Antibody Tests in Detecting SARS-CoV-2 Infection: A Meta-Analysis

  1. Panagiota I. Kontou
  2. Georgia G. Braliou
  3. Niki L. Dimou
  4. Georgios Nikolopoulos
  5. Pantelis G. Bagos

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Abstract

The emergence of Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 made imperative the need for diagnostic tests that can identify the infection. Although Nucleic Acid Test (NAT) is considered to be the gold standard, serological tests based on antibodies could be very helpful. However, individual studies are usually inconclusive, thus, a comparison of different tests is needed. We performed a systematic review and meta-analysis in PubMed, medRxiv and bioRxiv. We used the bivariate method for meta-analysis of diagnostic tests pooling sensitivities and specificities. We evaluated IgM and IgG tests based on Enzyme-linked immunosorbent assay (ELISA), Chemiluminescence Enzyme Immunoassays (CLIA), Fluorescence Immunoassays (FIA), and the Lateral Flow Immunoassays (LFIA). We identified 38 studies containing data from 7848 individuals. Tests using the S antigen are more sensitive than N antigen-based tests. IgG tests perform better compared to IgM ones and show better sensitivity when the samples were taken longer after the onset of symptoms. Moreover, a combined IgG/IgM test seems to be a better choice in terms of sensitivity than measuring either antibody alone. All methods yield high specificity with some of them (ELISA and LFIA) reaching levels around 99%. ELISA- and CLIA-based methods perform better in terms of sensitivity (90%–94%) followed by LFIA and FIA with sensitivities ranging from 80% to 89%. ELISA tests could be a safer choice at this stage of the pandemic. LFIA tests are more attractive for large seroprevalence studies but show lower sensitivity, and this should be taken into account when designing and performing seroprevalence studies.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.22.20074914: (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 variableData extracted for each study included (if available): first author’s last name, percentage of male patients, mean age of COVID-19 patients, mean number of days from onset and percentage of severe or critically-ill COVID-19 patients.

    Table 2: Resources

    Antibodies
    SentencesResources
    ) AND (IgM OR IgG or antibodies OR antibody OR ELISA or “rapid test”).
    IgM OR IgG
    suggested: None
    Eligible articles were required to meet the following criteria: a) studies that reported COVID-19 cases confirmed either by NAT such as RT-PCR or sequencing documenting SARS-CoV-2 infection, or by a combination of NAT and clinical findings, and b) results concerning IgM and/or IgG antibodies using a variety of methods.
    IgG
    suggested: None
    The immunoassay methods used for COVID-19 antibody (Ab) detection in all studies included in the present meta-analysis include Enzyme-linked immunosorbent assay (ELISA), Chemiluminescence Enzyme Immunoassays (CLIA)
    COVID-19
    suggested: None
    Because in most cases CLIA detected both anti-N and anti-S IgG and IgM antibodies, (with only one study detecting anti-N 33, 34), we assumed N and S based IgG and IgM CLIAs in studies without relevant information.
    anti-N
    suggested: None
    anti-S IgG
    suggested: None
    IgM
    suggested: None
    Software and Algorithms
    SentencesResources
    We conducted the literature search using PubMed (https://www.ncbi.nlm.nih.gov/pubmed/), medRxiv (https://medrxiv.org/) and bioRxiv (https://www.biorxiv.org/).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Data analysis: We performed a quality assessment of the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, offered by the Review Manager Software (RevMan 5.2.3).
    RevMan
    suggested: (RevMan, RRID:SCR_003581)

    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 10.3390/diagnostics10050319
  3. Version published to 10.1101/2020.04.22.20074914v1 on medRxiv