Biomechanical markers associations with pain, symptoms, and disability compared to radiographic severity in knee osteoarthritis patients: a secondary analysis from a cluster randomized controlled trial.

BACKGROUND: Conventional radiography is commonly used to diagnose knee osteoarthritis (OA), but also to guide clinical decision-making, despite a well-established discordance between radiographic severity and patient symptoms. The incidence and progression of OA is driven, in part, by biomechanical markers. Therefore, these dynamic markers may be a good metric of functional status and actionable targets for clinicians when developing conservative treatment plans. The aim of this study was to assess the associations between biomechanical markers and self-reported knee function compared to radiographic severity. METHODS: This was a secondary analysis of data from a randomized controlled trial (RCT) conducted in primary care clinics with knee OA participants. Correlation coefficients (canonical (ρ) and structural (Corr)) were assessed between the Knee Injury and Osteoarthritis Outcome Score (KOOS) and both, radiographic OA severity using the Kellgren-Lawrence grade, and three-dimensional biomechanical markers quantified by a knee kinesiography exam. Significant differences between coefficients were assessed using Fischer's z-transformation method to compare correlations from dependent samples. RESULTS: KOOS and biomechanical data were significantly more associated than KOOS and X-ray grading (ρ: 0.41 vs 0.20; p < 0.001). Structural correlation (Corr) between KOOS and X-ray grade was 0.202 (4% of variance explained), while individual biomechanical markers, such as the flexion during loading, explained up to 14% of KOOS variance (i.e., Corr2). Biomechanical markers showed the strongest associations with Pain and Activity of Daily Living KOOS subscales (both > 36% variance explained), while X-ray grading was most associated with Symptoms subscale (21% explained; all p ≤ 0.001). CONCLUSIONS: Knee biomechanical markers are associated with patient-reported knee function to a greater extent than X-ray grading, but both provide complementary information in the assessment of OA patients. Understanding how dynamic markers relate to function compared to radiographic severity is a valuable step towards precision medicine, allowing clinicians to refine and tailor therapeutic measures by prioritizing and targeting modifiable biomechanical markers linked to pain and function. TRIAL REGISTRATION: Original RCT was approved by the Research Ethics Boards of École de technologie supérieure (H20150505) and Centre hospitalier de l'Université de Montréal (CHUM-CE.14.339), first registered at https://www.isrctn.com/ (ID-ISRCTN16152290) on May 27, 2015.

Graphical-based multivariate analysis for knee joint clinical and kinematic data correlation assessment.

A large amount of data including joint kinematics, joint kinetics, clinical and functional measurements constitutes the clinical gait analysis basis which is a process whereby quantitative gait information are collected to aid in clinical decision-making. Therefore, better understanding the relationship between the biomechanical and clinical data for the knee osteoarthritis (OA) patient is for a relevant importance. It's the purpose of this paper, which aims to analyze and visualize the correlation structure between biomechanical characteristics and clinical symptoms, and thus to provide an additional knowledge from the coupling of these parameters that will be useful for the pathology assessment of knee-joint disease in the end-staged knee OA patients. We perform two multivariate statistical approaches, first, a Canonical Correlation Analysis (CCA) to assess the multivariate association and, second, a graphical- based representation of the multivariate correlation to better understand the association between these multivariate data. Results show the usefulness of using such multivariate approaches to highlight association and specific correlation structure between the features and to extract meaningful information.

A multivariate relationship between the kinematic and clinical parameters of knee osteoarthritis population.

BACKGROUND: Biomechanical and clinical parameters contribute very closely to functional evaluations of the knee joint. To better understand knee osteoarthritis joint function, the association between a set of knee biomechanical data and a set of clinical parameters of an osteoarthritis population (OA) is investigated in this study. METHODS: The biomechanical data used here are a set of characteristics derived from 3D knee kinematic patterns: flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, all determined during gait recording. The clinical parameters include a KOOS questionnaire and the patient's demographic characteristics. Canonical correlation analysis (CCA) is used (1) to evaluate the multivariate relationship between biomechanical data and clinical parameter sets, and (2) to cluster the most correlated parameters. Multivariate models were created within the identified clusters to determine the effect of each parameter's subset on the other. The analyses were performed on a large database containing 166 OA patients. RESULTS: The CCA results showed meaningful correlations that gave rise to three different clusters. Multivariate linear models were found explaining the subjective clinical parameters by evaluating the biomechanical data contained within each cluster. CONCLUSION: The results showed that a multivariate analysis of the clinical symptoms and the biomechanical characteristics of knee joint function allowed a better understanding of their relationships.