A Predictive Model for Postoperative Intracranial Infection in Patients Following Spontaneous Intracranial Aneurysm Rupture.

Abstract

Objective: To analyze the risk factors for postoperative intracranial infection in patients with intracranial aneurysm rupture and to use them to construct a predictive clinical model.

Methods: A total of 598 patients with intracranial aneurysm rupture admitted to Anhui Provincial Hospital between June 2020 and October 2022 were selected. Univariate and logistic regression analyses were conducted to identify the risk factors for postoperative intracranial infection. A predictive clinical model was constructed, and its effectiveness in clinical applications was systematically evaluated using receiver operating characteristic (ROC) curve analysis.

Results: Among the 598 patients with spontaneous intracranial aneurysm rupture, 71 developed an intracranial infection (11.87%). The results of logistic regression analysis showed that preoperative lung infection, Glasgow Coma Scale (GCS) score at admission, unplanned secondary surgery, surgical method, ventricular haematocele, Hunt-Hess score, and an indwelling drainage tube were factors influencing the development of intracranial infections in patients with spontaneous intracranial aneurysm rupture. The area under the ROC curve (AUC) of the prediction model based on these factors was 0.959, with an optimal critical value of 0.148, a sensitivity of 0.915, and a specificity of 0.907.

Conclusions: The authors' predictive model achieved excellent results and can help medical personnel quickly identify the risk of intracranial infection, screen populations with postoperative infection after intracranial aneurysm rupture and provide strategies for the prevention of intracranial infections.