在泌尿瘤学中对风险分层进行脆弱性指数的比较:选择哪个指数?
Ekamjit S Deol1, Vidit Sharma1, Anthony E Fadel1
1Department of Urology, Mayo Clinic, Rochester, MN.
Urology
|August 30, 2024
概括
修订后的风险分析指数 (RAI-Rev) 与修改后的脆弱性指数 (mFI) 相比,更好地预测大泌尿外科手术后的死亡率和非家庭出院率. 在这些患者中,RAI-Rev对手术风险分层的效用更大.
科学领域:
- 尿道瘤学 尿道瘤学
- 老年人手术是一门老年手术.
- 医疗保健服务研究 医疗服务研究
背景情况:
- 在主要的泌尿外科瘤手术中,脆弱性评估对于手术风险分层至关重要.
- 修改后的脆弱性指数 (mFI) 和修订后的风险分析指数 (RAI-Rev) 是常见的脆弱性查工具.
- 确定最佳工具对于准确的外科手术后期预测结果至关重要.
研究的目的:
- 为了比较MFI和RAI-Rev对30天外科手术后期结果的预测性表现.
- 确定在主要泌尿外科瘤外科手术中进行手术风险分层的优质脆弱性查工具.
主要方法:
- 来自NSQIP数据库的101739名接受重大泌尿外科手术 (2013-2017) 的患者的分析.
- 多变量后勤回归以评估mFI和RAI-Rev与重大并发症,Clavien等级≥4并发症,非家庭出院,再入院和死亡率的关联.
- 接收器运行特征 (ROC) 曲线分析和DeLong的测试来比较两个指数的预测能力 (C统计).
主要成果:
- 与mFI相比,RAI-Rev在死亡率 (C-统计:0.688-0.798) 和非家庭出院 (C-统计:0.638-0.734) 的区分能力更强 (C-统计:0.594-0.677和0.593-0.639).
- 这两种指数在重大外科手术后并发症的预测性表现相似 (C-统计:0.531-0.607).
- 在死亡率和非家庭出院 (P < .001) 方面证实了有利于RAI-Rev的统计学上显著差异.
结论:
- 在接受重大泌尿外科手术的患者中,RAI-Rev似乎是一个比mFI更有效的预后工具.
- 这些发现表明,RAI-Rev在手术风险分层方面可能具有更大的实用性.
- 未来的前性研究和临床试验在设计时应考虑这些比较结果.
相关概念视频
01:20Comparing the Survival Analysis of Two or More Groups
162
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...
162
01:21Cancer Survival Analysis
334
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...
334
01:24Kaplan-Meier Approach
111
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,...
111