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Characterizing the learning curve of a virtual intracorporeal suturing simulator VBLaST-SS©

  • 2019 SAGES Oral
  • Published:
Surgical Endoscopy Aims and scope Submit manuscript

Abstract

Background

The virtual basic laparoscopic skill trainer suturing simulator (VBLaST-SS©) was developed to simulate the intracorporeal suturing task in the FLS program. The purpose of this study was to evaluate the training effectiveness and participants’ learning curves on the VBLaST-SS© and to assess whether the skills were retained after 2 weeks without training.

Methods

Fourteen medical students participated in the study. Participants were randomly assigned to two training groups (7 per group): VBLaST-SS© or FLS, based on the modality of training. Participants practiced on their assigned system for one session (30 min or up to ten repetitions) a day, 5 days a week for three consecutive weeks. Their baseline, post-test, and retention (after 2 weeks) performance were also analyzed. Participants’ performance scores were calculated based on the original FLS scoring system. The cumulative summation (CUSUM) method was used to evaluate learning. Two-way mixed factorial ANOVA was used to compare the effects of group, time point (baseline, post-test, and retention), and their interaction on performance.

Results

Six out of seven participants in each group reached the predefined proficiency level after 7 days of training. Participants’ performance improved significantly (p < 0.001) after training within their assigned group. The CUSUM learning curve shows that one participant in each group achieved 5% failure rate by the end of the training period. Twelve out of fourteen participants’ CUSUM curves showed a negative trend toward achieving the 5% failure rate after further training.

Conclusion

The VBLaST-SS© is effective in training laparoscopic suturing skill. Participants’ performance of intracorporeal suturing was significantly improved after training on both systems and was retained after 2 weeks of no training.

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References

  1. Rogers SO, Gawande AA, Kwaan M, Puopolo AL, Yoon C, Brennan TA, Studdert DM (2006) Analysis of surgical errors in closed malpractice claims at 4 liability insurers. Surgery 140:25–33. https://doi.org/10.1016/j.surg.2006.01.008

    Article  PubMed  Google Scholar 

  2. Stefanidis D, Montero P, Urbach DR, Qureshi A, Perry K, Bachman SL, Madan A, Petersen R, Pryor AD (2014) SAGES research agenda in gastrointestinal and endoscopic surgery: updated results of a Delphi study. Surg Endosc 28:2763–2771. https://doi.org/10.1007/s00464-014-3535-5

    Article  PubMed  Google Scholar 

  3. Vassiliou MC, Dunkin BJ, Marks JM, Fried GM (2010) FLS and FES: comprehensive models of training and assessment. Surg Clin North Am 90:535–558

    Article  PubMed  Google Scholar 

  4. Scott DJ, Dunnington GL (2008) The new ACS/APDS skills curriculum: moving the learning curve out of the operating room. J Gastrointest Surg 12:213–221. https://doi.org/10.1007/s11605-007-0357-y

    Article  PubMed  Google Scholar 

  5. Fried GM, Feldman LS, Vassiliou MC, Fraser SA, Stanbridge D, Ghitulescu G, Andrew CG (2004) Proving the value of simulation in laparoscopic surgery. Ann Surg 240:518–528. https://doi.org/10.1097/01.sla.0000136941.46529.56

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fried GM (2008) FLS assessment of competency using simulated laparoscopic tasks. J Gastrointest Surg 12:210–212. https://doi.org/10.1007/s11605-007-0355-0

    Article  PubMed  Google Scholar 

  7. Peters JH, Fried GM, Swanstrom LL, Soper NJ, Sillin LF, Schirmer B, Hoffman K, the SAGES FLS Committee (2004) Development and validation of a comprehensive program of education and assessment of the basic fundamentals of laparoscopic surgery. Surgery 135:21–27. https://doi.org/10.1016/S0039-6060(03)00156-9

    Article  PubMed  Google Scholar 

  8. Qi D, Panneerselvam K, Ahn W, Arikatla V, Enquobahrie A, De S (2017) Virtual interactive suturing for the fundamentals of laparoscopic surgery (FLS). J Biomed Inform 75:48–62. https://doi.org/10.1016/j.jbi.2017.09.010

    Article  PubMed  PubMed Central  Google Scholar 

  9. Anton NE, Sawyer JM, Korndorffer JR, DuCoin CG, McRary G, Timsina LR, Stefanidis D (2018) Developing a robust suturing assessment: validity evidence for the intracorporeal suturing assessment tool. Surgery 163:560–564. https://doi.org/10.1016/j.surg.2017.10.029

    Article  PubMed  Google Scholar 

  10. Stefanidis D, Hope WW, Korndorffer JR, Markley S, Scott DJ (2010) Initial laparoscopic basic skills training shortens the learning curve of laparoscopic suturing and is cost-effective. J Am Coll Surg 210:436–440. https://doi.org/10.1016/j.jamcollsurg.2009.12.015

    Article  PubMed  Google Scholar 

  11. Mattar SG, Alseidi AA, Jones DB, Jeyarajah DR, Swanstrom LL, Aye RW, Wexner SD, Martinez JM, Ross SB, Awad MM, Franklin ME, Arregui ME, Schirmer BD, Minter RM (2013) General surgery residency inadequately prepares trainees for fellowship: results of a survey of fellowship program directors. Ann Surg 258:440–447. https://doi.org/10.1097/SLA.0b013e3182a191ca

    Article  PubMed  Google Scholar 

  12. Chellali A, Zhang L, Sankaranarayanan G, Arikatla VS, Ahn W, Derevianko A, Schwaitzberg SD, Jones DB, DeMoya M, Cao CGL (2014) Validation of the VBLaST peg transfer task: a first step toward an alternate training standard. Surg Endosc 28:2856–2862. https://doi.org/10.1007/s00464-014-3538-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Awtrey C, Chellali A, Schwaitzberg S, De S, Jones D, Cao C (2015) Validation of the VBLaST: a virtual peg transfer task in gynecologic surgeons. J Minim Invasive Gynecol 22:1271–1277. https://doi.org/10.1016/j.jmig.2015.07.015

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sankaranarayanan G, Lin H, Arikatla VS, Mulcare M, Zhang L, Derevianko A, Lim R, Fobert D, Cao C, Schwaitzberg SD, Jones DB, De S (2010) Preliminary face and construct validation study of a virtual basic laparoscopic skill trainer. J Laparoendosc Adv Surg Tech 20:153–157. https://doi.org/10.1089/lap.2009.0030

    Article  Google Scholar 

  15. American Educational Research Association, American Psychological Association, National Council on Measurement in Education, Joint Committee on Standards for Educational, Psychological Testing (US) (2014) Standards for educational and psychological testing. Amer Educational Research Assn

  16. Fu Y, Cavuoto L, Qi D, Panneerselvam K, Yang G, Artikala VS, Enquobahrie A, De S, Schwaitzberg SD (2018) Validation of a virtual intracorporeal suturing simulator. Surg Endosc. https://doi.org/10.1007/s00464-018-6531-3

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cook JA, Ramsaya CR, Fayers P (2004) Statistical evaluation of learning curve effects in surgical trials. Clin Trials J Soc Clin Trials 1:421–427. https://doi.org/10.1191/1740774504cn042oa

    Article  Google Scholar 

  18. Cook JA, Ramsay CR, Fayers P (2007) Using the literature to quantify the learning curve: a case study. Int J Technol Assess Health Care 23:255–260. https://doi.org/10.1017/S0266462307070341

    Article  PubMed  Google Scholar 

  19. Khan N, Abboudi H, Khan MS, Dasgupta P, Ahmed K (2014) Measuring the surgical “learning curve”: methods, variables and competency. BJU Int 113:504–508. https://doi.org/10.1111/bju.12197

    Article  PubMed  Google Scholar 

  20. Fraser SA, Klassen DR, Feldman LS, Ghitulescu GA, Stanbridge D, Fried GM (2003) Evaluating laparoscopic skills. Surg Endosc 17:964–967. https://doi.org/10.1007/s00464-002-8828-4

    Article  CAS  PubMed  Google Scholar 

  21. Feldman LS, Cao J, Andalib A, Fraser S, Fried GM (2009) A method to characterize the learning curve for performance of a fundamental laparoscopic simulator task: defining “learning plateau” and “learning rate”. Surgery 146:381–386. https://doi.org/10.1016/j.surg.2009.02.021

    Article  PubMed  Google Scholar 

  22. Diesen DL, Erhunmwunsee L, Bennett KM, Ben-David K, Yurcisin B, Ceppa EP, Omotosho PA, Perez A, Pryor A (2011) Effectiveness of laparoscopic computer simulator versus usage of box trainer for endoscopic surgery training of novices. J Surg Educ 68:282–289. https://doi.org/10.1016/j.jsurg.2011.02.007

    Article  PubMed  Google Scholar 

  23. Linsk AM, Monden KR, Sankaranarayanan G, Ahn W, Jones DB, De S, Schwaitzberg SD, Cao CGL (2018) Validation of the VBLaST pattern cutting task: a learning curve study. Surg Endosc Other Interv Tech 32:1990–2002. https://doi.org/10.1007/s00464-017-5895-0

    Article  Google Scholar 

  24. Fraser SA, Feldman LS, Stanbridge D, Fried GM (2005) Characterizing the learning curve for a basic laparoscopic drill. Surg Endosc Other Interv Tech 19:1572–1578. https://doi.org/10.1007/s00464-005-0150-5

    Article  CAS  Google Scholar 

  25. Zhang L, Sankaranarayanan G, Arikatla VS, Ahn W, Grosdemouge C, Rideout JM, Epstein SK, De S, Schwaitzberg SD, Jones DB, Cao CGL (2013) Characterizing the learning curve of the VBLaST-PT© (Virtual Basic Laparoscopic Skill Trainer). Surg Endosc 27:3603–3615. https://doi.org/10.1007/s00464-013-2932-5

    Article  PubMed  PubMed Central  Google Scholar 

  26. Nemani A, Ahn W, Cooper C, Schwaitzberg S, De S (2018) Convergent validation and transfer of learning studies of a virtual reality-based pattern cutting simulator. Surg Endosc 32:1265–1272. https://doi.org/10.1007/s00464-017-5802-8

    Article  PubMed  Google Scholar 

  27. Stefanidis D, Korndorffer JR, Markley S, Sierra R, Heniford BT, Scott DJ (2007) Closing the gap in operative performance between novices and experts: does harder mean better for laparoscopic simulator training? J Am Coll Surg 205:307–313. https://doi.org/10.1016/j.jamcollsurg.2007.02.080

    Article  PubMed  Google Scholar 

  28. Korndorffer JR, Dunne JB, Sierra R, Stefanidis D, Touchard CL, Scott DJ (2005) Simulator training for laparoscopic suturing using performance goals translates to the operating room. J Am Coll Surg 201:23–29. https://doi.org/10.1016/j.jamcollsurg.2005.02.021

    Article  PubMed  Google Scholar 

  29. Derossis AM, Fried GM, Abrahamowicz M, Sigman HH, Barkun JS, Meakins JL (1998) Development of a model for training and evaluation of laparoscopic skills. Am J Surg 175:482–487. https://doi.org/10.1016/S0002-9610(98)00080-4

    Article  CAS  PubMed  Google Scholar 

  30. McCarter FD, Luchette FA, Molloy M, Hurst JM, Davis K, Johannigman JA, Frame SB, Fischer JE (2000) Institutional and individual learning curves for focused abdominal ultrasound for trauma: cumulative sum analysis. Ann Surg 231:689–700. https://doi.org/10.1097/00000658-200005000-00009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. de Oliveira Filho GR (2002) The construction of learning curves for basic skills in anesthetic procedures: an application for the cumulative sum method. Anesth Analg 95:411–416. https://doi.org/10.1213/00000539-200208000-00033

    Article  PubMed  Google Scholar 

  32. Harrysson IJ, Cook J, Sirimanna P, Feldman LS, Darzi A, Aggarwal R (2014) Systematic review of learning curves for minimally invasive abdominal surgery. Ann Surg 260:37–45. https://doi.org/10.1097/SLA.0000000000000596

    Article  PubMed  Google Scholar 

  33. Vossen C, Van Ballaer P, Shaw RW, Koninckx PR (1997) Effect of training on endoscopic intracorporeal knot tying. Hum Reprod 12:2658–2663. https://doi.org/10.1093/humrep/12.12.2658

    Article  CAS  PubMed  Google Scholar 

  34. Stefanidis D, Korndorffer JR, Heniford BT, Scott DJ (2007) Limited feedback and video tutorials optimize learning and resource utilization during laparoscopic simulator training. Surgery 142:202–206. https://doi.org/10.1016/j.surg.2007.03.009

    Article  PubMed  Google Scholar 

  35. Stefanidis D, Korndorffer JR, Markley S, Sierra R, Scott DJ (2006) Proficiency maintenance: impact of ongoing simulator training on laparoscopic skill retention. J Am Coll Surg 202:599–603. https://doi.org/10.1016/j.jamcollsurg.2005.12.018

    Article  PubMed  Google Scholar 

  36. Jaffer U, Cameron AEP (2008) Laparoscopic appendectomy: a junior trainee’s learning curve. JSLS J Soc Laparoendosc Surg 12:288–291

    Google Scholar 

  37. Yap CH, Colson ME, Watters DA (2007) Cumulative sum techniques for surgeons: a brief review. ANZ J Surg 77:583–586. https://doi.org/10.1111/j.1445-2197.2007.04155.x

    Article  PubMed  Google Scholar 

  38. Waller HM, Connor SJ (2009) Cumulative sum (Cusum) analysis provides an objective measure of competency during training in endoscopic retrograde cholangio-pancreatography (ERCP). Hpb 11:565–569. https://doi.org/10.1111/j.1477-2574.2009.00091.x

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lee YK, Ha YC, Hwang DS, Koo KH (2013) Learning curve of basic hip arthroscopy technique: CUSUM analysis. Knee Surgery Sport Traumatol Arthrosc 21:1940–1944. https://doi.org/10.1007/s00167-012-2241-x

    Article  Google Scholar 

  40. Bartlett A, Parry B (2001) Cusum analysis of trends in operative selection and conversion rates for laparoscopic cholecystectomy. ANZ J Surg 71:453–456. https://doi.org/10.1046/j.1440-1622.2001.02163.x

    Article  CAS  PubMed  Google Scholar 

  41. O’Connor A, Schwaitzberg SD, Cao CGL (2008) How much feedback is necessary for learning to suture? Surg Endosc Other Interv Tech 22:1614–1619. https://doi.org/10.1007/s00464-007-9645-6

    Article  Google Scholar 

  42. Bonrath EM, Weber BK, Fritz M, Mees ST, Wolters HH, Senninger N, Rijcken E (2012) Laparoscopic simulation training: testing for skill acquisition and retention. Surg (United States) 152:12–20. https://doi.org/10.1016/j.surg.2011.12.036

    Article  Google Scholar 

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Acknowledgements

Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Number R44EB019802. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Department of Industrial and System Engineering, University at Buffalo, Buffalo, NY, 14260, USA

    Yaoyu Fu & Lora Cavuoto

  2. UB RIS²E², Department of Surgery, University at Buffalo, Buffalo, NY, USA

    Lora Cavuoto & Steven D. Schwaitzberg

  3. Center for Modeling, Simulation and Imaging in Medicine (CeMSIM), Rensselaer Polytechnic Institute, Troy, NY, USA

    Di Qi, Karthikeyan Panneerselvam & Suvranu De

  4. Medical Computing Group, Kitware, Inc., Carrboro, NC, USA

    Venkata Sreekanth Arikatla & Andinet Enquobahrie

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  1. Yaoyu Fu
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Correspondence to Lora Cavuoto.

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Disclosures

Dr. Schwaitzberg has no relevant conflicts related to this manuscript and is a consultant for Activ Surgical, Human Extensions, Arch Therapeutics, Acuity Bio, and Nu View Surgical.Yaoyu Fu , Lora Cavuoto, Di Qi, Karthikeyan Panneerselvam, Venkata Sreekanth Arikatla, Andinet Enquobahrie, and Suvranu De have no conflicts of interest or financial ties to disclose.

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Fu, Y., Cavuoto, L., Qi, D. et al. Characterizing the learning curve of a virtual intracorporeal suturing simulator VBLaST-SS©. Surg Endosc 34, 3135–3144 (2020). https://doi.org/10.1007/s00464-019-07081-6

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