Choisne Julie, Fourrier Nicolas, Handsfield Geoffrey, Signal Nada, Taylor Denise, Wilson Nichola, Stott Susan, Besier Thor F
Auckland Bioengineering Institute, University of Auckland, 70 Symonds street, Auckland 1010, New Zealand.
Léonard de Vinci Pôle Universitaire, Research Center, 92 916 Paris La Défense, France.
J Clin Med. 2020 May 12;9(5):1432. doi: 10.3390/jcm9051432.
Ankle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children's 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children's gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription.
踝足矫形器常用于给患有脑瘫（CP）的儿童开具处方。尚不清楚三维步态分析（3DGA）是否能为临床医生在开具矫形器处方时提供足够且可靠的信息，以确保一致性。数据驱动建模可以通过揭示诸如3DGA参数与矫形器使用的步态结果等变量之间的非直观关系来探究此类问题。本研究的目的是：（1）开发一种数据驱动模型，根据患有CP的儿童的步态生物力学对其进行分类；（2）确定矫形器类型与步态模式之间的关系。从25名发育正常的儿童和98名患有CP且额外开具了矫形器的儿童的步行试验中获取3DGA数据。开发了一种无监督自组织映射，随后进行k均值聚类，以根据儿童的3DGA对不同的步态模式进行分组。模型输入是从步态轮廓评分中提取的步态变量得分（GVSs），测量与发育正常儿童步态周期的均方根差异。该模型识别出了五种病理步态模式，其GVSs存在统计学差异。只有43%的儿童在佩戴矫形器时改善了步态模式。即使是步态模式相似的儿童，矫形器处方也存在差异。本研究表明，定量数据驱动方法可能会为支持矫形器处方提供更清晰和具体的信息。
