摘要
目的:探讨和分析达芬奇机器人辅助结直肠癌根治术的学习曲线.方法:回顾性分析北京医院胃肠外科同一主刀医师2020年11月-2023年3月连续开展的47例达芬奇机器人辅助结直肠癌根治术的临床资料.采用累积和分析曲线(CUSUM)法分析手术时间及术中出血量的学习曲线.结果:47例患者均顺利完成达芬奇机器人辅助结直肠癌根治术.结合手术时间及术中出血量的学习曲线的转折点将学习曲线分为3组(A、B、C组),比较各组患者的一般资料及围术期资料.3组的手术时间为(216.1± 46.1)min、(258.9±68.6)min 和(197.2±25.3)min,B 组显著大于 C 组;术中出血量为 50.0(50.0,100.0)mL、100.0(50.0,250.0)mL 和 50.0(30.0,75.0)mL,B 组显著大于 C 组,差异均有统计学意义(P＜0.05).根据学习曲线的趋势变化进一步划分A组为A1、A2组,4组手术频率分别为2.4例/月、1.4例/月、1.8例/月及1.6例/月.结论:达芬奇机器人辅助结直肠癌根治术CUSUM学习曲线可大致分为学习提高期、经验积累期、难度挑战期和熟练掌握期4个阶段,经过大约11～29例手术后基本跨越学习曲线,之后开始挑战更高难度的手术.
Abstract
Objective:To evaluate the learning curve of robotic-assisted laparoscopic surgery for colorectal cancer.Methods:From November 2020 to March 2023,a total of 47 patients imple-mented robotic-assisted laparoscopic colorectal surgery by the same surgeon at Beijing Hospital were retrospectively reviewed.The cumulative sum analysis curve(CUSUM)method was used to an-alyze the learning curve.Results:All 47 patients successfully underwent robotic-assisted laparo-scopic surgery for colorectal cancer.The learning curve was divided into 3 groups(A,B,C)based on the turning points of the learning curves.General data and perioperative data were analyzed.For these 3 groups,the corresponding operation time were(216.1±46.1)min,(258.9±68.6)min,and(197.2±25.3)min;The estimated blood loss were 50.0(50.0,100.0)mL,100.0(50.0,250.0)mL and 50.0(30.0,75.0)mL,and the difference was statistically significant(P＜0.05).According to the trend of the learning curve,group A is further divided into A1 and A2,and the surgical frequencies of the four groups were 2.4 cases/month,1.4 cases/month,1.8 cases/month,and 1.6 cases/month,respectively.Conclusions:Our data suggest that the CUSUM learning curve of robotic-assisted laparoscopic surgery for col-orectal cancer can be divided into initial learning stage,experience accumulation stage,difficulty chal-lenge stage and mastery stage,and after a learning curve phase of 11 to 29 cases,the surgeon may basically overcome the learning curve and then begin to challenge more difficult surgeries.
NETL
NSTL
维普
万方数据