Comparison of Online Patient Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study - Susan Park; So Hyun Choi; Yun-Kyoung Song; Jin-Won Kwon (JMIR Publications, 2022)
BackgroundTramadol is known to cause fewer adverse events (AEs) than other opioids. However, recent research has raised concerns about various safety issues. ObjectiveWe aimed to explore these new AEs related to tramadol using social media and conventional pharmacovigilance data. MethodsThis study used 2 data sets, 1 from patients' drug reviews on WebMD (January 2007 to January 2021) and 1 from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS; January 2016 to December 2020). We analyzed 2062 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. To analyze AEs from FAERS, a disproportionality analysis was performed with 3 measures: proportional reporting ratio, reporting odds ratio, and information component. ResultsFrom the 869 AEs reported, we identified 125 new signals related to tramadol use not listed on the drug label that satisﬁed all 3 signal detection criteria. In addition, 20 serious AEs 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. ConclusionsThis study detected several novel signals related to tramadol use, suggesting newly identified possible AEs. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data.