Arora Karan Singh, Khan Nuzhath, Abboudi Hamid, Khan Mohammed S, Dasgupta Prokar, Ahmed Kamran
Department of Urology, King's Health Partners, MRC Centre for Transplantation, King's College London, Guy's Hospital , St Thomas Street, London , UK.
Postgrad Med. 2015 Mar;127(2):202-14. doi: 10.1080/00325481.2014.996113. Epub 2014 Dec 22.
The aim of this systematic review is to evaluate the learning curve (LC) literature and identify the LC of cardiothoracic and vascular surgical procedures.
The LC describes an observation that a learner's performance improves over time during acquisition of new motor skills. Measuring the LC of surgical procedures has important implications for surgical innovation, education, and patient safety. Numerous studies have investigated LCs of isolated operations in cardiothoracic and vascular surgeries, but a lack of uniformity in the methods and variables used to measure LCs has led to a lack of systematic reviews.
The MEDLINE®, EMBASE™, and PsycINFO® databases were systematically searched until July 2013. Articles describing LCs for cardiothoracic and vascular procedures were included. The type of procedure, statistical analysis, number of participants, procedure setting, level of participants, outcomes, and LCs were reviewed.
A total of 48 studies investigated LCs in cardiothoracic and vascular surgeries. Based on operating time, the LC for coronary artery bypass surgery ranged between 15 and 100 cases; for endoscopic vessel harvesting and other cardiac vessel surgery between 7 and 35 cases; for valvular surgery, which included repair and replacement, between 20 and 135 cases; for video-assisted thoracoscopic surgery, between 15 and 35 cases; for vascular neurosurgical procedures between 100 and 500 cases, based on complications; for endovascular vessel repairs between 5 and 40 cases; and for ablation procedures between 25 and 60 cases. However there was a distinct lack of standardization in the variables/outcome measures used, case selection, prior experience, and supervision of participating surgeons and a range of statistical analyses to compute LCs was noted.
LCs in cardiothoracic and vascular procedures are hugely variable depending on the procedure type, outcome measures, level of prior experience, and methods/statistics used. Uniformity in methods, variables, and statistical analysis is needed to derive meaningful comparisons of LCs. Acknowledgment and application of learning processes other than those reliant on volume-outcomes relationship will benefit LC research and training of surgeons.
本系统评价旨在评估学习曲线（LC）相关文献，并确定心胸和血管外科手术的学习曲线。
学习曲线描述了一种观察结果，即在获得新运动技能的过程中，学习者的表现会随着时间的推移而提高。测量外科手术的学习曲线对外科创新、教育和患者安全具有重要意义。许多研究调查了心胸和血管外科中孤立手术的学习曲线，但用于测量学习曲线的方法和变量缺乏一致性，导致缺乏系统评价。
对MEDLINE®、EMBASE™和PsycINFO®数据库进行系统检索，直至2013年7月。纳入描述心胸和血管手术学习曲线的文章。对手术类型、统计分析、参与者数量、手术环境、参与者水平、结果和学习曲线进行了综述。
共有48项研究调查了心胸和血管外科的学习曲线。基于手术时间，冠状动脉搭桥手术的学习曲线在15至100例之间；内镜下血管采集和其他心脏血管手术在7至35例之间；瓣膜手术（包括修复和置换）在20至135例之间；电视辅助胸腔镜手术在15至35例之间；血管神经外科手术基于并发症在100至500例之间；血管腔内修复在5至40例之间；消融手术在25至60例之间。然而，在使用的变量/结果测量、病例选择、既往经验、参与手术医生的监督方面明显缺乏标准化，并且注意到用于计算学习曲线的一系列统计分析。
心胸和血管手术的学习曲线因手术类型、结果测量、既往经验水平以及使用的方法/统计而异。需要在方法、变量和统计分析方面保持一致，以便对学习曲线进行有意义的比较。认识并应用除依赖手术量-结果关系之外的学习过程将有利于学习曲线研究和外科医生培训。
