van Bragt Job J M H, Brinkman Paul, de Vries Rianne, Vijverberg Susanne J H, Weersink Els J M, Haarman Eric G, de Jongh Frans H C, Kester Sigrid, Lucas Annelies, In 't Veen Johannes C C M, Sterk Peter J, Bel Elisabeth H D, Maitland-van der Zee Anke H
Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands.
Breathomix BV, Leiden, The Netherlands.
ERJ Open Res. 2020 Dec 21;6(4). doi: 10.1183/23120541.00307-2020. eCollection 2020 Oct.
Molecular profiling of exhaled breath by electronic nose (eNose) might be suitable as a noninvasive tool that can help in monitoring of clinically unstable COPD patients. However, supporting data are still lacking. Therefore, as a first step, this study aimed to determine the accuracy of exhaled breath analysis by eNose to identify COPD patients who recently exacerbated, defined as an exacerbation in the previous 3 months. Data for this exploratory, cross-sectional study were extracted from the multicentre BreathCloud cohort. Patients with a physician-reported diagnosis of COPD (n=364) on maintenance treatment were included in the analysis. Exacerbations were defined as a worsening of respiratory symptoms requiring treatment with oral corticosteroids, antibiotics or both. Data analysis involved eNose signal processing, ambient air correction and statistics based on principal component (PC) analysis followed by linear discriminant analysis (LDA). Before analysis, patients were randomly divided into a training (n=254) and validation (n=110) set. In the training set, LDA based on PCs 1-4 discriminated between patients with a recent exacerbation or no exacerbation with high accuracy (receiver operating characteristic (ROC)-area under the curve (AUC)=0.98, 95% CI 0.97-1.00). This high accuracy was confirmed in the validation set (AUC=0.98, 95% CI 0.94-1.00). Smoking, health status score, use of inhaled corticosteroids or vital capacity did not influence these results. Exhaled breath analysis by eNose can discriminate with high accuracy between COPD patients who experienced an exacerbation within 3 months prior to measurement and those who did not. This suggests that COPD patients who recently exacerbated have their own exhaled molecular fingerprint that could be valuable for monitoring purposes.
通过电子鼻(eNose)对呼出气进行分子分析可能适合作为一种无创工具,有助于监测临床不稳定的慢性阻塞性肺疾病(COPD)患者。然而,目前仍缺乏支持性数据。因此,作为第一步,本研究旨在确定通过eNose进行呼出气分析以识别近期病情加重的COPD患者的准确性,近期病情加重定义为在过去3个月内出现过病情加重。这项探索性横断面研究的数据取自多中心呼吸云队列。分析纳入了接受维持治疗且经医生报告诊断为COPD的患者(n = 364)。病情加重定义为呼吸症状恶化,需要使用口服糖皮质激素、抗生素或两者进行治疗。数据分析包括eNose信号处理、环境空气校正以及基于主成分(PC)分析随后进行线性判别分析(LDA)的统计学分析。在分析前,患者被随机分为训练组(n = 254)和验证组(n = 110)。在训练组中,基于主成分1 - 4的线性判别分析能够以高精度区分近期病情加重或未加重的患者(受试者操作特征曲线下面积(ROC - AUC)= 0.98,95%置信区间0.97 - 1.00)。在验证组中也证实了这种高精度(AUC = 0.98,95%置信区间0.94 - 1.00)。吸烟、健康状况评分、吸入糖皮质激素的使用或肺活量均不影响这些结果。通过eNose进行的呼出气分析能够以高精度区分测量前3个月内病情加重的COPD患者和未加重的患者。这表明近期病情加重的COPD患者有其自身的呼出分子指纹,这对于监测目的可能具有重要价值。