title: The Mayo Clinic quadratic equation improves the prediction of glomerular filtration rate in diabetic subjects.
abstract: BACKGROUND
  Although recommended, both the Cockcroft and Gault formula (CG) and the modification of diet in renal disease (MDRD) equation are not ideally predictive of glomerular filtration rate (GFR) in diabetic subjects; we tested whether the new Mayo Clinic Quadratic (MCQ) equation performed better.
  
  
  METHODS
  In 200 diabetic subjects with a wide range of renal function, GFR was measured by 51Cr-EDTA clearance, and compared with the results of the three predictive equations by regression analysis and Bland and Altman procedures. The correlations with body mass index, age and albumin excretion rates were tested. The precisions (absolute difference as percentage), diagnostic accuracies [receiver operating characteristic (ROC) curves for the diagnosis of moderate and severe chronic kidney disease (CKD)], and the results of stratification according to the KDOQ classification were compared.
  
  
  RESULTS
  The CG and MCQ overestimated mean GFR, whereas the MDRD underestimated it. Correlation coefficients and areas under the ROC curves were better for the MDRD and the MCQ as compared with the CG, which was biased by body weight (+30% overestimation in obese diabetic subjects). The absolute differences with true GFR were slightly lower for the MDRD than the MCQ, and both better than the CG. Both the MDRD and MCQ correctly stratified 65% of the subjects (CG: 55%, P<0.05). In contrast with the MDRD, the MCQ did not underestimate normal GFR, and its performance for stratification was uniformly good over a wide GFR range.
  
  
  CONCLUSIONS
  In diabetic subjects, the MCQ has a similar diagnostic performance to the MDRD, but it does not underestimate normal GFR, which is an important advantage.
authors.affiliation: Université de Bordeaux 2--Victor Segalen, 33000 Bordeaux, France. vincent.rigalleau@wanadoo.fr
authors.forename: Vincent
authors.initials: V
authors.lastname: Rigalleau
authors.forename: Catherine
authors.initials: C
authors.lastname: Lasseur
authors.forename: Christelle
authors.initials: C
authors.lastname: Raffaitin
authors.forename: Caroline
authors.initials: C
authors.lastname: Perlemoine
authors.forename: Nicole
authors.initials: N
authors.lastname: Barthe
authors.forename: Philippe
authors.initials: P
authors.lastname: Chauveau
authors.forename: Christian
authors.initials: C
authors.lastname: Combe
authors.forename: Henri
authors.initials: H
authors.lastname: Gin
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000419:Albuminuria / Q000175:diagnosis / Q000209:etiology / Q000652:urine; D015415:Biomarkers / Q000097:blood / Q000652:urine; D015992:Body Mass Index; D002614:Chelating Agents / Q000493:pharmacokinetics; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000150:complications / Q000503:physiopathology*; D004492:Edetic Acid / Q000493:pharmacokinetics; D005260:Female; D005500:Follow-Up Studies; D005919:Glomerular Filtration Rate / Q000502:physiology*; D016304:Hospitals, Private / Q000706:statistics & numerical data*; D006801:Humans; D007676:Kidney Failure, Chronic / Q000150:complications / Q000175:diagnosis* / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009391:Nephelometry and Turbidimetry; D011336:Probability; D011379:Prognosis; D012372:ROC Curve; D012044:Regression Analysis; D012189:Retrospective Studies; D014563:Urodynamics
chemical_list: D015415:Biomarkers; D002614:Chelating Agents; D004492:Edetic Acid