Evaluation of renal function equations to predict amikacin clearance.

Objective: To evaluate the predictive performance of eight renal function equations to describe amikacin elimination in a large standard population with a wide range of age. Methods: Retrospective study of adult hospitalized patients treated with amikacin and monitored in the clinical pharmacokinetics laboratory of a pharmacy service. Renal function was calculated as Cockcroft-Gault with total, adjusted and ideal body weight, MDRD-4, CKD-EPI, rLM, BIS1, and FAS. One compartment model with first-order elimination, including interindividual variability on clearance and volume of distribution and combined residual error model was selected as a base structural model. A pharmaco-statistical analysis was performed following a non-linear mixed effects modeling approach (NONMEM 7.3 software). Results: 198 patients (61 years [18-93]) and 566 measured amikacin plasma concentrations were included. All the estimated glomerular filtration rate and creatinine clearance equations evaluated described properly the data. The linear relationship between clearance and glomerular filtration rate based on rLM showed a statistically significant improvement in the fit of the data. rLM must be evaluated carefully in renal failure for amikacin dose adjustment. Conclusions: Revised Lund-Malmö (rLM) and CKD-EPI showed the superior predictive performance of amikacin drug elimination comparing to all the alternative metrics evaluated.