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Surg Oncol. 2017 Mar;26(1):28-36. doi: 10.1016/j.suronc.2016.12.005. Epub 2016 Dec 19.

Robotic right hemicolectomy: Analysis of 108 consecutive procedures and multidimensional assessment of the learning curve.

Author information

1
Department of Digestive Surgery, Santa Maria Hospital, Terni, Italy.
2
Department of Economics, University of Perugia, Perugia, Italy.
3
Department of Surgery, Royal Free Hospital NHS Foundation Trust, London, UK.
4
Department of General and Oncologic Surgery, University of Perugia, Perugia, Italy.
5
Department of Surgical Science, "La Sapienza" University, Laparoscopic Surgery Unit, Rome, Italy.
6
Department of Surgical Science, "La Sapienza" University, Advanced Surgical Technologies PhD, Rome, Italy.
7
Department of Hygiene and Public Health, University of Perugia, Perugia, Italy.
8
Department of Digestive Surgery, Santa Maria Hospital, Terni, Italy; Department of Surgical and Biomedical Sciences, University of Perugia, Perugia, Italy. Electronic address: Stefano.trastulli@hotmail.it.

Abstract

PURPOSE:

Surgeons tend to view the robotic right colectomy (RRC) as an ideal beginning procedure to gain proficiency in robotic general and colorectal surgery. Nevertheless, oncological RRC, especially if performed with intracorporeal ileocolic anastomosis confectioning, cannot be considered a technically easier procedure. The aim of this study was to assess the learning curve of the RRC performed for oncological purposes and to evaluate its safety and efficacy investigating the perioperative and pathology outcomes in the different learning phases.

METHODS:

Data on a consecutive series of 108 patients undergoing RRC with intracorporeal anastomosis between June 2011 and September 2015 at our institution were prospectively collected to evaluate surgical and short-term oncological outcomes. CUSUM (Cumulative Sum) and Risk-Adjusted (RA) CUSUM analysis were performed in order to perform a multidimensional assessment of the learning curve for the RRC surgical procedure. Intraoperative, postoperative and pathological outcomes were compared among the learning curve phases.

RESULTS:

Based on the CUSUM and RA-CUSUM analyses, the learning curve for RRC could be divided into 3 different phases: phase 1, the initial learning period (1st-44th case); phase 2, the consolidation period (45th-90th case); and phase 3, the mastery period (91th-108th case). Operation time, conversion to open surgery rate and the number of harvested lymph nodes significantly improve through the three learning phases.

CONCLUSIONS:

The learning curve for oncological RRC with intracorporeal anastomosis is composed of 3 phases. Our data indicate that the performance of RRC is safe from an oncological point of view in all of the three phases of the learning curve. However, the technical skills necessary to significantly reduce operative time, conversion to open surgery rate and to significantly improve the number of harvested lymph nodes were achieved after 44 procedures. These data suggest that it might be prudent to start the RRC learning curve by treating only benign diseases and to reserve the performance of oncological resection to when at least the initial learning phase has been completed.

KEYWORDS:

Colon cancer; Colorectal cancer; Cumulative sum analyses; Ileocolic anastomosis; Learning curve; Right hemicolectomy; Robotic colectomy

PMID:
28317582
DOI:
10.1016/j.suronc.2016.12.005
[Indexed for MEDLINE]

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