Literature DB >> 22991294

The learning curve of robotic lobectomy.

Mark Meyer1, Farid Gharagozloo, Barbara Tempesta, Marc Margolis, Eric Strother, Douglas Christenson.   

Abstract

BACKGROUND: Robotic lobectomy has been shown to be feasible, safe and oncologically efficacious. The actual learning curve of robotic lobectomy has yet to be defined. This study was designed to define the learning curve of robotic lobectomy.
METHODS: We performed a retrospective review of prospectively accrued patients at our institution who underwent robotic lobectomy from January 2004 until December 2011. Six scatter graphs were constructed, comparing operative time, conversion rate, morbidity, mortality, length of stay and surgeon comfort with the number of consecutive cases. In each graph, a regression trendline was drawn and the change in the slope of the curve corresponding to the beginning of the plateau defined the learning curve. The overall learning curve was defined as mean ± SD of the sum of the individual learning curves.
RESULTS: Based on operative times, mortality and surgeon comfort, the overall learning curve was 18 ± 3 cases. The learning curve based on operative times, mortality and surgeon comfort was 15, 20 and 19 cases, respectively. There was no association between the need for conversion and number of consecutive cases. There was a trend towards lower morbidity and decreased length of stay with greater experience. However, these parameters did not define a specific learning curve.
CONCLUSIONS: Operative time, mortality and surgeon comfort were found to be key parameters for the learning curve of robotic lobectomy when performed by surgeons who are experienced with video-assisted thoracic surgery (VATS). The learning curve was 18 ± 3 cases.
Copyright © 2012 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2012        PMID: 22991294     DOI: 10.1002/rcs.1455

Source DB:  PubMed          Journal:  Int J Med Robot        ISSN: 1478-5951            Impact factor:   2.547


  27 in total

Review 1.  Robotic Surgery for Thoracic Disease.

Authors:  Shin-Ichi Yamashita; Yasuhiro Yoshida; Akinori Iwasaki
Journal:  Ann Thorac Cardiovasc Surg       Date:  2016-01-26       Impact factor: 1.520

Review 2.  [Application of the da Vinci robotic system in thoracic surgery].

Authors:  M Ismail; M Swierzy; M Ulrich; J C Rückert
Journal:  Chirurg       Date:  2013-08       Impact factor: 0.955

3.  Robotic anatomic lung resections: the initial experience and description of learning in 102 cases.

Authors:  Alper Toker; Mehmet Oğuzhan Özyurtkan; Erkan Kaba; Kemal Ayalp; Özkan Demirhan; Elena Uyumaz
Journal:  Surg Endosc       Date:  2015-06-20       Impact factor: 4.584

4.  Defining the learning curve of robotic thoracic surgery: what does it take?

Authors:  Alexandra D Power; Desmond M D'Souza; Susan D Moffatt-Bruce; Robert E Merritt; Peter J Kneuertz
Journal:  Surg Endosc       Date:  2019-08-02       Impact factor: 4.584

5.  Video-assisted thoracoscopic lobectomy: which is the learning curve of an experienced consultant?

Authors:  Antonio Mazzella; Anne Olland; Pierre Emmanuel Falcoz; Stephane Renaud; Nicola Santelmo; Gilbert Massard
Journal:  J Thorac Dis       Date:  2016-09       Impact factor: 2.895

6.  Evaluation of different time schedules in training with the Da Vinci simulator.

Authors:  C Güldner; A Orth; P Dworschak; I Diogo; M Mandapathil; A Teymoortash; U Walliczek-Dworschak
Journal:  Surg Endosc       Date:  2017-03-09       Impact factor: 4.584

Review 7.  Da Vinci© Skills Simulator™: is an early selection of talented console surgeons possible?

Authors:  Mark Meier; Kevin Horton; Hubert John
Journal:  J Robot Surg       Date:  2016-06-22

8.  Surgical outcomes associated with postoperative atrial fibrillation after robotic-assisted pulmonary lobectomy: retrospective review of 208 consecutive cases.

Authors:  Emily P Ng; Frank O Velez-Cubian; Kathryn L Rodriguez; Matthew R Thau; Carla C Moodie; Joseph R Garrett; Jacques P Fontaine; Eric M Toloza
Journal:  J Thorac Dis       Date:  2016-08       Impact factor: 2.895

Review 9.  Why comprehensive adoption of robotic assisted thoracic surgery is ideal for both simple and complex lung resections.

Authors:  Michael Mazzei; Abbas E Abbas
Journal:  J Thorac Dis       Date:  2020-02       Impact factor: 2.895

Review 10.  Transition from video-assisted thoracic surgery to robotic pulmonary surgery.

Authors:  Takashi Suda
Journal:  J Vis Surg       Date:  2017-04-10
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