Predicting motor recovery in tetraplegia during inpatient rehabilitation by motor unit action potentials and stimulated manual motor testing

通过运动单位动作电位和刺激性手动运动测试预测四肢瘫痪患者住院康复期间的运动功能恢复

阅读:2

Abstract

STUDY DESIGN: Diagnostic Study. OBJECTIVES: Early prognosis for recovery in traumatic cervical spinal cord injury resulting in tetraplegia may further guide rehabilitation and surgical interventions. This study assesses the feasibility and potential of using stimulated manual motor testing (SMMT) and needle electromyography (EMG) to predict gains in strength during acute inpatient rehabilitation. SETTING: Single academic inpatient rehabilitation facility (IRF). METHODS: Muscles with weak strength (manual motor test (MMT) <3) were assessed for lower motor neuron (LMN) integrity by SMMT using surface electrodes. Muscles without clinical strength (MMT=0) using SMMT and EMG. Correlations and prognostic models assessed the association and prediction of these measures with improvement in MMT values over 4 weeks. RESULTS: The missing data rate for SMMT and motor unit action potential (MUAP) testing was 9.5% and 24%, respectively. Wilcoxon Rank Sum tests of 4-week MMT changes with MUAP presence (P = 3.89×10(-6)) and SMMT improvement (P = 0.0156) were statistically significant, but the Spearman Rank Correlation Coefficient of changes in SMMT with MMT changes was not (P = 0.817). The receiver operating characteristic (ROC) Area Under the Curve (AUC) for combined MUAP and SMMT predictors of MMT improvement was 0.732, with an optimal sensitivity of 41.9% (95% CI 25.8% to 58.1%) and specificity of 90.3% (95% CI 84.5% to 96.1%). This model was superior to univariate models. CONCLUSIONS: With pragmatic compromises in test administration to reduce attrition, measuring the presence of voluntary MUAP and improvement in SMMT during acute rehabilitation retains value in predicting motor improvement in 4 weeks.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。