk mitigation strategies toward these individuals during percutaneous coronary intervention. Illuminating which risk models best stratify risk is an important foundation for such quality improvement efforts. METHODS AND
resultsSeven previously published risk prediction models for CA-AKI and 3 models for kidney injury requiring dialysis were validated using 2 definitions for CA-AKI (the Kidney Disease: Improving Global Outcomes definition of ≥0.3 mg/dL within 48 hours or ≥50% increase in serum creatinine from baseline within 7 days and the historical definition of ≥0.5 mg/dL or ≥25% increase in serum creatinine from baseline within 48 hours), and AKI requiring dialysis within 30 days of percutaneous coronary intervention. Model performance was compared based on discrimination, calibration, and categorical net reclassification index before and after model recalibration. Among 7888 patients who underwent percutaneous coronary intervention in Alberta Canada, CA-AKI occurred in 330 patients (4.2%) when CA-AKI was defined using the Kidney Disease: Improving Global Outcomes definition and 571 (7.3%) when using the historical definition. CA-AKI requiring dialysis occurred in 42 (0.6%) patients. When validated using the Kidney Disease: Improving Global Outcomes definition for CA-AKI, the 2 most recently published models for CA-AKI showed better discrimination (C statistics, 0.75-0.76) than older models (C statistics, 0.61-0.68). C statistics of models for kidney injury requiring dialysis ranged from 0.70 to 0.86. The calibration of all models for CA-AKI deviated from ideal, and the proportion of patients classified into different risk categories for CA-AKI differed substantially for the 2 most recent models. Recalibration significantly improved risk stratification of patients into clinical risk categories for some models.
conclusionsRecent prediction models for CA-AKI show better discrimination compared with older models; however, model recalibration should be examined in external cohorts to improve the accuracy of predictions, particularly if predicted risk strata are used to guide management approaches.
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