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Patient-specific prediction of long-term outcomes will change stroke rehabilitation for the better
  1. India Walford1,
  2. Jane Maryam Rondina2,
  3. Nick Ward1,3
  1. 1 Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
  2. 2 Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
  3. 3 Department of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
  1. Correspondence to Professor Nick Ward, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; n.ward{at}

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Stroke is the most common cause of neurological disability and yet our ability to predict long-term outcomes remains poor. The paper by Selles et al 1 used upper limb outcomes from 450 patients with first-time ischaemic stroke to take a refreshingly different approach to the prediction problem. First, allowing repeated clinical measures to contribute to the prediction acknowledges what most clinicians already know—that rate of clinical change is helpful in prognostication. Second, rather than predict outcome at a single future time point, they have created likely recovery trajectories (with CIs) for individual patients. Third, access to this predictive model is freely available online, so that stroke services around the world can more accurately begin to make predictions of individual recovery. Although the current approach concerns upper limb recovery, the principle should apply to all domains.

Current methodological approaches to prediction …

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  • Twitter @jmrondina, @dr_nickward

  • Contributors All authors have contributed to the writing and editing of this commentary.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Commissioned; internally peer reviewed.

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