Risk Prediction Models for Contrast-Induced Acute Kidney Injury Accompanying Cardiac Catheterization: Systematic Review and Meta-analysis
| Autor: | Kelvin C. W. Leung, Neesh Pannu, Mouhieddin Traboulsi, Michelle M. Graham, David W. Allen, Matthew T. James, Bryan Ma, Merril L. Knudtson, David M. Goodhart |
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| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
Cardiac Catheterization medicine.medical_treatment MEDLINE Contrast-induced nephropathy Contrast Media Coronary Artery Disease 030204 cardiovascular system & hematology Coronary Angiography Global Health 03 medical and health sciences 0302 clinical medicine Percutaneous Coronary Intervention Risk Factors Internal medicine medicine Humans 030212 general & internal medicine Intensive care medicine Dialysis Cardiac catheterization business.industry Incidence Acute kidney injury Percutaneous coronary intervention Acute Kidney Injury Models Theoretical Random effects model medicine.disease Meta-analysis Cardiology and Cardiovascular Medicine business |
| Zdroj: | The Canadian journal of cardiology. 33(6) |
| ISSN: | 1916-7075 |
| Popis: | Background Identification of patients at risk of contrast-induced acute kidney injury (CI-AKI) is valuable for targeted prevention strategies accompanying cardiac catheterization. Methods We searched MedLine and EMBASE for articles that developed or validated a clinical prediction model for CI-AKI or dialysis after angiography or percutaneous coronary intervention. Random effects meta-analysis was used to pool c-statistics of models. Heterogeneity was explored using stratified analyses and meta-regression. Results We identified 75 articles describing 74 models predicting CI-AKI, 10 predicting CI-AKI and dialysis, and 1 predicting dialysis. Sixty-three developed a new risk model whereas 20 articles reported external validation of previously developed models. Thirty models included sufficient information to obtain individual patient risk estimates; 9 using only preprocedure variables whereas 21 included preprocedural and postprocedure variables. There was heterogeneity in the discrimination of CI-AKI prediction models (median [total range] in c-statistic 0.78 [0.57-0.95]; I 2 = 95.8%, Cochran Q-statistic P P = 0.868). Models predicting dialysis had good discrimination without heterogeneity (median [total range] c-statistic: 0.88 [0.87-0.89]; I 2 = 0.0%, Cochran Q-statistic P = 0.981). Seven prediction models were externally validated; however, 2 of these models showed heterogeneous discriminative performance and 2 others lacked information on calibration in external cohorts. Conclusions Three published models were identified that produced generalizable risk estimates for predicting CI-AKI. Further research is needed to evaluate the effect of their implementation in clinical care. |
| Databáze: | OpenAIRE |
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