AUTHOR=Liu Shichao , Liang Risheng 

TITLE=Risk stratification for intracranial infection after high-grade gliomas surgery: a nomogram development and validation study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1697966

DOI=10.3389/fonc.2025.1697966

ISSN=2234-943X

ABSTRACT=BackgroundIntracranial infection (ICI) is a severe complication following high-grade gliomas (HGGs) surgery, leading to increased morbidity and mortality. This study aimed to identify risk factors for postoperative ICI and to develop and validate a nomogram for predicting its occurrence, providing a tool to guide clinical prevention.MethodsThe clinical data of 104 patients who underwent surgery for HGGs between January 2017 and August 2024 were retrospectively analyzed. Patients were randomly divided into a training set (n=72) and a validation set (n=32). Risk factors for ICI were assessed using univariate and multivariate logistic regression analyses. A predictive nomogram was constructed based on the identified independent risk factors and evaluated for its performance.ResultsSixteen of the 104 patients (15.4%) developed a postoperative ICI. Multivariate logistic regression analysis identified a preoperative Karnofsky Performance Status (KPS) score ≤ 70 (OR = 16.55, 95% CI: 2.57–106.54, P = 0.003) and a drainage tube placement time ≥ 48 hours (OR = 15.42, 95% CI: 1.10–215.46, P = 0.042) as independent risk factors for ICI. A nomogram incorporating these two factors was developed and showed excellent discrimination, with an area under the curve (AUC) of 0.93 (95% CI: 0.87–0.99) in the training set and 0.92 (95% CI: 0.79–1.00) in the validation set. Calibration plots and decision curve analysis confirmed the nomogram’s accuracy and clinical utility.ConclusionA low preoperative KPS score and prolonged drainage tube placement are significant independent predictors of ICI after glioblastoma surgery. The nomogram developed in this study provides a simple and accurate tool for risk stratification, which can assist clinicians in identifying high-risk patients and implementing targeted preventive measures.