The accuracy of an electronic nose to diagnose tuberculosis in patients referred to an expert centre

Abstract

Introduction An electronic nose (eNose) device has shown a high specificity and sensitivity to diagnose or rule out tuberculosis (TB) in the past. The aim of this study was to evaluate its performance in patients referred to INERAM. Methods Patients aged ≥15 years were included. A history, physical examination, chest radiography (CRX) and microbiological evaluation of a sputum sample were performed in all participants, as well as a 5-minute breath test with the eNose. TB diagnosis was preferably established by the gold standard and compared to the eNose predictions. Univariate and multivariate logistic regression analyses were performed to assess potential risk factors for erroneous classification results by the eNose. Results 107 participants with signs and symptoms of TB were enrolled of which 91 (85.0%) were diagnosed with TB. The blind eNose predictions resulted in an accuracy of 50%; a sensitivity of 52.3% (CI 95%: 39.6–64.7%) and a specificity of 36.4% (CI 95%: 12.4–68.4%). Risk factors for erroneous classifications by the eNose were older age (multivariate analysis: OR 1.55, 95% CI 1.10–2.18, p = 0.012) and antibiotic use (multivariate analysis: OR 3.19, 95% CI 1.06–9.66, p = 0.040). Conclusion In this study, the accuracy of the eNose to diagnose TB in a tertiary referral hospital was only 50%. The use of antibiotics and older age represent important factors negatively influencing the diagnostic accuracy of the eNose. Therefore, its use should probably be restricted to screening in high-risk communities in less complex healthcare settings.

Integration of Electrochemical Sensing and Machine Learning to Detect Tuberculosis via Methyl Nicotinate in Patient Breath

“…They found sensitivity and specificity values of 78% and 42%, respectively, in the validation phase [16]. When compared to the EAS approach, E-nose platforms are expensive and use a non-targeted VOC detection approach to produce a full-spectrum electronic signal that is fed into an algorithmic classifier [13]. While interesting, currently the lack of using a targeted biomarker contributes to their lower performance.…”