| Summary |
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. |
| General note | Presented to the Faculty of the Department of Engineering |
| General note | Advisor: Ali Vahdati |
| General note | Title from PDF t.p. (viewed February 29, 2024). |
| Dissertation note | M.S. East Carolina University 2022 |
| Bibliography note | Includes bibliographical references. |
| Technical details | System requirements: Adobe Reader. |
| Technical details | Mode of access: World Wide Web. |
| Genre/form | dissertations. |
| Genre/form | Academic theses. |
| Genre/form | Academic theses. |
| Genre/form | Thèses et écrits académiques. |
