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Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians

BACKGROUND: Spirometry is recommended for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in international guidelines and may be useful for distinguishing asthma from COPD. Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies h...

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Autores principales: He, Xiao-Ou, D’Urzo, Anthony, Jugovic, Pieter, Jhirad, Reuven, Sehgal, Prateek, Lilly, Evan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373504/
https://www.ncbi.nlm.nih.gov/pubmed/25763716
http://dx.doi.org/10.1038/npjpcrm.2015.8
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author He, Xiao-Ou
D’Urzo, Anthony
Jugovic, Pieter
Jhirad, Reuven
Sehgal, Prateek
Lilly, Evan
author_facet He, Xiao-Ou
D’Urzo, Anthony
Jugovic, Pieter
Jhirad, Reuven
Sehgal, Prateek
Lilly, Evan
author_sort He, Xiao-Ou
collection PubMed
description BACKGROUND: Spirometry is recommended for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in international guidelines and may be useful for distinguishing asthma from COPD. Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies highlight how different SIAs may influence the interpretation of the same spirometric data. AIMS: We examined how two different SIAs may influence decision making among primary-care physicians. METHODS: Data for this initiative were gathered from 113 primary-care physicians attending accredited workshops in Canada between 2011 and 2013. Physicians were asked to interpret nine spirograms presented twice in random sequence using two different SIAs and touch pad technology for anonymous data recording. RESULTS: We observed differences in the interpretation of spirograms using two different SIAs. When the pre-bronchodilator FEV(1)/FVC (forced expiratory volume in one second/forced vital capacity) ratio was >0.70, algorithm 1 led to a ‘normal’ interpretation (78% of physicians), whereas algorithm 2 prompted a bronchodilator challenge revealing changes in FEV(1) that were consistent with asthma, an interpretation selected by 94% of physicians. When the FEV(1)/FVC ratio was <0.70 after bronchodilator challenge but FEV(1) increased >12% and 200 ml, 76% suspected asthma and 10% suspected COPD using algorithm 1, whereas 74% suspected asthma versus COPD using algorithm 2 across five separate cases. The absence of a post-bronchodilator FEV(1)/FVC decision node in algorithm 1 did not permit consideration of possible COPD. CONCLUSIONS: This study suggests that differences in SIAs may influence decision making and lead clinicians to interpret the same spirometry data differently.
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spelling pubmed-43735042015-09-15 Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians He, Xiao-Ou D’Urzo, Anthony Jugovic, Pieter Jhirad, Reuven Sehgal, Prateek Lilly, Evan NPJ Prim Care Respir Med Article BACKGROUND: Spirometry is recommended for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in international guidelines and may be useful for distinguishing asthma from COPD. Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies highlight how different SIAs may influence the interpretation of the same spirometric data. AIMS: We examined how two different SIAs may influence decision making among primary-care physicians. METHODS: Data for this initiative were gathered from 113 primary-care physicians attending accredited workshops in Canada between 2011 and 2013. Physicians were asked to interpret nine spirograms presented twice in random sequence using two different SIAs and touch pad technology for anonymous data recording. RESULTS: We observed differences in the interpretation of spirograms using two different SIAs. When the pre-bronchodilator FEV(1)/FVC (forced expiratory volume in one second/forced vital capacity) ratio was >0.70, algorithm 1 led to a ‘normal’ interpretation (78% of physicians), whereas algorithm 2 prompted a bronchodilator challenge revealing changes in FEV(1) that were consistent with asthma, an interpretation selected by 94% of physicians. When the FEV(1)/FVC ratio was <0.70 after bronchodilator challenge but FEV(1) increased >12% and 200 ml, 76% suspected asthma and 10% suspected COPD using algorithm 1, whereas 74% suspected asthma versus COPD using algorithm 2 across five separate cases. The absence of a post-bronchodilator FEV(1)/FVC decision node in algorithm 1 did not permit consideration of possible COPD. CONCLUSIONS: This study suggests that differences in SIAs may influence decision making and lead clinicians to interpret the same spirometry data differently. Nature Publishing Group 2015-03-12 /pmc/articles/PMC4373504/ /pubmed/25763716 http://dx.doi.org/10.1038/npjpcrm.2015.8 Text en Copyright © 2015 Primary Care Respiratory Society UK/Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
spellingShingle Article
He, Xiao-Ou
D’Urzo, Anthony
Jugovic, Pieter
Jhirad, Reuven
Sehgal, Prateek
Lilly, Evan
Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
title Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
title_full Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
title_fullStr Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
title_full_unstemmed Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
title_short Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
title_sort differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373504/
https://www.ncbi.nlm.nih.gov/pubmed/25763716
http://dx.doi.org/10.1038/npjpcrm.2015.8
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