Linking Joint Impairment and Gait Biomechanics in Patients with Juvenile Idiopathic Arthritis

Abstract

Juvenile Idiopathic Arthritis (JIA) is a paediatric musculoskeletal disease of unknown aetiology, leading to walking alterations when the lower-limb joints are involved. Diagnosis of JIA is mostly clinical. Imaging can quantify impairments associated to inflammation and joint damage. However, treatment planning could be better supported using dynamic information, such as joint contact forces (JCFs). To this purpose, we used a musculoskeletal model to predict JCFs and investigate how JCFs varied as a result of joint impairment in eighteen children with JIA. Gait analysis data and magnetic resonance images (MRI) were used to develop patient-specific lower-limb musculoskeletal models, which were evaluated for operator-dependent variability (< 3.6°, 0.05 N kg^-1 and 0.5 BW for joint angles, moments, and JCFs, respectively). Gait alterations and JCF patterns showed high between-subjects variability reflecting the pathology heterogeneity in the cohort. Higher joint impairment, assessed with MRI-based evaluation, was weakly associated to overall joint overloading. A stronger correlation was observed between impairment of one limb and overload of the contralateral limb, suggesting risky compensatory strategies being adopted, especially at the knee level. This suggests that knee overloading during gait might be a good predictor of disease progression and gait biomechanics should be used to inform treatment planning.

Keywords: Biomechanics; Gait analysis; Juvenile arthritis; Lower-limb; MRI; Musculoskeletal; Musculoskeletal modelling; Opensim.

Figure 1

Experimental markers used in the stereophotogrammetric protocol (filled and empty dots) and retained during the imaging (filled dots) and relevant description.

Figure 2

Outline of the repeatability study.

Figure 3

Repeatability of the model output: example of mean and SD (shadow) over three walking trials of hip, knee and ankle JCFs for one model (left and right side in red and black, respectively) built by the same operator three times (a) and three different operators (c). Ranges of variation of JCFs for (b) intra-operator and (d) inter-operator analysis

Figure 4

Comparison (non-parametric 1D t test in SPM) between joint angles, moments, powers (Abs = absorbed, Gen = generated) and contact forces of the IM (fuchsia) and NI (grey) groups over the gait cycle. Vertical dotted lines represent the instant in which toe off occurs and black bars identify the regions of the gait cycle where statistical significance was meet (p < 0.05).

Figure 5

Radar plot visualisation of the JCF and JP parameters normalised using robust z score. * = BI group significantly different from MI; ^† = BI group significantly different from NI.

Figure 6

Spearman’s ρ non-parametric correlation between: (a) the MRI_Index of a single limb and the biomechanical parameters of the same limb, (b) the MRI_Index of a single limb and the biomechanical parameters of the contralateral limb, (c) the total MRI_Index and the sum of JCF peaks (JCF_Index) of the two limbs. Dashed black lines represent linear regression fitting; ρ and p are the correlation coefficient and statistical significance, respectively.