J Gynecol Oncol.  2013 Oct;24(4):303-312. 10.3802/jgo.2013.24.4.303.

Learning curve analysis of robot-assisted radical hysterectomy for cervical cancer: initial experience at a single institution

• Yim GW
• Kim SW
• Nam EJ
• Kim S
• Kim YT

• KMID: 2177871
• DOI: http://doi.org/10.3802/jgo.2013.24.4.303

Abstract

OBJECTIVE
The aim of this study was to evaluate the learning curve and perioperative outcomes of robot-assisted laparoscopic procedure for cervical cancer.
METHODS
A series of 65 cases of robot-assisted laparoscopic radical hysterectomies with bilateral pelvic lymph node dissection for early stage cervical cancer were included. Demographic data and various perioperative parameters including docking time, console time, and total operative time were reviewed from the prospectively collected database. Console time was set as a surrogate marker for surgical competency, in addition to surgical outcomes. The learning curve was evaluated using cumulative summation method.
RESULTS
The mean operative time was 190 minutes (range, 117 to 350 minutes). Two unique phases of the learning curve were derived using cumulative summation analysis; phase 1 (the initial learning curve of 28 cases), and phase 2 (the improvement phase of subsequent cases in which more challenging cases were managed). Docking and console times were significantly decreased after the first 28 cases compared with the latter cases (5 minutes vs. 4 minutes for docking time, 160 minutes vs. 134 minutes for console time; p<0.001 and p<0.001, respectively). There was a significant reduction in blood loss during operation (225 mL vs. 100 mL, p<0.001) and early postoperative complication rates (28% vs. 8.1%, p=0.003) in phase 2. No conversion to laparotomy occurred.
CONCLUSION
Improvement of surgical performance in robot-assisted surgery for cervical cancer can be achieved after 28 cases. The two phases identified by cumulative summation analysis showed significant reduction in operative time, blood loss, and complication rates in the latter phase of learning curve.

Keyword

Cervical neoplasms; Laparoscopic surgery; Learning curve; Robotics

MeSH Terms

Biomarkers
Hysterectomy
Laparoscopy
Laparotomy
Learning
Learning Curve
Lymph Node Excision
Operative Time
Postoperative Complications
Robotics
Uterine Cervical Neoplasms

Figure

• 

  Fig. 1 Console time (CT) plots. (A) The raw CT plotted against chronological case number. (B) Cumulative sum (CUSUM) of CT plotted against case number (solid line). CUSUM curve of best modeled fit for the plot (dashed line).

• 

  Fig. 2 Two phases and the lines of best fit for each phase of the cumulative sum (CUSUM) learning curve. (A) The CUSUM value 28 divides the learning curve of the console time (CT) into two phases. (B) Lines best fit for each phase. Phase 1 represents the initial learning curve. Phase 2 represents increasing competence of surgeon after the initial 28 case.

Reference

1. Mendivil A, Holloway RW, Boggess JF. Emergence of robotic assisted surgery in gynecologic oncology: American perspective. Gynecol Oncol. 2009; 114:2 Suppl. S24–S31.

2. Lin PS, Wakabayashi MT, Han ES. Role of robotic surgery in endometrial cancer. Curr Treat Options Oncol. 2009; 10:33–43.

3. Jung YW, Kim SW, Kim YT. Recent advances of robotic surgery and single port laparoscopy in gynecologic oncology. J Gynecol Oncol. 2009; 20:137–144.

4. Advincula AP, Wang K. Evolving role and current state of robotics in minimally invasive gynecologic surgery. J Minim Invasive Gynecol. 2009; 16:291–301.

5. Nezhat C, Lavie O, Lemyre M, Unal E, Nezhat CH, Nezhat F. Robot-assisted laparoscopic surgery in gynecology: scientific dream or reality. Fertil Steril. 2009; 91:2620–2622.

6. Chen CC, Falcone T. Robotic gynecologic surgery: past, present, and future. Clin Obstet Gynecol. 2009; 52:335–343.

7. Yim GW, Kim SW, Nam EJ, Kim YT. Role of robot-assisted surgery in cervical cancer. Int J Gynecol Cancer. 2011; 21:173–181.

8. Soliman PT, Frumovitz M, Sun CC, Dos Reis R, Schmeler KM, Nick AM, et al. Radical hysterectomy: a comparison of surgical approaches after adoption of robotic surgery in gynecologic oncology. Gynecol Oncol. 2011; 123:333–336.

9. Geisler JP, Orr CJ, Khurshid N, Phibbs G, Manahan KJ. Robotically assisted laparoscopic radical hysterectomy compared with open radical hysterectomy. Int J Gynecol Cancer. 2010; 20:438–442.

10. Magrina JF, Kho R, Magtibay PM. Robotic radical hysterectomy: technical aspects. Gynecol Oncol. 2009; 113:28–31.

11. Ramirez PT, Soliman PT, Schmeler KM, dos Reis R, Frumovitz M. Laparoscopic and robotic techniques for radical hysterectomy in patients with early-stage cervical cancer. Gynecol Oncol. 2008; 110:3 Suppl 2. S21–S24.

12. Wohl H. The cusum plot: its utility in the analysis of clinical data. N Engl J Med. 1977; 296:1044–1045.

13. Williams AK, Chalasani V, Martinez CH, Osbourne E, Stitt L, Izawa JI, et al. Cumulative summation graphs are a useful tool for monitoring positive surgical margin rates in robot-assisted radical prostatectomy. BJU Int. 2011; 107:1648–1652.

14. Biau DJ, Williams SM, Schlup MM, Nizard RS, Porcher R. Quantitative and individualized assessment of the learning curve using LC-CUSUM. Br J Surg. 2008; 95:925–929.

15. Secin FP. The learning curve of robotic assisted laparoscopic radical prostatectomy: what is the evidence? Arch Esp Urol. 2011; 64:830–838.

16. Pierorazio PM, Patel HD, Feng T, Yohannan J, Hyams ES, Allaf ME. Robotic-assisted versus traditional laparoscopic partial nephrectomy: comparison of outcomes and evaluation of learning curve. Urology. 2011; 78:813–819.

17. Lee J, Yun JH, Nam KH, Soh EY, Chung WY. The learning curve for robotic thyroidectomy: a multicenter study. Ann Surg Oncol. 2011; 18:226–232.

18. Bokhari MB, Patel CB, Ramos-Valadez DI, Ragupathi M, Haas EM. Learning curve for robotic-assisted laparoscopic colorectal surgery. Surg Endosc. 2011; 25:855–860.

19. Yu SC, Clapp BL, Lee MJ, Albrecht WC, Scarborough TK, Wilson EB. Robotic assistance provides excellent outcomes during the learning curve for laparoscopic Roux-en-Y gastric bypass: results from 100 robotic-assisted gastric bypasses. Am J Surg. 2006; 192:746–749.

20. Kim YT, Kim SW, Hyung WJ, Lee SJ, Nam EJ, Lee WJ. Robotic radical hysterectomy with pelvic lymphadenectomy for cervical carcinoma: a pilot study. Gynecol Oncol. 2008; 108:312–316.

21. Hawkins DM, Olwell DH. Cumulative sum charts and charting for quality improvement. New York: Springer;1998.

22. Bege T, Lelong B, Esterni B, Turrini O, Guiramand J, Francon D, et al. The learning curve for the laparoscopic approach to conservative mesorectal excision for rectal cancer: lessons drawn from a single institution's experience. Ann Surg. 2010; 251:249–253.

23. Seamon LG, Cohn DE, Richardson DL, Valmadre S, Carlson MJ, Phillips GS, et al. Robotic hysterectomy and pelvic-aortic lymphadenectomy for endometrial cancer. Obstet Gynecol. 2008; 112:1207–1213.

24. Schreuder HW, Zweemer RP, van Baal WM, van de Lande J, Dijkstra JC, Verheijen RH. From open radical hysterectomy to robot-assisted laparoscopic radical hysterectomy for early stage cervical cancer: aspects of a single institution learning curve. Gynecol Surg. 2010; 7:253–258.

25. Pitter MC, Anderson P, Blissett A, Pemberton N. Robotic-assisted gynaecological surgery-establishing training criteria; minimizing operative time and blood loss. Int J Med Robot. 2008; 4:114–120.

26. Payne TN, Dauterive FR. A comparison of total laparoscopic hysterectomy to robotically assisted hysterectomy: surgical outcomes in a community practice. J Minim Invasive Gynecol. 2008; 15:286–291.

27. Lowe MP, Johnson PR, Kamelle SA, Kumar S, Chamberlain DH, Tillmanns TD. A multiinstitutional experience with roboticassisted hysterectomy with staging for endometrial cancer. Obstet Gynecol. 2009; 114(2 Pt 1):236–243.

28. Lim PC, Kang E, Park DH. A comparative detail analysis of the learning curve and surgical outcome for robotic hysterectomy with lymphadenectomy versus laparoscopic hysterectomy with lymphadenectomy in treatment of endometrial cancer: a case-matched controlled study of the first one hundred twenty two patients. Gynecol Oncol. 2011; 120:413–418.

29. Lenihan JP Jr, Kovanda C, Seshadri-Kreaden U. What is the learning curve for robotic assisted gynecologic surgery? J Minim Invasive Gynecol. 2008; 15:589–594.

30. Fanning J, Hojat R, Johnson J, Fenton B. Robotic radical hysterectomy. Minerva Ginecol. 2009; 61:53–55.

31. Bell MC, Torgerson JL, Kreaden U. The first 100 da Vinci hysterectomies: an analysis of the learning curve for a single surgeon. S D Med. 2009; 62:9193–95.

32. Holloway RW, Ahmad S, DeNardis SA, Peterson LB, Sultana N, Bigsby GE 4th, et al. Robotic-assisted laparoscopic hysterectomy and lymphadenectomy for endometrial cancer: analysis of surgical performance. Gynecol Oncol. 2009; 115:447–452.

33. Leitao MM Jr, Briscoe G, Santos K, Winder A, Jewell EL, Hoskins WJ, et al. Introduction of a computer-based surgical platform in the surgical care of patients with newly diagnosed uterine cancer: outcomes and impact on approach. Gynecol Oncol. 2012; 125:394–399.

34. Iranmanesh P, Morel P, Wagner OJ, Inan I, Pugin F, Hagen ME. Set-up and docking of the da Vinci surgical system: prospective analysis of initial experience. Int J Med Robot. 2010; 6:57–60.

35. Dessolle L, Freour T, Barriere P, Jean M, Ravel C, Darai E, et al. How soon can I be proficient in embryo transfer? Lessons from the cumulative summation test for learning curve (LC-CUSUM). Hum Reprod. 2010; 25:380–386.

36. Bazot M, Darai E, Biau DJ, Ballester M, Dessolle L. Learning curve of transvaginal ultrasound for the diagnosis of endometriomas assessed by the cumulative summation test (LC-CUSUM). Fertil Steril. 2011; 95:301–303.

37. Balsyte D, Schaffer L, Burkhardt T, Wisser J, Zimmermann R, Kurmanavicius J. Continuous independent quality control for fetal ultrasound biometry provided by the cumulative summation technique. Ultrasound Obstet Gynecol. 2010; 35:449–455.

38. Papanna R, Biau DJ, Mann LK, Johnson A, Moise KJ Jr. Use of the Learning Curve-Cumulative Summation test for quantitative and individualized assessment of competency of a surgical procedure in obstetrics and gynecology: fetoscopic laser ablation as a model. Am J Obstet Gynecol. 2011; 204:218.e1–218.e9.