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- Title
A cross-sectional study: a breathomics based pulmonary tuberculosis detection method.
- Authors
Fu, 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
- 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. Key messages: What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies.
- Subjects
TUBERCULOSIS; TIME-of-flight mass spectrometers; MACHINE learning; CROSS-sectional method; LUNG diseases
- Publication
BMC Infectious Diseases, 2023, Vol 23, Issue 1, p1
- ISSN
1471-2334
- Publication type
Article
- DOI
10.1186/s12879-023-08112-3