Author + information
- Received January 15, 2013
- Revision received March 12, 2013
- Accepted March 26, 2013
- Published online June 4, 2013.
- *Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan
- †Department of Thrombosis and Hemostasis and Department of Nephrology, Leiden University Medical Center, Leiden, the Netherlands
- ‡Blue Cross Blue Shield of Michigan, Detroit, Michigan
Objectives 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).
Background 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.
Methods 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.
Results 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).
Conclusions The risk of CIN and NRD among patients undergoing PCI can be reliably calculated using a novel easy-to-use computational tool (https://bmc2.org/calculators/cin). This risk prediction algorithm may prove useful for both bedside clinical decision making and risk adjustment for assessment of quality.
The BMC2 registry is funded by Blue Cross Blue Shield of Michigan. The sponsor had no role in study design or review or the decision to submit the work for publication. Dr. Gurm receives research funding from Blue Cross Blue Shield of Michigan and the National Institutes of Health and Agency for Healthcare Research & Quality. Dr. Share is employed by Blue Cross Blue Shield of Michigan. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received January 15, 2013.
- Revision received March 12, 2013.
- Accepted March 26, 2013.
- American College of Cardiology Foundation