We used a latent class approach to obtain a diagnostic model of TBM for adults with suspected brain infection. Upon validation, our model showed good calibration and discrimination. Using cutpoints based on Youden’s Index, both our full and simplified prevalence models surpassed the most sensitive confirmatory test ZN-Smear (86% and 79%, compared with 65% of ZN-Smear), while still obtaining good specificity (88% and 75%, respectively).
The association direction of the diagnostic features and the individual TBM risk mostly followed prior knowledge. There are two differences compared with the UCD [5]. Firstly, we enforced a positive coefficient on HIV infection. Secondly, we found that a higher GCS was associated with a higher probability of TBM. Still, our estimated TBM risk was well-calibrated with the final hospital diagnosis made at discharge or death. This raises confidence that both are accurate. However, our model provides results far earlier in patients’ hospitalisation.
A novel quantification in our model is the latent mycobacterial burden. This estimate showed good correspondence with another laboratory-based quantification (Clinical supplementary appendix). We found that higher lymphocyte count in CSF was associated with increased probability of having TBM but with reduced mycobacillary burden. As lower mycobacillary burden is believed to be associated with reduced mortality, this was in line with a previous study, in which higher lymphocyte count was linked to increased survival from TBM [22].
As a by-product, we could re-evaluate the performance of the current microbiological assays. ZN-Smear had the highest sensitivity, 64.6% (95% CrI 50.9% − 81.8%), confirming prior studies from our centre and reflecting the high level of technical expertise in conducting the test [1, 11]. In our laboratory, Xpert performed poorly, especially for individuals without HIV, who on average had low mycobacillary burden. All tests performed much better for those with HIV, who tended to have higher burden. Within this group, ZN-Smear can be used as a reliable diagnostic standard, although the performance and utility is likely to be reduced outside of expert laboratories.
We are not the first to use LCA to help improve TBM diagnosis. A previous study from the Vietnam National Lung Hospital in Hanoi [6] had a different target population and estimated TBM prevalence amongst individuals with TB of any type. They made the strong assumption that all confirmatory test results were mutually independent. We could relax that assumption by including a model for mycobacterial burden. Unlike previous studies [4, 6, 8], we used non-confirmatory biomarkers as predictors, not as manifest variables. This implementation had two purposes: on the technical side, it lowered the risk of violating the aforementioned assumptions of independence if more manifest variables were included; and on the prediction side, it allowed us to develop a calibrated diagnostic model for TBM based on disease related clinical and laboratory characteristics.
Our study has some limitations. There were missing data, especially in test results and HIV status. When imputing missing values, we had to make several assumptions. These assumptions, despite the validity checks of the imputation (Statistical supplementary appendix), could have biased the results to some extent. In addition, this is a single-centre study conducted in a specialised brain infections centre. The prevalence of individuals with severe disease may be lower in other centres. The levels of clinical and laboratory expertise at our tertiary hospital may also be higher; especially the performance of CSF ZN-smear may not generalise well to other laboratories. There is less inter-laboratory variability in the performance of CSF Xpert and culture, but this does not detract from the need for external validation of our findings. In both cases, it highlights the benefit of a diagnostic tool that is less sensitive to expert judgement – which our prevalence models provide.
In conclusion, despite many past attempts to quantify microbiological test performance and develop diagnostic methods for TBM, this is amongst the first studies to utilise LCM and rigorously validate many assumptions made by the model. It was developed using a large cohort of adults with brain infection in Vietnam. Leveraging Bayesian inference, we extended the classical LCM and estimated individual mycobacillary burden. Our findings therefore have relevance for both clinical practice and research. Until a better gold standard for TBM diagnosis is developed, our model could be used as reference for both the diagnosis of TBM and the estimation of severity, both for research and clinical care.