Clinical Significance of an IgM and IgG Test for Diagnosis of Highly Suspected COVID-19

  1. Xingwang Jia
  2. Pengjun Zhang
  3. Yaping Tian
  4. Junli Wang
  5. Huadong Zeng
  6. Jun Wang
  7. Jiao Liu
  8. Zeyan Chen
  9. Lijun Zhang
  10. Haihong He
  11. Kunlun He
  12. Yajie Liu

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Abstract

Background: Nucleic acid detection and CT scanning have been reported in COVID-19 diagnosis. Here, we aimed to investigate the clinical significance of IgM and IgG testing for the diagnosis of highly suspected COVID-19.

Methods: A total of 63 patients with suspected COVID-19 were observed, 57 of whom were enrolled (24 males and 33 females). The selection was based on the diagnosis and treatment protocol for COVID-19 (trial Sixth Edition) released by the National Health Commission of the People's Republic of China. Patients were divided into positive and negative groups according to the first nucleic acid results from pharyngeal swab tests. Routine blood tests were detected on the second day after each patient was hospitalized. The remaining serum samples were used for detection of novel coronavirus-specific IgM/IgG antibodies.

Results: The rate of COVID-19 nucleic acid positivity was 42.10%. The positive detection rates with a combination of IgM and IgG testing for patients with COVID-19 negative and positive nucleic acid test results were 72.73 and 87.50%, respectively.

Conclusions: We report a rapid, simple, and accurate detection method for patients with suspected COVID-19 and for on-site screening for close contacts within the population. IgM and IgG antibody detection can identify COVID-19 after a negative nucleic acid test. Diagnostic accuracy of COVID-19 might be improved by nucleic acid testing in patients with a history of epidemic disease or with clinical symptoms, as well as CT scans when necessary, and serum-specific IgM and IgG antibody testing after the window period.

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  1. Version published to 10.3389/fmed.2021.569266
  2. ScreenIT

    SciScore for 10.1101/2020.02.28.20029025: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Patients: This is a retrospective study which was approved by the Ethics Committee of Shenzhen Hospital, Southern Medical University (NYSZYYEC20200009).
    Consent: The data were anonymous, so the requirement for informed consent was therefore waived.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: Statistical analyses were performed with the Statistical Analysis System software SPSS 19.0, and data are presented as Median (25% percentile, 75% percentile).
    Statistical Analysis System
    suggested: (Statistical Analysis System, RRID:SCR_008567)
    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:
    There are still some limitations in our study. First, the relatively small sample size, difference of IgM and IgG antigen binding site, difference of COVID-19 nucleic acid design, it may result in the bias of results. Second, because of the different time that from a patient who were firstly exposed to the virus to the detection, the detection positive rate of IgM and IgG may be affected. Earlier and different times should be performed to validate the detection value of IgM and IgG. Third, the detection value of IgM and IgG should be followed up in the future study. In summary, compared with the nucleic acid detection, the IgM and IgG may provide a quick, simple and accurate aided detection method for suspected COVID-19 patients. We should combine the nucleic acid, IgM, IgG, CT scan and clinical characteristics results together for the diagnosis of COVID-19.

    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 10.1101/2020.02.28.20029025 on medRxiv