Learning Curve of Robotic-assisted Hysterectomy With Pelvic Lymphadenectomy for Early Stage Endometrial Cancer: Analysis of 81 Cases

Akazawa, Munetoshi; Hashimoto, Kazunori; Lee, Sheng‐Lun; Liu, Weimin

doi:10.21873/anticanres.15222

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Moreover, robot-assisted surgery is also indicated for benign uterine myoma, and in the United States, the gynecological department comprised the largest number of robot-assisted surgery [3,4]. Some papers have reported that the learning curve for robotassisted surgery requires 20 to 30 cases for significant improvement in many departments, including gynecology and urology [5][6][7]. On the other hand, Hwang et al [8] and Chong et al [9] reported that the learning curve for laparoscopic surgery for gynecologic malignancies is generally 35 and 50 cases, respectively.…”

Section: Discussionmentioning

confidence: 99%

Robot-assisted surgery for early uterine corpus cancer: assessing the learning curve

2022

European Journal of Gynaecological Oncology

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Minimally invasive surgery (MIS) is performed for various human cancers, among which robot-assisted surgery is widely used in the United States, Europe, and other countries. Presently, the total number of robot-assisted surgeries in gynecology exceeds that in urology and any other clinical department. Our group began using robot-assisted surgery for early-stage uterine corpus cancer as from November 2017 and has already performed 40 robot-assisted operations. For this study, we examined the cases of 35 patients, excluding 3 in whom lymphadenectomy was not performed and 2 who underwent additional vaginal wall plasty. Compared with the first 20 cases, the latter 15 had reduced bleeding and shorter operation times. No serious complications were observed in the whole cohort, but infections, including urinary tract infections and peritonitis, were common among the first 20 patients. Although robot-assisted surgery is considered more advantageous for obese patients than laparoscopic and open surgery, we found no significant differences in bleeding, surgical time, or the number of resected lymph nodes between obese patients (Body Mass Index (BMI) ≥30) and other patients in this present study. Robot-assisted surgery is expected to be more widely used, and further technical improvements are needed.

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Section: Discussionmentioning

confidence: 99%

Robot-assisted surgery for early uterine corpus cancer: assessing the learning curve

2022

European Journal of Gynaecological Oncology

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CUSUM learning curves: what they can and can’t tell us

et al. 2023

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Introduction There has been increased interest in assessing the surgeon learning curve for new skill acquisition. While there is no consensus around the best methodology, one of the most frequently used learning curve assessments in the surgical literature is the cumulative sum curve (CUSUM) of operative time. To demonstrate the limitations of this methodology, we assessed the CUSUM of console time across cohorts of surgeons with differing case acquisition rates while varying the total number of cases used to calculate the CUSUM. Methods We compared the CUSUM curves of the average console times of surgeons who completed their first 20 robotic-assisted (RAS) cases in 13, 26, 39, and 52 weeks, respectively, for their first 50 and 100 cases, respectively. This analysis was performed for prostatectomy (1094 surgeons), malignant hysterectomy (737 surgeons), and inguinal hernia (1486 surgeons). Results In all procedures, the CUSUM curve of the cohort of surgeons who completed their first 20 procedures in 13 weeks demonstrated a lower slope than cohorts of surgeons with slower case acquisition rates. The case number at which the peak of the CUSUM curve occurs uniformly increases when the total number of cases used in generation of the CUSUM chart changes from 50 to 100 cases. Conclusion The CUSUM analyses of these three procedures suggests that surgeons with fast initial case acquisition rates have less variability in their operative times over the course of their learning curve. The peak of the CUSUM curve, which is often used in surgical learning curve literature to denote “proficiency” is predictably influenced by the total number of procedures evaluated, suggesting that defining the peak as the point at which a surgeon has overcome the learning curve is subject to routine bias. The CUSUM peak, by itself, is an insufficient measure of “conquering the learning curve.”

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Safe Implementation of Robotic Surgery for Gynecologic Diseases at a Tertiary Center: Retrospective Analysis of 149 Cases and Review of the Literature

Yoshimura,

Nishio,

Sakai

et al. 2024

Gynecology and Minimally Invasive Therapy

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Objectives: The initial learning curve is a barrier to introducing robotic surgery. Evidence regarding appropriate simulation programs that allow for a smooth introduction of gynecological robotic surgery remains limited. Materials and Methods: We retrospectively analyzed 149 patients who underwent robotic surgery for gynecologic diseases. Before their first procedure, the surgeons completed a robotic surgery training program. Assistant surgeons also completed simulation programs, including setup procedures and manipulation of the robotic arm. Results: The mean (± standard deviation) operative, setup, and console times were 170 ± 54 min, 22 ± 8 min, and 126 ± 51 min, respectively. No patient required blood transfusion or conversion to laparoscopy or laparotomy. Patients undergoing surgery by the same surgeon were divided into three groups (first-third, middle-third, and last-third of patients undergoing surgery) to assess chronological changes. No statistically significant differences were found between the operative and console times among these groups. The setup times for the middle and last third of patients were 20 ± 7 min and 18 ± 7 min, respectively, which were statistically significantly shorter than those for the first third of patients. No significant differences in the operative and console times done by five physicians who completed programs were observed between the first 75 and the latter 74 procedures; however, the setup times of the latter 74 procedures were significantly shorter than those of the first 74 procedures (25 ± 9 min vs. 19 ± 6 min; P < 0.001). Conclusion: The setup time was influenced by clinical experience. An appropriate simulation program allowed a safe implementation of robotic surgery.

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Learning Curve of Robotic-assisted Hysterectomy With Pelvic Lymphadenectomy for Early Stage Endometrial Cancer: Analysis of 81 Cases

Cited by 3 publications

References 9 publications

Robot-assisted surgery for early uterine corpus cancer: assessing the learning curve

Robot-assisted surgery for early uterine corpus cancer: assessing the learning curve

CUSUM learning curves: what they can and can’t tell us

Safe Implementation of Robotic Surgery for Gynecologic Diseases at a Tertiary Center: Retrospective Analysis of 149 Cases and Review of the Literature

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