BACKGROUND AND OBJECTIVES: 51Cr EDTA clearance (CL) from plasma is used to estimate glomerular filtration rate (GFR). We propose that current methods for analysing the raw 51Cr EDTA measurements over-simplifies the disposition of 51Cr EDTA and therefore could produce biased GFR estimates. The aim of this study was to develop a population pharmacokinetic model for 51Cr EDTA disposition and to compare model-predicted GFR to other methods of estimating renal function. PATIENTS AND METHODS: Data from 40 individuals who received ~7.4 MBq of 51Cr EDTA, as an intravenous bolus, were available for analysis. Plasma radioactivity (counts/min) was measured from timed collection points at 2, 4, 6 and 24 h after the dose. A population analysis was conducted using NONMEM® version 7.2. Model-predicted GFR was compared with other methods for estimating renal function using mean prediction error (MPE). RESULTS: A two-compartment pharmacokinetic model with first-order elimination best fit the data. Compared with the model predictions, creatinine CL from 24 h urine data was unbiased. The commonly used 'slope-intercept' method for estimating isotopic GFR was positively biased compared with the model (MPE 15.5 mL/min/1.73 m2 [95% confidence interval {CI} 8.9-22.2]. The Cockcroft Gault, Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equations led to negatively biased GFR estimates (MPE -19.0 [95% CI -25.4 to -12.7], -20.1 [95% CI -27.2 to -13.1] and -16.5 [95% CI -22.2 to -10.1] mL/min/1.73 m2, respectively). CONCLUSIONS: The biased GFR estimates were most obvious in patients with relatively normal renal function. This may lead to inaccurate dosing in patients who are receiving drugs with a narrow therapeutic range where dosing is adjusted according to GFR estimates (e.g. carboplatin). STUDY REGISTRATION: The study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR), number: ACTRN 12611000035921.
BACKGROUND AND OBJECTIVES:51Cr EDTA clearance (CL) from plasma is used to estimate glomerular filtration rate (GFR). We propose that current methods for analysing the raw 51Cr EDTA measurements over-simplifies the disposition of 51Cr EDTA and therefore could produce biased GFR estimates. The aim of this study was to develop a population pharmacokinetic model for 51Cr EDTA disposition and to compare model-predicted GFR to other methods of estimating renal function. PATIENTS AND METHODS: Data from 40 individuals who received ~7.4 MBq of 51Cr EDTA, as an intravenous bolus, were available for analysis. Plasma radioactivity (counts/min) was measured from timed collection points at 2, 4, 6 and 24 h after the dose. A population analysis was conducted using NONMEM® version 7.2. Model-predicted GFR was compared with other methods for estimating renal function using mean prediction error (MPE). RESULTS: A two-compartment pharmacokinetic model with first-order elimination best fit the data. Compared with the model predictions, creatinine CL from 24 h urine data was unbiased. The commonly used 'slope-intercept' method for estimating isotopic GFR was positively biased compared with the model (MPE 15.5 mL/min/1.73 m2 [95% confidence interval {CI} 8.9-22.2]. The Cockcroft Gault, Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equations led to negatively biased GFR estimates (MPE -19.0 [95% CI -25.4 to -12.7], -20.1 [95% CI -27.2 to -13.1] and -16.5 [95% CI -22.2 to -10.1] mL/min/1.73 m2, respectively). CONCLUSIONS: The biased GFR estimates were most obvious in patients with relatively normal renal function. This may lead to inaccurate dosing in patients who are receiving drugs with a narrow therapeutic range where dosing is adjusted according to GFR estimates (e.g. carboplatin). STUDY REGISTRATION: The study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR), number: ACTRN 12611000035921.
Authors: Sarayut Janmahasatian; Stephen B Duffull; Susan Ash; Leigh C Ward; Nuala M Byrne; Bruce Green Journal: Clin Pharmacokinet Date: 2005 Impact factor: 6.447
Authors: Andrew S Levey; Lesley A Stevens; Christopher H Schmid; Yaping Lucy Zhang; Alejandro F Castro; Harold I Feldman; John W Kusek; Paul Eggers; Frederick Van Lente; Tom Greene; Josef Coresh Journal: Ann Intern Med Date: 2009-05-05 Impact factor: 25.391