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Aims

Functional alignment (FA) in total knee arthroplasty (TKA) aims to achieve balanced gaps by adjusting implant positioning while minimizing changes to constitutional joint line obliquity (JLO). Although FA uses kinematic alignment (KA) as a starting point, the final implant positions can vary significantly between these two approaches. This study used the Coronal Plane Alignment of the Knee (CPAK) classification to compare differences between KA and final FA positions.

Methods

A retrospective analysis compared pre-resection and post-implantation alignments in 2,116 robotic-assisted FA TKAs. The lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) were measured to determine the arithmetic hip-knee-ankle angle (aHKA = MPTA – LDFA), JLO (JLO = MPTA + LDFA), and CPAK type. The primary outcome was the proportion of knees that varied ≤ 2° for aHKA and ≤ 3° for JLO from their KA to FA positions, and direction and magnitude of those changes per CPAK phenotype. Secondary outcomes included proportion of knees that maintained their CPAK phenotype, and differences between sexes.


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.