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

SUBMITTER: Stadelman-Behar AM 

PROVIDER: S-EPMC11376156 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature