Background
Improved cancer control with increasing surgical experience—the learning curve—was demonstrated for open and laparoscopic prostatectomy. In a prior single–center study, we found that this might not be the case for robot-assisted radical prostatectomy (RARP).
Objective
To investigate the relationship between prior experience of a surgeon and biochemical recurrence (BCR) after RARP.
Design, setting, and participants
We retrospectively analyzed the data of 8101 patients with prostate cancer treated with RARP by 46 surgeons at nine institutions between 2003 and 2021. Surgical experience was coded as the total number of robotic prostatectomies performed by the surgeon before the patient operation.
Outcome measurements and statistical analysis
We evaluated the relationship of prior surgeon experience with the probability of BCR adjusting for preoperative prostate-specific antigen, pathologic stage, grade, lymph-node involvement, and year of surgery.
Results and limitations
Overall, 1047 patients had BCR. The median follow-up for patients without BCR was 33 mo (interquartile range: 14, 61). After adjusting for case mix, the relationship between surgical experience and the risk of BCR after surgery was not statistically significant (p = 0.2). The 5-yr BCR-free survival rates for a patient treated by a surgeon with prior 10, 250, and 1000 procedures performed were, respectively, 82.0%, 82.7%, and 84.8% (absolute difference between 10 and 1000 prior procedures: 1.6% [95% confidence interval: 0.4%, 3.3%). Results were robust to a number of sensitivity analyses.
Conclusions
These findings suggest that, as opposed to open and laparoscopic radical prostatectomy, surgeons performing RARP achieve adequate cancer control in the early phase of their career. Further research should explore why the learning curve for robotic surgery differs from prior findings for open and laparoscopic radical prostatectomy. We hypothesize that surgical education, including simulation training and the adoption of objective performance metrics, is an important mechanism for flattening the learning curve.
