COVID-19 associated autoimmunity is a feature of severe respiratory disease - a Bayesian analysis

  1. Uriel Trahtemberg
  2. Robert Rottapel
  3. Claudia C dos Santos
  4. Alex P Di Battista
  5. Arthur S. Slutsky
  6. Andrew J Baker
  7. Marvin J Fritzler

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

Serological and clinical features with similarities to systemic autoimmunity have been reported in severe COVID-19, but there is a lack of studies that include contemporaneous controls who do not have COVID-19.

Methods

Observational cohort study of adult patients admitted to an intensive care unit with acute respiratory failure. Patients were divided into COVID + and COVID − based on SARS-CoV-2 PCR from nasopharyngeal swabs and/or endotracheal aspirates. No COVID-19 specific interventions were given. The primary clinical outcome was death in the ICU within 3 months; secondary outcomes included in-hospital death and disease severity measures. Measurements including autoantibodies, were done longitudinally. ANOVA and Fisher’s exact test were used with α=0.05, with a false discovery rate of q=0.05. Bayesian analysis was performed to provide credible estimates of the possible states of nature compatible with our results.

Results

22 COVID + and 20 COVID − patients were recruited, 69% males, median age 60.5 years. Overall, 64% had anti-nuclear antibodies, 38% had antigen-specific autoantibodies, 31% had myositis related autoantibodies, and 38% had high levels of anti-cytokine autoantibodies. There were no statistically significant differences between COVID + and COVID − for any of the clinical or autoantibody parameters. A specific pattern of anti-nuclear antibodies was associated with worse clinical severity for both cohorts.

Conclusions

Severe COVID + patients have similar humoral autoimmune features as comparably ill COVID − patients, suggesting that autoantibodies are a feature of critical illness regardless of COVID-19 status. The clinical significance of autoimmune serology and the correlation with severity in critical illness remains to be elucidated.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.02.17.21251953: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Research Ethics Board of St.
    Consent: Informed consent was obtained from the patients or their legal representatives.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    A HEp-2 indirect immunofluorescence assay (IFA) was used to detect anti-cellular antibodies (referred to as anti-nuclear antibodies (ANA); NOVA Lite HEp-2, Inova Diagnostics, San Diego, CA) and images read and archived on an automated instrument (Nova View, Inova Diagnostics).
    anti-cellular
    suggested: None
    anti-nuclear
    suggested: None
    Anti-Cytokine antibodies were assayed using a multiplexed addressable laser bead immunoassay (Millipore, Oakville, ON, Canada; HCYTAAB-17K-15) on a Luminex 200 system.
    Anti-Cytokine
    suggested: None
    Experimental Models: Organisms/Strains
    SentencesResources
    Experimental procedures: All autoantibody and serology assays were performed by Mitogen Diagnostics Laboratory (MitogenDx, Calgary, AB, Canada).
    AB
    suggested: RRID:BDSC_203)
    Software and Algorithms
    SentencesResources
    All statistical and graphical analyses were performed on R (RStudio, version 1.3.1093, Boston, United States) and JMP Pro (version 15.2.1; SAS Institute Inc, Cary, NC, USA).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04747782RecruitingCOVID-19 Longitudinal Biomarkers in Lung Injury

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2021.02.17.21251953v1 on medRxiv