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Erschienen in: Clinical Pharmacokinetics 8/2020

05.03.2020 | Original Research Article

Development of Population and Bayesian Models for Applied Use in Patients Receiving Cefepime

verfasst von: Jiajun Liu, Michael Neely, Jeffrey Lipman, Fekade Sime, Jason A. Roberts, Patrick J. Kiel, Sean N. Avedissian, Nathaniel J. Rhodes, Marc H. Scheetz

Erschienen in: Clinical Pharmacokinetics | Ausgabe 8/2020

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Abstract

Background and Objective

Understanding pharmacokinetic disposition of cefepime, a β-lactam antibiotic, is crucial for developing regimens to achieve optimal exposure and improved clinical outcomes. This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric and adult patients receiving cefepime treatment.

Methods

Multiple physiologically relevant models were fit to pediatric and adult subject data. To evaluate the final model performance, a withheld group of 12 pediatric patients and two separate adult populations were assessed.

Results

Seventy subjects with a total of 604 cefepime concentrations were included in this study. All adults (n = 34) on average weighed 82.7 kg and displayed a mean creatinine clearance of 106.7 mL/min. All pediatric subjects (n = 36) had mean weight and creatinine clearance of 16.0 kg and 195.6 mL/min, respectively. A covariate-adjusted two-compartment model described the observed concentrations well (population model R2, 87.0%; Bayesian model R2, 96.5%). In the evaluation subsets, the model performed similarly well (population R2, 84.0%; Bayesian R2, 90.2%).

Conclusion

The identified model serves well for population dosing and as a Bayesian prior for precision dosing.
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Metadaten
Titel
Development of Population and Bayesian Models for Applied Use in Patients Receiving Cefepime
verfasst von
Jiajun Liu
Michael Neely
Jeffrey Lipman
Fekade Sime
Jason A. Roberts
Patrick J. Kiel
Sean N. Avedissian
Nathaniel J. Rhodes
Marc H. Scheetz
Publikationsdatum
05.03.2020
Verlag
Springer International Publishing
Erschienen in
Clinical Pharmacokinetics / Ausgabe 8/2020
Print ISSN: 0312-5963
Elektronische ISSN: 1179-1926
DOI
https://doi.org/10.1007/s40262-020-00873-3

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