Background: Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor outcome, is caused in part by lack of availability of diagnostic tests that are both rapid and accurate. Several attempts have been made to develop clinical scoring systems to fill this gap, but none have performed sufficiently well to be broadly implemented. We aim to identify and validate a set of clinical predictors that accurately classify TBM using individual patient data (IPD) from published studies. Methods: We will perform a systematic review and obtain IPD from studies published from the year 1990 which undertook diagnostic testing for TBM in adolescents or adults using at least one of, microscopy for acid-fast bacilli, commercial nucleic acid amplification test for Mycobacterium tuberculosis or mycobacterial culture of cerebrospinal fluid. Clinical data that have previously been shown to be associated with TBM, and can inform the final diagnosis, will be requested. The data-set will be divided into training and test/validation data-sets for model building. A predictive logistic model will be built using a training set with patients with definite TBM and no TBM. Should it be warranted, factor analysis may be employed, depending on evidence for multicollinearity or the case for including latent variables in the model. Discussion: We will systematically identify and extract key clinical parameters associated with TBM from published studies and use a 'big data' approach to develop and validate a clinical prediction model with enhanced generalisability. The final model will be made available through a smartphone application. Further work will be external validation of the model and test of efficacy in a randomised controlled trial.
Keywords: Tuberculous meningitis; diagnostics; machine learning; multivariable prediction rule.
Copyright: © 2021 Boyles T et al.