Implementing the da Vinci SP Without Increasing Positive Surgical Margins: Experience and Pathologic Outcomes of a Prostate Cancer Referral Center.

Abstract

Background: Different consoles have been described for the da Vinci single-port (SP) surgery since it was cleared by the FDA in November 2018. However, the literature still lacks studies identifying factors related to the SP learning curve and how to overcome the technological limitations, especially in terms of maintaining acceptable positive surgical margins (PSMs). This study describes our perioperative experience implementing a safe SP approach to radical prostatectomy (RP) while minimizing PSM, especially during the initial learning period. Materials and Methods: We performed a retrospective analysis of 100 consecutive patients with prostate cancer who underwent RP with the SP robot from June 2019 to December 2020 (IRB 237998). We accessed the perioperative data, pathology report, and short-term oncologic outcomes. We also represented our PSM trends in 100 consecutive cases, discussing potential factors for minimizing the learning curve impact on positive margins and outcomes. Medians and interquartile ranges, as well as frequencies and proportions, were reported for continuous and categorical variables, respectively. Results and Limitations: The median follow-up is 14 months (8-17). The cohort has a median age of 62 years (56-68), median prostate-specific antigen of 5.5 (4.3-7.7), median preoperative Sexual Health Inventory for Men (SHIM) of 20, median American Urological Association (AUA) of 7 (3-11), and median body mass index of 25.4 (23.4-27.4). The median total operative time was 114 minutes (104-124), the median console time was 80 minutes (75-90). No intraoperative complications were reported. The overall rate of PSMs was 15% (5% were pT2 and 10% were pT3). Conclusions: The SP approach to RP is feasible, safe, and with acceptable intraoperative performance. In this study, we have described crucial factors for considering selection criteria in candidates for SP-robot-assisted RP. We believe that with an appropriate patient selection, this robot can be safely implemented without increasing positive margins and compromising the outcomes, especially during the learning curve period.