Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis

Abstract Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorith...

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Autores principales:	Scott Claxton, Paul Porter, Joanna Brisbane, Natasha Bear, Javan Wood, Vesa Peltonen, Phillip Della, Claire Smith, Udantha Abeyratne
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/98a599c7ae8948ad9536e72f01b18131
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spelling	oai:doaj.org-article:98a599c7ae8948ad9536e72f01b181312021-12-02T16:32:00ZIdentifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis10.1038/s41746-021-00472-x2398-6352https://doaj.org/article/98a599c7ae8948ad9536e72f01b181312021-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00472-xhttps://doaj.org/toc/2398-6352Abstract Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9–89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4–96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0–87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.Scott ClaxtonPaul PorterJoanna BrisbaneNatasha BearJavan WoodVesa PeltonenPhillip DellaClaire SmithUdantha AbeyratneNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Scott Claxton Paul Porter Joanna Brisbane Natasha Bear Javan Wood Vesa Peltonen Phillip Della Claire Smith Udantha Abeyratne Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
description	Abstract Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9–89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4–96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0–87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.
format	article
author	Scott Claxton Paul Porter Joanna Brisbane Natasha Bear Javan Wood Vesa Peltonen Phillip Della Claire Smith Udantha Abeyratne
author_facet	Scott Claxton Paul Porter Joanna Brisbane Natasha Bear Javan Wood Vesa Peltonen Phillip Della Claire Smith Udantha Abeyratne
author_sort	Scott Claxton
title	Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
title_short	Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
title_full	Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
title_fullStr	Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
title_full_unstemmed	Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
title_sort	identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/98a599c7ae8948ad9536e72f01b18131
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Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis

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