Population pharmacokinetic modeling and optimal sampling strategy for Bayesian estimation of amikacin exposure in critically ill septic patients.

摘要: Because the sepsis-induced pharmacokinetic (PK) modifications need to be considered in aminoglycoside dosing, present study aimed develop a population PK model for amikacin (AMK) severe sepsis and subsequently propose an optimal sampling strategy suitable Bayesian estimation of drug parameters. Concentration-time profiles AMK were obtained from 88 critically ill septic patients during first 24 hours antibiotic treatment. The was developed using nonlinear mixed effects modeling approach. Covariate analysis included demographic data, pathophysiological characteristics, comedication. Optimal times selected based on robust design criterion. Taking into account clinical constraints, two-point approach investigated. A two-compartment with first-order elimination best fitted concentrations. Population estimates 19.2 9.34 L central peripheral volume distribution 4.31 2.21 L/h intercompartmental total body clearance. Creatinine clearance estimated Cockcroft-Gault equation retained final model. two 1 hour 6 after onset infusion. Predictive performance individual Bayes computed proposed reported: mean prediction errors less than 5% root square 30%. confirmed significant influence creatinine disposition treatment patients. Based estimates, parameters developed, meeting practice.