医学
冲程(发动机)
临床试验
临床神经学
领域(数学分析)
物理医学与康复
内科学
梅德林
神经科学
心理学
生物
数学
生物化学
机械工程
工程类
数学分析
作者
Robynne Braun,Laura Heitsch,John W. Cole,Arne Lindgren,Adam de Havenon,Jason A. Dude,Keith R. Lohse,Steven C. Cramer,Bradford B. Worrall
出处
期刊:Neurology
[Ovid Technologies (Wolters Kluwer)]
日期:2021-08-24
卷期号:97 (8): 367-377
被引量:20
标识
DOI:10.1212/wnl.0000000000012231
摘要
Global outcome measures that are widely used in stroke clinical trials, such as the modified Rankin Scale (mRS), lack sufficient detail to detect changes within specific domains (e.g., sensory, motor, visual, linguistic, or cognitive function). Yet such data are vital for understanding stroke recovery and its mechanisms. Poststroke deficits in specific domains differ in their rate and degree of recovery and in their effects on overall independence and quality of life. For example, even in a patient with complete recovery of strength, persistent deficits in the nonmotor domains such as language and cognition may make a return to independent living impossible. In such cases, global measures based solely on the patient's degree of independence would overlook a complete recovery in the motor domain. Capturing these important aspects of recovery demands a domain-specific approach. If stroke outcomes trials are to incorporate finer-grained recovery metrics-which can require substantial time, effort, and expertise to implement-efficiency must be a priority. In this article, we discuss how commonly collected clinical data from the NIH Stroke Scale can guide the judicious selection of relevant recovery domains for more detailed testing. Our overarching goal is to make the implementation of domain-specific testing more feasible for large-scale clinical trials on stroke recovery.
科研通智能强力驱动
Strongly Powered by AbleSci AI