BACKGROUND: The Modification of Diet in Renal Disease (MDRD) formula and a modified version of the Cockcroft-Gault (CG(m)) formula adjusting for body surface area have been found to more accurately estimate glomerular filtration rate (GFR) compared with the original CG equation in specific patient populations. To date, the use of these formulas in determining drug dosage and estimating drug elimination has not been thoroughly investigated. OBJECTIVE: To evaluate the ability of the MDRD and CG(m) formulas to predict aminoglycoside elimination rate and clearance. METHODS: A 6-month prospective, noninterventional, pharmacokinetic study was conducted at a university teaching hospital. Patients receiving aminoglycoside antibiotics (amikacin, gentamicin, or tobramycin) were eligible for study inclusion. Predicted elimination rate and aminoglycoside clearance were calculated for each patient using the MDRD and CG(m) formulas. Actual (patient-specific) elimination rate and aminoglycoside clearance were calculated for each patient using measured aminoglycoside serum concentrations. Predictive ability of the formulas was compared through Spearman correlations and Student's t-tests. Accuracy of formula estimates was also evaluated. RESULTS: Seventy-one patients met study inclusion criteria; the majority (82%) were in an intensive care unit. The 6-variable MDRD formula was found to be a significantly better predictor of aminoglycoside clearance (p = 0.035) compared with CG(m). There was no statistically significant difference between the 2 methods in predicting patient-specific elimination rates (p = 0.167). Among subgroups, the MDRD formula was a significantly better predictor of aminoglycoside clearance for patients with an estimated GFR less than 60 mL/min (p = 0.027). CONCLUSIONS: The 6-variable MDRD performs better than the CG(m) formula in predicting aminoglycoside clearance and may be considered as a tool in aminoglycoside dosing recommendations.
BACKGROUND: The Modification of Diet in Renal Disease (MDRD) formula and a modified version of the Cockcroft-Gault (CG(m)) formula adjusting for body surface area have been found to more accurately estimate glomerular filtration rate (GFR) compared with the original CG equation in specific patient populations. To date, the use of these formulas in determining drug dosage and estimating drug elimination has not been thoroughly investigated. OBJECTIVE: To evaluate the ability of the MDRD and CG(m) formulas to predict aminoglycoside elimination rate and clearance. METHODS: A 6-month prospective, noninterventional, pharmacokinetic study was conducted at a university teaching hospital. Patients receiving aminoglycoside antibiotics (amikacin, gentamicin, or tobramycin) were eligible for study inclusion. Predicted elimination rate and aminoglycoside clearance were calculated for each patient using the MDRD and CG(m) formulas. Actual (patient-specific) elimination rate and aminoglycoside clearance were calculated for each patient using measured aminoglycoside serum concentrations. Predictive ability of the formulas was compared through Spearman correlations and Student's t-tests. Accuracy of formula estimates was also evaluated. RESULTS: Seventy-one patients met study inclusion criteria; the majority (82%) were in an intensive care unit. The 6-variable MDRD formula was found to be a significantly better predictor of aminoglycoside clearance (p = 0.035) compared with CG(m). There was no statistically significant difference between the 2 methods in predicting patient-specific elimination rates (p = 0.167). Among subgroups, the MDRD formula was a significantly better predictor of aminoglycoside clearance for patients with an estimated GFR less than 60 mL/min (p = 0.027). CONCLUSIONS: The 6-variable MDRD performs better than the CG(m) formula in predicting aminoglycoside clearance and may be considered as a tool in aminoglycoside dosing recommendations.
Authors: Nicolas Charhon; Michael N Neely; Laurent Bourguignon; Pascal Maire; Roger W Jelliffe; Sylvain Goutelle Journal: Antimicrob Agents Chemother Date: 2012-01-30 Impact factor: 5.191
Authors: Aghogho A Okparavero; Hocine Tighiouart; Zipporah Krishnasami; Christina M Wyatt; Hiba Graham; James Hellinger; Lesley A Inker Journal: Antivir Ther Date: 2013-08-20