Comparison of 16 Serological SARS-CoV-2 Immunoassays in 16 Clinical Laboratories

  1. Lene H. Harritshøj
  2. Mikkel Gybel-Brask
  3. Shoaib Afzal
  4. Pia R. Kamstrup
  5. Charlotte S. Jørgensen
  6. Marianne Kragh Thomsen
  7. Linda Hilsted
  8. Lennart Friis-Hansen
  9. Pal B. Szecsi
  10. Lise Pedersen
  11. Lene Nielsen
  12. Cecilie B. Hansen
  13. Peter Garred
  14. Trine-Line Korsholm
  15. Susan Mikkelsen
  16. Kirstine O. Nielsen
  17. Bjarne K. Møller
  18. Anne T. Hansen
  19. Kasper K. Iversen
  20. Pernille B. Nielsen
  21. Rasmus B. Hasselbalch
  22. Kamille Fogh
  23. Jakob B. Norsk
  24. Jonas Henrik Kristensen
  25. Kristian Schønning
  26. Nikolai S. Kirkby
  27. Alex C. Y. Nielsen
  28. Lone H. Landsy
  29. Mette Loftager
  30. Dorte K. Holm
  31. Anna C. Nilsson
  32. Susanne G. Sækmose
  33. Birgitte Grum-Schwensen
  34. Bitten Aagaard
  35. Thøger G. Jensen
  36. Dorte M. Nielsen
  37. Henrik Ullum
  38. Ram B. Dessau

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Abstract

Serological assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to support clinical diagnosis and epidemiological investigations. Recently, assays for large-scale detection of total antibodies (Ab), immunoglobulin G (IgG), and IgM against SARS-CoV-2 antigens have been developed, but there are limited data on the diagnostic accuracy of these assays.

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  1. Version published to 10.1128/jcm.02596-20
  2. ScreenIT

    SciScore for 10.1101/2020.07.30.20165373: (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: We detected the following sentences addressing limitations in the study:
    A potential limitation for the use of immunoassays can be interference due to x-reactivity in individuals with autoimmune disease or acute infections with EBV or CMV. We tested this directly by using samples from patients with these diseases. This did not seem to be an issue in most assays, except for in the Diasorin/LiaisonXL IgG and YHLO/iFlash IgM assays, which showed interference in some samples. In the US, the FDA (US Food and Drug Administration) requires a minimum sensitivity of 90% and a specificity of 95% for emergency use authorization of serologic anti-SARS-CoV-2 assays (12). In the UK, the Medicines and Healthcare Products Regulatory Agency (MHRA) has defined a Target Product Profile for Enzyme Immunoassays detecting antibodies against SARS-CoV-2, with an acceptable sensitivity and specificity of ≥98% for anti-SARS-CoV-2-immunoassays among patients with a history of SARS-CoV-2 ≥20 days after symptom onset (13). In our study, the sensitivities calculated from the case samples with a known TSO >21 days (N=123) did not reach 98% in any assay evaluated (Table 3a-c). However, we prioritized high diagnostic specificity (≥99%) as our main criterion, since Denmark has a low anti-SARS-CoV-2 seroprevalence (1.7%) (14). For example, in a population with a low anti-SARS-CoV-2 prevalence, an assay specificity of 97.2% (Diasorin/LiaisonXL) would lead to low positive predictive values. Interestingly, many manufacturers use a TSO ≥14 days, in contrast to ≥20 or >21 days, as a cut...

    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.

  3. Version published to 10.1101/2020.07.30.20165373 on medRxiv