Literature DB >> 35325433

The robotic learning curve for a newly appointed colorectal surgeon.

Sabah Uddin Saqib1, Muhammad Zeeshan Raza2, Charles Evans3, Adeel Ahmad Bajwa3.   

Abstract

Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies. However, the learning curve of robotic surgery in this cohort is unclear as established training pathways are not formalized. This study looks at the experience and learning curve of a single laparoscopic trained surgeon in performing safe and effective resections, mainly for low rectal and anal malignancies using the da Vinci robotic system by evaluating metrics related to surgical process and patient outcome. A serial retrospective review of the robotic colorectal surgery database, in the University Hospital Coventry and Warwickshire (UHCW), was undertaken. All 48 consecutive cases, performed by a recently qualified colorectal surgeon, were included in our study. The surgical process was evaluated using both console and total operative time recorded in each case along with the adequacy of resections performed; in addition, patient-related outcomes including intraoperative and postoperative complications were analyzed to assess differences in the learning curve. Forty eight sequential recto-sigmoid resections were included in the study performed by a single surgeon. The cases were divided into four cohorts in chronological order with comparable demographics, tumour stage, location, and complexity of the operation (mean age 65, male 79%, and female 29%). The results showed that the mean console time dropped from 3 to 2.5 h, while total operative time dropped from 6 h to 5.5 h as the surgeon became more experienced; however, this was not found to be statistically significant. In addition, no significant difference in pathological staging was seen over the study period. No major intra-op and post-op complications were observed and no 30-day mortality was recorded. Moreover, after 30 cases, the learning curve developed the plateau phase, suggesting the gain of maximum proficiency of skills required for robotic colorectal resections. The learning curve in robotic rectal surgery is short and flattens early; complication rates are low during the learning curve and continue to decrease with time. This shows that with proper training and proctoring, new colorectal surgeons can be trained in a short time to perform elective colorectal pelvic resections.
© 2022. Crown.

Entities:  

Keywords:  Colorectal cancer; Da Vinci; Learning curve; Robotic surgery

Year:  2022        PMID: 35325433     DOI: 10.1007/s11701-022-01400-1

Source DB:  PubMed          Journal:  J Robot Surg        ISSN: 1863-2483


  19 in total

Review 1.  Robotic surgery: a current perspective.

Authors:  Anthony R Lanfranco; Andres E Castellanos; Jaydev P Desai; William C Meyers
Journal:  Ann Surg       Date:  2004-01       Impact factor: 12.969

2.  Learning curves in surgical practice.

Authors:  A N Hopper; M H Jamison; W G Lewis
Journal:  Postgrad Med J       Date:  2007-12       Impact factor: 2.401

3.  Learning curve in robotic colorectal surgery.

Authors:  Yosef Nasseri; Isabella Stettler; Wesley Shen; Ruoyan Zhu; Arman Alizadeh; Anderson Lee; Jason Cohen; Moshe Barnajian
Journal:  J Robot Surg       Date:  2020-08-04

Review 4.  Ergonomics in Surgery: A Review.

Authors:  Tatiana Catanzarite; Jasmine Tan-Kim; Emily L Whitcomb; Shawn Menefee
Journal:  Female Pelvic Med Reconstr Surg       Date:  2018 Jan/Feb       Impact factor: 2.091

Review 5.  Robotic versus laparoscopic versus open colorectal surgery: towards defining criteria to the right choice.

Authors:  Matthew Zelhart; Andreas M Kaiser
Journal:  Surg Endosc       Date:  2017-08-15       Impact factor: 4.584

6.  Developing a robotic colorectal cancer surgery program: understanding institutional and individual learning curves.

Authors:  Hamza Guend; Maria Widmar; Sunil Patel; Garrett M Nash; Philip B Paty; José G Guillem; Larissa K Temple; Julio Garcia-Aguilar; Martin R Weiser
Journal:  Surg Endosc       Date:  2016-11-04       Impact factor: 4.584

7.  Learning curve for robotic-assisted laparoscopic rectal cancer surgery.

Authors:  Rosa M Jiménez-Rodríguez; José Manuel Díaz-Pavón; Fernando de la Portilla de Juan; Emilio Prendes-Sillero; Hisnard Cadet Dussort; Javier Padillo
Journal:  Int J Colorectal Dis       Date:  2012-12-15       Impact factor: 2.571

8.  The robotic colorectal experience: an outcomes and learning curve analysis of 502 patients.

Authors:  Salvatore A Parascandola; Michael L Horsey; Salini Hota; Jessie Osborne Paull; Ada Graham; Natalie Pudalov; Savannah Smith; Richard Amdur; Vincent Obias
Journal:  Colorectal Dis       Date:  2020-11-04       Impact factor: 3.788

Review 9.  Training in robotics: The learning curve and contemporary concepts in training.

Authors:  Christian Bach; Arkadiusz Miernik; Martin Schönthaler
Journal:  Arab J Urol       Date:  2013-12-05

Review 10.  Robotic vs. Standard Laparoscopic Technique - What is Better?

Authors:  Ferdinand Köckerling
Journal:  Front Surg       Date:  2014-05-15
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