Scholarlink

Background <p>Several studies have used demographic characteristics to examine differences in survival time for patients with malignant meningioma (MM). Latent class analysis (LCA), with its ability to identify mutually patterns of patients in a heterogeneous population. The aim of our study was to analyze the heterogeneity of sociodemographic characteristics in meningioma.</p> Methods <p>The data of patients diagnosed with malignant meningioma (n = 1,562, age &gt; 18&#xa0;years old) were extracted from the Surveillance, Epidemiology, and End Result database. Data on sociodemographic characteristics such as age, sex, race, NHIA, marital status, household income, rural or urban residential area, and overall survival time were included. LCA was used to identify heterogeneous patterns of MM. each group was explored using Bayesian network analysis.</p> Results <p>In total, 1562 patients with MM were processed by the LCA model; the 4-class latent class models were the best fit. LCA identified four survival groups: highest, intermediate-high, low-to-moderate, and lowest survival groups. Patients with the longest survival times—93.59&#xa0;months—were 40–59&#xa0;years old, female, Black, non-Hispanic, married, and had a family income of $60,000–$74,999 and lived in densely populated areas. Bayesian networks revealed correlations between patients with MM and sociodemographic characteristics in different latent class groups.</p> Conclusion <p>We identified and verified differences in clinical and sociodemographic characteristics between survival groups. A comprehensive understanding of the “people-oriented” subgroup characteristics will greatly benefit the diagnosis and treatment of MM.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Survival differences in malignant meningiomas: a latent class analysis using SEER data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月27日

查看全文
  • Bo Zhong,
  • Yan Zhang

摘要

Background

Several studies have used demographic characteristics to examine differences in survival time for patients with malignant meningioma (MM). Latent class analysis (LCA), with its ability to identify mutually patterns of patients in a heterogeneous population. The aim of our study was to analyze the heterogeneity of sociodemographic characteristics in meningioma.

Methods

The data of patients diagnosed with malignant meningioma (n = 1,562, age > 18 years old) were extracted from the Surveillance, Epidemiology, and End Result database. Data on sociodemographic characteristics such as age, sex, race, NHIA, marital status, household income, rural or urban residential area, and overall survival time were included. LCA was used to identify heterogeneous patterns of MM. each group was explored using Bayesian network analysis.

Results

In total, 1562 patients with MM were processed by the LCA model; the 4-class latent class models were the best fit. LCA identified four survival groups: highest, intermediate-high, low-to-moderate, and lowest survival groups. Patients with the longest survival times—93.59 months—were 40–59 years old, female, Black, non-Hispanic, married, and had a family income of $60,000–$74,999 and lived in densely populated areas. Bayesian networks revealed correlations between patients with MM and sociodemographic characteristics in different latent class groups.

Conclusion

We identified and verified differences in clinical and sociodemographic characteristics between survival groups. A comprehensive understanding of the “people-oriented” subgroup characteristics will greatly benefit the diagnosis and treatment of MM.