Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA.
Neurorehabil Neural Repair. 2011 Jun;25(5):458-68. doi: 10.1177/1545968310390222. Epub 2011 Feb 8.
The link between spasticity and impaired voluntary movement after stroke remains unclear because of the lack of suitable tools characterizing properties of spastic muscles. Describing this relationship early poststroke can potentially help predict the extent and time course of recovery.
To describe the time course of changes in neuromuscular properties after stroke using the upper extremity Fugl-Meyer Assessment (FMA) at 1 month to predict recovery patterns over 1 year.
Using a parallel cascade system identification technique, this study characterized intrinsic and reflex behaviors for different mean elbow joint angles, at specified times poststroke. Then the "growth mixture" model was used to characterize recovery patterns over 1 year. Logistic regression analyses were applied to predict these patterns. The impact of patient characteristics was also investigated.
In 21 stroke survivors, 14 had sustained hemorrhage and 7 had thromboses. The study observed several recovery classes, relating intrinsic and reflex stiffness magnitudes with changing elbow angle at different time points. The largest group (48%) showed progressive increase in reflex stiffness over time, but 33% showed declining reflex stiffness over the same period. A third class (19%) showed invariant reflex properties. These differences were linked to the initial reflex magnitudes. The FMA at 1 month showed an inverse relationship with key reflex patterns and proved to be a strong predictor of class membership. Stroke type was also influential.
The logistical regression class may enable us to accurately predict reflex responses during the first year, allowing us to apportion impairment between central and peripheral mechanisms.
由于缺乏能够描述痉挛肌肉特性的合适工具，痉挛与卒中后运动功能受损之间的联系仍不清楚。早期描述这种关系有助于预测恢复的程度和时间进程。
使用上肢 Fugl-Meyer 评估（FMA）在 1 个月时描述卒中后神经肌肉特性的变化时间过程，以预测 1 年内的恢复模式。
本研究使用并行级联系统识别技术，在特定时间点描述不同平均肘关节角度的固有和反射行为。然后使用“增长混合”模型来描述 1 年内的恢复模式。应用逻辑回归分析来预测这些模式。还研究了患者特征的影响。
在 21 名卒中幸存者中，14 人发生持续性出血，7 人发生血栓形成。该研究观察到几个恢复类别，与不同时间点改变的肘部角度相关的固有和反射刚度的大小。最大的一组（48%）表现出反射刚度随时间的逐渐增加，但同期有 33%的反射刚度下降。第三组（19%）表现出不变的反射特性。这些差异与初始反射幅度有关。1 个月时的 FMA 与关键反射模式呈负相关，并证明是分类成员的有力预测指标。卒中类型也有影响。
逻辑回归分类可能使我们能够准确预测第一年的反射反应，从而将损伤分配到中枢和外周机制。
