(A202) A Nomogram Model to Predict Meningitis Occurrence in Cerebrospinal Fluid Leak Patients

Introduction: Meningitis following cerebrospinal fluid (CSF) leak leads to significant morbidity and mortality. The present study analyzed the factors influencing meningitis risk and constructed a model for timely detection of meningitis development in patients with CSF leaks.

Methods: 179 patients with CSF leaks were randomly divided into training and validation sets in a 6:4 ratio. Predictive factors affecting the development of meningitis following CSF leak were refined to construct the nomogram with univariate and multivariable logistic regression analyses. Verification of the model's accuracy and applicability was conducted with the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).

Results: 40 patients (22.3%) developed meningitis, predominantly males (70.0%). Smoking and chronic rhinosinusitis were more common in patients with meningitis. The multiple logistic regression analysis revealed that pneumonia and iatrogenic CSF leak are independent risk factors for developing meningitis. A nomogram consisting of four factors - smoking, sex, pneumonia and etiology of CSF leak was constructed. The under the curve (AUC) values for the predictive model of meningitis were 0.75 (95%CI: 0.63-0.86) in the training set and 0.76 (95%CI: 0.63-0.89) in the validation set. The calibration plots and DCA curves confirmed the excellent performance of the nomogram in predicting meningitis occurrence in CSF leak patients.

Conclusions: Pneumonia and iatrogenic CSF leaks are independent risk factors for developing meningitis in patients with CSF leak. The nomograph model developed in this study effectively predicts meningitis occurrence, aiding in early evaluation.