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An electronic nose in the discrimination of patients with asthma and controls

2007; Elsevier BV; Volume: 120; Issue: 4 Linguagem: Inglês

10.1016/j.jaci.2007.05.043

1097-6825

Silvano Dragonieri, Robert Schot, Bart Mertens, Saskia le Cessie, Stefanie A. Gauw, Antonio Spanevello, Onofrio Resta, N. P. Willard, T.J. Vink, Klaus F. Rabe, Elisabeth H. Bel, Peter J. Sterk,

Olfactory and Sensory Function Studies

BackgroundExhaled 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.ObjectiveWe hypothesized that an electronic nose can discriminate exhaled air of patients with asthma from healthy controls, and between patients with different disease severities.MethodsTen young patients with mild asthma (25.1 ± 5.9 years; FEV1, 99.9 ± 7.7% predicted), 10 young controls (26.8 ± 6.4 years; FEV1, 101.9 ± 10.3), 10 older patients with severe asthma (49.5 ± 12.0 years; FEV1, 62.3 ± 23.6), and 10 older controls (57.3 ± 7.1 years; FEV1, 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.ResultsSmellprints 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.ConclusionAn electronic nose can discriminate exhaled breath of patients with asthma from controls but is less accurate in distinguishing asthma severities.Clinical implicationThese findings warrant validation of electronic noses in diagnosing newly presented patients with asthma. 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. We hypothesized that an electronic nose can discriminate exhaled air of patients with asthma from healthy controls, and between patients with different disease severities. Ten young patients with mild asthma (25.1 ± 5.9 years; FEV1, 99.9 ± 7.7% predicted), 10 young controls (26.8 ± 6.4 years; FEV1, 101.9 ± 10.3), 10 older patients with severe asthma (49.5 ± 12.0 years; FEV1, 62.3 ± 23.6), and 10 older controls (57.3 ± 7.1 years; FEV1, 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. 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. An electronic nose can discriminate exhaled breath of patients with asthma from controls but is less accurate in distinguishing asthma severities.