Should experienced open prostatic surgeons convert to robotic surgery? The real learning curve for one surgeon over 3 years

Authors


Nicolas Doumerc, Department of Urology, Saint Vincent’s Clinic, Suite 905, 438 Victoria Street, Darlinghurst 2010, Sydney, Australia.
e-mail: ndoumerc@gmail.com

Abstract

Study Type – Therapy (case series)
Level of Evidence 4

OBJECTIVE

To critically analyse the learning curve for one experienced open surgeon converting to robotic surgery for radical prostatectomy (RP).

PATIENTS AND METHODS

From February 2006 to December 2008, 502 patients had retropubic RP (RRP) while concurrently 212 had robot-assisted laparoscopic RP (RALP) by one urologist. We prospectively compared the baseline patient and tumour characteristics, variables during and after RP, histopathological features and early urinary functional outcomes in the two groups.

RESULTS

The patients in both groups were similar in age, preoperative prostate-specific antigen level, and prostatic volume. However, there were more high-stage (T2b and T3, P= 0.02) and -grade (Gleason 9, P= 0.01) tumours in the RRP group. The mean (range) operative duration was 147 (75–330) min for RRP and 192 (119–525) min for RALP (P < 0.001); 110 cases were required to achieve ‘3-h proficiency’. Major complication rates were 1.8% and 0.8% for RALP and RRP, respectively. The overall positive surgical margin (PSM) rate was 21.2% in the RALP and 16.7% in the RRP group (P= 0.18). PSM rates for pT2 were comparable (11.6% vs 10.1%, P= 0.74). pT3 PSM rates were higher for RALP than RRP (40.5% vs 28.8%, P= 0.004). The learning curve started to plateau in the overall PSM rate after 150 cases. For the pT2 and pT3 PSM rates, the learning curve tended to flatten after 140 and 170 cases, respectively. The early continence rates were comparable (P= 0.07) but showed a statistically significant improvement after 200 cases.

CONCLUSIONS

Our analysis of the learning curve has shown that certain components of the curve for an experienced open surgeon transferring skills to the robotic platform take different times. We suggest that patient selection is guided by these milestones, to maximize oncological outcomes.

Ancillary