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Lise N. Tchouta, MD; Henry S. Park, MD, MPH; Daniel J. Boffa, MD; Justin D. Blasberg, MD; Frank C. Detterbeck, MD; Anthony W. Kim, MD
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FINANCIAL/NONFINANCIAL DISCLOSURES: See earlier cited article for author conflicts of interest.

Section of Thoracic Surgery, Yale School of Medicine, New Haven, CT

CORRESPONDENCE TO: Lise N. Tchouta, MD, Section of Thoracic Surgery, Yale School of Medicine, 330 Cedar St, BB 205, New Haven, CT, 06520


Copyright 2017, American College of Chest Physicians. All Rights Reserved.


Chest. 2017;151(4):942-943. doi:10.1016/j.chest.2017.01.017
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The authors would like to thank Novellis et al for their letter. It is important to first clarify that the goal of our study was to determine the outcomes of robotic lobectomies, assuming they are performed by surgeons who are qualified and trained appropriately. We recognize that this fact may not have been applicable to all the cases included, but at the same time, there was no information to indicate otherwise, and in the current practice environment, the majority of surgeons most likely had a baseline level of training. The number of cases each surgeon completed to develop their skills on the robotic platform was beyond the scope of this manuscript. Like Novellis et al, others have cited the figure of approximately 20 operations to define the learning curve. This figure arises from limited institutional experience., A retrospective study of prospectively accrued data, spanning 7 years between 2004 and 2011, established proficiency at 18 ± 3 consecutive cases based on operative times, mortality, and surgeon comfort. This number has been confirmed by Veronesi et al. Ultimately, one must also appreciate the fact that accumulating ≥ 15 cases per year could amount to far more than 20 consecutive operations over an indefinite time frame. Without greater rigor in studying the learning curve, it is challenging to know exactly how this difference matters both qualitatively and quantitatively.

Our data, as evidenced in Figure 3 of our article, suggests that the pattern of adoption of robotic-assisted thoracic surgery (RobATS) is likely explained by new centers increasingly offering RobATS rather than old ones growing their practice. We do not disagree with Novellis et al in their assessment that a high proportion of low-volume centers may be providing a bit of a distorted picture of what can be achieved with the robotic platform. However, these low-volume centers are a significant part of the RobATS landscape, and thus should be included in outcomes studies such as ours, since it is reflective of current practice patterns and will continue to be until this technology becomes more mainstream. This trend does not invalidate the results presented in the manuscript because the data were examined as discrete aggregates, with the following concern in mind: to look at the outcomes at different strata.

Finally, the outcomes of RobATS do not just reflect those of the surgeon but, more globally, those of the centers with the entire team and resources allocated to this approach. The fact remains though, that at present, robotic surgery is still a relatively novel technology, and more data will be generated over the years to come to fully appreciate its outcomes and benefits, including redefining what high volume indicates and how it applies to proficiency.

References

Melfi F.M. .Mussi A. . Robotically assisted lobectomy: learning curve and complications. Thorac Surg Clin. 2008;18:289-295 [PubMed]journal. [CrossRef] [PubMed]
 
Gharagozloo F. .Margolis M. .Tempesta B. .Strother E. .Najam F. . Robot-assisted lobectomy for early-stage lung cancer: report of 100 consecutive cases. Ann Thorac Surg. 2009;88:380-384 [PubMed]journal. [CrossRef] [PubMed]
 
Meyer M. .Gharagozloo F. .Tempesta B. .Margolis M. .Strother E. .Christenson D. . The learning curve of robotic lobectomy. Int J Med Robot. 2012;8:448-452 [PubMed]journal. [CrossRef] [PubMed]
 
Veronesi G. .Galetta D. .Maisonneuve P. .et al Four-arm robotic lobectomy for the treatment of early-stage lung cancer. J Thorac Cardiovasc Surg. 2010;140:19-25 [PubMed]journal. [CrossRef] [PubMed]
 
Tchouta L.N. .Park H.S. .Boffa D.J. .Blasberg J.D. .Detterbeck F.C. .Kim A.W. . Hospital volume and outcomes of robot-assisted lobectomies. Chest. 2017;151:329-339 [PubMed]journal. [CrossRef] [PubMed]
 

Figures

Tables

References

Melfi F.M. .Mussi A. . Robotically assisted lobectomy: learning curve and complications. Thorac Surg Clin. 2008;18:289-295 [PubMed]journal. [CrossRef] [PubMed]
 
Gharagozloo F. .Margolis M. .Tempesta B. .Strother E. .Najam F. . Robot-assisted lobectomy for early-stage lung cancer: report of 100 consecutive cases. Ann Thorac Surg. 2009;88:380-384 [PubMed]journal. [CrossRef] [PubMed]
 
Meyer M. .Gharagozloo F. .Tempesta B. .Margolis M. .Strother E. .Christenson D. . The learning curve of robotic lobectomy. Int J Med Robot. 2012;8:448-452 [PubMed]journal. [CrossRef] [PubMed]
 
Veronesi G. .Galetta D. .Maisonneuve P. .et al Four-arm robotic lobectomy for the treatment of early-stage lung cancer. J Thorac Cardiovasc Surg. 2010;140:19-25 [PubMed]journal. [CrossRef] [PubMed]
 
Tchouta L.N. .Park H.S. .Boffa D.J. .Blasberg J.D. .Detterbeck F.C. .Kim A.W. . Hospital volume and outcomes of robot-assisted lobectomies. Chest. 2017;151:329-339 [PubMed]journal. [CrossRef] [PubMed]
 
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