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Bringing Proportional Recovery into Proportion: Bayesian Modelling of Post-Stroke Motor Impairment

Bonkhoff, Anna, Hope, Thomas M.H., Bzdok, Danilo, Guggisberg, Adrian, Hawe, Rachel, Dukelow, Sean, Rehme, Anne, Fink, Gereon, Grefkes, Christian, Bowman, Howard and others. (2020) Bringing Proportional Recovery into Proportion: Bayesian Modelling of Post-Stroke Motor Impairment. Brain, 143 (7). pp. 2189-2206. ISSN 0006-8950. (doi:10.1093/brain/awaa146) (KAR id:81216)

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Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and health care system. More than ten years ago, the proportional recovery rule was introduced by promising just that: high-fidelity predictions of recovery following stroke 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 analyzed n=385 post-stroke trajectories from six separate studies – one of the currently 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 >80%. When instead analyzing 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 behavior scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

Item Type: Article
DOI/Identification number: 10.1093/brain/awaa146
Uncontrolled keywords: Motor outcome post-stroke, Proportional recovery, Bayesian hierarchical models, Bayesian model comparison, learning from data
Subjects: R Medicine > RC Internal medicine > RC321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Howard Bowman
Date Deposited: 13 May 2020 14:39 UTC
Last Modified: 28 Jun 2021 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)
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