Evidence map›Paper›PMID 31775289›Full record

ArticleSensors (Basel, Switzerland)2019

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

Hamid Khodakarami, Lucia Ricciardi, Maria Fiorella Contarino, Rajesh Pahwa, Kelly E Lyons, Victor J Geraedts, Francesca Morgante, Alison Leake, Dominic Paviour, Andrea De Angelis and 1 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Hamid KhodakaramiGlobal Kinetics Pty Ltd., 31 Queens St., Melbourne 3000, Australia.
Lucia RicciardiNeurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, Crammer Terrace, London SW18 0RE, UK.
Maria Fiorella ContarinoDepartment of Neurology, Leiden University Medical Center, K5-Q103 Albinusdreef 2, 2333 ZA Leiden, The Netherlands.ORCID 0000-0002-0312-184X
Rajesh PahwaParkinson's Disease and Movement Disorder Center, University of Kansas Medical Center, 3599 Rainbow Blvd, MS 3042, Kansas City, KS 66160, USA.
Kelly E LyonsParkinson's Disease and Movement Disorder Center, University of Kansas Medical Center, 3599 Rainbow Blvd, MS 3042, Kansas City, KS 66160, USA.
Victor J GeraedtsDepartment of Neurology, Leiden University Medical Center, K5-Q103 Albinusdreef 2, 2333 ZA Leiden, The Netherlands.ORCID 0000-0002-6604-8707
Francesca MorganteNeurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, Crammer Terrace, London SW18 0RE, UK.ORCID 0000-0002-9834-3639
Alison LeakeNeurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, Crammer Terrace, London SW18 0RE, UK.
Dominic PaviourNeurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, Crammer Terrace, London SW18 0RE, UK.
Andrea De AngelisNeurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, Crammer Terrace, London SW18 0RE, UK.
Malcolm HorneFlorey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC 3010, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AgedAntiparkinson AgentsFemaleHumansLevodopaMaleMiddle AgedParkinson DiseaseReproducibility of ResultsWearable Electronic DevicesWristWrist JointAntiparkinson AgentsLevodopaambulatory systemslevodopa challenge testlevodopa responsemachine learningParkinson’s Diseasewearable devices

Identifiers

PMID31775289
PMCPMC6928714

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.