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

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.
• 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.