Uppsala Monitoring Centre, Box 1051, Uppsala, 75140, Sweden.
Bayer AG, Berlin, Germany.
Drug Saf. 2018 Dec;41(12):1355-1369. doi: 10.1007/s40264-018-0699-2.
Social media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events.
Performance was assessed using a reference set by Harpaz et al., consisting of 62 US Food and Drug Administration labelling changes, and an internal WEB-RADR reference set consisting of 200 validated safety signals. In total, 75 drugs were studied. Twitter/Facebook posts were retrieved for the period March 2012 to March 2015, and drugs/events were extracted from the posts. We retrieved 4.3 million and 2.0 million posts for the WEB-RADR and Harpaz drugs, respectively. Individual case reports were extracted from VigiBase for the same period. Disproportionality algorithms based on the Information Component or the Proportional Reporting Ratio and crude post/report counting were applied in Twitter/Facebook and VigiBase. Receiver operating characteristic curves were generated, and the relative timing of alerting was analysed.
Across all algorithms, the area under the receiver operating characteristic curve for Twitter/Facebook varied between 0.47 and 0.53 for the WEB-RADR reference set and between 0.48 and 0.53 for the Harpaz reference set. For VigiBase, the ranges were 0.64-0.69 and 0.55-0.67, respectively. In Twitter/Facebook, at best, 31 (16%) and four (6%) positive controls were detected prior to their index dates in the WEB-RADR and Harpaz references, respectively. In VigiBase, the corresponding numbers were 66 (33%) and 17 (27%).
Our results clearly suggest that broad-ranging statistical signal detection in Twitter and Facebook, using currently available methods for adverse event recognition, performs poorly and cannot be recommended at the expense of other pharmacovigilance activities.
社交媒体已被提议作为药物警戒信号检测的一种可能有用的数据来源。本研究主要旨在评估已建立的统计信号检测算法在 Twitter/Facebook 上对广泛药物和不良事件的性能。
使用 Harpaz 等人的参考集评估性能,该参考集由 62 项美国食品和药物管理局标签变更和由 200 项验证的安全信号组成的内部 WEB-RADR 参考集组成。共研究了 75 种药物。检索了 2012 年 3 月至 2015 年 3 月期间的 Twitter/Facebook 帖子,并从帖子中提取药物/事件。我们分别为 WEB-RADR 和 Harpaz 药物检索了 430 万和 200 万条帖子。同期还从 VigiBase 中提取了个体病例报告。在 Twitter/Facebook 和 VigiBase 中应用了基于信息成分或比例报告比的比例失衡算法和原始帖子/报告计数。生成了接收者操作特性曲线,并分析了警报的相对时间。
对于 WEB-RADR 参考集,所有算法的 Twitter/Facebook 曲线下面积在 0.47 到 0.53 之间,对于 Harpaz 参考集,在 0.48 到 0.53 之间。对于 VigiBase,范围分别为 0.64-0.69 和 0.55-0.67。在 Twitter/Facebook 中,最好的情况下,WEB-RADR 和 Harpaz 参考集中分别有 31 个(16%)和 4 个(6%)阳性对照在其索引日期之前被检测到。在 VigiBase 中,相应的数字分别为 66(33%)和 17(27%)。
我们的结果清楚地表明,使用目前用于不良事件识别的方法,在 Twitter 和 Facebook 中进行广泛的统计信号检测效果不佳,不能推荐以牺牲其他药物警戒活动为代价。