Surgery of the Alimentary Tract, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Bologna, Italy.
Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Via Massarenti 9, 40138, Bologna, Italy.
Int J Colorectal Dis. 2024 Oct 1;39(1):154. doi: 10.1007/s00384-024-04728-2.
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.
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.
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.
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.
2023 年 6 月,我院采用美敦力 Hugo RAS 系统进行结直肠手术。该系统的独立机器臂可实现个性化的对接配置。本研究提出了我们改良的机器人低位前切除术(LAR)和深部盆腔手术的多对接策略,旨在最大限度地发挥 Hugo RAS 系统在直肠手术中的潜力,并评估相关的学习曲线。
本回顾性分析纳入了 31 例采用 Hugo RAS 系统行机器人 LAR 手术的患者,均采用我们新的多对接策略。对接时间是主要的结局指标。采用曼-肯德尔检验、斯皮尔曼相关系数和累积和(CUSUM)分析评估策略相关的学习曲线和效率提高。
对接时间呈显著负向趋势(p<0.01),表明经验增加效率提高。CUSUM 分析证实了明确的学习曲线,在第 15 次手术左右达到熟练水平。中位对接时间为 6 分钟,与其他机器人平台在熟练后相似。
本研究表明,采用 Hugo RAS 系统的机器人 LAR 中,多对接策略具有可行性和有效性。我们的个性化方法利用了系统的独特特点,实现了高效的对接时间和简化的手术流程。对于从腹腔镜手术过渡到机器人手术的外科医生来说,这种方法可能特别有益,有助于更顺利地采用新技术。需要进一步的研究来验证这些发现在不同的手术环境和经验水平下的普遍性。