2002
   Paris, France

Cefepime Monitoring in ICU Patients Using a Population Pharmacokinetic Approach

Georges B., Saivin S., Archambaud M., Conil JM., Decun JF., Cougot P., Virenque C., Houin G.

Introduction: Cefepime is used in the treatment of severe infections caused by Gram-negative bacilli. Pharmacokinetic modifications observed in ICU patients may lead to inadequate serum drug concentrations of the drug. The purpose of this study was to determine the influence of some patients' characteristics on the pharmacokinetics of cefepime, using a population-pharmacokinetic approach and leading to an adequate individual dosing strategy.

Patients and Methods: After ethical committee approval and informed consent, 34 patients were included. They received cefepime as 2g x 2 or 4 g continuously and 8 to 11 blood samples were drawn. Drug concentrations were measured by HPLC with UV detection. Individual clearances were calculated using Kinetica software. The population pharmacokinetic analysis was carried out using NONMEM. A base-line model was constructed, then the influence of demographic and biological variables was studied.

Results: A two-compartment model associated to a proportional error model was the most suitable. The index of gravity score (IGS) and weight (WT kg) were significantly correlated with total plasma clearance (CL l/h). Hemoglobin levels (HB g/dl) and WT were significantly correlated with the central volume of distribution (V1 l). The final model was: CL = 16.6 - (0.762 IGS) - 0.239 (70 - WT). V1 = 0.173 - 2.73 HB + 0.574 WT. Q (inter-compartmental clearance) = 12.8. V2 (peripheral volume of distribution) = 22.3 l. We validated our model by comparing the observed individual clearances and the NONMEM predicted clearances (R2 = 0.8082).

Conclusion: Our data were consistent with those previously reported, concerning the significant variability in pharmacokinetics between ICU patients. We showed that IGS score, weight and hemoglobin levels are factors that may influence the standard dosing of cefepime. Our model enabled us to predict cefepime concentrations in new patients.