Gong Ze, Lo Wai Leung Ambrose, Wang Ruoli, Li Le
Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.
Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China.
Front Aging Neurosci. 2023 Mar 16;15:1130230. doi: 10.3389/fnagi.2023.1130230. eCollection 2023.
Aging is a non-modifiable risk factor for stroke and the global burden of stroke is continuing to increase due to the aging society. Muscle dysfunction, common sequela of stroke, has long been of research interests. Therefore, how to accurately assess muscle function is particularly important. Electrical impedance myography (EIM) has proven to be feasible to assess muscle impairment in patients with stroke in terms of micro structures, such as muscle membrane integrity, extracellular and intracellular fluids. However, EIM alone is not sufficient to assess muscle function comprehensively given the complex contributors to paretic muscle after an insult. This article discusses the potential to combine EIM and other common quantitative methods as ways to improve the assessment of muscle function in stroke survivors. Clinically, these combined assessments provide not only a distinct advantage for greater accuracy of muscle assessment through cross-validation, but also the physiological explanation on muscle dysfunction at the micro level. Different combinations of assessments are discussed with insights for different purposes. The assessments of morphological, mechanical and contractile properties combined with EIM are focused since changes in muscle structures, tone and strength directly reflect the muscle function of stroke survivors. With advances in computational technology, finite element model and machine learning model that incorporate multi-modal evaluation parameters to enable the establishment of predictive or diagnostic model will be the next step forward to assess muscle function for individual with stroke.
衰老作为中风的一个不可改变的风险因素,且由于社会老龄化,全球中风负担持续增加。肌肉功能障碍是中风常见的后遗症,长期以来一直是研究热点。因此,如何准确评估肌肉功能尤为重要。电阻抗肌电图(EIM)已被证明在评估中风患者肌肉损伤的微观结构方面是可行的,如肌膜完整性、细胞外液和细胞内液。然而,鉴于中风后瘫痪肌肉的复杂影响因素,仅靠EIM不足以全面评估肌肉功能。本文探讨了将EIM与其他常见定量方法相结合的可能性,以此作为改善中风幸存者肌肉功能评估的方法。临床上,这些联合评估不仅通过交叉验证在提高肌肉评估准确性方面具有显著优势,还能在微观层面提供肌肉功能障碍的生理学解释。针对不同目的,讨论了不同的评估组合及其见解。重点讨论了形态学、力学和收缩特性评估与EIM的结合,因为肌肉结构、张力和力量的变化直接反映了中风幸存者的肌肉功能。随着计算技术的进步,纳入多模态评估参数以建立预测或诊断模型的有限元模型和机器学习模型将是下一步评估中风个体肌肉功能的发展方向。