Combination of Antibody based rapid diagnostic tests used in an algorithm may improve their performance in SARS CoV-2 diagnosis

  1. Grace Esther Kushemererwa
  2. Ismail Kayongo
  3. Patrick Semanda
  4. Hellen Nansumba
  5. Iga Tadeo
  6. Christine Namulindwa
  7. Wilson Nyegenye
  8. Charles Kiyaga
  9. Patrick Ogwok
  10. Susan Ndidde
  11. Isaac Ssewanyana

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Abstract

Background

Globally response to the SARS-CoV-2 pandemic is highly limited by diagnostic methods. Currently, World Health Organization (WHO) recommends the use of molecular assays for confirmation of SARS-CoV-2 infection which are highly expensive and require specialized laboratory equipment. This is a limitation in mass testing and in low resource settings. SARS CoV-2 IgG/IgM antibody tests have had poor diagnostic performance that do not guarantee their use in diagnostics. In this study we demonstrate a concept of using a combination of RDTs in an algorithm to improve their performance for diagnostics.

Method

Eighty six (86) EDTA whole blood samples were collected from SARS-CoV-2 positive cases admitted at Masaka and Mbarara Regional Referral Hospitals in Uganda. These were categorized from day when confirmed positive as follows; category A (0-3 days, 10 samples), category B (4-7 days, 20 samples), Category C (8-17 days, 11 samples) and Category D (18-28 days, 20 samples). Plasma was prepared, transported to the testing laboratory and stored at −20 0 C prior to testing. A total of 13 RDTS were tested following manufacturer’s instructions. Data was entered in Microsoft Excel exported to STATA for computation of sensitivity and specificity. We computed for all possible combinations of 2 of the 13 RDTS (13C 2) that were evaluated in parallel algorithm.

Results

The individual sensitives of the RDTs ranged between 74% and 18% and there was a general increasing trend across the categories with days since PCR confirmation. A total of 78 possible combinations of the RDTs to be used in parallel was computated. The combinations of the 2 RDTS improved the sensitivities to 90%.

Discussion

We demonstrate that use of RDTs in combinations can improve their overall sensitivity. This approach when used on a wider range of combination of RDTs may yield combinations that can give sensitivities that are of diagnostics relevance in mass testing and low resource setting.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.26.20140806: (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

    Antibodies
    SentencesResources
    All the 13 COVID-19 kits use anti human IgM antibody (test line IgM), anti-human IgG (test Line IgG) and IgG (control line) immobilized on a nitrocellulose strip.
    anti human IgM
    suggested: None
    anti-human IgG
    suggested: None
    anti-human IgG (test Line IgG)
    suggested: None
    IgG (control line)
    suggested: (GeneTex Cat# GTX79844, RRID:AB_11178182)
    Software and Algorithms
    SentencesResources
    Results were recorded on a paper-based study record form (SRF), dated and initiated by a laboratory technologist and reviewed by another laboratory technologist Data Management and Analysis: All data was entered into Microsoft excel from the paper-based study record form (SRF).
    Microsoft excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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 10.1101/2020.06.26.20140806v1 on medRxiv