A Model for Intracranial Bacterial Infection After Neurosurgery Integrating Bacterial Culture and Genetic Testing

Early diagnosis of intracranial bacterial infections following neurosurgical procedures remains challenging because of delayed or inconclusive pathogen identification. This study aimed to develop and validate a predictive model for early detection of post-neurosurgical procedure bacterial infections by integrating clinical features and cerebrospinal fluid (CSF) biomarkers.

We conducted a retrospective study including 429 patients who underwent neurosurgery at Tianjin Huanhu Hospital between November 2018 and December 2024. Patients were randomly divided into training (n = 300) and validation (n = 129) cohorts. Predictive parameters were selected via uni-variable analysis and LASSO regression, followed by multi-variable logistic regression to construct a nomogram. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA).

Among 429 patients, 147 (34.3%) had confirmed CSF bacterial infections. Seven independent predictors were identified: CSF time to operation, CSF color, CSF white blood cell count ≥ 2000 × 10⁶/L, CSF glucose < 1.89 mmol/L, CSF lactate concentration, hyperpyrexia, and slower reaction time. The model achieved an area under the ROC curve of 0.822 (95% confidence interval [CI]: 0.771-0.873) in the training cohort and 0.715 (95% CI: 0.604-0.827) in the validation cohort. DCA demonstrated substantial clinical net benefit across a threshold probability range of 0.1-0.25.

This nomogram-based model provides a practical and reliable tool for early risk stratification of post-neurosurgical procedure intracranial bacterial infections, supporting timely diagnostic and therapeutic decision-making in clinical practice.