Diagnosis of Tuberculous Meningitis: Integrating Clinical Assessment and Molecular Diagnostics

TL;DR

This paper reviews how to diagnose tuberculous meningitis by combining clinical signs and molecular tests to improve accuracy and early treatment.

Contribution

The paper integrates clinical and molecular diagnostic approaches to improve the accuracy of diagnosing tuberculous meningitis.

Findings

Molecular assays enable rapid detection of Mycobacterium tuberculosis but are influenced by factors like HIV status and disease stage.

Integrated diagnostic algorithms combining clinical and molecular methods improve diagnostic accuracy and treatment timing.

Challenges remain in accessing molecular diagnostics and standardizing pathways across different healthcare settings.

Abstract

Tuberculous meningitis is the most severe form of tuberculosis and remains associated with high mortality and substantial neurological disability, particularly among children and people living with HIV. Early diagnosis is challenging because of nonspecific clinical manifestations, the limited discriminatory value of cerebrospinal fluid cytochemical analysis, and the low sensitivity of conventional microbiological methods. This narrative review synthesizes contemporary evidence on the diagnostic approach to tuberculous meningitis, integrating clinical assessment, paraclinical cerebrospinal fluid findings, conventional microbiology, and molecular diagnostic tools. Clinical scoring systems, including the uniform case definition (Lancet consensus score), improve diagnostic stratification but do not replace microbiological confirmation. Molecular assays have transformed diagnostic pathways by enabling rapid detection of Mycobacterium tuberculosis, although their performance is influenced by bacillary burden, cerebrospinal fluid volume, HIV status, and disease stage. Complementary molecular techniques and advanced sequencing approaches provide additional diagnostic value in selected paucibacillary cases or when first-line tests are negative. Integrated diagnostic algorithms that combine clinical evaluation with stepwise molecular testing improve diagnostic accuracy and support earlier treatment initiation. Ongoing challenges include limited access to molecular platforms, variability in laboratory capacity, and the need for standardized multimodal diagnostic pathways applicable across diverse healthcare settings.

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