The Yale algorithm. Special workshop--clinical.

M S Kramer, T A Hutchinson
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引用次数: 14

Abstract

The assessment of causality in drug-event associations depends on the setting and purpose of such an assessment. Epidemiologists are primarily interested in population-based inferences about whether a given drug can cause a certain adverse drug reaction (ADR), and if so, how often it does so. Pharmaceutical industries and regulatory agencies are also concerned with population-based risks, but in addition must worry about individual cases. Clinicians are primarily interested in the individual, ie, whether a given drug did cause a certain adverse event in a particular patient. The authors describe an algorithm that provides specific, detailed criteria for ranking the probability that an observed untoward clinical manifestation was caused by a given drug. The criteria are subdivided into six axes of decision strategy with a built-in scoring system that ordinally ranks the probability of an adverse drug reaction as definite, probable, possible, or unlikely. To illustrate the use of the algorithm, the authors assess a reference case of pancreatitis occurring after administration of methyldopa.

耶鲁算法。特殊的研讨会——临床。
对药物-事件关联的因果关系的评估取决于这种评估的设定和目的。流行病学家主要感兴趣的是基于人群的推断,即某种药物是否会引起某种药物不良反应(ADR),如果会,发生的频率是多少。制药业和监管机构也关注基于人群的风险,但除此之外还必须担心个别病例。临床医生主要对个体感兴趣,也就是说,某种药物是否会在特定患者身上引起某种不良事件。作者描述了一种算法,该算法提供了具体、详细的标准,用于对观察到的不良临床表现由给定药物引起的可能性进行排序。这些标准被细分为决策策略的六个轴,有一个内置的评分系统,通常将药物不良反应的概率分为确定、可能、可能或不可能。为了说明该算法的使用,作者评估了甲多巴给药后发生的胰腺炎的参考病例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Drug Information Journal
Drug Information Journal 医学-卫生保健
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审稿时长
6-12 weeks
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