UC3M Medialab, Department of Communication and Media Studies, University Carlos III of Madrid, Madrid, Spain.
Eu2P Programme, University of Bordeaux, Bordeaux, France.
Int J Risk Saf Med. 2023;34(1):41-61. doi: 10.3233/JRS-210024.
As Twitter has gained significant popularity, tweets can serve as large pool of readily available data to estimate the adverse events (AEs) of medications.
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.
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.
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.
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.
随着 Twitter 的日益普及，推文可以作为一个大型的现成数据池，用于估计药物的不良事件 (AE)。
本研究评估了推文是否是潜在安全警告的早期指标。此外，还比较了 Twitter 上发布的 AE 与英国黄卡系统中的 AE 趋势。
收集了 2017-2019 年期间 35 个药物-事件对的英文推文，这是在 EMA 药物警戒风险评估委员会 (PRAC) 会议日期前两年。使用 MedDRA 词典手动识别和编码信号和非信号 AE。还收集了同期的黄卡 AE。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。
在总共 61661 条英文推文中，有 1411 条具有负面或中性情绪，并提到了至少一种 AE。在 35 种药物中的 15 种药物（42.9%）的推文中包含与信号相关的 AE。在将 Twitter 和黄卡的数据汇总后，在各自的 PRAC 会议之前，发现了 35 个药物-事件对中的 24 对（68.6%）。两个数据源在 AE 的严重程度上显示出相似的分布，但基于标签的分布则不同。
Twitter 不能单独用于当前药物警戒 (PV) 系统中的信号检测。然而，它可以与传统的 PV 系统结合使用，用于早期信号检测，因为它可以提供药物安全性的全貌。
