Department of Biomedical Engineering, University of Michigan, Ann Arbor.
Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor.
JAMA Netw Open. 2023 Feb 1;6(2):e230982. doi: 10.1001/jamanetworkopen.2023.0982.
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.
DESIGN, SETTING, AND PARTICIPANTS: 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.
呼吸分析已被探索作为一种非侵入性手段来检测 COVID-19。然而，新兴的 SARS-CoV-2 变体（如奥密克戎）对呼气谱和呼吸分析的诊断准确性的影响尚不清楚。
评估当 SARS-CoV-2 的德尔塔和奥密克戎变体最为流行时，呼吸分析检测 COVID-19 患者的诊断准确性。
设计、设置和参与者：这项诊断研究包括了一组在 2021 年 4 月至 2022 年 5 月期间使用逆转录酶聚合酶链反应对 COVID-19 检测呈阳性和阴性的患者队列，涵盖了德尔塔变体被奥密克戎变体超越成为主要变体的时期。患者通过密歇根大学卫生系统的重症监护病房和急诊部招募。患者的呼吸通过便携式气相色谱进行分析。
确定了不同的 VOC 生物标志物集，这些标志物可以区分 COVID-19（SARS-CoV-2 德尔塔和奥密克戎变体）和非 COVID-19 疾病。
总体而言，分析了 167 名成年患者的 205 个呼吸样本。共有 77 名患者（平均[标准差]年龄，58.5[16.1]岁；41[53.2%]男性患者；13[16.9%]黑人患者和 59[76.6%]白人患者）患有 COVID-19，91 名患者（平均[标准差]年龄，54.3[17.1]岁；43[47.3%]男性患者；11[12.1%]黑人患者和 76[83.5%]白人患者）患有非 COVID-19 疾病。一些患者在多个日子进行了分析。在 94 个阳性样本中，根据密歇根州和美国疾病控制与预防中心的监测数据，41 个样本来自 2021 年感染德尔塔或其他变体的患者，53 个样本来自 2022 年感染奥密克戎变体的患者。发现 4 个 VOC 生物标志物可区分 COVID-19（德尔塔和其他 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奥密克戎变体时，准确率大幅下降至 82.1%。发现 4 个新的 VOC 生物标志物可区分奥密克戎变体和非 COVID-19 疾病（准确率，90.9%）。呼吸分析对奥密克戎变体的准确率为 91.5%，对所有 SARS-CoV-2 变体的 COVID-19（所有 SARS-CoV-2 变体）与非 COVID-19 疾病的准确率为 90.2%。
这项诊断研究的结果表明，呼吸分析在 COVID-19 检测方面具有潜力。然而，与快速抗原检测类似，新变体的出现带来了诊断挑战。本研究的结果需要进一步评估如何克服这些挑战，以利用呼吸分析来改善患者的诊断和护理。
