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A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation

Jason Carson Orcid Logo, Raoul van Loon Orcid Logo, Hari Arora Orcid Logo

Computers in Biology and Medicine

Swansea University Authors: Jason Carson Orcid Logo, Raoul van Loon Orcid Logo, Hari Arora Orcid Logo

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...

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Published in: Computers in Biology and Medicine
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URI: https://cronfa.swan.ac.uk/Record/cronfa67771
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spelling v2 67771 2024-09-23 A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation c1f2c28fbe6a41c5134b45abde5abb93 0000-0001-6634-9123 Jason Carson Jason Carson true false 880b30f90841a022f1e5bac32fb12193 0000-0003-3581-5827 Raoul van Loon Raoul van Loon true false ed7371c768e9746008a6807f9f7a1555 0000-0002-9790-0907 Hari Arora Hari Arora true false 2024-09-23 EAAS 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. Journal Article Computers in Biology and Medicine lung modelling, COVID-19, computational fluid dynamics, mechanical ventilation, reduced-order modelling 0 0 0 0001-01-01 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University 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. 2024-09-23T13:26:00.6873585 2024-09-23T13:21:25.1466945 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Jason Carson 0000-0001-6634-9123 1 Raoul van Loon 0000-0003-3581-5827 2 Hari Arora 0000-0002-9790-0907 3
title A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
spellingShingle A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
Jason Carson
Raoul van Loon
Hari Arora
title_short A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
title_full A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
title_fullStr A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
title_full_unstemmed A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
title_sort A Personalised Computational Model of the Impact of COVID-19 on Lung Function Under Mechanical Ventilation
author_id_str_mv c1f2c28fbe6a41c5134b45abde5abb93
880b30f90841a022f1e5bac32fb12193
ed7371c768e9746008a6807f9f7a1555
author_id_fullname_str_mv c1f2c28fbe6a41c5134b45abde5abb93_***_Jason Carson
880b30f90841a022f1e5bac32fb12193_***_Raoul van Loon
ed7371c768e9746008a6807f9f7a1555_***_Hari Arora
author Jason Carson
Raoul van Loon
Hari Arora
author2 Jason Carson
Raoul van Loon
Hari Arora
format Journal article
container_title Computers in Biology and Medicine
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 0
active_str 0
description 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.
published_date 0001-01-01T13:25:59Z
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score 11.028798

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