Introduction: In June 2023, our institution adopted the Medtronic Hugo RAS system for colorectal procedures. This system's independent robotic arms enable personalized docking configurations. This study presents our refined multi-docking strategy for robotic low anterior resection (LAR) and deep pelvic procedures, designed to maximize the Hugo RAS system's potential in rectal surgery, and evaluates the associated learning curve.
Methods: This retrospective analysis included 31 robotic LAR procedures performed with the Hugo RAS system using our novel multi-docking strategy. Docking times were the primary outcome. The Mann-Kendall test, Spearman's correlation, and cumulative sum (CUSUM) analysis were used to assess the learning curve and efficiency gains associated with the strategy.
Results: Docking times showed a significant negative trend (p < 0.01), indicating improved efficiency with experience. CUSUM analysis confirmed a distinct learning curve, with proficiency achieved around the 15th procedure. The median docking time was 6 min, comparable to other robotic platforms after proficiency.
Conclusion: This study demonstrates the feasibility and effectiveness of a multi-docking strategy in robotic LAR using the Hugo RAS system. Our personalized approach, capitalizing on the system's unique features, resulted in efficient docking times and streamlined surgical workflow. This approach may be particularly beneficial for surgeons transitioning from laparoscopic to robotic surgery, facilitating a smoother adoption of the new technology. Further research is needed to validate the generalizability of these findings across different surgical settings and experience levels.
Keywords: Learning curve; Low anterior resection; Medtronic Hugo RAS; Multi-docking strategy; Rectal cancer; Robotic surgery.
© 2024. The Author(s).