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.
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.