Journal Basic Info

  • Impact Factor: 1.995**
  • H-Index: 8
  • ISSN: 2474-1647
  • DOI: 10.25107/2474-1647
**Impact Factor calculated based on Google Scholar Citations. Please contact us for any more details.

Major Scope

  •  Surgical Oncology
  •  Ophthalmic Surgery
  •  Obstetrics Surgery
  •  Oral and Maxillofacial Surgery
  •  Robotic Surgery
  •  Transplant Surgery
  •  Urology
  •  General Surgery

Abstract

Citation: Clin Surg. 2016;1(1):1112.Research Article | Open Access

Learning Curve of Robot-Assisted Laparoscopy in Gynecologic Oncology Surgery: Team Training and Impact on Morbidity

Jauffret C;, Lambaudie E;, Esterni B;, Bannier M;, Buttarelli M; and Houvenaeghel G;

¹Department of Surgical Oncology, Paoli-Calmettes Institute, France
²Department of Biostatistics Unit, Paoli-Calmettes Institute, France

*Correspondance to: Camille Jauffret 

 PDF  Full Text DOI: 10.25107/2474-1647.1112

Abstract

Objectives: The aim of this study was to evaluate the learning curve of robot-assisted laparoscopy for gynecologic oncologic surgical procedures, regarding post-operative morbidity
Methods: Between February 2007 and October 2010, 225 robot-assisted procedures has been performed by a single team specialized in gynecologic oncology. Dates were prospectively collected. Isolated and poorly reproducible procedures were excluded, so a total of 187 procedures were finally included to assess the learning curve. Three parameters have been used: overall rate of postoperative complications, operative time and number of lymph nodes. CUSUM statistical analysis was used to investigate the learning curve. After determining the number of cases necessary for learning, a comparative analysis was conducted to compare the learning period to the efficiency period.Results: Fifty cases were necessary to master postoperative complications. The operative time evolves in three phases: the learning phase, with a gradual decrease in operative time; the consolidation phase, steady; and the final phase, increasing, corresponding to the introduction of more difficult cases and to the beginning of training program. There was a growing trend in the number of nodes removed. Comparative analysis of the learning period (senior surgeons) and the efficiency period (with increasing involvement of juniors) showed a significant evolution of procedures, and did not reveal any difference in terms of complications (44% vs. 31%, non significant), or mean number of lymph nodes removed (12.3 vs. 13.2, non significant).
Conclusion: Team training of robot-assisted laparoscopy requires an average of fifty cases looking at per operative complications.

Keywords

Robot-assisted laparoscopy; Gynecologic; Oncology

Cite the article

Jauffret C, Lambaudie E, Esterni B, Bannier M, Buttarelli M, Houvenaeghel G. Learning Curve of Robot-Assisted Laparoscopy in Gynecologic Oncology Surgery: Team Training and Impact on Morbidity. Clin Surg. 2016; 1: 1112.

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