Probabilistic computational modeling of total knee replacement wear

Polyethylene wear remains a clinically relevant issue affecting total knee replacement (TKR) performance, with considerable variability observed in both clinical retrieval and experimental wear studies. Recently, computational wear simulations have been shown to predict similar results to in vitro and retrieval studies. The objectives of this study were to develop a probabilistic wear prediction model capable of incorporating uncertainty in component alignment, constraint and environmental conditions, to compare computational predictions with experimental results from a knee wear simulator, and to identify the most significant parameters affecting predicted wear performance during simulated gait. The current study utilizes a previously verified wear model; the Archardʹs law-based wear formulation represents a composite measure, incorporating the effects and relative contributions of kinematics and contact pressure. Predicted wear was in reasonable agreement in trend and magnitude with experimental results. After 5 million cycles, the predicted ranges (1–99%) of variability in linear wear penetration and gravimetric wear were 0.13 mm and 25 mg, respectively, for the input variability levels evaluated. Using correlation-based sensitivity factors, the coefficient of friction, insert tilt and femoral flexion–extension alignment, and the wear coefficient were identified as the parameters most affecting predicted wear. Comparisons of stability, accuracy and efficiency for the Monte Carlo and advanced mean value (AMV) probabilistic methods are also described. The probabilistic wear prediction model provides a time and cost efficient framework to evaluate wear performance, including considerations of malalignment and variability, during the design phase of new implants.