title: Comparison of the accuracy and precision of pharmacokinetic equations to predict free meropenem concentrations in critically ill patients.
abstract: Population pharmacokinetic analyses can be applied to predict optimized dosages for individual patients. The aim of this study was to compare the prediction performance of the published population pharmacokinetic models for meropenem in critically ill patients. We coded the published population pharmacokinetic models with covariate relationships into dosing software to predict unbound meropenem concentrations measured in a separate cohort of critically ill patients. The agreements between the observed and predicted concentrations were evaluated with Bland-Altman plots. The absolute and relative bias and precision of the models were determined. The clinical implications of the results were evaluated according to whether dose adjustments were required from the predictions to achieve a meropenem concentration of >2 mg/liter throughout the dosing interval. A total of 157 free meropenem concentrations from 56 patients were analyzed. Eight published population pharmacokinetic models were compared. The models showed an absolute bias in predicting the unbound meropenem concentrations from a mean percent difference (95% confidence interval [CI]) of -108.5% (-119.9% to -97.3%) to 19.9% (7.3% to 32.7%), while absolute precision ranged from -249.1% (-263.4% to -234.8%) to 31.9% (17.6% to 46.2%) and -178.9% (-196.9% to -160.9%) to 175.0% (157.0% to 193.0%). A dose change was required in 44% to 64% of the concentration results. Seven of the eight equations evaluated underpredicted free meropenem concentrations. In conclusion, the overall accuracy of these models supports their inclusion in dosing software and application for individualizing meropenem doses in critically ill patients to increase the likelihood of achievement of optimal antibiotic exposures.
authors.affiliation: Burns, Trauma and Critical Care Research Centre, The University of Queensland, Brisbane, Queensland, Australia.
authors.forename: Gloria
authors.initials: G
authors.lastname: Wong
authors.affiliation: Department of Pharmacy, Vassar Brothers Medical Center, Poughkeepsie, New York, USA Optimum Dosing Strategies, Bloomingdale, New Jersey, USA.
authors.forename: Andras
authors.initials: A
authors.lastname: Farkas
authors.affiliation: Department of Pharmacy, St. Barnabas Hospital, Bronx, New York, USA.
authors.forename: Rachel
authors.initials: R
authors.lastname: Sussman
authors.affiliation: EasyStats LTD, London, United Kingdom.
authors.forename: Gergely
authors.initials: G
authors.lastname: Daroczi
authors.affiliation: Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom.
authors.forename: William W
authors.initials: WW
authors.lastname: Hope
authors.affiliation: Burns, Trauma and Critical Care Research Centre, The University of Queensland, Brisbane, Queensland, Australia Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Lipman
authors.affiliation: Burns, Trauma and Critical Care Research Centre, The University of Queensland, Brisbane, Queensland, Australia Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia Pharmacy Department, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia j.roberts2@uq.edu.au.
authors.forename: Jason A
authors.initials: JA
authors.lastname: Roberts
mesh_terms: D000328:Adult; D000900:Anti-Bacterial Agents / Q000493:pharmacokinetics*; D016638:Critical Illness; D016903:Drug Monitoring; D006801:Humans; D000077731:Meropenem; D008875:Middle Aged; D008954:Models, Biological; D013845:Thienamycins / Q000493:pharmacokinetics*
chemical_list: D000900:Anti-Bacterial Agents; D013845:Thienamycins; D000077731:Meropenem