Mining multi-item drug adverse effect associations in spontaneous reporting systems.
Mining multi-item drug adverse effect associations in spontaneous reporting systems.
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DOI：
10.1186/1471-2105-11-s9-s7
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发表时间：
2010-10-28
影响因子：
3
通讯作者：
Friedman C
中科院分区：
文献类型：
--
作者：
Harpaz R;Chase HS;Friedman C
多项药物不良事件（ADE）关联是指多种药物与可能的多种不良事件相关。目前药物警戒的标准是双变量关联分析，其中单独研究每种单一药物-不良反应组合。在几项重要的药物警戒研究中注意到了检测多项ADE相关性的重要性和困难性。在本文中，我们研究了一个良好的数据挖掘方法，称为关联规则挖掘，我们量身定制的上述问题的应用，并证明其价值。该方法被应用于FDA自发不良事件报告系统（AERS），其输出的限制和期望最小，以前没有做过的规模和一般性的实验，在这项工作中提出。基于2008年向AERS报告并发表的162，744份疑似ADE报告，我们的方法确定了1167种多项ADE关联。一个具有代表性的样本的基础上开发的协会特征的分类。确定的大量（占总数的67%）潜在多项ADE相关性由领域专家表征和临床验证为先前公认的ADE相关性。还发现了几种潜在的新型ADE。较小比例（4%）的关联被表征和验证为已知的药物-药物相互作用。我们的研究结果表明，多项ADE是存在的，并可以使用我们的方法从FDA的不良反应报告系统中提取，这表明我们的方法是一种有效的方法，用于多项ADE的初始识别。该研究还揭示了一些局限性和挑战，这些局限性和挑战可归因于方法和数据质量。
Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work. Based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions. Our findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.