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A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children

BACKGROUND: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic events. Although highly effective, the use of warfarin is limited by a narrow therapeutic range combined with a more than ten-fold difference in the dose required for adequate anticoag...

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Autores principales: Hamberg, Anna-Karin, Hellman, Jacob, Dahlberg, Jonny, Jonsson, E Niclas, Wadelius, Mia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4324411/
https://www.ncbi.nlm.nih.gov/pubmed/25889768
http://dx.doi.org/10.1186/s12911-014-0128-0
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author Hamberg, Anna-Karin
Hellman, Jacob
Dahlberg, Jonny
Jonsson, E Niclas
Wadelius, Mia
author_facet Hamberg, Anna-Karin
Hellman, Jacob
Dahlberg, Jonny
Jonsson, E Niclas
Wadelius, Mia
author_sort Hamberg, Anna-Karin
collection PubMed
description BACKGROUND: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic events. Although highly effective, the use of warfarin is limited by a narrow therapeutic range combined with a more than ten-fold difference in the dose required for adequate anticoagulation in adults. An optimal dose that leads to a favourable balance between the wanted antithrombotic effect and the risk of bleeding as measured by the prothrombin time International Normalised Ratio (INR) must be found for each patient. A model describing the time-course of the INR response can be used to aid dose selection before starting therapy (a priori dose prediction) and after therapy has been initiated (a posteriori dose revision). RESULTS: In this paper we describe a warfarin decision support tool. It was transferred from a population PKPD-model for warfarin developed in NONMEM to a platform independent tool written in Java. The tool proved capable of solving a system of differential equations that represent the pharmacokinetics and pharmacodynamics of warfarin with a performance comparable to NONMEM. To estimate an a priori dose the user enters information on body weight, age, baseline and target INR, and optionally CYP2C9 and VKORC1 genotype. By adding information about previous doses and INR observations, the tool will suggest a new dose a posteriori through Bayesian forecasting. Results are displayed as the predicted dose per day and per week, and graphically as the predicted INR curve. The tool can also be used to predict INR following any given dose regimen, e.g. a fixed or an individualized loading-dose regimen. CONCLUSIONS: We believe that this type of mechanism-based decision support tool could be useful for initiating and maintaining warfarin therapy in the clinic. It will ensure more consistent dose adjustment practices between prescribers, and provide efficient and truly individualized warfarin dosing in both children and adults. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-014-0128-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-43244112015-02-12 A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children Hamberg, Anna-Karin Hellman, Jacob Dahlberg, Jonny Jonsson, E Niclas Wadelius, Mia BMC Med Inform Decis Mak Software BACKGROUND: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic events. Although highly effective, the use of warfarin is limited by a narrow therapeutic range combined with a more than ten-fold difference in the dose required for adequate anticoagulation in adults. An optimal dose that leads to a favourable balance between the wanted antithrombotic effect and the risk of bleeding as measured by the prothrombin time International Normalised Ratio (INR) must be found for each patient. A model describing the time-course of the INR response can be used to aid dose selection before starting therapy (a priori dose prediction) and after therapy has been initiated (a posteriori dose revision). RESULTS: In this paper we describe a warfarin decision support tool. It was transferred from a population PKPD-model for warfarin developed in NONMEM to a platform independent tool written in Java. The tool proved capable of solving a system of differential equations that represent the pharmacokinetics and pharmacodynamics of warfarin with a performance comparable to NONMEM. To estimate an a priori dose the user enters information on body weight, age, baseline and target INR, and optionally CYP2C9 and VKORC1 genotype. By adding information about previous doses and INR observations, the tool will suggest a new dose a posteriori through Bayesian forecasting. Results are displayed as the predicted dose per day and per week, and graphically as the predicted INR curve. The tool can also be used to predict INR following any given dose regimen, e.g. a fixed or an individualized loading-dose regimen. CONCLUSIONS: We believe that this type of mechanism-based decision support tool could be useful for initiating and maintaining warfarin therapy in the clinic. It will ensure more consistent dose adjustment practices between prescribers, and provide efficient and truly individualized warfarin dosing in both children and adults. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-014-0128-0) contains supplementary material, which is available to authorized users. BioMed Central 2015-02-07 /pmc/articles/PMC4324411/ /pubmed/25889768 http://dx.doi.org/10.1186/s12911-014-0128-0 Text en © Hamberg et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Hamberg, Anna-Karin
Hellman, Jacob
Dahlberg, Jonny
Jonsson, E Niclas
Wadelius, Mia
A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children
title A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children
title_full A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children
title_fullStr A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children
title_full_unstemmed A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children
title_short A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children
title_sort bayesian decision support tool for efficient dose individualization of warfarin in adults and children
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4324411/
https://www.ncbi.nlm.nih.gov/pubmed/25889768
http://dx.doi.org/10.1186/s12911-014-0128-0
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