Athena Institute, Vrije Universiteit Amsterdam, The Netherlands.
AISSR, University of Amsterdam, Amsterdam, The Netherlands.
Pharmacoepidemiol Drug Saf. 2022 Sep;31(9):1003-1006. doi: 10.1002/pds.5502. Epub 2022 Jul 6.
Adverse drug reaction (ADR) reports in pharmacovigilance databases often contain coded information and large amounts of unstructured or semi-structured information in plain text format. The unstructured format and sheer volume of these data often render them neglected. Structural topic modelling (STM) represents a potentially insightful way of harnessing these valuable data and to detect grouping or themes in spontaneous reports to aid signal detection.
This was an explorative study of the potential for structural topic modelling to identify useful patterns in ADR reports involving opioid drugs in a pharmacovigilance database.
A dataset of ADR reports on opioid drugs reported to the Netherlands Pharmacovigilance Centre Lareb from 1991 to December 2020 was used, comprising a total of 3069 unique reports. Qualitative text analysis was combined with STM, an automated text analysis method, to examine these data.
In reports submitted directly by patients and healthcare professionals, 11 meaningful topics were identified, whereby patient experience reports, particularly in relation to pain (relief), and the timing of intake and ADRs of tramadol and paracetamol, were the most common. Of the 12 topics identified in reports received via marketing authorization holders, patch and skin-related side effects, addiction and constipation were the most prevalent.
The STM-based analysis identified information that cannot always be captured by coding with the Medical Dictionary for Regulatory Activities (MedDRA®). The identified topics reflect findings in the literature on opioids.
药物不良反应（ADR）报告在药物警戒数据库中通常包含编码信息和大量以纯文本格式呈现的非结构化或半结构化信息。这些数据的非结构化格式和庞大数量往往导致它们被忽视。结构主题建模（STM）代表了一种有潜力的方法，可以利用这些有价值的数据，并在自发报告中检测分组或主题，以帮助信号检测。
本研究旨在探索结构主题建模在药物警戒数据库中识别阿片类药物 ADR 报告中有用模式的潜力。
使用了从 1991 年至 2020 年 12 月向荷兰药物警戒中心 Lareb 报告的阿片类药物 ADR 报告数据集，共包含 3069 个独特报告。定性文本分析与 STM（一种自动文本分析方法）相结合，用于检查这些数据。
在患者和医疗保健专业人员直接提交的报告中，确定了 11 个有意义的主题，其中患者体验报告，特别是与疼痛（缓解）、曲马多和对乙酰氨基酚的摄入时间和 ADR 有关的报告，以及报告接收方通过营销授权持有人的报告中与贴片和皮肤相关的副作用、成瘾和便秘最为常见。
基于 STM 的分析确定了无法通过使用监管活动医学词典（MedDRA®）编码捕获的信息。所确定的主题反映了阿片类药物文献中的发现。
