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Prediction of the Levodopa Challenge Test in Parkinson's Disease Using Data from a Wrist-Worn Sensor.

Khodakarami, H; Ricciardi, L; Contarino, MF; Pahwa, R; Lyons, KE; Geraedts, VJ; Morgante, F; Leake, A; Paviour, D; De Angelis, A; et al. Khodakarami, H; Ricciardi, L; Contarino, MF; Pahwa, R; Lyons, KE; Geraedts, VJ; Morgante, F; Leake, A; Paviour, D; De Angelis, A; Horne, M (2019) Prediction of the Levodopa Challenge Test in Parkinson's Disease Using Data from a Wrist-Worn Sensor. Sensors (Basel), 19 (23). p. 5153. ISSN 1424-8220 https://doi.org/10.3390/s19235153
SGUL Authors: Morgante, Francesca

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Abstract

The response to levodopa (LR) is important for managing Parkinson's Disease and is measured with clinical scales prior to (OFF) and after (ON) levodopa. The aim of this study was to ascertain whether an ambulatory wearable device could predict the LR from the response to the first morning dose. The ON and OFF scores were sorted into six categories of severity so that separating Parkinson's Kinetigraph (PKG) features corresponding to the ON and OFF scores became a multi-class classification problem according to whether they fell below or above the threshold for each class. Candidate features were extracted from the PKG data and matched to the class labels. Several linear and non-linear candidate statistical models were examined and compared to classify the six categories of severity. The resulting model predicted a clinically significant LR with an area under the receiver operator curve of 0.92. This study shows that ambulatory data could be used to identify a clinically significant response to levodopa. This study has also identified practical steps that would enhance the reliability of this test in future studies.

Item Type: Article
Additional Information: © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). Correction available at: https://doi.org/10.3390/s20154167
Keywords: Parkinson’s Disease, ambulatory systems, levodopa challenge test, levodopa response, machine learning, wearable devices, 0301 Analytical Chemistry, 0906 Electrical And Electronic Engineering, Analytical Chemistry
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: Sensors (Basel)
ISSN: 1424-8220
Language: eng
Dates:
DateEvent
25 November 2019Published
12 November 2019Accepted
PubMed ID: 31775289
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/111456
Publisher's version: https://doi.org/10.3390/s19235153

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