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Are there different gait profiles in patients with advanced knee osteoarthritis? A machine learning approach
Clinical Biomechanics ( IF 1.8 ) Pub Date : 2021-08-14 , DOI: 10.1016/j.clinbiomech.2021.105447
Gustavo Leporace 1 , Felipe Gonzalez 2 , Leonardo Metsavaht 1 , Marcelo Motta 3 , Felipe P Carpes 4 , Jorge Chahla 5 , Marcus Luzo 6
Affiliation  

Background

Determine whether knee kinematics features analyzed using machine-learning algorithms can identify different gait profiles in knee OA patients.

Methods

3D gait kinematic data were recorded from 42 patients (Kellgren-Lawrence stages III and IV) walking barefoot at individual maximal gait speed (0.98 ± 0.34 m/s). Principal component analysis, self-organizing maps, and k-means were applied to the data to identify the most relevant and discriminative knee kinematic features and to identify gait profiles.

Findings

Four different gait profiles were identified and clinically characterized as type 1: gait with the knee in excessive varus and flexion (n = 6, 14%, increased knee adduction and increased maximum and minimum knee flexion, p < 0.01); type 2: gait with knee external rotation, either in varus or valgus (n = 11, 26%, excessive maximum and minimum external rotation, p < 0.001); type 3: gait with a stiff knee (n = 17, 40%, decreased knee flexion range of motion, p < 0.001); and type 4: gait with knee varus ‘thrust’ and decreased rotation (n = 8, 19%, increased and reduced range of motion in the coronal and transverse plane, respectively, p < 0.05).

Interpretation

In a group of patients with homogeneous Kellgren-Lawrence classification of knee OA, gait kinematics data permitted to identify four different gait profiles. These gait profiles can be a valuable tool for helping surgical decisions and treatment. To allow generalization, further studies should be carried with a larger and heterogeneous population.



中文翻译:

晚期膝骨关节炎患者的步态特征是否不同?机器学习方法

背景

确定使用机器学习算法分析的膝关节运动学特征是否可以识别膝关节 OA 患者的不同步态特征。

方法

记录了 42 名患者(Kellgren-Lawrence III 期和 IV 期)以个人最大步态速度(0.98 ± 0.34 m/s)赤脚行走的 3D 步态运动学数据。将主成分分析、自组织图和 k-means 应用于数据,以识别最相关和最具辨别力的膝关节运动学特征并识别步态轮廓。

发现

四种不同的步态特征被确定并在临床上被定性为 1 型:膝关节过度内翻和屈曲的步态(n = 6, 14%,膝关节内收增加,膝关节最大和最小屈曲增加,p  < 0.01);2型:膝关节外旋步态,内翻或外翻(n = 11, 26%,最大和最小外旋过度,p  < 0.001);3 型:膝关节僵硬的步态(n = 17, 40%,膝关节屈曲活动范围减小,p  < 0.001);和 4 型:膝内翻“推力”和减少旋转的步态(n = 8, 19%,分别增加和减少冠状面和横切面的运动范围,p  < 0.05)。

解释

在一组具有同质 Kellgren-Lawrence 膝关节 OA 分类的患者中,步态运动学数据允许识别四种不同的步态轮廓。这些步态曲线可以成为帮助手术决策和治疗的宝贵工具。为了进行概括,应在更大且异质的人群中进行进一步的研究。

更新日期:2021-08-15
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