Patel Rikesh, Belousov Maksim, Jani Meghna, Dasgupta Nabarun, Winokur Carly, Nenadic Goran, Dixon William G
Arthritis Research UK Centre for Epidemiology, University of Manchester.
School of Computer Science, University of Manchester.
NPJ Digit Med. 2018 Feb 12;1. doi: 10.1038/s41746-017-0007-z.
In recent years, social media websites have been suggested as a novel, vast source of data which may be useful for deriving drug safety information. Despite this, there are few published reports of drug safety profiles derived in this way. The aims of this study were to detect and quantify glucocorticoid-related adverse events using a computerised system for automated detection of suspected adverse drug reactions (ADR) from narrative text in Twitter, and to compare the frequency of specific ADR mentions within Twitter to the frequency and patterns of spontaneous ADR reporting to a national drug regulatory body. Of 159,297 tweets mentioning either prednisolone or prednisone between 1 October 2012 and 30 June 2015, 20,206 tweets were deemed to contain information resembling an ADR. The top AE MedDRA Preferred Terms were 'insomnia' and 'weight increased', both recognised non-serious but common side effects. These were proportionally over-reported in Twitter when compared to spontaneous reports in the UK regulator's ADR reporting scheme. Serious glucocorticoid related AEs were reported less frequently. Pharmacovigilance using Twitter data has the potential to be a valuable, supplementary source of drug safety information. In particular, it can illustrate which drug side effects patients discuss most commonly, potentially because of important impacts on quality of life. This information could help clinicians to inform patients about frequent and relevant non-serious side effects as well as more serious side effects.
近年来，社交媒体网站被认为是一种新型的、海量的数据来源，可能有助于获取药物安全信息。尽管如此，很少有已发表的报告介绍通过这种方式得出的药物安全概况。本研究的目的是使用一个计算机系统，从推特中的叙述性文本自动检测疑似药物不良反应（ADR），以检测和量化与糖皮质激素相关的不良事件，并将推特中特定ADR提及的频率与向国家药品监管机构自发报告ADR的频率和模式进行比较。在2012年10月1日至2015年6月30日期间提及泼尼松龙或泼尼松的159,297条推文当中，有20,206条推文被认为包含类似ADR的信息。最常见的医学术语词典（MedDRA）首选术语是“失眠”和“体重增加”，这两种都是公认的非严重但常见的副作用。与英国监管机构ADR报告计划中的自发报告相比，这些副作用在推特中的报告比例过高。与糖皮质激素相关的严重不良事件报告频率较低。利用推特数据进行药物警戒有可能成为有价值的药物安全信息补充来源。特别是，它可以说明患者最常讨论的药物副作用，这可能是因为这些副作用对生活质量有重要影响。这些信息可以帮助临床医生告知患者常见且相关的非严重副作用以及更严重的副作用。
