Related Experiment Video
Updated: Jun 27, 2026
A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury
Published on: March 26, 2019
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.
Purpose:
Early prediction of post-craniotomy meningitis (PCM) is critical for optimizing prognosis. Given the limitations of existing prediction tools, this study sought to develop and externally validate a novel model for early postoperative risk stratification and prediction of PCM.
Patients And Methods:
Data were retrospectively collected from three Chinese hospitals. Least absolute shrinkage and selection operator (LASSO) and backward stepwise logistic regression were utilized to select independent predictors for nomogram construction. Nomogram performance was further evaluated by areas under the receiver operating characteristic curves (AUC) (discrimination), calibration plots (calibration) and decision curve analysis (clinical applicability). Meanwhile, pairwise comparisons of AUCs were performed to compare predictive performance between the model and the individual independent predictors. Additionally, a practical probability threshold of 30% was proposed based on the decision curve analysis to evaluate the model's performance in terms of sensitivity and specificity.
Results:
According to their admitted hospitals, 694 patients were allocated to the training cohort, and 309 comprised the external validation cohort. Within the training cohort, postoperative CSF leak, intraoperative/postoperative ventricular drain placement, and trans-sinusal surgery were identified as independent predictors (all P<0.001). Then, a new nomogram was built based on these predictors and demonstrated good discrimination in both the training (AUC=0.890, 95% CI 0.864-0.912) and external validation (AUC=0.824, 95% CI 0.777-0.865) cohorts, alongside satisfactory calibration and clinical utility. Further pairwise comparisons confirmed the nomogram's superiority over each single predictor across both cohorts (all P<0.001). Furthermore, applying a practical decision threshold of 30% yielded well-balanced early-warning performance for the model, with sensitivities >0.70 and specificities >0.90 in both groups.
Conclusion:
Our novel model exhibited favorable performance and clinical applicability, and was found to outperform the individual predictors. Accordingly, this newly built model seems a practical warning tool, though better-designed studies are needed for its further refinement.
Insights
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.
- 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.
