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

机译:一种贝叶斯决策支持工具,用于成人和儿童中华法林的有效剂量个性化

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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.
机译:背景华法林是预防和治疗血栓栓塞事件的处方最广泛的抗凝剂。尽管非常有效,但华法林的使用受到狭窄治疗范围的限制,并且成人中充分抗凝所需的剂量差异超过十倍。必须为每位患者找到一个最佳剂量,该剂量可在所需的抗血栓形成作用和出血风险之间找到有利的平衡,凝血酶原时间为国际标准化比率(INR)。在开始治疗之前(先验剂量预测)和开始治疗后(后验剂量修订),可以使用描述INR反应时程的模型来辅助剂量选择。结果本文描述了一种华法林决策支持工具。它已从NONMEM中开发的华法林种群PKPD模型转移到用Java编写的平台无关工具。该工具证明能够解决代表华法林药代动力学和药效学的微分方程组,其性能可与NONMEM媲美。为了估算先验剂量,用户输入有关体重,年龄,基线和目标INR以及可选的CYP2C9和VKORC1基因型的信息。通过添加有关先前剂量和INR观测值的信息,该工具将通过贝叶斯预测建议后代新剂量。结果显示为每天和每周的预测剂量,并以图形形式显示为预测的INR曲线。该工具还可用于根据任何给定的剂量方案(例如,固定或个体化的剂量方案。结论我们认为,这种基于机制的决策支持工具可用于在临床中启动和维持华法林治疗。它将确保开处方者之间的剂量调整实践更加一致,并为儿童和成人提供有效且真正个性化的华法林剂量。

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