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Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification

Christopher Phillips Orcid Logo, Neil Parthaláin, Yasir Syed, Davide Deganello Orcid Logo, Timothy Claypole, Keir Lewis Orcid Logo

Metabolites, Volume: 4, Issue: 2, Pages: 300 - 318

Swansea University Authors: Christopher Phillips Orcid Logo, Davide Deganello Orcid Logo, Timothy Claypole, Keir Lewis Orcid Logo

DOI (Published version): 10.3390/metabo4020300

Abstract

Exhaled volatile organic compounds (VOCs) are of interest for their potential to diagnose disease non-invasively. However, most breath VOC studies have analyzed single breath samples from an individual and assumed them to be wholly consistent representative of the person. This provided the motivatio...

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Published in: Metabolites
Published: 2014
URI: https://cronfa.swan.ac.uk/Record/cronfa22363
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last_indexed 2019-09-23T19:27:33Z
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However, most breath VOC studies have analyzed single breath samples from an individual and assumed them to be wholly consistent representative of the person. This provided the motivation for an investigation of the variability of breath profiles when three breath samples are taken over a short time period (two minute intervals between samples) for 118 stable patients with Chronic Obstructive Pulmonary Disease (COPD) and 63 healthy controls and analyzed by gas chromatography and mass spectroscopy (GC/MS). The extent of the variation in VOC levels differed between COPD and healthy subjects and the patterns of variation differed for isoprene versus the bulk of other VOCs. In addition, machine learning approaches were applied to the breath data to establish whether these samples differed in their ability to discriminate COPD from healthy states and whether aggregation of multiple samples, into single data sets, could offer improved discrimination. The three breath samples gave similar classification accuracy to one another when evaluated separately (66.5% to 68.3% subjects classified correctly depending on the breath repetition used). Combining multiple breath samples into single data sets gave better discrimination (73.4% subjects classified correctly). Although accuracy is not sufficient for COPD diagnosis in a clinical setting, enhanced sampling and analysis may improve accuracy further. 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spelling 2019-09-23T15:17:12.0588046 v2 22363 2015-07-16 Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification cc734f776f10b3fb9b43816c9f617bb5 0000-0001-8011-710X Christopher Phillips Christopher Phillips true false ea38a0040bdfd3875506189e3629b32a 0000-0001-8341-4177 Davide Deganello Davide Deganello true false 7735385522f1e68a8775b4f709e91d55 Timothy Claypole Timothy Claypole true false bc53c343c975d6e0ad88c1d8b9ddd70c 0000-0002-8248-6774 Keir Lewis Keir Lewis true false 2015-07-16 EAAS Exhaled volatile organic compounds (VOCs) are of interest for their potential to diagnose disease non-invasively. However, most breath VOC studies have analyzed single breath samples from an individual and assumed them to be wholly consistent representative of the person. This provided the motivation for an investigation of the variability of breath profiles when three breath samples are taken over a short time period (two minute intervals between samples) for 118 stable patients with Chronic Obstructive Pulmonary Disease (COPD) and 63 healthy controls and analyzed by gas chromatography and mass spectroscopy (GC/MS). The extent of the variation in VOC levels differed between COPD and healthy subjects and the patterns of variation differed for isoprene versus the bulk of other VOCs. In addition, machine learning approaches were applied to the breath data to establish whether these samples differed in their ability to discriminate COPD from healthy states and whether aggregation of multiple samples, into single data sets, could offer improved discrimination. The three breath samples gave similar classification accuracy to one another when evaluated separately (66.5% to 68.3% subjects classified correctly depending on the breath repetition used). Combining multiple breath samples into single data sets gave better discrimination (73.4% subjects classified correctly). Although accuracy is not sufficient for COPD diagnosis in a clinical setting, enhanced sampling and analysis may improve accuracy further. Variability in samples, and short-term effects of practice or exertion, need to be considered in any breath testing program to improve reliability and optimize discrimination. Journal Article Metabolites 4 2 300 318 9 5 2014 2014-05-09 10.3390/metabo4020300 This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University 2019-09-23T15:17:12.0588046 2015-07-16T13:23:42.9049401 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Christopher Phillips 0000-0001-8011-710X 1 Neil Parthaláin 2 Yasir Syed 3 Davide Deganello 0000-0001-8341-4177 4 Timothy Claypole 5 Keir Lewis 0000-0002-8248-6774 6 0022363-15072016123555.pdf Phillips2014.pdf 2016-07-15T12:35:55.0930000 Output 314739 application/pdf Version of Record true 2016-07-15T00:00:00.0000000 false
title Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification
spellingShingle Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification
Christopher Phillips
Davide Deganello
Timothy Claypole
Keir Lewis
title_short Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification
title_full Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification
title_fullStr Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification
title_full_unstemmed Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification
title_sort Short-Term Intra-Subject Variation in Exhaled Volatile Organic Compounds (VOCs) in COPD Patients and Healthy Controls and Its Effect on Disease Classification
author_id_str_mv cc734f776f10b3fb9b43816c9f617bb5
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bc53c343c975d6e0ad88c1d8b9ddd70c
author_id_fullname_str_mv cc734f776f10b3fb9b43816c9f617bb5_***_Christopher Phillips
ea38a0040bdfd3875506189e3629b32a_***_Davide Deganello
7735385522f1e68a8775b4f709e91d55_***_Timothy Claypole
bc53c343c975d6e0ad88c1d8b9ddd70c_***_Keir Lewis
author Christopher Phillips
Davide Deganello
Timothy Claypole
Keir Lewis
author2 Christopher Phillips
Neil Parthaláin
Yasir Syed
Davide Deganello
Timothy Claypole
Keir Lewis
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container_start_page 300
publishDate 2014
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college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
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description Exhaled volatile organic compounds (VOCs) are of interest for their potential to diagnose disease non-invasively. However, most breath VOC studies have analyzed single breath samples from an individual and assumed them to be wholly consistent representative of the person. This provided the motivation for an investigation of the variability of breath profiles when three breath samples are taken over a short time period (two minute intervals between samples) for 118 stable patients with Chronic Obstructive Pulmonary Disease (COPD) and 63 healthy controls and analyzed by gas chromatography and mass spectroscopy (GC/MS). The extent of the variation in VOC levels differed between COPD and healthy subjects and the patterns of variation differed for isoprene versus the bulk of other VOCs. In addition, machine learning approaches were applied to the breath data to establish whether these samples differed in their ability to discriminate COPD from healthy states and whether aggregation of multiple samples, into single data sets, could offer improved discrimination. The three breath samples gave similar classification accuracy to one another when evaluated separately (66.5% to 68.3% subjects classified correctly depending on the breath repetition used). Combining multiple breath samples into single data sets gave better discrimination (73.4% subjects classified correctly). Although accuracy is not sufficient for COPD diagnosis in a clinical setting, enhanced sampling and analysis may improve accuracy further. Variability in samples, and short-term effects of practice or exertion, need to be considered in any breath testing program to improve reliability and optimize discrimination.
published_date 2014-05-09T06:42:50Z
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