Recovery after stroke: the severely impaired are a distinct group

So what? The goal is still 100% recovery.

Recovery after stroke: the severely impaired are a distinct group

1. http://orcid.org/0000-0002-5927-1089Anna K Bonkhoff1,
2. http://orcid.org/0000-0003-0714-8545Tom Hope2,
3. Danilo Bzdok3,4,5,
4. Adrian G Guggisberg6,
5. Rachel L Hawe7,8,
6. Sean P Dukelow8,
7. François Chollet9,
8. David J Lin10,
9. Christian Grefkes11,12,
10. Howard Bowman13,14

1. Correspondence to Dr Anna K Bonkhoff, J. Philip Kistler Stroke Research Center, Boston, MA 02114, USA; abonkhoff@mgh.harvard.edu

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 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.(You blithering idiots need to solve stroke to 100% recovery, not just use biomarkers to predict failure to recover! I'd fire the lot of you!)