Critical complications of COVID-19: A descriptive meta-analysis study

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

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Abstract

The coronavirus disease 2019 (COVID-19) is a novel coronavirus infection that has rapidly spread worldwide, causing a pandemic. The main objective of this meta-analysis was to evaluate the prevalence of the most common symptoms and complications of COVID-19. All relevant studies on the clinical complications of COVID-19 have been identified by searching two web databases (i.e., PubMed and Scopus). Afterward, the relevant data were extracted from the selected studies, and then analyzed by the STATA (Version 14) random-effects model. The 30 studies selected for our meta-analysis covered 6,389 infected patients. The prevalence rates of the most common symptoms were as follows: fever: 84.30% (95% CI: 77.13-90.37; I2 = 97.74%), cough: 63.01% (95% CI: 57.63-68.23; I2 = 93.73%), dyspnea: 37.16% (95% CI: 27.31-47.57%; I2 = 98.32%), fatigue: 34.22% (95% CI: 26.29-42.62; I2 = 97.29%), and diarrhea: 11.47% (95% CI: 6.96-16.87; I2 = 95.58%). Moreover, the most prevalent complications were found to be acute respiratory distress syndrome (ARDS) with 33.15% (95% CI: 23.35-43.73; I2 = 98.56%), arrhythmia with 16.64% (95% CI: 9.34-25.5; I2 = 92.29%), acute cardiac injury with 15.68% (95% CI: 11.1-20.97; I2 = 92.45%), heart failure with 11.50% (95% CI: 3.45-22.83; I2 = 89.48%), and acute kidney injury (AKI) with 9.87% (95% CI: 6.18-14.25; I2 = 95.64%). In this study, we assessed the prevalence of the main clinical complications of COVID-19, and found that following respiratory complications, cardiac and renal complications are the most common clinical complications of COVID-19.

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  1. Version published to 10.31083/j.rcm.2020.03.129
  2. ScreenIT

    SciScore for 10.1101/2020.06.14.20130955: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was carried out under the approval of ethics committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.RETECH.REC.1399.083).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Study selection: To find eligible cross-sectional and case-control studies published on COVID-19 until 1st May 2020, we conducted a web search with citations in the PubMed.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    For data analysis, STATA software (version 14) was applied.
    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 10.1101/2020.06.14.20130955v1 on medRxiv