Department of Health Sciences, University of York, York, YO10 5DD, UK.
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Drug Saf. 2022 Sep;45(9):971-981. doi: 10.1007/s40264-022-01212-0. Epub 2022 Aug 7.
Statin discontinuation can have major negative health consequences. Studying the reasons for discontinuation can be challenging as traditional data collection methods have limitations. We propose an alternative approach using social media.
We used natural language processing and machine learning to extract mentions of discontinuation of statin therapy from an online health forum, WebMD ( http://www.webmd.com ). We then extracted data according to themes and identified key attributes of the people posting for themselves.
We identified 2121 statin reviews that contained information on discontinuing at least one named statin. Sixty percent of people posting declared themselves as female and the most common age category was 55-64 years. Over half the people taking statins did so for < 6 months. By far the most common reason given (90%) was patient experience of adverse events, the most common of which were musculoskeletal and connective tissue disorders. The rank order of adverse events reported in WebMD was largely consistent with those reported to regulatory agencies in the US and UK. Data were available on age, sex, duration of statin use, and, in some instances, adverse event resolution and rechallenge. In some instances, details were presented on resolution of the adverse event and rechallenge.
Social media may provide data on the reasons for switching or discontinuation of a medication, as well as unique patient perspectives that may influence continuation of a medication. This information source may provide unique data for novel interventions to reduce medication discontinuation.
停止使用他汀类药物可能会产生严重的健康后果。由于传统的数据收集方法存在局限性,因此研究停药的原因具有挑战性。我们提出了一种使用社交媒体的替代方法。
我们使用自然语言处理和机器学习从在线健康论坛 WebMD(http://www.webmd.com)中提取他汀类药物治疗中断的提及。然后,我们根据主题提取数据,并确定发布者自身的关键属性。
我们确定了 2121 条包含至少一种已命名他汀类药物停药信息的他汀类药物评论。60%的发帖人宣布自己为女性,最常见的年龄组为 55-64 岁。超过一半服用他汀类药物的人使用时间<6 个月。迄今为止,给出的最常见原因(90%)是患者经历了不良事件,最常见的是肌肉骨骼和结缔组织疾病。在 WebMD 报告的不良事件的排序与在美国和英国向监管机构报告的不良事件基本一致。数据可用于年龄、性别、他汀类药物使用时间,在某些情况下还可用于不良事件的解决和重新挑战。在某些情况下,还介绍了不良事件的解决和重新挑战的详细信息。
社交媒体可能提供有关药物转换或停药原因的信息,以及可能影响药物继续使用的独特患者观点。这种信息来源可能为减少药物停药的新干预措施提供独特的数据。