Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis - Stadelman-Behar, Anna M.; Tiffin, Nicki; Ellis, Jayne; Creswell, Fiona V.; Ssebambulidde, Kenneth; Nuwagira, Edwin; Richards, Lauren; Lutje, Vittoria; Hristea, Adriana; et al (The American Journal of Tropical Medicine and Hygiene, 2024)
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 5 0.82), and logistic regression and random forest had the same accuracy (C 5 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.
Keywords: Diagnostic Prediction Model; Tuberculous Meningitis; Participants