Background

Recovery of hand function after stroke represents the hardest target for clinicians. Robot-assisted therapy has been proved to be effective for hand recovery. Nevertheless, studies aimed to refer patients to the best therapy are missing.

Methods

With the aim to identify which clinical features are predictive for referring to robot-assisted hand therapy, 174 stroke patients were assessed with: Fugl-Meyer Assessment (FMA), Functional Independence Measure (FIM), Reaching Performance Scale (RPS), Box and Block Test (BBT), Modified Ashworth Scale (MAS), Nine Hole Pegboard Test (NHPT). Moreover, patients ability to control the robot with residual force and surface EMG (sEMG) independently, was checked. ROC curves were calculated to determine which of the measures were the predictors of the event.

Results

sEMG control (AUC = 0.925) was significantly determined by FMA upper extremity (FMUE) (>24/66) and sensation (>23/24) sections, MAS at Flexor Carpi (<3/4) and total MAS (>4/20). Force control (AUC = 0.928) was correlated only with FMUE (>24/66).

Conclusions

FMUE and MAS were the best predictors of preserved ability to control the device by two different modalities. This finding opens the possibility to plan specific therapies aimed at maximizing the highest functional outcome achievable after stroke.

中文翻译：

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卒中后机器人辅助手功能的康复：开发预测模型以供参考

背景

中风后手功能的恢复代表临床医生最困难的目标。机器人辅助疗法已被证明对手部康复有效。然而，缺少旨在使患者接受最佳治疗的研究。

方法

为了确定哪些临床特征可预测机器人辅助手部治疗，对174名卒中患者进行了以下评估：Fugl-Meyer评估（FMA），功能独立性量度（FIM），伸手可及性量表（RPS），Box和方块测试（BBT），改良的Ashworth量表（MAS），九孔钉板测试（NHPT）。此外，检查了患者用残余力和表面肌电图（sEMG）独立控制机器人的能力。计算ROC曲线以确定哪些度量是事件的预测因子。

结果

sEMG对照（AUC = 0.925）由FMA上肢（FMUE）（> 24/66）和感觉（> 23/24）切片，屈肌腕（<3/4）的MAS和总MAS（> 4 / 20）。力控制（AUC = 0.928）仅与FMUE（> 24/66）相关。

结论

FMUE和MAS是保留通过两种不同方式控制设备的能力的最佳预测指标。这一发现为计划最大程度地中风后可获得的最高功能结局的特定疗法提供了可能性。