Subscribe to RSS

DOI: 10.1055/a-2606-9826
Introduction of Robot-assisted Surgery for Benign Total Hysterectomy with Salpingectomy: Learning Curve, Safety and Experience in a Tertiary Surgical Center
Article in several languages: English | deutsch
Abstract
Background
The dynamic advances in robot-assisted surgery have particularly affected gynecological surgery. An analysis of the surgical data of robot-assisted procedures, starting when the procedure is first introduced into a surgical center, provides valuable insights into the initial use and integration of the da Vinci system in routine clinical practice and the impact on patient care. This article aims to investigate the learning curve and show the increased proficiency with this approach. This analysis focuses on the most common procedure performed during the introductory phase: benign total hysterectomy with salpingectomy.
Material and Method
A retrospective data analysis was carried out of the first 250 patients operated on between February 2020 and June 2022 by five different surgeons in Freiburg University Hospital using the da Vinci surgical system. The evaluation includes classic surgical parameters such as preparation times, incision-to-suture times, and console times as well as the learning curves of the surgeons and the surgical team (incl. CUSUM analysis). Perioperative patient characteristics (e.g., blood loss, hospitalization times, conversion rate) are also presented.
Results
Most procedures (30%) were carried out for uterine fibroids. Operating times decreased significantly over time as more and more robot-assisted procedures were carried out: the surgical preparation time decreased over the first 30 procedures from 28.1 ± 8.6 min to 23.8 ± 7.2 min. The initial incision-to-suture time for benign total hysterectomies with salpingectomy was 94.0 ± 42.2 min and had decreased significantly by the end of the first 20 procedures. The average console time was 66.8 ± 36.1 min, and the decrease was particularly visible over the first 20 procedures. The individual learning curves of the surgeons showed significant decreases in time. For example, the average console time of surgeon A decreased over the first ten procedures from 70.5 ± 23.0 min to 46.9 ± 13.5 min. The conversion rate for the whole cohort was 0.8%.
Discussion
The evaluation of the first 250 da Vinci surgeries demonstrates the easy learnability of robot-assisted surgery. The conversion rate was very low, coming in at just 0.8%. A positive effect on the learning curve of individual surgeons was found after about 20 procedures. Both the preparation times and the incision-to-suture times decreased rapidly, meaning that there were no problems integrating the new approach into routine clinical practice.
Publication History
Received: 26 February 2025
Accepted after revision: 11 May 2025
Article published online:
18 July 2025
© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
-
References/Literatur
- 1
Reich H,
DeCaprio J,
McGlynn F.
Laparoscopic Hysterectomy. J Gynecol Surg 1989; 5: 213-216
MissingFormLabel
- 2
Diaz-Arrastia C,
Jurnalov C,
Gomez G.
et al.
Laparoscopic hysterectomy using a computer-enhanced surgical robot. Surg Endosc 2002;
16: 1271-1273
MissingFormLabel
- 3
Nezhat C,
Lavie O,
Hsu S.
et al.
Robotic-assisted laparoscopic myomectomy compared with standard laparoscopic myomectomy—a
retrospective matched control study. Fertil Steril 2009; 91: 556-559
MissingFormLabel
- 4
Orady ME,
Hrynewych A,
Nawfal AK.
et al.
Comparison of Robotic-Assisted Hysterectomy to Other Minimally Invasive Approaches.
JSLS 2012; 16: 542-548
MissingFormLabel
- 5
Paraiso MFR,
Ridgeway B,
Park AJ.
et al.
A randomized trial comparing conventional and robotically assisted total laparoscopic
hysterectomy. Am J Obstet Gynecol 2013; 208: 368.e1-368.e7
MissingFormLabel
- 6
Deimling TA,
Eldridge JL,
Riley KA.
et al.
Randomized controlled trial comparing operative times between standard and robot-assisted
laparoscopic hysterectomy. Int J Gynaecol Obstet 2017; 136: 64-69
MissingFormLabel
- 7
Shashoua AR,
Gill D,
Locher SR.
Robotic-Assisted Total Laparoscopic Hysterectomy Versus Conventional Total Laparoscopic
Hysterectomy. JSLS 2009; 13: 364-369
MissingFormLabel
- 8
Sananès N,
Garbin O,
Hummel M.
et al.
Setting up robotic surgery in gynaecology: the experience of the Strasbourg teaching
hospital. J Robot Surg 2011; 5: 133-136
MissingFormLabel
- 9
Page ES.
Continuous Inspection Schemes. Biometrika 1954; 41: 100-115
MissingFormLabel
- 10
Lin PL,
Zheng F,
Shin M.
et al.
CUSUM learning curves: what they can and can’t tell us. Surg Endosc 2023; 37: 7991-7999
MissingFormLabel
- 11
Lenihan JP,
Kovanda C,
Seshadri-Kreaden U.
What is the Learning Curve for Robotic Assisted Gynecologic Surgery?. J Minim Invasive
Gynecol 2008; 15: 589-594
MissingFormLabel
- 12
Carbonnel M,
Moawad GN,
Tarazi MM.
et al.
Robotic Hysterectomy for Benign Indications: What Have We Learned from a Decade?.
JSLS 2021; 25: e2020.00091
MissingFormLabel
- 13
Rajanbabu A,
Patel V,
Anandita A.
et al.
An analysis of operating time over the years for robotic-assisted surgery in gynecology
and gynecologic oncology. J Robot Surg 2021; 15: 215-219
MissingFormLabel
- 14
Lin JF,
Frey M,
Huang JQ.
Learning Curve Analysis of the First 100 Robotic-assisted Laparoscopic Hysterectomies
Performed by a Single Surgeon. Int J Gynaecol Obstet 2013; 124: 88-91
MissingFormLabel
- 15
Lee J,
Kim S.
Charting Proficiency: The Learning Curve in Robotic Hysterectomy for Large Uteri Exceeding
1000 g. J Clin Med 2024; 13: 4347
MissingFormLabel
- 16
Favre A,
Huberlant S,
Carbonnel M.
et al.
Pedagogic Approach in the Surgical Learning: The First Period of “Assistant Surgeon”
May Improve the Learning Curve for Laparoscopic Robotic-Assisted Hysterectomy. Front
Surg 2016; 3: 58
MissingFormLabel
- 17
Corrado G,
Vizza E,
Cela V.
et al.
Laparoscopic versus robotic hysterectomy in obese and extremely obese patients with
endometrial cancer: A multi-institutional analysis. Eur J Surg Oncol 2018; 44: 1935-1941
MissingFormLabel
- 18
Borse M,
Godbole G,
Kelkar D.
et al.
Early evaluation of a next- generation surgical system in robot-assisted total laparoscopic
hysterectomy: A prospective clinical cohort study. Acta Obstet Gynecol Scand 2022;
101: 978-986
MissingFormLabel
- 19
Lim PC,
Crane JT,
English EJ.
et al.
Multicenter analysis comparing robotic, open, laparoscopic, and vaginal hysterectomies
performed by high-volume surgeons for benign indications. Int J Gynaecol Obstet 2016;
133: 359-364
MissingFormLabel
- 20
Patzkowsky KE,
As-Sanie S,
Smorgick N.
et al.
Perioperative outcomes of robotic versus laparoscopic hysterectomy for benign disease.
JSLS 2013; 17: 100-106
MissingFormLabel
- 21
Lawrie TA,
Liu H,
Lu D.
et al.
Robot-assisted surgery in gynaecology. Cochrane Database Syst Rev 2019; (04) CD011422
MissingFormLabel
- 22
Imran H,
Shuja H,
Abid M.
et al.
Robotic surgery: augmenting surgeons’ skills or replacing them?. IJS Global Health
2024; 7: e00515
MissingFormLabel