Donagher Joni, Martin Jennifer H, Barras Michael A
Department of Pharmacy, Royal North Shore Hospital, Sydney, New South Wales, Australia.
Discipline of Clinical Pharmacology, School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia.
Intern Med J. 2017 May;47(5):593-600. doi: 10.1111/imj.13412.
Individualised drug dosing has been shown to improve patient outcomes and reduce adverse drug events. One method of individualised medicine is the Bayesian approach, which uses prior information about how the population responds to therapy, to inform clinicians about how a specific individual is responding to their current therapy. This information is then used to make changes to the dose. Studies using a Bayesian approach to adjust drug dosing have shown that clinicians are able to achieve a therapeutic range quicker than standard practice. If concentration is related to a pharmacodynamic end-point, this means that the drug will be more effective, and the side-effects will be minimised. Unfortunately, the software options to assist with Bayesian dosing in Australia are limited. The aims of this article are to demystify the concepts of Bayesian dosing, set the context of the Bayesian approach using reference to other dosing strategies and discuss its benefits over current dosing methods for a number of drugs. The article is targeted to medical and pharmacy clinicians, and there is a practical clinical case to demonstrate how this method could be used in everyday clinical practice.
个体化给药已被证明可改善患者预后并减少药物不良事件。个体化医学的一种方法是贝叶斯方法，该方法利用有关人群对治疗反应的先验信息，向临床医生告知特定个体对其当前治疗的反应情况。然后利用这些信息来调整剂量。使用贝叶斯方法调整药物剂量的研究表明，临床医生能够比标准做法更快地达到治疗范围。如果浓度与药效学终点相关，这意味着药物将更有效，且副作用将降至最低。不幸的是，澳大利亚用于辅助贝叶斯给药的软件选项有限。本文的目的是揭开贝叶斯给药概念的神秘面纱，通过参考其他给药策略来设定贝叶斯方法的背景，并讨论其相对于当前多种药物给药方法的优势。本文面向医学和药学临床医生，并有一个实际临床案例来说明该方法如何在日常临床实践中使用。
