Ploeger Hilde E, Bus Sicco A, Nollet Frans, Brehm Merel-Anne
Department of Rehabilitation, Academic Medical Center, University of Amsterdam, The Netherlands.
Department of Rehabilitation, Academic Medical Center, University of Amsterdam, The Netherlands.
Gait Posture. 2017 Oct;58:146-153. doi: 10.1016/j.gaitpost.2017.07.107. Epub 2017 Jul 23.
The objective was to identify gait patterns in polio survivors with calf muscle weakness and associate them to underlying lower extremity impairments, which are expected to help in the search for an optimal orthosis. Unilaterally affected patients underwent barefoot 3D-gait analyses. Gait pattern clusters were created based on the ankle and knee angle and ankle moment shown in midstance of the affected limb. Impairment clusters were created based on plantarflexor and knee-extensor strength, and ankle and knee joint range-of-motion. The association between gait patterns and underlying impairments were examined descriptively. The Random Forest Algorithm and regression analyses were used to predict gait patterns and parameters. Seven gait patterns in 73 polio survivors were identified, with two dominant patterns: one with a mildly/non-deviant ankle angle, ankle moment and knee angle (n=23), and one with a strongly deviant ankle angle and a mildly/non-deviant ankle moment and knee angle (n=18). Gait pattern prediction from underlying impairments was 49% accurate with best prediction performance for the second dominant gait pattern (sensitivity 78% and positive predictive value 74%). The underlying impairments explained between 20 and 32% of the variance in individual gait parameters. Polio survivors with calf muscle weakness who present a similar impairment profile do not necessarily walk the same. From physical examination alone, the gait pattern nor the individual gait parameters could be accurately predicted. The patient's gait should therefore be measured to help in the prescription and evaluation of orthoses for these patients.
目的是识别小腿肌肉无力的脊髓灰质炎幸存者的步态模式,并将其与潜在的下肢损伤相关联,这有望有助于寻找最佳矫形器。单侧受影响的患者接受了赤足三维步态分析。根据患侧肢体站立中期的踝关节和膝关节角度以及踝关节力矩创建步态模式聚类。根据跖屈肌和膝关节伸肌力量以及踝关节和膝关节活动范围创建损伤聚类。对步态模式与潜在损伤之间的关联进行了描述性研究。使用随机森林算法和回归分析来预测步态模式和参数。在73名脊髓灰质炎幸存者中识别出七种步态模式,其中两种为主导模式:一种踝关节角度、踝关节力矩和膝关节角度轻度/无偏差(n = 23),另一种踝关节角度严重偏差,踝关节力矩和膝关节角度轻度/无偏差(n = 18)。根据潜在损伤对步态模式的预测准确率为49%,对第二种主导步态模式的预测性能最佳(敏感性78%,阳性预测值74%)。潜在损伤解释了个体步态参数中20%至32%的方差。小腿肌肉无力且损伤情况相似的脊髓灰质炎幸存者行走方式不一定相同。仅通过体格检查,无法准确预测步态模式或个体步态参数。因此,应该测量患者的步态,以帮助为这些患者开具矫形器并进行评估。