Literature DB >> 32055993

Can better surgical outcomes be obtained in the learning process of robotic rectal cancer surgery? A propensity score-matched comparison between learning phases.

Jong Min Lee1, Seung Yoon Yang1, Yoon Dae Han1, Min Soo Cho1, Hyuk Hur1, Byung Soh Min1, Kang Young Lee1, Nam Kyu Kim2.   

Abstract

BACKGROUND: Although studies of robotic rectal cancer surgery have demonstrated the effects of learning on operation time, comparisons have failed to demonstrate differences in clinicopathological outcomes between unadjusted learning phases. This study aimed to investigate the learning curve of robotic rectal cancer surgery for clinicopathological outcomes and compare surgical outcomes between adjusted learning phases. Study design We enrolled 506 consecutive patients with rectal adenocarcinoma who underwent robotic resection by a single surgeon between 2007 and 2018. Risk-adjusted cumulative sum (RA-CUSUM) for surgical failure was used to analyze the learning curve. Surgical failure was defined as the occurrence of any of the following: conversion to open surgery, severe complications (Clavien-Dindo grade ≥ 3a), insufficient number of harvested lymph nodes (LNs), or R1 resection. Comparisons between learning phases analyzed by RA-CUSUM were performed before and after propensity score matching.
RESULTS: In RA-CUSUM analysis, the learning curve was divided into two learning phases: phase 1 (1st-177th cases, n = 177) and phase 2 (178th-506th cases, n = 329). Before matching, patients in phase 2 had deeper tumor invasion and higher rates of positive LNs on pretreatment images and preoperative chemoradiotherapy. After matching, phase 1 (n = 150) and phase 2 (n = 150) patients exhibited similar clinical characteristics. Phase 2 patients had lower rates of surgical failure overall and these components: conversion to open surgery, severe complications, and insufficient harvested LNs.
CONCLUSIONS: For robotic rectal cancer surgery, surgical outcomes improved after the 177th case. Further studies by other robotic surgeons are required to validate our results.

Entities:  

Keywords:  Learning curve; Rectal cancer; Robot; Surgical outcome

Year:  2020        PMID: 32055993     DOI: 10.1007/s00464-020-07445-3

Source DB:  PubMed          Journal:  Surg Endosc        ISSN: 0930-2794            Impact factor:   4.584


  3 in total

1.  [Pathological insights of radiotherapy-related damage to surgical margin after preoperative radiotherapy in patients with rectal cancer].

Authors:  Q H Zhong; P H Wu; Q Y Qin; Y Y Kuang; T H Ma; H M Wang; Y X Zhu; D C Chen; J P Wang; L Wang
Journal:  Zhonghua Wai Ke Za Zhi       Date:  2017-07-01

2.  Clinicopathological Factors Influencing Lymph Node Yield in Colorectal Cancer: A Retrospective Study.

Authors:  Elena Orsenigo; Giulia Gasparini; Michele Carlucci
Journal:  Gastroenterol Res Pract       Date:  2019-01-22       Impact factor: 2.260

3.  Factors Predicting Difficulty of Laparoscopic Low Anterior Resection for Rectal Cancer with Total Mesorectal Excision and Double Stapling Technique.

Authors:  Weiping Chen; Qiken Li; Yongtian Fan; Dechuan Li; Lai Jiang; Pengnian Qiu; Lilong Tang
Journal:  PLoS One       Date:  2016-03-18       Impact factor: 3.240

  3 in total
  2 in total

1.  Learning Curve of Robotic-Assisted Total Mesorectal Excision for Rectal Cancer.

Authors:  Bo Tang; Tao Li; Gengmei Gao; Jun Shi; Taiyuan Li
Journal:  Front Oncol       Date:  2022-07-11       Impact factor: 5.738

2.  Teaching and learning robotic surgery at the dual console: a video-based qualitative analysis.

Authors:  Hélène Cristofari; Minoa Karin Jung; Nadja Niclauss; Christian Toso; Laure Kloetzer
Journal:  J Robot Surg       Date:  2021-03-16
  2 in total

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