Rapid Ventilator Splitting During COVID-19 Pandemic Using 3D Printed Devices and Numerical Modeling of 200 Million Patient Specific Air Flow Scenarios.

Journal Article

There has been a pressing need for an expansion of the ventilator capacity in response to the recent COVID19 pandemic. To address this need, we present a system to enable rapid and efficacious splitting between two or more patients with varying lung compliances and tidal volume requirements. Reserved for dire situations, ventilator splitting is complex, and has been limited to patients with similar pulmonary compliances and tidal volume requirements. Here, we report a 3D printed ventilator splitter and resistor system (VSRS) that uses interchangeable airflow resistors to deliver optimal tidal volumes to patients with differing respiratory physiologies, thereby expanding the applicability of ventilator splitting to a larger patient pool. We demonstrate the capability of the VSRS using benchtop test lungs and standard-of-care ventilators, which produced data used to validate a complementary, patient-specific airflow computational model. The computational model allows clinicians to rapidly select optimal resistor sizes and predict delivered pressures and tidal volumes on-demand from different patient characteristics and ventilator settings. Due to the inherent need for rapid deployment, all simulations for the wide range of clinically-relevant patient characteristics and ventilator settings were pre-computed and compiled into an easy to use mobile app. As a result, over 200 million individual computational simulations were performed to maximize the number of scenarios for which the VSRS can provide assistance. The VSRS will help address the pressing need for increased ventilator capacity by allowing ventilator splitting to be used with patients with differing pulmonary physiologies and respiratory requirements, which will be particularly useful for developing countries and rural communities with a limited ventilator supply.

Full Text

Duke Authors

Cited Authors

  • Bishawi, M; Kaplan, M; Chidyagwai, S; Cappiello, J; Cherry, A; MacLeod, D; Gall, K; Evans, N; Kim, M; Shaha, R; Whittle, J; Hollidge, M; Truskey, G; Randles, A

Published Date

  • August 12, 2020

Published In

  • Res Sq

PubMed ID

  • 32818206

Pubmed Central ID

  • PMC7430577

Digital Object Identifier (DOI)

  • 10.21203/rs.3.rs-48165/v1

Language

  • eng

Conference Location

  • United States