Abstract
Introduction
The intrinsic constraint of a total knee replacement (TKR) implant system is considered an important characteristic which plays a large role in determining stability following surgery. Established techniques for evaluating the constraint of TKR implants, as described in ASTM F 1223-14, do not necessarily map directly to physiologically relevant loading scenarios where instability can occur, and thus give an incomplete picture of the constraint characteristics of a candidate implant design. Sophisticated joint motion simulators now allow for more physiologically representative joint loading (eg. gait), including the contributions of virtual soft tissues. In this study, we employ a function-based constraint measurement technique for evaluating the kinematics of two TKR designs during gait. Furthermore, we employ simulated soft tissues in order to create three “virtual” knees on which the TKR are tested.
Methods
The constraint characteristics of TKR implants were evaluated using a function-based measurement technique on a VIVO joint motion simulator (AMTI, Waltham, MA). The AVG75 standardized load and motion profiles for gait (Bergmann et al. 2014), were applied to an ultra-congruent cruciate-sacrificing TKR (Zimmer-Biomet, Warsaw, IN). Ligaments were simulated as point-to-point spring elements between the femur and tibia (3 bundles for MCL, 3 bundles for LCL). Ligament bundle origin, insertion, stiffness, and resting length properties were adapted from the publically available MB Knee project (simtk.org/home/mb_knee) to create three knees. AP and IE kinematics were recorded during simulated gait after approximately 500 “learning” cycles at 0.75 Hz. Trials were then repeated with superimposed AP forces or IE torques. The amount of superimposed load varied with the amount of compressive load, such that the superimposed load was ±25 N AP force or ±1 Nm IE torque, per 1000 N of compressive force. AP and IE laxities were calculated based on changes in AP and IE motions, respectively (Fig 1). Experiments were repeated with a second TKR design; using the same femoral component but replacing the ultra-congruent UHMWPE bearing with a 3D printed ABS plastic bearing featuring a less congruent sagittal profile. In total, there were 2 implants × 3 virtual knees × 5 simulated loading profiles = 30 different simulated gait trials.
Results
The baseline (normal gait) AP and IE motions for both TKR designs, averaged across three knees, are shown in Fig. 2. The average AP and IE laxities for each knee are shown in Table 1, with results averaged for each TKR design.
Discussion
Differences in AP motion between the two TKR designs are large compared to the differences in IE motion. Predictably, the overall AP and IE motions and average laxities for the less congruent TKR are greater. While this trend was generally consistent across all knees, the actual differences in laxities between the two TKR designs varied between knees. This suggests that the importance of the intrinsic constraint of TKR varies on subject-to-subject basis, and thus variable soft tissue stabilization models should be considered during pre-clinical testing.
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