Abstract
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.
Funder
National Natural Science Foundation of China
Key Technologies Research and Development Program
Basic and Applied Basic Research Foundation of Guangdong Province
Guangdong Provincial Key Laboratory of Prevention and Control for Severe Clinical Animal Diseases
Shenzhen Scientific and Technological Foundation
Summit Plan for Foshan High-level Hospital Construction
Shenzhen Third People's Hospital
The Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties
Shenzhen Natural Science Foundation
the Shenzhen Clinical Research Center for Tuberculosis
the Special fund of Shenzhen Central-leading-local Scientific and Technological Foundation
Publisher
Springer Science and Business Media LLC
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