Longitudinal Alterations in Gait Features in Growing Children With Duchenne Muscular Dystrophy

Abstract

Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Three-dimensional gait analysis (3DGA) could provide sensitive parameters to study the efficacy of clinical trials aiming to preserve ambulation. However, quantitative descriptions of the natural history of gait features in DMD are first required. The overall goal was to provide a full delineation of the progressive gait pathology in children with DMD, covering the entire period of ambulation, by performing a so-called mixed cross-sectional longitudinal study. Firstly, to make our results comparable with previous literature, we aimed to cross-sectionally compare 31 predefined gait features between children with DMD and a typically developing (TD) database (1). Secondly, we aimed to explore the longitudinal changes in the 31 predefined gait features in growing boys with DMD using follow-up 3DGA sessions (2). 3DGA-sessions (n = 124) at self-selected speed were collected in 27 boys with DMD (baseline age: 4.6–15 years). They were repeatedly measured over a varying follow-up period (range: 6 months–5 years). The TD group consisted of 27 children (age: 5.4–15.6 years). Per measurement session, the spatiotemporal parameters, and the kinematic and kinetic waveforms were averaged over the selected gait cycles. From the averaged waveforms, discrete gait features (e.g., maxima and minima) were extracted. Mann-Whitney U tests were performed to cross-sectionally analyze the differences between DMD at baseline and TD (1). Linear mixed effect models were performed to assess the changes in gait features in the same group of children with DMD from both a longitudinal (i.e., increasing time) as well as a cross-sectional perspective (i.e., increasing baseline age) (2). At baseline, the boys with DMD differed from the TD children in 17 gait features. Additionally, 21 gait features evolved longitudinally when following-up the same boys with DMD and 25 gait features presented a significant cross-sectional baseline age-effect. The current study quantitatively described the longitudinal alterations in gait features in boys with DMD, thereby providing detailed insight into how DMD gait deteriorates. Additionally, our results highlight that gait features extracted from 3DGA are promising outcome measures for future clinical trials to quantify the efficacy of novel therapeutic strategies.

Population longitudinal analysis of Gait Profile Score and North Star Ambulatory Assessment in children with Duchenne muscular dystrophy

“…Based on these results, 3DGA could be considered as potential biomarker or even surrogate endpoint, in addition to functional endpoints such as 6MWT or NSAA, to enhance detection of treatment effect in clinical trials, particularly for those with more preserved muscle function. From their mixed crosssectional longitudinal study, Vandekerckhove et al 52 reported on the overall gait index GPS, which represents the difference in 16 kinematic parameters between an individual DMD subject and the control group. The increase in GPS confirmed the overall progressive gait pathology seen in children with DMD.…”

“…Vandekerckhove et al 52 . described a mixed cross‐sectional longitudinal study in 27 DMD patients, with a cross‐sectional component (children measured only once) and a longitudinal component (children with repeated measures over time).…”

“…Subject demographics of boys with DMD at baseline from the 3DGA cross-sectional longitudinal study reported by Vandekerckhove et al52 …”

Longitudinal trajectory of medial gastrocnemius muscle growth in the first years of life

“…The workflow to build the linear mixed-effects models as described by Vandekerckhove et al was applied. 22 With the jth observation in child i, for covariate c, the response for the different muscle outcomes was modelled as follows:…”

“…random intercept and random slope respectively. The workflow to build the linear mixed‐effects models as described by Vandekerckhove et al was applied 22 . With the j th observation in child i , for covariate c , the response for the different muscle outcomes was modelled as follows:where α 1 is the intercept, a 1 i the random intercept, β 1 the regression coefficient, b 1 i the random slope, and ε (1) ij the measurement error.…”