PRE-OPERATIVE GAIT PATTERNS PREDICT UNICONDYLAR KNEE REPLACEMENT CANDIDATES THAT ULTIMATELY RECEIVED TOTAL KNEE REPLACEMENTS

Abstract

The objective of this study was to determine if pre-operative gait patterns could predict which patients selected for unicondylar knee replacement (UKR) actually received a UKR or a total knee replacement (TKR). At the time of surgery, ten of the twenty-two UKR candidates presented with extensive degenerative changes and received total knee replacements. We analyzed gait, radiographic, and anthropometric data with a pattern recognition technique designed to detect biomechanical differences between the two groups. The groups were indistinguishable clinically, and radiographically, yet the pattern recognition technique identified features that completely separated the two groups based on the biomechanical differences.

The objective of this study was to determine if pre-operative gait patterns could predict which patients selected for UKR actually received a UKR or a TKR.

The UKR and TKR groups were indistinguishable visibly, clinically, and radiographically, yet the pattern recognition technique employed in this analysis identified features that completely separated the two groups.

Biomechanical differences between the pre-operative groups could lead to more accurate diagnosis of unicompartmental knee OA as well as further understanding of the pathomechanics of knee OA.

Twenty-two patients were initially diagnosed with unicompartmental knee OA of the medial side, and prescribed to receive unicompartmental knee replacements (UKR). At the time of surgery, ten of the twenty-two UKR candidates presented with more extensive degenerative changes and received total knee replacements (TKR). We measured gait data including knee joint angles forces and moments, velocity, stride length, stance percentage, and stance time as well as body mass index. Furthermore radiographic measures were taken including the Hip Knee Ankle (HKA) angle, the standing knee flexion angle, and the medial and lateral condyle joint spaces.

The data were analysed using a pattern recognition technique that used principal component analysis to extract features from the data and discriminant analysis to separate the two groups.

The discriminant function completely separated the UKR and TKR patients based on their pre-operative data. The most discriminatory feature represented a difference in early swing phase in the knee internal rotation moments.