Breath analysis as a potential diagnostic tool for tuberculosis

Int J Tuberc Lung Dis. 2012 Jun;16(6):777-82. doi: 10.5588/ijtld.11.0576. Epub 2012 Apr 9.


Setting: Cape Town, South Africa.

Objectives: We investigated the potential of breath analysis by gas chromatography-mass spectrometry (GC-MS) to discriminate between samples collected prospectively from patients with suspected tuberculosis (TB).

Design: Samples were obtained in a TB-endemic setting in South Africa, where 28% of culture-proven TB patients had Ziehl-Neelsen (ZN) negative sputum smear. A training set of breath samples from 50 sputum culture-proven TB patients and 50 culture-negative non-TB patients was analysed using GC-MS. We used support vector machine analysis for classification of the patient samples into TB and non-TB.

Results: A classification model with seven compounds had a sensitivity of 72%, a specificity of 86% and an accuracy of 79% compared with culture. The classification model was validated with breath samples from a different set of 21 TB and 50 non-TB patients from the same area, giving a sensitivity of 62%, a specificity of 84% and an accuracy of 77%.

Conclusion: This study shows that GC-MS breath analysis is able to differentiate between TB and non-TB breath samples even among patients with a negative ZN sputum smear but a positive culture for Mycobacterium tuberculosis. We conclude that breath analysis by GC-MS merits further research.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adult
  • Breath Tests*
  • Endemic Diseases*
  • Female
  • Gas Chromatography-Mass Spectrometry*
  • Humans
  • Male
  • Middle Aged
  • Mycobacterium tuberculosis / isolation & purification
  • Predictive Value of Tests
  • Prospective Studies
  • Reproducibility of Results
  • Sensitivity and Specificity
  • South Africa / epidemiology
  • Sputum / microbiology
  • Support Vector Machine
  • Tuberculosis / diagnosis*
  • Tuberculosis / epidemiology
  • Tuberculosis / microbiology
  • Young Adult