Details

Autor(en) / Beteiligte

• Freifeld, Clark C.
• Brownstein, John S.
• Menone, Christopher M.
• Bao, Wenjie
• Filice, Ross
• Kass-Hout, Taha
• Dasgupta, Nabarun

Titel

Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

Ist Teil von

• Drug safety, 2014-05, Vol.37 (5), p.343-350

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2014

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Background Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. Objective The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. Methods We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA ® ). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Results Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 ( p  < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. Conclusion Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.