Author: Ibrahim, Wadah; Cordell, Rebecca L.; Wilde, Michael J.; Richardson, Matthew; Carr, Liesl; Sundari Devi Dasi, Ananga; Hargadon, Beverley; Free, Robert C.; Monks, Paul S.; Brightling, Christopher E.; Greening, Neil J.; Siddiqui, Salman
Title: Diagnosis of COVID-19 by exhaled breath analysis using gas chromatography–mass spectrometry Cord-id: n9l7ch9s Document date: 2021_7_5
ID: n9l7ch9s
Snippet: BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has claimed over two and a half million lives worldwide so far. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is perceived to be seasonally recurrent, and a rapid noninvasive biomarker to accurately diagnose patients early on in their disease course will be necessary to meet the operational demands for COVID-19 control in the coming years. OBJECTIVE: The aim of this study was to evaluate the role of exh
Document: BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has claimed over two and a half million lives worldwide so far. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is perceived to be seasonally recurrent, and a rapid noninvasive biomarker to accurately diagnose patients early on in their disease course will be necessary to meet the operational demands for COVID-19 control in the coming years. OBJECTIVE: The aim of this study was to evaluate the role of exhaled breath volatile biomarkers in identifying patients with suspected or confirmed COVID-19 infection, based on their underlying PCR status and clinical probability. METHODS: A prospective, real-world, observational study was carried out, recruiting adult patients with suspected or confirmed COVID-19 infection. Breath samples were collected using a standard breath collection bag, modified with appropriate filters to comply with local infection control recommendations, and samples were analysed using gas chromatography–mass spectrometry (TD-GC-MS). RESULTS: 81 patients were recruited between April 29 and July 10, 2020, of whom 52 out of 81 (64%) tested positive for COVID-19 by reverse transcription–polymerase chain reaction (RT-PCR). A regression analysis identified a set of seven exhaled breath features (benzaldehyde, 1-propanol, 3,6-methylundecane, camphene, beta-cubebene, iodobenzene and an unidentified compound) that separated PCR-positive patients with an area under the curve (AUC): 0.836, sensitivity: 68%, specificity: 85%. CONCLUSIONS: GC-MS-detected exhaled breath biomarkers were able to identify PCR-positive COVID-19 patients. External replication of these compounds is warranted to validate these results.
Search related documents:
Co phrase search for related documents- accurate rapid and acute respiratory distress syndrome: 1, 2, 3, 4, 5, 6
- accurate rapid and admission point: 1
- accurate rapid and logistic regression model: 1
- accurate rapid and low middle: 1, 2
- accurate rapid and lung cancer: 1, 2
- accurate rapid and lymphocyte count: 1
- achieve difficult and acute respiratory distress syndrome: 1, 2
- achieve difficult and low middle: 1
- acute respiratory distress syndrome and admission point: 1, 2, 3, 4
- acute respiratory distress syndrome and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
- acute respiratory distress syndrome and low middle: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
- acute respiratory distress syndrome and lung cancer: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
- acute respiratory distress syndrome and lymphocyte count: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64
- admission point and logistic regression model: 1
- admission point and lymphocyte count: 1
- logistic regression model and low middle: 1, 2, 3, 4, 5, 6
- logistic regression model and lung cancer: 1, 2, 3, 4, 5, 6
- logistic regression model and lymphocyte count: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
- lung cancer and lymphocyte count: 1, 2, 3, 4, 5
Co phrase search for related documents, hyperlinks ordered by date