A Patient-Specific Multiscale Model of Mechanical Ventilation of COVID-19-afflicted Lungs

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Shea Taran Middleton (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: Coronavirus disease-2019 (COVID-19) is a respiratory disease that caused a worldwide pandemic and, in some cases, manifests as an acute respiratory distress syndrome. Severe cases of COVID-19 are often treated with mechanical ventilation, which has a high risk of causing ventilator-induced lung injury. However, COVID-19 is a relatively recent disease, and there is a lack of detailed understanding of its response to mechanical ventilation. This thesis aims to create a multiscale physics-based computational modeling framework for COVID-19-related acute respiratory distress syndrome (CARDS) to examine region-specific and overall lung dynamics for patients subject to mechanical ventilation. This goal is accomplished by developing patient-specific image-based models of free-breathing and mechanically ventilated patients using four-dimensional computed tomography (4DCT) imaging data from COVID-19 patients. Models presented in this thesis were designed to provide insight into airflow redistribution and volume and pressure differentials on a regional basis. One model was developed as a patient-specific proof-of-concept of realistic simulation of healthy and COVID-19 free-breathing mechanics. The free-breathing model was then modified to simulate pressure-control mechanical ventilation conditions and applied to four patients with advanced COVID-19. This in silico mechanical ventilation model reasonably predicted redistribution of ventilation from severely damaged lung lobes to the lobes less affected by COVID-19 damage, potentially revealing a risk factor of mechanical ventilation volutrauma due to COVID-19 damage heterogeneity. Each mechanical ventilation simulation was validated and showed reasonable agreement with existing image- or clinical data-based studies of COVID-19 and other lung pathologies. This study exhibits a foundation for future COVID-19 patient-specific multiscale lung modeling.

Additional Information

Publication

Thesis

Language: English

Date: 2023

Subjects

Pulmonary mechanics, COVID-19, Pulmonary ventilation, Computer modeling, Acute respiratory distress syndrome, SARS-CoV-2, Lung mechanics, Mechanical ventilation, Patient-specific modeling

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