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BMC Infect Dis

Title:		A cross-sectional study: a breathomics based pulmonary tuberculosis detection method
Author(s):		Fu L; Wang L; Wang H; Yang M; Yang Q; Lin Y; Guan S; Deng Y; Liu L; Li Q; He M; Zhang P; Chen H; Deng G;
Address:		"Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China. Peking University Clinical Research Institute, Peking University First Hospital, Beijing, 100000, China. Institute for Hepatology, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. Medical Examination Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. Pulmonary Diseases Out-Patient Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. 82880246@qq.com. Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China. haibinc@hotmail.com. Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China. jxxk1035@yeah.net"
Journal Title:		BMC Infect Dis
Year:		2023
Volume:		20230310
Issue:		1
Page Number:		148 -
DOI:		10.1186/s12879-023-08112-3
ISSN/ISBN:		1471-2334 (Electronic) 1471-2334 (Linking)
Abstract:		"BACKGROUND: Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection. METHOD: Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients. RESULTS: The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961. CONCLUSIONS: The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis"
Keywords:		"Humans Cross-Sectional Studies *Tuberculosis, Pulmonary/diagnosis *Lung Diseases Algorithms Machine Learning Breathomics Pulmonary tuberculosis Volatile organic compounds;"
Notes:		"MedlineFu, Liang Wang, Lei Wang, Haibo Yang, Min Yang, Qianting Lin, Yi Guan, Shanyi Deng, Yongcong Liu, Lei Li, Qingyun He, Mengqi Zhang, Peize Chen, Haibin Deng, Guofang eng 82070016/National Natural Science Foundation of China/ 2020YFA0907200/Key Technologies Research and Development Program/ 2019YFC0840602/Key Technologies Research and Development Program/ 2019B1515120041/Basic and Applied Basic Research Foundation of Guangdong Province/ 2020B1111170014/Guangdong Provincial Key Laboratory of Prevention and Control for Severe Clinical Animal Diseases/ KCXFZ202002011007083/Shenzhen Scientific and Technological Foundation/ JCYJ20180228162112889/Shenzhen Scientific and Technological Foundation/ FSSYKF-2020001/Summit Plan for Foshan High-level Hospital Construction/ G2022051/Shenzhen Third People's Hospital/ SZGSP010/The Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties/ JCYJ20220530163212027/Shenzhen Natural Science Foundation/ 20210617141509001/the Shenzhen Clinical Research Center for Tuberculosis/ LCYX20220620105200001/the Special fund of Shenzhen Central-leading-local Scientific and Technological Foundation/ England 2023/03/11 BMC Infect Dis. 2023 Mar 10; 23(1):148. doi: 10.1186/s12879-023-08112-3"