Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
J Allergy Clin Immunol. 2020 Nov;146(5):1045-1055. doi: 10.1016/j.jaci.2020.05.038. Epub 2020 Jun 10.
Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma.
We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma.
Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics.
Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics.
eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping.
电子鼻（eNose）是一种新兴的即时检测工具，它可能有助于对哮喘等慢性呼吸道疾病进行亚表型分类。
我们旨在研究电子鼻是否可以对哮喘患儿和成人患者的特应性进行分类。
纳入了来自 4 个独立队列的哮喘和/或喘息患者；BreathCloud 参与者（n=429）、无偏生物标志物预测呼吸疾病结局成人队列（n=96）、无偏生物标志物预测呼吸疾病结局儿科队列（n=100）和儿童哮喘药物的遗传药理学：具有抗炎作用的药物 2 参与者（n=30）。特应性定义为阳性皮肤点刺试验结果（≥3 mm）和/或常见过敏原的特异性 IgE 水平（≥0.35 kU/L）阳性。通过使用集成电子鼻平台或 SpiroNose 来测量呼气谱。根据使用的技术，将数据分为 2 个训练集和 2 个验证集。有监督数据分析包括使用 3 种不同的机器学习算法来对特应性与非特应性哮喘患者进行分类，并报告作为模型性能衡量指标的接收者操作特征曲线下面积。此外，还通过使用贝叶斯网络对电子鼻挥发性有机化合物谱与哮喘特征之间的数据驱动关系进行了无监督分析。
本研究纳入了 655 名参与者（n=601 名成人和学龄期儿童哮喘患者和 54 名学龄前儿童喘息患者[其中 68.2%为特应性]）的呼吸谱。利用挥发性有机化合物谱的机器学习模型在训练集和验证集的接收者操作特征曲线下面积至少为 0.84 和 0.72，从而区分了特应性和非特应性参与者。无监督方法表明，对特应性进行分类的呼吸谱不受其他患者特征的干扰。
电子鼻可以准确检测出哮喘和喘息患者的特应性，并且在具有不同年龄组的队列中都具有良好的效果，因此可用于哮喘表型分析。
