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Canadian Journal of Cardiology

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

Published:January 31, 2017DOI:https://doi.org/10.1016/j.cjca.2017.01.018

      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.

      Résumé

      Introduction

      L’identification des patients exposés au risque d’insuffisance rénale aiguë induite par les produits de contraste (CI-AKI, de l’anglais contrast-induced acute kidney injury) est très utile pour les stratégies de prévention ciblées qui accompagnent le cathétérisme cardiaque.

      Méthodes

      Nous avons cherché dans MedLine et EMBASE des articles qui rapportaient l’élaboration ou la validation d’un modèle de prédiction clinique de la CI-AKI ou de dialyse après l’angiographie ou l’intervention coronarienne percutanée. Nous avons utilisé une méta-analyse à effets aléatoires pour regrouper les statistiques C des modèles. Nous avons étudié l’hétérogénéité au moyen d’analyses stratifiées et d’une méta-régression.

      Résultats

      Nous avons trouvé 75 articles qui décrivaient 74 modèles de prédiction de la CI-AKI, 10 modèles de prédiction de la CI-AKI et de la dialyse, et 1 modèle de prédiction de la dialyse. Soixante-trois articles rapportaient l’élaboration d’un nouveau modèle de risque alors que 20 articles rapportaient la validation externe de modèles déjà élaborés. Trente modèles comprenaient une information suffisante pour obtenir des estimations individuelles du risque auquel le patient est exposé ; 9 utilisaient seulement les variables avant l’intervention alors que 21 comprenaient les variables avant l’intervention et après l’intervention. Il existait une hétérogénéité dans la discrimination des modèles de prédiction de la CI-AKI (médiane [intervalle total] de la statistique C 0,78 [0,57-0,95]; I2 = 95,8 %, statistique Q de Cochran P < 0,001). Cependant, il n’y avait aucune différence dans la discrimination des modèles qui utilisaient seulement les variables avant l’intervention comparativement aux modèles qui comprenaient les variables après l’intervention (P = 0,868). Les modèles qui prédisaient la dialyse avaient une bonne discrimination sans hétérogénéité (médiane [intervalle total] de la statistique C : 0,88 [0,87-0,89] ; I2 = 0,0 %, statistique Q de Cochran P = 0,981). Sept modèles de prédiction faisaient l’objet d’une validation externe. Cependant, 2 de ces modèles montraient une performance de discrimination hétérogène et 2 autres manquaient d’informations sur la calibration des cohortes externes.

      Conclusions

      Trois modèles publiés ont démontré produire des estimations généralisables du risque pour prédire la CI-AKI. D’autres recherches sont nécessaires pour évaluer l’effet de leur mise en œuvre aux soins cliniques.
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