Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

Area of Science:

• Neuroscience
• Infectious Diseases
• Clinical Diagnostics

Background:

• Differentiating tuberculous meningitis (TBM) from bacterial meningitis (BM) presents a significant clinical challenge.
• Accurate and timely diagnosis is crucial for effective treatment and patient outcomes.

Purpose of the Study:

• To develop and validate a diagnostic model for the accurate differentiation between TBM and BM.
• To identify key indicators in cerebrospinal fluid (CSF) and T-SPOT assay for improved diagnostic accuracy.

Main Methods:

• A multivariate logistic regression model was developed using data from two hospital cohorts (Qiaokou and Caidian).
• Indicators analyzed included CSF lymphocyte proportion, CSF chlorine, CSF nucleated cell count, and T-SPOT assay (TBAg/PHA ratio).
• The model's performance was evaluated using receiver operating characteristic (ROC) curve analysis, sensitivity, and specificity.

Main Results:

• The TBAg/PHA ratio alone showed a high area under the ROC curve (AUC) of 0.889 for distinguishing TBM from BM.
• A combined diagnostic model incorporating TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion demonstrated excellent performance.
• The model achieved an AUC of 0.949 in the Qiaokou cohort and was validated with an AUC of 0.923 in the Caidian cohort.

Conclusions:

• A novel diagnostic model combining CSF parameters and T-SPOT assay effectively discriminates between TBM and BM.
• This model offers high sensitivity and specificity, improving diagnostic utility in clinical practice.
• The validated model provides a reliable tool for differentiating these two serious forms of meningitis.