LUNG ULTRASOUND FINDINGS IN PATIENTS WITH COVID-19 IN AN URBAN EMERGENCY DEPARTMENT IN THE UK – AN OBSERVATIONAL STUDY

  1. Julia Burkert
  2. Hannah Dunlop
  3. Rachel Stewart
  4. Adam Treacy
  5. Robert Jarman
  6. Paramjeet Deol

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Abstract

COVID-19 can present with respiratory symptoms ranging from mild cough to viral pneumonia and ARDS. Lung ultrasonography has emerged as a promising imaging modality during the pandemic, but there is still a paucity of systematic analysis of lung ultrasound findings.

In this retrospective observational study, 12 Zone ultrasound scans of COVID-19 positive patients were systematically analysed for pleural irregularities, subpleural consolidations, B lines, deep consolidations and effusions. Lung abnormalities were analysed according to overall frequency, frequency distribution in coronal and sagittal lung planes and were also correlated to clinical severity groups as determined by oxygenation deficit.

Our results show that lung zones with abnormalities can occur juxtaposed to normal lung. Irregular pleural and small subpleural consolidations appear ubiquitously distributed throughout both lungs and occur early in the disease process. Wide B-lines are a predominant feature in COVID-19 infection. B-lines are found in a variety of patterns with number and width correlated to disease severity. In our analysis we also describe a previously unrecognised finding of small peri-pleural effusions in 8.7% of scans occurring in all areas of the lung.

The current results form the basis for a more thorough understanding of the lung changes occurring in COVID-19 and the incorporation of lung ultrasound in the setting of COVID-19 infection including triage, diagnosis, treatment approach and prognosis.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.28.20182626: (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
    Statistical analyses were carried out using MS Excel [Version16.41] Microsoft Corporation, Redmond, WA, USA.
    MS Excel
    suggested: None

    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:
    There are several limitations to our study. The sample size was small but given the unique situation with a fast-evolving pandemic we analysed important lung ultrasound characteristics in the limited time available and used statistical measures to allow for small sample sizes. In addition, our most significant findings had non-overlapping confidence intervals which provides additional confidence in the validity of our findings. A potential shortcoming is also our classification of disease severity, primarily that the Berlin criteria classify severity based on PaO2, measured at a PEEP of 5(29). As not all our patients with mild symptoms were subjected to arterial blood gas analysis, we refer to research conducted by Rice and colleagues which showed that S/F can be reliably related and converted to P/F ratio(31). This has also been acknowledged by the authors of the original Berlin criteria(32). We use the Berlin criteria merely as a guide to classify severity. In summary, in this study we have systematically analysed the lung ultrasound findings typical for COVID-19 respiratory disease for their overall frequencies as well as anatomical prevalence within the lung. Pleural irregularities, small peripheral consolidations and wide B-lines occur in all severities of COVID-19 infection, but their frequency also directly correlates to disease severity as defined by oxygenation deficit. Small localised peripleural effusions are a feature in COVID-19 respiratory disease. This informat...

    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

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  2. Version published to 10.1101/2020.09.28.20182626v2 on medRxiv
  3. Version published to 10.1101/2020.09.28.20182626v1 on medRxiv