Successful Sleeve Resection as a Marker for Proficiency for Robotic Pulmonary Resection
23 April 2019
02 August 2019
15 September 2019 (online)
Background Robot technology is a revolutionary technique to overcome limitations of minimal invasive surgery. The proficiency level varies from study to study. We considered the first sleeve lobectomy as a benchmark procedure to evaluate the proficiency level.
Methods We retrospectively analyzed 197 patients who underwent robot-assisted thoracoscopic surgery (RATS) for primary lung cancer between December 2011 and May 2018. Patients were divided into two groups based on undergoing surgery earlier period (EP) or later period (LP) than the first sleeve lobectomy by RATS (May 25, 2015). The preoperative, operative, and short- and long-term postoperative outcomes were compared. Seven-year survival was also compared between two periods for T1N0 and T2N0 diseases.
Results Preoperative features were similar. The mean operative time was 166.8 ± 55.1 and 142.4 ± 43.9 minutes in EP and LP, respectively (p = 0.005). The mean number of dissected lymph nodes in LP was also significantly higher than that in EP (24.4 ± 9.4 vs. 20.8 ± 10.4, p = 0.035). The complication rate was significantly lower in LP (29/86 vs. 25/111, p = 0.048). The extended resection (ER) rate was significantly higher in LP (p = 0.023). The 7-year survival was comparable in EP and LP in both patients with T1N0 and T2N0 (p = 0.28 and p = 0.11, respectively).
Conclusion Perioperative outcomes, such as duration of surgery, number of dissected lymph nodes, complications, and ERs are favorable in patients who underwent surgeries after the first sleeve resection. The first sleeve lobectomy may be considered as the benchmark procedure for the proficiency level in RATS.
Conception and design: T. Cosgun and A Toker; administrative support: T. Cosgun and E. Kaba; provision of study materials or patients: K. Ayalp and A. Toker; collection and assembly of data: T. Cosgun; data analysis and interpretation: T Cosgun; and article writing and final approval of article: all authors.
- 1 Owen RM, Force SD, Gal AA. , et al. Routine intraoperative frozen section analysis of bronchial margins is of limited utility in lung cancer resection. Ann Thorac Surg 2013; 95 (06) 1859-1865 , discussion 1865–1866
- 2 Tomaszek SC, Kim Y, Cassivi SD. , et al. Bronchial resection margin length and clinical outcome in non-small cell lung cancer. Eur J Cardiothorac Surg 2011; 40 (05) 1151-1156
- 3 Spaggiari L, Tessitore A, Casiraghi M. , et al. Survival after extended resection for mediastinal advanced lung cancer: lessons learned on 167 consecutive cases. Ann Thorac Surg 2013; 95 (05) 1717-1725
- 4 Wright TP. Factors affecting the cost of airplanes. J Aeronaut Sci 1936; 3: 122-128
- 5 Yao F, Wang J, Yao J, Hang F, Cao S, Cao Y. Video-assisted thoracic surgical lobectomy for lung cancer: description of a learning curve. J Laparoendosc Adv Surg Tech A 2017; 27 (07) 696-703
- 6 Maruthappu M, Duclos A, Orgill D, Carty MJ. A monitoring tool for performance improvement in plastic surgery at the individual level. Plast Reconstr Surg 2013; 131 (05) 702e-710e
- 7 Dawe SR, Windsor JA, Broeders JA, Cregan PC, Hewett PJ, Maddern GJ. A systematic review of surgical skills transfer after simulation-based training: laparoscopic cholecystectomy and endoscopy. Ann Surg 2014; 259 (02) 236-248
- 8 Maruthappu M, Duclos A, Lipsitz SR, Orgill D, Carty MJ. Surgical learning curves and operative efficiency: a cross-specialty observational study. BMJ Open 2015; 5 (03) e006679
- 9 Li X, Wang J, Ferguson MK. Competence versus mastery: the time course for developing proficiency in video-assisted thoracoscopic lobectomy. J Thorac Cardiovasc Surg 2014; 147 (04) 1150-1154
- 10 Dufourq N, Nicole Goldstein L, Botha M. Competence in performing emergency skills: how good do doctors really think they are?. Afr J Emerg Med 2017; 7 (04) 151-156
- 11 Toker A, Tanju S, Ziyade S, Kaya S, Dilege S. Learning curve in videothoracoscopic thymectomy: how many operations and in which situations?. Eur J Cardiothorac Surg 2008; 34 (01) 155-158
- 12 Zhao H, Bu L, Yang F, Li J, Li Y, Wang J. Video-assisted thoracoscopic surgery lobectomy for lung cancer: the learning curve. World J Surg 2010; 34 (10) 2368-2372
- 13 Bilgic E, Watanabe Y, Nepomnayshy D. , et al; Simulation Committee of the Association for Surgical Education. Multicenter proficiency benchmarks for advanced laparoscopic suturing tasks. Am J Surg 2017; 213 (02) 217-221
- 14 Lin MW, Kuo SW, Yang SM, Lee JM. Robotic-assisted thoracoscopic sleeve lobectomy for locally advanced lung cancer. J Thorac Dis 2016; 8 (07) 1747-1752
- 15 Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg 2004; 240 (02) 205-213
- 16 Toker A, Kaba E, Ayalp K, Özyurtkan MO. Robotic lung resections: video-assisted thoracic surgery based approach. J Vis Surg 2017; 3: 15
- 17 Mazzella A, Olland A, Falcoz PE, Renaud S, Santelmo N, Massard G. Video-assisted thoracoscopic lobectomy: which is the learning curve of an experienced consultant?. J Thorac Dis 2016; 8 (09) 2444-2453
- 18 Osugi H, Takemura M, Higashino M. , et al. Learning curve of video-assisted thoracoscopic esophagectomy and extensive lymphadenectomy for squamous cell cancer of the thoracic esophagus and results. Surg Endosc 2003; 17 (03) 515-519
- 19 Melfi FM, Mussi A. Robotically assisted lobectomy: learning curve and complications. Thorac Surg Clin 2008; 18 (03) 289-295 , vi–vii
- 20 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 (01) 19-25
- 21 Özyurtkan MO, Kaba E, Toker A. What happens while learning robotic lobectomy for lung cancer?. J Vis Surg 2017; 3: 27
- 22 Toker A, Özyurtkan MO, Kaba E, Ayalp K, Demirhan Ö, Uyumaz E. Robotic anatomic lung resections: the initial experience and description of learning in 102 cases. Surg Endosc 2016; 30 (02) 676-683
- 23 Melfi FM, Fanucchi O, Davini F, Mussi A. VATS-based approach for robotic lobectomy. Thorac Surg Clin 2014; 24 (02) 143-149 , v
- 24 Cosgun T, Kaba E, Ayalp K, Alomari MR, Toker A. Bronchial sleeve anastomosis and primary closures with the da Vinci system: an advanced minimally invasive technique. Video-assist Thorac Surg 2017; 2: 49 . Doi: 10.21037/vats.2017.08.06
- 25 Zhao Y, Jiao W, Ren X. , et al. Left lower lobe sleeve lobectomy for lung cancer using the Da Vinci surgical system. J Cardiothorac Surg 2016; 11 (01) 59
- 26 Meyer M, Gharagozloo F, Tempesta B, Margolis M, Strother E, Christenson D. The learning curve of robotic lobectomy. Int J Med Robot 2012; 8 (04) 448-452
- 27 Cheufou DH, Mardanzai K, Ploenes T. , et al. Effectiveness of robotic lobectomy-outcome and learning curve in a high volume center. Thorac Cardiovasc Surg 2018 . Doi: 10.1055/s-0038-1639477
- 28 Seder CW, Cassivi SD, Wigle DA. Navigating the pathway to robotic competency in general thoracic surgery. Innovations (Phila) 2013; 8 (03) 184-189