Abstract
Objective: The objectives of this study are, first, to measure concordance between 5 different renal function estimates (methods) in terms of recommended drug doses, and, subsequently, to establish the potential for significant clinical differences between Cockroft–Gault (CG) and Modification of Diet in Renal Disease (MDRD) equations in dosing a specific medication, namely, meropenem. Design and setting: This study used a Monte Carlo simulation, and this is a computer–based study with no actual patient data. Patients: A total of 1200 and 8701 simulated cases to study the concordance for the 5 methods and the potential clinical significance of discordance between CG and MDRD, respectively, were chosen for the study. Methods: Simulated factors were age, sex, height, weight, serum creatinine, ethnicity, and albumin. We estimated the renal function using 5 formulas (ie, 10 combinations) including CG, MDRD, and Chronic Kidney Disease Epidemiology Collaboration (CKD–EPI). Next, the team evaluated concordance for each combination in dosing 22 drugs. Finally, our researchers reviewed and simulated data from the literature to show how CG versus MDRD use can result in clinically significant differences for meropenem. Results: Pairwise combinations yielded statistically significant differences ( P < .0001) except for CG and MDRD ( P < .5147). In addition, the highest concordance was for MDRD and CKD–EPI. Average discordance is in the range of 25% to 30% with the lowest being between CG and albumin–based estimates. Both CG and MDRD were largely discordant which can reach up to 40% with a drug like meropenem and may be associated with significant adverse outcomes. Conclusions: Both CG and MDRD in our simulation are statistically comparable. Clinically, nonetheless, they are significantly inconsistent in terms of recommended drug dosing. We encourage practical comparisons of outcomes for individual or groups of medications (eg, meropenem and antibiotics) empirically dosed in renal patients on the basis of equations used in distinct populations.
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