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.

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).

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.

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).

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.