Sofie A M Dhaese1, Andras Farkas2, Pieter Colin3,4, Jeffrey Lipman5,6, Veronique Stove7,8, Alain G Verstraete7,8, Jason A Roberts5,6,9, Jan J De Waele1. 1. Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium. 2. Department of Pharmacy, Mount Sinai West Hospital, New York, NY, USA. 3. Department of Anesthesiology, University Medical Center Groningen, Groningen, The Netherlands. 4. Laboratory of Medical Biochemistry and Clinical Analysis, Ghent University, Ghent, Belgium. 5. Centre for Clinical Research, University of Queensland, Brisbane, Australia. 6. Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Australia. 7. Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium. 8. Department of Clinical Chemistry, Microbiology and Immunology, Ghent University, Ghent, Belgium. 9. Department of Pharmacy, Royal Brisbane and Women's Hospital, Brisbane, Australia.
Abstract
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.
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.
Authors: Uwe Liebchen; Marian Klose; Michael Paal; Michael Vogeser; Michael Zoller; Ines Schroeder; Lisa Schmitt; Wilhelm Huisinga; Robin Michelet; Johannes Zander; Christina Scharf; Ferdinand A Weinelt; Charlotte Kloft Journal: Antibiotics (Basel) Date: 2021-04-20
Authors: Abdullah Alsultan; Shereen A Dasuqi; Fadi Aljamaan; Rasha A Omran; Saeed Ali Syed; Turki AlJaloud; Abdullah AlAhmadi; Saeed Alqahtani; Mohammed A Hamad Journal: Saudi Pharm J Date: 2021-10-08 Impact factor: 4.330
Authors: Renata Černá Pařízková; Jiřina Martínková; Eduard Havel; Petr Šafránek; Milan Kaška; David Astapenko; Jan Bezouška; Jaroslav Chládek; Vladimír Černý Journal: Crit Care Date: 2021-07-17 Impact factor: 9.097