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J Am Coll Cardiol. 2013 Jun 4;61(22):2242-8. doi: 10.1016/j.jacc.2013.03.026.

A novel tool for reliable and accurate prediction of renal complications in patients undergoing percutaneous coronary intervention.

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Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA.



The aim of the study was to develop and validate a tool for predicting risk of contrast-induced nephropathy (CIN) in patients undergoing contemporary percutaneous coronary intervention (PCI).


CIN is a common complication of PCI and is associated with adverse short- and long-term outcomes. Previously described risk scores for predicting CIN either have modest discrimination or include procedural variables and thus cannot be applied for pre-procedural risk stratification.


Random forest models were developed using 46 pre-procedural clinical and laboratory variables to estimate the risk of CIN in patients undergoing PCI. The 15 most influential variables were selected for inclusion in a reduced model. Model performance estimating risk of CIN and new requirement for dialysis (NRD) was evaluated in an independent validation data set using area under the receiver-operating characteristic curve (AUC), with net reclassification improvement used to compare full and reduced model CIN prediction after grouping in low-, intermediate-, and high-risk categories.


Our study cohort comprised 68,573 PCI procedures performed at 46 hospitals between January 2010 and June 2012 in Michigan, of which 48,001 (70%) were randomly selected for training the models and 20,572 (30%) for validation. The models demonstrated excellent calibration and discrimination for both endpoints (CIN AUC for full model 0.85 and for reduced model 0.84, p for difference <0.01; NRD AUC for both models 0.88, p for difference = 0.82; net reclassification improvement for CIN 2.92%, p = 0.06).


The risk of CIN and NRD among patients undergoing PCI can be reliably calculated using a novel easy-to-use computational tool ( This risk prediction algorithm may prove useful for both bedside clinical decision making and risk adjustment for assessment of quality.

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