Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study.
Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study.
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DOI：
10.1016/j.eclinm.2020.100609
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发表时间：
2020-12
影响因子：
15.1
通讯作者：
Eddleston M
中科院分区：
文献类型：
--
作者：
Ruszkiewicz DM;Sanders D;O'Brien R;Hempel F;Reed MJ;Riepe AC;Bailie K;Brodrick E;Darnley K;Ellerkmann R;Mueller O;Skarysz A;Truss M;Wortelmann T;Yordanov S;Thomas CLP;Schaaf B;Eddleston M
迫切需要在首次出现时迅速区分COVID-19与其他呼吸道疾病，包括流感。不需要实验室支持的护理点检测将加快诊断速度并保护卫生保健工作人员。我们研究了使用呼吸分析来区分这些条件与近患者气相色谱-离子迁移谱（GC-IMS）的可行性。在英国爱丁堡和德国多特蒙德进行的独立观察性患病率研究招募了在医院就诊时可能患有COVID-19的成年患者。参与者提供了一个单一的呼吸样本，通过GC-IMS进行VOC分析。通过口/鼻拭子的转录聚合酶链反应（RT-qPCR）以及临床审查确定COVID-19感染。在对环境污染物进行校正后，通过多变量分析和与GC-IMS数据库的比较，确定了潜在的COVID-19呼吸生物标志物。根据一组挥发性有机化合物的相对丰度提出了COVID-19呼吸评分，并根据队列数据进行了测试。招募了98名患者，其中爱丁堡和多特蒙德分别有21/33（63.6%）和10/65（15.4%）人患有COVID-19。其他诊断包括哮喘、COPD、细菌性肺炎和心脏病。多变量分析确定了醛类（乙醛、辛醛）、酮类（丙酮、丁酮）和甲醇，它们将COVID-19与其他条件区分开来。在爱丁堡分离出一种对严重程度/死亡具有显著预测能力的未鉴定特征，而在多特蒙德鉴定出庚醛。确诊患者的鉴别在爱丁堡和多特蒙德，将COVID-19与非COVID-19区分开来（25和65）的准确率分别为80%和81.5%（敏感性/特异性82.4%/75%;受试者操作者特征下面积[AUROC] 0.87 95% CI 0.67 - 1）和多特蒙德（灵敏度/特异性90%/80%; AUROC 0.91 95%CI 0.87至1）。这两项研究独立表明，在首次医疗接触时，COVID-19患者可以迅速与其他疾病患者区分开来。标记化合物的特性与COVID-19通过酮症、胃肠道效应和炎症过程导致的呼吸生物化学紊乱一致。这种方法的开发和验证可能有助于在即将到来的地方性流感季节快速诊断COVID-19。MR得到了NHS研究苏格兰职业研究员临床医生奖的支持。DMR得到了爱丁堡大学（参考COV_29）的支持。
There is an urgent need to rapidly distinguish COVID-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory- support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS). Independent observational prevalence studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible COVID-19 at hospital presentation. Participants gave a single breath-sample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT- qPCR) of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential COVID-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A COVID-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data. Ninety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had COVID-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated COVID-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of COVID-19 from non-COVID-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver- operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity / specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1). These two studies independently indicate that patients with COVID-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with COVID-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of COVID-19 in the coming endemic flu seasons. MR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV_29.
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