Learning curve analysis of robot-assisted radical hysterectomy for cervical cancer: Initial experience at a single institution

Ga Won Yim, Sang Wun Kim, Eun Ji Nam, Sunghoon Kim, Young Tae Kim

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29 Citations (Scopus)

Abstract

Objective: The aim of this study was to evaluate the learning curve and perioperative outcomes of robot-assisted laparoscopic procedure for cervical cancer. Methods: A series of 65 cases of robot-assisted laparoscopic radical hysterectomies with bilateral pelvic lymph node dissection for early stage cervical cancer were included. Demographic data and various perioperative parameters including docking time, console time, and total operative time were reviewed from the prospectively collected database. Console time was set as a surrogate marker for surgical competency, in addition to surgical outcomes. The learning curve was evaluated using cumulative summation method. Results: The mean operative time was 190 minutes (range, 117 to 350 minutes). Two unique phases of the learning curve were derived using cumulative summation analysis; phase 1 (the initial learning curve of 28 cases), and phase 2 (the improvement phase of subsequent cases in which more challenging cases were managed). Docking and console times were significantly decreased after the first 28 cases compared with the latter cases (5 minutes vs. 4 minutes for docking time, 160 minutes vs. 134 minutes for console time; p<0.001 and p<0.001, respectively). There was a significant reduction in blood loss during operation (225 mL vs. 100 mL, p<0.001) and early postoperative complication rates (28% vs. 8.1%, p=0.003) in phase 2. No conversion to laparotomy occurred. Conclusion: Improvement of surgical performance in robot-assisted surgery for cervical cancer can be achieved after 28 cases. The two phases identified by cumulative summation analysis showed significant reduction in operative time, blood loss, and complication rates in the latter phase of learning curve.

Original languageEnglish
Pages (from-to)303-312
Number of pages10
JournalJournal of Gynecologic Oncology
Volume24
Issue number4
DOIs
Publication statusPublished - 2013 Oct 1

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All Science Journal Classification (ASJC) codes

  • Oncology
  • Obstetrics and Gynaecology

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