title: Predictive performance of ten equations for estimating creatinine clearance in cardiac patients. Iohexol Cooperative Study Group.
abstract: OBJECTIVE
  The predictive performance of 10 equations used to estimate creatinine clearance (Clcr) was assessed retrospectively from data collected on 420 patients.
  
  
  METHODS
  This study is a retrospective data analysis of information collected on hemodynamically stable patients awaiting coronary angiography during the Iohexol Cooperative Study.
  
  
  METHODS
  The Iohexol Cooperative Study was a multicenter study that compared nephrotoxicity of high- and low-osmolar contrast media in patients undergoing coronary angiography. Data used for this analysis were preangiography 24-hour urine collections that were primarily collected in hospitalized patients.
  
  
  METHODS
  Patients selected from the Iohexol Cooperative Study database for analysis were participants categorized into one or more of six subgroups: elderly (n = 222), hypoalbuminemic (n = 25), chronic renal insufficiency (n = 128), low serum creatinine (n = 115), obese (n = 208), and diabetic (n = 191) who had baseline urine collections of at least 24 hours.
  
  
  METHODS
  Predictive performance was assessed using bias, precision, slopes, and y-intercepts.
  
  
  RESULTS
  The Salazar-Corcoran equation was unbiased in the entire group as well as in five of the subgroups. The Cockcroft-Gault equation was unbiased in three of the subgroups. All other equations were biased in predicting Clcr in the entire group as well as in at least four of the subgroups. Precision was generally poor. All slopes were significantly different than one and all y-intercepts were significantly different than zero (p < 0.01). Correlation coefficients were between 0.63 and 0.79 with the exceptions of the low serum creatinine subgroup (r values 0.35-0.64) and the Davis-Chandler equation (r values 0.35-0.71 across groups).
  
  
  CONCLUSIONS
  Of the equations studied, Salazar-Corcoran and Cockcroft-Gault appear to be the best for predicting Clcr.
authors.affiliation: Philadelphia College of Pharmacy, University of the Sciences in Philadelphia, PA 19104, USA. s.spinle@usip.edu
authors.forename: S A
authors.initials: SA
authors.lastname: Spinler
authors.forename: J J
authors.initials: JJ
authors.lastname: Nawarskas
authors.forename: E G
authors.initials: EG
authors.lastname: Boyce
authors.forename: J E
authors.initials: JE
authors.lastname: Connors
authors.forename: S L
authors.initials: SL
authors.lastname: Charland
authors.forename: S
authors.initials: S
authors.lastname: Goldfarb
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D003404:Creatinine / Q000097:blood / Q000378:metabolism*; D003920:Diabetes Mellitus / Q000378:metabolism; D005260:Female; D006331:Heart Diseases / Q000378:metabolism*; D006801:Humans; D016014:Linear Models; D008297:Male; D008433:Mathematics; D015337:Multicenter Studies as Topic; D009765:Obesity / Q000378:metabolism; D011237:Predictive Value of Tests; D012189:Retrospective Studies
chemical_list: D003404:Creatinine