Park Eun Jung, Kim Chang Woo, Cho Min Soo, Baik Seung Hyuk, Kim Dong Wook, Min Byung Soh, Lee Kang Young, Kim Nam Kyu
Section of Colon and Rectal Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea.
Surg Endosc. 2014 Oct;28(10):2821-31. doi: 10.1007/s00464-014-3569-8. Epub 2014 Jun 6.
Robotic surgery has advantages to perform rectal cancer by its ergonomic designs and advanced technologies. However, it was uncertain whether these core robotic technologies could shorten the learning curve. The aim of this study is to investigate the learning curve of robotic rectal cancer surgery and to compare the learning curve phases with respect to perioperative clinicopathologic outcomes.
From April 2006 to August 2011, a total of 130 consecutive patients who were diagnosed with rectal cancer underwent a robotic low anterior resection (LAR) using the hybrid technique by a single surgeon at Severance Hospital. The moving average method and the cumulative sum (CUSUM) were used to analyze the learning curve. The risk-adjusted CUSUM (RA-CUSUM) analysis was used to evaluate the points, which showed completion of surgical procedures in terms of R1 resection, conversion, postoperative complications, harvested lymph nodes less than 12, and local recurrence. Perioperative clinical outcomes and pathologic results were compared among the learning curve phases.
According to the CUSUM, the learning curve was divided into three phases: phase 1 [the initial learning period (1st-44th case), n = 44], phase 2 [the competent period (45th-78th case), n = 34], and phase 3 [the challenging period (79th-130th case), n = 52]. RA-CUSUM showed the minimum value at the 75th case, which suggested technical competence to satisfy feasible perioperative outcomes. The total operation time tended to decrease after phase 1 and so did the surgeon console time and docking time. Postoperative complications and pathologic outcomes were not significantly different among the learning phases.
The learning curve of robotic LAR consisted of three phases. The primary technical competence was achieved at phase 1 of the 44th case according to the CUSUM. The technical completion to assure feasible perioperative outcomes was achieved at phase 2 at the 75th case by the RA-CUSUM method.
机器人手术凭借其符合人体工程学的设计和先进技术，在直肠癌手术中具有优势。然而，这些核心机器人技术能否缩短学习曲线尚不确定。本研究旨在探讨机器人直肠癌手术的学习曲线，并比较学习曲线各阶段围手术期临床病理结果。
2006年4月至2011年8月，在延世大学Severance医院，一名外科医生连续为130例诊断为直肠癌的患者采用混合技术进行了机器人低位前切除术（LAR）。采用移动平均法和累积和（CUSUM）分析学习曲线。采用风险调整累积和（RA-CUSUM）分析评估在R1切除、中转、术后并发症、获取淋巴结少于12枚以及局部复发方面完成手术操作的情况。比较学习曲线各阶段的围手术期临床结果和病理结果。
根据CUSUM，学习曲线分为三个阶段：第1阶段[初始学习期（第1 - 44例），n = 44]，第2阶段[熟练期（第45 - 78例），n = 34]，第3阶段[挑战期（第79 - 130例），n = 52]。RA-CUSUM在第75例时显示最小值，这表明具备满足可行围手术期结果的技术能力。第1阶段后总手术时间趋于下降，外科医生控制台时间和对接时间也如此。学习阶段之间术后并发症和病理结果无显著差异。
机器人LAR的学习曲线包括三个阶段。根据CUSUM，在第1阶段第44例时达到初步技术能力。通过RA-CUSUM方法，在第2阶段第75例时实现了确保可行围手术期结果的技术完善。
