Learning curve in robotic rectal cancer surgery: current state of affairs

• Published in: International journal of colorectal disease Vol. 31; no. 12; pp. 1807 - 1815
• Main Authors: Jiménez-Rodríguez, Rosa M., Rubio-Dorado-Manzanares, Mercedes, Díaz-Pavón, José Manuel, Reyes-Díaz, M. Luisa, Vazquez-Monchul, Jorge Manuel, Garcia-Cabrera, Ana M., Padillo, Javier, De la Portilla, Fernando
• Format: Journal Article Book Review
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2016; Springer; Springer Nature B.V
• Subjects: Colorectal cancer; Comparative analysis; Gastroenterology; Hepatology; Humans; Internal Medicine; Intraoperative Care; Laparoscopic surgery; Laparoscopy; Learning Curve; Medicine; Medicine & Public Health; Postoperative Complications - etiology; Proctology; Rectal Neoplasms - surgery; Review; Robotic surgery; Robotics; Robots; Surgery; Robotic surgery; Minimally invasive surgery; Rectal cancer; Learning curve; Rectal surgery
• ISSN: 0179-1958; 1432-1262
• DOI: 10.1007/s00384-016-2660-0

Introduction
Robotic-assisted rectal cancer surgery offers multiple advantages for surgeons, and it seems to yield the same clinical outcomes as regards the short-time follow-up of patients compared to conventional laparoscopy. This surgical approach emerges as a technique aiming at overcoming the limitations posed by rectal cancer and other surgical fields of difficult access, in order to obtain better outcomes and a shorter learning curve.
Material and methods
A systematic review of the literature of robot-assisted rectal surgery was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The search was conducted in October 2015 in PubMed, MEDLINE and the Cochrane Central Register of Controlled Trials, for articles published in the last 10 years and pertaining the learning curve of robotic surgery for colorectal cancer. It consisted of the following key words: “rectal cancer/learning curve/robotic-assisted laparoscopic surgery”.
Results
A total of 34 references were identified, but only 9 full texts specifically addressed the analysis of the learning curve in robot-assisted rectal cancer surgery, 7 were case series and 2 were non-randomised case-comparison series. Eight papers used the cumulative sum (CUSUM) method, and only one author divided the series into two groups to compare both. The mean number of cases for phase I of the learning curve was calculated to be 29.7 patients; phase II corresponds to a mean number 37.4 patients. The mean number of cases required for the surgeon to be classed as an expert in robotic surgery was calculated to be 39 patients.
Conclusion
Robotic advantages could have an impact on learning curve for rectal cancer and lower the number of cases that are necessary for rectal resections.