A personalised computational model of the impact of COVID-19 on lung function under mechanical ventilation
Year: 2024
Type: article
Abstract: This work proposes a modelling framework to analyse flow and pressure distributions throughout the lung of mechanically ventilated COVID-19 patients. The methodology involves: segmentation of the lungs and major airways from patient CT images; a volume filling algorithm that creates a dichotomous airway network in the remaining volume of the lung; an estimate of resistance and compliance within the lung based on Hounsfield unit values from the CT scan; and a computational fluid dynamics model to analyse flow, lung inflation, and pressure throughout the airway network. Mechanically ventilated patients with differing progression and severity of the disease were simulated. The results indicate that the flow distribution within the lung can be significantly affected when there are competing types of lung damage. These competing types are primarily fibrosis-like lung damage that creates higher resistance and lower compliance in that region; and emphysema, which causes a decrease in resistance and increase in compliance. In a patient with severe disease, the model predicted an increase in inflation by 33% in an area affected by emphysema-like conditions. This could increase the risk of alveolar rupture. The framework could readily be adapted to study other respiratory diseases. Early interventions in critical respiratory care could be facilitated through such efficient patient-specific modelling approaches. • Lung boundary and major airways are acquired from CT images to create personalised airway networks. • Estimates major airway parameters and their distribution, from ‘healthy’ to pathological states. • Simulates airflow and pressure distributions in a structured airway network for a ventilated lung. • Demonstrates impact of both total and locally distributed respiratory compliance and resistance.
Authors: Jason M. Carson, Raoul Van Loon, Hari Arora
Institution: Swansea University
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,640
OpenAlex ID: W4403461427