A Novel Diagnostic Prediction Model for Distinguishing Between Tuberculous and Cryptococcal Meningitis

Background and aim: Tuberculous meningitis (TBM) and cryptococcal meningitis (CM) are easily misdiagnosed due to atypical clinical symptoms. It is difficult for physcians to achieve a rapid and accurate differential diagnosis of TBM in the early stages of disease onset. The aim of this study was to construct a diagnostic prediction model for TBM and CM. Methods: In this retrospective study, 194 patients with TBM and CM were divided into two groups: training group (163 patients) and validation group (31 patients). Univariate and multivariate analyses were performed on the training group patients. The diagnostic factors were selected to construct the differential diagnostic prediction model for TBM and CM, and the prediction model was verified. A receiver operating characteristics curve (ROC) was constructed and used to evaluate the diagnostic value of the novel model. Results: Univariate analysis of clinical characteristics revealed differences in eight parameters ( P P =0.088). A differential diagnosis model for TBM and CM was constructed based on those factors. A ROC was constructed with an area under curve [AUC] of 94.5%, a sensitivity of 85.71%, and specificity of 94.59% in the training group. Conclusion: The novel diagnostic scoring model for TBM and CM has greater differential diagnosis potential than previous reports, which can provide more reliable preliminary diagnosis results for primary hospitals, effectively reduce misdiagnosis, and provide references for early treatment.