Predicting early death in elderly patients with bladder urothelial carcinoma: a population-based study.

BACKGROUND

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.