Literature DB >> 9183346

Measurement properties of the NIH Stroke Scale during acute rehabilitation.

A W Heinemann1, R L Harvey, J R McGuire, D Ingberman, L Lovell, P Semik, E J Roth.   

Abstract

BACKGROUND AND
PURPOSE: The scale of stroke impairment characteristics by Brott and associates, the National Institutes of Health (NIH) Stroke Scale, has been used widely in various studies of stroke outcome; however, the measurement properties of the items applied to patients during medical rehabilitation have not been evaluated thoroughly. This study evaluated the extent to which scale items cohere to define a unidimensional construct and have a useful range for application to patients during medical rehabilitation.
METHODS: Rating scale (or Rasch) analysis of the 15 NIH Stroke Scale items was conducted using the BIGSTEPS computer program to evaluate (1) the range of impairment assessed by the items, (2) the items' coherence with an underlying construct of impairment, and (3) range of impairment measured in rehabilitation patients. We sought to maximize the range of impairment measured by conducting analyses recursively; at each subsequent step, the worst fitting item was deleted or rescored. The sample comprised 1291 admission and discharge records from 693 rehabilitation inpatients with stroke.
RESULTS: Thirteen items arrayed the sample across a sufficient range of impairment. The limb ataxia item fit poorly and was deleted; lower ratings for this item were associated with higher scores on the total scale. Pupillary response was also deleted because ratings reflected poor congruence with the total score. Best language was rescored because intermediate ratings were inconsistently related to the total score. Patients with hemorrhagic strokes had poorer fitting measures than did patients with ischemic strokes.
CONCLUSIONS: The items in a revised NIH Stroke Scale worked well together to define the severity of impairment resulting from stroke that is observed during medical rehabilitation. Directions regarding limb ataxia should be modified to indicate untestability due to hemiplegia.

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Year:  1997        PMID: 9183346     DOI: 10.1161/01.str.28.6.1174

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  3 in total

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Authors:  Elisabeth A Wilde; Stephen R McCauley; Tara M Kelly; Harvey S Levin; Claudia Pedroza; Guy L Clifton; Claudia S Robertson; Alex B Valadka; Paolo Moretti
Journal:  J Neurotrauma       Date:  2010-06       Impact factor: 5.269

2.  Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms.

Authors:  Xinping Lin; Shiteng Lin; XiaoLi Cui; Daizun Zou; FuPing Jiang; JunShan Zhou; NiHong Chen; Zhihong Zhao; Juan Zhang; Jianjun Zou
Journal:  Front Neurol       Date:  2021-12-23       Impact factor: 4.003

3.  The UK Functional Assessment Measure (UK FIM+FAM): Psychometric Evaluation in Patients Undergoing Specialist Rehabilitation following a Stroke from the National UK Clinical Dataset.

Authors:  Meenakshi Nayar; Roxana Vanderstay; Richard J Siegert; Lynne Turner-Stokes
Journal:  PLoS One       Date:  2016-01-29       Impact factor: 3.240

  3 in total

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