Universität zu Köln

Bonkhoff, Anna K., Hope, Tom, Bzdok, Danilo, Guggisberg, Adrian G., Hawe, Rachel L., Dukelow, Sean P., Chollet, Francois, Lin, David J., Grefkes, Christian and Bowman, Howard (2022). Recovery after stroke: the severely impaired are a distinct group. J. Neurol. Neurosurg. Psychiatry, 93 (4). S. 369 - 379. LONDON: BMJ PUBLISHING GROUP. ISSN 1468-330X

Full text not available from this repository.

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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Bonkhoff, Anna K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Hope, Tom UNSPECIFIED UNSPECIFIED UNSPECIFIED Bzdok, Danilo UNSPECIFIED UNSPECIFIED UNSPECIFIED Guggisberg, Adrian G. UNSPECIFIED UNSPECIFIED UNSPECIFIED Hawe, Rachel L. UNSPECIFIED UNSPECIFIED UNSPECIFIED Dukelow, Sean P. UNSPECIFIED UNSPECIFIED UNSPECIFIED Chollet, Francois UNSPECIFIED UNSPECIFIED UNSPECIFIED Lin, David J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Grefkes, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Bowman, Howard UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-603453
DOI:	10.1136/jnnp-2021-327211
Journal or Publication Title:	J. Neurol. Neurosurg. Psychiatry
Volume:	93
Number:	4
Page Range:	S. 369 - 379
Date:	2022
Publisher:	BMJ PUBLISHING GROUP
Place of Publication:	LONDON
ISSN:	1468-330X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language MOTOR RECOVERY; PROPORTIONAL RECOVERY; UPPER EXTREMITY; PREDICTION; BIOMARKER; MODEL Multiple languages Clinical Neurology; Psychiatry; Surgery Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/60345