八种现代手术前评分系统的比较,用于预测四肢转移患者的存活率
Tse-Ying Lee1,2, Yu-An Chen2, Olivier Q Groot3,4
1Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Cancer medicine
|June 12, 2023
概括
对于骨转移生存预测的手术前评分系统 (PSSs) 在台湾患者中显示出不同的表现. 骨瘤学研究小组的机器学习算法 (SORG-MLA) 在这个人群中表现出卓越的准确性和临床实用性.
科学领域:
- 在瘤学瘤学.
- 医学统计 医学统计
- 手术决策 - 手术决策
背景情况:
- 生存预测对于骨转移的治疗决策至关重要.
- 手术前评分系统 (PSS) 帮助预测生存率,但它们的性能在不同人群中各不相同.
- 骨瘤学研究小组机器学习算法 (SORG-MLA) 之前的验证仅限于特定的种族群体.
研究的目的:
- 在台湾队列中验证和比较八个现有PSS的表现.
- 确定在台湾的骨转移患者中存活的最佳预测准确度的PSS.
- 评估不同PSS在手术治疗决策中的临床实用性.
主要方法:
- 对356名在台湾因四肢转移而接受手术的患者进行了回顾性分析.
- 使用歧视 (c指数),决策曲线分析 (DCA),校准和Brier分数验证和比较八个PSS.
- 评估模型在预测3个月和12个月生存时间方面的表现.
主要成果:
- 与西方验证相比,所有PSS在台湾队列中都表现出较低的歧视能力.
- 在这个人群中,SORG-MLA是唯一一个在这个人群中保持良好的歧视 (c指数>0.8) 的PSS.
- 在DCA的广泛风险概率中,SORG-MLA提供了最大的净收益.
结论:
- 民族地理差异显著影响PSS表现;临床医生在应用模型时必须考虑这一点.
- 国际验证研究对于确保PSS可用于共享决策的普遍性至关重要.
- 未来的预测模型可以通过结合反映当前癌症护理进展的最新患者数据来改进.
相关概念视频
01:21Cancer Survival Analysis
402
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...
402
01:20Comparing the Survival Analysis of Two or More Groups
228
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...
228
01:24Kaplan-Meier Approach
195
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,...
195