识别难以从混合辅助肢体步态训练中获益的中风患者的特征:一项回顾性队列研究

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2024-02-08 DOI:10.3389/fnbot.2024.1336812
Shingo Taki, Takeshi Imura, Tsubasa Mitsutake, Yuji Iwamoto, Ryo Tanaka, Naoki Imada, Hayato Araki, Osamu Araki
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引用次数: 0

摘要

机器人辅助步态训练对中风康复中的独立行走很有效,混合辅助肢体(HAL)就是一个例子。然而,使用 HAL 进行步态训练并非对每个人都有效,而且目前还不清楚哪些人无法从中获益。因此,我们旨在确定难以从使用 HAL 进行步态训练中获益的中风患者的特征。我们进行了一项单一机构的回顾性队列研究。研究对象是 82 名在住院期间接受过 HAL 步态训练的脑卒中患者。因变量是衡量脑卒中患者步态独立性的功能性行走类别(FAC),五个自变量(年龄、美国国立卫生研究院脑卒中量表、Brunnstrom 恢复阶段(BRS)、脑卒中发病天数和功能独立性测量总分(认知项目))均选自既往研究,并通过逻辑回归分析进行了分析。我们使用曲线下面积(AUC)和混淆矩阵等指标评估了逻辑回归分析的有效性。结果表明,年龄、卒中发生到开始 HAL 训练的天数以及 BRS 是对通过 HAL 步态训练实现独立行走有显著影响的因素。AUC为0.86。此外,在建立混淆矩阵后,计算出的二元准确度、灵敏度(召回)和特异度分别为 0.80、0.80 和 0.81,表明准确度很高。我们的研究结果证实,年龄越大、瘫痪程度越重、卒中发生后开始 HAL 辅助训练的时间越晚,出院后出现行走依赖的可能性就越大。
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Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study
Robot-assisted gait training is effective for walking independence in stroke rehabilitation, the hybrid assistive limb (HAL) is an example. However, gait training with HAL may not be effective for everyone, and it is not clear who is not expected to benefit. Therefore, we aimed to identify the characteristics of stroke patients who have difficulty gaining benefits from gait training with HAL. We conducted a single-institutional retrospective cohort study. The participants were 82 stroke patients who had received gait training with HAL during hospitalization. The dependent variable was the functional ambulation category (FAC) that a measure of gait independence in stroke patients, and five independent [age, National Institutes of Health Stroke Scale, Brunnstrom recovery stage (BRS), days from stroke onset, and functional independence measure total score (cognitive items)] variables were selected from previous studies and analyzed by logistic regression analysis. We evaluated the validity of logistic regression analysis by using several indicators, such as the area under the curve (AUC), and a confusion matrix. Age, days from stroke onset to HAL initiation, and BRS were identified as factors that significantly influenced walking independence through gait training with HAL. The AUC was 0.86. Furthermore, after building a confusion matrix, the calculated binary accuracy, sensitivity (recall), and specificity were 0.80, 0.80, and 0.81, respectively, indicated high accuracy. Our findings confirmed that older age, greater degree of paralysis, and delayed initiation of HAL-assisted training after stroke onset were associated with increased likelihood of walking dependence upon hospital discharge.
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
期刊最新文献
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