A statistically-augmented computational platform for evaluating meniscal function

Abstract

Meniscal implants have been developed in an attempt to provide pain relief and prevent pathological degeneration of articular cartilage. However, as yet there has been no systematic and comprehensive analysis of the effects of the meniscal design variables on meniscal function across a wide patient population, and there are no clear design criteria to ensure the functional performance of candidate meniscal implants. Our aim was to develop a statistically-augmented, experimentally-validated, computational platform to assess the effect of meniscal properties and patient variables on knee joint contact mechanics during the activity of walking. Our analysis used Finite Element Models (FEMs) that represented the geometry, kinematics as based on simulated gait and contact mechanics of three laboratory tested human cadaveric knees. The FEMs were subsequently programmed to represent prescribed meniscal variables (circumferential and radial/axial moduli - Ecm, Erm, stiffness of the meniscal attachments - Slpma, Slamp) and patient variables (varus/valgus alignment – VVA, and articular cartilage modulus - Ec). The contact mechanics data generated from the FEM runs were used as training data to a statistical interpolator which estimated joint contact data for untested configurations of input variables. Our data suggested that while Ecm and Erm of a meniscus are critical in determining knee joint mechanics in early and late stance (peak 1 and peak 3 of the gait cycle), for some knees that have greater laxity in the mid-stance phase of gait, the stiffness of the articular cartilage, Ec, can influence force distribution across the tibial plateau. We found that the medial meniscus plays a dominant load-carrying role in the early stance phase and less so in late stance, while the lateral meniscus distributes load throughout gait. Joint contact mechanics in the medial compartment are more sensitive to Ecm than those in the lateral compartment. Finally, throughout stance, varus-valgus alignment can overwhelm these relationships while the stiffness of meniscal attachments in the range studied have minimal effects on the knee joint mechanics. In summary, our statistically-augmented, computational platform allowed us to study how meniscal implant design variables (which can be controlled at the time of manufacture or implantation) interact with patient variables (which can be set in FEMs but cannot controlled in patient studies) to affect joint contact mechanics during the activity of simulated walking.

Meniscus mechanics and mechanobiology

“…Several studies also utilized FE techniques to examine the effect of knee joint geometry and tissue properties on joint mechanics: Khoshgoftar et al (2015) and Párraga Quiroga et al (2015) analyzed the effect of meniscus implant size and shape on joint mechanics, Łuczkiewicz et al (2015) studied the effect of meniscal shape in the cross-sectional plane on knee joint mechanics, and Guo et al (2015) used a statistically augmented model to study the effect of meniscal design variables (such as tensile modulus in the radial and circumferential directions) and patient variables (such as differences in knee joint alignment and articular cartilage properties) on joint behavior. Such models were also used to quantify the changes in mechanical loading of the chondrocytes in meniscectomized knees .…”

“…Such models were also used to quantify the changes in mechanical loading of the chondrocytes in meniscectomized knees . These studies highlight the unique information that can be obtained regarding the functional performance of the meniscus via modeling, while emphasizing the need to compare the output data to experimentally derived data to provide confidence in the models (Guo et al, 2015).…”

Reducing uncertainty when using knee‐specific finite element models by assessing the effect of input parameters

“…A total of 201 FE runs were made at 21 input vectors that were as space‐filling as possible, based off a Latin hypercube design in nine space. An additional 180 inputs was identified for which the FE models were also run to form a grid across the design space . Once the FE model derived training data was input to the kriging predictor, it was used to assess the range of each output for Condition 1 inputs and Condition 2 inputs.…”

“…The locations of the input vectors formed a Latin hypercube design (LHD) in nine space which, additionally, maximized the minimum run interpoint distance among all LHDs . Secondly, FE runs were made at an additional 180 inputs that formed a grid . In sum, 201 FE models were run for each knee.…”

Shape of chondrocytes within articular cartilage affects the solid but not the fluid microenvironment under unconfined compression

“…In future studies, the multiscale modeling approach of the current study can also be used to design new tissue engineering constructs. By changing the material properties of the construct the model is able to mimic the cellular microenvironment within the extracellular matrix, such that a statistically-augmented finite element approach [52, 53] can then be used to systematically investigate the influence of the material properties of the tissue construct on the cellular microenvironment.…”