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Oral Bacterial Infection and Shedding in Drosophila melanogaster
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A Model for Intracranial Bacterial Infection After Neurosurgery Integrating Bacterial Culture and Genetic Testing
Baoli Lin1,2,3, Xianbing Meng1,2,4, Ke Pu1,2
1Department of Neurosurgery, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.
Surgical Infections
|January 27, 2026
Summary
A new model aids early detection of bacterial infections after neurosurgery. It integrates clinical data and cerebrospinal fluid (CSF) biomarkers for improved risk stratification and timely treatment decisions.
Area of Science:
- Neurosurgery
- Infectious Diseases
- Biomarkers
- Predictive Modeling
Background:
- Intracranial bacterial infections post-neurosurgery pose diagnostic challenges due to delayed pathogen identification.
- Early detection is crucial for effective management and improved patient outcomes.
- Current diagnostic methods can be inconclusive, necessitating novel approaches.
Purpose of the Study:
- To develop and validate a predictive model for early detection of bacterial infections following neurosurgical procedures.
- To integrate clinical features and cerebrospinal fluid (CSF) biomarkers for enhanced diagnostic accuracy.
- To provide a tool for timely risk stratification in post-neurosurgical patients.
Main Methods:
- Retrospective study of 429 neurosurgery patients (November 2018 - December 2024).
- Patients divided into training (n=300) and validation (n=129) cohorts.
- LASSO regression and multivariable logistic regression used to construct a nomogram model.
- Model performance assessed using ROC curves, calibration plots, and decision curve analysis (DCA).
Main Results:
- 147 out of 429 patients (34.3%) had confirmed CSF bacterial infections.
- Seven independent predictors identified: CSF time to operation, CSF color, CSF white blood cell count, CSF glucose, CSF lactate, hyperpyrexia, and reaction time.
- The model demonstrated good predictive performance with AUCs of 0.822 (training) and 0.715 (validation).
- DCA indicated significant clinical utility within a specific probability threshold.
Conclusions:
- A nomogram-based predictive model offers a practical tool for early risk stratification of post-neurosurgical bacterial infections.
- The model supports timely diagnostic and therapeutic decision-making in clinical practice.
- Integration of clinical and CSF biomarkers enhances early detection capabilities.
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