文章：

机器人结直肠手术的学习曲线

Learning Curve for Robotic Colorectal Surgery

原文发布日期：8 October 2024

DOI: 10.3390/cancers16193420

类型: Article

开放获取: 是

英文摘要：

With the increasing adoption of robotic surgery in clinical practice, institutions intending to adopt this technology should understand the learning curve in order to develop strategies to help its surgeons and operating theater teams overcome it in a safe manner without compromising on patient care. Various statistical methods exist for the analysis of learning curves, of which a cumulative sum (CUSUM) analysis is more commonly described in the literature. Variables used for analysis can be classified into measures of the surgical process (e.g., operative time and pathological quality) and measures of patient outcome (e.g., postoperative complications). Heterogeneity exists in how performance thresholds are defined during the interpretation of learning curves. Factors that influence the learning curve include prior surgical experience in colorectal surgery, being in a mature robotic surgical unit, case mix and case complexity, robotic surgical simulation, spending time as a bedside first assistant, and being in a structured training program with proctorship.

摘要翻译： 

随着机器人手术在临床实践中的日益普及，有意引进该技术的机构应充分理解其学习曲线，以便制定相应策略，帮助外科医生及手术团队在保障患者安全的前提下平稳度过技术适应期。目前存在多种用于分析学习曲线的统计学方法，其中累积和（CUSUM）分析法在文献中更为常见。分析变量可分为手术过程指标（如手术时间、病理质量）和患者结局指标（如术后并发症）。在解读学习曲线时，关于技术熟练度阈值的界定标准尚存在异质性。影响学习曲线的因素包括：结直肠手术既往经验、是否处于成熟的机器人手术单元、病例组合与复杂程度、机器人手术模拟训练、作为床旁第一助手的实践时长，以及是否参与有导师指导的规范化培训项目。

原文链接：

Learning Curve for Robotic Colorectal Surgery