Methods for evaluating effects of unloader knee braces on joint health: a review

Biomechanical Effect of Valgus Knee Braces on the Treatment of Medial Gonarthrosis: A Systematic Review

Background

Valgus braces are prescribed as a common conservative treatment option for patients with medial gonarthrosis to improve their quality of life. Many studies had reviewed the effects of the valgus braces on patients with medial gonarthrosis, while they mainly focused on the knee adduction moment (KAM), with less attention paid to other parameters such as spatiotemporal and morphological parameters.

Objectives

The purpose of this study was to review the effects of valgus braces on the spatiotemporal, kinematic/kinetic, morphological, and muscle parameters.

Methods

Based on the selected keywords, a survey of literatures was performed in Web of Science, PubMed, Scopus, and Google Scholar using the PRISMA methods, and the search period was established from January 2000 to March 2022.

Results

Thirty-four articles were included. According to the conclusion of these articles, the valgus brace can be used to relieve the symptoms of patients with medial gonarthrosis by decreasing the varus angle, decreasing the KAM, and redistributing the knee compartment loads. However, the effects of valgus braces on other biomechanical parameters (e.g., walking speed, cadence, joint angle, and joint space) had not reached a consensus.

Conclusions

The valgus knee brace can effectively relieve the symptoms of medial gonarthrosis through multiple mechanisms, while there is still some confusion about the effectiveness of the valgus brace on the other biomechanical parameters.

Potential Instability and Malfunction of Knee Joints with Vastus Medialis Impairment after Total Knee Arthroplasty

Four pairs of fresh-frozen cadaver knees (eight knees, four male knees) with a mean age of 72 ± 7 years were used for tests involving a customized simulator capable of controlling quadriceps loading conditions. The muscle force distribution of the quadriceps for the normal loading condition was applied on the basis of muscle cross-sectional area data, as previously reported (VM: 31 N; RF/VI: 49 N; VL: 45 N). To simulate vastus medialis (VM) impairment, we set the muscle force for VM in the muscle force distribution of the quadriceps at zero (VM: 0 N; RF/VI: 49 N; VL: 45 N). The joint reaction forces and moments on knee joints that underwent total knee arthroplasty (TKA) did not differ significantly according to VM impairment status for all flexion angles (p > 0.05). Nevertheless, the vectors of internal–external moments mostly showed a tendency for alteration from external to internal due to VM impairment. This tendency was evident in 9 cases in 12 total test pairs (with and without VM impairment). Furthermore, the vectors of the anterior–posterior reaction forces mostly showed a tendency to increase anteriorly due to VM impairment. This tendency was also evident in 9 cases in 12 total test pairs (with and without VM impairment). These results indicate that posterior dislocation of the tibia may be induced if VM impairment occurs after TKA. In conclusion, VM impairment in knee joints undergoing TKA may contribute to posterior dislocation of the tibia by a paradoxical roll-back with enhancements of the anterior joint reaction force and external moment during knee-joint flexion. Our findings may be valuable for understanding the mechanism of potential instability and malfunction due to VM impairment in knee joints after TKA, and may help to optimize clinical/rehabilitation training plans to improve the prognosis (stability and function) of knee joints undergoing TKA.

Non-anatomical placement adversely affects the functional performance of the meniscal implant: a finite element study

Non-anatomical placement may occur during the surgical implantation of the meniscal implant, and its influence on the resulting biomechanics of the knee joint has not been systematically studied. The purpose of this study was to evaluate the biomechanical effects of non-anatomical placement of the meniscal implant on the knee joint during a complete walking cycle. Three-dimensional finite element (FE) analyses of the knee joint were performed, based on the model developed from magnetic resonance images and the loading conditions derived from the gait pattern of a healthy male subject, for the following physiological conditions: (i) knee joint with intact native meniscus, (ii) medial meniscectomized knee joint, (iii) knee joint with anatomically placed meniscal implant, and (iv) knee joint with the meniscal implant placed in four different in vitro determined non-anatomical locations. While the native menisci were modeled using the nonlinear hyperelastic Holzapfel-Gasser-Ogden (HGO) constitutive model, the meniscal implant was modeled using the isotropic hyperelastic neo-Hookean model. Placement of the meniscal implant in the non-anatomical lateral-posterior and lateral-anterior locations significantly increased the peak contact pressure in the medial compartment. Placement of the meniscal implant in non-anatomical locations significantly altered the tibial rotational kinematics and increased the total force acting at the meniscal horns. Results suggest that placement of the meniscal implant in non-anatomical locations may restrain its ability to be chondroprotective and may initiate or accelerate cartilage degeneration. In conclusion, clinicians should endeavor to place the implant as closest as possible to the anatomical location to restore the normal knee biomechanics.

A machine learning-based diagnostic model associated with knee osteoarthritis severity

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student's t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student's t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.