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Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?

Introduction: Parkinson's disease hinders the ability of a person to perform daily activities. However, the varying impact of specific symptoms and their interactions on a person's motor repertoire is not understood. The current study investigates the possibility to predict global motor di...

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Autores principales: Lebel, Karina, Duval, Christian, Goubault, Etienne, Bogard, Sarah, Blanchet, Pierre. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105871/
https://www.ncbi.nlm.nih.gov/pubmed/32266228
http://dx.doi.org/10.3389/fbioe.2020.00189
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author Lebel, Karina
Duval, Christian
Goubault, Etienne
Bogard, Sarah
Blanchet, Pierre. J.
author_facet Lebel, Karina
Duval, Christian
Goubault, Etienne
Bogard, Sarah
Blanchet, Pierre. J.
author_sort Lebel, Karina
collection PubMed
description Introduction: Parkinson's disease hinders the ability of a person to perform daily activities. However, the varying impact of specific symptoms and their interactions on a person's motor repertoire is not understood. The current study investigates the possibility to predict global motor disabilities based on the patient symptomatology and medication. Methods: A cohort of 115 patients diagnosed with Parkinson's disease (mean age = 67.0 ± 8.7 years old) participated in the study. Participants performed different tasks, including the Timed-Up & Go, eating soup and the Purdue Pegboard test. Performance on these tasks was judged using timing, number of errors committed, and count achieved. K-means method was used to cluster the overall performance and create different motor performance groups. Symptomatology was objectively assessed for each participant from a combination of wearable inertial sensors (bradykinesia, tremor, dyskinesia) and clinical assessment (rigidity, postural instability). A multinomial regression model was derived to predict the performance cluster membership based on the patients' symptomatology, socio-demographics information and medication. Results: Clustering exposed four distinct performance groups: normal behavior, slightly affected in fine motor tasks, affected only in TUG, and affected in all areas. The statistical model revealed that low to moderate level of dyskinesia increased the likelihood of being in the normal group. A rise in postural instability and rest tremor increase the chance to be affected in TUG. Finally, LEDD did not help distinguishing between groups, but the presence of Amantadine as part of the medication regimen appears to decrease the likelihood of being part of the groups affected in TUG. Conclusion: The approach allowed to demonstrate the potential of using clinical symptoms to predict the impact of Parkinson's disease on a person's mobility performance.
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spelling pubmed-71058712020-04-07 Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology? Lebel, Karina Duval, Christian Goubault, Etienne Bogard, Sarah Blanchet, Pierre. J. Front Bioeng Biotechnol Bioengineering and Biotechnology Introduction: Parkinson's disease hinders the ability of a person to perform daily activities. However, the varying impact of specific symptoms and their interactions on a person's motor repertoire is not understood. The current study investigates the possibility to predict global motor disabilities based on the patient symptomatology and medication. Methods: A cohort of 115 patients diagnosed with Parkinson's disease (mean age = 67.0 ± 8.7 years old) participated in the study. Participants performed different tasks, including the Timed-Up & Go, eating soup and the Purdue Pegboard test. Performance on these tasks was judged using timing, number of errors committed, and count achieved. K-means method was used to cluster the overall performance and create different motor performance groups. Symptomatology was objectively assessed for each participant from a combination of wearable inertial sensors (bradykinesia, tremor, dyskinesia) and clinical assessment (rigidity, postural instability). A multinomial regression model was derived to predict the performance cluster membership based on the patients' symptomatology, socio-demographics information and medication. Results: Clustering exposed four distinct performance groups: normal behavior, slightly affected in fine motor tasks, affected only in TUG, and affected in all areas. The statistical model revealed that low to moderate level of dyskinesia increased the likelihood of being in the normal group. A rise in postural instability and rest tremor increase the chance to be affected in TUG. Finally, LEDD did not help distinguishing between groups, but the presence of Amantadine as part of the medication regimen appears to decrease the likelihood of being part of the groups affected in TUG. Conclusion: The approach allowed to demonstrate the potential of using clinical symptoms to predict the impact of Parkinson's disease on a person's mobility performance. Frontiers Media S.A. 2020-03-24 /pmc/articles/PMC7105871/ /pubmed/32266228 http://dx.doi.org/10.3389/fbioe.2020.00189 Text en Copyright © 2020 Lebel, Duval, Goubault, Bogard and Blanchet. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Lebel, Karina
Duval, Christian
Goubault, Etienne
Bogard, Sarah
Blanchet, Pierre. J.
Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?
title Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?
title_full Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?
title_fullStr Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?
title_full_unstemmed Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?
title_short Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?
title_sort can we predict the motor performance of patients with parkinson's disease based on their symptomatology?
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105871/
https://www.ncbi.nlm.nih.gov/pubmed/32266228
http://dx.doi.org/10.3389/fbioe.2020.00189
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