TY - GEN
T1 - Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic
AU - Bishawi, Muath
AU - Kaplan, Michael
AU - Chidyagwai, Simbarashe
AU - Cappiello, Jhaymie
AU - Cherry, Anne
AU - MacLeod, David
AU - Gall, Ken
AU - Evans, Nathan
AU - Kim, Michael
AU - Shaha, Rajib
AU - Whittle, John
AU - Hollidge, Melanie
AU - Truskey, George
AU - Randles, Amanda
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Airflow
KW - Cloud computing
KW - Ventilator modeling
UR - http://www.scopus.com/inward/record.url?scp=85134293768&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-08757-8_13
DO - 10.1007/978-3-031-08757-8_13
M3 - Conference contribution
AN - SCOPUS:85134293768
SN - 9783031087561
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 149
BT - Computational Science - ICCS 2022, 22nd International Conference, Proceedings
A2 - Groen, Derek
A2 - de Mulatier, Clélia
A2 - Krzhizhanovskaya, Valeria V.
A2 - Sloot, Peter M.A.
A2 - Paszynski, Maciej
A2 - Dongarra, Jack J.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd Annual International Conference on Computational Science, ICCS 2022
Y2 - 21 June 2022 through 23 June 2022
ER -