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Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling

PURPOSE: The aim of the present study was to evaluate the performance of the forced oscillation technique (FOT) for the early diagnosis of the effects of smoking and COPD. The contributions of the integer-order (InOr) and fractional-order (FrOr) models were also evaluated. PATIENTS AND METHODS: In t...

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Autores principales: Ribeiro, Caroline Oliveira, Faria, Alvaro Camilo Dias, Lopes, Agnaldo José, de Melo, Pedro Lopes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188181/
https://www.ncbi.nlm.nih.gov/pubmed/30349233
http://dx.doi.org/10.2147/COPD.S173686
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author Ribeiro, Caroline Oliveira
Faria, Alvaro Camilo Dias
Lopes, Agnaldo José
de Melo, Pedro Lopes
author_facet Ribeiro, Caroline Oliveira
Faria, Alvaro Camilo Dias
Lopes, Agnaldo José
de Melo, Pedro Lopes
author_sort Ribeiro, Caroline Oliveira
collection PubMed
description PURPOSE: The aim of the present study was to evaluate the performance of the forced oscillation technique (FOT) for the early diagnosis of the effects of smoking and COPD. The contributions of the integer-order (InOr) and fractional-order (FrOr) models were also evaluated. PATIENTS AND METHODS: In total, 120 subjects were analyzed: 40 controls, 40 smokers (20.3±9.3 pack-years) and 40 patients with mild COPD. RESULTS: Initially, it was observed that traditional FOT parameters and the InOr and FrOr models provided a consistent description of the COPD pathophysiology. Mild COPD introduced significant increases in the FrOr inertance, damping factor and hysteresivity (P<0.0001). These parameters were significantly correlated with the spirometric parameters of central and small airway obstruction (P<0.0001). The diagnostic accuracy analyses indicated that FOT parameters and InOr modeling may adequately identify these changes (area under the receiver operating characteristic curve – AUC >0.8). The use of FrOr modeling significantly improved this process (P<0.05), allowing the early diagnosis of smokers and patients with mild COPD with high accuracy (AUC >0.9). CONCLUSION: FrOr modeling improves our knowledge of modifications that occur in the early stages of COPD. Additionally, the findings of the present study provide evidence that these models may play an important role in the early diagnosis of COPD, which is crucial for improving the clinical management of the disease.
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spelling pubmed-61881812018-10-22 Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling Ribeiro, Caroline Oliveira Faria, Alvaro Camilo Dias Lopes, Agnaldo José de Melo, Pedro Lopes Int J Chron Obstruct Pulmon Dis Original Research PURPOSE: The aim of the present study was to evaluate the performance of the forced oscillation technique (FOT) for the early diagnosis of the effects of smoking and COPD. The contributions of the integer-order (InOr) and fractional-order (FrOr) models were also evaluated. PATIENTS AND METHODS: In total, 120 subjects were analyzed: 40 controls, 40 smokers (20.3±9.3 pack-years) and 40 patients with mild COPD. RESULTS: Initially, it was observed that traditional FOT parameters and the InOr and FrOr models provided a consistent description of the COPD pathophysiology. Mild COPD introduced significant increases in the FrOr inertance, damping factor and hysteresivity (P<0.0001). These parameters were significantly correlated with the spirometric parameters of central and small airway obstruction (P<0.0001). The diagnostic accuracy analyses indicated that FOT parameters and InOr modeling may adequately identify these changes (area under the receiver operating characteristic curve – AUC >0.8). The use of FrOr modeling significantly improved this process (P<0.05), allowing the early diagnosis of smokers and patients with mild COPD with high accuracy (AUC >0.9). CONCLUSION: FrOr modeling improves our knowledge of modifications that occur in the early stages of COPD. Additionally, the findings of the present study provide evidence that these models may play an important role in the early diagnosis of COPD, which is crucial for improving the clinical management of the disease. Dove Medical Press 2018-10-11 /pmc/articles/PMC6188181/ /pubmed/30349233 http://dx.doi.org/10.2147/COPD.S173686 Text en © 2018 Ribeiro et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Ribeiro, Caroline Oliveira
Faria, Alvaro Camilo Dias
Lopes, Agnaldo José
de Melo, Pedro Lopes
Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling
title Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling
title_full Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling
title_fullStr Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling
title_full_unstemmed Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling
title_short Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling
title_sort forced oscillation technique for early detection of the effects of smoking and copd: contribution of fractional-order modeling
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188181/
https://www.ncbi.nlm.nih.gov/pubmed/30349233
http://dx.doi.org/10.2147/COPD.S173686
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