Prevalence and mortality of lung comorbidities among patients with COVID-19

  1. Mohammed G. Alkhathami
  2. Shailesh M. Advani
  3. Adil A. Abalkhail
  4. Fahad M. Alkhathami
  5. Mohammed K. Alshehri
  6. Ebtisam E. Albeashy
  7. Jihad A. Alsalamah

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Abstract

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  1. Version published to 10.4103/lungindia.lungindia_497_20
  2. Version published to 10.1101/2020.06.01.20119271v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.01.20119271: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Also, it was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.10 Authors performed a literature search of scientific databases including PubMed (Medline), Embase (Ovid), and the Cochrane Library.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Furthermore, we did an additional search on Google Scholar.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    From each study, various details including the authors’ names, study design/country, study population, age, and acknowledged features and mortality of Asthma, COPD, Lung cancer and Cystic fibrosis, extracted into Microsoft excel sheet.
    Microsoft excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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 has limitations and strengths. COVID-19 is rapidly evolving globally. Hence as new literature continues to be reported, estimates of individuals with COVID-19 and chronic comorbidities might change. Further, the majority of our studies were from China, the epicenter of the COVID-19 outbreak. Hence our results might not be an adequate representation of COVID-19 cases from the global perspective. Furthermore, certain studies reported low to medium risk of bias which might be due to heterogeneity due to sample size differences, data collection procedures, differences in assessment of comorbidities or evaluation of COVID-19, and assessment of other characteristics. Our strengths include the updated use of all available literature on lung-specific comorbidities and calculating pooled estimates as compared to earlier reports.46, 47, 59 To conclude, our results identify key areas for future work. First, designing and implementing smoking cessation interventions on a nationwide scale to help reduce risk to high-risk smokers. Second, identifying high risk individuals in community settings with underlying lung comorbidities including those with uncontrolled COPD and asthma. Third, promoting and identifying new treatment strategies for these conditions that reduce the risk of immunosuppression, thereby reducing the risk of COVID-19 infection. Finally, isolating, tracking and tracing COVID-19 patients with lung comorbidities to prevent adverse outcomes. In summary, our review a...

    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.

  4. Version published to 10.1101/2020.06.01.20119271v1 on medRxiv