Clinica chimica acta; international journal of clinical chemistry · 2021
Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis.
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Oliveira Luciana Fontes de, Mallafré-Muro Celia, Giner Jordi, Perea Lidia, Sibila Oriol, Pardo Antonio, Marco Santiago
Аннотация
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.
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.