Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Sep 1, 2021
Date Accepted: Nov 27, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Comparison of Online Patient’s Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study
ABSTRACT
Background:
Tramadol is known to cause fewer adverse events (AE) than other opioids. However, recent research has raised concerns about various safety issues.
Objective:
We aimed to explore these new AE related to tramadol using social media and conventional pharmacovigilance data.
Methods:
This study used two datasets, one from patients’ drug reviews on WebMD and one from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We analyzed 2,062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities (MedDRA). To analyze AE from FAERS, a disproportionality analysis was performed with three measures: the proportional reporting ratio (PRR), the reporting odds ratio (ROR), and the information component (IC).
Results:
From the 869 AE reported, we identified 125 new signals related to tramadol use not listed on the drug label that satisfied all three signal detection criteria. In addition, 20 serious AE were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients’ symptom descriptions, tramadol-induced pain might also be an unexpected AE.
Conclusions:
This study detected several novel signals related to tramadol use, suggesting newly identified possible AE. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. Clinical Trial: N/A
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