Journal
CLINICA CHIMICA ACTA
Volume 526, Issue -, Pages 6-13Publisher
ELSEVIER
DOI: 10.1016/j.cca.2021.12.019
Keywords
Breath analysis; Bronchiectasis; Signal processing; E-nose; GC-MS
Categories
Funding
- European Commission [712754]
- Severo Ochoa program of the Spanish Ministry of Science and Competitiveness [SEV-2014-0425]
- Departament d'Universitats, Recerca i Societat de la Informacio de la Generalitat de Catalunya [2017 SGR 1721]
- Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya
- European Social Fund (ESF)
- Institut de Bioenginyeria de Catalunya (IBEC), Spain
- Spanish MINECO [RTI2018-098577-B-C22]
- Sociedad Espanola de Neumologia y Cirugia Toracica (SEPAR)
- Societat Catalana de Pneumologia (SOCAP)
- Fundacio Catalana de Pneumologia (FUCAP)
- Instituto de SaludCarlos III -Fondos FEDER [PI18/00311]
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This study demonstrates that breath sample analysis can effectively differentiate between control, bronchiectasis, and bronchiectasis with Pseudomonas Aeruginosa infection samples.
Background and aims: In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients. Materials and methods: A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples. Results: Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI95%: 84-100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test. Conclusions: Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.
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