Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Largo F. Vito, 1, 00168 Rome, Italy.
Chest. 2010 Apr;137(4):790-6. doi: 10.1378/chest.09-1836. Epub 2010 Jan 15.
Analysis of exhaled breath by biosensors discriminates between patients with asthma and healthy subjects. An electronic nose consists of a chemical sensor array for the detection of volatile organic compounds (VOCs) and an algorithm for pattern recognition. We compared the diagnostic performance of a prototype of an electronic nose with lung function tests and fractional exhaled nitric oxide (FENO) in patients with atopic asthma.
A cross-sectional study was undertaken in 27 patients with intermittent and persistent mild asthma and in 24 healthy subjects. Two procedures for collecting exhaled breath were followed to study the differences between total and alveolar air. Seven patients with asthma and seven healthy subjects participated in a study with mass spectrometry (MS) fingerprinting as an independent technique for assessing between group discrimination. Classification was based on principal component analysis and a feed-forward neural network.
The best results were obtained when the electronic nose analysis was performed on alveolar air. Diagnostic performance for electronic nose, FENO, and lung function testing was 87.5%, 79.2%, and 70.8%, respectively. The combination of electronic nose and FENO had the highest diagnostic performance for asthma (95.8%). MS fingerprints of VOCs could discriminate between patients with asthma and healthy subjects.
The electronic nose has a high diagnostic performance that can be increased when combined with FENO. Large studies are now required to definitively establish the diagnostic performance of the electronic nose. Whether this integrated noninvasive approach will translate into an early diagnosis of asthma has to be clarified.
EUDRACT https://eudralink.emea.europa.eu; Identifier: 2007-000890-51; and clinicaltrials.gov; Identifier: NCT00819676.
生物传感器通过分析呼出气可区分哮喘患者和健康受试者。电子鼻由用于检测挥发性有机化合物 (VOC) 的化学传感器阵列和用于模式识别的算法组成。我们比较了电子鼻原型与肺功能测试和呼出气一氧化氮分数 (FENO) 在特应性哮喘患者中的诊断性能。
我们进行了一项横断面研究，纳入 27 例间歇性和持续性轻度哮喘患者和 24 例健康受试者。为了研究总呼出气和肺泡气之间的差异，我们分别采用两种方法收集呼出气。7 例哮喘患者和 7 例健康受试者参加了一项使用质谱 (MS) 指纹图谱的研究，MS 指纹图谱是评估组间差异的独立技术。分类基于主成分分析和前馈神经网络。
当对肺泡气进行电子鼻分析时，得到的结果最佳。电子鼻、FENO 和肺功能测试的诊断性能分别为 87.5%、79.2%和 70.8%。电子鼻和 FENO 的联合检测对哮喘的诊断性能最高（95.8%）。MS 图谱可区分哮喘患者和健康受试者的 VOC 指纹。
电子鼻具有较高的诊断性能，与 FENO 联合使用时可进一步提高。目前需要进行大规模研究以明确电子鼻的诊断性能。这种综合的非侵入性方法是否可以早期诊断哮喘尚需阐明。
EUDRACT https://eudralink.emea.europa.eu；标识符：2007-000890-51；和 clinicaltrials.gov；标识符：NCT00819676。
