Analysis of the learning curve for robotic hysterectomy for benign gynaecological disease.

Fatih Sendag, Burak Zeybek, Ali Akdemir, Banu Ozgurel, Kemal Oztekin
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引用次数: 13

Abstract

Background: The objective was to evaluate the learning curve for performing a robotic hysterectomy to treat benign gynaecological disease.

Methods: Thirty-six patients underwent robotic hysterectomy for benign indications. A systematic chart review of consecutive cases was conducted. The collected data included age, BMI, operating time, set-up time, docking time, uterine weight, blood loss, intraoperative complications, postoperative complications, conversions to laparotomy and length of hospital stay.

Results: The mean operating, set-up and docking times were 169 ± 54.5, 52.9 ± 12.4 and 7.8 ± 7.6 min, respectively. The learning curve analysis revealed a decrease in both docking and operating times, with both curves plateauing after case 9.

Conclusions: The learning curve analysis revealed a decrease in docking time and operating time after case 9, suggesting that there might be a fast, learning curve for experienced laparoscopic surgeons to master robotic hysterectomy, and that the docking process does not have a significant negative influence on the overall operating time.

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妇科良性疾病机器人子宫切除术的学习曲线分析。
背景:目的是评估进行机器人子宫切除术治疗良性妇科疾病的学习曲线。方法:36例患者行良性子宫切除手术。对连续病例进行了系统的图表回顾。收集的数据包括年龄、BMI、手术时间、准备时间、对接时间、子宫重量、出血量、术中并发症、术后并发症、转开腹手术和住院时间。结果:平均操作时间为169±54.5 min,设置时间为52.9±12.4 min,对接时间为7.8±7.6 min。学习曲线分析显示,对接和操作时间均有所减少,在病例9后,两条曲线均趋于平稳。结论:学习曲线分析显示,病例9后对接时间和手术时间减少,提示经验丰富的腹腔镜外科医生可能有一个快速的学习曲线来掌握机器人子宫切除术,对接过程对整体手术时间没有显著的负面影响。
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