OPTIMISATION OF LIGAMENT PROPERTIES USING AN INSTRUMENTED TRIAL KNEE IMPLANT FOR MULTIPLE SPECIMENS

Introduction Joint mechanics and implant performance have been shown to be sensitive to ligament properties [1]. Computational models have helped establish this understanding, where optimization is typically used to estimate ligament properties for recreation of physically measured specimen-specific kinematics [2]. If available, contact metrics from physical tests could be used to improve the robustness and validity of these predictions. Understanding specimen-specific relationships between joint kinematics, contact metrics, and ligament properties could further highlight factors affecting implant survivorship and patient satisfaction. Instrumented knee implants offer a means to measure joint contact data both in-vivo and intra-operatively, and can also be used in a controlled experimental environment. This study extends on previous work presented at ISTA [3], and the purpose here was to evaluate the use of instrumented implant contact metrics during optimization of ligament properties for two specimens. The overarching goal of this work is to inform clinical joint balancing techniques and identify factors that are critical to implant performance. Methods Total knee arthroplasties were performed on 4 (two specimens modeled) cadeveric specimens by an experienced orthopaedic surgeon. An instrumented trial implant (VERASENSE, OrthoSensor, Inc., Dania Beach, FL) was used in place of a standard insert. Experimentation was performed using a simVITROTM controlled robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH) to apply intra-operative style loading and measure tibiofemoral kinematics. Three successive laxity style tests were performed at 10° knee flexion: anterior-posterior force (±100 N), varus-valgus moment (±5 Nm), and internal-external moment (±3 Nm). Tibiofemoral kinematics and instrumented implant contact metrics were measured throughout testing (Fig. 1). Specimen-specific finite element models were developed for two of the tested specimens and solved using Abaqus/Explicit (Dassault Systemes). Relevant ligaments and rigid bone geometries were defined using specimen-specific MRIs. Virtual implantation was achieved using registration and each ligament was modeled as a set of nonlinear elastic springs (Fig. 1). Stiffness values were adopted from the literature [2] while the ligament slack lengths served as control variables during optimization. The objective was to minimize the root mean square difference between VERASENSE measured tibiofemoral contact metrics and the corresponding model results (Fig. 1). Results and Discussion The models for both specimens successfully recreated joint kinematics with average errors less than 4° in rotations, and 3 mm in translations (not shown). Minus a systematic offset in θ for specimen 3, AFD and θ contact kinematics also realized good agreement for both specimens (Fig. 2). Contact forces were generally over-predicted, though both specimens recreated the experimental trends (Fig. 2). The present work shows continued progress towards simulation based tools that can be used for both research and to support the clinical decision making process. A separate ISTA submission presents assessment of these model9s predictive capacity, while future work will evaluate additional specimens, and explore the sensitivity to uncertainties in experimental and modeling parameters. Acknowledgements This work was supported by Orthosensor Inc. For any figures or tables, please contact authors directly (see Info & Metrics tab above).