Pharmacovigilance Analysis on Cerebrovascular Accidents and Coronavirus disease 2019 Vaccines

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Abstract

Introduction

Recently, there have been reports of cerebrovascular accidents (CVA) occurring in individuals who have received the Coronavirus disease 2019 (COVID-19) vaccine.

Objective

The objective of this analysis was to determine if a statistically significant signal exists in post-marketing safety reports between CVA and the three COVID-19 vaccines being administered in the United States of America (Pfizer, Moderna, Janssen).

Methods

A pharmacovigilance disproportionality analysis on adverse events reported with COVID-19 vaccines was conducted using data from Vaccine Adverse Event Reporting System.

Results

A statistically significant signal was found between CVA events and each of the three COVID-19 vaccines (Pfizer/BioNTech’s, Moderna’s and Janssen’s) in the VAERS database. Females and individuals of age 65 or older had higher number of case reports of CVA events with the COVID-19 vaccines. Females had also more COVID-19 adverse event reports in which a CVA was reported and resulted in the patient having permanent disability or death.

Limitations

Randomized controlled trials are needed to further analyze this signal.

Conclusion

Patients should be made aware of the risk-benefit and symptoms to watch out for that may indicate the onset of a CVA and informed to seek medical care as soon as possible if they develop these symptoms.

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  1. SciScore for 10.1101/2021.04.19.21255768: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: 7 Approval by institutional review board, or human subjects’ committee was not required as the analysis was performed on de-identified retrospective public domain safety data.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.