Patient satisfaction and decision regret in patients undergoing radical prostatectomy: a multicenter analysis.

Journal: International Urology And Nephrology
Published:
Abstract

Objective: Prostate cancer (PCa) remains the most common non-cutaneous cancer among men in Europe, with treatment advances improving mortality outcomes. Radical prostatectomy (RP) is a key treatment option for localized PCa, anyway complications such as urinary incontinence and erectile dysfunction can influence patient satisfaction and decision regret. This multicenter study aims to identify factors that can assist urologists in improving preoperative counseling through levels of satisfaction or regret, with the goal of increasing patient awareness regarding the potential risks and complications associated with RP.

Methods: A prospective analysis was conducted on 590 patients undergoing RP across 4 Italian centers (from 2019 to 2022). Decision regret was assessed using the validated Decision Regret Scale (DRS). The mean score of the five items of the DRS was calculated and then converted to a 100-points scale, with a cutoff score of 25 distinguishing low from high regret. Logistic regression analysis evaluated predictors of treatment regret.

Results: At a median follow-up of 23 months, 79% of patients reported low decision regret (DRS ≤ 25). Lower decision regret was associated with lower rates of urinary incontinence and erectile dysfunction. Multivariate analysis identified urinary continence (OR 3.8, p < 0.001), erectile function (OR 0.3, p < 0.001), and robotic surgery (OR 3.7, p = 0.009) as significant independent predictors of satisfaction. In addition, poorer quality of life correlated with increased regret.

Conclusions: Our study emphasizes the importance of evaluating surgical outcomes to predict patient satisfaction, optimizing preoperative counseling, and reducing long-term regret. Urinary and sexual function, and surgical technique influence satisfaction and quality of life. Further research should explore patient-specific predictors and optimize timing for outcome evaluations.