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Prior experience in laparoscopic rectal surgery can minimise the learning curve for robotic rectal resections: a cumulative sum analysis

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The learning curve for robotic colorectal surgery is ill-defined. This study aimed to investigate the learning curve of experienced laparoscopic rectal surgeons when starting with robotic total mesorectal excision (TME) using cumulative sum (CUSUM) charts.


This retrospective case series analysed patients who underwent curative and elective laparoscopic or robotic TMEs for rectal cancer performed by two surgeons. The first consecutive robotic TME cases of each surgeon were 1:1 propensity score matched to their laparoscopic TME cases using age, body mass index, American Society of Anesthesiologists grade, T stage (AJCC) and tumour location height. The matched laparoscopic cases defined individual standards for the quality indicators: operating time, R stage, lymph node harvest, length of hospital stay and major complications (Clavien–Dindo grade 3–5). Deviation of more than a quarter of a standard deviation from the mean for the continuous indicators, or exceeding the observed risk for the binary indicators was defined as off-target with an upward inflection in the CUSUM curve.


From 2006 to 2015, 384 (294 laparoscopic; 90 robotic) TMEs met the inclusion criteria. Surgeon A performed 206 (70.1%) of the laparoscopic and 43 (47.8%) of the robotic cases. Surgeon B performed 88 (29.9%) of the laparoscopic and 47 (52.2%) of the robotic cases. After matching, no covariate exhibited an absolute standardised mean difference >0.25. For surgeon A, the CUSUM curves showed no apparent learning process compared to his laparoscopic standards. For surgeon B, a learning process for operation time, lymph node harvest and major complications was demonstrated by an initial upward inflection of the CUSUM curves; after 15 cases, all quality indicators were generally on target.


For experienced laparoscopic colorectal surgeons, the formal learning process for robotic TME may be short to reach a similar performance level as obtained in conventional laparoscopy.

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  1. Shah J, Vyas A, Vyas D (2014) The History of Robotics in Surgical Specialties. Am J Robot Surg 1:12–20

    Article  PubMed  PubMed Central  Google Scholar 

  2. Feroci F, Vannucchi A, Bianchi PP, Cantafio S, Garzi A, Formisano G, Scatizzi M (2016) Total mesorectal excision for mid and low rectal cancer: Laparoscopic vs robotic surgery. World J Gastroenterol 22:3602–3610

    Article  PubMed  PubMed Central  Google Scholar 

  3. D’Annibale A, Pernazza G, Monsellato I, Pende V, Lucandri G, Mazzocchi P, Alfano G (2013) Total mesorectal excision: a comparison of oncological and functional outcomes between robotic and laparoscopic surgery for rectal cancer. Surg Endosc 27:1887–1895

    Article  PubMed  Google Scholar 

  4. Bianchi PP, Ceriani C, Locatelli A, Spinoglio G, Zampino MG, Sonzogni A, Crosta C, Andreoni B (2010) Robotic versus laparoscopic total mesorectal excision for rectal cancer: a comparative analysis of oncological safety and short-term outcomes. Surg Endosc 24:2888–2894

    Article  CAS  PubMed  Google Scholar 

  5. Araujo SE, Seid VE, Klajner S (2014) Robotic surgery for rectal cancer: current immediate clinical and oncological outcomes. World J Gastroenterol 20:14359–14370

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wang M, Kang X, Wang H, Guan W (2014) A meta-analysis on the outcomes and potential benefits of hybrid robotic technique in rectal cancer surgery. Zhonghua Wei Chang Wai Ke Za Zhi 17:785–790

    PubMed  Google Scholar 

  7. Trastulli S, Farinella E, Cirocchi R, Cavaliere D, Avenia N, Sciannameo F, Gulla N, Noya G, Boselli C (2012) Robotic resection compared with laparoscopic rectal resection for cancer: systematic review and meta-analysis of short-term outcome. Colorectal Dis 14:e134–e156

    Article  CAS  PubMed  Google Scholar 

  8. Collinson FJ, Jayne DG, Pigazzi A, Tsang C, Barrie JM, Edlin R, Garbett C, Guillou P, Holloway I, Howard H, Marshall H, McCabe C, Pavitt S, Quirke P, Rivers CS, Brown JM (2012) An international, multicentre, prospective, randomised, controlled, unblinded, parallel-group trial of robotic-assisted versus standard laparoscopic surgery for the curative treatment of rectal cancer. Int J Colorectal Dis 27:233–241

    Article  PubMed  Google Scholar 

  9. Yamaguchi T, Kinugasa Y, Shiomi A, Sato S, Yamakawa Y, Kagawa H, Tomioka H, Mori K (2015) Learning curve for robotic-assisted surgery for rectal cancer: use of the cumulative sum method. Surg Endosc 29:1679–1685

    Article  PubMed  Google Scholar 

  10. Dindo D, Demartines N, Clavien PA (2004) Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg 240:205–213

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ahmed J, Kuzu MA, Figueiredo N, Khan J, Parvaiz A (2016) Three-step standardized approach for complete mobilization of the splenic flexure during robotic rectal cancer surgery. Colorectal Dis 18:O171–O174

    Article  CAS  PubMed  Google Scholar 

  12. Bolsin S, Colson M (2000) The use of the Cusum technique in the assessment of trainee competence in new procedures. Int J Qual Health Care 12:433–438

    Article  CAS  PubMed  Google Scholar 

  13. Chang WR, McLean IP (2006) CUSUM: a tool for early feedback about performance? BMC Med Res Methodol 6:8

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kestin IG (1995) A statistical approach to measuring the competence of anaesthetic trainees at practical procedures. Br J Anaesth 75:805–809

    Article  CAS  PubMed  Google Scholar 

  15. Montgomery DC (2009) Introduction to statistical quality control, 6th edn. Wiley, Hoboken

    Google Scholar 

  16. Thoemmes F (2012) Propensity score matching in SPSS. asXiv:1201.6385; Accessed 18 Augu 2016.

  17. R Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  18. Ho DE, Imai K, King G, Stuart EA (2011) MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw 42:28

    Article  Google Scholar 

  19. Bowers J, Fredrickson M, Hansen B (2010) RItools: Randomization Inference Tools. pp R package

  20. Iacus S, King G, Porro G (2009) cem: Sorftware for Coarsened Exact Matching. pp R package

  21. Colquhoun PH (2008) CUSUM analysis of J-pouch surgery reflects no learning curve after board certification. Can J Surg 51:296–299

    PubMed  PubMed Central  Google Scholar 

  22. Biau DJ, Resche-Rigon M, Godiris-Petit G, Nizard RS, Porcher R (2007) Quality control of surgical and interventional procedures: a review of the CUSUM. Qual Saf Health Care 16:203–207

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bowles TA, Watters DA (2007) Time to CUSUM: simplified reporting of outcomes in colorectal surgery. ANZ J Surg 77:587–591

    Article  PubMed  Google Scholar 

  24. Trinh BB, Jackson NR, Hauch AT, Hu T, Kandil E (2014) Robotic versus laparoscopic colorectal surgery. JSLS 18:3

    Google Scholar 

  25. Fung AK, Aly EH (2013) Robotic colonic surgery: is it advisable to commence a new learning curve? Dis Colon Rectum 56:786–796

    Article  PubMed  Google Scholar 

  26. de Jesus JP, Valadao M, de Castro Araujo RO, Cesar D, Linhares E, Iglesias AC (2016) The circumferential resection margins status: A comparison of robotic, laparoscopic and open total mesorectal excision for mid and low rectal cancer. Eur J Surg Oncol 42:808–812

  27. Baxter NN (2009) Is lymph node count an ideal quality indicator for cancer care? J Surg Oncol 99:265–268

    Article  PubMed  Google Scholar 

  28. Wang J, Kulaylat M, Rockette H, Hassett J, Rajput A, Dunn KB, Dayton M (2009) Should total number of lymph nodes be used as a quality of care measure for stage III colon cancer? Ann Surg 249:559–563

    Article  PubMed  Google Scholar 

  29. Cooper MA, Ibrahim A, Lyu H, Makary MA (2015) Underreporting of robotic surgery complications. J Healthc Qual 37:133–138

    Article  PubMed  Google Scholar 

  30. Scarpinata R, Aly EH (2013) Does robotic rectal cancer surgery offer improved early postoperative outcomes? Dis Colon Rectum 56:253–262

    Article  PubMed  Google Scholar 

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The authors thank Sarah Marley, M.Sc., for her support in reviewing and providing statistical advice on this manuscript. Special thanks to Karen Flashman for data collection.

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Correspondence to Manfred Odermatt.

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Manfred Odermatt, Jamil Ahmed, Sofoklis Panteleimonitis, Jim Khan and Amjad Parvaiz have no conflicts of interest or financial ties to disclose.

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Odermatt, M., Ahmed, J., Panteleimonitis, S. et al. Prior experience in laparoscopic rectal surgery can minimise the learning curve for robotic rectal resections: a cumulative sum analysis. Surg Endosc 31, 4067–4076 (2017).

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