Risk Prediction Models for Contrast-Induced Acute Kidney Injury Accompanying Cardiac Catheterization: Systematic Review and Meta-analysis

Abstract

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]; I² = 95.8%, Cochran Q-statistic P < 0.001). However, there was no difference in the discrimination of models using only preprocedure variables compared with models that included postprocedural variables (P = 0.868). Models predicting dialysis had good discrimination without heterogeneity (median [total range] c-statistic: 0.88 [0.87-0.89]; I² = 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.