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

Abstract

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.

Keywords: Diagnosis; Gold standard; Latent class model; Tuberculosis; Tuberculous meningitis.

Fig. 1

Basic model design. Unknown TBM status is linked with test results. The probability of a positive test depends on bacillary burden, which in turn depends on modulating factors. TBM risk factors help determining an individual’s TBM status. The distribution of test results shown on the bar plots are for demonstration only and do not correspond to the actual numbers

Fig. 2

Participant recruitment flow

Fig. 3

Venn diagram for ZN-Smear, MGIT, and Xpert profile in the study population

Fig. 4

Posterior estimates of our selected model. A Prevalence model: TBM odds ratios by diagnostic features, except WCC; B TBM odds ratio by CSF WCC ($cells/m{m}^3$) over reference value = 154 $cells/m{m}^3$; C Modulating factors impacting individual bacillary burden, given they have TBM. In A and C: dot, thick and thin lines are medians, 50% and 95% credible intervals. In B: the blue line is median odds ratio, the inner and outer ribbon are 50% and 95% credible intervals

Fig. 5

Performance of the selected prevalence model, assuming the final hospital diagnosis is the true status. A ROC curve and AUC: AUC values are presented as “average (min—max over 5 repetitions of cross-validation)”; B Calibration plot, showing the relationship between the predicted probability and observe outcome, smoothed by a loess curve. The grey lines are fitted curves from each 20-fold cross validation and coloured lines represents their average. The cross-validation procedure is explained in the Statistical supplementary appendix

Fig. 6

Posterior estimates and performance of the simplified prevalence model, assuming the final hospital diagnosis is the true status. A Posterior estimates of coefficients of clinical TBM risk factors. Points, thick and thin lines are medians, 50% and 95% credible intervals. B ROC plot and AUC. AUC values are presented as “average (min—max over 20 repetitions of cross-validation)”. C Calibration plot, showing the relationship between the predicted probability and observe outcome, smoothed by a loess curve. The grey lines are fitted curves from each 20-fold cross validation and coloured lines represents their average. The cross-validation procedure is explained in the Statistical supplementary appendix