LAUSR

Background Different consoles have been described for the da Vinci SP (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 (PSM). This study describes our perioperative experience implementing a safe SP approach to radical prostatectomy while minimizing PSM, especially during the initial learning period. Material and methods We performed a retrospective analysis of 100 consecutive patients with prostate cancer who underwent radical prostatectomy with the SP robot from June 2019 to December 2020. We accessed the perioperative data, pathology report, and short-term oncological outcomes. We also represented our positive surgical margin (PSM) trends in 100 consecutive cases, discussing potential factors for minimizing the learning curve impact on positive margins and outcomes. Medians and interquartile ranges (IQR), 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 old (56-68), median PSA of 5.5 (4.3-7.7), median preoperative SHIM of 20, median AUA of 7 (3-11), and median BMI of 25.4 (23.4-27.4). The median total operative time was 114 minutes (104-124), median console time was 80 minutes (75-90). No intraoperative complications were reported. The overall rate of positive surgical margins was 15% (5% were pT2 and 10% were pT3). Conclusions The SP approach to radical prostatectomy is feasible, safe, with acceptable intraoperative performance. In this study, we have described crucial factors for considering selection criteria in candidates for SP-RARP. 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.