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Orthopaedic Proceedings
Vol. 99-B, Issue SUPP_6 | Pages 118 - 118
1 Mar 2017
Zaylor W Halloran J
Full Access

Introduction

Loads acting on the knee are tied to the long term performance of implants, and are directly related to ligament function [1]. Previous work has used computational models coupled with optimization to estimate ligament properties based on experimental joint kinematics [2]. Our group recently utilized a similar optimization scheme that estimated ligament slack lengths based on experimental implant contact metrics [3]. A comparison with surgically relevant loading conditions that were excluded from the optimization would help establish the utility of the simulation framework. Hence, the purpose of this study was to assess the predictive capability of two simulated knees using comparisons with experimentally determined trends found after systematic removal of key tissues. Similar techniques may support clinical joint balancing techniques, as well as identify factors that dictate long term implant performance.

Methods

Knee arthroplasty was performed by orthopedic surgeons for four cadaveric specimens. Instrumented trial inserts (VERASENSE, OrthoSensor, Inc., Dania Beach, FL) were used and experimentation utilized the simVITROTM robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH) to measure tibiofemoral kinematics under interoperative style loading. Three successive laxity style tests were performed at 10° flexion: anterior-posterior force (±100 N), varus-valgus moment (±5 Nm), and internal-external moment (±3 Nm). Kinematics and implant forces were measured throughout testing. Specimens were first tested in the intact state, then the laxity tests were repeated after systematic release of the posterior cruciate ligament (PCL), superficial medial collateral ligament (sMCL), or popliteus (POP). Significant changes in kinematics and contact metrics were determined using regression analysis between the intact versus the tissue released states.

Finite element models were developed for two specimens, and optimized ligament slack lengths were found using methods described previously [3] (Fig. 1). The experimental laxity style loads were applied to both optimized models with intact ligaments, and with individually released PCL, sMCL, or POP ligaments. Knee kinematics and tibial contact loads were predicted, and trended responses from the intact simulations to those with released ligaments were determined (i.e. higher, lower or no change). Simulation results were then compared with the statistically significant findings from the experimental tests.


Orthopaedic Proceedings
Vol. 99-B, Issue SUPP_6 | Pages 119 - 119
1 Mar 2017
Zaylor W Halloran J
Full Access

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 Systèmes). 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).


Orthopaedic Proceedings
Vol. 98-B, Issue SUPP_8 | Pages 19 - 19
1 May 2016
Halloran J Zadzilka J Colbrunn R Bonner T Anderson C Klika A Barsoum W
Full Access

Introduction

Improper soft-tissue balancing can result in postoperative complications after total knee arthroplasty (TKA) and may lead to early revision. A single-use tibial insert trial with embedded sensor technology (VERASENSE from OrthoSensor Inc., Dania Beach, FL) was designed to provide feedback to the surgeon intraoperatively, with the goal to achieve a “well-balanced” knee throughout the range of motion (Roche et al. 2014). The purpose of this study was to quantify the effects of common soft-tissue releases as they related to sensor measured joint reactions and kinematics.

Methods

Robotic testing was performed using four fresh-frozen cadaveric knee specimens implanted with appropriately sized instrumented trial implants (geometry based on a currently available TKA system). Sensor outputs included the locations and magnitudes of medial and lateral reaction forces. As a measure of tibiofemoral joint kinematics, medial and lateral reaction locations were resolved to femoral anterior-posterior displacement and internal-external tibial rotation (Fig 1.). Laxity style joint loading included discrete applications of ± 100 N A-P, ± 3 N/m I-E and ± 5 N/m varus-valgus (V-V) loads, each applied at 10, 45, and 90° of flexion. All tests included 20 N of compressive force. Laxity tests were performed before and after a specified series of soft-tissue releases, which included complete transection of the posterior cruciate ligament (PCL), superficial medial collateral ligament (sMCL), and the popliteus ligament (Table 1). Sensor outputs were recorded for each quasi-static test. Statistical results were quantified using regression formulas that related sensor outputs (reaction loads and kinematics) as a function of tissue release across all loading conditions. Significance was set for p-values ≤ 0.05.


Orthopaedic Proceedings
Vol. 98-B, Issue SUPP_8 | Pages 18 - 18
1 May 2016
Halloran J Colbrunn R Anderson C
Full Access

INTRODUCTION

Understanding the relationship between knee specific tissue behavior and joint contact mechanics remains an area of focus. Seminal work from 1990's established the possibility to optimize tissue properties for recreation of laxity driven kinematics (Mommersteeg et al., 1996). Yet, the uniqueness and validity of such predictions could be strengthened, especially as they relate to joint contact conditions. Understanding this interplay has implications for the long term performance of joint replacements.

Development of instrumented knee implants, highlighted by a single use tibial insert trial with embedded sensor technology (VERASENSE, Orthosensor Inc.), may offer an avenue to establish the relationship between tissue state and joint mechanics. Utilization of related data also has the potential to confirm computational predictions, where both rigid body motions and associated reactions are explicitly accounted for. Hence, the goal of this work was to evaluate an approach for optimization of ligament properties using joint mechanics data from an instrumented implant during laxity style testing. Such a framework could be used to inform joint balancing techniques, improve long term implant performance, and alternatively, qualify factors that may lead to poor outcomes

METHODS

Experimentation was performed on a 52 year old male, left, cadaveric specimen. Joint arthroplasty was performed using standard practice by an experienced orthopedic surgeon. To mimic passive intraoperative loading, laxity loading at 10°, 45° and 90° flexion, which consisted of discrete application of anterior-posterior (± 100N), varus-valgus (± 5 Nm) and internal-external (± 3 Nm) loads at each angle, was performed using a simVITROTM robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH). Experimental results included relative tibiofemoral kinematics and sensor measured metrics (Fig 1).

The finite element model was developed from specimen-specific MRIs and solved using Abaqus/Explicit. The model included the rigid bones, appropriately placed implants and relevant soft-tissue structures (Fig. 1). Ligament stiffness values were adopted from the literature and included a 6% strain toe region. Sets of nonlinear springs, defined using MR imaging, comprised each ligament/bundle. Optimization was performed, which minimized the root mean squared difference between VERASENSE measured tibiofemoral mechanics and the model predicted values. Ligament slack lengths were the control variables and the objective included each loading state and all contact metrics (θ, AFD, ML, and LL).


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_34 | Pages 268 - 268
1 Dec 2013
Colbrunn R Bonner T Barsoum W Halloran J
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Introduction

Experimental testing reproducing activity specific joint-level loading has the potential to quantify structure-function relationships, evaluate intervention possibilities, perform device analysis, and quantify joint kinematics. Many recent technological advancements have been made in this field and inspire this study's aim to present a framework for the application of activity dependent tibiofemoral loading in a specific custom developed 6 degree of freedom (DOF) robotic test frame. This study demonstrates a pipeline wherein kinetic and kinematic data from subjects were collected in a gait lab, analyzed through musculoskeletal modeling techniques, and applied to cadaveric specimens in the robotic testing system in a real-time manner. This pipeline (Figure 1 blue dotted region) fits into a framework for synergistic development and refinement of arthroplasty techniques and devices.

Methods

Gait lab kinetic and kinematic data for walking was collected from 5 subjects. Subject-specific musculoskeletal modeling was performed to determine 6 DOF active component joint loading (OpenSim version 2.4, simtk.org). Kinetic profiles of the stance phase of gait were estimated and experimentally prescribed in a clinically relevant joint coordinate frame (as a function of time). Of note, knee flexion angle was the only kinematically applied DOF in the robotic testing system. Six fresh-frozen left cadaveric knee specimens (3 male, 3 female, age 49–70) were acquired. The specimens were rigidly secured to the robotic Universal Musculoskeletal Simulator (UMS) custom testing apparatus [1], which controlled joint loads with a real-time force feedback controller. Joint loads were scaled to 40% of predicted loads determined through modeling, because of system load capacity limitations and to prevent joint soft tissue damage potentially caused by additional loads without active muscle constraints. The loading profile for the walking activity was applied to each of the knees and the resulting kinematics were recorded. In addition, the force feedback controller performance was evaluated by calculating the root-mean-square (RMS) error between the desired and actual loads throughout these dynamic loading profiles.