{"id":{"repo_id":"ecu","oai_identifier":"oai:thescholarship.ecu.edu:10342/12194"},"canonical_url":"https://search.dev.ndltd.org/etd/ecu/oai:thescholarship.ecu.edu:10342/12194","repository":{"repo_id":"ecu","name":"East Carolina University","base_url":"https://thescholarship.ecu.edu/server/oai/request"},"display":{"title":"A Patient-Specific Multiscale Model of Mechanical Ventilation of COVID-19-afflicted Lungs","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Middleton, Shea Taran"],"institution":"East Carolina University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Engineering","school":null,"contributors":[],"advisors":["Vahdati, Ali"],"committee_chairs":[],"committee_members":["George, Stephanie M","Maddipati, Veeranna","Lust, Robert M"],"year":2022,"date_issued":"2022-07-24","date_published":"2022-07-24","updated_at":"2026-07-24T02:13:39Z","subjects":["Pulmonary mechanics, COVID-19, Pulmonary ventilation, Computer modeling, Acute respiratory distress syndrome, SARS-CoV-2, Lung mechanics, Mechanical ventilation, Patient-specific modeling"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10342/12194","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Vahdati, Ali"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["George, Stephanie M","Maddipati, Veeranna","Lust, Robert M"]},{"key":"dc:contributor.department","label":"Department","values":["Engineering"]},{"key":"dc:creator","label":"Author","values":["Middleton, Shea Taran"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-02-07T18:34:05Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-07-01T08:01:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-07-24"]},{"key":"dc:publisher","label":"Institution","values":["East Carolina University"]},{"key":"dc:type","label":"Dc Type","values":["Master's Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pulmonary mechanics, COVID-19, Pulmonary ventilation, Computer modeling, Acute respiratory distress syndrome, SARS-CoV-2, Lung mechanics, Mechanical ventilation, Patient-specific modeling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10342/12194"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A Patient-Specific Multiscale Model of Mechanical Ventilation of COVID-19-afflicted Lungs"]}]}],"canonical_facts":{"dc:contributor.advisor":["Vahdati, Ali"],"dc:contributor.committeemember":["George, Stephanie M","Maddipati, Veeranna","Lust, Robert M"],"dc:contributor.department":["Engineering"],"dc:creator":["Middleton, Shea Taran"],"dc:date.accessioned":["2023-02-07T18:34:05Z"],"dc:date.available":["2024-07-01T08:01:58Z"],"dc:date.issued":["2022-07-24"],"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:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/10342/12194"],"dc:language.iso":["en"],"dc:publisher":["East Carolina University"],"dc:subject":["Pulmonary mechanics, COVID-19, Pulmonary ventilation, Computer modeling, Acute respiratory distress syndrome, SARS-CoV-2, Lung mechanics, Mechanical ventilation, Patient-specific modeling"],"dc:title":["A Patient-Specific Multiscale Model of Mechanical Ventilation of COVID-19-afflicted Lungs"],"dc:type":["Master's Thesis"]},"updated_at":"2026-07-24T02:13:39Z"}