Louise A Connell1, Marie-Claire Smith2,3, Winston D Byblow3,4, Cathy M Stinear2,3. 1. School of Health Sciences, University of Central Lancashire, Preston, UK. 2. Department of Medicine, University of Auckland, Auckland, New Zealand. 3. Centre for Brain Research, University of Auckland, Auckland, New Zealand. 4. Department of Exercise Sciences, University of Auckland, Auckland, New Zealand.
Abstract
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.
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.
Authors: Mirjam R Heldner; Caroline Chalfine; Marion Houot; Roza M Umarova; Jan Rosner; Julian Lippert; Laura Gallucci; Anne Leger; Flore Baronnet; Yves Samson; Charlotte Rosso Journal: Front Neurol Date: 2022-02-17 Impact factor: 4.003