Clinical characteristics of 50466 patients with 2019-nCoV infection

  1. Pengfei Sun
  2. Shuyan Qie
  3. Zongjan Liu
  4. Jizhen Ren
  5. Jianing Xi

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Abstract

Objective

We aim to summarize reliable evidences of evidence-based medicine for the treatment and prevention of the 2019 novel coronavirus (2019-nCoV) by analyzing all the published studies on the clinical characteristics of patients with 2019-nCoV.

Methods

PubMed, Cochrane Library, Embase, and other databases were searched. Several studies on the clinical characteristics of 2019-nCoV infection were collected for Meta-analysis.

Results

Ten studies were included in Meta-analysis, including a total number of 50466 patients with 2019-nCoV infection. Meta-analysis shows that, among these patients, the incidence of fever was 89.1%, the incidence of cough was 72.2%, and the incidence of muscle soreness or fatigue was 42.5%. The incidence of acute respiratory distress syndrome (ARDS) was 14.8%, the incidence of abnormal chest computer tomography (CT) was 96.6%, the percentage of severe cases in all infected cases was 18.1%, and the case fatality rate of patients with 2019-nCoV infection was 4.3%.

Conclusion

Fever and cough are the most common symptoms in patients with 2019-nCoV infection, and most of these patients have abnormal chest CT examination. Several people have muscle soreness or fatigue as well as ARDS. Diarrhea, hemoptysis, headache, sore throat, shock, and other symptoms only occur in a small number of patients. The case fatality rate of patients with 2019-nCoV infection is lower than that of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS).

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  1. ScreenIT

    SciScore for 10.1101/2020.02.18.20024539: (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
    2.1 Search strategy: Three popular medical databases including PubMed, Cochrane Library, and Embase databases were searched for related literatures, using the following keywords: “2019-nCoV”, “Coronavirus”, “COVID-19”, “SARS-CoV-2” and “Wuhan Coronavirus”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)

    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:
    However, this Meta-analysis also has some limitations: 1) All studies included in this Meta-analysis are retrospective studies with large heterogeneity; 2) Most patients in our Meta-analysis are Chinese, and we aimed to use the conclusions of this study to predict patients in general, including other countries and races; 3) There was publication bias in the Meta-analysis of the ARDS group. The analytical conclusion of the ARDS group may be influenced by publication bias. Therefore, based on the above limitations, the conclusions of this Meta-analysis still need to be verified by more relevant studies with more careful design, more rigorous execution, and larger sample size.

    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.

  2. Version published to 10.1101/2020.02.18.20024539 on medRxiv