title: Creatinine Clearance and Estimated Glomerular Filtration Rate--When are they Interchangeable.
abstract: Study goal was to examine which of glomerular rate equations is most suitable for prediction of creatinine clearance (CrCl). Using a retrospective review of data from 500 hospital patients we calculated glomerular filtration rate according to Cockcroft-Gault equation (C-G), Modification of Diet in Renal Disease Study equation (MDRD) and Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI). We determined if results of these equations were compatible with CrCl, and does stage of kidney disease, body-mass index (BMI), diabetes or old age have an impact on their ability to predict creatinine clearance. All of the equations showed high correlations with CrCl, regardless of diabetes, overweight or old age. There was no significant difference (p<0.05) between diagnostic accuracy when comparing ROC plots for MDRD and CKD-EPIat CrCl cut offs of 60 ml/min/1.73 m2 and 90 ml/min/1.73 m2 when analyzing data for all patients, older patients (>65 years) and diabetics. The percentage of overweight patients (BMI > or = 25) in patients with normal CrCl and decreased GFR was 64.81% for C-G, 92.04% for MDRD and 91.36% for CKD-EPI. Large number of overweight patients with normal CrCl and decreased GFR would indicate that CrCl overestimates GFR in overweight patients. The simple correction in CrCl for obese subjects is purposed. Passing-Bablok regression showed agreement between CrCl and MDRD and CrCl and CKD-EPI only in cases of severely decreased GFR (G4 and G5 stage of chronic kidney disease). Only in these stages of chronic kidney disease can CrCl and MDRD or CrCl and CKD-EPI be used simultaneously.
authors.forename: Lucija
authors.initials: L
authors.lastname: Simetić
authors.forename: Lada
authors.initials: L
authors.lastname: Zibar
authors.forename: Sandra
authors.initials: S
authors.lastname: Drmić
authors.forename: Ivana
authors.initials: I
authors.lastname: Begić
authors.forename: Vatroslav
authors.initials: V
authors.lastname: Serić
mesh_terms: D000367:Age Factors; D000368:Aged; D015992:Body Mass Index; D015331:Cohort Studies; D003404:Creatinine / Q000378:metabolism*; D048909:Diabetes Complications / Q000378:metabolism; D003920:Diabetes Mellitus / Q000378:metabolism*; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007668:Kidney / Q000378:metabolism*; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000150:complications / Q000378:metabolism*; D050177:Overweight / Q000150:complications / Q000378:metabolism; D051436:Renal Insufficiency, Chronic / Q000150:complications / Q000175:diagnosis / Q000378:metabolism*; D012189:Retrospective Studies
chemical_list: D003404:Creatinine