Data Mining in Pharmacovigilance -Detecting the Unexpected
Data Mining in Pharmacovigilance -Detecting the Unexpected
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药物警戒中的数据挖掘 - 检测意外情况
DOI：
10.2165/00002018-200932050-00005
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发表时间：
2009
期刊：
影响因子：
4.2
通讯作者：
P. Hallberg
中科院分区：
文献类型：
--
作者：
A. Sundström;P. Hallberg
摘要背景：药物警戒的最重要目的之一是尽早发现药物不良反应（ADR）信号。然而，一些ADR很难检测，一个例子是所谓的“C型”反应。这些效应表现为药物治疗过程中出现的看似“自发”的疾病，例如患者服用罗非昔布时发生心血管事件。由于这种类型的ADR经常被误认为是自发性疾病，致病因子可能会以无辜的旁观者的身份出现。
目的：本研究的主要目的是探讨使用数据挖掘方法来检测“C型”反应信号的可能性。我们假设，通过将合并用药而不仅仅是可疑药物纳入非特异性分析的计算中，我们将能够识别此类反应。
研究设计：我们使用来自瑞典药物信息系统SWEDIS的数据，其中包含瑞典医生向瑞典医疗产品管理局提交的自发报告，并应用贝叶斯置信传播神经网络（BCPNN）方法计算属于解剖治疗化学（ATC）类心血管系统药物的药物-事件组合的信息成分（IC）值，肌肉骨骼系统和神经系统（报告数量= 51 270），其中仅考虑可疑药物，也考虑合并药物和可疑药物。然后，我们根据截至2007年11月在瑞典批准的产品特性概要（SPC），将IC值统计学显著升高的药物-事件组合分类为标示或未标示，并将其进一步分类为“C型”反应或非“C型”反应。
主要结果测量：与仅考虑可疑药物相比，当考虑合并药物和可疑药物时，“C型”反应的比例信号。
结果：在考虑可疑药物时，标签药物-事件组合的比例为78.6%。与仅考虑可疑药物相比，当考虑合并用药和可疑药物时，更有可能发现归类为“C型”反应的药物-事件组合;当考虑仅通过其中一种方法发出信号的药物-事件组合时，分别为18/449和0/248。此类药物-事件组合包括，例如，猝死和塞来昔布、心肌梗死和双氯芬酸、自杀相关事件和几种抗抑郁药。
结论：在数据挖掘实践中包括合并用药和可疑药物可能是早期检测“C型”反应的一种方法。这可能是药物警戒实践中数据挖掘的一个进步。
AbstractBackground: One of the most important aims of pharmacovigilance is to detect signals of adverse drug reactions (ADRs) as early as possible. However, some ADRs are difficult to detect, one example being so called ‘type C’ reactions. These are effects that present as seemingly ‘spontaneous’ diseases occurring during treatment with a drug, such as the occurrence of a cardiovascular event while the patient is taking rofecoxib. As this type of ADR is often mistaken for a spontaneous disease, the causative agent may appear as an innocent bystander.
Objective: The primary aim of this study was to investigate the possibility of using data mining approaches to detect signals of ‘type C’ reactions. We hypothesized that by including concomitant, and not only suspected medications in the calculations of disproportionality analyses, we would be able to identify such reactions.
Study design: We used data from the Swedish Drug Information System, SWEDIS, which contains spontaneous reports submitted by Swedish physicians to the Swedish Medical Products Agency, and applied Bayesian confidence propagation neural network (BCPNN) methodology to calculate the information component (IC) value for drug-event combinations for drugs belonging to the Anatomic Therapeutic Chemical (ATC) classes cardiovascular system, musculoskeletal system and nervous system (number of reports = 51 270) where only the suspected drug was considered, and also where both concomitant and suspected drugs were considered. We then classified drug-event combinations that were signalled by a statistically significantly raised IC value as labelled or unlabelled based on the approved summary of product characteristics (SPC) in Sweden as of November 2007, and further classified them as ‘type C’ reactions or not ‘type C’.
Main outcome measure: The proportion of ‘type C’ reactions signalled when considering both concomitant and suspected drugs compared with suspected drugs only.
Results: The proportion of labelled drug-event combinations when considering suspected drugs was 78.6%. Drug-event combinations classified as ‘type C’ reactions were more likely to be found when considering both concomitant and suspected drugs compared with suspected drugs only; 18/449 versus 0/248 when considering drug-event combinations that were signalled exclusively by one of the approaches. Such drug-event combinations included, for example, sudden death and celecoxib, myocardial infarction and diclofenac, suicide-related events and several antidepressants.
Conclusion: Including both concomitant and suspected drugs in data mining practices may be a way of detecting ‘type C’ reactions earlier. This could constitute an advance in data mining for pharmacovigilance practices.