*Department of Pediatrics, Stanford University, Stanford, CA †Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK ‡Department of Surgery & Cancer, Imperial College London, London, UK §Department of Surgery, McGill University, Montreal, Canada ¶Department of Surgery, University of Pennsylvania, Philadelphia, PA.
Ann Surg. 2014 Jul;260(1):37-45. doi: 10.1097/SLA.0000000000000596.
To determine how minimally invasive surgical learning curves are assessed and define an ideal framework for this assessment.
Learning curves have implications for training and adoption of new procedures and devices. In 2000, a review of the learning curve literature was done by Ramsay et al and it called for improved reporting and statistical evaluation of learning curves. Since then, a body of literature is emerging on learning curves but the presentation and analysis vary.
A systematic search was performed of MEDLINE, EMBASE, ISI Web of Science, ERIC, and the Cochrane Library from 1985 to August 2012. The inclusion criteria are minimally invasive abdominal surgery formally analyzing the learning curve and English language. 592 (11.1%) of the identified studies met the selection criteria.
Time is the most commonly used proxy for the learning curve (508, 86%). Intraoperative outcomes were used in 316 (53%) of the articles, postoperative outcomes in 306 (52%), technical skills in 102 (17%), and patient-oriented outcomes in 38 (6%) articles. Over time, there was evidence of an increase in the relative amount of laparoscopic and robotic studies (P < 0.001) without statistical evidence of a change in the complexity of analysis (P = 0.121).
Assessment of learning curves is needed to inform surgical training and evaluate new clinical procedures. An ideal analysis would account for the degree of complexity of individual cases and the inherent differences between surgeons. There is no single proxy that best represents the success of surgery, and hence multiple outcomes should be collected.
确定微创外科学习曲线是如何评估的,并定义一个理想的评估框架。
学习曲线对培训和采用新程序和设备有影响。2000 年,Ramsay 等人对学习曲线文献进行了回顾,呼吁改进学习曲线的报告和统计评估。从那时起,关于学习曲线的文献不断涌现,但呈现和分析方式各不相同。
对 1985 年至 2012 年 8 月期间的 MEDLINE、EMBASE、ISI Web of Science、ERIC 和 Cochrane Library 进行了系统搜索。纳入标准是正式分析学习曲线和英语的微创腹部手术。确定的 592 项研究中有 11.1%符合选择标准。
时间是最常用的学习曲线代理(508,86%)。316 篇文章(53%)使用了术中结果,306 篇文章(52%)使用了术后结果,102 篇文章(17%)使用了技术技能,38 篇文章(6%)使用了以患者为导向的结果。随着时间的推移,腹腔镜和机器人研究的相对数量有所增加(P < 0.001),但分析复杂性的变化没有统计学证据(P = 0.121)。
评估学习曲线对于指导外科培训和评估新的临床程序是必要的。理想的分析应考虑到个别病例的复杂程度和外科医生之间的固有差异。没有一个单一的代理能够最好地代表手术的成功,因此应该收集多个结果。