Risk Factor Considerations in Statistical Signal Detection: Using Subgroup Disproportionality to Uncover Risk Groups for Adverse Drug Reactions in VigiBase
Risk Factor Considerations in Statistical Signal Detection: Using Subgroup Disproportionality to Uncover Risk Groups for Adverse Drug Reactions in VigiBase
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DOI：
10.1007/s40264-020-00957-w
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发表时间：
2020-06-20
期刊：
影响因子：
4.2
通讯作者：
Noren, G. Niklas
中科院分区：
文献类型：
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作者：
Sandberg, Lovisa;Taavola, Henric;Noren, G. Niklas
简介 在治疗个体患者时，我们的愿景是在获益与危害之间实现最佳平衡。这种量身定制的治疗依赖于药物不良反应危险因素的识别和表征。与风险因素考虑相关的信息可以在不良事件报告中捕获，并可用于统计信号检测。目的 本研究的目的是探讨在全球不良事件报告数据库中对各种危险因素进行统计筛查是否可以揭示药物不良反应风险群体的信号。方法 对截至 2017 年 8 月输入世界卫生组织 (WHO) 全球个案安全报告数据库 VigiBase 的 1540 万份报告进行亚组不成比例分析。对药物不良事件对进行不成比例分析 (1) 在完整数据库中，以及 (2) 在由以下协变量定义的一系列亚组中进行：患者年龄、性别、体重指数、妊娠、基础疾病、报告国家和地理区域。药物不良事件对在此类亚组中不成比例地过度报告，但在完整数据库中没有，并且两个观察到的预期比率之间存在显着差异，被强调为统计信号。通过过滤和分类，对这些进行进一步优先排序，以进行临床评估，然后将临床相关信号传达给药物警戒界和公众。结果 对 354 个优先统计信号进行了评估，产生了 7 个传达的信号，描述了以前未识别的与年龄（老年人）、性别（男性和女性）、体重指数（体重不足和肥胖）和地理区域（亚洲）相关的潜在风险群体，所有信号除了一个已确定的药物不良反应外。评估中考虑的重要方面包括通过审查类似药物/不良事件/亚组的替代解释和报告模式来评估亚组中不成比例的过度报告，以及寻找支持风险假设的合理机制。结论 这项研究表明，通过将广泛的危险因素筛查纳入全球不良事件报告数据库中的统计信号检测，有可能发现药物不良反应风险群体的信号。我们的研究结果表明，有可能使用此类统计方法来描述所关注的亚人群的风险特征。
Introduction In the treatment of the individual patient, a vision is to achieve the best possible balance between benefit and harm. Such tailored therapy relies upon the identification and characterisation of risk factors for adverse drug reactions. Information relevant to risk factor considerations can be captured in adverse event reports and could be utilised in statistical signal detection. Objective The aim of this study was to explore whether statistical screening of a broad range of risk factors within a global database of adverse event reports could uncover signals of risk groups for adverse drug reactions. Methods Subgroup disproportionality analysis was applied to 15.4 million reports entered in VigiBase, the World Health Organization (WHO) global database of individual case safety reports, up to August 2017. Disproportionality analyses for drug-adverse event pairs were performed (1) in the full database and (2) across a range of subgroups defined by the following covariates: patient age, sex, body mass index, pregnancy, underlying condition, reporting country, and geographical region. Drug-adverse event pairs disproportionately over-reported in such subgroups, but not in the full database, and with a substantial difference between the two observed-to-expected ratios, were highlighted as statistical signals. These were further prioritised, through filtering and sorting, for clinical assessment, whereafter clinically relevant signals were communicated to the pharmacovigilance community and the public. Results Assessments were performed for 354 prioritised statistical signals, resulting in seven communicated signals describing previously unrecognised potential risk groups related to age (elderly), sex (male and female), body mass index (underweight and obese), and geographical region (Asia), all except one for already established adverse drug reactions. Important aspects considered in the assessments included an evaluation of the disproportionate over-reporting in the subgroup by reviewing alternative explanations and reporting patterns for similar drugs/adverse events/subgroups, and a search for plausible mechanisms to support the risk hypothesis. Conclusions This study reveals that it is possible to uncover signals of risk groups for adverse drug reactions through incorporation of broad risk factor screening into statistical signal detection in a global database of adverse event reports. Our findings suggest the potential to use such statistical methodologies for risk characterisation in subpopulations of concern.