Literature DB >> 30833424

External Evaluation of Population Pharmacokinetic Models of Vancomycin in Large Cohorts of Intensive Care Unit Patients.

Tingjie Guo1,2, Reinier M van Hest2, Luca F Roggeveen3, Lucas M Fleuren3, Patrick J Thoral3, Rob J Bosman4, Peter H J van der Voort4, Armand R J Girbes3, Ron A A Mathot2, Paul W G Elbers3.   

Abstract

Dosing of vancomycin is often guided by therapeutic drug monitoring and population pharmacokinetic models in the intensive care unit (ICU). The validity of these models is crucial, as ICU patients have marked pharmacokinetic variability. Therefore, we set out to evaluate the predictive performance of published population pharmacokinetic models of vancomycin in ICU patients. The PubMed database was used to search for population pharmacokinetic models of vancomycin in adult ICU patients. The identified models were evaluated in two independent data sets which were collected from two large hospitals in the Netherlands (Amsterdam UMC, Location VUmc, and OLVG Oost). We also tested a one-compartment model with fixed values for clearance and volume of distribution, in which a clinical standard dosage regimen (SDR) was mimicked to assess its predictive performance. Prediction error was calculated to assess the predictive performance of the models. Six models plus the SDR model were evaluated. The model of Roberts et al. (J. A. Roberts, F. S. Taccone, A. A. Udy, J.-L. Vincent, F. Jacobs, and J. Lipman, Antimicrob Agents Chemother 55:2704-2709, 2011, https://doi.org/10.1128/AAC.01708-10) performed satisfactorily, with mean and median values of prediction error of 5.1% and -7.5%, respectively, for Amsterdam UMC, Location VUmc, patients, and -12.6% and -17.2% respectively, for OLVG Oost patients. The other models, including the SDR model, yielded high mean values (-49.7% to 87.7%) and median values (-56.1% to 66.1%) for both populations. In conclusion, only the model of Roberts et al. was able to validly predict the concentrations of vancomycin for our data, whereas other models and standard dosing were largely inadequate. Extensive evaluation should precede the adoption of any model in clinical practice for ICU patients.
Copyright © 2019 American Society for Microbiology.

Entities:  

Keywords:  ICU patients; NONMEM; antibiotics; model validation; population pharmacokinetics; vancomycin

Year:  2019        PMID: 30833424      PMCID: PMC6496102          DOI: 10.1128/AAC.02543-18

Source DB:  PubMed          Journal:  Antimicrob Agents Chemother        ISSN: 0066-4804            Impact factor:   5.191


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6.  Pharmacokinetic/pharmacodynamic analysis of vancomycin in ICU patients.

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7.  Population pharmacokinetic analysis of vancomycin using serum cystatin C as a marker of renal function.

Authors:  Akihiro Tanaka; Tetsuya Aiba; Takashi Otsuka; Katsuya Suemaru; Tatsuya Nishimiya; Tomoyoshi Inoue; Mitsuharu Murase; Yuji Kurosaki; Hiroaki Araki
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8.  Can population pharmacokinetic modelling guide vancomycin dosing during continuous renal replacement therapy in critically ill patients?

Authors:  Andrew A Udy; Cecilia Covajes; Fabio Silvio Taccone; Frédérique Jacobs; Jean-Louis Vincent; Jeffrey Lipman; Jason A Roberts
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Review 10.  Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions.

Authors:  Jason A Roberts; Mohd H Abdul-Aziz; Jeffrey Lipman; Johan W Mouton; Alexander A Vinks; Timothy W Felton; William W Hope; Andras Farkas; Michael N Neely; Jerome J Schentag; George Drusano; Otto R Frey; Ursula Theuretzbacher; Joseph L Kuti
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2.  Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling.

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Review 5.  Aminoglycosides in the Intensive Care Unit: What Is New in Population PK Modeling?

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6.  Population Pharmacokinetic Modeling of Vancomycin in Thai Patients With Heterogeneous and Unstable Renal Function.

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7.  Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients.

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8.  Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs?

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9.  Prospective validation of a model-informed precision dosing tool for vancomycin in intensive care patients.

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10.  Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.

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