A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis.

BACKGROUND: Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known. METHODS: We included 659 individuals aged [Formula: see text] years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl-Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally. RESULTS: Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden's Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively. CONCLUSION: Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.

Developing biomarker assays to accelerate tuberculosis drug development: defining target product profiles.

Drug development for tuberculosis is hindered by the methodological limitations in the definitions of patient outcomes, particularly the slow organism growth and difficulty in obtaining suitable and representative samples throughout the treatment. We developed target product profiles for biomarker assays suitable for early-phase and late-phase clinical drug trials by consulting subject-matter experts on the desirable performance and operational characteristics of such assays for monitoring of tuberculosis treatment in drug trials. Minimal and optimal criteria were defined for scope, intended use, pricing, performance, and operational characteristics of the biomarkers. Early-stage trial assays should accurately quantify the number of viable bacilli, whereas late-stage trial assays should match the number, predict relapse-free cure, and replace culture conversion endpoints. The operational criteria reflect the infrastructure and resources available for drug trials. The effective tools should define the sterilising activity of the drug and lower the probability of treatment failure or relapse in people with tuberculosis. The target product profiles outlined in this Review should guide and de-risk the development of biomarker-based assays suitable for phase 2 and 3 clinical drug trials.