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Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke

Proprioceptive impairments occur in ~50% of stroke survivors, with 20–40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, cli...

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Autores principales: Chilvers, Matthew J., Rajashekar, Deepthi, Low, Trevor A., Scott, Stephen H., Dukelow, Sean P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296129/
https://www.ncbi.nlm.nih.gov/pubmed/37371431
http://dx.doi.org/10.3390/brainsci13060953
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author Chilvers, Matthew J.
Rajashekar, Deepthi
Low, Trevor A.
Scott, Stephen H.
Dukelow, Sean P.
author_facet Chilvers, Matthew J.
Rajashekar, Deepthi
Low, Trevor A.
Scott, Stephen H.
Dukelow, Sean P.
author_sort Chilvers, Matthew J.
collection PubMed
description Proprioceptive impairments occur in ~50% of stroke survivors, with 20–40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke.
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spelling pubmed-102961292023-06-28 Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke Chilvers, Matthew J. Rajashekar, Deepthi Low, Trevor A. Scott, Stephen H. Dukelow, Sean P. Brain Sci Article Proprioceptive impairments occur in ~50% of stroke survivors, with 20–40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke. MDPI 2023-06-15 /pmc/articles/PMC10296129/ /pubmed/37371431 http://dx.doi.org/10.3390/brainsci13060953 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chilvers, Matthew J.
Rajashekar, Deepthi
Low, Trevor A.
Scott, Stephen H.
Dukelow, Sean P.
Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke
title Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke
title_full Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke
title_fullStr Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke
title_full_unstemmed Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke
title_short Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke
title_sort clinical, neuroimaging and robotic measures predict long-term proprioceptive impairments following stroke
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296129/
https://www.ncbi.nlm.nih.gov/pubmed/37371431
http://dx.doi.org/10.3390/brainsci13060953
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