Can Twitter posts serve as early indicators for potential safety signals? A retrospective analysis.

Pub Date : 2023-01-01 DOI:10.3233/JRS-210024
Revati Pathak, Daniel Catalan-Matamoros
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引用次数: 1

Abstract

Background: As Twitter has gained significant popularity, tweets can serve as large pool of readily available data to estimate the adverse events (AEs) of medications.

Objective: This study evaluated whether tweets were an early indicator for potential safety warnings. Additionally, the trend of AEs posted on Twitter was compared with AEs from the Yellow Card system in the United Kingdom.

Methods: English Tweets for 35 drug-event pairs for the period 2017-2019, two years prior to the date of EMA Pharmacovigilance Risk Assessment Committee (PRAC) meeting, were collected. Both signal and non-signal AEs were manually identified and encoded using the MedDRA dictionary. AEs from Yellow Card were also gathered for the same period. Descriptive and inferential statistical analysis was conducted using Fisher's exact test to assess the distribution and proportion of AEs from the two data sources.

Results: Of the total 61,661 English tweets, 1,411 had negative or neutral sentiment and mention of at least one AE. Tweets for 15 out of the 35 drugs (42.9%) contained AEs associated with the signals. On pooling data from Twitter and Yellow Card, 24 out of 35 drug-event pairs (68.6%) were identified prior to the respective PRAC meetings. Both data sources showed similar distribution of AEs based on seriousness, however, the distribution based on labelling was divergent.

Conclusion: Twitter cannot be used in isolation for signal detection in current pharmacovigilance (PV) systems. However, it can be used in combination with traditional PV systems for early signal detection, as it can provide a holistic drug safety profile.

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推特帖子能作为潜在安全信号的早期指示器吗?回顾性分析。
背景:随着Twitter的普及,Twitter可以作为估计药物不良事件(ae)的大量可用数据池。目的:本研究评估推特是否是潜在安全预警的早期指标。此外,还将Twitter上发布的ae趋势与英国黄牌系统的ae趋势进行了比较。方法:收集2017-2019年(EMA药物警戒风险评估委员会(PRAC)会议召开前两年)期间35对药物事件对的英文推文。使用MedDRA字典手动识别和编码信号和非信号ae。黄牌的ae也在同一时期被收集。使用Fisher精确检验进行描述性和推断性统计分析,以评估来自两个数据源的ae的分布和比例。结果:在总共61661条英文推文中,1411条有负面或中立的情绪,并且提到了至少一个AE。35种药物中有15种(42.9%)的推文包含与信号相关的ae。通过汇总Twitter和黄卡的数据,在各自的PRAC会议之前确定了35对药物事件中的24对(68.6%)。两个数据来源显示基于严重程度的ae分布相似,然而,基于标签的分布是不同的。结论:Twitter不能单独用于当前药物警戒(PV)系统的信号检测。然而,它可以与传统的PV系统结合使用,用于早期信号检测,因为它可以提供整体的药物安全性概况。
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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