Population pharmacokinetics and evaluation of the predictive performance of pharmacokinetic models in critically ill patients receiving continuous infusion meropenem: a comparison of eight pharmacokinetic models.


Journal

The Journal of antimicrobial chemotherapy
ISSN: 1460-2091
Titre abrégé: J Antimicrob Chemother
Pays: England
ID NLM: 7513617

Informations de publication

Date de publication:
01 02 2019
Historique:
received: 07 06 2018
accepted: 26 09 2018
pubmed: 31 10 2018
medline: 8 5 2020
entrez: 31 10 2018
Statut: ppublish

Résumé

Several population pharmacokinetic (PopPK) models for meropenem dosing in ICU patients are available. It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously. A PopPK model was developed with concentration-time data collected from routine care of 21 ICU patients (38 samples) receiving continuous infusion meropenem. The predictability of this model and seven other published PopPK models was studied using an independent dataset that consisted of 47 ICU patients (161 samples) receiving continuous infusion meropenem. A statistical comparison of imprecision (mean square prediction error) and bias (mean prediction error) was conducted. A one-compartment model with linear elimination and creatinine clearance as a covariate of clearance best described our data. The mean ± SD parameter estimate for CL was 9.89 ± 3.71 L/h. The estimated volume of distribution was 48.1 L. The different PopPK models showed a bias in predicting serum concentrations from the validation dataset that ranged from -8.76 to 7.06 mg/L. Imprecision ranged from 9.90 to 42.1 mg/L. Published PopPK models for meropenem vary considerably in their predictive performance when validated in an external dataset of ICU patients receiving continuous infusion meropenem. It is necessary to validate PopPK models in a target population before implementing them in a therapeutic drug monitoring program aimed at optimizing meropenem dosing.

Sections du résumé

Background
Several population pharmacokinetic (PopPK) models for meropenem dosing in ICU patients are available. It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously.
Patients and methods
A PopPK model was developed with concentration-time data collected from routine care of 21 ICU patients (38 samples) receiving continuous infusion meropenem. The predictability of this model and seven other published PopPK models was studied using an independent dataset that consisted of 47 ICU patients (161 samples) receiving continuous infusion meropenem. A statistical comparison of imprecision (mean square prediction error) and bias (mean prediction error) was conducted.
Results
A one-compartment model with linear elimination and creatinine clearance as a covariate of clearance best described our data. The mean ± SD parameter estimate for CL was 9.89 ± 3.71 L/h. The estimated volume of distribution was 48.1 L. The different PopPK models showed a bias in predicting serum concentrations from the validation dataset that ranged from -8.76 to 7.06 mg/L. Imprecision ranged from 9.90 to 42.1 mg/L.
Conclusions
Published PopPK models for meropenem vary considerably in their predictive performance when validated in an external dataset of ICU patients receiving continuous infusion meropenem. It is necessary to validate PopPK models in a target population before implementing them in a therapeutic drug monitoring program aimed at optimizing meropenem dosing.

Identifiants

pubmed: 30376103
pii: 5148147
doi: 10.1093/jac/dky434
doi:

Substances chimiques

Anti-Bacterial Agents 0
Meropenem FV9J3JU8B1

Types de publication

Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

432-441

Auteurs

Sofie A M Dhaese (SAM)

Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium.

Andras Farkas (A)

Department of Pharmacy, Mount Sinai West Hospital, New York, NY, USA.

Pieter Colin (P)

Department of Anesthesiology, University Medical Center Groningen, Groningen, The Netherlands.
Laboratory of Medical Biochemistry and Clinical Analysis, Ghent University, Ghent, Belgium.

Jeffrey Lipman (J)

Centre for Clinical Research, University of Queensland, Brisbane, Australia.
Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Australia.

Veronique Stove (V)

Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium.
Department of Clinical Chemistry, Microbiology and Immunology, Ghent University, Ghent, Belgium.

Alain G Verstraete (AG)

Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium.
Department of Clinical Chemistry, Microbiology and Immunology, Ghent University, Ghent, Belgium.

Jason A Roberts (JA)

Centre for Clinical Research, University of Queensland, Brisbane, Australia.
Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Australia.
Department of Pharmacy, Royal Brisbane and Women's Hospital, Brisbane, Australia.

Jan J De Waele (JJ)

Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium.

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Classifications MeSH