Asthma in COVID-19 patients: An extra chain fitting around the neck?

  1. Mohammad H. Hussein
  2. Eman A. Toraih
  3. Abdallah S. Attia
  4. Nicholas Burley
  5. Allen D. Zhang
  6. Jackson Roos
  7. August Houghton
  8. Nedum Aniemeka
  9. Mahmoud Omar
  10. Mohamed Aboueisha
  11. Mohamed A. Shama
  12. Juan Duchesne
  13. Emad Kandil

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.rmed.2020.106205
  2. ScreenIT

    SciScore for 10.1101/2020.07.13.20153130: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study population: Following Institutional Review Board (IRB) approval, data of hospitalized patients with laboratory-confirmed SARS-CoV-2 infection was gathered and entered into the web-based data collection platform, RedCap.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Study population: Following Institutional Review Board (IRB) approval, data of hospitalized patients with laboratory-confirmed SARS-CoV-2 infection was gathered and entered into the web-based data collection platform, RedCap.
    RedCap
    suggested: (REDCap, RRID:SCR_003445)
    Berlin criteria were also utilized to identify cases of acute respiratory distress syndrome (ARDS) [16] Statistical analysis: Data management was performed in SAS 9.4 (SAS Institute Inc.,
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)
    SAS 9.4, Cary, NC: SAS Institute Inc., 2013) and statistical analysis was carried out using SPSS 26.0 (SPSS, Inc., Chicago, IL, USA) and STATA 16.0 (StataCorp. 2019. Stata Statistical Software: Release 16.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    STATA
    suggested: (Stata, RRID:SCR_012763)
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    The potential limitations of this study include self-reporting of asthma history and the inability to identify the severity of asthma. To prevent false reporting, each patient’s home medications were reviewed to confirm the diagnosis. Asthma severity may be assessed retrospectively based on symptoms or via risk assessment requiring measurement of airflow limitation using spirometry or Peak flow meter. However, these Aerosol Generating Procedures (AGPs) may put healthcare workers at an increased risk of coronavirus exposure. The results of the current study show that while pre-existing asthma is more prevalent in our COVID-19 cohort than in the general population, after adjustment for other covariates, it was neither associated with disease severity nor negative outcomes. Therefore, although a seemingly poor prognosis, asthma does not imply a worse outcome as compared to non-asthmatics. This information can help physicians and researchers identify people with other underlying medical conditions at risk for more severe COVID-19 disease.

    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 10.1101/2020.07.13.20153130v1 on medRxiv