Joint Detection of Serum IgM/IgG Antibody Is an Important Key to Clinical Diagnosis of SARS-CoV-2 Infection

  1. Fang Hu
  2. Xiaoling Shang
  3. Meizhou Chen
  4. Changliang Zhang

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Abstract

Background . This study was aimed to investigate the application of SARS-CoV-2 IgM and IgG antibodies in diagnosis of COVID-19 infection. Method . This study enrolled a total of 178 patients at Huangshi Central Hospital from January to February 2020. Among them, 68 patients were SARS-CoV-2 infected, confirmed with nucleic acid test (NAT) and CT imaging. Nine patients were in the suspected group (NAT negative) with fever and other respiratory symptoms. 101 patients were in the control group with other diseases and negative to SARS-CoV-2 infection. After serum samples were collected, SARS-CoV-2 IgG and IgM antibodies were tested by chemiluminescence immunoassay (CLIA) for all patients. Results . The specificity of serum IgM and IgG antibodies to SARS-CoV-2 was 99.01% (100/101) and 96.04% (97/101), respectively, and the sensitivity was 88.24% (60/68) and 97.06% (66/68), respectively. The combined detection rate of SARS-CoV-2 IgM and IgG antibodies was 98.53% (67/68). Conclusion . Combined detection of serum SARS-CoV-2 IgM and IgG antibodies had better sensitivity compared with single IgM or IgG antibody testing, which can be used as an important diagnostic tool for SARS-CoV-2 infection and a screening tool of potential SARS-CoV-2 carriers in clinics, hospitals, and accredited scientific laboratories.

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  1. Version published to 10.1155/2020/1020843
  2. ScreenIT

    SciScore for 10.1101/2020.07.07.20146902: (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 variableThe patients included 91 males (51.1%) and 87 females (48.9%) with a mean age of 54.3 years (ranging from 2 months to 94 years).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: The statistical analysis was performed using SPSS 19.0 statistical software (IBM SPSS, Chicago, IL, USA).
    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 also has some limitations. For example, we didn’t investigate the cross-reaction with other pathogens (e.g., hCoV-NL-63 or others), MERS-COV, SARS-COV and some auto-antibodies that could cause interference for immunoassay. We also didn’t perform dynamic monitoring of the change of antibodies titer for in-depth study.

    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 10.1101/2020.07.07.20146902v1 on medRxiv