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Updated: Jan 9, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk stratification for intracranial infection after high-grade gliomas surgery: a nomogram development and
1Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Background:
Intracranial 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.
Methods:
The 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.
Results:
Sixteen 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.
Conclusion:
A 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.
