Clinical Neuroscience Laboratory, Department of Medicine, The University of Auckland, New Zealand (H.T.J., J.C., C.M.S.).
School of Medicine, Monash University, Melbourne, Australia (J.C.).
Stroke. 2022 Feb;53(2):578-585. doi: 10.1161/STROKEAHA.121.035170. Epub 2021 Oct 4.
The ARAT (Action Research Arm Test) has been used to classify upper limb motor outcome after stroke in 1 of 3, 4, or 5 categories. The COVID-19 pandemic has encouraged the development of assessments that can be performed quickly and remotely. The aim of this study was to derive and internally validate decision trees for categorizing upper limb motor outcomes at the late subacute and chronic stages of stroke using a subset of ARAT tasks.
This study retrospectively analyzed ARAT scores obtained in-person at 3 months poststroke from 333 patients. In-person ARAT scores were used to categorize patients' 3-month upper limb outcome using classification systems with 3, 4, and 5 outcome categories. Individual task scores from in-person assessments were then used in classification and regression tree analyses to determine subsets of tasks that could accurately categorize upper limb outcome for each of the 3 classification systems. The decision trees developed using 3-month ARAT data were also applied to in-person ARAT data obtained from 157 patients at 6 months poststroke.
The classification and regression tree analyses produced decision trees requiring 2 to 4 ARAT tasks. The overall accuracy of the cross-validated decision trees ranged from 87.7% (SE, 1.0%) to 96.7% (SE, 2.0%). Accuracy was highest when classifying patients into one of 3 outcome categories and lowest for 5 categories. The decision trees are referred to as FOCUS (Fast Outcome Categorization of the Upper Limb After Stroke) assessments and they remained accurate for 6-month poststroke ARAT scores (overall accuracy range 83.4%-91.7%).
A subset of ARAT tasks can accurately categorize upper limb motor outcomes after stroke. Future studies could investigate the feasibility and accuracy of categorizing outcomes using the FOCUS assessments remotely via video call.
ARAT（动作研究臂测试）已被用于将中风后上肢运动功能结果分为 3 个、4 个或 5 个类别之一。COVID-19 大流行鼓励开发可以快速远程进行的评估。本研究的目的是使用 ARAT 任务的子集，为中风后晚期亚急性期和慢性期的上肢运动功能结果分类推导出并内部验证决策树。
本研究回顾性分析了 333 例患者中风后 3 个月的 ARAT 评分。使用 3 个、4 个和 5 个结果类别的分类系统，根据现场评估的 ARAT 评分对患者 3 个月的上肢结果进行分类。然后，使用个体任务评分进行分类和回归树分析，以确定可准确分类每个 3 个分类系统上肢结果的任务子集。使用 3 个月 ARAT 数据开发的决策树也应用于 157 例患者中风后 6 个月的现场 ARAT 数据。
分类和回归树分析产生了需要 2 到 4 个 ARAT 任务的决策树。交叉验证决策树的整体准确性范围为 87.7%（SE，1.0%）至 96.7%（SE，2.0%）。将患者分为 3 个结果类别之一的准确率最高，分为 5 个类别的准确率最低。这些决策树被称为 FOCUS（中风后上肢快速结果分类）评估，它们对 6 个月后的 ARAT 评分仍具有准确性（总体准确性范围为 83.4%-91.7%）。
ARAT 任务的一个子集可以准确分类中风后的上肢运动功能结果。未来的研究可以探讨通过视频通话远程使用 FOCUS 评估进行分类的可行性和准确性。
