Department of Surgery, Boston Medical Center, 88 E. Newton Street, Boston, MA, 02118, USA.
Harvard Medical School, Boston, MA, USA.
Surg Endosc. 2017 Nov;31(11):4583-4596. doi: 10.1007/s00464-017-5520-2. Epub 2017 Apr 14.
Robotic-assisted surgery is used with increasing frequency in general surgery for a variety of applications. In spite of this increase in usage, the learning curve is not yet defined. This study reviews the literature on the learning curve in robotic general surgery to inform adopters of the technology.
PubMed and EMBASE searches yielded 3690 abstracts published between July 1986 and March 2016. The abstracts were evaluated based on the following inclusion criteria: written in English, reporting original work, focus on general surgery operations, and with explicit statistical methods.
Twenty-six full-length articles were included in final analysis. The articles described the learning curves in colorectal (9 articles, 35%), foregut/bariatric (8, 31%), biliary (5, 19%), and solid organ (4, 15%) surgery. Eighteen of 26 (69%) articles report single-surgeon experiences. Time was used as a measure of the learning curve in all studies (100%); outcomes were examined in 10 (38%). In 12 studies (46%), the authors identified three phases of the learning curve. Numbers of cases needed to achieve plateau performance were wide-ranging but overlapping for different kinds of operations: 19-128 cases for colorectal, 8-95 for foregut/bariatric, 20-48 for biliary, and 10-80 for solid organ surgery.
Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.
机器人辅助手术在普通外科中越来越多地用于各种应用。尽管使用频率增加，但学习曲线尚未确定。本研究回顾了机器人普通外科学习曲线的文献，为该技术的采用者提供信息。
在 1986 年 7 月至 2016 年 3 月期间，通过 PubMed 和 EMBASE 搜索共获得 3690 篇摘要。根据以下纳入标准评估摘要：用英文书写，报告原始工作，重点是普通外科手术，并有明确的统计方法。
最终分析纳入了 26 篇全文文章。这些文章描述了结直肠（9 篇，35%）、前肠/减重（8 篇，31%）、胆道（5 篇，19%）和实体器官（4 篇，15%）手术的学习曲线。26 篇文章中有 18 篇（69%）报告了单外科医生的经验。所有研究均以时间作为学习曲线的衡量标准（100%）；10 项研究（38%）检查了结果。在 12 项研究（46%）中，作者确定了学习曲线的三个阶段。不同手术类型的达到平台表现所需的病例数量范围很广但重叠：结直肠为 19-128 例，前肠/减重为 8-95 例，胆道为 20-48 例，实体器官手术为 10-80 例。
尽管机器人手术在普通外科中越来越多地使用，但文献中几乎没有关于采用学习曲线的指南。在本综述文章的异质样本中，达到平台表现所需的病例数量因手术类型而异，并且学习曲线可能具有多个阶段，因为外科医生随着经验的增长将更复杂的病例添加到他们的病例组合中。时间是学习曲线最常见的决定因素。文献缺乏对结果和并发症的统一评估，这可能比单独操作时间更能反映专业水平。
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