Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19. (June 2022)

• Record Type: Journal Article
• Authors: Middleton, Shea; Dimbath, Elizabeth; Pant, Anup; George, Stephanie M.; Maddipati, Veeranna; Peach, M. Sean; Yang, Kaida; Ju, Andrew W.; Vahdati, Ali
• Is Part Of: Computers in biology and medicine. Volume 145(2022)
• Publication Date: 2022-06
• Subjects: Pulmonary mechanics -- COVID-19 -- Pulmonary ventilation -- Computer modeling -- Acute respiratory distress syndrome -- SARS-CoV-2 -- Lung mechanics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105513
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics. Highlights: Developed CT-based modeling approach to simulate heterogeneous COVID-19 lung damage. Ventilation was redistributed to less injured lung lobes. Overall tidal volume decreased as tissue injury and surfactant loss increased. Implemented a multi-scale subject-specific computational model of COVID-19 lung.