Relationship between clinical and biologic variables and chloramphenicol pharmacokinetic parameters in pediatric patients with sepsis.

OBJECTIVE: To evaluate the influence of several clinical and biologic factors on the disposition kinetics of oral chloramphenicol in pediatric patients and to determine the usefulness of this information to predict chloramphenicol serum concentrations. STUDY DESIGN: The clinical, biologic, and pharmacokinetic data of 30 consecutive pediatric patients diagnosed with sepsis and admitted to a tertiary care center were analyzed retrospectively. The patients were randomly assigned to a study group and a validation group. The model was developed by a three-step approach involving Bayesian estimation of pharmacokinetic parameters, selection of covariates by principal component analysis, and final selection by stepwise multiple linear regression. The model was tested in the study group and compared with a general population model using a prediction error analysis. RESULTS: Regression analysis revealed that weight, albumin, and white blood cell (WBC) count were the most important determinants for chloramphenicol distribution volume, whereas age, WBC count, and serum creatinine were the most important determinants for chloramphenicol clearance. The performance of the constructed population model improved significantly in terms of both bias and precision compared with the general model when tested in the validation group. CONCLUSIONS: Clinical and biologic factors may significantly influence chloramphenicol's disposition in pediatric patients with sepsis and therefore should be considered in programming dosage regimens.

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

OBJECTIVE: 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. DESIGN: 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. RESULTS: 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. CONCLUSIONS: 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.

[Bayesian prediction of chloramphenicol blood levels in children with sepsis and malnutrition]. / Predicción bayesiana de las concentraciones séricas de cloranfenicol en niños con sepsis y desnutrición.

OBJECTIVE: To validate the population pharmacokinetic parameters of chloramphenicol in pediatric patients with sepsis and malnutrition (PPSM) using a bayesian forecasting program. DESIGN: Retrospective evaluation of predictive performance of a bayesian program in PPSM. SETTING: Tertiary care center. PATIENTS: Fifteen MPSP and ten NMPSP that receiving treatment with chloramphenicol. METHODS AND MAIN RESULTS: In the first part of the study, the medical records of 10 MPSP and 10 NMPSP who had received treatment with chloramphenicol were reviewed. The population pharmacokinetic parameter values for each group were estimated using a nonparametric expectation maximization algorithm (NPEM). In the second part, data gathered from five other MPSP receiving chloramphenicol were entered into a bayesian program. Chloramphenicol pharmacokinetic values for each of these five patients were estimated, first using the values of NMPSP as a priori distribution and then repeating the analysis using the MPSP values. The bayesian serum chloramphenicol concentrations predicted for each population model were compared with the actual peaks and troughs. The specific model for MPSP permitted forecasting the peak and trough serum chloramphenicol concentrations with less bias and a better precision compared with the NMPSP population model. CONCLUSIONS: These data indicate that chloramphenicol pharmacokinetics in PPSM can be predicted with minimal bias and good precision using a bayesian forecasting program, allowing a better control of the chloramphenicol serum concentrations. In addition, the limited number of samples required by the bayesian method may represent an important economical benefit for the patient.

[Cefuroxime Bayesian pharmacokinetics in severely ill septic children]. / Farmacocinética bayesiana de cefuroxima en niños infectados gravemente enfermos.

OBJECTIVE: To estimate the cefuroxime pharmacokinetic parameters in critically ill pediatric septic patients using a Bayesian pharmacokinetic method and three serum drug assays per patient. DESIGN: Cross-sectional study of critically ill pediatric patients undergoing therapeutic monitoring of cefuroxime serum concentrations. SETTING: Tertiary care center. PATIENTS: Nine critically ill pediatric patients with sepsis and septic shock treated with cefuroxime. METHODS: Timed serum concentrations of cefuroxime were obtained in each patient. The Vd (volume of distribution) and the Kel (constant of elimination) were estimated by means of a Bayesian iterative two-stage algorithm, using the information of three serum drug concentrations per patient at optimal times. The parameters were also estimated by the traditional method of non linear least square regression in eight samples. RESULTS: The Bayesian Vd was very similar to the traditional Vd with a correlation coefficient of 0.99 and a small bias of -0.04 L/kg whereas the Kel had a correlation of 0.90 and bias of -0.29 h-1. The mean Bayesian Vd was 0.68 L/kg, a larger value than that reported in non critically ill patients. CONCLUSIONS: We offer a tentative cefuroxime pharmacokinetic model for critically ill pediatric septic patients which may be useful for the control of cefuroxime serum concentrations. Also, our study underscored the potential usefulness of a Bayesian pharmacokinetic approach as a tool for therapeutic drug monitoring in critically ill patients.