A novel training program: laparoscopic versus robotic-assisted low anterior resection for rectal cancer can be trained simultaneously
Yanlei Wang, Dongpeng Wen, Cheng Zhang, Zhi-kai Wang, Jiancheng Zhang
- 发表年份
- 2023
- 引用次数
- 3
- 访问权限
- 开放获取
摘要
Background: Current expectations are that surgeons should be technically proficient in minimally invasive low anterior resection (LAR)-both laparoscopic and robotic-assisted surgery. However, methods to effectively train surgeons for both approaches are under-explored. We aimed to compare two different training programs for minimally invasive LAR, focusing on the learning curve and perioperative outcomes of two trainee surgeons. Methods: We reviewed 272 consecutive patients undergoing laparoscopic or robotic LAR by surgeons A and B, who were novices in conducting minimally invasive colorectal surgery. Surgeon A was trained by first operating on 80 cases by laparoscopy and then 56 cases by robotic-assisted surgery. Surgeon B was trained by simultaneously performing 80 cases by laparoscopy and 56 by robotic-assisted surgery. The cumulative sum (CUSUM) method was used to evaluate the learning curves of operative time and surgical failure. Results: For laparoscopic surgery, the CUSUM plots showed a longer learning process for surgeon A than surgeon B (47 vs. 32 cases) for operative time, but a similar trend in surgical failure (23 vs. 19 cases). For robotic surgery, the plots of the two surgeons showed similar trends for both operative times (23 vs. 25 cases) and surgical failure (17 vs. 19 cases). Therefore, the learning curves of surgeons A and B were respectively divided into two phases at the 47th and 32nd cases for laparoscopic surgery and at the 23rd and 25th cases for robotic surgery. The clinicopathological outcomes of the two surgeons were similar in each phase of the learning curve for each surgery. Conclusions: For surgeons with rich experience in open colorectal resections, simultaneous training for laparoscopic and robotic-assisted LAR of rectal cancer is safe, effective, and associated with accelerated learning curves.
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