A novel diagnostic model for tuberculous meningitis using Bayesian latent class

Participants

We used data from a prospective observational study on individuals with suspected brain infections who were admitted to the neuro-infection ward of the Hospital for Tropical Diseases (HTD), Ho Chi Minh City, Vietnam [11]. The HTD is a 550-bed centre providing secondary and tertiary treatment for a wide range of tropical infections in Southern Vietnam [3]. The study received ethical approval from HTD and the Oxford Tropical Research Ethics Committee and written informed consent was obtained from all participants or their relatives if they were incapacitated [11].

Participants were \(\ge 16\) years old and were enrolled between 29th August 2017 and 22nd January 2021. All were suspected of neurological infection and underwent lumbar puncture at baseline as a routine diagnostic procedure. Patients were ineligible for enrolment if performing a lumbar puncture was contraindicated and excluded from our analysis if the mycobacterial cultures of CSF taken within the first week were contaminated.

Data collection

Clinical and imaging data

Demographics (age, gender) and relevant medical history were collected. HIV tests were only conducted on those with identified risk factors for HIV infection. All participants underwent a chest X-ray. Brain imaging was not performed routinely and not included in the database.

Cerebrospinal fluid (CSF) analysis

White cell count (WCC) and cellular differential, protein, glucose (with paired blood glucose taken at the same time), and lactate were measured at enrolment. A Gram stain was performed to screen for bacterial meningitis, PCR/serology for viral meningitis, PCR for Angiostrongylus cantonensis, and India-ink staining and a cryptococcal antigen lateral flow test if cryptococcal meningitis was suspected. Where possible at least 6 mL CSF was used for mycobacterial testing by ZN-Smear, MGIT, and either Xpert MTB/RIF or Xpert MTB/RIF Ultra (Xpert Ultra). We combined Xpert Ultra with Xpert as they were diagnostically comparable for TBM in our setting [11]. In case an insufficient sample (11].

Diagnosis and treatment

All patients received treatment according to the national and local guidelines. At the time of discharge or death, all were given a final diagnosis, based on the available clinical and laboratory information, including treatment response. If at least one of ZN-Smear, Xpert, or MGIT from CSF was positive at any time, the patient had definite TBM. Patients had suspected TBM if confirmatory microbiological and molecular tests were negative, but TBM was clinically suggested and anti-TB drugs were started. Those who recovered without anti-TB chemotherapy or had an alternative diagnosis confirmed microbiologically (e.g., by culture, PCR, or antigen tests) were assigned another diagnosis (i.e., not TBM).

Statistical analysis

Our latent class model for TBM has two components, both consisting of logistic regression models (Fig. 1). In the indicator model, we made the results of three observed confirmatory tests ZN-Smear, MGIT, and Xpert depending on TBM status. The prevalence model quantifies the probability to have TBM. This probability is purely based on the diagnostic features, prior to any confirmatory tests. As the three tests share similar mechanisms of detecting the presence of Mtb, we added an individual random effect – which we call the mycobacillary burden – to eliminate their collinearity [7, 12]. The latent mycobacillary burden was related to a set of modulating factors (bacillary burden sub-model).

The choice of diagnostic features and modulating factors was based on prior knowledge of their association with TBM status and bacterial burden [5] (Clinical supplementary appendix). We added three indicators of an alternative diagnosis to the prevalence model: (1) positive CSF eosinophil count—a strong biomarker for eosinophilic meningitis, a relatively common condition in Vietnam, usually caused by Angiostrongylus cantonensis; (2) positive CSF Indian-ink staining or cryptococcal antigen lateral flow test; and (3) positive CSF Gram stain for non-acid-fast bacterial meningitis. We also included CSF red cell count as it is a marker of a traumatic lumbar puncture, which requires corrections to WCC and biochemical features [13,14,15]. A complete formulation of our model design is reported in the Statistical supplementary appendix.

We used the individual estimated TBM risk from the above model to fit a…

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