TY - JOUR T1 - Recovery after stroke: the severely impaired are a distinct group JF - Journal of Neurology, Neurosurgery & Psychiatry JO - J Neurol Neurosurg Psychiatry DO - 10.1136/jnnp-2021-327211 SP - jnnp-2021-327211 AU - Anna K Bonkhoff AU - Tom Hope AU - Danilo Bzdok AU - Adrian G Guggisberg AU - Rachel L Hawe AU - Sean P Dukelow AU - François Chollet AU - David J Lin AU - Christian Grefkes AU - Howard Bowman Y1 - 2021/12/22 UR - http://jnnp.bmj.com/content/early/2021/12/21/jnnp-2021-327211.abstract N2 - Introduction Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.Methods We designed a Bayesian hierarchical model to estimate 3–6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5–30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.Results Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3–6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%).Conclusions Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.Data are available upon reasonable request. Data is available from the authors on reasonable request. Jupyter notebook scripts (python 3.7, predominantly pymc3) is openly available: https://github.com/AnnaBonkhoff/to_be_added_upon_acceptance. ER -