Abstract：
Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.
摘要：
＆＃xD;背景＆＃xD;严重急性呼吸系统综合症冠状病毒2（SARS-CoV-2）的检测依赖于鼻咽拭子的实时逆转录酶聚合酶链反应（RT-PCR）。当病毒负荷和感染位于下气道和肺实质的远端时，RT-PCR的假阴性率可能很高。备用保险箱，需要简单易得的下气道采样方法,以帮助COVID-19肺炎的早期和快速诊断.
方法
在一项前瞻性非盲观察研究中，从安大略省的三家医院纳入RT-PCR阳性且有SARS-CoV-2感染症状的患者，加拿大。将健康个体或RT-PCR阴性且无呼吸道症状的住院患者纳入对照组。通过激光吸收光谱法（LAS）收集并分析了呼吸样品中的挥发性有机化合物（VOC），并通过机器学习（ML）方法进行了分类，以识别SARS-CoV-2的独特LAS光谱模式（呼吸纹）。
结果
在135名患者中，115名患者提供了可分析的呼吸样本。使用LAS呼吸指纹训练ML分类器模型，在区分SARS-CoV2阳性和阴性组方面的准确率为72·2-81·7％。不同年龄的亚组的表现是一致的，性别,BMI,SARS-CoV-2变种,发病时间和需氧量。整体性能高于VOC训练的分类器模型，准确率为63-74·7%。＆＃xD;结论＆＃xD;这项研究表明，使用呼气的LAS分析基于ML的呼吸纹模型可能是研究下气道和检测SARS-CoV-2和其他呼吸道病原体的有价值的非侵入性方法。该技术和ML方法可以轻松地部署在任何解决方案中，只需最少的培训。这将极大地改善访问和可扩展性以满足浪涌能力;允许早期和快速检测以告知治疗;并且在为未来爆发快速开发新的分类器模型方面提供极大的多功能性。
