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<p>This study aimed to estimate the learning curve for benign robotic-assisted laparoscopic hysterectomy, defined as the number of cases required to stabilize intra- and postoperative complications, estimated blood loss and operative time. This is a retrospective single-center cohort study with prospectively collected data. Patients who underwent a robotic-assisted laparoscopic hysterectomy between 2013 and 2021 were included. Six surgeons performed the surgeries. Analysis was conducted using linear and logistic generalized estimating equation (GEE) regression. The estimand was change in the speed of learning. The cohort comprised 1281 consecutive cases. An inverse association was observed between the number of robot-assisted laparoscopic hysterectomies and the number of complications with a breakpoint at 150 surgeries (adjusted Odds Ratio (aOR) 0.996, 95% Confidence Interval (CI) 0.992–0.999, <i>P</i> = 0.03) This decrease continued with surgeon experience. Moreover, there was a significant decrease in operative time after 50 operations (aCoeff -0.62&#xa0;min, 95% CI − 0.89 to − 0.34&#xa0;min, <i>P</i> &lt; 0.001), with an operative time of approximately 100&#xa0;min. No significant difference in intraoperative blood loss was observed throughout the learning curve (Knotbreak 50 operations, aCoeff 0.69&#xa0;ml, 95% CI − 0.44 – 1.81&#xa0;ml, <i>P</i> = 0.23). In this large observational study of learning curve for robotic-assisted laparoscopic hysterectomy, a plateau was reached at 50 cases for operative time and 150 cases for intra- and postoperative complications.</p>

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From practice to perfection—complications and operative time learning curves in benign robotic-assisted laparoscopic hysterectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月4日

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  • Klara Krantz Andersson,
  • Anna Oksanen,
  • Henrik Falconer,
  • Malin Brunes

摘要

This study aimed to estimate the learning curve for benign robotic-assisted laparoscopic hysterectomy, defined as the number of cases required to stabilize intra- and postoperative complications, estimated blood loss and operative time. This is a retrospective single-center cohort study with prospectively collected data. Patients who underwent a robotic-assisted laparoscopic hysterectomy between 2013 and 2021 were included. Six surgeons performed the surgeries. Analysis was conducted using linear and logistic generalized estimating equation (GEE) regression. The estimand was change in the speed of learning. The cohort comprised 1281 consecutive cases. An inverse association was observed between the number of robot-assisted laparoscopic hysterectomies and the number of complications with a breakpoint at 150 surgeries (adjusted Odds Ratio (aOR) 0.996, 95% Confidence Interval (CI) 0.992–0.999, P = 0.03) This decrease continued with surgeon experience. Moreover, there was a significant decrease in operative time after 50 operations (aCoeff -0.62 min, 95% CI − 0.89 to − 0.34 min, P < 0.001), with an operative time of approximately 100 min. No significant difference in intraoperative blood loss was observed throughout the learning curve (Knotbreak 50 operations, aCoeff 0.69 ml, 95% CI − 0.44 – 1.81 ml, P = 0.23). In this large observational study of learning curve for robotic-assisted laparoscopic hysterectomy, a plateau was reached at 50 cases for operative time and 150 cases for intra- and postoperative complications.