A Patient-Specific Multiscale Model of Mechanical Ventilation of COVID-19-afflicted Lungs
dc.access.option | Restricted Campus Access Only | |
dc.contributor.advisor | Vahdati, Ali | |
dc.contributor.author | Middleton, Shea Taran | |
dc.contributor.department | Engineering | |
dc.date.accessioned | 2022-09-12T14:44:21Z | |
dc.date.available | 2024-07-01T08:02:00Z | |
dc.date.created | 2022-07 | |
dc.date.issued | 2022-07-24 | |
dc.date.submitted | July 2022 | |
dc.date.updated | 2022-08-30T19:22:43Z | |
dc.degree.department | Engineering | |
dc.degree.discipline | MS-Biomedical Engineering | |
dc.degree.grantor | East Carolina University | |
dc.degree.level | Masters | |
dc.degree.name | M.S. | |
dc.description.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. | |
dc.embargo.lift | 2024-07-01 | |
dc.format.mimetype | application/pdf | |
dc.identifier.uri | http://hdl.handle.net/10342/11121 | |
dc.language.iso | en | |
dc.publisher | East Carolina University | |
dc.subject | Pulmonary mechanics | |
dc.subject | Pulmonary ventilation | |
dc.subject | Acute respiratory distress syndrome | |
dc.subject | Lung mechanics | |
dc.subject | Mechanical ventilation | |
dc.subject | Patient-specific modeling | |
dc.subject.lcsh | COVID-19 (Disease)--Evaluation | |
dc.subject.lcsh | Computer simulation | |
dc.subject.lcsh | Lungs--Diseases | |
dc.subject.lcsh | Pulmonary function tests | |
dc.subject.lcsh | Artificial respiration | |
dc.title | A Patient-Specific Multiscale Model of Mechanical Ventilation of COVID-19-afflicted Lungs | |
dc.type | Master's Thesis | |
dc.type.material | text |