Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients
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RESEARCH PAPER
Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients Tingjie Guo 1,2,3 & Reinier M. van Hest 2 & Laura B. Zwep 3,4 & Luca F. Roggeveen 1 & Lucas M. Fleuren 1 & Rob J. Bosman 5 & Peter H. J. van der Voort 5 & Armand R. J. Girbes 1 & Ron A. A. Mathot 2 & Paul W. G. Elbers 1 & Johan G. C. van Hasselt 3
Received: 15 June 2020 / Accepted: 11 August 2020 # The Author(s) 2020
ABSTRACT Purpose Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for modelbased TDM. Methods We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days. Results The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were − 7.7%, −4.5% and − 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition,
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11095-020-02908-7) contains supplementary material, which is available to authorized users.
regardless of MAP method, including historical TDM data further in the past will increase prediction errors. Conclusions The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance.
KEY WORDS bayesian forecasting . ICU . MAP . NONMEM . TDM . vancomycin
ABBREVIATIONS CL ICU MAP NONMEM PE PK SD TDM V
Clearance Intensive care unit Maximum a posteriori Nonlinear mixed-effects modeling Percentage error Pharmacokinetic Standard deviation Therapeutic drug monitoring Volume of distribution
* Tingjie Guo [email protected]
INTRODUCTION 1
Department of Intensive Care Medicine | Research VUmc Intensive Care (REVIVE) | Amsterdam Cardiovascular Sciences (ACS) | Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
2
Department of Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
3
Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
4
Mathematical Institute, Leiden University, Leiden, The Netherlands
5
Intensive Care Unit, OLVG Oost, Amsterdam, The Netherlands
Therapeutic drug monitoring (TDM) concerns the measurement of drug concentrations in patients to
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