Predicting epiglottic collapse in patients with obstructive sleep apnoea
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNIFESP |
Texto Completo: | http://repositorio.unifesp.br/handle/11600/51266 http://dx.doi.org/10.1183/13993003.00345-2017 |
Resumo: | Obstructive sleep apnoea (OSA) is characterised by pharyngeal obstruction occurring at different sites. Endoscopic studies reveal that epiglottic collapse renders patients at higher risk of failed oral appliance therapy or accentuated collapse on continuous positive airway pressure. Diagnosing epiglottic collapse currently requires invasive studies (imaging and endoscopy). As an alternative, we propose that epiglottic collapse can be detected from the distinct airflow patterns it produces during sleep. 23 OSA patients underwent natural sleep endoscopy. 1232 breaths were scored as epiglottic/nonepiglottic collapse. Several flow characteristics were determined from the flow signal (recorded simultaneously with endoscopy) and used to build a predictive model to distinguish epiglottic from nonepiglottic collapse. Additionally, 10 OSA patients were studied to validate the pneumotachograph flow features using nasal pressure signals. Epiglottic collapse was characterised by a rapid fall(s) in the inspiratory flow, more variable inspiratory and expiratory flow and reduced tidal volume. The cross-validated accuracy was 84%. Predictive features obtained from pneumotachograph flow and nasal pressure were strongly correlated. This study demonstrates that epiglottic collapse can be identified from the airflow signal measured during a sleep study. This method may enable clinicians to use clinically collected data to characterise underlying physiology and improve treatment decisions. |
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Azarbarzin, AliMarques, MelaniaSands, Scott A.de Beeck, Sara OpGenta, Pedro R.Taranto-Montemurro, Luigide Melo, Camila M. [UNIFESP]Messineo, LudovicoVanderveken, Olivier M.White, David P.Wellman, Andrew2019-08-19T11:48:30Z2019-08-19T11:48:30Z2017European Respiratory Journal. Sheffield, v. 50, n. 3, p. -, 2017.0903-1936http://repositorio.unifesp.br/handle/11600/51266http://dx.doi.org/10.1183/13993003.00345-201710.1183/13993003.00345-2017WOS:000412289900007Obstructive sleep apnoea (OSA) is characterised by pharyngeal obstruction occurring at different sites. Endoscopic studies reveal that epiglottic collapse renders patients at higher risk of failed oral appliance therapy or accentuated collapse on continuous positive airway pressure. Diagnosing epiglottic collapse currently requires invasive studies (imaging and endoscopy). As an alternative, we propose that epiglottic collapse can be detected from the distinct airflow patterns it produces during sleep. 23 OSA patients underwent natural sleep endoscopy. 1232 breaths were scored as epiglottic/nonepiglottic collapse. Several flow characteristics were determined from the flow signal (recorded simultaneously with endoscopy) and used to build a predictive model to distinguish epiglottic from nonepiglottic collapse. Additionally, 10 OSA patients were studied to validate the pneumotachograph flow features using nasal pressure signals. Epiglottic collapse was characterised by a rapid fall(s) in the inspiratory flow, more variable inspiratory and expiratory flow and reduced tidal volume. The cross-validated accuracy was 84%. Predictive features obtained from pneumotachograph flow and nasal pressure were strongly correlated. This study demonstrates that epiglottic collapse can be identified from the airflow signal measured during a sleep study. This method may enable clinicians to use clinically collected data to characterise underlying physiology and improve treatment decisions.OMPA Corporation, Kaifeng, ChinaPhilips Respironics research grantNational Institutes of Health (NIH)Harvard Catalyst Clinical Research Center (HCCRC)Natural Sciences and Engineering Research Council of Canada (NSERC)National Health and Medical Research Council of Australia (NHMRC)Menzies FoundationAmerican Heart Association (AHA)American Thoracic Society FoundationFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Brigham & Womens Hosp, Div Sleep & Circadian Disorders, 75 Francis St, Boston, MA 02115 USAHarvard Med Sch, Boston, MA USAUniv São Paulo, Hosp Clin HCFMUSP, Heart Inst InCor, Pulm Div,Fac Med, São Paulo, BrazilAlfred & Monash Univ, Dept Allergy Immunol & Resp Med, Melbourne, Vic, AustraliaAlfred & Monash Univ, Cent Clin Sch, Melbourne, Vic, AustraliaUniv Antwerp, Fac Med & Hlth Sci, Translat Neurosci, Antwerp, BelgiumUniv Fed São Paulo UNIFESP, Dept Psychobiol, São Paulo, BrazilAntwerp Univ Hosp, Dept ENT Head & Neck Surg, Edegem, BelgiumUniv Fed São Paulo UNIFESP, Dept Psychobiol, São Paulo, BrazilNIH: R01 HL 128658NIH: 2R01HL102321NIH: P01 NIH HL095491HCCRC: UL1 RR 025758-01NHMRC: 1053201AHA: 15SDG25890059AHA: 17POST33410436Web of Science-engEuropean Respiratory Soc Journals LtdPredicting epiglottic collapse in patients with obstructive sleep apnoeainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNIFESPinstname:Universidade Federal de São Paulo (UNIFESP)instacron:UNIFESP11600/512662021-10-05 21:33:38.884metadata only accessoai:repositorio.unifesp.br:11600/51266Repositório InstitucionalPUBhttp://www.repositorio.unifesp.br/oai/requestopendoar:34652021-10-06T00:33:38Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP)false |
dc.title.en.fl_str_mv |
Predicting epiglottic collapse in patients with obstructive sleep apnoea |
title |
Predicting epiglottic collapse in patients with obstructive sleep apnoea |
spellingShingle |
Predicting epiglottic collapse in patients with obstructive sleep apnoea Azarbarzin, Ali |
title_short |
Predicting epiglottic collapse in patients with obstructive sleep apnoea |
title_full |
Predicting epiglottic collapse in patients with obstructive sleep apnoea |
title_fullStr |
Predicting epiglottic collapse in patients with obstructive sleep apnoea |
title_full_unstemmed |
Predicting epiglottic collapse in patients with obstructive sleep apnoea |
title_sort |
Predicting epiglottic collapse in patients with obstructive sleep apnoea |
author |
Azarbarzin, Ali |
author_facet |
Azarbarzin, Ali Marques, Melania Sands, Scott A. de Beeck, Sara Op Genta, Pedro R. Taranto-Montemurro, Luigi de Melo, Camila M. [UNIFESP] Messineo, Ludovico Vanderveken, Olivier M. White, David P. Wellman, Andrew |
author_role |
author |
author2 |
Marques, Melania Sands, Scott A. de Beeck, Sara Op Genta, Pedro R. Taranto-Montemurro, Luigi de Melo, Camila M. [UNIFESP] Messineo, Ludovico Vanderveken, Olivier M. White, David P. Wellman, Andrew |
author2_role |
author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Azarbarzin, Ali Marques, Melania Sands, Scott A. de Beeck, Sara Op Genta, Pedro R. Taranto-Montemurro, Luigi de Melo, Camila M. [UNIFESP] Messineo, Ludovico Vanderveken, Olivier M. White, David P. Wellman, Andrew |
description |
Obstructive sleep apnoea (OSA) is characterised by pharyngeal obstruction occurring at different sites. Endoscopic studies reveal that epiglottic collapse renders patients at higher risk of failed oral appliance therapy or accentuated collapse on continuous positive airway pressure. Diagnosing epiglottic collapse currently requires invasive studies (imaging and endoscopy). As an alternative, we propose that epiglottic collapse can be detected from the distinct airflow patterns it produces during sleep. 23 OSA patients underwent natural sleep endoscopy. 1232 breaths were scored as epiglottic/nonepiglottic collapse. Several flow characteristics were determined from the flow signal (recorded simultaneously with endoscopy) and used to build a predictive model to distinguish epiglottic from nonepiglottic collapse. Additionally, 10 OSA patients were studied to validate the pneumotachograph flow features using nasal pressure signals. Epiglottic collapse was characterised by a rapid fall(s) in the inspiratory flow, more variable inspiratory and expiratory flow and reduced tidal volume. The cross-validated accuracy was 84%. Predictive features obtained from pneumotachograph flow and nasal pressure were strongly correlated. This study demonstrates that epiglottic collapse can be identified from the airflow signal measured during a sleep study. This method may enable clinicians to use clinically collected data to characterise underlying physiology and improve treatment decisions. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017 |
dc.date.accessioned.fl_str_mv |
2019-08-19T11:48:30Z |
dc.date.available.fl_str_mv |
2019-08-19T11:48:30Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
European Respiratory Journal. Sheffield, v. 50, n. 3, p. -, 2017. |
dc.identifier.uri.fl_str_mv |
http://repositorio.unifesp.br/handle/11600/51266 http://dx.doi.org/10.1183/13993003.00345-2017 |
dc.identifier.issn.none.fl_str_mv |
0903-1936 |
dc.identifier.doi.none.fl_str_mv |
10.1183/13993003.00345-2017 |
dc.identifier.wos.none.fl_str_mv |
WOS:000412289900007 |
identifier_str_mv |
European Respiratory Journal. Sheffield, v. 50, n. 3, p. -, 2017. 0903-1936 10.1183/13993003.00345-2017 WOS:000412289900007 |
url |
http://repositorio.unifesp.br/handle/11600/51266 http://dx.doi.org/10.1183/13993003.00345-2017 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
- |
dc.publisher.none.fl_str_mv |
European Respiratory Soc Journals Ltd |
publisher.none.fl_str_mv |
European Respiratory Soc Journals Ltd |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UNIFESP instname:Universidade Federal de São Paulo (UNIFESP) instacron:UNIFESP |
instname_str |
Universidade Federal de São Paulo (UNIFESP) |
instacron_str |
UNIFESP |
institution |
UNIFESP |
reponame_str |
Repositório Institucional da UNIFESP |
collection |
Repositório Institucional da UNIFESP |
repository.name.fl_str_mv |
Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP) |
repository.mail.fl_str_mv |
|
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1802764145825677312 |