Biostatistics Research Today is a free monthly online journal that collates and summarizes the latest research about Biostatistics, including details on statistics, uncertainty, probability, modeling.

Lean body mass-adjusted Cockcroft and Gault formula improves the estimation of glomerular filtration rate in subjects with normal-range serum creatinine.

Lim WH, Lim EM, McDonald S

Renal Unit, The Queen Elizabeth Hospital, Woodville, South Australia, Australia. wai.lim@nwahs.sa.gov.au

BACKGROUND: Assessment of glomerular filtration rate (GFR) in individuals with normal-range serum creatinine is important in certain clinical situations, such as in potential living kidney donors. Accurate measurements of GFR invariably involve using an invasive method (e.g. inulin clearances), but is inconvenient. The aim of the present study was to determine whether serum creatinine-based prediction formulae adjusted for lean body mass (LBM) could improve the accuracy of GFR estimation in these subjects. METHODS: Glomerular filtration rate was determined by the clearance of technetium-99m-labelled diethylenetriamine penta-acetic acid ((99m)Tc DTPA) from plasma in 56 subjects with normal serum creatinine. For each subject, GFR was estimated using prediction formulae +/- LBM adjustment and compared with measured GFR. Formulae analysed include Cockcroft-Gault, Levey, Gates, Mawer, Hull, Toto, Jellife and Bjornsson. RESULTS: All formulae +/- LBM adjustment underestimated measured GFR, with poor precision, poor agreement and correlation (r (2) <or= 0.25). Between 69% and 95% of the estimated GFR determined by the formulae correctly classified those with a normal measured GFR. LBM-adjusted formulae significantly improved the accuracy of GFR estimation compared with unadjusted formulae. CONCLUSION: The lean body mass-adjusted Cockcroft-Gault formula was the closest to measured GFR but is not accurate enough to replace radionuclide GFR measurement. Prediction formulae should be adjusted for LBM to improve GFR estimation.

Published 7 June 2006 in Nephrology (Carlton), 11(3): 250-6.
Full-text of this article is available online (may require subscription).

© 2005-2008 Biostatistics Research Today. All Rights Reserved.