Developing a model for predicting suicide risk among prostate cancer survivors - Yang, Jie; Liu, Hai-ming; Qu, Xiang; Jiang, Fan; Hao, Jie-wei; Rong, Pei-rong; Ning, Peng; Zheng, An-jie (Frontiers in Medicine, 2025)
ObjectiveGiven the significantly higher suicide risk among cancer survivors compared to the general population, and considering that prostate cancer survivors make up the largest group of cancer survivors, it is imperative to develop a model for predicting suicide risk among prostate cancer survivors.MethodsClinical data of prostate cancer patients were extracted from the surveillance, epidemiology, and end results (SEER) database and randomly divided into a training cohort and a validation cohort in a 7:3 ratio. Initial variable selection was performed using univariate Cox regression, Best Subset Regression (BSR), and Least Absolute Shrinkage and Selection Operator (LASSO). Variables to be included in the final model were selected using backward stepwise Cox regression. Model performance was evaluated using the Concordance Index (C-index), Receiver Operating Characteristic (ROC) curves, and calibration curves.ResultsData from 238,534 prostate cancer patients were obtained from the SEER database, of which 370 (0.16%) died by suicide. Seven variables including age, race, marital status, household income, PSA levels, M stage, and surgical status were included in the final model. The model demonstrated good discriminative ability in both the training and validation cohorts, with C-indices of 0.702 and 0.688, respectively. ROC values at 3, 5, and 10 years were 0.727/0.644, 0.700/0.698, and 0.735/0.708, respectively. Calibration curves indicated a high degree of consistency between model predictions and actual outcomes. High-risk prostate cancer survivors had a 3.5 times higher risk of suicide than the low-risk group (0.007 vs. 0.002, P < 0.001), a finding supported by data from the validation cohort and the entire cohort.ConclusionA reliable predictive model for suicide risk among prostate cancer survivors was successfully established based on seven readily obtainable clinical predictors. This model can effectively aid healthcare professionals in quickly identifying high-risk prostate cancer survivors and timely implementation of preventive interventions.