Clinical and Paraclinical Characteristics of COVID-19 patients: A Systematic Review and Meta-Analysis

  1. Keyvan Heydari
  2. Parisa Lotfi
  3. Nima Shadmehri
  4. Mohammad Yousefi
  5. Maedeh Raei
  6. Pouya Houshmand
  7. Mohammad Zahedi
  8. Reza Alizadeh-Navaei
  9. Sahar Bathaeian
  10. Sahar Rismantab

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Abstract

Introduction: Recently, a new strain of coronaviruses, which originated from Wuhan City, Hubei Province, China has been identified. According to the high prevalence of new coronavirus, further investigation on the clinical and paraclinical features of this disease seems essential. Hence, we carried out this systematic review and meta-analysis to figure out the unknown features. Methods:  This study was performed using databases of Web of Science, Scopus and PubMed. We considered English cross-sectional and case-series papers which reported clinical, radiological, and laboratory characteristics of patients with COVID-19. We used STATA v.11 and random effect model for data analysis. Results: In the present meta-analysis, 32 papers including 49504 COVID-19 patients were studied. The most common clinical symptoms were fever (84%), cough (65%) and fatigue (42%), respectively. The most common radiological and paraclinical features were bilateral pneumonia (61%), ground-glass opacity (50%), thrombocytopenia (36%) and lymphocytopenia (34%). The study also showed that the frequency of comorbidities and early symptoms was higher in critically severe patients. Moreover, we found the overall mortality rate of three percent. Conclusion: According to that there are many cases without Computed Tomography Scan findings or clear clinical symptoms, it is recommended to use other confirming methods such RNA sequencing in order to identification of suspicious undiagnosed patients. Moreover, while there is no access to clinical and paraclinical facilities in in public places such as airports and border crossings, it is recommended to consider factors such as fever, cough, sputum and fatigue.

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  1. Version published to 10.18502/tbsrj.v4i1.8772
  2. ScreenIT

    SciScore for 10.1101/2020.03.26.20044057: (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
    We searched databases of Web of Science, Scopus, and PubMed without any time limitation for publications up to March 13, 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    As manual search, the list of imported references, list of related reviews, and the results of Google Scholar have been investigated.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Eligibility Criteria: We considered following criteria for study selection: Study Selection: Duplicated papers were deleted using EndNote software.
    EndNote
    suggested: (EndNote, RRID:SCR_014001)
    Statistical Analysis: Statistical analysis was performed using STATA v.
    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: We detected the following sentences addressing limitations in the study:
    The limitations of this study include: Conclusion: Given the high proportion that may occur without CT-Scan findings or clinical symptoms, it is advisable to use several combination methods to better diagnose the disease, to minimize undiagnosed patients. Moreover, while there is no access to clinical and paraclinical facilities in in public places such as airports and border crossings, it is recommended to consider factors such as fever, cough, sputum and fatigue. Since the prevalence of underlying diseases is higher in patients with more severe conditions, the risk of serious illness in those with underlying diseases should be considered.

    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 10.1101/2020.03.26.20044057v1 on medRxiv