应用德尔菲过程客观化肌电图在脑瘫治疗中的系统应用

Robert Reisig, Mehrdad Davoudi, Marco Götze, Firooz Salami, Sebastian Wolf
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摘要

脑瘫(CP)是一种影响运动功能和协调的神经发育障碍。虽然没有治愈的治疗方法,但可以使用手术和保守等各种方法来优化患者的身体表现。[1]治疗计划包括体格检查、影像学和步态分析。[2]尽管肌电图是除体格检查外评估肌肉无力和痉挛的唯一方法,但肌电图数据在决策中的作用却鲜为人知。[3]然而,它可以有效地执行,并可以大大改善治疗决策树。[4]这个德尔菲过程补充了具有相同研究目标的数据驱动方法,以便两者的发现可以集成。肌电图如何增强对CP患者的诊断和治疗方法?我们的目标包括确定推动决策过程的关键肌电数据特征,并确定最合适和最具影响力的数据评估描述符。此外,正在调查目前的利用情况。采用了德尔福程序,由53名专家组成的初步小组进行步态分析。其中44家已同意继续参与该项目。这些专家是根据他们与ESMAC的关系和其他参与者的介绍选出的。在第一轮中,小组成员被问及他们目前或过去在CP患者步态分析中使用肌电图的情况。问题包括有效性、可靠性、评估肌肉、数据处理、涉及肌电图数据的决策过程、规范数据的使用以及用于评估肌电图的描述符。参赛者将收到前几轮的评估结果,并可根据此信息作出决定。第二轮计划于2023年4月底开始。第三轮计划在2023年9月ESMAC之前完成并评估。德尔菲进程目前正在进行中,第一轮已经完成。90%的参与者认为肌电图信息在CP的背景下至少有些帮助,79%的人认为它至少有些可靠。虽然至少32%的参与者完全依赖原始数据,但超过21%的参与者完全使用封装数据。主要用于决策过程的肌肉是股直肌和胫骨前肌。最广泛使用的描述词包括“延迟”、“延长”、“过早”、“收缩”、“异相”、“缺席”、“早期”和“连续”。目前的研究结果表明,在CP的背景下,肌电图数据的有用性和可靠性是主要的共识。同时,数据评估似乎有两种主要的方法——一种使用原始数据,另一种使用信封。在未来的几轮过程中,我们的目标是从专家那里收集基于肌电图数据的治疗决策树——可能是由经验或证据驱动的——并尝试通过纯粹的数据驱动的方法复制这些决策树。
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A Delphi Process is being applied to objectify the systematic use of EMG in therapy of Cerebral Palsy
Cerebral Palsy (CP) is a neurodevelopmental disorder that affects motor function and coordination. While there is no curative treatment, various methods, surgical and conservative, can be used to optimize patients' physical performance. [1] Treatment planning involves physical examination, imaging, and gait analysis. [2] Despite being the only method apart from physical examination to assess muscle weakness and spasticity, the role of EMG data in decision-making is little understood. [3] However, it can be efficient to perform and could substantially improve treatment decision trees. [4] This Delphi Process complements a data driven approach with identical research goals so that findings of both can be integrated. How can EMG enhance diagnostic and therapeutic methods for patients with CP? Our objectives include identifying key EMG data features that advance decision-making processes and determining the most appropriate and impactful descriptors for data evaluation. Additionally, present-day utilization is being investigated. A Delphi Process is being employed, engaging an initial panel of 53 experts in gait analysis. Of these, 44 have agreed to continue their participation in the project. These experts were selected based on their affiliation with ESMAC and referrals from other participants. In the first round, panelists were asked about their current or past use of EMG in gait analysis for patients with CP. Questions covered the topics effectiveness, reliability, assessed muscles, data processing, decision-making processes involving EMG data, use of normative data, and descriptors being used to evaluate EMG. Participants will receive the evaluated results from the previous rounds and may base their decisions on this information. The second round is scheduled to begin by the end of April 2023. The third round is planned for completion and evaluation before ESMAC in September 2023. The Delphi Process is currently underway, and the first round has been completed. 90% of participants found EMG information in the context of CP to be at least somewhat helpful, and 79% considered it at least somewhat reliable. While at least 32% of participants rely solely on raw data, more than 21% solely use enveloped data. The muscles predominantly used for decision processes are rectus femoris and tibialis anterior. Statistic assessed musclesDownload : Download high-res image (86KB)Download : Download full-size image The most widespread descriptors used include 'delayed,' 'prolonged,' 'premature,' 'cocontraction,' 'out of phase,' 'absent,' 'early' and 'continuous. Current results show predominant consensus about helpfulness and reliability of EMG data in the context of CP. Simultaneously, there seem to be two major approaches in data evaluation – one using raw data and the other using envelopes. In future rounds of the process we aim to collect treatment decision trees from experts which are based on EMG data – may they be driven by experience or evidence – and try to replicate these decision trees by purely data driven approaches.
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