Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis

Abstract

No accurate and rapid diagnostic test exists for tuberculous meningitis (TBM), leading to delayed diagnosis. We leveraged data from multiple studies to improve the predictive performance of diagnostic models across different populations, settings, and subgroups to develop a new predictive tool for TBM diagnosis. We conducted a systematic review to analyze eligible datasets with individual-level participant data (IPD). We imputed missing data and explored three approaches: stepwise logistic regression, classification and regression tree (CART), and random forest regression. We evaluated performance using calibration plots and C-statistics via internal-external cross-validation. We included 3,761 individual participants from 14 studies and nine countries. A total of 1,240 (33%) participants had "definite" (30%) or "probable" (3%) TBM by case definition. Important predictive variables included cerebrospinal fluid (CSF) glucose, blood glucose, CSF white cell count, CSF differential, cryptococcal antigen, HIV status, and fever presence. Internal validation showed that performance varied considerably between IPD datasets with C-statistic values between 0.60 and 0.89. In external validation, CART performed the worst (C = 0.82), and logistic regression and random forest had the same accuracy (C = 0.91). We developed a mobile app for TBM clinical prediction that accounted for heterogeneity and improved diagnostic performance (https://tbmcalc.github.io/tbmcalc). Further external validation is needed.

Figure 1.

Preferred Reporting Items for Systematic Review and Meta-Analysis individual-level participant data (IPD) flow diagram of study selection process. TBM = tuberculous meningitis.

Figure 2.

Receiver operating characteristic (ROC) curves for logistic regression, classification and regression tree (CART), and random forest models. Logistic = logistic regression. Numbers behind models indicate C-statistic (i.e., area under the receiver operator characteristic curve).

Figure 3.

Calibration plot for Logistic, classification and regression tree analysis (CART), and random forest multivariate prediction models. Logistic = logistic regression; RF = random forest.