An Advanced Knee Simulator Model Can Reproducibly Be Used for Ligament Balancing Training during Total Knee Arthroplasty

Abstract

A critical and difficult aspect of total knee arthroplasty (TKA) is ligamentous balancing for which cadavers and models have played a large role in the education and training of new arthroplasty surgeons, although they both have several shortcomings including cost, scarcity, and dissimilarity to in vivo ligament properties. An advanced knee simulator (AKS) model based on computed tomography (CT) scans was developed in the setting of these challenges with cadavers and previous models. In this study, we compared the ligament balancing between AKS and human cadaveric knees to assess the validity of using the AKS for ligament balancing training during TKA. A CT scan of a TKA patient with varus deformity was used to design the AKS model with modular components, using three-dimensional printing. Three fellowship-trained arthroplasty surgeons used technology-assisted TKA procedure to plan and balance three cadaver knees and the AKS model. Medial and lateral laxity data were captured using manual varus and valgus stress assessments for cadavers and the model in an extension pose (10 degrees of flexion from terminal extension) and between 90 and 95 degrees for flexion. After preresection assessments, surgeons planned a balanced cruciate-retaining TKA. Following bony cuts and trialing, extension and flexion ligament laxity values were recorded in a similar manner. Descriptive statistics and Student's t-tests were performed to compare the cadavers and model with a p-value set at 0.05. Preresection medial/lateral laxity data for both extension and flexion were plotted and showed that the highest standard deviation (SD) for the cadavers was 0.67 mm, whereas the highest SD for the AKS was 1.25 mm. A similar plot for trialing demonstrated that the highest SD for the cadavers was 0.6 mm, whereas the highest SD for the AKS was 0.61 mm. The AKS trialing data were highly reproducible when compared with cadaveric data, demonstrating the value of the AKS model as a tool to teach ligament balancing for TKA and for future research endeavors.

Keywords

ligament balancing - model - cadaver - robotic arthroplasty - total knee arthroplasty

Publication History

Received: 21 March 2024

Accepted: 11 June 2024

Accepted Manuscript online:
12 June 2024

Article published online:
05 July 2024

• References

• 1 Roche M. The MAKO robotic-arm knee arthroplasty system. Arch Orthop Trauma Surg 2021; 141 (12) 2043-2047
• 2 Mitchell J, Wang J, Bukowski B. et al. Relative clinical outcomes comparing manual and robotic-assisted total knee arthroplasty at minimum 1-year follow-up. HSS J 2021; 17 (03) 267-273
• 3 Kolessar DJ, Hayes DS, Harding JL, Rudraraju RT, Graham JH. Robotic-arm assisted technology's impact on knee arthroplasty and associated healthcare costs. J Health Econ Outcomes Res 2022; 9 (02) 57-66
• 4 Joo PY, Chen AF, Richards J. et al. Clinical results and patient-reported outcomes following robotic-assisted primary total knee arthroplasty: a multicentre study. Bone Jt Open 2022; 3 (08) 589-595
• 5 Marchand RC, Bhowmik-Stoker M, Chen Z, Mont MA. Clinical outcomes after computed tomography-based total knee arthroplasty: a surgeon's first 1,000 cases. Surg Technol Int 2022; 40: 391-395
• 6 Marchand KB, Moody R, Scholl LY. et al. Results of robotic-assisted versus manual total knee arthroplasty at 2-year follow-up. J Knee Surg 2023; 36 (02) 159-166
• 7 Elmallah RK, Mistry JB, Cherian JJ. et al. Can we really “feel” a balanced total knee arthroplasty?. J Arthroplasty 2016; 31 (09) 102-105
• 8 Hoerlesberger N, Glehr M, Amerstorfer F, Hauer G, Leithner A, Sadoghi P. Residents' learning curve of total knee arthroplasty based on radiological outcome parameters: a retrospectibve comparative study. J Arthroplasty 2021; 36 (01) 154-159
• 9 Probst T, Akalin ER, Giannouchos A, Schnurr C. Learning curves of robotic technology in an orthopedic teaching hospital. Orthopädie 2022; 51 (09) 739-747
• 10 Griffin FM, Insall JN, Scuderi GR. Accuracy of soft tissue balancing in total knee arthroplasty. J Arthroplasty 2000; 15 (08) 970-973
• 11 Lee DH, Park JH, Song DI, Padhy D, Jeong WK, Han SB. Accuracy of soft tissue balancing in TKA: comparison between navigation-assisted gap balancing and conventional measured resection. Knee Surg Sports Traumatol Arthrosc 2010; 18 (03) 381-387
• 12 Elmasry SS, Sculco PK, Kahlenberg CA. et al. Arthroplasty surgeons differ in their intraoperative soft tissue assessments: a study in human cadavers to quantify surgical decision-making in TKA. Clin Orthop Relat Res 2022; 480 (08) 1604-1615
• 13 Varner C, Dixon L, Simons MC. The past, present, and future: a discussion of cadaver use in medical and veterinary education. Front Vet Sci 2021; 8: 720740
• 14 Scholl L, Caba M, Hampp E. et al. Robotic-assisted total knee arthroplasty technology provides a repeatable and reproducible method of assessing soft tissue balance. J Knee Surg 2024; 37 (08) 607-611
• 15 Ro J, Ro DH, Kang Y, Han HS, Shin CS. Biomechanical effect of coronal alignment and ligament laxity in total knee arthroplasty: a simulation study. Front Bioeng Biotechnol 2022; 10: 851495
• 16 Nielsen ES, Hsu A, Patil S, Colwell Jr CW, D'Lima DD. Second-generation electronic ligament balancing for knee arthroplasty: a cadaver study. J Arthroplasty 2018; 33 (07) 2293-2300