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一种用于预测老年上尿路尿路上皮癌患者术后并发症和生存率的简化衰弱指数及列线图。

A simplified frailty index and nomogram to predict the postoperative complications and survival in older patients with upper urinary tract urothelial carcinoma.

作者信息

Liu Jianyong, Wang Haoran, Wu Pengjie, Wang Jiawen, Wang Jianye, Hou Huimin, Wang Jianlong, Zhang Yaoguang

机构信息

Beijing Hospital, National Center of Gerontology, Institute of the Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Beijing Hospital Continence Center, Beijing, China.

出版信息

Front Oncol. 2023 Oct 11;13:1187677. doi: 10.3389/fonc.2023.1187677. eCollection 2023.

DOI:10.3389/fonc.2023.1187677
PMID:37901313
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10600399/
Abstract

PURPOSE

This study was designed to investigate the clinical value of a simplified five-item frailty index (sFI) for predicting short- and long-term outcomes in older patients with upper urinary tract urothelial carcinoma (UTUC) patients after radical nephroureterectomy (RNU).

METHOD

This retrospective study included 333 patients (aged ≥65 years) with UTUC. Patients were classified into five groups: 0, 1, 2, 3, and 3+, according to sFI score. The variable importance and minimum depth methods were used to screen for significant variables, and univariable and multivariable logistic regression models applied to investigated the relationships between significant variables and postoperative complications. Survival differences between groups were analyzed using Kaplan-Meier plots and log-rank tests. Cox proportional hazards regression was used to evaluate risk factors associated with overall survival (OS) and cancer-specific survival (CSS). Further, we developed a nomogram based on clinicopathological features and the sFI. The area under the curve (AUC), Harrel's concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram.

RESULT

Of 333 cases identified, 31.2% experienced a Clavien-Dindo grade of 2 or greater complication. Random forest-logistic regression modeling showed that sFI significantly influenced the incidence of postoperative complications in older patients (AUC= 0.756). Compared with patients with low sFI score, those with high sFI scores had significantly lower OS and CSS (p < 0.001). Across all patients, the random survival forest-Cox regression model revealed that sFI score was an independent prognostic factor for OS and CSS, with AUC values of 0.815 and 0.823 for predicting 3-year OS and CSS, respectively. The nomogram developed was clinically valuable and had good ability to discriminate abilities for high-risk patients. Further, we developed a survival risk classification system that divided all patients into high-, moderate-, and low-risk groups based on total nomogram points for each patient.

CONCLUSION

A simple five-item frailty index may be considered a prognostic factor for the prognosis and postoperative complications of UTUC following RNU. By using this predictive model, clinicians may increase their accuracy in predicting complications and prognosis and improve preoperative decision-making.

摘要

目的

本研究旨在探讨简化五项衰弱指数(sFI)对预测老年上尿路尿路上皮癌(UTUC)患者根治性肾输尿管切除术(RNU)后短期和长期预后的临床价值。

方法

这项回顾性研究纳入了333例年龄≥65岁的UTUC患者。根据sFI评分,患者被分为五组:0、1、2、3和3+。采用变量重要性和最小深度法筛选显著变量,并应用单变量和多变量逻辑回归模型研究显著变量与术后并发症之间的关系。使用Kaplan-Meier曲线和对数秩检验分析组间生存差异。采用Cox比例风险回归评估与总生存期(OS)和癌症特异性生存期(CSS)相关的危险因素。此外,我们基于临床病理特征和sFI构建了列线图。采用曲线下面积(AUC)、Harrel一致性指数(C指数)、校准曲线和决策曲线分析(DCA)对列线图进行评估。

结果

在333例确诊病例中,31.2%发生了Clavien-Dindo 2级或更高等级的并发症。随机森林-逻辑回归模型显示,sFI对老年患者术后并发症的发生率有显著影响(AUC = 0.756)。与sFI评分低的患者相比,sFI评分高的患者的OS和CSS显著更低(p < 0.001)。在所有患者中,随机生存森林-Cox回归模型显示,sFI评分是OS和CSS的独立预后因素,预测3年OS和CSS的AUC值分别为0.815和0.823。所构建的列线图具有临床价值,对高危患者具有良好的鉴别能力。此外,我们开发了一种生存风险分类系统,根据每个患者的列线图总分将所有患者分为高、中、低风险组。

结论

一个简单的五项衰弱指数可被视为RNU后UTUC预后及术后并发症的一个预后因素。通过使用这种预测模型,临床医生可以提高预测并发症和预后的准确性,并改善术前决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/7a31a0dc4780/fonc-13-1187677-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/34b64ef370ec/fonc-13-1187677-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/64035aefd930/fonc-13-1187677-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/b6db6576a558/fonc-13-1187677-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/b7ed6effa730/fonc-13-1187677-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/795752f0995b/fonc-13-1187677-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/86d77ab8fc87/fonc-13-1187677-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/7a31a0dc4780/fonc-13-1187677-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/34b64ef370ec/fonc-13-1187677-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/64035aefd930/fonc-13-1187677-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/b6db6576a558/fonc-13-1187677-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/b7ed6effa730/fonc-13-1187677-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/795752f0995b/fonc-13-1187677-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/86d77ab8fc87/fonc-13-1187677-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4d/10600399/7a31a0dc4780/fonc-13-1187677-g007.jpg

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