Literature DB >> 26388199

Quantifying the shape of maximal expiratory flow-volume curves in healthy humans and asthmatic patients.

Paolo B Dominelli1, Yannick Molgat-Seon2, Glen E Foster3, Giulio S Dominelli4, Hans C Haverkamp5, William R Henderson2, A William Sheel2.   

Abstract

Differences in the absolute flow and volume of maximal expiratory flow-volume (MEFV) curves have been studied extensively in health and disease. However, the shapes of MEFV curves have received less attention. We questioned if the MEFV curve shape was associated with (i) expiratory flow limitation (EFL) in health and (ii) changes in bronchial caliber in asthmatics. Using the slope-ratio (SR) index, we quantified MEFV curve shape in 84 healthy subjects and 8 matched asthmatics. Healthy subjects performed a maximal exercise test to assess EFL. Those with EFL during had a greater SR (1.15 ± 0.20 vs. 0.85 ± 0.20, p<0.05) yet, there was no association between maximal oxygen consumption and SR (r=0.14, p>0.05). Asthmatics average SR was greater than the healthy subjects (1.35 ± 0.03 vs. 0.90 ± 0.11, p<0.05), but there were no differences when bronchial caliber was manipulated. In conclusion, a greater SR is related to EFL and this metric could aid in discriminating between groups known to differ in the absolute size of MEFV curves.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Expiratory flow limitation; Pulmonary system limitation; Ventilator constraints

Mesh:

Substances:

Year:  2015        PMID: 26388199      PMCID: PMC6238952          DOI: 10.1016/j.resp.2015.09.007

Source DB:  PubMed          Journal:  Respir Physiol Neurobiol        ISSN: 1569-9048            Impact factor:   1.931


  39 in total

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Journal:  Respir Physiol Neurobiol       Date:  2014-06-27       Impact factor: 1.931

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Authors:  Tony G Babb
Journal:  Exerc Sport Sci Rev       Date:  2013-01       Impact factor: 6.230

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10.  Evidence for dysanapsis using computed tomographic imaging of the airways in older ex-smokers.

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  2 in total

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Authors:  Yannick Molgat-Seon; Paolo B Dominelli; Carli M Peters; Jordan A Guenette; A William Sheel; Igor M Gladstone; Andrew T Lovering; Joseph W Duke
Journal:  Am J Physiol Regul Integr Comp Physiol       Date:  2019-08-21       Impact factor: 3.619

2.  Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Authors:  Paresh C Giri; Anand M Chowdhury; Armando Bedoya; Hengji Chen; Hyun Suk Lee; Patty Lee; Craig Henriquez; Neil R MacIntyre; Yuh-Chin T Huang
Journal:  Front Physiol       Date:  2021-06-24       Impact factor: 4.566

  2 in total

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