Learning Curve of Docking Time in Robot-Assisted Radical Prostatectomy with the Hugo RAS System: How Many Procedures to Achieve Efficiency?

Objectives: Recently, the Hugo RAS System has been introduced on the market and features a modular design comprising four separate, independent arm carts. In this study we aim to identify the number of consecutive robotic-assisted radical prostatectomies (RARP) required to achieve optimal docking time with this new robotic platform. Methods: Data from 68 patients who underwent RARP with the New Hugo RAS System were analyzed. A three-arm setting was used in every case. The docking was executed by the same urology resident who had successfully completed the training course as a bed assistant provided by Medtronic at the ORSI Academy in Aalst, Belgium. Statistical analysis included univariate linear regression to evaluate the association between the number of consecutive procedures (independent variable) and docking time (dependent variable). Additionally, a cumulative sum (CUSUM) analysis was conducted to assess the learning curve, identifying the point at which docking time stabilized. Results: The analysis included 68 patients. The median “skin to skin” operative time was 198 min (IQR 90–375), with a total console time median of 150 min (IQR 60–335) and a docking time median of 5 min (IQR 4–13). Linear regression analysis showed a significant negative correlation between the number of procedures performed and docking time (p < 0.0017), indicating that increased experience correlates with reduced docking time. CUSUM analysis revealed that after the sixth procedure, docking time consistently declined, suggesting that the learning curve for achieving optimal docking time was reached around this point. Conclusions: These findings suggest that, despite being a new platform with four independent arms, the Hugo RAS System allows for a brief docking time to be achieved with just a few procedures, thus not impacting the overall duration of the surgical procedure.