Bayesian approach to control of amikacin serum concentrations in critically ill patients with sepsis

To compare the predictive performance of a Bayesian program incorporating a population model with and without severity of illness covariates in intensive care unit (ICU) patients with sepsis.

The clinical, physiologic, and pharmacokinetic data of 62 patients with sepsis admitted to a tertiary-care center were analyzed retrospectively. The patients were randomly assigned to a active group and a validation group. The model was developed using a three-step approach involving Bayesian estimation of pharmacokinetic parameters, selection of covariates by principal component analysis, and final selection of covariates by stepwise multiple linear regression. The predictive performance of this model was tested in patients from the validation group and compared with that of a general population model without covariates.

Regression analysis revealed that the Acute Physiologic and Chronic Health Evaluation (APACHE II) score was the most important determinant for amikacin volume of distribution (1.5 L/kg, APACHE II; r2 = 0.77). For amikacin clearance (CIamik), creatinine clearance (CIcr), positive end-expiratory pressure (PEEP), and use of catecholamines (CAT) were the most important predictors (CIamik = 44.5 + 0.67 CIcr - 1.29 PEEP - 8.34 CAT; r2 = 0.72). The relative mean error (deltaME) and root mean-square error (deltaRMSE) (95% CI) were -0.62 (-1.2 to 0.01) and 3.78 (2.3 to 4.8) mg/L, respectively. Since the 95% CI for deltaRMSE did not include zero, it appears that the model with covariates is significantly improved in terms of precision.

Our results show that, in ICU patients treated with amikacin, it is relevant to consider covariates related to pathophysiologic status and therapeutic measures. Application of a Bayesian program allows improved control of the pharmacokinetic parameters in patients who exhibit rapidly changing physiologic conditions.