Comparison of severe and non-severe COVID-19 pneumonia: review and meta-analysis

  1. Weiping Ji
  2. Jing Zhang
  3. Gautam Bishnu
  4. Xudong Du
  5. Xinxin Chen
  6. Hui Xu
  7. Xiaoling Guo
  8. Zhenzhai Cai
  9. Xian Shen

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Abstract

Objective

To compare the difference between severe and non-severe COVID-19 pneumonia and figure out the potential symptoms lead to severity.

Methods

Articles from PubMed, Embase, Cochrane database, and google up-to 24 February 2020 were systematically reviewed. Eighteen Literatures were identified with cases of COVID-19 pneumonia. The extracted data includes clinical symptoms, age, gender, sample size and region et al were systematic reviewed and meta analyzed.

Results

14 eligible studies including 1,424 patients were analyzed. Symptoms like fever (89.2%), cough (67.2%), fatigue (43.6%) were common, dizziness, hemoptysis, abdominal pain and conjunctival congestion/conjunctivitis were rare. Polypnea/dyspnea in severe patients were significantly higher than non-severe (42.7% vs.16.3%, P<0.0001). Fever and diarrhea were higher in severe patients(p=0.0374and0.0267). Further meta-analysis showed incidence of fever(OR1.70,95%CI 1.01-2.87), polypnea/dyspnea(OR3.53, 95%CI 1.95-6.38) and diarrhea(OR1.80,95%CI 1.06-3.03) was higher in severe patients, which meant the severe risk of patients with fever, polypnea/dyspnea, diarrhea were 1.70, 3.53, 1.80 times higher than those with no corresponding symptoms.

Conclusions

Fever, cough and fatigue are common symptoms in COVID-19 pneumonia. Compared with non-severe patients, the symptoms as fever, polypnea/dyspnea and diarrhea are potential symptoms lead to severity.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.04.20030965: (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
    Sources and search criteria: We conducted a comprehensive systematic search of PubMed, Embase, Cochrane database, and google to find all published studies that describe the clinical characteristics of COVID-19, using the search terms, “novel coronavirus”, “SARS-CoV-2”, “COVID-19”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane
    suggested: (Cochrane Library, RRID:SCR_013000)
    Statistical analysis: SAS 9.4 and Review Manager 5.3 software were used for analysis and drawing.
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)

    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 systematic review had limitations. Firstly, most of the data in this study are from retrospective studies and case reports, which usually report successful management and are affected by selection and publication bias. Secondly, the datacollection of some cases is incomplete. So the statistical test and the discovery of p-value should be carefully explained. Thirdly, the number of included studies is not enough, the test efficiency is insufficient, symmetry can be observed, but it is difficult to evaluate symmetry.

    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 10.1101/2020.03.04.20030965v1 on medRxiv