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Can acute clinical outcomes predict health-related quality of life after stroke: a one-year prospective study of stroke survivors.

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Date
2018-11-21
Authors
Yeoh, Yen Shing
Koh, Gerald Choon-Huat
Tan, Chuen Seng
Lee, Kim En
Tu, Tian Ming
Singh, Rajinder
Chang, Hui Meng
De Silva, Deidre A
Ng, Yee Sien
Ang, Yan Hoon
Yap, Philip
Chew, Effie
Merchant, Reshma Aziz
Yeo, Tseng Tsai
Chou, Ning
Venketasubramanian, N
Young, Sherry H
Hoenig, Helen
Matchar, David Bruce
Luo, Nan
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Abstract
<h4>Background</h4>Health-related quality of life (HRQoL) is a key metric to understand the impact of stroke from patients' perspective. Yet HRQoL is not readily measured in clinical practice. This study aims to investigate the extent to which clinical outcomes during admission predict HRQoL at 3 months and 1 year post-stroke.<h4>Methods</h4>Stroke patients admitted to five tertiary hospitals in Singapore were assessed with Shah-modified Barthel Index (Shah-mBI), National Institute of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), Mini-Mental State Examination (MMSE), and Frontal Assessment Battery (FAB) before discharge, and the EQ-5D questionnaire at 3 months and 12 months post-stroke. Association of clinical measures with the EQ index at both time points was examined using multiple linear regression models. Forward stepwise selection was applied and consistently significant clinical measures were analyzed for their association with individual dimensions of EQ-5D in multiple logistic regressions.<h4>Results</h4>All five clinical measures at baseline were significant predictors of the EQ index at 3 months and 12 months, except that MMSE was not significantly associated with the EQ index at 12 months. NIHSS (3-month standardized β = - 0.111; 12-month standardized β = - 0.109) and mRS (3-month standardized β = - 0.122; 12-month standardized β = - 0.080) were shown to have a larger effect size than other measures. The contribution of NIHSS and mRS as significant predictors of HRQoL was mostly explained by their association with the mobility, self-care, and usual activities dimensions of EQ-5D.<h4>Conclusions</h4>HRQoL at 3 months and 12 months post-stroke can be predicted by clinical outcomes in the acute phase. NIHSS and mRS are better predictors than BI, MMSE, and FAB.
Type
Journal article
Subject
Humans
Linear Models
Prospective Studies
Quality of Life
Aged
Middle Aged
Survivors
Female
Male
Stroke
Surveys and Questionnaires
Outcome Assessment, Health Care
Permalink
https://hdl.handle.net/10161/22793
Published Version (Please cite this version)
10.1186/s12955-018-1043-3
Publication Info
Yeoh, Yen Shing; Koh, Gerald Choon-Huat; Tan, Chuen Seng; Lee, Kim En; Tu, Tian Ming; Singh, Rajinder; ... Luo, Nan (2018). Can acute clinical outcomes predict health-related quality of life after stroke: a one-year prospective study of stroke survivors. Health and quality of life outcomes, 16(1). pp. 221. 10.1186/s12955-018-1043-3. Retrieved from https://hdl.handle.net/10161/22793.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Hoenig

Helen Marie Hoenig

Professor of Medicine
1. General Focus and Goals of Research: Dr. Hoenig's research focuses on rehabilitation, and more specifically on assistive technology and teletechnology. Patient populations of interest include geriatric patients with diverse medical problems including stroke, spinal and/or musculoskeletal disorders. 2. Specific Approaches or Techniques: Randomized controlled trials, epidemiological studies including large data base analyses and survey research. Clinical trials include studi
Matchar

David Bruce Matchar

Professor of Medicine
My research relates to clinical practice improvement - from the development of clinical policies to their implementation in real world clinical settings. Most recently my major content focus has been cerebrovascular disease. Other major clinical areas in which I work include the range of disabling neurological conditions, cardiovascular disease, and cancer prevention. Notable features of my work are: (1) reliance on analytic strategies such as meta-analysis, simulation, decision analy
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