Abstract
In the early days of the COVID-19 pandemic, there was a pressing need for an expansion of the ventilator capacity in response to the COVID19 pandemic. Reserved for dire situations, ventilator splitting is complex, and has previously been limited to patients with similar pulmonary compliances and tidal volume requirements. To address this need, we developed a system to enable rapid and efficacious splitting between two or more patients with varying lung compliances and tidal volume requirements. We present here a computational framework to both drive device design and inform patient-specific device tuning. By creating a patient- and ventilator-specific airflow model, we were able to identify pressure-controlled splitting as preferable to volume-controlled as well create a simulation-guided framework to identify the optimal airflow resistor for a given patient pairing. In this work, we present the computational model, validation of the model against benchtop test lungs and standard-of-care ventilators, and the methods that enabled simulation of over 200 million patient scenarios using 800,000 compute hours in a 72 h period.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Optimizing ventilator use during the COVID-19 pandemic. https://www.hhs.gov/sites/default/files/optimizing-ventilator-use-during-covid19-pandemic.pdf. Accessed 20 Apr 2020
Wilgis, J.: Strategies for providing mechanical ventilation in a mass casualty incident: distribution versus stockpiling. Respir. Care 53(1), 96–103 (2008)
Truog, R.D., Mitchell, C., Daley, G.Q.: The toughest triage-allocating ventilators in a pandemic. N. Engl. J. Med. 382(21), 1973–1975 (2020)
Guérin, C., Lévy, P.: Easier access to mechanical ventilation worldwide: an urgent need for low income countries, especially in face of the growing COVID-19 crisis (2020)
Hadar, S.S., et al.: Adaptive split ventilator system enables parallel ventilation, individual monitoring and ventilation pressures control for each lung simulators. medRxiv (2020)
Tronstad, C., et al.: Splitting one ventilator for multiple patients-a technical assessment (2020)
Beitler, J.R., et al.: Ventilator sharing during an acute shortage caused by the COVID-19 pandemic. Am. J. Respir. Crit. Care Med. 202(4), 600–604 (2020)
Lai, B.K., Erian, J.L., Pew, S.H., Eckmann, M.S.: Emergency open-source three-dimensional printable ventilator circuit splitter and flow regulator during the COVID-19 pandemic. Anesthesiology 133(1), 246–248 (2020)
Clarke, A., Stephens, A., Liao, S., Byrne, T., Gregory, S.: Coping with COVID-19: ventilator splitting with differential driving pressures using standard hospital equipment. Anaesthesia 75(7), 872–880 (2020)
Srinivasan, S.S., et al.: A rapidly deployable individualized system for augmenting ventilator capacity. Sci. Transl. Med. 12(549), eabb9401 (2020)
Clarke, A.: 3D printed circuit splitter and flow restriction devices for multiple patient lung ventilation using one anaesthesia workstation or ventilator. Anaesthesia 75(6), 819–820 (2020)
Joint statement on multiple patients per ventilator. https://www.asahq.org/about-asa/newsroom/news-releases/2020/03/joint-statement-on-multiple-patients-per-ventilator. Accessed 25 Apr 2020
Schmidt, M., Foitzik, B., Hochmuth, O., Schmalisch, G.: Computer simulation of the measured respiratory impedance in newborn infants and the effect of the measurement equipment. Med. Eng. Phys. 20(3), 220–228 (1998)
Kaplan, M., et al.: Cloud computing for COVID-19: lessons learned from massively parallel models of ventilator splitting. Comput. Sci. Eng. 22(6), 37–47 (2020)
Cherry, A.D., Cappiello, J., Bishawi, M., Hollidge, M.G., MacLeod, D.B.: Shared ventilation: toward safer ventilator splitting in resource emergencies. Anesthesiology 133(3), 681–683 (2020)
Acknowledgements
The authors would like to thank the COVID-19 HPC Consortium for both the compute hours and the broader Microsoft team for all of the support. They would like to thank T. Milledge, C. Kneifel, V. Orlikowski, J. Dorff, and M. Newton for critical computing support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bishawi, M. et al. (2022). Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-08757-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08756-1
Online ISBN: 978-3-031-08757-8
eBook Packages: Computer ScienceComputer Science (R0)