de Vries R, Brinkman P, van der Schee M P, Fens N, Dijkers E, Bootsma S K, de Jongh F H C, Sterk P J
Department of Respiratory Medicine, Academic Medical Centre, Amsterdam, The Netherlands. Faculty of Science and Technology, University of Twente, Enschede, The Netherlands.
J Breath Res. 2015 Oct 15;9(4):046001. doi: 10.1088/1752-7155/9/4/046001.
New 'omics'-technologies have the potential to better define airway disease in terms of pathophysiological and clinical phenotyping. The integration of electronic nose (eNose) technology with existing diagnostic tests, such as routine spirometry, can bring this technology to 'point-of-care'. We aimed to determine and optimize the technical performance and diagnostic accuracy of exhaled breath analysis linked to routine spirometry. Exhaled breath was collected in triplicate in healthy subjects by an eNose (SpiroNose) based on five identical metal oxide semiconductor sensor arrays (three arrays monitoring exhaled breath and two reference arrays monitoring ambient air) at the rear end of a pneumotachograph. First, the influence of flow, volume, humidity, temperature, environment, etc, was assessed. Secondly, a two-centre case-control study was performed using diagnostic and monitoring visits in day-to-day clinical care in patients with a (differential) diagnosis of asthma, chronic obstructive pulmonary disease (COPD) or lung cancer. Breathprint analysis involved signal processing, environment correction based on alveolar gradients and statistics based on principal component (PC) analysis, followed by discriminant analysis (Matlab2014/SPSS20). Expiratory flow showed a significant linear correlation with raw sensor deflections (R(2) = 0.84) in 60 healthy subjects (age 43 ± 11 years). No correlation was found between sensor readings and exhaled volume, humidity and temperature. Exhaled data after environment correction were highly reproducible for each sensor array (Cohen's Kappa 0.81-0.94). Thirty-seven asthmatics (41 ± 14.2 years), 31 COPD patients (66 ± 8.4 years), 31 lung cancer patients (63 ± 10.8 years) and 45 healthy controls (41 ± 12.5 years) entered the cross-sectional study. SpiroNose could adequately distinguish between controls, asthma, COPD and lung cancer patients with cross-validation values ranging between 78-88%. We have developed a standardized way to integrate eNose technology with spirometry. Signal processing techniques and environmental background correction ensured that the multiple sensor arrays within the SpiroNose provided repeatable and interchangeable results. SpiroNose discriminated controls and patients with asthma, COPD and lung cancer with promising accuracy, paving the route towards point-of-care exhaled breath diagnostics.
新的“组学”技术有潜力在病理生理学和临床表型分析方面更好地界定气道疾病。将电子鼻（eNose）技术与现有诊断测试（如常规肺活量测定）相结合，可使该技术应用于“床边检测”。我们旨在确定并优化与常规肺活量测定相关的呼出气分析的技术性能和诊断准确性。在健康受试者中，通过基于五个相同金属氧化物半导体传感器阵列的电子鼻（SpiroNose）（三个阵列监测呼出气，两个参考阵列监测环境空气）在呼吸流速计后端对呼出气进行三次采集。首先，评估流量、体积、湿度、温度、环境等的影响。其次，在日常临床护理中，针对哮喘、慢性阻塞性肺疾病（COPD）或肺癌（鉴别）诊断的患者，进行了一项双中心病例对照研究，包括诊断和监测访视。呼吸指纹分析包括信号处理、基于肺泡梯度的环境校正以及基于主成分（PC）分析的统计，随后进行判别分析（Matlab2014/SPSS20）。在60名健康受试者（年龄43±11岁）中，呼气流量与传感器原始偏转呈显著线性相关（R² = 0.84）。未发现传感器读数与呼出气体体积、湿度和温度之间存在相关性。环境校正后的呼出数据在每个传感器阵列中具有高度可重复性（科恩kappa系数为0.81 - 0.94）。37名哮喘患者（41±14.2岁）、31名COPD患者（66±8.4岁）、31名肺癌患者（63±10.8岁）和45名健康对照者（41±12.5岁）进入横断面研究。SpiroNose能够充分区分对照组、哮喘患者、COPD患者和肺癌患者，交叉验证值在78% - 88%之间。我们已经开发出一种将eNose技术与肺活量测定相结合的标准化方法。信号处理技术和环境背景校正确保了SpiroNose内的多个传感器阵列能够提供可重复且可互换的结果。SpiroNose能够以可观的准确性区分对照组以及哮喘、COPD和肺癌患者，为床边呼出气诊断铺平了道路。
