# Construction and evaluation of a diagnostic prediction model for bacterial meningitis based on clinical and laboratory data

## TL;DR

This study developed a diagnostic model for bacterial meningitis using clinical and lab data to help doctors make faster and more accurate diagnoses.

## Contribution

A new diagnostic prediction model for bacterial meningitis based on clinical and laboratory variables was developed and validated.

## Key findings

- The model achieved an AUC of 0.84 in the training cohort and 0.77 in the validation cohort.
- Key predictors included CRP, lymphocyte percentage, and cerebrospinal fluid parameters.
- The model provides a visual nomogram for risk assessment in clinical settings.

## Abstract

Bacterial meningitis refers to the rapid inflammation of the meninges caused by bacteria or their byproducts, impacting the pia mater, arachnoid mater, and the subarachnoid space. This condition is a serious infectious illness affecting the central nervous system, if not diagnosed and treated promptly, it may result in severe neurological complications or even fatalities, making prompt and precise diagnosis essential for better outcomes. The objective of this research was to develop and assess a diagnostic prediction model for bacterial meningitis utilizing clinical and laboratory information. A retrospective study was carried out on patients with central nervous system infections who were admitted to the First Hospital of Hebei Medical University between January 2022 and February 2025. Both univariate and multivariate logistic regression analyses were utilized to create the prediction model, identifying key independent factors such as intracerebral hemorrhage, hydrocephalus, C-reactive protein (CRP), lymphocyte percentage (LY), cerebrospinal fluid chloride level (CSFCL), and the white blood cell count in cerebrospinal fluid. The results of logistic regression analysis were used to construct a nomogram to visualize the risk of bacterial meningitis in patients. The effectiveness of the model was assessed through calibration curves, the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Findings indicated that the AUC for the prediction model was 0.84 (95% CI: 0.78–0.89) for the training cohort and 0.77 (95% CI: 0.66–0.87) for the validation cohort. In summary, this model exhibits strong diagnostic capabilities and serves as a valuable tool for the swift clinical identification of bacterial meningitis.