Comparative analysis of chemical breath-prints through olfactory technology for the discrimination between SARS-CoV-2 infected patients and controls
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Background: We identified a global chemical pattern of volatile organic compounds in exhaled breath capable of discriminating between COVID-19 patients and controls (without infection) using an electronic nose. Methods: The study focused on 42 SARS-CoV-2 RT-qPCR positive subjects as well as 42 negative subjects. Principal component analysis indicated a separation of the study groups and provides a cumulative percentage of explanation of the variation of 98.3%25. Results: The canonical analysis of principal coordinates model shows a separation by the first canonical axis CAP1 (r2 = 0.939 and 95.23%25 of correct classification rate), the cut-off point of 0.0089; 100%25 sensitivity (CI 95%25:91.5–100%25) and 97.6%25 specificity (CI 95%25:87.4–99.9%25). The predictive model usefulness was tested on 30 open population subjects without prior knowledge of SARS-CoV-2 RT-qPCR status. Of these 3 subjects exhibited COVID-19 suggestive breath profiles, all asymptomatic at the time, two of which were later shown to be SARS-CoV-2 RT-qPCR positive. An additional subject had a borderline breath profile and SARS-CoV-2 RT-qPCR positive. The remaining 27 subjects exhibited healthy breath profiles as well as SARS-CoV-2 RT-qPCR test results. Conclusions: In all, the use of olfactory technologies in communities with high transmission rates as well as in resource-limited settings where targeted sampling is not viable represents a practical COVID-19 screening approach capable of promptly identifying COVID-19 suspect patients and providing useful epidemiological information to guide community health strategies in the context of COVID-19. © 2021 Elsevier B.V.
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Asymptomatic; COVID-19 screening; Electronic nose; Exhaled breath; Volatile organic compounds adult; Article; asymptomatic infection; clinical article; comparative study; controlled study; coronavirus disease 2019; female; human; male; olfactory discrimination; prediction; public health; screening test; sensitivity and specificity; virus transmission; mass screening; technology; COVID-19; Humans; Mass Screening; SARS-CoV-2; Sensitivity and Specificity; Technology
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