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A Novel Model for Predicting Post-Craniotomy Meningitis Using Early Postoperative Risk Stratification: A Multi-Center

Jingwei Zhao¹, Xiyu Chen¹, Lei Wu¹

¹Department of Critical Care, Beijing Tiantan Hospital of Capital Medical University, Beijing, 100070, People's Republic of China.

Infection and Drug Resistance

|June 26, 2026

Summary

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This summary is machine-generated.

A new model accurately predicts post-craniotomy meningitis (PCM) risk. This tool identifies patients needing closer monitoring, improving outcomes by detecting early signs of infection after brain surgery.

Area of Science:

Neurosurgery
Infectious Disease Epidemiology
Medical Informatics

Background:

Post-craniotomy meningitis (PCM) poses significant risks to patient prognosis.
Existing prediction tools for PCM have limitations in accuracy and applicability.
Early identification of high-risk patients is crucial for timely intervention and improved outcomes.

Purpose of the Study:

To develop and externally validate a novel predictive model for early risk stratification of PCM.
To enhance the accuracy and clinical utility of postoperative meningitis prediction.
To provide a practical tool for identifying patients at high risk of developing PCM.

Main Methods:

Retrospective data collection from three Chinese hospitals.
Development of a nomogram using LASSO and logistic regression to identify independent predictors.

Keywords:

craniotomy external validation independent predictor meningitis pairwise comparison predictive modeling

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External validation of the nomogram using discrimination (AUC), calibration plots, and decision curve analysis.

Main Results:

Postoperative CSF leak, ventricular drain placement, and trans-sinusal surgery were identified as key predictors.
The novel nomogram demonstrated strong discrimination in both training (AUC=0.890) and external validation (AUC=0.824) cohorts.
The model showed superior performance compared to individual predictors, with good calibration and clinical utility.

Conclusions:

The developed nomogram is a practical and effective tool for early prediction of PCM.
The model exhibits favorable performance and clinical applicability, outperforming individual predictors.
Further studies are warranted for refinement, but the model shows promise for clinical implementation.