A personalised computational model of the impact of COVID-19 on lung function under mechanical ventilation

Carson, Jason M.; Loon, Raoul van; Arora, Hari

doi:10.1016/j.compbiomed.2024.109177

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A personalised computational model of the impact of COVID-19 on lung function under mechanical ventilation

Jason M. Carson

, Raoul van Loon

, Hari Arora

Computers in Biology and Medicine, Volume: 183, Start page: 109177

Swansea University Authors: Jason M. Carson , Raoul van Loon , Hari Arora

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DOI (Published version): 10.1016/j.compbiomed.2024.109177

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
ISSN:	0010-4825
Published:	Elsevier BV 2024
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URI:	https://cronfa.swan.ac.uk/Record/cronfa67771
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first_indexed	2024-09-23T12:25:59Z
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Carson</name><active>true</active><ethesisStudent>true</ethesisStudent></author><author><sid>880b30f90841a022f1e5bac32fb12193</sid><ORCID>0000-0003-3581-5827</ORCID><firstname>Raoul</firstname><surname>van Loon</surname><name>Raoul van Loon</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>ed7371c768e9746008a6807f9f7a1555</sid><ORCID>0000-0002-9790-0907</ORCID><firstname>Hari</firstname><surname>Arora</surname><name>Hari Arora</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-09-23</date><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.</abstract><type>Journal Article</type><journal>Computers in Biology and Medicine</journal><volume>183</volume><journalNumber/><paginationStart>109177</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0010-4825</issnPrint><issnElectronic/><keywords>lung modelling, COVID-19, computational fluid dynamics, mechanical ventilation, reduced-order modelling</keywords><publishedDay>1</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-12-01</publishedDate><doi>10.1016/j.compbiomed.2024.109177</doi><url/><notes/><college>COLLEGE NANME</college><department>College of Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><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.</funders><projectreference/><lastEdited>2024-10-17T11:07:24.1743134</lastEdited><Created>2024-09-23T13:21:25.1466945</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Biomedical Engineering</level></path><authors><author><firstname>Jason M.</firstname><surname>Carson</surname><orcid>0000-0001-6634-9123</orcid><order>1</order></author><author><firstname>Raoul</firstname><surname>van Loon</surname><orcid>0000-0003-3581-5827</orcid><order>2</order></author><author><firstname>Hari</firstname><surname>Arora</surname><orcid>0000-0002-9790-0907</orcid><order>3</order></author></authors><documents><document><filename>67771__32615__6b489835a6394e469a9da10fb00057ce.pdf</filename><originalFilename>67771.VoR.pdf</originalFilename><uploaded>2024-10-17T11:03:36.5615449</uploaded><type>Output</type><contentLength>2553120</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2024 The Authors. 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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 d0fe636d559f9023182e4315c2940595 0000-0001-6634-9123 Jason M. Carson Jason M. Carson true true 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 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 183 109177 Elsevier BV 0010-4825 lung modelling, COVID-19, computational fluid dynamics, mechanical ventilation, reduced-order modelling 1 12 2024 2024-12-01 10.1016/j.compbiomed.2024.109177 COLLEGE NANME College of Engineering COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) 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. 2024-10-17T11:07:24.1743134 2024-09-23T13:21:25.1466945 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Jason M. Carson 0000-0001-6634-9123 1 Raoul van Loon 0000-0003-3581-5827 2 Hari Arora 0000-0002-9790-0907 3 67771__32615__6b489835a6394e469a9da10fb00057ce.pdf 67771.VoR.pdf 2024-10-17T11:03:36.5615449 Output 2553120 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
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 M. 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	d0fe636d559f9023182e4315c2940595 880b30f90841a022f1e5bac32fb12193 ed7371c768e9746008a6807f9f7a1555
author_id_fullname_str_mv	d0fe636d559f9023182e4315c2940595_*_Jason M. Carson 880b30f90841a022f1e5bac32fb12193__Raoul van Loon ed7371c768e9746008a6807f9f7a1555_**_Hari Arora
author	Jason M. Carson Raoul van Loon Hari Arora
author2	Jason M. Carson Raoul van Loon Hari Arora
format	Journal article
container_title	Computers in Biology and Medicine
container_volume	183
container_start_page	109177
publishDate	2024
institution	Swansea University
issn	0010-4825
doi_str_mv	10.1016/j.compbiomed.2024.109177
publisher	Elsevier BV
college_str	Faculty of Science and Engineering
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department_str	School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
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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	2024-12-01T11:07:22Z
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score	11.037603

A personalised computational model of the impact of COVID-19 on lung function under mechanical ventilation

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