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The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings

BACKGROUND: In general practice (GP), the diagnosis of obstructive airway diseases much relies on diagnostic questions, in view of the limited availability of lung function. We systematically assessed the relative importance of such questions for diagnosing asthma and chronic obstructive pulmonary d...

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Autores principales: Jankrift, Neele, Kellerer, Christina, Magnussen, Helgo, Nowak, Dennis, Jörres, Rudolf A., Schneider, Antonius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264721/
https://www.ncbi.nlm.nih.gov/pubmed/34277033
http://dx.doi.org/10.21037/jtd-20-3539
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author Jankrift, Neele
Kellerer, Christina
Magnussen, Helgo
Nowak, Dennis
Jörres, Rudolf A.
Schneider, Antonius
author_facet Jankrift, Neele
Kellerer, Christina
Magnussen, Helgo
Nowak, Dennis
Jörres, Rudolf A.
Schneider, Antonius
author_sort Jankrift, Neele
collection PubMed
description BACKGROUND: In general practice (GP), the diagnosis of obstructive airway diseases much relies on diagnostic questions, in view of the limited availability of lung function. We systematically assessed the relative importance of such questions for diagnosing asthma and chronic obstructive pulmonary disease (COPD), either without or with information from spirometry. METHODS: We used data obtained in a pulmonary practice to ensure the validity of diagnoses and assessments. Subjects with a diagnosis of COPD (n=260), or asthma (n=433), or other respiratory diseases (n=230), and subjects without respiratory diseases (n=364, controls) were included. The diagnostic questions comprised eight items, covering smoking history, self-attributed allergic rhinitis, dyspnea, cough, phlegm and wheeze. Optionally standard parameters of the flow-volume-curve were included. Decision trees for the diagnosis of COPD and asthma were constructed, moreover a probabilistic diagnostic network based on the results of path analyses describing the relationship between variables. RESULTS: In the decision trees, age, sex, current smoking, wheezing, dyspnea upon mild exertion, self-attributed allergic rhinitis, phlegm, forced expiratory volume in one second (FEV(1)), and expiratory flow rates were relevant, depending on the diagnostic comparison, while cough, dyspnea upon strong exertion and ex-smoker status were not relevant. In contrast, the probabilistic network for the diagnosis of COPD and asthma versus controls incorporated all diagnostic questions, i.e., dyspnea upon mild or strong exertion, current smoking, ex-smoking, wheezing, cough and phlegm but from spirometry only FEV(1). Depending on the individual pattern, the probability for COPD could raise from 25% to 81%, while the diagnostic gain for asthma was lower. CONCLUSIONS: The study developed simple diagnostic algorithms for asthma and COPD that take into account the relative importance of clinical signs and history, as well as spirometric data if available. The diagnostic accuracy was especially high for COPD. These algorithms may be helpful as a starting point in the standardisation of diagnostic strategies in GP practices. TRIAL REGISTRATION: The study is registered under DRKS00013935 at German Clinical Trials Register (DRKS, Date of registration 01/03/2018).
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spelling pubmed-82647212021-07-16 The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings Jankrift, Neele Kellerer, Christina Magnussen, Helgo Nowak, Dennis Jörres, Rudolf A. Schneider, Antonius J Thorac Dis Original Article BACKGROUND: In general practice (GP), the diagnosis of obstructive airway diseases much relies on diagnostic questions, in view of the limited availability of lung function. We systematically assessed the relative importance of such questions for diagnosing asthma and chronic obstructive pulmonary disease (COPD), either without or with information from spirometry. METHODS: We used data obtained in a pulmonary practice to ensure the validity of diagnoses and assessments. Subjects with a diagnosis of COPD (n=260), or asthma (n=433), or other respiratory diseases (n=230), and subjects without respiratory diseases (n=364, controls) were included. The diagnostic questions comprised eight items, covering smoking history, self-attributed allergic rhinitis, dyspnea, cough, phlegm and wheeze. Optionally standard parameters of the flow-volume-curve were included. Decision trees for the diagnosis of COPD and asthma were constructed, moreover a probabilistic diagnostic network based on the results of path analyses describing the relationship between variables. RESULTS: In the decision trees, age, sex, current smoking, wheezing, dyspnea upon mild exertion, self-attributed allergic rhinitis, phlegm, forced expiratory volume in one second (FEV(1)), and expiratory flow rates were relevant, depending on the diagnostic comparison, while cough, dyspnea upon strong exertion and ex-smoker status were not relevant. In contrast, the probabilistic network for the diagnosis of COPD and asthma versus controls incorporated all diagnostic questions, i.e., dyspnea upon mild or strong exertion, current smoking, ex-smoking, wheezing, cough and phlegm but from spirometry only FEV(1). Depending on the individual pattern, the probability for COPD could raise from 25% to 81%, while the diagnostic gain for asthma was lower. CONCLUSIONS: The study developed simple diagnostic algorithms for asthma and COPD that take into account the relative importance of clinical signs and history, as well as spirometric data if available. The diagnostic accuracy was especially high for COPD. These algorithms may be helpful as a starting point in the standardisation of diagnostic strategies in GP practices. TRIAL REGISTRATION: The study is registered under DRKS00013935 at German Clinical Trials Register (DRKS, Date of registration 01/03/2018). AME Publishing Company 2021-06 /pmc/articles/PMC8264721/ /pubmed/34277033 http://dx.doi.org/10.21037/jtd-20-3539 Text en 2021 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Jankrift, Neele
Kellerer, Christina
Magnussen, Helgo
Nowak, Dennis
Jörres, Rudolf A.
Schneider, Antonius
The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings
title The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings
title_full The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings
title_fullStr The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings
title_full_unstemmed The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings
title_short The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings
title_sort role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264721/
https://www.ncbi.nlm.nih.gov/pubmed/34277033
http://dx.doi.org/10.21037/jtd-20-3539
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