对结直肠癌患者的基于人口的数据中的脆弱性指标进行比较
Rebecca Birch1, John Taylor1, Tameera Rahman2,3
1Leeds Institute for Medical Research at St James's, University of Leeds, Leeds, UK.
Age and ageing
|May 24, 2024
概括
在患有结直肠癌的老年人中量化虚弱是具有挑战性的. 在行政数据中,三个指标 - - 医院脆弱性风险评分 (HFRS),SCARF指数和脆弱性综合征 (FS) - - 已被证明是可行的,有效的和可靠的.
科学领域:
- 老年医学 老年医学
- 在瘤学瘤学.
- 医疗保健服务研究 医疗服务研究
背景情况:
- 结直肠癌治疗中的与年龄相关的差异已经得到了充分的证据.
- 人口老龄化的脆弱性可能会加剧这些不平等.
- 在大规模研究中,准确地评估脆弱性是很困难的.
研究的目的:
- 评估三个脆弱性指标的可行性,有效性和可靠性:医院脆弱性风险评分 (HFRS),二级护理行政记录脆弱性指数 (SCARF) 和脆弱性综合征 (FS).
- 评估这些措施在结直肠癌患者的国家队列中.
主要方法:
- 在英国国家卫生服务中对136,008名结直肠癌患者进行了回顾性分析.
- 在数据集中生成的HFRS,SCARF和FS指标.
- 与查尔森并发症指数 (CCI) 进行诊断代码比较;通过脆弱性流行率和1年生存率评估有效性.
- 使用Brier分数和c-statistic评估模型性能.
主要成果:
- 所有三个脆弱性测量 (HFRS,SCARF,FS) 都证明了可行性,有效性和可靠性.
- 在SCARF和CCI诊断码之间存在显著的重叠.
- 虚弱的患病率各不相同:SCARF在最低风险组中发现了55.4%,HFRS为85.1%,FS为81.2%.
- HFRS和FS在脆弱程度之间的1年生存率中显示出最大的差异;模型性能差异很小.
结论:
- HFRS,SCARF和FS是使用常规管理健康数据量化脆弱性的有价值工具.
- 最佳的脆弱性测量取决于流行病学研究的具体需求和背景.
- 这些经过验证的措施可以帮助理解和解决结直肠癌护理中的脆弱性.
更多相关视频
相关概念视频
Comparing the Survival Analysis of Two or More Groups
177
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
177
Cancer Survival Analysis
343
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
343
Kaplan-Meier Approach
132
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
132


