Recovery after stroke: the severely impaired are a distinct group

Abstract

IntroductionStroke 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.MethodsWe 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.ResultsRecovery 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%).ConclusionsOur 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.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

“…In particular, a linear regression model of raw outcome scores could be transformed into a change score model, which would then additionally allow for the interpretation of coefficients with respect to the classic proportional recovery concept. 102 , 181 Second, interpreting recovery solely on the basis of the change between follow-up and initial scores may mix up different patient subgroups and neurobiological mechanisms underlying different forms of functional recovery. For example, it is likely that a recovery change score of 10 points on the Fugl–Meyer assessment scale is driven by very different neurobiological processes depending on whether recovery started with an initial score of 5 (very severely affected) or 55 (almost no deficits).…”

Arguments for the biological and predictive relevance of the proportional recovery rule

“… Zarahn et al, 2011 , for example, used a subjective approach to classify patients with baseline FM ≤ 10 as severely affected. More recently, Bonkhoff et al, 2022 , used Bayesian hierarchical models and formal model selection approaches to identify a threshold (also found to be FM = 10) above and below which patients exhibit different recovery patterns. Our point in this section, meanwhile, is that the severely affected population contains biologically meaningful subgroups – recoverers and non-recoverers – and that these can be identified using appropriate unsupervised learning methods.…”

“…There is growing consensus around the need for careful comparisons of different models for recovery and for analytic strategies that minimize the impact of ceiling effects. Bonkhoff et al, 2020 , and Bonkhoff et al, 2022 , use a statistically rigorous model selection approach based on leave-one-out cross-validated deviances, which is particularly well suited to comparing Bayesian hierarchical models. These papers also consider only patients with initial impairments below 45 to avoid ceiling effects induced by mildly affected stroke patients.…”

“… Senesh and Reinkensmeyer, 2019 (title: ‘Breaking proportional recovery after stroke’) summarized these two papers in their abstract by saying they ‘explained that [proportional recovery] should be expected because of mathematical coupling between the baseline and change score’. Some of the authors of the original criticisms have since moderated their stance and important points of consensus are emerging; see, for example, Bonkhoff et al, 2020 , and Bonkhoff et al, 2022 . Against this rapidly evolving and potentially confusing backdrop, we hope to bring together all the thorny issues so that readers can reach some degree of closure on the PRR.…”

Responsiveness and trajectory of changes in the rating of everyday arm-use in the community and home (REACH) scale over the first-year post-stroke

“…The severe impairment group was defined by baseline Fugl-Meyer Upper Limb scores of 10 or less and the mild/moderate impairment group was defined by scores of 11 and greater. 13 Finally, age and sex were added to the interaction model to examine their effect on REACH scores. Linear mixed-effects models were selected due to their ability to handle missing data and provide interpretable trajectories over time within the sample size constraints of this hypothesis-generating study.…”