Implementing biomarkers to predict motor recovery after stroke.

BACKGROUND: There is growing interest in using biomarkers to predict motor recovery and outcomes after stroke. The PREP2 algorithm combines clinical assessment with biomarkers in an algorithm, to predict upper limb functional outcomes for individual patients. To date, PREP2 is the first algorithm to...

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Publicado en:NeuroRehabilitation Vol. 43; no. 1; pp. 41 - 51
Autores principales: Connell, Louise A., Smith, Marie-Claire, Byblow, Winston D., Stinear, Cathy M., Harvey, Richard L.
Formato: algorithm review tables/charts Journal Article
Publicado: Sage Publications Inc. 2018
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Sage Publications Inc.
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        atl: Implementing biomarkers to predict motor recovery after stroke.
      aug:
        au:
          Connell, Louise A.
          Smith, Marie-Claire
          Byblow, Winston D.
          Stinear, Cathy M.
          Harvey, Richard L.
        affil: School of Health Sciences, University of Central Lancashire, Preston, UK
      sug:
        subj:
          Movement Disorders Diagnosis
          Movement Disorders Rehabilitation
          Biological Markers
          Stroke Complications
          Movement Disorders Etiology
          Recovery
          Algorithms
          Conceptual Framework
          Health Care Delivery
          Medical Practice
      ab: BACKGROUND: There is growing interest in using biomarkers to predict motor recovery and outcomes after stroke. The PREP2 algorithm combines clinical assessment with biomarkers in an algorithm, to predict upper limb functional outcomes for individual patients. To date, PREP2 is the first algorithm to be tested in clinical practice, and other biomarker-based algorithms are likely to follow. PURPOSE: This review considers how algorithms to predict motor recovery and outcomes after stroke might be implemented in clinical practice. FINDINGS: There are two tasks: first the prediction information needs to be obtained, and then it needs to be used. The barriers and facilitators of implementation are likely to differ for these tasks. We identify specific elements of the Consolidated Framework for Implementation Research that are relevant to each of these two tasks, using the PREP2 algorithm as an example. These include the characteristics of the predictors and algorithm, the clinical setting and its staff, and the healthcare environment. CONCLUSIONS: Active, theoretically underpinned implementation strategies are needed to ensure that biomarkers are successfully used in clinical practice for predicting motor outcomes after stroke, and should be considered in parallel with biomarker development.
      pubtype: Academic Journal
      doctype:
        algorithm
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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