The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature-retrieved amikacin population pharmacokinetic models in patients who were critically ill.Methods
Four population pharmacokinetic models, three of them customized for critically-ill patients, were applied using pharmacokinetic software to fifty-one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient-specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration-time curve (AUC) and maximum serum drug concentration (Cmax) targets was obtained.Key findings
Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and Cmax values.Conclusions
Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.