Impacts on functional and oncological outcomes of Robotic-assisted Radical Prostatectomy 10 years after the US Preventive Service Taskforce recommendations against PSA screening

Abstract

Objective: In the following years after the United States Preventive Service Task Force (USPSTF) recommendation against prostate cancer screening with PSA in 2012, several authors worldwide described an increase in higher grades and aggressive prostate tumors. In this scenario, we aim to evaluate the potential impacts of USPSTF recommendations on the functional and oncological outcomes in patients undergoing robotic-assisted radical prostatectomy (RARP) in a referral center.

Material and methods: We included 11396 patients who underwent RARP between 2008 and 2021. Each patient had at least a 12-month follow-up. The cohort was divided into two groups based on an inflection point in the outcomes at the end of 2012 and the beginning of 2013. The inflection point period was detected by Bayesian regression with multiple change points and regression with unknown breakpoints. We reported continuous variables as median and interquartile range (IQR) and categorical variables as absolute and relative percent frequencies.

Results: Group 1 had 4760 patients, and Group 2 had 6636 patients, with a median follow-up of 109 and 38 months, respectively. In the final pathology, Group 2 had 9.5% increase in tumor volume, 24% increase on Gleason ≥ 4+3 (ISUP 3) , and 18% increase on ≥ pT3. This translated to a 6% increase in positive surgical margins and 24% reduction in full nerve sparing in response to the worsening pathology. There was a significant decline in post-operative outcomes in Group 2, including a 12-month continence reduction of 9%, reduction in potency by 27%, and reduction of trifecta by 22%.

Conclusions: The increasing number of high-risk patients has led to worse functional and oncologic outcomes. The initial rapid rise in PSM was leveled by the move towards more partial nerve sparing. Among some historical changes in prostate cancer diagnosis and management in the period of our study, the USPSTF recommendation coincided with worse outcomes of prostate cancer treatment in a population who could benefit from PSA screening at the appropriate time.

Keywords: Diagnosis; Prostatic Neoplasms; Robotic Surgical Procedures.

Figure 1. (A) Trend change analysis (logit scale) before and after 2012 showing increasing Gleason ≥7 or ≥ pT3 and increasing positive surgical margins (PSM). Biochemical recurrence (BCR) was considered in patients with follow-up ≥ 5 years to avoid a curve drop and false impression of BCR reduction after 2017. (B) Trend change analysis (logit scale) before and after 2012 illustrating functional outcomes reduction (potency, continence, and trifecta).

Supplementary Figure 1. The plots display the results of Bayesian regression with multiple change points (left) and regression with unknown breakpoints (right) for the potency outcome. The left plot illustrates the observed (blue points) and fitted logit of potency rates by years (solid gray lines), along with the 2.5% and 97.5% quantiles (red dashed lines). The distribution displayed at the bottom represents the posterior density of the change point. The right plot shows the fitted broken-line model with the estimated change point.

Supplementary Figure 2. The plots display the results of Bayesian regression with multiple change points (left) and regression with unknown breakpoints (right) for the continence outcome. The left plot illustrates the observed (blue points) and fitted logit of continence rates by years (solid gray lines), along with the 2.5% and 97.5% quantiles (red dashed lines). The distribution displayed at the bottom represents the posterior density of the change point. The right plot shows the fitted broken-line model with the estimated change point.

Supplementary Figure 3. The plots display the results of Bayesian regression with multiple change points (left) and regression with unknown breakpoints (right) for the trifecta outcome. The left plot illustrates the observed (blue points) and fitted logit of trifecta rates by years (solid gray lines), along with the 2.5% and 97.5% quantiles (red dashed lines). The distribution displayed at the bottom represents the posterior density of the change point. The right plot shows the fitted broken-line model with the estimated change point.

Supplementary Figure 4. The plots display the results of Bayesian regression with multiple change points (left) and regression with unknown breakpoints (right) for the PSM outcome. The left plot illustrates the observed (blue points) and fitted logit of PSM rates by years (solid gray lines), along with the 2.5% and 97.5% quantiles (red dashed lines). The distribution displayed at the bottom represents the posterior density of the change point. The right plot shows the fitted broken-line model with the estimated change point.