Development and validation of a nomogram for predicting suicide risk and prognostic factors in bladder cancer patients following diagnosis: A population-based retrospective study.

This study sought to identify independent risk factors associated with suicide following a diagnosis of bladder cancer and to develop a predictive model with the potential to contribute to suicide rate reduction. Harnessing data from the Surveillance, Epidemiology, and End Results (SEER) database, we identified bladder cancer patients diagnosed between 2004 and 2015, randomly assigning them to training and validation cohorts. The Cox proportional hazard model was employed to identify relevant predictors, leading to the construction of prediction nomogram models. Validation of prognostic nomograms involved assessing the consistency index (C-index), receiver operating characteristic (ROC) curve, and calibration curve. A total of 109,961 eligible bladder cancer patients were enrolled, randomly divided into training and validation sets. Multivariate Cox regression analysis revealed that sex, marital status, tumor local status (T Stage), and lymph node metastatic conditions (N Stage) were independent predictors for suicide in bladder cancer patients. Evaluation of the nomogram's accuracy through the C-index and ROC curve demonstrated acceptable performance in both training and validation sets. Moreover, the calibration plot indicated moderate accuracy of the nomogram in both datasets. Overall, this study successfully identified risk factors for suicide among bladder cancer patients and developed a nomogram, offering individualized diagnosis, intervention, and risk assessment to mitigate the risk of suicide in this patient population. • By utilizing easily accessible and understandable predictors, we developed a simple yet reliable prediction model. • This model could assist oncologists or urologists in rapidly identifying individuals at risk of suicide, enabling them to implement appropriate precautions promptly. • The application of such prediction models has the potential to prevent unnecessary deaths and significantly reduce the burden of bladder cancer. [ABSTRACT FROM AUTHOR]