Abstract
Introduction:
There is substantial range in kinematics and joint loading in the total knee arthroplasty (TKA) patient population. Prospective TKA designs should be evaluated across the spectrum of loading conditions observed in vivo. Recent research has implanted telemetric tibial trays into TKA patients and measured loads at the tibiofemoral (TF) joint [1]. However, the number of patients for which telemetric data is available is limited and restricts the variability in loading conditions to a small subset of those which may be encountered in vivo. However, there is a substantial amount of fluoroscopic data available from numerous TKA patients and component designs [2]. The purpose of this study was to develop computational simulations which incorporate population-based variability in loading conditions derived from in vivo fluoroscopy, for eventual use in computational as well as experimental activity models.
Methods:
Fluoroscopic kinematic data was obtained during squat for several patients with fixed bearing and rotating platform (RP) components. Anterior-posterior (A-P) and internal-external (I-E) motions of the TF joint were extracted from full extension to maximum flexion. Joint compressive loading was estimated using an inverse-dynamics approach. Previously-developed computational models of the knee, lower limb, and Kansas knee simulator were virtually implanted with the same design as the fluoroscopy patients. A control system was integrated with the computational models such that external loading at the hip and ankle were determined in order to reproduce the measured in vivo motions and compressive load (Fig. 1). Accuracy of the model in matching the in vivo motions was assessed, in addition to the resulting joint A-P and I-E loading.
The external loading determined for a broader range of patients can subsequently be utilized in a force-controlled simulation to assess the robustness of implant concepts to patient loading variability. The applicability of this work as a comparative tool was illustrated by assessing the kinematics of two PS RP designs under three patient-specific loading conditions.
Results:
External hip and ankle loading conditions were determined for each computational model that reproduced in vivo A-P, I-E and flexion-extension joint motions and estimated compressive load. For example, RMS accuracy of 0.4 mm, 0.2° and 0.7° were achieved for A-P, I-E and flexion, respectively (Fig. 1, 2). There was good agreement in both trend and magnitude of joint loads predicted from the externally-loaded models compared to telemetric measurements. Comparative analysis of two designs under multiple loading conditions illustrated variability in joint mechanics as a result of design factors and variation between subjects for the same design (Fig. 3).
Discussion:
Pre-clinical evaluation of new devices under physiological joint loading conditions is crucial to robust functionality across the TKA population. The loads applied to a TKA system will affect fixation, wear, and functional performance. Harnessing in vivo kinematic data to develop population-based loading profiles will facilitate development of a platform for comprehensive design-phase evaluation of prospective designs. In addition, loading conditions for experimental simulators can be developed in order to test new devices under the range of variability likely to be encountered in vivo.
