Universität zu Köln

Bonkhoff, Anna K., Hope, Thomas, Bzdok, Danilo, Guggisberg, Adrian G., Hawe, Rachel L., Dukelow, Sean P., Rehme, Anne K., Fink, Gereon R. ORCID: 0000-0002-8230-1856, Grefkes, Christian ORCID: 0000-0002-1656-720X and Bowman, Howard (2020). Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment. Brain, 143. S. 2189 - 2207. OXFORD: OXFORD UNIV PRESS. ISSN 1460-2156

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Abstract

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More than 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies-one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called 'fitters', pointed to a combination of proportional to lost function and constant recovery. 'Proportional to lost' here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of 480%. When instead analysing the complete spectrum of subjects, 'fitters' and 'non-fitters', a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Bonkhoff, Anna K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Hope, Thomas 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 Rehme, Anne K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Fink, Gereon R. UNSPECIFIED orcid.org/0000-0002-8230-1856 UNSPECIFIED Grefkes, Christian UNSPECIFIED orcid.org/0000-0002-1656-720X UNSPECIFIED Bowman, Howard UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-327221
DOI:	10.1093/brain/awaa146
Journal or Publication Title:	Brain
Volume:	143
Page Range:	S. 2189 - 2207
Date:	2020
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1460-2156
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language STROKE; GENERALIZABILITY; PREDICTION; DEPENDS; NEGLECT Multiple languages Clinical Neurology; Neurosciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/32722