Robotic-assisted radical prostatectomy-impact of a mentorship program on oncological outcomes during the learning curve

Background and aims The learning curve for robotic-assisted radical prostatectomy (RARP) is estimated to be about 50-200 cases. This study will evaluate the benefit of a mentorship programme after completing a mini-fellowship in RARP by an experienced surgeon who previously trained in open and laparoscopic surgery. Methods Our study was a retrospective comparative analysis of RARP performed by a single consultant urologist. A retrospective chart review of the first 120 cases was performed. The 120 patients were divided into three groups of 40 cases. For the first 40 cases, an appropriately qualified mentor was present. The peri-operative and oncological outcomes were compared between the three groups. Results Operative times significantly decreased with experience (250 min vs 234 min vs 225 min, p < 0.05). Complication rates, estimated blood loss, and length of stay were similar between all groups. There was a higher rate of positive margins in the final group (20% vs 17.5% vs 32.5%, p < 0.5). There was a greater number of pT3 tumours in group 3 (42%, n = 17) compared to groups 1 and 2 (20%, n = 8, and 22.5%, n = 9) which may account for the higher rate of positive margins in this group. Conclusion In the transition of an experienced laparoscopic surgeon to robotic surgery, we showed that there is a benefit of a mentorship programme after a mini-fellowship in reducing the impact of the learning curve on patient outcomes. Ongoing mentorship may be of benefit in cases where a high volume of tumour is suspected and should be avoided in the early part of the learning curve to maximise oncological outcomes.

Automated summary

The study retrospectively analyzed 120 cases of RARP performed by an experienced surgeon and found that operative times decreased significantly with experience, while complication rates remained similar. The results suggest that a mentorship program after completing a mini-fellowship may help reduce the impact of the learning curve on patient outcomes when transitioning to robotic surgery.