Lung Ultrasound Findings in Patients Hospitalized With COVID‐19

  1. Andre Kumar
  2. Yingjie Weng
  3. Youyou Duanmu
  4. Sally Graglia
  5. Farhan Lalani
  6. Kavita Gandhi
  7. Viveta Lobo
  8. Trevor Jensen
  9. Sukyung Chung
  10. Jeffrey Nahn
  11. John Kugler

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Abstract

Lung ultrasound (LUS) can accurately diagnose several pulmonary diseases, including pneumothorax, effusion, and pneumonia. LUS may be useful in the diagnosis and management of COVID‐19.

Methods

This study was conducted at two United States hospitals from 3/21/2020 to 6/01/2020. Our inclusion criteria included hospitalized adults with COVID‐19 (based on symptomatology and a confirmatory RT‐PCR for SARS‐CoV‐2) who received a LUS. Providers used a 12‐zone LUS scanning protocol. The images were interpreted by the researchers based on a pre‐developed consensus document. Patients were stratified by clinical deterioration (defined as either ICU admission, invasive mechanical ventilation, or death within 28 days from the initial symptom onset) and time from symptom onset to their scan.

Results

N = 22 patients (N = 36 scans) were included. Eleven (50%) patients experienced clinical deterioration. Among N = 36 scans, only 3 (8%) were classified as normal. The remaining scans demonstrated B‐lines (89%), consolidations (56%), pleural thickening (47%), and pleural effusion (11%). Scans from patients with clinical deterioration demonstrated higher percentages of bilateral consolidations (50 versus 15%; P  = .033), anterior consolidations (47 versus 11%; P  = .047), lateral consolidations (71 versus 29%; P  = .030), pleural thickening (69 versus 30%; P  = .045), but not B‐lines (100 versus 80%; P  = .11). Abnormal findings had similar prevalences between scans collected 0–6 days and 14–28 days from symptom onset.

Discussion

Certain LUS findings may be common in hospitalized COVID‐19 patients, especially for those that experience clinical deterioration. These findings may occur anytime throughout the first 28 days of illness. Future efforts should investigate the predictive utility of these findings on clinical outcomes.

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  1. Version published to 10.1002/jum.15683
  2. ScreenIT

    SciScore for 10.1101/2020.06.25.20140392: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Our Institutional Review Board approved this study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04384055Enrolling by invitationPredicting Outcomes for Covid-19 Using Sonography

    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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  3. Version published to 10.1101/2020.06.25.20140392 on medRxiv