Multi-site Validation of a SARS-CoV-2 IgG/IgM Rapid Antibody Detection Kit

  1. Christopher Minteer
  2. Arnau Casanovas-Massana
  3. Tao Li
  4. David McDonald
  5. Linda Wang
  6. Si Hui Pan
  7. David Caianiello
  8. Jesse Collinski
  9. Edward deRamon
  10. Robert Hale
  11. Rebecca Howell
  12. Jason Ray
  13. Joseph Vinetz
  14. Morgan Levine
  15. Albert I. Ko
  16. David A. Spiegel

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Abstract

Deaths from coronavirus disease (COVID-19) have exceeded 300,000 persons globally, calling for rapid development of mobile diagnostics that can assay widespread prevalence and infection rates. Data provided in this study supports the utility of a newly-designed lateral flow immunoassay (LFA) for detecting SARS-CoV-2 IgM and IgG antibodies. We employed a clinical cohort of 1,892 SARS-CoV-2 patients and controls, including individuals diagnosed by RT-qPCR at Yale New Haven Hospital, The First Affiliated Hospital of Anhui Medical University, the Chinese Center for Disease Control and Prevention of Hefei City (Hefei CDC), Anhui Province (Anhui Province CDC), and Fuyang City (Fuyang CDC). The LFA studied here detects SARS-CoV-2 IgM and IgG antibodies with a specificity of 97.9–100% for IgM, 99.7–100% for IgG, and sensitivities ranging from 94.1–100% for patients >14-days post symptom onset. Sensitivity decreases in patients <14-days post symptom onset, which is likely due to lower IgG/IgM antibody levels in this population. Finally, we developed a visual intensity reporting system that we believe will be suitable for laboratory and point-of-care settings, and will provide granular information about antibody levels. Overall our results support the widespread utility of this and other LFAs in assessing population-level epidemiological statistics.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.25.20112227: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot 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.

    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.

  2. Version published to 10.1101/2020.05.25.20112227v1 on medRxiv