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Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants
UCL, Inst Child Hlth, Inflammat Infect & Rheumatol Sect, London, England..
St Georges Univ London, Inst Infect & Immun, Paediat Infect Dis Res Grp, London, England..
Univ Tartu, Dept Microbiol, Tartu, Estonia..
Univ Tartu, Dept Microbiol, Tartu, Estonia..
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2016 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 60, no 8, 4869-4877 p.Article in journal (Refereed) Published
Abstract [en]

Trough gentamicin therapeutic drug monitoring (TDM) is time-consuming, disruptive to neonatal clinical care, and a patient safety issue. Bayesian models could allow TDM to be performed opportunistically at the time of routine blood tests. This study aimed to develop and prospectively evaluate a new gentamicin model and a novel Bayesian computer tool (neoGent) for TDM use in neonatal intensive care. We also evaluated model performance for predicting peak concentrations and the area under the concentration-time curve from time 0 h to time t h (AUC(0-t)). A pharmacokinetic meta-analysis was performed on pooled data from three studies (1,325 concentrations from 205 patients). A 3-compartment model was used with the following covariates: allometric weight scaling, postmenstrual and postnatal age, and serum creatinine concentration. Final parameter estimates (standard errors) were as follows: clearance, 6.2 (0.3) liters/h/70 kg of body weight; central volume (V), 26.5 (0.6) liters/70 kg; intercompartmental disposition (Q), 2.2 (0.3) liters/h/70 kg; peripheral volume V2, 21.2 (1.5) liters/70 kg; intercompartmental disposition (Q2), 0.3 (0.05) liters/h/70 kg; peripheral volume V3, 148 (52.0) liters/70 kg. The model's ability to predict trough concentrations from an opportunistic sample was evaluated in a prospective observational cohort study that included data from 163 patients and 483 concentrations collected in five hospitals. Unbiased trough predictions were obtained; the median (95% confidence interval [CI]) prediction error was 0.0004 (-1.07, 0.84) mg/liter. Results also showed that peaks and AUC(0-t) values could be predicted (from one randomly selected sample) with little bias but relative imprecision, with median (95% CI) prediction errors being 0.16 (-4.76, 5.01) mg/liter and 10.8 (-24.9, 62.2) mg center dot h/liter, respectively. neoGent was implemented in R/NONMEM and in the freely available TDMx software.

Place, publisher, year, edition, pages
2016. Vol. 60, no 8, 4869-4877 p.
National Category
Pharmacology and Toxicology
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URN: urn:nbn:se:uu:diva-303286DOI: 10.1128/AAC.00577-16ISI: 000380792600051PubMedID: 27270281OAI: oai:DiVA.org:uu-303286DiVA: diva2:971333
Available from: 2016-09-16 Created: 2016-09-15 Last updated: 2016-09-16Bibliographically approved

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