Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

A team of researchers at NDM’s Oxford University Clinical Research Unit (OUCRU) has pioneered a novel, microbiological assay-independent diagnostic model for adult Tuberculous Meningitis, leveraging Bayesian latent class analysis to improve the accuracy of diagnoses. This innovative approach, promises to enhance the detection and treatment of one of the most severe forms of tuberculosis (TB).

Tuberculous Meningitis, caused by Mycobacterium tuberculosis invading the meningeal structure, is the deadliest form of TB. It affects the layers of tissue covering the brain and spinal cord, leading to almost certain death if left untreated.

Early and accurate diagnosis of Tuberculous Meningitis is crucial but has historically been complicated due to the lack of a definitive, rapid gold standard test. Traditionally, the diagnosis of Tuberculous Meningitis heavily relied on clinicians’ judgement. This study published in BMC Infectious Diseases, introduces a new model that could change this landscape significantly, allowing clinicians to detect Tuberculous Meningitis cases as early as admission time.

The research, conducted by Dr Dong Huu Khanh Trinh, DPhil student, Dr Ronald Geskus, Head of Biostatistics Group and colleagues at OUCRU, analysed data from 659 individuals aged 16 years and above with suspected brain Tuberculous Meningitis infections admitted to the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam. Using a latent class model, the team developed a tool that uses early clinical, biochemical and haematological features to estimate the likelihood of Tuberculous Meningitis in patients before Mycobacterium tuberculosis confirmatory test results are available.

The model’s prediction was blind validated with a definitive diagnosis, later independently made by experts at the Ho Chi Minh City Hospital for Tropical Diseases (at the point of the patients’ death or discharge), demonstrating excellent accuracy (with Area Under the Curve = 94%). It shows particular promise for use in resource-limited settings such as Vietnam, where access to advanced diagnostic tests is often challenging.

Bayesian latent class analysis is a statistical tool that delivers insights into problems where definitive measurement is ambiguous—like diagnosing Tuberculous Meningitis. A sophisticated method of deduction, it uses a combination of indirect clues—symptoms and test results—to infer the presence of a condition. 

This approach allows doctors to make more informed decisions about diagnosis and treatment, even when a direct or clear-cut test result isn’t available. It’s a way of connecting the dots to form a clearer picture of a patient’s health, using both the data at hand and the wisdom of past experiences.

The findings indicate that factors such as HIV infection, miliary TB, prolonged symptom duration, and elevated cerebrospinal fluid lymphocyte count are indicative of a higher likelihood of Tuberculous Meningitis. Furthermore, the research revealed that Tuberculous Meningitis patients with HIV infection, lower cerebrospinal fluid lymphocyte count and increased cerebrospinal fluid protein levels are associated with higher mycobacterial burden.

The simplified diagnostic model, which includes a scoring table for early screening using only clinical information and chest X-ray results, could be particularly useful in settings where comprehensive cerebrospinal fluid analysis is not feasible.

Both models are also accessible as a progressive web app, bringing this innovative tool directly to clinicians, regardless of where they practice. Easy to use and compatible with various devices, the progressive web app ensures that healthcare professionals can leverage this assistive tool for Tuberculous Meningitis diagnosis, helping to expedite decision-making and potentially improve patient outcomes. Funding for the study is provided by Wellcome

First author Dr Dong Huu Khanh Trinh emphasises the model’s potential impact: ‘Our diagnostic tool holds the promise of being a crucial decision assistant for clinicians across different settings. It can help in rapidly identifying Tuberculous Meningitis cases, thereby facilitating timely and appropriate treatment interventions that can save lives.’

To read the full study results, visit: