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

Abstract

Objectives: 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 an adverse short and long term outcome. 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 utilizing 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 dataset using area under the ROC curve (AUC), with net reclassification improvement (NRI) used to compare full and reduced model CIN prediction after grouping in low, intermediate, and high risk categories. Results: Our study cohort was comprised of 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: full model 0.85, reduced model 0.84, p for difference <0.01; NRD AUC: both models 0.88, p for difference = 0.82; NRI 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-vic.org/calculators/cin). This risk prediction algorithm may prove useful for both bed side clinical decision making and risk adjustment for assessment of quality.