Competing risk model to determine the prognostic factors for patients with gliosarcoma.

Cancer-related death Competing risk model Gliosarcoma Nomogram SEER

Journal

World neurosurgery
ISSN: 1878-8769
Titre abrégé: World Neurosurg
Pays: United States
ID NLM: 101528275

Informations de publication

Date de publication:
27 Dec 2023
Historique:
received: 14 12 2023
accepted: 21 12 2023
medline: 2 1 2024
pubmed: 2 1 2024
entrez: 29 12 2023
Statut: aheadofprint

Résumé

Glioma (GSM) is a highly aggressive variant of brain cancer with an extremely unfavorable prognosis. Prognosis is not feasible by traditional methods due to lack of staging criteria, and the present study aims to screen more detailed demographic factors to predict the prognostic factors of the tumors. For this study, we extracted data of diagnosed with GSM from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019.. To account for the influence of competing risks, we employed a cumulative incidence function (CIF). Subsequently, univariate analysis was conducted to evaluate the individual variables under investigation. Specifically for GSM patients, we generated cumulative risk curves for specific mortality outcomes and events related to competing risks. Additionally, we utilized both univariate and multivariate Cox analysis to account for non-GSM-related deaths that may potentially confound our research. The competing risk model revealed that age, marital, tumor size, and adjuvant therapy were prognostic factors in GSM-related death. The analysis results showed that older age (60-70 years, ≥ 71 years) and larger tumor size (≥ 5.3 cm) significantly increased the risk of GSM-related death. Conversely, surgical intervention, chemotherapy and being single were identified as protective factors against GSM-related death. Our study using a competing risk model provided valuable insights into the prognostic factors associated with GSM-related death. Further research and clinical interventions targeted at minimizing these risk factors and promoting the utilization of protective measures may potentially contribute to improved outcomes and reduced mortality rates for GSM patients.

Sections du résumé

BACKGROUND BACKGROUND
Glioma (GSM) is a highly aggressive variant of brain cancer with an extremely unfavorable prognosis. Prognosis is not feasible by traditional methods due to lack of staging criteria, and the present study aims to screen more detailed demographic factors to predict the prognostic factors of the tumors.
METHODS METHODS
For this study, we extracted data of diagnosed with GSM from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019.. To account for the influence of competing risks, we employed a cumulative incidence function (CIF). Subsequently, univariate analysis was conducted to evaluate the individual variables under investigation. Specifically for GSM patients, we generated cumulative risk curves for specific mortality outcomes and events related to competing risks. Additionally, we utilized both univariate and multivariate Cox analysis to account for non-GSM-related deaths that may potentially confound our research.
RESULTS RESULTS
The competing risk model revealed that age, marital, tumor size, and adjuvant therapy were prognostic factors in GSM-related death. The analysis results showed that older age (60-70 years, ≥ 71 years) and larger tumor size (≥ 5.3 cm) significantly increased the risk of GSM-related death. Conversely, surgical intervention, chemotherapy and being single were identified as protective factors against GSM-related death.
CONCLUSION CONCLUSIONS
Our study using a competing risk model provided valuable insights into the prognostic factors associated with GSM-related death. Further research and clinical interventions targeted at minimizing these risk factors and promoting the utilization of protective measures may potentially contribute to improved outcomes and reduced mortality rates for GSM patients.

Identifiants

DOI: 10.1016/j.wneu.2023.12.123 PMID: 38157982
pubmed: 38157982
pii: S1878-8750(23)01856-9
doi: 10.1016/j.wneu.2023.12.123
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Auteurs

Mingyi Chen (M)

Department of Neurology and Stroke Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China; Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.

Liying Huang (L)

Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.

Fang Wang (F)

Department of Neurology and Stroke Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China; Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.

Xiaoxin Xu (X)

Department of Neurology and Stroke Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China; Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.

Xiaohong Xu (X)

Department of Neurology and Stroke Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China; Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China,. Electronic address: xiaohongxu86@jnu.edu.cn.

Classifications MeSH