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Learning curve for active robotic total knee arthroplasty

Knee Surgery, Sports Traumatology, Arthroscopy volume 30, pages 2666–2676 (2022)Cite this article

Abstract

Purpose

Total Knee Arthroplasty (TKA) procedures incorporate technology in an attempt to improve outcomes. The Active Robot (ARo) performs a TKA with automated resections of the tibia and femur in efforts to optimize bone cuts. Evaluating the Learning Curve (LC) is essential with a novel tool. The purpose of this study was to assess the associated LC of ARo for TKA.

Methods

A multi-center prospective FDA cohort study was conducted from 2017 to 2018 including 115 patients that underwent ARo. Surgical time of the ARo was defined as Operative time (OT), segmented as surgeon-dependent time (patient preparation and registration) and surgeon-independent time (autonomous bone resection by the ARo). An average LC for all surgeons was computed. Complication rates and patient-reported outcome (PRO) scores were recorded and examined to evaluate for any LC trends in these patient related factors.

Results

The OT for the cases 10–12 were significantly quicker than the OT time of cases 1–3 (p < 0.028), at 36.5 ± 7.4 down from 49.1 ± 17 min. CUSUM and confidence interval analysis of the surgeon-dependent time showed different LCs for each surgeon, ranging from 12 to 19 cases. There was no difference in device related complications or PRO scores over the study timeframe.

Conclusion

Active Robotic total knee arthroplasty is associated with a short learning curve of 10–20 cases. The learning curve was associated with the surgical time dedicated to the robotic specific portion of the case. There was no learning curve-associated device-related complications, three-dimensional component position, or patient-reported outcome scores.

Level of evidence

Level II.

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References

  1. Berndt K, Rahm S, Dora C, Zingg PO (2019) Total hip arthroplasty with accolade/trident through the direct minimally invasive anterior approach without traction table: learning curve and results after a minimum of 5 years. Orthop Traumatol Surg Res 105:931–936

    Article  Google Scholar 

  2. Boylan M, Suchman K, Vigdorchik J, Slover J, Bosco J (2018) Technology-assisted hip and knee arthroplasties: an analysis of utilization trends. J Arthroplasty 33:1019–1023

    Article  Google Scholar 

  3. Camp CL, Krych AJ, Stuart MJ, Regnier TD, Mills KM, Turner NS (2016) Improving resident performance in knee arthroscopy: a prospective value assessment of simulators and cadaveric skills laboratories. J Bone Joint Surg Am 98:220–225

    Article  Google Scholar 

  4. Cannon WD, Garrett WE Jr, Hunter RE, Sweeney HJ, Eckhoff DG, Nicandri GT et al (2014) Improving residency training in arthroscopic knee surgery with use of a virtual-reality simulator. A randomized blinded study. J Bone Joint Surg Am 96:1798–1806

    Article  Google Scholar 

  5. Christ AB, Pearle AD, Mayman DJ, Haas SB (2018) Robotic-assisted unicompartmental knee arthroplasty: state-of-the art and review of the literature. J Arthroplasty 33:1994–2001

    Article  Google Scholar 

  6. de Steiger RN, Lorimer M, Solomon M (2015) What is the learning curve for the anterior approach for total hip arthroplasty? Clin Orthop Relat Res 473:3860–3866

    Article  Google Scholar 

  7. Ezzibdeh RM, Barrett AA, Arora P, Amanatullah DF (2020) Learning curve for the direct superior approach to total hip arthroplasty. Orthopedics. https://doi.org/10.3928/01477447-20200404-051-7

    Article  PubMed  Google Scholar 

  8. Geller JA, Rossington A, Mitra R, Jaramaz B, Khare R, Netravali NA. Rate of learning curve and alignment accuracy of an image-free handheld robot for total knee arthroplasty. Abstract number O44 presented at: European Knee Society; May 2–3, 2019; Valencia, Spain

  9. Gofton WT, Papp SR, Gofton T, Beaule PE (2016) Understanding and taking control of surgical learning curves. Instr Course Lect 65:623–631

    PubMed  Google Scholar 

  10. Gofton WT, Solomon M, Gofton T, Page A, Kim PR, Netting C et al (2016) What do reported learning curves mean for orthopaedic surgeons? Instr Course Lect 65:633–643

    PubMed  Google Scholar 

  11. Hartford JM, Bellino MJ (2017) The learning curve for the direct anterior approach for total hip arthroplasty: a single surgeon’s first 500 cases. Hip Int 27:483–488

    Article  Google Scholar 

  12. Jacofsky DJ, Allen M (2016) Robotics in arthroplasty: a comprehensive review. J Arthroplasty 31:2353–2363

    Article  Google Scholar 

  13. Jeon SW, Kim KI, Song SJ (2019) Robot-assisted total knee arthroplasty does not improve long-term clinical and radiologic outcomes. J Arthroplasty 34:1656–1661

    Article  Google Scholar 

  14. Kaper B (2020) Learning curve and time commitment assessment in the adoption of NAVIO robotic-assisted total knee arthroplasty. Orthopaedic proceedings. J Bone Joint Surg Br 102(Suppl 1):59

    Article  Google Scholar 

  15. Kayani B, Konan S, Huq SS, Tahmassebi J, Haddad FS (2019) Robotic-arm assisted total knee arthroplasty has a learning curve of seven cases for integration into the surgical workflow but no learning curve effect for accuracy of implant positioning. Knee Surg Sports Traumatol Arthrosc 27:1132–1141

    Article  Google Scholar 

  16. Kayani B, Konan S, Pietrzak JRT, Huq SS, Tahmassebi J, Haddad FS (2018) The learning curve associated with robotic-arm assisted unicompartmental knee arthroplasty: a prospective cohort study. Bone Joint J 100-B:1033–1042

    CAS  Article  Google Scholar 

  17. Keggi J, Plaskos C (2017) Learning curve and early patient satisfaction of robotic-assisted total knee arthroplasty. Orthopaedic proceedings. J Bone Joint Surg Br 99(Suppl 4):59–59

    Google Scholar 

  18. Liow MHL, Chin PL, Pang HN, Tay DK, Yeo SJ (2017) THINK surgical TSolution-One((R)) (Robodoc) total knee arthroplasty. SICOT J 3:63

    Article  Google Scholar 

  19. Lonner JH, Fillingham YA (2018) Pros and cons: a balanced view of robotics in knee arthroplasty. J Arthroplasty 33:2007–2013

    Article  Google Scholar 

  20. Mabrey JD, Reinig KD, Cannon WD (2010) Virtual reality in orthopaedics: is it a reality? Clin Orthop Relat Res 468:2586–2591

    Article  Google Scholar 

  21. Smith AF, Eccles CJ, Bhimani SJ, Denehy KM, Bhimani RB, Smith LS et al (2019) Improved patient satisfaction following robotic-assisted total knee arthroplasty. J Knee Surg. https://doi.org/10.1055/s-0039-1700837

    Article  PubMed  Google Scholar 

  22. Sodhi N, Khlopas A, Piuzzi NS, Sultan AA, Marchand RC, Malkani AL et al (2018) The learning curve associated with robotic total knee arthroplasty. J Knee Surg 31:17–21

    Article  Google Scholar 

  23. Stulberg B, Zadzilka J, Kreuzer SW, Long W, Kissin Y, Liebelt RA, Zuhars J (2020) Results of an active robotic total knee arthroplasty us trial. Orthopaedic proceedings. J Bone Joint Surg Br 102(Suppl 2):9

    Google Scholar 

  24. Vermue H, Luyckx T, Winnock de Grave P, Ryckaert A, Cools AS, Himpe N, Victor J (2020) Robot-assisted total knee arthroplasty is associated with a learning curve for surgical time but not for component alignment, limb alignment and gap balancing. Knee Surg Sports Traumatol Arthrosc. https://doi.org/10.1007/s00167-020-06341-6

    Article  PubMed  Google Scholar 

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Funding

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Authors and Affiliations

  1. Orthopaedic Surgery Resident, Department of Orthopaedic Surgery, New York University, Langone Orthopaedic Hospital, 301 East 17th Street, New York, NY, 10003, USA

    Siddharth A. Mahure & William J. Long

  2. Insall Scott Kelly Institute, 260 East 66th Street, 1st Floor, New York, NY, 10065, USA

    Greg Michael Teo & William J. Long

  3. Hackensack University Medical Center, Hackensack, NJ, 07601, USA

    Yair D. Kissin

  4. St. Vincent Charity Medical Center, Cleveland, OH, 44115, USA

    Bernard N. Stulberg

  5. Memorial Bone and Joint Research Foundation, Houston, TX, 77043, USA

    Stefan Kreuzer

Authors
  1. Siddharth A. Mahure
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  2. Greg Michael Teo
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  3. Yair D. Kissin
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  4. Bernard N. Stulberg
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  5. Stefan Kreuzer
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  6. William J. Long
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Corresponding author

Correspondence to Siddharth A. Mahure.

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Conflicts of interest

The authors that they have no conflict of interest.

Ethics approval

Institutional review board (IRB) approval was obtained to conduct a multi-center, prospective, non-randomized US investigational device exemption clinical trial to evaluate the safety and effectiveness of robotic-assisted TKA using the TSolution One Total Knee Application (THINK Surgical).

Informed consent

Written informed consent was obtained from all study participants after IRB approval for the study.

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Mahure, S.A., Teo, G.M., Kissin, Y.D. et al. Learning curve for active robotic total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 30, 2666–2676 (2022). https://doi.org/10.1007/s00167-021-06452-8

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  • DOI: https://doi.org/10.1007/s00167-021-06452-8

Keywords

  • Learning curve
  • Robotic
  • Autonomous
  • Safety
  • Active