Chest computed tomography for the diagnosis of patients with coronavirus disease 2019 (COVID-19): a rapid review and meta-analysis

  1. on behalf of COVID-19 Evidence and Recommendations Working Group
  2. Meng Lv
  3. Mengshu Wang
  4. Nan Yang
  5. Xufei Luo
  6. Wei Li
  7. Xin Chen
  8. Yunlan Liu
  9. Mengjuan Ren
  10. Xianzhuo Zhang
  11. Ling Wang
  12. Yanfang Ma
  13. Junqiang Lei
  14. Toshio Fukuoka
  15. Hyeong Sik Ahn
  16. Myeong Soo Lee
  17. Zhengxiu Luo
  18. Yaolong Chen
  19. Enmei Liu
  20. Jinhui Tian
  21. Xiaohui Wang

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.04.14.20064733: (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 variableData extraction: Eight researchers (N Yang, X Luo, W Li, X Chen, Y Liu, M Ren, X Zhang and L Wang) were divided into four groups to extract the data and collect the following information for each study: basic information (title, first author, country or region of participants, date of publication/posting, journal, and study type), patient information (sample size, female/male ratio, adult/children ratio, age range, mean age), outcome information (primary outcome: sensitivity of chest CT imaging using reverse transcription polymerase chain reaction (RT-PCR) results as reference; other outcomes, including probability of bilateral or unilateral pneumonia, ground-glass opacities (GGO) and consolidation, number of lobes affected, location of lobe involvement, rounded morphology, linear opacities, crazy-paving pattern, air bronchogram, interlobular septum thickening, pleural thickening, halo sign, reverse halo sign, pleural effusion and lymphadenopathy).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: We searched Medline (via PubMed), Embase, Cochrane library, Web of Science, China Biology Medicine disc (CBM), China National Knowledge Infrastructure (CNKI) between 1 January 2020 and 31 March 2020, using terms with (“2019-novel coronavirus” OR “Novel CoV” OR “2019-nCoV” OR “2019-CoV” OR “Wuhan-Cov” OR “Wuhan Coronavirus” OR “Wuhan seafood market pneumonia virus” OR COVID-19 OR SARS-CoV-2 OR “novel coronavirus pneumonia”) AND
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane library
    suggested: (Cochrane Library, RRID:SCR_013000)
    We also searched Google Scholar and the preprint servers, including SSRN (https://www.ssrn.com/index.cfm/en/), medRxiv (https://www.medrxiv.org/) and bioRxiv (https://www.biorxiv.org/), as well as reference lists of the identified articles, to find additional studies.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Data synthesis: We performed a meta-analysis using STATA 15.1.
    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:
    However, this review also has some limitations: 1) though we conducted a systematic search, we only included articles published or posted in English and Chinese, which may introduce publication bias; 2) we only included case series and case reports, cases selection of included studies may introduce bias; 3) due to most of studies conducted in China, some cases may be overlapping between studies; and 4) there was large heterogeneity between included studies.

    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 10.21037/atm-20-3311
  3. Version published to 10.1101/2020.04.14.20064733v1 on medRxiv