Risk model for depression in patients with chronic prostatitis/chronic pelvic pain syndrome: A retrospective cross-sectional study

Objective The correlation between depression and prostatitis is widely acknowledged; however, there is a dearth of comprehensive risk models that can predict the risk of depression in patients with chronic prostatitis/chronic pelvic pain syndrome. In this investigation, we devised a predictive model to ascertain the likelihood of depression in these patients. Methods This prospective study enrolled 599 patients with chronic prostatitis/chronic pelvic pain syndrome between January 2022 and January 2025. The patients were randomly divided into training (70%, n = 419) and validation (30%, n = 180) cohorts. Using depression (Patient Health Questionnaire-9 score ≥10) as the primary outcome, we developed a nomogram using Boruta and least absolute shrinkage and selection operator feature selection followed by multivariate logistic regression. Model performance was assessed using receiver operating characteristic curve analysis (area under the receiver operating characteristic curve), calibration plots, and decision curve analysis, with internal validation performed in the validation cohort. Results The nomogram integrated seven readily available predictors (prostate progression, hypertension, International Prostate Symptom Score, number of nights with sleep disturbance, white blood cell count, triglyceride level, and hemoglobin level) and showed excellent performance (area under the receiver operating characteristic curve values = 0.864 in the training cohort and 0.911 in the validation cohort). Discussion This nomogram can help urologists quickly identify chronic prostatitis/chronic pelvic pain syndrome patients at high risk of depression, enable early psychological intervention, and improve the quality of life of these patients.