title: A novel tool for reliable and accurate prediction of renal complications in patients undergoing percutaneous coronary intervention.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA. hgurm@med.umich.edu
authors.forename: Hitinder S
authors.initials: HS
authors.lastname: Gurm
authors.forename: Milan
authors.initials: M
authors.lastname: Seth
authors.forename: Judith
authors.initials: J
authors.lastname: Kooiman
authors.forename: David
authors.initials: D
authors.lastname: Share
mesh_terms: D058186:Acute Kidney Injury / Q000139:chemically induced* / Q000517:prevention & control; D000368:Aged; D000465:Algorithms; D015331:Cohort Studies; D003287:Contrast Media / Q000009:adverse effects*; D005919:Glomerular Filtration Rate; D006801:Humans; D008875:Middle Aged; D062645:Percutaneous Coronary Intervention; D011785:Quality Assurance, Health Care; D012372:ROC Curve; D012044:Regression Analysis; D006435:Renal Dialysis; D018570:Risk Assessment / Q000379:methods*
chemical_list: D003287:Contrast Media