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.
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.
Authors: Theodore R Mellors; Lionel Blanchet; JoAnne L Flynn; Jaime Tomko; Melanie O'Malley; Charles A Scanga; Philana L Lin; Jane E Hill Journal: J Appl Physiol (1985) Date: 2017-01-05
Authors: Marco Beccaria; Carly Bobak; Boitumelo Maitshotlo; Theodore R Mellors; Giorgia Purcaro; Flavio A Franchina; Christiaan A Rees; Mavra Nasir; Wendy S Stevens; Lesley E Scott; Andrew Black; Jane E Hill Journal: J Breath Res Date: 2018-11-05 Impact factor: 3.262
Authors: Ngoc A Dang; Sjoukje Kuijper; Elisabetta Walters; Mareli Claassens; Dick van Soolingen; Gabriel Vivo-Truyols; Hans-Gerd Janssen; Arend H J Kolk Journal: PLoS One Date: 2013-10-17 Impact factor: 3.240