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The Bone & Joint Journal
Vol. 105-B, Issue 1 | Pages 35 - 46
1 Jan 2023
Mills K Wymenga AB Bénard MR Kaptein BL Defoort KC van Hellemondt GG Heesterbeek PJC

Aims. The aim of this study was to compare a bicruciate-retaining (BCR) total knee arthroplasty (TKA) with a posterior cruciate-retaining (CR) TKA design in terms of kinematics, measured using fluoroscopy and stability as micromotion using radiostereometric analysis (RSA). Methods. A total of 40 patients with end-stage osteoarthritis were included in this randomized controlled trial. All patients performed a step-up and lunge task in front of a monoplane fluoroscope one year postoperatively. Femorotibial contact point (CP) locations were determined at every flexion angle and compared between the groups. RSA images were taken at baseline, six weeks, three, six, 12, and 24 months postoperatively. Clinical and functional outcomes were compared postoperatively for two years. Results. The BCR-TKA demonstrated a kinematic pattern comparable to the natural knee’s screw-home mechanism in the step-up task. In the lunge task, the medial CP of the BCR-TKA was more anterior in the early flexion phase, while laterally the CP was more posterior during the entire movement cycle. The BCR-TKA group showed higher tibial migration. No differences were found for the clinical and functional outcomes. Conclusion. The BCR-TKA shows a different kinematic pattern in early flexion/late extension compared to the CR-TKA. The difference between both implants is mostly visible in the flexion phase in which the anterior cruciate ligament is effective; however, both designs fail to fully replicate the motion of a natural knee. The higher migration of the BCR-TKA was concerning and highlights the importance of longer follow-up. Cite this article: Bone Joint J 2023;105-B(1):35–46


The Bone & Joint Journal
Vol. 98-B, Issue 10_Supple_B | Pages 16 - 21
1 Oct 2016
Jones GG Kotti M Wiik AV Collins R Brevadt MJ Strachan RK Cobb JP

Aims

To compare the gait of unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) patients with healthy controls, using a machine-learning approach.

Patients and Methods

145 participants (121 healthy controls, 12 patients with cruciate-retaining TKA, and 12 with mobile-bearing medial UKA) were recruited. The TKA and UKA patients were a minimum of 12 months post-operative, and matched for pattern and severity of arthrosis, age, and body mass index.

Participants walked on an instrumented treadmill until their maximum walking speed was reached. Temporospatial gait parameters, and vertical ground reaction force data, were captured at each speed. Oxford knee scores (OKS) were also collected. An ensemble of trees algorithm was used to analyse the data: 27 gait variables were used to train classification trees for each speed, with a binary output prediction of whether these variables were derived from a UKA or TKA patient. Healthy control gait data was then tested by the decision trees at each speed and a final classification (UKA or TKA) reached for each subject in a majority voting manner over all gait cycles and speeds. Top walking speed was also recorded.