title: Assessing creatinine clearance from modification of diet in renal disease study equations in the ADEMEX cohort: limitations and potential applications.
abstract: OBJECTIVE
  Twenty-four-hour urine and dialysate collections provide accepted means to assess adequacy in peritoneal dialysis (PD). Recent publications suggest that creatinine clearance (CrCl) estimated from the Modification of Diet in Renal Disease (MDRD) equations (eCrCl) accurately approximates measured CrCl (mCrCl) derived from 24-hour collections of urine and dialysate and might serve as an alternative means to assess small-solute clearance and adequacy in PD.
  
  
  METHODS
  Post hoc analysis of data from patients enrolled in ADEMEX was used to assess agreement between mCrCl and eCrCl derived by four- and six-variable MDRD equations (4V-MDRDE and 6V-MDRDE, respectively). Additionally, associations among mCrCl, eCrCl, and survival were determined.
  
  
  RESULTS
  Acceptable precision was observed between mCrCl and 4V-MDRDE-eCrCl and 6V-MDRDE-eCrCl for the entire cohort. Precision was markedly diminished when analysis was limited to functionally anuric patients with mCrCl < 12 ml/min per 1.73 m². Although there was no association between survival and mCrCl, for every 1-ml/min per 1.73 m² increase in 4V- and 6V-MDRDE-eCrCl, there was a 6% and 4% increase in risk of death, respectively. There was a negative association between MDRDE-eCrCl and creatinine appearance rates, suggesting MDRDE-eCrCl is significantly confounded by individual differences in muscle mass.
  
  
  CONCLUSIONS
  MDRDE-eCrCl provides demographically comparable values to 24-hour urine and dialysate collections across the ADEMEX cohort. However, MDRDEs should not be used to assess small-solute removal or adequacy in individual PD patients or to predict outcome in any cohort of patients over narrow ranges of limited clearance.
authors.affiliation: Baxter Healthcare Corporation, Renal Division, McGaw Park, IL 60085, USA. james_sloand@baxter.com
authors.forename: James A
authors.initials: JA
authors.lastname: Sloand
authors.forename: John K
authors.initials: JK
authors.lastname: Leypoldt
authors.forename: Bruce F
authors.initials: BF
authors.lastname: Culleton
authors.forename: Mary E
authors.initials: ME
authors.lastname: Gellens
authors.forename: Ramon
authors.initials: R
authors.lastname: Paniagua
authors.forename: Dante
authors.initials: D
authors.lastname: Amato
authors.forename: Edward F
authors.initials: EF
authors.lastname: Vonesh
mesh_terms: D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood / Q000652:urine; D003404:Creatinine / Q000097:blood* / Q000652:urine; D015314:Dialysis Solutions / Q000378:metabolism / Q000627:therapeutic use*; D004032:Diet; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007676:Kidney Failure, Chronic / Q000097:blood / Q000401:mortality / Q000503:physiopathology / Q000628:therapy* / Q000652:urine; D016018:Least-Squares Analysis; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D010530:Peritoneal Dialysis / Q000009:adverse effects / Q000401:mortality; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011446:Prospective Studies; D015203:Reproducibility of Results; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D015314:Dialysis Solutions; D003404:Creatinine