BACKGROUND: Exhaled breath contains thousands of volatile organic compounds (VOCs) that could serve as biomarkers of lung disease. Electronic noses can distinguish VOC mixtures by pattern recognition. OBJECTIVE: We hypothesized that an electronic nose can discriminate exhaled air of patients with asthma from healthy controls, and between patients with different disease severities. METHODS: Ten young patients with mild asthma (25.1 +/- 5.9 years; FEV(1), 99.9 +/- 7.7% predicted), 10 young controls (26.8 +/- 6.4 years; FEV(1), 101.9 +/- 10.3), 10 older patients with severe asthma (49.5 +/- 12.0 years; FEV(1), 62.3 +/- 23.6), and 10 older controls (57.3 +/- 7.1 years; FEV(1), 108.3 +/- 14.7) joined a cross-sectional study with duplicate sampling of exhaled breath with an interval of 2 to 5 minutes. Subjects inspired VOC-filtered air by tidal breathing for 5 minutes, and a single expiratory vital capacity was collected into a Tedlar bag that was sampled by electronic nose (Cyranose 320) within 10 minutes. Smellprints were analyzed by linear discriminant analysis on principal component reduction. Cross-validation values (CVVs) were calculated. RESULTS: Smellprints of patients with mild asthma were fully separated from young controls (CVV, 100%; Mahalanobis distance [M-distance], 5.32), and patients with severe asthma could be distinguished from old controls (CVV, 90%; M-distance, 2.77). Patients with mild and severe asthma could be less well discriminated (CVV, 65%; M-distance, 1.23), whereas the 2 control groups were indistinguishable (CVV, 50%; M-distance, 1.56). The duplicate samples replicated these results. CONCLUSION: An electronic nose can discriminate exhaled breath of patients with asthma from controls but is less accurate in distinguishing asthma severities. CLINICAL IMPLICATION: These findings warrant validation of electronic noses in diagnosing newly presented patients with asthma.
BACKGROUND: Exhaled breath contains thousands of volatile organic compounds (VOCs) that could serve as biomarkers of lung disease. Electronic noses can distinguish VOC mixtures by pattern recognition. OBJECTIVE: We hypothesized that an electronic nose can discriminate exhaled air of patients with asthma from healthy controls, and between patients with different disease severities. METHODS: Ten young patients with mild asthma (25.1 +/- 5.9 years; FEV(1), 99.9 +/- 7.7% predicted), 10 young controls (26.8 +/- 6.4 years; FEV(1), 101.9 +/- 10.3), 10 older patients with severe asthma (49.5 +/- 12.0 years; FEV(1), 62.3 +/- 23.6), and 10 older controls (57.3 +/- 7.1 years; FEV(1), 108.3 +/- 14.7) joined a cross-sectional study with duplicate sampling of exhaled breath with an interval of 2 to 5 minutes. Subjects inspired VOC-filtered air by tidal breathing for 5 minutes, and a single expiratory vital capacity was collected into a Tedlar bag that was sampled by electronic nose (Cyranose 320) within 10 minutes. Smellprints were analyzed by linear discriminant analysis on principal component reduction. Cross-validation values (CVVs) were calculated. RESULTS: Smellprints of patients with mild asthma were fully separated from young controls (CVV, 100%; Mahalanobis distance [M-distance], 5.32), and patients with severe asthma could be distinguished from old controls (CVV, 90%; M-distance, 2.77). Patients with mild and severe asthma could be less well discriminated (CVV, 65%; M-distance, 1.23), whereas the 2 control groups were indistinguishable (CVV, 50%; M-distance, 1.56). The duplicate samples replicated these results. CONCLUSION: An electronic nose can discriminate exhaled breath of patients with asthma from controls but is less accurate in distinguishing asthma severities. CLINICAL IMPLICATION: These findings warrant validation of electronic noses in diagnosing newly presented patients with asthma.
Authors: Peter J Mazzone; Xiao-Feng Wang; Yaomin Xu; Tarek Mekhail; Mary C Beukemann; Jie Na; Jonathan W Kemling; Kenneth S Suslick; Madhu Sasidhar Journal: J Thorac Oncol Date: 2012-01 Impact factor: 15.609
Authors: Wadah Ibrahim; Liesl Carr; Rebecca Cordell; Michael J Wilde; Dahlia Salman; Paul S Monks; Paul Thomas; Chris E Brightling; Salman Siddiqui; Neil J Greening Journal: Thorax Date: 2021-01-07 Impact factor: 9.139
Authors: Michael Schivo; Felicia Seichter; Alexander A Aksenov; Alberto Pasamontes; Daniel J Peirano; Boris Mizaikoff; Nicholas J Kenyon; Cristina E Davis Journal: J Breath Res Date: 2013-02-27 Impact factor: 3.262