The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters

Abstract Digital assessments of motor severity could improve the sensitivity of clinical trials and personalise treatment in Parkinson’s disease (PD) but have yet to be widely adopted. Their ability to capture individual change across the heterogeneous motor presentations typical of PD remains inade...

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Autores principales:	Ashwani Jha, Elisa Menozzi, Rebecca Oyekan, Anna Latorre, Eoin Mulroy, Sebastian R. Schreglmann, Cosmin Stamate, Ioannis Daskalopoulos, Stefan Kueppers, Marco Luchini, John C. Rothwell, George Roussos, Kailash P. Bhatia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/dbd853c5a047468e8202a0decd920ae5
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spelling	oai:doaj.org-article:dbd853c5a047468e8202a0decd920ae52021-12-02T12:33:17ZThe CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters10.1038/s41531-020-00135-w2373-8057https://doaj.org/article/dbd853c5a047468e8202a0decd920ae52020-12-01T00:00:00Zhttps://doi.org/10.1038/s41531-020-00135-whttps://doaj.org/toc/2373-8057Abstract Digital assessments of motor severity could improve the sensitivity of clinical trials and personalise treatment in Parkinson’s disease (PD) but have yet to be widely adopted. Their ability to capture individual change across the heterogeneous motor presentations typical of PD remains inadequately tested against current clinical reference standards. We conducted a prospective, dual-site, crossover-randomised study to determine the ability of a 16-item smartphone-based assessment (the index test) to predict subitems from the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III) as assessed by three blinded clinical raters (the reference-standard). We analysed data from 60 subjects (990 smartphone tests, 2628 blinded video MDS-UPDRS III subitem ratings). Subject-level predictive performance was quantified as the leave-one-subject-out cross-validation (LOSO-CV) accuracy. A pre-specified analysis classified 70.3% (SEM 5.9%) of subjects into a similar category to any of three blinded clinical raters and was better than random (36.7%; SEM 4.3%) classification. Post hoc optimisation of classifier and feature selection improved performance further (78.7%, SEM 5.1%), although individual subtests were variable (range 53.2–97.0%). Smartphone-based measures of motor severity have predictive value at the subject level. Future studies should similarly mitigate against subjective and feature selection biases and assess performance across a range of motor features as part of a broader strategy to avoid overly optimistic performance estimates.Ashwani JhaElisa MenozziRebecca OyekanAnna LatorreEoin MulroySebastian R. SchreglmannCosmin StamateIoannis DaskalopoulosStefan KueppersMarco LuchiniJohn C. RothwellGeorge RoussosKailash P. BhatiaNature PortfolioarticleNeurology. Diseases of the nervous systemRC346-429ENnpj Parkinson's Disease, Vol 6, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neurology. Diseases of the nervous system RC346-429
spellingShingle	Neurology. Diseases of the nervous system RC346-429 Ashwani Jha Elisa Menozzi Rebecca Oyekan Anna Latorre Eoin Mulroy Sebastian R. Schreglmann Cosmin Stamate Ioannis Daskalopoulos Stefan Kueppers Marco Luchini John C. Rothwell George Roussos Kailash P. Bhatia The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters
description	Abstract Digital assessments of motor severity could improve the sensitivity of clinical trials and personalise treatment in Parkinson’s disease (PD) but have yet to be widely adopted. Their ability to capture individual change across the heterogeneous motor presentations typical of PD remains inadequately tested against current clinical reference standards. We conducted a prospective, dual-site, crossover-randomised study to determine the ability of a 16-item smartphone-based assessment (the index test) to predict subitems from the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III) as assessed by three blinded clinical raters (the reference-standard). We analysed data from 60 subjects (990 smartphone tests, 2628 blinded video MDS-UPDRS III subitem ratings). Subject-level predictive performance was quantified as the leave-one-subject-out cross-validation (LOSO-CV) accuracy. A pre-specified analysis classified 70.3% (SEM 5.9%) of subjects into a similar category to any of three blinded clinical raters and was better than random (36.7%; SEM 4.3%) classification. Post hoc optimisation of classifier and feature selection improved performance further (78.7%, SEM 5.1%), although individual subtests were variable (range 53.2–97.0%). Smartphone-based measures of motor severity have predictive value at the subject level. Future studies should similarly mitigate against subjective and feature selection biases and assess performance across a range of motor features as part of a broader strategy to avoid overly optimistic performance estimates.
format	article
author	Ashwani Jha Elisa Menozzi Rebecca Oyekan Anna Latorre Eoin Mulroy Sebastian R. Schreglmann Cosmin Stamate Ioannis Daskalopoulos Stefan Kueppers Marco Luchini John C. Rothwell George Roussos Kailash P. Bhatia
author_facet	Ashwani Jha Elisa Menozzi Rebecca Oyekan Anna Latorre Eoin Mulroy Sebastian R. Schreglmann Cosmin Stamate Ioannis Daskalopoulos Stefan Kueppers Marco Luchini John C. Rothwell George Roussos Kailash P. Bhatia
author_sort	Ashwani Jha
title	The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters
title_short	The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters
title_full	The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters
title_fullStr	The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters
title_full_unstemmed	The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters
title_sort	cloudupdrs smartphone software in parkinson’s study: cross-validation against blinded human raters
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/dbd853c5a047468e8202a0decd920ae5
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The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters

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