Huang Qingmai, Hu Qianghua, Jiang Manfei, Wang Baofeng, Che Xianping
Department of Urology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China.
Transl Androl Urol. 2025 Jun 30;14(6):1551-1565. doi: 10.21037/tau-2025-225. Epub 2025 Jun 23.
The incidence and mortality rates of bladder urothelial carcinoma significantly increase with age after the age of 60 years. In our study, we aimed to identify and analyze the risk factors for early death (death within 6 months) in elderly patients with bladder urothelial carcinoma and established a reliable Nomogram model, thereby assisting clinicians to choose the best clinical decision-making.
Data of elderly patients with bladder urothelial carcinoma from the Surveillance, Epidemiology, and End Results (SEER) database (version 8.4.4) between 2010 and 2015 were selected. Multivariate logistic regression analysis was used to identify independent risk factors associated with early death. A predictive Nomogram was constructed based on these risk factors to assess the risk of early death. During the training and validation processes, the clinical applicability and predictability of the model was evaluated using receiver operating characteristic (ROC) curves, calibration analysis and decision curve analysis (DCA).
In this study, a total of 5,087 patients with bladder urothelial carcinoma were collected, among whom 1,163 experienced early death. Age, marital status, tumor (T)-stage, metastasis (M)-stage, surgery, radiation, chemotherapy, brain metastasis, and tumor size were all identified as independent risk factors for early death. Based on these factors, we constructed a nomogram that can effectively predict early death in elderly patients with bladder urothelial carcinoma. The nomogram shows that the areas under the curve (AUCs) were 0.7938 and 0.8107 for the training and validation cohorts respectively, and the DCA showed that the predictive model performed well and could be applied in the clinic. Limitations of this study: potential selection bias, lack of relevant variables such as comorbidities, family history, and lack of external validation.
In this study, we constructed and validated a predictive model (Nomogram) to accurately predict the clinical prognosis of elderly patients with bladder urothelial carcinoma. This predictive tool provides clinicians with an individualized prognostic assessment that can optimize the development of treatment regimens and improve patients' clinical outcomes and quality of survival.
膀胱尿路上皮癌的发病率和死亡率在60岁以后随年龄显著增加。在我们的研究中，我们旨在识别和分析老年膀胱尿路上皮癌患者早期死亡（6个月内死亡）的危险因素，并建立一个可靠的列线图模型，从而协助临床医生做出最佳临床决策。
选取2010年至2015年监测、流行病学和最终结果（SEER）数据库（版本8.4.4）中老年膀胱尿路上皮癌患者的数据。采用多因素逻辑回归分析确定与早期死亡相关的独立危险因素。基于这些危险因素构建预测列线图以评估早期死亡风险。在训练和验证过程中，使用受试者操作特征（ROC）曲线、校准分析和决策曲线分析（DCA）评估模型的临床适用性和预测性。
本研究共收集5087例膀胱尿路上皮癌患者，其中1163例发生早期死亡。年龄、婚姻状况、肿瘤（T）分期、转移（M）分期、手术、放疗、化疗、脑转移和肿瘤大小均被确定为早期死亡的独立危险因素。基于这些因素，我们构建了一个可以有效预测老年膀胱尿路上皮癌患者早期死亡的列线图。该列线图显示，训练队列和验证队列的曲线下面积（AUC）分别为0.7938和0.8107，DCA显示预测模型表现良好，可应用于临床。本研究的局限性：潜在的选择偏倚，缺乏合并症、家族史等相关变量，且缺乏外部验证。
在本研究中，我们构建并验证了一个预测模型（列线图）以准确预测老年膀胱尿路上皮癌患者的临床预后。这个预测工具为临床医生提供了个体化的预后评估，可优化治疗方案的制定，改善患者的临床结局和生存质量。
