The prognostic value of comorbidity for the severity of COVID-19: A systematic review and meta-analysis study

  1. Mobina Fathi
  2. Kimia Vakili
  3. Fatemeh Sayehmiri
  4. Ashraf Mohamadkhani
  5. Mohammadreza Hajiesmaeili
  6. Mostafa Rezaei-Tavirani
  7. Owrang Eilami

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Abstract

With the increase in the number of COVID-19 infections, the global health apparatus is facing insufficient resources. The main objective of the current study is to provide additional data regarding the clinical characteristics of the patients diagnosed with COVID-19, and in particular to analyze the factors associated with disease severity, lack of improvement, and mortality.

Methods

102 studies were included in the present meta-analysis, all of which were published before September 24, 2020. The studies were found by searching a number of databases, including Scopus, MEDLINE, Web of Science, and Embase. We performed a thorough search from early February until September 24. The selected papers were evaluated and analyzed using Stata software application version 14.

Results

Ultimately, 102 papers were selected for this meta- analysis, covering 121,437 infected patients. The mean age of the patients was 58.42 years. The results indicate a prevalence of 79.26% for fever (95% CI: 74.98–83.26; I 2 = 97.35%), 60.70% for cough (95% CI: 56.91–64.43; I 2 = 94.98%), 33.21% for fatigue or myalgia (95% CI: 28.86–37.70; I 2 = 96.12%), 31.30% for dyspnea (95% CI: 26.14–36.69; I 2 = 97.67%), and 10.65% for diarrhea (95% CI: 8.26–13.27; I 2 = 94.20%). The prevalence for the most common comorbidities was 28.30% for hypertension (95% CI: 23.66–33.18; I 2 = 99.58%), 14.29% for diabetes (95% CI: 11.88–16.87; I 2 = 99.10%), 12.30% for cardiovascular diseases (95% CI: 9.59–15.27; I 2 = 99.33%), and 5.19% for chronic kidney disease (95% CI: 3.95–6.58; I 2 = 96.42%).

Conclusions

We evaluated the prevalence of some of the most important comorbidities in COVID-19 patients, indicating that some underlying disorders, including hypertension, diabetes, cardiovascular diseases, and chronic kidney disease, can be considered as risk factors for patients with COVID-19 infection. Furthermore, the results show that an elderly male with underlying diseases is more likely to have severe COVID-19.

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  1. Version published to 10.1371/journal.pone.0246190
  2. ScreenIT

    SciScore for 10.1101/2020.06.11.20128835: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This paper was performed under the approval of ethics committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.RETECH.REC.1399.084).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We searched PubMed and Scopus databases to obtain qualified studies that were published on COVID-19 until May 1, 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    The Metaprop (meta-analysis for proportion) was used in STATA and when p was near to 0 or 1.
    STATA
    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: 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: 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 10.1101/2020.06.11.20128835 on medRxiv