Literature DB >> 31080658

Learning curve and postoperative outcomes of minimally invasive esophagectomy.

Linda Claassen1, Frans van Workum1, Camiel Rosman1.   

Abstract

Surgical innovation is necessary to increase surgical effectiveness and to decrease postoperative complications, but can be associated with learning curves. The significance of surgical learning curves is increasing and it is important to take surgical learning curves into account when interpreting outcome data that is acquired during an implementation period. This may especially be the case for a technically challenging procedure like minimally invasive esophagectomy (MIE). This review article provides an overview of the published literature that has described a learning curve for MIE, with particular interest in the relationship between the learning curve and postoperative complications. Twenty two studies reported learning curves of different types of MIE. These studies showed that the length of the learning curve of MIE can be significant, but most studies are single center studies of limited methodological quality. In addition, several learning curve analysis methods are used but a clear recommendation regarding the preferred method is lacking. Most studies use intraoperative parameters (e.g., operative time) to define the length of the learning curve. However, significant learning curve effects have been found for clinically more relevant parameters (e.g., anastomotic leak), especially for Ivor Lewis MIE. These studies suggest that patient safety can be substantially compromised during learning curves. To increase patient safety and shorten the learning curve, evidence based and effective safe implementation programs are necessary.

Entities:  

Keywords:  Minimally invasive esophagectomy (MIE); learning curve; proficiency gain curve; safe implementation

Year:  2019        PMID: 31080658      PMCID: PMC6503284          DOI: 10.21037/jtd.2018.12.54

Source DB:  PubMed          Journal:  J Thorac Dis        ISSN: 2072-1439            Impact factor:   2.895


  17 in total

1.  Robot-assisted minimally invasive esophagectomy (RAMIE): tips and tricks from the bedside assistant view-expert experiences.

Authors:  S van der Horst; C Voli; I A Polanco; R van Hillegersberg; J P Ruurda; B Park; D Molena
Journal:  Dis Esophagus       Date:  2020-11-26       Impact factor: 3.429

Review 2.  Today's Mistakes and Tomorrow's Wisdom in the Surgical Treatment of Barrett's Adenocarcinoma.

Authors:  Giovanni Maria Garbarino; Mark Ivo van Berge Henegouwen; Suzanne Sarah Gisbertz; Wietse Jelle Eshuis
Journal:  Visc Med       Date:  2022-05-24

Review 3.  Why pay more for robot in esophageal cancer surgery?

Authors:  Fabrizio Rebecchi; Elettra Ugliono; Marco Ettore Allaix; Mario Morino
Journal:  Updates Surg       Date:  2022-08-11

4.  Robotic-assisted minimally invasive Ivor Lewis esophagectomy within the prospective multicenter German da Vinci Xi registry trial.

Authors:  Jan-Hendrik Egberts; Thilo Welsch; Felix Merboth; Sandra Korn; Christian Praetorius; Daniel E Stange; Marius Distler; Matthias Biebl; Johann Pratschke; Felix Nickel; Beat Müller-Stich; Daniel Perez; Jakob R Izbicki; Thomas Becker; Jürgen Weitz
Journal:  Langenbecks Arch Surg       Date:  2022-05-02       Impact factor: 2.895

5.  Totally minimally invasive esophagectomy versus hybrid minimally invasive esophagectomy: systematic review and meta-analysis.

Authors:  Frans van Workum; Bastiaan R Klarenbeek; Nikolaj Baranov; Maroeska M Rovers; Camiel Rosman
Journal:  Dis Esophagus       Date:  2020-08-03       Impact factor: 3.429

6.  Prospective validation of classification of intraoperative adverse events (ClassIntra): international, multicentre cohort study.

Authors:  Salome Dell-Kuster; Nuno V Gomes; Larsa Gawria; Soheila Aghlmandi; Maame Aduse-Poku; Ian Bissett; Catherine Blanc; Christian Brandt; Richard B Ten Broek; Heinz R Bruppacher; Cillian Clancy; Paolo Delrio; Eloy Espin; Konstantinos Galanos-Demiris; I Ethem Gecim; Shahbaz Ghaffari; Olivier Gié; Barbara Goebel; Dieter Hahnloser; Friedrich Herbst; Ioannidis Orestis; Sonja Joller; Soojin Kang; Rocio Martín; Johannes Mayr; Sonja Meier; Jothi Murugesan; Deirdre Nally; Menekse Ozcelik; Ugo Pace; Michael Passeri; Simone Rabanser; Barbara Ranter; Daniela Rega; Paul F Ridgway; Camiel Rosman; Roger Schmid; Philippe Schumacher; Alejandro Solis-Pena; Laura Villarino; Dionisios Vrochides; Alexander Engel; Greg O'Grady; Benjamin Loveday; Luzius A Steiner; Harry Van Goor; Heiner C Bucher; Pierre-Alain Clavien; Philipp Kirchhoff; Rachel Rosenthal
Journal:  BMJ       Date:  2020-08-25

7.  From McKeown to Ivor Lewis, the learning curve for thoracic lymphadenectomy over the first 100 robotic esophagectomy cases: a retrospective study.

Authors:  Ze-Guo Zhuo; Gang Li; Tie-Niu Song; Gu-Ha Alai; Xu Shen; Yun Wang; Yi-Dan Lin
Journal:  J Thorac Dis       Date:  2021-03       Impact factor: 2.895

8.  Learning Curve for Lymph Node Dissection Around the Recurrent Laryngeal Nerve in McKeown Minimally Invasive Esophagectomy.

Authors:  Zi-Yi Zhu; Rao-Jun Luo; Zheng-Fu He; Yong Xu; Shao-Hua Xu; Qiang Zhang
Journal:  Front Oncol       Date:  2021-05-20       Impact factor: 6.244

9.  Robot assisted versus laparoscopic suturing learning curve in a simulated setting.

Authors:  Erik Leijte; Ivo de Blaauw; Frans Van Workum; Camiel Rosman; Sanne Botden
Journal:  Surg Endosc       Date:  2019-11-21       Impact factor: 4.584

10.  Differences in the Exposure of the Lumbar Nerve Root Between Experts and Novices: Results From a Realistic Simulation Pilot Study With Force Sensors.

Authors:  Christoph Mehren; Werner Korb; Esther Fenyöházi; Davide Iacovazzi; Luis Bernal; Michael H Mayer
Journal:  Global Spine J       Date:  2020-04-08
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.