Computer simulations of knee movement allow Total Knee Arthroplasty (TKA) dynamic outcomes to be studied. This study aims to build a model predicting patient reported outcome from a simulation of post-operative TKA joint dynamics.
Landmark localisation was performed on 239 segmented pre-operative computerized tomography (CT) scans to capture patient specific soft tissue attachments. The pre-operative bones and 3D implant files were registered to post-operative CT scans following TKA. Each post-operative knee was simulated undergoing a deep knee bend with assumed ligament balancing of the extension space. The kinematic results from this simulation were used in a Multivariate Adaptive Regression Spline algorithm, predicting attainment of a Patient Acceptable Symptom State (PASS) score in captured 12 month post-operative Knee Injury and Osteoarthritis Outcome Scores (KOOS). An independent series of 250 patients was evaluated by the predictive model to assess how the predictive model behaved in a pre-operative planning context.
The generated predictive algorithm, called the Dynamic Knee Score (DKS) contained features, in order of significance, related to tibio-femoral force, patello-femoral motion and tibio-femoral motion. Area Under the Curve for predicting attainment of the PASS KOOS Score was 0.64. The predictive model produced a bimodal spread of predictions, reflecting a tendency to either strongly prefer one alignment plan over another or be ambivalent.
A predictive algorithm relating patient reported outcome to the outputs of a computational simulation of a deep knee bend has been derived (the DKS). Simulation outcomes related to tibio-femoral balance had the highest correlation with patient reported outcome.