Adverse drug events in the prevention and treatment of COVID-19: A data mining study on the FDA adverse event reporting system

Background: In the emergent situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients more adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs to help health professionals and patients get rid of the risk. Objective: This pharmacovigilance study aimed to investigate the ADEs of some attractive drugs (i.e., "hot drugs " in this study) in COVID-19 prevention and treatment based on the data from the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). Methods: The FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2022 were retrieved with hot drugs and frequent ADEs were recognized. A combination of support, lower bound of 95% confidence interval (CI) of the proportional reporting ratio (PRR) was applied to detect significant hot drug and ADE signals by the Python programming language on the Jupyter notebook. Results: A total of 66,879 COVID-19 associated cases were retrieved with 22 hot drugs and 1,109 frequent ADEs on the "preferred term " (PT) level. The algorithm finally produced 992 significant ADE signals on the PT level among which unexpected signals such as "hypofibrinogenemia " of tocilizumab and "disease recurrence " of nirmatrelvir\ritonavir stood out. A picture of signals on the "system organ class " (SOC) level was also provided for a comprehensive understanding of these ADEs. Conclusion: Data mining is a promising and efficient way to assist pharmacovigilance work, and the result of this study could help timely recognize ADEs in the prevention and treatment of COVID-19.