The utility of lung ultrasound in COVID-19: A systematic scoping review

  1. Michael M Trauer
  2. Ashley Matthies
  3. Nick Mani
  4. Cian McDermott
  5. Robert Jarman

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Abstract

Lung ultrasound (LUS) has an established evidence base and has proven useful in previous viral epidemics. An understanding of the utility of LUS in COVID-19 is crucial to determine its most suitable role based on local circumstances.

Method

Online databases, specialist websites and social media platforms were searched to identify studies that explore the utility of LUS in COVID-19. Case reports and recommendations were excluded.

Findings

In total, 33 studies were identified which represent a rapidly expanding evidence base for LUS in COVID-19. The quality of the included studies was relatively low; however, LUS certainly appears to be a highly sensitive and fairly specific test for COVID-19 in all ages and in pregnancy.

Discussion

There may be LUS findings and patterns that are relatively specific to COVID-19; however, specificity may also be influenced by factors such as disease severity, pre-existing lung disease, operator experience, disease prevalence and the reference standard.

Conclusion

LUS is almost certainly more sensitive than chest radiograph for COVID-19 and has several advantages over computed tomography and real-time polymerase chain reaction. High-quality research is needed into various aspects of LUS including: diagnostic accuracy in undifferentiated patients; triage and prognostication; monitoring progression and guiding interventions; the persistence of residual LUS findings; inter-observer agreement and the role of contrast-enhanced LUS.

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  1. Version published to 10.1177/1742271x20950779
  2. ScreenIT

    SciScore for 10.1101/2020.06.15.20130344: (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
    Protocol and registration: The protocol was drafted in line with PRISMA [20] and registered on https://figshare.com/ on 13/6/2020 (10.6084/m9.figshare.12478820) Inclusion criteria: Exclusion criteria: Information sources: Traditional online databases were searched including: Medline, Embase, SCOPUS, The Cochrane Library, The TRIP database, Google Scholar and www.clinicaltrials.gov.
    https://figshare.com/
    suggested: (FigShare, RRID:SCR_004328)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    This initial search was performed on two databases (Medline and Embase) (See Appendix II).
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)

    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:
    Limitations: The recent emergence and dynamic nature of the COVID-19 pandemic has led to the rapid publication of research and it is inevitable that new studies will continue to be released before this review is published. A thorough and systematic literature search was performed including non-traditional sources (see Appendix I) however all relevant evidence may not have been identified due to publication bias and non-English language publications being excluded.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.06.15.20130344v2 on medRxiv
  4. Version published to 10.1101/2020.06.15.20130344v1 on medRxiv