Case Series of Thrombosis With Thrombocytopenia Syndrome After COVID-19 Vaccination—United States, December 2020 to August 2021

  1. Isaac See
  2. Allison Lale
  3. Paige Marquez
  4. Michael B. Streiff
  5. Allison P. Wheeler
  6. Naomi K. Tepper
  7. Emily Jane Woo
  8. Karen R. Broder
  9. Kathryn M. Edwards
  10. Ruth Gallego
  11. Andrew I. Geller
  12. Kelly A. Jackson
  13. Shashi Sharma
  14. Kawsar R. Talaat
  15. Emmanuel B. Walter
  16. Imo J. Akpan
  17. Thomas L. Ortel
  18. Victor C. Urrutia
  19. Shannon C. Walker
  20. Jennifer C. Yui
  21. Tom T. Shimabukuro
  22. Adamma Mba-Jonas
  23. John R. Su
  24. David K. Shay

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Abstract

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  1. Version published to 10.7326/m21-4502
  2. ScreenIT

    SciScore for 10.1101/2021.11.10.21266063: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Tier 2 cases had new-onset thrombocytopenia, thrombosis in an extremity vein or pulmonary artery in the absence of thrombosis at a Tier 1 location, and a positive anti-PF4 antibody ELISA test or functional HIT platelet test occurring any time after receipt of COVID-19 vaccine.
    anti-PF4
    suggested: None
    Software and Algorithms
    SentencesResources
    REDCap is a secure, web-based software platform designed to support data entry and management [22,23].
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    Data analyses were conducted with SAS 9.4 (SAS Institute, Cary, NC)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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: We detected the following sentences addressing limitations in the study:
    This report is subject to limitations. VAERS is a passive surveillance system, so the TTS reporting rates described likely underestimate true rates of occurrence. In addition, lack of anti-PF4 antibody ELISA testing could lead to both underreporting and underrepresentation of Tier 2 cases, compared to Tier 1 TTS cases. However, a strength of the current analysis is that because the COVID-19 immunization program was federally coordinated, all U.S. public health jurisdictions reported the number of COVID-19 vaccines that were administered, allowing a better estimate of reporting rates than usually possible when using VAERS data. We estimate reporting rates of TTS following COVID-19 vaccination and describe the epidemiology of this emerging syndrome through a systematic surveillance effort. The epidemiology and hypothesized pathogenesis of TTS support a causal association with receipt of the Ad26.COV2.S COVID-19 vaccine. However, the much lower reported rates and difference in demographics for TTS cases following mRNA-based COVID-19 vaccines suggest that reports occurring after mRNA-based vaccine likely represents a background rate of cases of autoimmune HIT. Continued vigilance for TTS cases following Ad26.COV2.S vaccination is needed; the reporting rates and epidemiologic data about TTS following Ad26.COV2.S vaccination will be useful in formulating benefit-risk assessments for COVID-19 immunization programs in the United States and other countries [33-34].

    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.

  3. Version published to 10.1101/2021.11.10.21266063v1 on medRxiv