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?:abstract
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BACKGROUND The COVID-19 pandemic is creating ventilator shortages in many countries that is sparking a conversation about placing multiple patients on a single ventilator. However, on March 26, 2020, six leading medical organizations released a joint statement warning clinicians that attempting this technique could lead to poor outcomes and high mortality. Nevertheless, hospitals around the United States and abroad are considering this technique out of desperation (eg, New York), but there is little data to guide their approach. The overall objective of this study is to utilize a computational model of mechanically ventilated lungs to assess how patient-specific lung mechanics and ventilator settings impact lung tidal volume (VT). METHODS We developed a lumped-parameter computational model of multiple patients connected to a shared ventilator and validated it against a similar experimental study. We used this model to evaluate how patient-specific lung compliance and resistance would impact VT under 4 ventilator settings of pressure control level, PEEP, breathing frequency, and inspiratory:expiratory ratio. RESULTS Our computational model predicts VT within 10% of experimental measurements. Using this model to perform a parametric study, we provide proof-of-concept for an algorithm to better match patients in different hypothetical scenarios of a single ventilator shared by > 1 patient. CONCLUSIONS Assigning patients to preset ventilators based on their required level of support on the lower PEEP/higher [Formula: see text] scale of the National Institute of Health\'s National Heart, Lung, and Blood Institute ARDS Clinical Network (ARDSNet), secondary to lung mechanics, could be used to overcome some of the legitimate concerns of placing multiple patients on a single ventilator. We emphasize that our results are currently based on a computational model that has not been validated against any preclinical or clinical data. Therefore, clinicians considering this approach should not look to our study as an exact estimate of predicted patient VT values.
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