Quantifying the competing relationship between durability and kinematics of total knee replacements using multiobjective design optimization and validated computational models

Durability and kinematics are two critical factors which must be considered during total knee replacement (TKR) implant design. It is hypothesized, however, that there exists a competing relationship between these two performance measures, such that improvement of one requires sacrifice with respect to the other. No previous studies have used rigorous and systematic methods to quantify this relationship. During this study, multiobjective design optimization (MOO) using the adaptive weighted sum (AWS) method is used to determine a set of Pareto-optimal implant designs considering durability and kinematics simultaneously. Previously validated numerical simulations and a parametric modeller are used in conjunction with the AWS method in order to generate a durability-versus-kinematics Pareto curve. In terms of kinematics, a design optimized for kinematics alone outperformed a design optimized for durability by 61.8%. In terms of durability, the design optimized for durability outperformed the kinematics-optimized design by 70.6%. Considering the entire Pareto curve, a balanced (1:1) trade-off could be obtained when equal weighting was placed on both performance measures; however improvement of one performance measure required greater sacrifices with respect to the other when the weighting was extremized. For the first time, the competing relationship between durability and kinematics was confirmed and quantified using optimization methods. This information can aid future developments in TKR design and can be expanded to other total joint replacement designs.