Suppr超能文献

开发一种新型风险评分,以在转移性肾细胞癌患者中选择最佳细胞减瘤性肾切除术候选者。多机构登记处(REMARCC)的结果。

Development of a Novel Risk Score to Select the Optimal Candidate for Cytoreductive Nephrectomy Among Patients with Metastatic Renal Cell Carcinoma. Results from a Multi-institutional Registry (REMARCC).

机构信息

Department of Medical, Oral and Biotechnological Sciences, Laboratory of Biostatistics, University "G. D'Annunzio" Chieti-Pescara, Chieti, Italy; Department of Urology, SS Annunziata Hospital, "G. D'Annunzio" University of Chieti, Chieti, Italy.

Department of Urology, University Medical Centre Mannheim, Mannheim, Germany.

出版信息

Eur Urol Oncol. 2021 Apr;4(2):256-263. doi: 10.1016/j.euo.2020.12.010. Epub 2020 Dec 29.

Abstract

BACKGROUND

Selection of patients for upfront cytoreductive nephrectomy (CN) in metastatic renal cell carcinoma (mRCC) has to be improved.

OBJECTIVE

To evaluate a new scoring system for the prediction of overall mortality (OM) in mRCC patients undergoing CN.

DESIGN, SETTING, AND PARTICIPANTS: We identified a total of 519 patients with synchronous mRCC undergoing CN between 2005 and 2019 from a multi-institutional registry (Registry for Metastatic RCC [REMARCC]).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Cox proportional hazard regression was used to test the main predictors of OM. Restricted mean survival time was estimated as a measure of the average overall survival time up to 36 mo of follow-up. The concordance index (C-index) was used to determine the model's discrimination. Decision curve analyses were used to compare the net benefit from the REMARCC model with International mRCC Database Consortium (IMDC) or Memorial Sloan Kettering Cancer Center (MSKCC) risk scores.

RESULTS AND LIMITATIONS

The median follow-up period was 18 mo (interquartile range: 5.9-39.7). Our models showed lower mortality rates in obese patients (p = 0.007). Higher OM rates were recorded in those with bone (p = 0.010), liver (p = 0.002), and lung metastases (p < 0.001). Those with poor performance status (<80%) and those with more than three metastases had also higher OM rates (p = 0.026 and 0.040, respectively). The C-index of the REMARCC model was higher than that of the MSKCC and IMDC models (66.4% vs 60.4% vs 60.3%). After stratification, 113 (22.0%) patients were classified to have a favorable (no risk factors), 202 (39.5%) an intermediate (one or two risk factors), and 197 (38.5%) a poor (more than two risk factors) prognosis. Moreover, 72 (17.2%) and 51 (13.9%) patients classified as having an intermediate and a poor prognosis according to MSKCC and IMDC categories, respectively, would be reclassified as having a good prognosis according to the REMARCC score.

CONCLUSIONS

Our findings confirm the relevance of tumor and patient features for the risk stratification of mRCC patients and clinical decision-making regarding CN. Further prospective external validations are required for the scoring system proposed herein.

PATIENT SUMMARY

Current stratification systems for selecting patients for kidney removal when metastatic disease is shown are controversial. We suggest a system that includes tumor and patient features besides the systems already in use, which are based on blood tests.

摘要

背景

在转移性肾细胞癌(mRCC)患者中,需要改进对初始细胞减灭性肾切除术(CN)的患者选择。

目的

评估一种新的评分系统,用于预测接受 CN 的 mRCC 患者的总死亡率(OM)。

设计、地点和参与者:我们从多机构登记处(转移性 RCC 登记处 [REMARCC])中总共确定了 519 名患有同步 mRCC 并接受 CN 的患者。

结果测量和统计分析

使用 Cox 比例风险回归来检验 OM 的主要预测因子。限制平均生存时间被估计为平均总生存时间的指标,直到 36 个月的随访。一致性指数(C 指数)用于确定模型的区分能力。决策曲线分析用于比较 REMARCC 模型与国际 mRCC 数据库联盟(IMDC)或纪念斯隆凯特琳癌症中心(MSKCC)风险评分的净收益。

结果和局限性

中位随访时间为 18 个月(四分位距:5.9-39.7)。我们的模型显示肥胖患者的死亡率较低(p=0.007)。骨转移(p=0.010)、肝转移(p=0.002)和肺转移(p<0.001)的 OM 发生率更高。那些表现状态较差(<80%)和有三个以上转移的患者也有更高的 OM 发生率(p=0.026 和 0.040)。REMARCC 模型的 C 指数高于 MSKCC 和 IMDC 模型(66.4%比 60.4%比 60.3%)。分层后,113 名(22.0%)患者被归类为预后良好(无风险因素),202 名(39.5%)为中间(一个或两个风险因素),197 名(38.5%)为预后不良(两个以上风险因素)。此外,根据 MSKCC 和 IMDC 分类,分别有 72 名(17.2%)和 51 名(13.9%)被归类为中间和预后不良的患者,根据 REMARCC 评分将重新归类为预后良好。

结论

我们的发现证实了肿瘤和患者特征对于 mRCC 患者的风险分层和关于 CN 的临床决策的相关性。需要进一步进行前瞻性的外部验证,以验证本文提出的评分系统。

患者总结

目前用于选择转移性疾病患者进行肾脏切除的分层系统存在争议。我们建议一种系统,该系统除了基于血液检查的现有系统外,还包括肿瘤和患者特征。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验