Recovery after stroke: the severely impaired are a distinct group. Issue 4 (22nd December 2021)

• Record Type: Journal Article
• Authors: Bonkhoff, Anna K; Hope, Tom; Bzdok, Danilo; Guggisberg, Adrian G; Hawe, Rachel L; Dukelow, Sean P; Chollet, François; Lin, David J; Grefkes, Christian; Bowman, Howard
• Is Part Of: Journal of neurology, neurosurgery and psychiatry. Volume 93:Issue 4(2022)
• Page Start: 369
• Page End: 378
• Publication Date: 2021-12-22
• Subjects: stroke -- cerebrovascular disease -- statistics -- rehabilitation; Neurology -- Periodicals; Nervous system -- Surgery -- Periodicals; Psychiatry -- Periodicals; 616.8
• DOI: 10.1136/jnnp-2021-327211
• Languages: English
• ISSNs: 0022-3050

Abstract

Abstract : 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 R 2 =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.