Collection 8.

Rapid point of care tests for respiratory infections are associated with high rates of false negative results which can drive empiric, and potentially inappropriate, antibiotic use. Because infectious pathogens alter VOC composition, unique VOC signatures in biospecimens hold the potential to discriminate bacterial and viral infections from uninfected controls. One approach for rapid identification of respiratory pathogens is the electronic nose (e-nose), a sensor device that uses artificial intelligence to recognize disease-specific patterns in VOC profiles of gaseous mixtures. In this preclinical proof of concept study, we tested the validity of an e-nose to discriminate PCR-confirmed cases of infection with three viral pathogens (SARS-CoV-2, RSV, influenza A) from uninfected controls using nasopharyngeal test swab media. Using exploratory factor analysis, the e-nose discriminated both influenza A and SAR-CoV-2 from uninfected controls. To assess sensitivity and specificity, we applied factor analysis-based threshold values and obtained high levels of sensitivity (96.30%) and specificity (90.62%) for influenza A and more modest levels for SARS-CoV-2 (sensitivity=75%, specificity=68.57%). We did not apply threshold values to RSV samples because the e-nose sensors showed low discriminatory power for that pathogen. Our findings support proof of concept of the validity of the e-nose to discriminate common viral respiratory pathogens. Our use of binary thresholds for influenza A, which are easily adapted to point-of-care settings, yielded superior sensitivity results and comparable specificity results when compared to rapid tests. We recommend that future studies apply our analytic approach to samples of human breath to determine if these findings can be replicated or improved.呼吸道感染快速床旁检测往往伴随较高的假阴性率，这可能引发经验性乃至不恰当的抗生素使用。由于感染性病原体可改变挥发性有机化合物（volatile organic compounds, VOC）的组成，生物样本中的独特VOC特征有望区分细菌、病毒感染与未感染对照样本。用于快速识别呼吸道病原体的一种技术手段是电子鼻（electronic nose, e-nose）——这是一类借助人工智能识别气体混合物VOC谱中疾病特异性模式的传感设备。在本临床前概念验证研究中，我们采用鼻咽拭子送检培养基，验证了电子鼻区分经聚合酶链式反应（polymerase chain reaction, PCR）确认的三种病毒病原体（SARS-CoV-2、呼吸道合胞病毒（respiratory syncytial virus, RSV）、甲型流感病毒）感染病例与未感染对照样本的有效性。通过探索性因子分析，电子鼻可将甲型流感病毒、SARS-CoV-2感染样本与未感染对照样本区分开来。为评估检测灵敏度与特异度，我们采用基于因子分析的阈值，针对甲型流感病毒获得了较高的灵敏度（96.30%）与特异度（90.62%），而针对SARS-CoV-2的检测性能则相对适中（灵敏度=75%，特异度=68.57%）。我们未对RSV样本设置阈值，因电子鼻传感器对该病原体的区分能力较弱。本研究结果为电子鼻区分常见病毒性呼吸道病原体的有效性提供了概念验证支持。我们针对甲型流感病毒采用的二元阈值可轻松适配床旁检测场景，与传统快速检测相比，该阈值获得了更优的灵敏度结果与相当的特异度结果。我们建议未来研究将本研究的分析方法应用于人类呼吸样本，以验证该研究结果是否可复现或得到进一步优化。