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A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
Jason Carson 
,
Raoul van Loon ,
Hari Arora
,
Raoul van Loon ,
Hari Arora
Computers in Biology and Medicine
Swansea University Authors:
Jason Carson , Raoul van Loon , Hari Arora
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 adichotomous air...
| Published in: | Computers in Biology and Medicine |
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| Published: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa67771 |
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| 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 adichotomous 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. |
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| Keywords: |
lung modelling, COVID-19, computational fluid dynamics, mechanical ventilation, reduced-order modelling |
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors acknowledge funding support from Welsh Government, WG, (MA/KW/1457/20) and the Engineering and Physical Sciences Research Council, EPSRC, (EP/V041789/1) for the development and exploitation of the model framework. The authors also acknlowedge the Research Impact Fund from EPSRC for wider dissemination activities realted to this research article. The authors are also grateful for valuable discussions with healthcare professionals from Hywel Dda University Health Board, Swansea Bay University Health Board, and Cwm Taf Morgannwg University Health Board providing insight on their experiences with COVID-19 and data access. |