Chest computed tomography scan findings of coronavirus disease 2019 (COVID-19) patients: a comprehensive systematic review and meta-analysis

  1. Mohammad Karimian
  2. Milad Azami

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Abstract

Numerous cases of pneumonia caused by coronavirus disease 2019 (COVID-19) were reported in Wuhan, China. Chest computed tomography (CT) scan is highly important in the diagnosis and follow-up of lung disease treatment. The present meta-analysis was performed to evaluate chest CT scan findings in COVID-19 patients.

Material and methods

All research steps were taken according to the Meta-Analysis of Observational Studies In Epidemiology (MOOSE) protocol and the final report was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We registered this review at the International Prospective Register of Systematic Reviews (PROSPERO, CRD42019127858).

Results

Forty eligible studies including 4598 patients with COVID-19 were used for meta-analysis. The rate of posi­tive chest CT scan in patients with COVID-19 was 94.5% (95% CI: 91.7-96.3). Bilateral lung involvement, pure ground-glass opacity (GGO), mixed (GGO pulse consolidation or reticular), consolidation, reticular, and presence of nodule findings in chest CT scan of COVID-19 pneumonia patients were respectively estimated to be 79.1% (95% CI: 70.8-85.5), 64.9% (95% CI: 54.1-74.4), 49.2% (95% CI: 35.7-62.8), 30.3% (95% CI: 19.6-43.6), 17.0% (95% CI: 3.9-50.9) and 16.6% (95% CI: 13.6-20.2). The distribution of lung lesions in patients with COVID-19 pneumonia was peripheral (70.0% [95% CI: 57.8-79.9]), central (3.9% [95% CI: 1.4-10.6]), and peripheral and central (31.1% [95% CI: 19.5-45.8]). The pulmonary lobes most commonly involved were the right lower lobe (86.5% [95% CI: 57.7-96.8]) and left lower lobe (81.0% [95% CI: 50.5-94.7]).

Conclusions

The most important outcomes in chest CT scan of patients with COVID-19 pneumonia were bilateral lung involvement, GGO or mixed (GGO pulse consolidation or reticular) patterns, thickened interlobular septa, vascular enlargement, air bronchogram sign, peripheral distribution, and left and right lower lobes involvement. Our study showed that chest CT scan has high sensitivity in the diagnosis of COVID-19, and may therefore serve as a standard method for diagnosis of COVID-19.

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  1. Version published to 10.5114/pjr.2021.103379
  2. ScreenIT

    SciScore for 10.1101/2020.04.22.20075382: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Since the present study was based on a regular review of previous studies, approval of the organizational review board and patient satisfaction was not necessary.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableData summary form includes the following items: First author’s name and year of publication, country and province, article references, study design, mean age and standard deviation, average duration from onset of symptoms until admission, time of performing CT scan, COVID-19 detection method, patient description, sample (respiratory secretions, blood, etc.), sample location (nasal, pharyngeal, etc.), number of patients (total, male and female), number of patients referred to the intensive care unit (ICU), quality of articles, positive chest CT scan in COVID-19 patients, number of positive chest CT scan findings in COVID-19 patients available in the studies. 2.5.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Literature search: We searched the Web of Science (ISI), Ovid, Science Direct, Scopus, EMBASE, PubMed/Medline, Cochrane Library (Cochrane Database of Systematic Reviews - CDSR), EBSCO, CINAHL and Google scholar databases using the following keywords: “2019 nCoV”, “Novel coronavirus”,”COVID-19”, “Novel coronavirus 2019”, “Wuhan pneumonia”, “Wuhan coronavirus”, “acute respiratory infection”, “COVID-19”, and “SARS-CoV-2”, “CT scan”, “Computed tomography”, “Radiology”, “Radiography”, “Clinical Characteristics”, “clinical features”, and “COVID-19”.
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Cochrane Database of Systematic Reviews
    suggested: None
    Google scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    An example of a combined search within PubMed is as follows: (“2019 nCoV”, OR “Novel coronavirus”, OR “COVID-19”, OR “Novel coronavirus 2019”, OR “Wuhan pneumonia”, OR “Wuhan coronavirus”, OR “acute respiratory infection”, OR “COVID- 19”, OR “SARS-CoV-2”) AND (“CT scan” OR “Computed tomography” OR “Radiology” OR “Radiography” OR “Clinical characteristics” OR “clinical features” OR “COVID-19”).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Duplicate studies were identified manually or using EndNote X9.
    EndNote
    suggested: (EndNote, RRID:SCR_014001)

    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 10.1101/2020.04.22.20075382v1 on medRxiv