B Kayani1, S Konan2, J R T Pietrzak2, S S Huq2, J Tahmassebi1, F S Haddad3. 1. Department of Trauma and Orthopaedics, University College Hospital, London, UK and Princess Grace Hospital, London, UK. 2. Department of Trauma and Orthopaedics, University College Hospital and Princess Grace Hospital, London, UK. 3. University College London Hospitals, The Princess Grace Hospital, and The NIHR Biomedical Research Centre at UCLH, London, UK.
Abstract
Aims: The primary aim of this study was to determine the surgical team's learning curve for introducing robotic-arm assisted unicompartmental knee arthroplasty (UKA) into routine surgical practice. The secondary objective was to compare accuracy of implant positioning in conventional jig-based UKA versus robotic-arm assisted UKA. Patients and Methods: This prospective single-surgeon cohort study included 60 consecutive conventional jig-based UKAs compared with 60 consecutive robotic-arm assisted UKAs for medial compartment knee osteoarthritis. Patients undergoing conventional UKA and robotic-arm assisted UKA were well-matched for baseline characteristics including a mean age of 65.5 years (sd 6.8) vs 64.1 years (sd 8.7), (p = 0.31); a mean body mass index of 27.2 kg.m2 (sd 2.7) vs 28.1 kg.m2 (sd 4.5), (p = 0.25); and gender (27 males: 33 females vs 26 males: 34 females, p = 0.85). Surrogate measures of the learning curve were prospectively collected. These included operative times, the Spielberger State-Trait Anxiety Inventory (STAI) questionnaire to assess preoperative stress levels amongst the surgical team, accuracy of implant positioning, limb alignment, and postoperative complications. Results: Robotic-arm assisted UKA was associated with a learning curve of six cases for operating time (p < 0.001) and surgical team confidence levels (p < 0.001). Cumulative robotic experience did not affect accuracy of implant positioning (p = 0.52), posterior condylar offset ratio (p = 0.71), posterior tibial slope (p = 0.68), native joint line preservation (p = 0.55), and postoperative limb alignment (p = 0.65). Robotic-arm assisted UKA improved accuracy of femoral (p < 0.001) and tibial (p < 0.001) implant positioning with no additional risk of postoperative complications compared to conventional jig-based UKA. Conclusion: Robotic-arm assisted UKA was associated with a learning curve of six cases for operating time and surgical team confidence levels but no learning curve for accuracy of implant positioning. Cite this article: Bone Joint J 2018;100-B:1033-42.
Aims: The primary aim of this study was to determine the surgical team's learning curve for introducing robotic-arm assisted unicompartmental knee arthroplasty (UKA) into routine surgical practice. The secondary objective was to compare accuracy of implant positioning in conventional jig-based UKA versus robotic-arm assisted UKA. Patients and Methods: This prospective single-surgeon cohort study included 60 consecutive conventional jig-based UKAs compared with 60 consecutive robotic-arm assisted UKAs for medial compartment knee osteoarthritis. Patients undergoing conventional UKA and robotic-arm assisted UKA were well-matched for baseline characteristics including a mean age of 65.5 years (sd 6.8) vs 64.1 years (sd 8.7), (p = 0.31); a mean body mass index of 27.2 kg.m2 (sd 2.7) vs 28.1 kg.m2 (sd 4.5), (p = 0.25); and gender (27 males: 33 females vs 26 males: 34 females, p = 0.85). Surrogate measures of the learning curve were prospectively collected. These included operative times, the Spielberger State-Trait Anxiety Inventory (STAI) questionnaire to assess preoperative stress levels amongst the surgical team, accuracy of implant positioning, limb alignment, and postoperative complications. Results: Robotic-arm assisted UKA was associated with a learning curve of six cases for operating time (p < 0.001) and surgical team confidence levels (p < 0.001). Cumulative robotic experience did not affect accuracy of implant positioning (p = 0.52), posterior condylar offset ratio (p = 0.71), posterior tibial slope (p = 0.68), native joint line preservation (p = 0.55), and postoperative limb alignment (p = 0.65). Robotic-arm assisted UKA improved accuracy of femoral (p < 0.001) and tibial (p < 0.001) implant positioning with no additional risk of postoperative complications compared to conventional jig-based UKA. Conclusion: Robotic-arm assisted UKA was associated with a learning curve of six cases for operating time and surgical team confidence levels but no learning curve for accuracy of implant positioning. Cite this article: Bone Joint J 2018;100-B:1033-42.
Authors: Ricardo Larrainzar-Garijo; Elisa M Molanes-López; Miguel Cañones-Martín; David Murillo-Vizuete; Natalia Valencia-Santos; Raul Garcia-Bogalo; Fernando Corella-Montoya Journal: Indian J Orthop Date: 2022-06-22 Impact factor: 1.033
Authors: Nana O Sarpong; Carl L Herndon; Michael B Held; Alexander L Neuwirth; Thomas R Hickernell; Jeffrey A Geller; H John Cooper; Roshan P Shah Journal: Curr Rev Musculoskelet Med Date: 2020-12
Authors: K Sekiguchi; S Nakamura; S Kuriyama; K Nishitani; H Ito; Y Tanaka; M Watanabe; S Matsuda Journal: Bone Joint Res Date: 2019-04-02 Impact factor: 5.853