The learning curve for da Vinci robot-assisted mediastinal tumor resection by using cumulative sum analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIUBo , WANGMingmin , XUShiguang ,  WANGShumin , LAIXiangmin

Department of Thoracic Surgery, The General Hospital of Shenyang Military, Shenyang, 110016, P.R.China;

Corresponding author：

WANGShumin, Email: sureman2003congo@163.com

Keywords：

CUSUM learning curve; mediastinal tumor resection; da Vinci robot-assisted surgery; thoracic surgery

DOI：

10.7507/1007-4848.201603036

Video：

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Objective To investigate the learning curve for da Vinci robot-assisted mediastinal tumor resection (DRMTR). Methods A total of 50 consecutive patients received DRMTR between March 2011 and September 2012 in our hospital. Clinical data of the 50 patients were collected and analyzed. There were 23 males, 27 females aged 46.9(17–80) years. The learning curve was evaluated by using the cumulative sum (CUSUM) analysis. Results The mean operation time was 124.6 min. The CUSUM learning curve was best modeled as a third-order polynomial curve with the equation: CUSUM=0.046×case-number³–4.681×case-number²+127.508×case-number–237.940, which had a highR² value of 0.868. The fitting curve reached the top after the 19th case, which suggested that the surgeons master the technique after they finished 19 cases. As a cut-off point, the 19th case divided the learning curve into two phases, in which there was statistical diffference in operation time (P<0.01), intraoperative blood loss (P<0.01), the postoperative duration of chest tube drainage (P<0.01 ) and the rate of postoperative complications (P<0.05 ). Conclusion The DRMTR identified by CUSUM analysis represents two characteristic stages of DRMTR: the learning stage and the mastery stage. It is suggested from our data that the surgeons need finish about 19 cases to master DRMTR.

Citation： LIUBo, WANGMingmin, XUShiguang, WANGShumin, LAIXiangmin. The learning curve for da Vinci robot-assisted mediastinal tumor resection by using cumulative sum analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2017, 24(2): 127-131. doi: 10.7507/1007-4848.201603036 Copy

1.	嵇武, 李宁, 黎介寿. 我国手术机器人外科面临的机遇和挑战. 中国微创外科杂志, 2012, 12(7): 577-579.
2.	Bolsin S, Colson M. The use of the Cusum technique in the assessment of trainee competence in new procedures. Int J Qual Health Care, 2000, 12(5): 433-438.
3.	王述民, 许世广, 童向东, 等. 达芬奇机器人手术系统在胸外科纵隔肿瘤切除术中的应用体会. 临床军医杂志, 2013, 41(12): 1247-1249.
4.	Biswas P, Kalbfleisch JD. A risk-adjusted CUSUM in continuous time based on the Cox model. Stat Med, 2008, 27(17): 3382-3406.
5.	Steiner SH, Cook RJ, Farewell VT, et al. Monitoring surgical performance using risk-adjusted cumulative sum charts. Biostatistics, 2000, 1(4): 441-452.
6.	高峰, 许李力. CUSUM 绘制在心外科住院医师临床培养中的应用. 中华医学教育探索杂志, 2015, 14(4): 397-400.
7.	Byrn JC, Schluender S, Divino CM, et al. Three-dimensional imaging improves surgical performance for both novice and experienced operators using the da Vinci Robot System. Am J Surg, 2007, 193(4): 519-522.
8.	Yin H, Wang Z, Xu J, et al. A specific instrument to facilitate single-incision laparoscopic cholecystectomy: an initial experience. Surg Innov, 2011, 18(3): 289-293.
9.	Moore MJ, Bennett CL. The learning curve for laparoscopic cholecystectomy. The Southern Surgeons Club. Am J Surg, 1995, 170(1): 55-59.
10.	Zhang YL, Wang GJ. Robotics in urological surgery: review of current status and maneuverability, and comparison of robot-assisted and traditional laparoscopy. Comput Aided Surg, 2011, 16(1): 38-45.
11.	Zhou NX, Chen JZ, Liu Q, et al. Outcomes of pancreatoduodenectomy with robotic surgery versus open surgery. Int J Med Robot, 2011, 7(2): 131-137.
12.	Najarian S, Fallahnezhad M, Afshari E. Advances in medical robotic systems with specific applications in surgery—A review. J Med Eng Technol, 2011, 35(1): 19-33.
13.	Chaput de Saintonge DM, Vere DW. Why don't doctors use cusums? Lancet, 1974, 303(7848): 120-121.
14.	Wohl H. The cusum plot: its utility in the analysis of clinical data. N Eng J Med, 1977, 296(18): 1044-1045.
15.	吴伟彬, 黄邵洪, 张健, 等. 胸腔镜下交感神经切除术治疗手汗症的学习曲线. 新医学, 2012, 43(7): 473-475.
16.	Ninomiya I, Osugi H, Tomizawa N, et al. Learning of thoracoscopic radical esophagectomy: how can the learning curve be made short and flat? Dis Esophagus, 2010, 23(8): 618-626.
17.	郑轶峰, 姜建青, 杨列, 等. 全胸腔镜肺叶切除术学习曲线的分析. 重庆医学, 2014, 43(14): 1797-1799.
18.	Kim MC, Jung GJ, Kim HH. Learning curve of laparoscopy-assisted distal gastrectomy with systemic lymphadenectomy for early gastric cancer. World J Gastroenterol, 2005, 11(11): 7508-7511.
19.	许建功, 蔡瑞君, 王禹冰, 等. 胸腔镜联合腹腔镜食管癌根治术学习曲线研究. 中国全科医学, 2012, 15(6): 631-634.
20.	Jones RO, Gianluca C, Walker WS. Does failed video-assisted lobectomy for lung cancer prejudice immediate and long-term outcomes? Ann Thorac Surg, 2008, 86(1): 235-239.

1. 嵇武, 李宁, 黎介寿. 我国手术机器人外科面临的机遇和挑战. 中国微创外科杂志, 2012, 12(7): 577-579.
2. Bolsin S, Colson M. The use of the Cusum technique in the assessment of trainee competence in new procedures. Int J Qual Health Care, 2000, 12(5): 433-438.
3. 王述民, 许世广, 童向东, 等. 达芬奇机器人手术系统在胸外科纵隔肿瘤切除术中的应用体会. 临床军医杂志, 2013, 41(12): 1247-1249.
4. Biswas P, Kalbfleisch JD. A risk-adjusted CUSUM in continuous time based on the Cox model. Stat Med, 2008, 27(17): 3382-3406.
5. Steiner SH, Cook RJ, Farewell VT, et al. Monitoring surgical performance using risk-adjusted cumulative sum charts. Biostatistics, 2000, 1(4): 441-452.
6. 高峰, 许李力. CUSUM 绘制在心外科住院医师临床培养中的应用. 中华医学教育探索杂志, 2015, 14(4): 397-400.
7. Byrn JC, Schluender S, Divino CM, et al. Three-dimensional imaging improves surgical performance for both novice and experienced operators using the da Vinci Robot System. Am J Surg, 2007, 193(4): 519-522.
8. Yin H, Wang Z, Xu J, et al. A specific instrument to facilitate single-incision laparoscopic cholecystectomy: an initial experience. Surg Innov, 2011, 18(3): 289-293.
9. Moore MJ, Bennett CL. The learning curve for laparoscopic cholecystectomy. The Southern Surgeons Club. Am J Surg, 1995, 170(1): 55-59.
10. Zhang YL, Wang GJ. Robotics in urological surgery: review of current status and maneuverability, and comparison of robot-assisted and traditional laparoscopy. Comput Aided Surg, 2011, 16(1): 38-45.
11. Zhou NX, Chen JZ, Liu Q, et al. Outcomes of pancreatoduodenectomy with robotic surgery versus open surgery. Int J Med Robot, 2011, 7(2): 131-137.
12. Najarian S, Fallahnezhad M, Afshari E. Advances in medical robotic systems with specific applications in surgery—A review. J Med Eng Technol, 2011, 35(1): 19-33.
13. Chaput de Saintonge DM, Vere DW. Why don't doctors use cusums? Lancet, 1974, 303(7848): 120-121.
14. Wohl H. The cusum plot: its utility in the analysis of clinical data. N Eng J Med, 1977, 296(18): 1044-1045.
15. 吴伟彬, 黄邵洪, 张健, 等. 胸腔镜下交感神经切除术治疗手汗症的学习曲线. 新医学, 2012, 43(7): 473-475.
16. Ninomiya I, Osugi H, Tomizawa N, et al. Learning of thoracoscopic radical esophagectomy: how can the learning curve be made short and flat? Dis Esophagus, 2010, 23(8): 618-626.
17. 郑轶峰, 姜建青, 杨列, 等. 全胸腔镜肺叶切除术学习曲线的分析. 重庆医学, 2014, 43(14): 1797-1799.
18. Kim MC, Jung GJ, Kim HH. Learning curve of laparoscopy-assisted distal gastrectomy with systemic lymphadenectomy for early gastric cancer. World J Gastroenterol, 2005, 11(11): 7508-7511.
19. 许建功, 蔡瑞君, 王禹冰, 等. 胸腔镜联合腹腔镜食管癌根治术学习曲线研究. 中国全科医学, 2012, 15(6): 631-634.
20. Jones RO, Gianluca C, Walker WS. Does failed video-assisted lobectomy for lung cancer prejudice immediate and long-term outcomes? Ann Thorac Surg, 2008, 86(1): 235-239.

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