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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Prognosis of Oligodendroglioma Patients Stratified by Age: A SEER Population-Based Analysis
Kai Jin1, Shu-Yuan Zhang1, Li-Wen Li1
1Department of Neurosurgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, People's Republic of China.
Purpose:
Glioma may affect patients of any age. So far, only a limited number of big data studies have been conducted concerning oligodendroglioma (OG) in diverse age groups. This study evaluated the risk factors for OG in different age groups using the Surveillance, Epidemiology, and End Results (SEER) database built by the National Cancer Institute, which is part of the National Institutes of Health.
Patients And Methods:
A total of 5437 cases within the SEER database were included. These patients were divided into seven age groups. The Kaplan-Meier method was employed for survival analysis. The independent risk factors for the survival of OG patients were identified using the Cox regression model. A nomogram was drawn with R software based on the independent risk factors. The X-tile software was adopted to find the optimal age group at diagnosis.
Results:
The all-cause mortality and the tumor-specific mortality increased with age. The univariate analysis showed that the patients' age, gender, primary lesion location, side affected by the primary lesion (left or right), surgery for the primary lesion, and tumor size were correlated with survival (P<0.05). Multivariate Cox regression analysis showed that age was an independent risk factor for the survival of OG patients (P<0.05). The optimal cutoff value of age in terms of overall survival (OS) and cause-specific survival (CSS) were identified as 48 and 61 years and 48 and 59 years, respectively.
Conclusion:
The older the age, the worse the survival would be. That's, the mortality increased with age. In the clinic, healthcare professionals should be fully aware of the variability in the prognosis of OG patients in different age groups. Therefore, individualized treatments are recommended to OG patients in different age groups to optimize the prognosis.
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