Suppr超能文献

利用社交媒体报告药物不良反应来补充美国食品和药物管理局不良事件报告系统:比较分析。

Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis.

机构信息

College of Pharmacy, Purdue University, West Lafayette, IN, United States.

出版信息

JMIR Public Health Surveill. 2020 Sep 30;6(3):e19266. doi: 10.2196/19266.

Abstract

BACKGROUND

Adverse drug reactions (ADRs) can occur any time someone uses a medication. ADRs are systematically tracked and cataloged, with varying degrees of success, in order to better understand their etiology and develop methods of prevention. The US Food and Drug Administration (FDA) has developed the FDA Adverse Event Reporting System (FAERS) for this purpose. FAERS collects information from myriad sources, but the primary reporters have traditionally been medical professionals and pharmacovigilance data from manufacturers. Recent studies suggest that information shared publicly on social media platforms related to medication use could be of benefit in complementing FAERS data in order to have a richer picture of how medications are actually being used and the experiences people are having across large populations.

OBJECTIVE

The aim of this study is to validate the accuracy and precision of social media methodology and conduct evaluations of Twitter ADR reporting for commonly used pharmaceutical agents.

METHODS

ADR data from the 10 most prescribed medications according to pharmacy claims data were collected from both FAERS and Twitter. In order to obtain data from FAERS, the SafeRx database, a curated collection of FAERS data, was used to collect data from March 1, 2016, to March 31, 2017. Twitter data were manually scraped during the same time period to extract similar data using an algorithm designed to minimize noise and false signals in social media data.

RESULTS

A total of 40,539 FAERS ADR reports were obtained via SafeRx and more than 40,000 tweets containing the drug names were obtained from Twitter's Advanced Search engine. While the FAERS data were specific to ADRs, the Twitter data were more limited. Only hydrocodone/acetaminophen, prednisone, amoxicillin, gabapentin, and metformin had a sufficient volume of ADR content for review and comparison. For metformin, diarrhea was the side effect that resulted in no difference between the two platforms (P=.30). For hydrocodone/acetaminophen, ineffectiveness as an ADR that resulted in no difference (P=.60). For gabapentin, there were no differences in terms of the ADRs ineffectiveness and fatigue (P=.15 and P=.67, respectively). For amoxicillin, hypersensitivity, nausea, and rash shared similar profiles between platforms (P=.35, P=.05, and P=.31, respectively).

CONCLUSIONS

FAERS and Twitter shared similarities in types of data reported and a few unique items to each data set as well. The use of Twitter as an ADR pharmacovigilance platform should continue to be studied as a unique and complementary source of information rather than a validation tool of existing ADR databases.

摘要

背景

药物不良反应(ADR)可能在任何人使用药物时发生。为了更好地了解其病因并开发预防方法,ADR 被系统地跟踪和编目,取得了不同程度的成功。为此,美国食品和药物管理局(FDA)开发了 FDA 不良事件报告系统(FAERS)。FAERS 从众多来源收集信息,但传统上主要报告者是医疗专业人员和制药商的药物警戒数据。最近的研究表明,与药物使用相关的社交媒体平台上共享的信息可能有助于补充 FAERS 数据,以便更全面地了解药物的实际使用情况以及人群中的用药体验。

目的

本研究旨在验证社交媒体方法的准确性和精密度,并对常用药物的 Twitter 药物不良反应报告进行评估。

方法

从药房索赔数据中收集了 10 种最常开处方的药物的 ADR 数据,这些数据来自 FAERS 和 Twitter。为了从 FAERS 获得数据,使用了经过精心整理的 FAERS 数据 SafeRx 数据库,从 2016 年 3 月 1 日至 2017 年 3 月 31 日收集数据。在同一时期,通过设计用于最小化社交媒体数据中的噪声和虚假信号的算法,手动从 Twitter 上提取类似数据。

结果

通过 SafeRx 获得了总共 40539 份 FAERS ADR 报告,并且从 Twitter 的高级搜索引擎中获得了超过 40000 条包含药物名称的推文。虽然 FAERS 数据专门针对 ADR,但 Twitter 数据则更有限。只有氢可酮/对乙酰氨基酚、泼尼松、阿莫西林、加巴喷丁和二甲双胍有足够的 ADR 内容供审查和比较。对于二甲双胍,腹泻是导致两个平台之间没有差异的副作用(P=.30)。对于氢可酮/对乙酰氨基酚,无效是导致没有差异的不良反应(P=.60)。对于加巴喷丁,在无效和疲劳这两个不良反应方面没有差异(P=.15 和 P=.67,分别)。对于阿莫西林,在平台之间,过敏反应、恶心和皮疹具有相似的特征(P=.35、P=.05 和 P=.31,分别)。

结论

FAERS 和 Twitter 在报告的数据类型上有相似之处,并且每个数据集也有一些独特的项目。应继续将 Twitter 用作 ADR 药物警戒平台进行研究,将其视为一种独特而互补的信息来源,而不是现有 ADR 数据库的验证工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de84/7557434/f132ecb7f00f/publichealth_v6i3e19266_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验