Linking Joint Impairment and Gait Biomechanics in Patients with Juvenile Idiopathic Arthritis
Montefiori, Erica; Modenese, Luca; Di Marco, Roberto; Magni-Manzoni, Silvia; Malattia, Clara; Petrarca, Maurizio; Ronchetti, Anna; de Horatio, Laura Tanturri; van Dijkhuizen, Pieter; Wang, Anqi; Wesarg, Stefan; Viceconti, Marco; Mazza, Claudia
(2019) Annals of Biomedical Engineering, volume 47, issue 11, pp. 2155 - 2167
(Article)
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
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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.
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Keywords: Biomechanics, Gait analysis, Juvenile arthritis, Lower-limb, MRI, Musculoskeletal, Musculoskeletal modelling, Opensim, Biomedical Engineering
ISSN: 0090-6964
Publisher: Springer Netherlands
Note: Funding Information: The authors would like to acknowledge Dr Norman Powell for the writing assistance, and Mr Giorgos Marinou and Mr Michael Woodward for their contribution to data processing. This research was supported by the European Commission (MD-PAEDIGREE project, FP7-ICT Programme, Project ID: 600932), the UK EPSRC (Multisim project, Grant Number: EP/K03877X/1) and the NIHR Sheffield Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care (DHSC). Data used to build the models will be made publicly available as on-line material on Figshare (10.15131/shef.data.6237146). The authors declare that they do not have any financial or personal relationship with other people or organisations that could have inappropriately influenced this study. Funding Information: The authors would like to acknowledge Dr Norman Powell for the writing assistance, and Mr Giorgos Marinou and Mr Michael Woodward for their contribution to data processing. This research was supported by the European Commission (MD-PAEDIGREE project, FP7-ICT Programme, Project ID: 600932), the UK EPSRC (Multisim project, Grant Number: EP/K03877X/1) and the NIHR Sheffield Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care (DHSC). Data used to build the models will be made publicly available as on-line material on Figshare (https://doi.org/10.15131/shef.da ta.6237146). Publisher Copyright: © 2019, The Author(s).
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