Pio Luca, Musleh Layla, Paraboschi Irene, Pistorio Angela, Mantica Guglielmo, Clermidi Pauline, Leonelli Lorenzo, Mattioli Girolamo, Sarnacki Sabine, Blanc Thomas
Department of Pediatric Surgery, Necker Enfants-Malades Hospital, Paris, France.
Université Paris Descartes, Sorbonne Paris Cite, Paris, France.
J Robot Surg. 2020 Aug;14(4):531-541. doi: 10.1007/s11701-019-01026-w. Epub 2019 Sep 17.
The learning curve (LC) of a new technique is fundamental for its application and assessment, and for the training. Literature was analysed to define the LCs of different surgical procedures and the impact of fellowship programs. MEDLINE, EMBASE and paediatric surgical journals' databases from January 1995 to December 2018 were systematically analysed. Two independent residents sought for articles providing description of robotic-assisted procedures' LCs in paediatric age/population. Seventeen articles were selected, describing LC of robotic-assisted pyeloplasty (n = 9), fundoplication (n = 4), cholecystectomy (n = 2), choledochal cyst resection (n = 1) and lingual tonsillectomy (n = 1), with 721 procedures. Ten studies refer to one single surgeon; six to more than one; one does not specify the number of operators. Eleven studies are unicentric retrospective, two multicentric retrospective, three prospective and one is a comparative analysis between a retrospective case series and a prospective cohort. The most recruited parameter is operative time alone in 3 articles, associated with complications in 12, length of hospital stay in 6, blood loss in 3, resolution in 4 and narcotic use in 2. The LC is described as impacting procedural planning (n = 17), training (n = 9) and economic costs (n = 2). To date, operative time is the most reported outcome to measure LC and proficiency. Efforts are needed to consider measures of surgical expertise and patient status. Robotic training should be standardized on targeted programs planned upon well-defined LCs.
新技术的学习曲线（LC）对于其应用、评估及培训至关重要。我们分析了相关文献，以确定不同外科手术的学习曲线以及 fellowship 项目的影响。系统分析了 1995 年 1 月至 2018 年 12 月期间的 MEDLINE、EMBASE 以及儿科外科期刊数据库。两名独立的住院医师查找了有关描述小儿年龄/人群中机器人辅助手术学习曲线的文章。共筛选出 17 篇文章，描述了机器人辅助肾盂成形术（n = 9）、胃底折叠术（n = 4）、胆囊切除术（n = 2）、胆总管囊肿切除术（n = 1）和舌扁桃体切除术（n = 1）的学习曲线，涉及 721 例手术。10 项研究涉及单一外科医生；6 项涉及不止一名外科医生；1 项未指明手术医生数量。11 项研究为单中心回顾性研究，2 项为多中心回顾性研究，3 项为前瞻性研究，1 项为回顾性病例系列与前瞻性队列的对比分析。在 3 篇文章中，最常纳入的参数仅为手术时间，12 篇文章将其与并发症相关联，6 篇文章将其与住院时间相关联，3 篇文章将其与失血量相关联，4 篇文章将其与恢复情况相关联，2 篇文章将其与麻醉药物使用相关联。学习曲线被描述为对外科手术规划（n = 17）、培训（n = 9）和经济成本（n = 2）有影响。迄今为止手术时间是衡量学习曲线和熟练程度时报道最多的结果。需要努力考虑手术专业技能和患者状况的衡量指标。机器人培训应在基于明确学习曲线制定的目标项目上实现标准化。
