Ruszkiewicz Dorota M, Sanders Daniel, O'Brien Rachel, Hempel Frederik, Reed Matthew J, Riepe Ansgar C, Bailie Kenneth, Brodrick Emma, Darnley Kareen, Ellerkmann Richard, Mueller Oliver, Skarysz Angelika, Truss Michael, Wortelmann Thomas, Yordanov Simeon, Thomas C L Paul, Schaaf Bernhard, Eddleston Michael
Centre for Analytical Science, Chemistry, School of Science, Loughborough University, LE11 3TU, United Kingdom.
G.A.S. Gesellschaft für analytische Sensorsysteme mbH BioMedizinZentrumDortmund, Dortmund, DE, Germany.
EClinicalMedicine. 2020 Dec;29:100609. doi: 10.1016/j.eclinm.2020.100609. Epub 2020 Oct 24.
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.
迫切需要在初次就诊时快速将新型冠状病毒肺炎（COVID-19）与其他呼吸道疾病（包括流感）区分开来。无需实验室支持的即时检测将加快诊断速度并保护医护人员。我们研究了使用呼吸分析通过床边气相色谱-离子迁移谱（GC-IMS）区分这些疾病的可行性。
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初次到医院就诊的可能患有COVID-19的成年患者。参与者提供单次呼吸样本，通过GC-IMS进行挥发性有机化合物（VOC）分析。通过对口腔/鼻腔拭子进行逆转录聚合酶链反应（RT-qPCR）并结合临床检查来确定COVID-19感染情况。在对环境污染物进行校正后，通过多变量分析并与GC-IMS数据库进行比较，确定潜在的COVID-19呼吸生物标志物。提出了一种基于一组挥发性有机化合物相对丰度的COVID-19呼吸评分，并针对队列数据进行了测试。
共招募了98名患者，其中爱丁堡的33名患者中有21名（63.6%）、多特蒙德的65名患者中有10名（15.4%）患有COVID-19。其他诊断包括哮喘、慢性阻塞性肺疾病（COPD）、细菌性肺炎和心脏疾病。多变量分析确定了可将COVID-19与其他疾病区分开来的醛类（乙醛、辛醛）、酮类（丙酮、丁酮）和甲醇。在爱丁堡分离出一种对病情严重程度/死亡具有显著预测能力的未知特征，而在多特蒙德鉴定出庚醛。在爱丁堡和多特蒙德，分别以80%和81.5%的准确率区分确诊COVID-19患者（分别为25名和65名）与非COVID-19患者（敏感性/特异性82.4%/75%；受试者工作特征曲线下面积[AUROC] 0.87，95%可信区间0.67至1）以及多特蒙德（敏感性/特异性90%/80%；AUROC 0.91，95%可信区间0.87至1）。
这两项研究独立表明，在初次医疗接触时可快速将COVID-19患者与其他疾病患者区分开来。标志物化合物的特性与COVID-19通过酮症、胃肠道影响和炎症过程导致的呼吸生化紊乱一致。该方法的开发和验证可能使在即将到来的地方性流感季节中快速诊断COVID-19成为可能。
MR获得了苏格兰国民保健服务研究职业研究员临床医生奖的支持。DMR获得了爱丁堡大学编号为COV_29的资助。
