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J Breath Res

Title:		A feasibility study of Covid-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometry
Author(s):		Zhang P; Ren T; Chen H; Li Q; He M; Feng Y; Wang L; Huang T; Yuan J; Deng G; Lu H;
Address:		"Department of Pulmonary Disease and Tuberculosis, The Third People's Hospital of Shenzhen, No. 29, Bulan Road, Longgang District, Shenzhen 518112, Guangdong, People's Republic of China. Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, People's Republic of China. Department of Disease Control, The Third People's Hospital of Shenzhen, No. 29, Bulan Road, Longgang District, Shenzhen 518112, Guangdong, People's Republic of China. Department of Infectious Disease, The Third People's Hospital of Shenzhen, No. 29, Bulan Road, Longgang District, Shenzhen 518112, Guangdong, People's Republic of China"
Journal Title:		J Breath Res
Year:		2022
Volume:		20220912
Issue:		4
Page Number:		-
DOI:		10.1088/1752-7163/ac8ea1
ISSN/ISBN:		1752-7163 (Electronic) 1752-7155 (Linking)
Abstract:		"Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a tremendous threat to global health. polymerase chain reaction (PCR) and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, the food and drug administration (FDA) emergency approved volatile organic component (VOC) as an alternative test for COVID-19 detection. In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by high-pressure photon ionization time-of-flight mass spectrometry for VOCs. Machine learning algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation. The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls. This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection"
Keywords:		Breath Tests/methods *covid-19 Case-Control Studies Feasibility Studies Humans Mass Spectrometry/methods Pandemics SARS-CoV-2 *Volatile Organic Compounds/analysis Covid-19 breath test machine learning volatile organic compounds;
Notes:		"MedlineZhang, Peize Ren, Tantan Chen, Haibin Li, Qingyun He, Mengqi Feng, Yong Wang, Lei Huang, Ting Yuan, Jing Deng, Guofang Lu, Hongzhou eng Randomized Controlled Trial Research Support, Non-U.S. Gov't England 2022/09/03 J Breath Res. 2022 Sep 12; 16(4). doi: 10.1088/1752-7163/ac8ea1"