New technologies for diagnosing active TB: the VANTDET diagnostic accuracy study

• Main Authors: Alice Halliday, Pooja Jain, Long Hoang, Robert Parker, Mica Tolosa-Wright, Tereza Masonou, Nathan Green, Aime Boakye, Yemisi Takwoingi, Shea Hamilton, Vinay Mandagere, Anastasia Fries, Lachlan Coin, Jon Deeks, Peter J White, Michael Levin, Peter Beverley, Onn Min Kon, Ajit Lalvani
• Format: Article
• Language: English
• Published: NIHR Journals Library 2021-04-01
• Series: Efficacy and Mechanism Evaluation
• Subjects: tuberculosis; mycobacterium tuberculosis; diagnostic test; transcriptomic; proteomics; cellular immune; signatures; validation
• Online Access: https://doi.org/10.3310/eme08050

Background: Tuberculosis (TB) is a devastating disease for which new diagnostic tests are desperately needed. Objective: To validate promising new technologies [namely whole-blood transcriptomics, proteomics, flow cytometry and quantitative reverse transcription-polymerase chain reaction (qRT-PCR)] and existing signatures for the detection of active TB in samples obtained from individuals with suspected active TB. Design: Four substudies, each of which used samples from the biobank collected as part of the interferon gamma release assay (IGRA) in the Diagnostic Evaluation of Active TB study, which was a prospective cohort of patients recruited with suspected TB. Setting: Secondary care. Participants: Adults aged ≥ 16 years presenting as inpatients or outpatients at 12 NHS hospital trusts in London, Slough, Oxford, Leicester and Birmingham, with suspected active TB. Interventions: New tests using genome-wide gene expression microarray (transcriptomics), surface-enhanced laser desorption ionisation time-of-flight mass spectrometry/liquid chromatography–mass spectrometry (proteomics), flow cytometry or qRT-PCR. Main outcome measures: Area under the curve (AUC), sensitivity and specificity were calculated to determine diagnostic accuracy. Positive and negative predictive values were calculated in some cases. A decision tree model was developed to calculate the incremental costs and quality-adjusted life-years of changing from current practice to using the novels tests. Results: The project, and four substudies that assessed the previously published signatures, measured each of the new technologies and performed a health economic analysis in which the best-performing tests were evaluated for cost-effectiveness. The diagnostic accuracy of the transcriptomic tests ranged from an AUC of 0.81 to 0.84 for detecting all TB in our cohort. The performance for detecting culture-confirmed TB or pulmonary TB was better than for highly probable TB or extrapulmonary tuberculosis (EPTB), but was not high enough to be clinically useful. None of the previously described serum proteomic signatures for active TB provided good diagnostic accuracy, nor did the candidate rule-out tests. Four out of six previously described cellular immune signatures provided a reasonable level of diagnostic accuracy (AUC = 0.78–0.92) for discriminating all TB from those with other disease and latent TB infection in human immunodeficiency virus-negative TB suspects. Two of these assays may be useful in the IGRA-positive population and can provide high positive predictive value. None of the new tests for TB can be considered cost-effective. Limitations: The diagnostic performance of new tests among the HIV-positive population was either underpowered or not sufficiently achieved in each substudy. Conclusions: Overall, the diagnostic performance of all previously identified ‘signatures’ of TB was lower than previously reported. This probably reflects the nature of the cohort we used, which includes the harder to diagnose groups, such as culture-unconfirmed TB or EPTB, which were under-represented in previous cohorts. Future work: We are yet to achieve our secondary objective of deriving novel signatures of TB using our data sets. This was beyond the scope of this report. We recommend that future studies using these technologies target specific subtypes of TB, specifically those groups for which new diagnostic tests are required. Funding: This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and NIHR partnership.