A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Tao Chen; Xiangpeng Zhan; Junfu Du; Xiaoqiang Liu; Wen Deng; Shuaishuai Zhao; Ming Jiang; Yunqiang Xiong; Xiaohai Zhang; Luyao Chen; Bin Fu

doi:10.3389/fsurg.2022.871577

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Front Surg. 2022 Mar 22:9:871577. doi: 10.3389/fsurg.2022.871577. eCollection 2022.

Authors

Tao Chen¹, Xiangpeng Zhan¹, Junfu Du², Xiaoqiang Liu¹, Wen Deng¹, Shuaishuai Zhao¹, Ming Jiang¹, Yunqiang Xiong¹, Xiaohai Zhang¹, Luyao Chen^{1

3}, Bin Fu^{1

3}

Affiliations

¹ Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
² Department of Urology, Wuning People's Hospital, Jiujiang, China.
³ Jiangxi Institute of Urology, Nanchang, China.

Abstract

Background: Metastatic renal cell carcinoma (mRCC) is usually considered to have a poor prognosis, which has a high risk of early death (≤3 months). Our aim was to developed a predictive nomogram for early death of mRCC.

Methods: The SEER database was accessed to obtain the related information of 6,005 mRCC patients between 2010 and 2015. They were randomly divided into primary cohort and validation cohort in radio of 7:3. The optimal cut-off point regarding age at diagnosis and tumor size were identified by the X-tile analysis. Univariate and multivariate logistic regression models were applied to determine significant independent risk factors contributed to early death. A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA).

Results: There were 6,005 patients with mRCC included in the predictive model, where 1,816 patients went through early death (death within ≤3 months of diagnosis), and among them 1,687 patients died of mRCC. Based on 11 significant risk factors, including age, grade, N-stage, histologic type, metastatic sites (bone, lung, liver and brain) and treatments (surgery, radiation, and chemotherapy), a practical nomogram was developed. The model's excellent effectiveness, discrimination and clinical practicality were proved by the AUC value, calibration plots and DCA, respectively.

Conclusions: The nomogram may play a major part in distinguishing the early death of mRCC patients, which can assist clinicians in individualized medicine.

Keywords: SEER database; early death; nomograms; prognosis; renal cell carcinoma.