Inexpensive Multipatient Respiratory Monitoring System for Helmet Ventilation During COVID-19 Pandemic

  1. The Princeton Open Ventilation Monitor Collaboration
  2. Philippe Bourrianne
  3. Stanley Chidzik
  4. Daniel J. Cohen
  5. Peter Elmer
  6. Thomas Hallowell
  7. Todd J. Kilbaugh
  8. David Lange
  9. Andrew M. Leifer
  10. Daniel R. Marlow
  11. Peter D. Meyers
  12. Edna Normand
  13. Janine Nunes
  14. Myungchul Oh
  15. Lyman Page
  16. Talmo Periera
  17. Jim Pivarski
  18. Henry Schreiner
  19. Howard A. Stone
  20. David W. Tank
  21. Stephan Thiberge
  22. Christopher Tully

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Abstract

Helmet continuous positive applied pressure is a form of noninvasive ventilation (NIV) that has been used to provide respiratory support to COVID-19 patients. Helmet NIV is low-cost, readily available, provides viral filters between the patient and clinician, and may reduce the need for invasive ventilation. Its widespread adoption has been limited, however, by the lack of a respiratory monitoring system needed to address known safety vulnerabilities and to monitor patients. To address these safety and clinical needs, we developed an inexpensive respiratory monitoring system based on readily available components suitable for local manufacture. Open-source design and manufacturing documents are provided. The monitoring system comprises flow, pressure, and CO2 sensors on the expiratory path of the helmet circuit and a central remote station to monitor up to 20 patients. The system is validated in bench tests, in human-subject tests on healthy volunteers, and in experiments that compare respiratory features obtained at the expiratory path to simultaneous ground-truth measurements from proximal sensors. Measurements of flow and pressure at the expiratory path are shown to deviate at high flow rates, and the tidal volumes reported via the expiratory path are systematically underestimated. Helmet monitoring systems exhibit high-flow rate, nonlinear effects from flow and helmet dynamics. These deviations are found to be within a reasonable margin and should, in principle, allow for calibration, correction, and deployment of clinically accurate derived quantities.

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  1. Version published to 10.1115/1.4053386
  2. Version published to 10.1101/2020.06.29.20141283v3 on medRxiv
  3. Version published to 10.1101/2020.06.29.20141283v2 on medRxiv
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    SciScore for 10.1101/2020.06.29.20141283: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: 2.2 Methods for testing and evaluation of the device and software: 2.3 Methods for human subject test: Human subject tests were conducted in accordance with IRB protocol # 12857 approved by Princeton University’s Institutional Review Board.
    Consent: Written consent was obtained from a healthy adult volunteer.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    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.

  5. Version published to 10.1101/2020.06.29.20141283v1 on medRxiv