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Guo Q, Duan S, Liu Y, Yuan Y. Adverse drug events in the prevention and treatment of COVID-19: A data mining study on the FDA adverse event reporting system. Front Pharmacol 2022; 13:954359. [PMID: 36506542 PMCID: PMC9730807 DOI: 10.3389/fphar.2022.954359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Qiang Guo
- Department of Pharmacy, Jincheng People’s Hospital, Jincheng, China
| | - Shaojun Duan
- Department of Pharmacy, Jincheng People’s Hospital, Jincheng, China
| | - Yaxi Liu
- Department of Information Technology, Jincheng People’s Hospital, Jincheng, China
| | - Yinxia Yuan
- Department of Pharmacy, Jincheng People’s Hospital, Jincheng, China,*Correspondence: Yinxia Yuan,
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Guo W, Deguise J, Tian Y, Huang PCE, Goru R, Yang Q, Peng S, Zhang L, Zhao L, Xie J, He Y. Profiling COVID-19 Vaccine Adverse Events by Statistical and Ontological Analysis of VAERS Case Reports. Front Pharmacol 2022; 13:870599. [PMID: 35814246 PMCID: PMC9263450 DOI: 10.3389/fphar.2022.870599] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/23/2022] [Indexed: 12/28/2022] Open
Abstract
Since the beginning of the COVID-19 pandemic, vaccines have been developed to mitigate the spread of SARS-CoV-2, the virus that causes COVID-19. These vaccines have been effective in reducing the rate and severity of COVID-19 infection but also have been associated with various adverse events (AEs). In this study, data from the Vaccine Adverse Event Reporting System (VAERS) was queried and analyzed via the Cov19VaxKB vaccine safety statistical analysis tool to identify statistically significant (i.e., enriched) AEs for the three currently FDA-authorized or approved COVID-19 vaccines. An ontology-based classification and literature review were conducted for these enriched AEs. Using VAERS data as of 31 December 2021, 96 AEs were found to be statistically significantly associated with the Pfizer-BioNTech, Moderna, and/or Janssen COVID-19 vaccines. The Janssen COVID-19 vaccine had a higher crude reporting rate of AEs compared to the Moderna and Pfizer COVID-19 vaccines. Females appeared to have a higher case report frequency for top adverse events compared to males. Using the Ontology of Adverse Event (OAE), these 96 adverse events were classified to different categories such as behavioral and neurological AEs, cardiovascular AEs, female reproductive system AEs, and immune system AEs. Further statistical comparison between different ages, doses, and sexes was also performed for three notable AEs: myocarditis, GBS, and thrombosis. The Pfizer vaccine was found to have a closer association with myocarditis than the other two COVID-19 vaccines in VAERS, while the Janssen vaccine was more likely to be associated with thrombosis and GBS AEs. To support standard AE representation and study, we have also modeled and classified the newly identified thrombosis with thrombocytopenia syndrome (TTS) AE and its subclasses in the OAE by incorporating the Brighton Collaboration definition. Notably, severe COVID-19 vaccine AEs (including myocarditis, GBS, and TTS) rarely occur in comparison to the large number of COVID-19 vaccinations administered in the United States, affirming the overall safety of these COVID-19 vaccines.
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Affiliation(s)
- Wenxin Guo
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Jessica Deguise
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Yujia Tian
- Department of Cell Biology and Neuroscience, Rutgers University, New Brunswick, NJ, United States
| | - Philip Chi-En Huang
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Rohit Goru
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Qiuyue Yang
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Suyuan Peng
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing, China
- Department of Medicine, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Lili Zhao
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Jiangan Xie
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
- *Correspondence: Jiangan Xie, ; Yongqun He,
| | - Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
- *Correspondence: Jiangan Xie, ; Yongqun He,
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