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Almost half of stroke patients face permanent impairment, such as motor weakness
.
Therefore, a comprehensive and more mechanistic understanding of post-stroke recovery is essential to successfully guide clinical decision-making and neurorehabilitation
The prevailing view in the field of upper extremity motor recovery is that patients should be divided into severely and non-severely affected groups when modeling motor recovery from stroke in general and when studying biomarkers and rehabilitation development in particular
.
For new patients who have just experienced a stroke and the results are not yet clear, it is most clinically valuable to accurately predict the recovery situation
In particular, accurate predictions could allow clinicians to stratify patients based on their predicted neurobiological recovery potential for additional interventions and even help demonstrate the benefits of novel rehabilitation treatments
This article was published in the Journal of Neurology, Neurosurgery & Psychiatry .
We designed a Bayesian hierarchical model to estimate upper extremity Fugl-Meyer (FM) scores 3-6 months after stroke
.
In focusing on interpretation of recovery patterns, we addressed confounding factors affecting previous recovery studies and considered only patients with an initial FM score <45
Out-of-sample predictive performance was estimated for patients across the entire initial injury range
When modeling and interpreting the recovery process for these severe and non-severe stroke patients simultaneously, significant similarities and differences in recovery patterns were found
Motion recovery outcomes across the entire injury range in 489 patients
Both patient groups recovered a fairly constant amount in FM manifestations of the affected upper extremities
.
However, severely affected patients (FM-initial ≤10) additionally exhibited additional features proportional to the spared component, whereas non-severely affected patients (FM-initial >10) exhibited additional features proportional to the lost recovery component
The results suggest that these presented out-of-sample predictive performance can serve as a benchmark for assessing other novel biomarkers of stroke recovery
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