A cross-sectional study: a breathomics based pulmonary tuberculosis detection method

Fu，Liang1; Wang，Lei2; Wang，Haibo3; Yang，Min1; Yang，Qianting4; Lin，Yi1; Guan，Shanyi5; Deng，Yongcong6; Liu，Lei1; Li，Qingyun2; He，Mengqi2; Zhang，Peize1; Chen，Haibin2; Deng，Guofang1

2023-12-01

10.1186/s12879-023-08112-3

BMC Infectious Diseases   影响因子和分区

1471-2334

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.

Breathomics Machine learning Pulmonary tuberculosis Volatile organic compounds

SCI

英语

第一 ; 通讯

Infectious Diseases

Infectious Diseases

WOS:000947963200004

BMC

IMMUNOLOGY

2-s2.0-85150004324

Scopus

1.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
2.Breax Laboratory,PCAB Research Center of Breath and Metabolism,Beijing,100074,China
3.Peking University Clinical Research Institute,Peking University First Hospital,Beijing,100000,China
4.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
5.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
6.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

Fu，Liang,Wang，Lei,Wang，Haibo,et al. A cross-sectional study: a breathomics based pulmonary tuberculosis detection method[J]. BMC Infectious Diseases,2023,23(1).

Fu，Liang.,Wang，Lei.,Wang，Haibo.,Yang，Min.,Yang，Qianting.,...&Deng，Guofang.(2023).A cross-sectional study: a breathomics based pulmonary tuberculosis detection method.BMC Infectious Diseases,23(1).

Fu，Liang,et al."A cross-sectional study: a breathomics based pulmonary tuberculosis detection method".BMC Infectious Diseases 23.1(2023).