Development and validation of a nomogram for predicting intracranial infection after intracranial aneurysm surgery

Intracranial infection is a severe complication following intracranial aneurysm surgery, associated with higher rates of morbidity and mortality. This study aimed to develop and validate a nomogram to predict the risk for intracranial infection after intracranial aneurysm surgery. This nomogram was designed to assist clinicians in identifying high-risk patients and implementing targeted preventive measures, ultimately improving postoperative outcomes.

This retrospective cohort study included patients who underwent intracranial aneurysm surgery at a single center. Data regarding potential predictors, including clinical characteristics, surgical details, and laboratory test results, were collected. Independent risk factors for intracranial infection were identified using univariate and multivariate logistic regression analyses. A nomogram was constructed on the basis of these predictors. Nomogram performance was evaluated using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration plots for predictive accuracy, and decision curve analysis (DCA) for clinical utility.

Data from 612 patients who underwent intracranial aneurysm surgery were analyzed, with 428 and 184 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis identified pneumonia, external ventricular drainage, tracheotomy, procalcitonin, C-reactive protein, and albumin levels as independent risk factors for intracranial infections (p < 0.05). A nomogram, constructed on the basis of these predictors, exhibited excellent discrimination, with an AUC of 0.91 (95% confidence interval [CI] 0.88-0.93) in the training cohort and 0.89 (95% CI 0.84-0.93) in the validation cohort. DCA demonstrated that the nomogram provided a significant net clinical benefit across a range of risk thresholds, supporting its utility in clinical decision making.

The nomogram developed was a robust and practical tool for predicting the risk for intracranial infection after intracranial aneurysm surgery. It demonstrated strong predictive accuracy and calibration, with potential applications in identifying high-risk patients and guiding individualized preventive strategies. However, validation using a broader and more diverse population is recommended to enhance the generalizability of the model.