Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant.
Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant.
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DOI：
10.1001/jamanetworkopen.2023.0982
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发表时间：
2023-02-01
影响因子：
13.8
通讯作者：
Fan, Xudong
中科院分区：
文献类型：
--
作者：
Sharma, Ruchi;Zang, Wenzhe;Tabartehfarahani, Ali;Lam, Andres;Huang, Xiaheng;Sivakumar, Anjali Devi;Thota, Chandrakalavathi;Yang, Shuo;Dickson, Robert P.;Sjoding, MichaelW.;Bisco, Erin;Mahmood, Carmen Colmenero;Diaz, Kristen Machado;Sautter, Nicholas;Ansari, Sardar;Ward, Kevin R.;Fan, Xudong
呼出气体中的挥发性有机化合物（VOCs）能否提供COVID-19疾病和SARS-CoV-2变体的诊断信息？在这项有167名参与者的诊断研究中，发现了4种VOC生物标志物，可以区分SARS-CoV-2的Delta变体（以及2021年发生的其他变体）与非covid -19疾病。2022年出现的Omicron变体极大地影响了VOC特征，需要一组不同的VOC来区分Omicron变体和非covid -19疾病。这些发现证明了呼吸分析区分COVID-19和非COVID-19疾病的能力，但它们也揭示了随着新的SARS-CoV-2变体的出现，COVID-19患者的呼吸特征发生了显著变化。这项诊断研究评估了呼吸分析在Delta和Omicron变异最普遍的时期检测COVID-19患者的准确性。呼气分析已被探索作为一种无创检测新冠肺炎的手段。然而，新出现的SARS-CoV-2变体（如Omicron）对呼出气体特征和呼吸分析诊断准确性的影响尚不清楚。评价呼吸分析在SARS-CoV-2 δ型和欧米克隆型变异最流行时检测COVID-19患者的诊断准确性。这项诊断研究包括2021年4月至2022年5月期间使用逆转录酶聚合酶链反应检测COVID-19阳性和阴性结果的患者队列，这段时间涵盖了Delta变体被Omicron取代为主要变体的时期。患者通过密歇根大学卫生系统的重症监护病房和急诊科登记。用便携式气相色谱法分析患者呼吸。发现了不同组的VOC生物标志物，可区分COVID-19 （SARS-CoV-2 Delta和Omicron变体）和非COVID-19疾病。总共分析了来自167名成年患者的205个呼吸样本。共有77例患者（平均[SD]年龄58.5[16.1]岁，男性41例[53.2%]，黑人13例[16.9%]，白人59例[76.6%]）患有COVID-19，非COVID-19患者91例（平均[SD]年龄54.3[17.1]岁，男性43例[47.3%]，黑人11例[12.1%]，白人76例[83.5%]）。对几名患者进行了数天的分析。根据密歇根州和美国疾病控制与预防中心的监测数据，在94份阳性样本中，41份样本来自2021年感染Delta或其他变体的患者，53份样本来自2022年感染Omicron变体的患者。发现四种VOC生物标志物可以区分COVID-19 （Delta和其他2021变体）和非COVID-19疾病，准确率为94.7%。然而，当这些生物标志物应用于Omicron变体时，准确率大幅下降至82.1%。发现4种新的VOC生物标志物可区分Omicron变异和非covid -19疾病（准确率为90.9%）。呼气分析将Omicron与早期变体区分开来的准确率为91.5%，将COVID-19（所有SARS-CoV-2变体）与非COVID-19疾病区分开来的准确率为90.2%。这项诊断研究的结果表明，呼吸分析有望用于COVID-19检测。然而，与快速抗原检测类似，新变异的出现给诊断带来了挑战。这项研究的结果需要进一步评估如何克服这些挑战，使用呼吸分析来改善患者的诊断和护理。
Can volatile organic compounds (VOCs) in exhaled breath provide diagnostic information on COVID-19 disease and SARS-CoV-2 variants? In this diagnostic study with 167 participants, 4 VOC biomarkers were found to distinguish between the Delta variant of SARS-CoV-2 (and other variants occurring in 2021) from non–COVID-19 illness. The emergence of the Omicron variant in 2022 substantially affected the VOC profiles, requiring a different set of VOCs to distinguish between the Omicron variant and non–COVID-19 illness. These findings demonstrate the ability of breath analysis to distinguish between COVID-19 and non–COVID-19 illness, but they also reveal the significant variations in the breath profile among patients with COVID-19 as new SARS-CoV-2 variants emerge. This diagnostic study evaluates the accuracies of breath analysis on detecting patients with COVID-19 in periods when the Delta and Omicron variants were most prevalent. Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown. To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent. This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography. Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non–COVID-19 illness. Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non–COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non–COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non–COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non–COVID-19 illness with 90.2% accuracy. The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.
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DOI：
10.18632/aging.203655
发表时间：
2021-10-28
期刊：
Aging
影响因子：
--
作者：
Hu K;Lin L;Liang Y;Shao X;Hu Z;Luo H;Lei M
通讯作者：
Lei M
DOI：
10.1183/16000617.0011-2019
发表时间：
2019-06-30
期刊：
European respiratory review : an official journal of the European Respiratory Society
影响因子：
--
作者：
通讯作者：
--
影响因子：
15.1
作者：
Shlomo IB;Frankenthal H;Laor A;Greenhut AK
通讯作者：
Greenhut AK
影响因子：
11.1
作者：
Davis CE;Schivo M;Kenyon NJ
通讯作者：
Kenyon NJ
影响因子：
3.8
作者：
Filipiak, Wojciech;Beer, Ronny;Amann, Anton
通讯作者：
Amann, Anton