Does experience translate? A multicenter evaluation of the learning curve in single-port robotic prostatectomy based...

Single-port robot-assisted radical prostatectomy (SP-RARP) offers potential benefits over multiport RARP (MP-RARP), including fewer incisions and reduced postoperative pain. However, the influence of prior MP-RARP experience on early SP-RARP learning curves remains unclear. We retrospectively analyzed 622 consecutive SP-RARP cases performed by four surgeons with differing MP-RARP caseloads (> 1,000, > 400, > 200, and > 100) across multiple centers. The primary endpoint of this study was the difference between the trends of four operators with different levels of MP-RARP experience in (1) operative time, (2) console time, (3) 90-day complications, (4) 1-pad continence duration, and (5) positive surgical margins in organ-confirmed disease. Operative time and console time were inversely correlated with previous MP-RARP volume, with statistically significant reductions observed in surgeons with higher prior experience, except for Surgeon A, who exhibited increasing trends (general linear model p < 0.001 for operative time and p = 0.005 for console time). The 90-day complication rate decreased proportionally with increasing MP-RARP experience (log-rank p < 0.001), with significant inter-surgeon variability, excluding Surgeon A (p = 0.011). While the average time to achieve 1-pad continence varied among surgeons, no statistically significant trend was identified (log-rank p = 0.127). Positive surgical margin rates differed significantly in patients with pT2 or lower stage (log-rank p < 0.001), although inter-surgeon differences excluding Surgeon A were not significant (log-rank p = 0.810). Extensive MP-RARP experience facilitated procedural efficiency and reduced complications during the SP-RARP learning curve without significantly affecting early functional recovery or oncologic control. Future prospective studies should focus on standardized SP training programs and long-term functional and oncologic outcomes to optimize patient benefits.