Clinical characterization and chest CT findings in laboratory-confirmed COVID-19: a systematic review and meta-analysis

  1. Golnaz Vaseghi
  2. Marjan Mansourian
  3. Raheleh Karimi
  4. Kiyan Heshmat-Ghahdarijani
  5. Sadegh Baradaran Mahdavi
  6. Amirhossein Pezeshki
  7. Behrooz Ataei
  8. Alireza Zandifar
  9. Omid Shafaat
  10. Shaghayegh Haghjoo Javanmard

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Abstract

Background

Imagery techniques have been used as essential parts of diagnostic workup for patients suspected for 2019-nCoV infection, Multiple studies have reported the features of chest computed tomography (CT) scans among a number of 2019-nCoV patients.

Method

Study Identification was carried out in databases (PubMed, Embase and Cochrane Library) to identify published studies examining the diagnosis, the 2019 novel coronavirus (2019-nCoV). Heterogeneity among reported prevalence was assessed by computing p-values of Cochrane Q-test and I 2 -statics. The pooled prevalence of treatment failure was carried out with a fixed effects meta-analysis model, generating the pooled 95% confidence interval. A random-effect model was used to pool the results since this model could incorporate the heterogeneity of the studies and therefore proved a more generalized result.

Results

According to the combined results of meta-analysis, the total 55% of corona patients were males. The mean age of the patients was 41.31 (34.14, 48.47). Two prevalent clinical symptoms between patients were fever, cough with prevalence of 85%, and 62%, respectively. Either Ground Glass Opacity GGO or consolidation was seen in 86% but 14% had NO GGO or consolidation .

The other rare CT symptoms were pericardial effusion, and pleural effusion with 4, 5, 7% prevalence, respectively. The most prevalent event was Either GGO or consolidation in 85% of patients.

Conclusion

The most CT-scan abnormality is Either Ground Glass Opacity GGO or consolidation however in few patients none of them might be observed, so trusting in just CT findings will lead to miss some patients.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.05.20031518: (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
    Study Identification and Selection: A systematic search was carried out in databases (PubMed, Embase and Cochrane Library) to identify published studies examining the diagnosis, the 2019 novel coronavirus (2019-nCoV), in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Statistical analysis: Data on study design, sample size, study population and publication year were extracted in Microsoft Excel format, and then analysis was carried out using Stata software (version 13.1, Stata Corp, College Station, TX, USA).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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.

  2. Version published to 10.1101/2020.03.05.20031518v1 on medRxiv