Clinical, laboratory, and temporal predictors of neutralizing antibodies against SARS-CoV-2 among COVID-19 convalescent plasma donor candidates

  1. Jim Boonyaratanakornkit
  2. Chihiro Morishima
  3. Stacy Selke
  4. Danniel Zamora
  5. Sarah McGuffin
  6. Adrienne E. Shapiro
  7. Victoria L. Campbell
  8. Christopher L. McClurkan
  9. Lichen Jing
  10. Robin Gross
  11. Janie Liang
  12. Elena Postnikova
  13. Steven Mazur
  14. Vladimir V. Lukin
  15. Anu Chaudhary
  16. Marie K. Das
  17. Susan L. Fink
  18. Andrew Bryan
  19. Alex L. Greninger
  20. Keith R. Jerome
  21. Michael R. Holbrook
  22. Terry B. Gernsheimer
  23. Mark H. Wener
  24. Anna Wald
  25. David M. Koelle

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  1. ScreenIT

    SciScore for 10.1101/2020.10.06.20207472: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study approval: Institutional Review Board approval was obtained from the University of Washington and all participants underwent informed consent form.
    Consent: Study approval: Institutional Review Board approval was obtained from the University of Washington and all participants underwent informed consent form.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The anti-SARS-CoV-2 IgG ELISA from Euroimmun (Lubeck, Germany) measures antibodies to recombinant spike protein (S1 domain), containing the receptor binding domain that interacts with ACE2(27).
    anti-SARS-CoV-2 IgG
    suggested: None
    ACE2
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    In these experiments, a fixed volume of diluted virus was incubated with an equivalent volume of test plasma for 1 hour at 37 °C prior to adding the suspension to Vero E6 cells (ATCC, Manassas, VA, USA, #CRL-1586).
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    ODR were converted to z-scores as follows: For Euroimmun, z-score = (ODR – 0.26)/0.13 and for Abbott, z-score = (index result – 0.08)/0.14).
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    SPSS software (IBM, Chicago, IL, USA) was used for statistical analysis and to generate ROC curves, the latter with default parameters.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    Our study has some limitations. First, although our sample population included a clinically diverse population with respect to age, sex, and clinical presentation, the cohort was predominantly white. Additional studies in minority populations at risk for worse outcomes will be needed to determine the generalizability of these results. Second, our study was mainly cross-sectional, and longitudinal data were obtained on only a subset of the cohort. Third, in analyzing the decline of neutralizing antibody titers, a titer of 1:20 was used as a surrogate for all titers <1:40, since this value was the next serial 2-fold titer below the lowest positive result. We chose this conservative surrogate value to avoid the possibility of over-estimating the rate of decline in neutralizing antibody titers. Fourth, we focused on blood immune responses. It is possible that the seronegative individuals in our study mounted a mucosal humoral or T cell response that could provide protection against re-infection. Probing mucosal and T cell immunity may be important in understanding the immune response of subjects with milder infection. In summary, our data provide important information regarding the predictive value of clinical factors and commercially available immunoassay results for high nAb titers. This information could aid in streamlining the selection of potential convalescent plasma donors and increase access to and efficiency of donor sample testing. Since transfusion of plasma with highe...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04344977Active, not recruitingCollection of Anti-SARS-CoV-2 Immune Plasma
    NCT04338360Approved for marketingExpanded Access to Convalescent Plasma for the Treatment of …

    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 10.1172/jci144930
  3. Version published to 10.1101/2020.10.06.20207472v2 on medRxiv
  4. Version published to 10.1101/2020.10.06.20207472v1 on medRxiv