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Predicting Severity of Huntington's Disease With Wearable Sensors

The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must...

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Autores principales: Scheid, Brittany H., Aradi, Stephen, Pierson, Robert M., Baldassano, Steven, Tivon, Inbar, Litt, Brian, Gonzalez-Alegre, Pedro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013843/
https://www.ncbi.nlm.nih.gov/pubmed/35445206
http://dx.doi.org/10.3389/fdgth.2022.874208
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author Scheid, Brittany H.
Aradi, Stephen
Pierson, Robert M.
Baldassano, Steven
Tivon, Inbar
Litt, Brian
Gonzalez-Alegre, Pedro
author_facet Scheid, Brittany H.
Aradi, Stephen
Pierson, Robert M.
Baldassano, Steven
Tivon, Inbar
Litt, Brian
Gonzalez-Alegre, Pedro
author_sort Scheid, Brittany H.
collection PubMed
description The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must be administered by a trained and experienced rater. An objective, automated method of quantifying disease severity would facilitate superior patient care and could be used to better track severity over time. We conducted the present study to evaluate the feasibility of using wearable sensors, coupled with machine learning algorithms, to rate motor function in patients with HD. Fourteen participants with symptomatic HD and 14 healthy controls participated in the study. Each participant wore five adhesive biometric sensors applied to the trunk and each limb while completing brief walking, sitting, and standing tasks during a single office visit. A two-stage machine learning method was employed to classify participants by HD status and to predict UHDRS motor subscores. Linear discriminant analysis correctly classified all participants' HD status except for one control subject with abnormal gait (96.4% accuracy, 92.9% sensitivity, and 100% specificity in leave-one-out cross-validation). Two regression models accurately predicted individual UHDRS subscores for gait, and dystonia within a 10% margin of error. Our regression models also predicted a composite UHDRS score–a sum of left and right arm rigidity, total chorea, total dystonia, bradykinesia, gait, and tandem gait subscores–with an average error below 15%. Machine learning classifiers trained on brief in-office datasets discriminated between controls and participants with HD, and could accurately predict selected motor UHDRS subscores. Our results could enable the future use of biosensors for objective HD assessment in the clinic or remotely and could inform future studies for the use of this technology as a potential endpoint in clinical trials.
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spelling pubmed-90138432022-04-19 Predicting Severity of Huntington's Disease With Wearable Sensors Scheid, Brittany H. Aradi, Stephen Pierson, Robert M. Baldassano, Steven Tivon, Inbar Litt, Brian Gonzalez-Alegre, Pedro Front Digit Health Digital Health The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must be administered by a trained and experienced rater. An objective, automated method of quantifying disease severity would facilitate superior patient care and could be used to better track severity over time. We conducted the present study to evaluate the feasibility of using wearable sensors, coupled with machine learning algorithms, to rate motor function in patients with HD. Fourteen participants with symptomatic HD and 14 healthy controls participated in the study. Each participant wore five adhesive biometric sensors applied to the trunk and each limb while completing brief walking, sitting, and standing tasks during a single office visit. A two-stage machine learning method was employed to classify participants by HD status and to predict UHDRS motor subscores. Linear discriminant analysis correctly classified all participants' HD status except for one control subject with abnormal gait (96.4% accuracy, 92.9% sensitivity, and 100% specificity in leave-one-out cross-validation). Two regression models accurately predicted individual UHDRS subscores for gait, and dystonia within a 10% margin of error. Our regression models also predicted a composite UHDRS score–a sum of left and right arm rigidity, total chorea, total dystonia, bradykinesia, gait, and tandem gait subscores–with an average error below 15%. Machine learning classifiers trained on brief in-office datasets discriminated between controls and participants with HD, and could accurately predict selected motor UHDRS subscores. Our results could enable the future use of biosensors for objective HD assessment in the clinic or remotely and could inform future studies for the use of this technology as a potential endpoint in clinical trials. Frontiers Media S.A. 2022-04-04 /pmc/articles/PMC9013843/ /pubmed/35445206 http://dx.doi.org/10.3389/fdgth.2022.874208 Text en Copyright © 2022 Scheid, Aradi, Pierson, Baldassano, Tivon, Litt and Gonzalez-Alegre. https://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 Digital Health
Scheid, Brittany H.
Aradi, Stephen
Pierson, Robert M.
Baldassano, Steven
Tivon, Inbar
Litt, Brian
Gonzalez-Alegre, Pedro
Predicting Severity of Huntington's Disease With Wearable Sensors
title Predicting Severity of Huntington's Disease With Wearable Sensors
title_full Predicting Severity of Huntington's Disease With Wearable Sensors
title_fullStr Predicting Severity of Huntington's Disease With Wearable Sensors
title_full_unstemmed Predicting Severity of Huntington's Disease With Wearable Sensors
title_short Predicting Severity of Huntington's Disease With Wearable Sensors
title_sort predicting severity of huntington's disease with wearable sensors
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013843/
https://www.ncbi.nlm.nih.gov/pubmed/35445206
http://dx.doi.org/10.3389/fdgth.2022.874208
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