Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device

Intoduction: In veterinary medicine, airway management of cats under general anesthesia is performed with an endotracheal tube (ETT) or supraglottic airway device (SGAD). This study aims to describe the use of computational fluid dynamics (CFD) to assess the velocities, pressures, and resistances of...

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Detalhes bibliográficos
Autores: Zamora Perarnau, Carla, Malvè, Mauro, Fernández Parra, Rocío
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universidad Católica de Valencia San Vicente Mártir
Repositorio:RIUCV. Repositorio de la Universidad Católica de Valencia San Vicente Mártir
Idioma:inglés
OAI Identifier:oai:riucv.ucv.es:20.500.12466/3382
Acesso em linha:http://hdl.handle.net/20.500.12466/3382
Access Level:acceso abierto
Palavra-chave:In silico
Airway management
V-gel®
Feline
General anesthesia
ETT
Flow
2204.03 Flujo de Fluidos
3109 Ciencias Veterinarias
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spelling Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway deviceZamora Perarnau, CarlaMalvè, MauroFernández Parra, RocíoIn silicoAirway managementV-gel®FelineGeneral anesthesiaETTFlow2204.03 Flujo de Fluidos3109 Ciencias VeterinariasIntoduction: In veterinary medicine, airway management of cats under general anesthesia is performed with an endotracheal tube (ETT) or supraglottic airway device (SGAD). This study aims to describe the use of computational fluid dynamics (CFD) to assess the velocities, pressures, and resistances of cats with ETT or SGAD. Methods: A geometrical reconstruction model of the device, trachea, and lobar bronchi was carried out from computed tomography (CT) scans that include the head, neck, and thorax. Twenty CT scans of cats under general anesthesia using ETT (n = 10) and SGAD (n = 10) were modeled and analyzed. An inspiratory flow of 2.4 L/min was imposed in each model and velocity (m/s), general and regional pressures (cmH2O) were computed. General resistance (cmH2O/L/min) was calculated using differential pressure differences between the device inlet and lobar bronchi. Additionally, regional resistances were calculated at the device’s connection with the breathing circuit (region A), at the glottis area for the SGAD, and the area of the ETT exit (bevel) (region B) and the device itself (region C). Results: Recirculatory flow and high velocities were found at the ETT’s bevel and at the glottis level in the SGAD group. The pressure gradient (Δp) was more enhanced in the ETT cases compared with the SGAD cases, where the pressure change was drastic. In region A, the Δp was higher in the ETT group, while in regions B and C, it was higher in the SGAD group. The general resistance was not statistically significant between groups (p = 0.48). Higher resistances were found at the region A (p = <0.001) in the ETT group. In contrast, the resistance was higher in the SGAD cases at the region B (p = 0.001). Discussion: Overall, the provided CT-based CFD analysis demonstrated regional changes in airway pressure and resistance between ETT and SGAD during anesthetic flow conditions. Correct selection of the airway device size is recommended to avoid upper airway obstruction or changes in flow parameters.20232023-12-2220232023-09-2520232023-09-25journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/20.500.12466/3382reponame:RIUCV. Repositorio de la Universidad Católica de Valencia San Vicente Mártirinstname:Universidad Católica de Valencia San Vicente MártirInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riucv.ucv.es:20.500.12466/33822026-06-19T08:32:07Z
dc.title.none.fl_str_mv Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
title Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
spellingShingle Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
Zamora Perarnau, Carla
In silico
Airway management
V-gel®
Feline
General anesthesia
ETT
Flow
2204.03 Flujo de Fluidos
3109 Ciencias Veterinarias
title_short Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
title_full Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
title_fullStr Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
title_full_unstemmed Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
title_sort Computational fluid dynamics comparison of the upper airway velocity, pressure, and resistance in cats using an endotracheal tube or a supraglottic airway device
dc.creator.none.fl_str_mv Zamora Perarnau, Carla
Malvè, Mauro
Fernández Parra, Rocío
author Zamora Perarnau, Carla
author_facet Zamora Perarnau, Carla
Malvè, Mauro
Fernández Parra, Rocío
author_role author
author2 Malvè, Mauro
Fernández Parra, Rocío
author2_role author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv In silico
Airway management
V-gel®
Feline
General anesthesia
ETT
Flow
2204.03 Flujo de Fluidos
3109 Ciencias Veterinarias
topic In silico
Airway management
V-gel®
Feline
General anesthesia
ETT
Flow
2204.03 Flujo de Fluidos
3109 Ciencias Veterinarias
description Intoduction: In veterinary medicine, airway management of cats under general anesthesia is performed with an endotracheal tube (ETT) or supraglottic airway device (SGAD). This study aims to describe the use of computational fluid dynamics (CFD) to assess the velocities, pressures, and resistances of cats with ETT or SGAD. Methods: A geometrical reconstruction model of the device, trachea, and lobar bronchi was carried out from computed tomography (CT) scans that include the head, neck, and thorax. Twenty CT scans of cats under general anesthesia using ETT (n = 10) and SGAD (n = 10) were modeled and analyzed. An inspiratory flow of 2.4 L/min was imposed in each model and velocity (m/s), general and regional pressures (cmH2O) were computed. General resistance (cmH2O/L/min) was calculated using differential pressure differences between the device inlet and lobar bronchi. Additionally, regional resistances were calculated at the device’s connection with the breathing circuit (region A), at the glottis area for the SGAD, and the area of the ETT exit (bevel) (region B) and the device itself (region C). Results: Recirculatory flow and high velocities were found at the ETT’s bevel and at the glottis level in the SGAD group. The pressure gradient (Δp) was more enhanced in the ETT cases compared with the SGAD cases, where the pressure change was drastic. In region A, the Δp was higher in the ETT group, while in regions B and C, it was higher in the SGAD group. The general resistance was not statistically significant between groups (p = 0.48). Higher resistances were found at the region A (p = <0.001) in the ETT group. In contrast, the resistance was higher in the SGAD cases at the region B (p = 0.001). Discussion: Overall, the provided CT-based CFD analysis demonstrated regional changes in airway pressure and resistance between ETT and SGAD during anesthetic flow conditions. Correct selection of the airway device size is recommended to avoid upper airway obstruction or changes in flow parameters.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-12-22
2023
2023-09-25
2023
2023-09-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12466/3382
url http://hdl.handle.net/20.500.12466/3382
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:RIUCV. Repositorio de la Universidad Católica de Valencia San Vicente Mártir
instname:Universidad Católica de Valencia San Vicente Mártir
instname_str Universidad Católica de Valencia San Vicente Mártir
reponame_str RIUCV. Repositorio de la Universidad Católica de Valencia San Vicente Mártir
collection RIUCV. Repositorio de la Universidad Católica de Valencia San Vicente Mártir
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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