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

Khodakarami, Hamid; Ricciardi, Lucia; Contarino, Maria Fiorella; Pahwa, Rajesh; Lyons, Kelly E.; Geraedts, Victor J.; Morgante, Francesca; Leake, Alison; Paviour, Dominic; Angelis, Andrea De; Horne, Malcolm

doi:10.3390/s19235153

Cited by 41 publications

(58 citation statements)

References 53 publications

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“…The numerical output is summarised in the Glossary that follows. The reader is referred to the company's website for further details (http://www.globalkineticscorporation.com.au/) and to other publications (15,(23)(24)(25)(26)(27)(28).…”

Section: The Pkg Systemmentioning

confidence: 99%

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A Method for Measuring Time Spent in Bradykinesia and Dyskinesia in People With Parkinson’s Disease Using an Ambulatory Monitor

Khodakarami

Shokouhi

Horne

2021

Preprint

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BackgroundFluctuations in motor function in Parkinson’s Disease (PD) are frequent and cause significant disability. Frequently device assisted therapies are required to treat them. Currently, fluctuations are self-reported through diaries and history yet frequently people with PD do not accurately identify and report fluctuations. As the management of fluctuations and the outcomes of many clinical trials depend on accurately measuring fluctuations a means of objectively measuring time spent with bradykinesia or dyskinesia would be important. The aim of this study was to present a system that uses wearable sensors to measure the percentage of time that bradykinesia or dyskinesia scores are above a target as a means for assessing levels of treatment and fluctuations in PD.MethodsData in a database of 228 people with Parkinson’s Disease and 157 control subjects, who had worn the Parkinsons Kinetigraph ((PKG, Global Kinetics CorporationTM, Australia) and scores from the Unified Parkinsons Disease Rating Scale (UPDRS) and other clinic scales were used. The PKG’s provided score for bradykinesia and dyskinesia every two minutes and these were compared to a previously established target range representing a UPDRS III score of 35. The proportion of these scores above target over the 6days that the PKG was worn were used to derive the percent time in bradykinesia (PTB) and percent time in dyskinesia (PTD). As well, a previously describe algorithm for estimating the amplitude of the levodopa response was used to determine whether a subject was a fluctuator or non-fluctuator. ResultsUsing this approach, a normal range of PTB and PTD based on Control subject was developed. The level of PTB and PTD experienced by people with PD was compared with their levels of fluctuation. There was a correlation (Pearsons ρ=0.4) between UPDRS II scores and PTB: the correlation between Parkinson Disease Questionnaire scores and UPDRS Total scores and PTB and slightly lower. PTB and PTD fell in response to treatment for bradykinesia or dyskinesia (respectively) with greater sensitivity than clinical scales.ConclusionsThis approach provides an objective assessment of the severity of fluctuations in Parkinsons Disease that could readily easily be used in routine care and in clinical trials.

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Section: The Pkg Systemmentioning

confidence: 99%

“…Bradykinesia is considered "in target" and "controlled" when "Severity Level<2.5" and "above target" and "uncontrolled" when the "Severity Level≥2.5" and a "signi cant" LR is when the Severity Level changes by ≥1.15. This is based on a previous study (24) that used data from clinical levodopa challenge tests to…”

Section: Targetsmentioning

confidence: 99%

A Method for Measuring Time Spent in Bradykinesia and Dyskinesia in People With Parkinson’s Disease Using an Ambulatory Monitor

Khodakarami

Shokouhi

Horne

2021

Preprint

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“…The BKS and DKS are plotted against the time of day and the time when medications are due is also provided. The numerical output is described in detail in other publications 4,[10][11][12][13][14][15][16][17][18][19] . All outputs are derived from data recorded between 09:00 and 18:00 of the six recording days and those relevant to this study are summarised below.…”

Section: The Pkg Systemmentioning

confidence: 99%

“…A BKS of 26 corresponds to a UPDRS III of approximately 30 (ref. 17 ). The Dyskinesia target was a DKS < 7 corresponding to an Abnormal Involuntary Movement Score of~9 (ref.…”

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confidence: 99%

A blinded, controlled trial of objective measurement in Parkinson’s disease

Woodrow

Horne

Fernando

et al. 2020

npj Parkinsons Dis.

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Medical conditions with effective therapies are usually managed with objective measurement and therapeutic targets. Parkinson’s disease has effective therapies, but continuous objective measurement has only recently become available. This blinded, controlled study examined whether management of Parkinson’s disease was improved when clinical assessment and therapeutic decisions were aided by objective measurement. The primary endpoint was improvement in the Movement Disorder Society-United Parkinson’s Disease Rating Scale’s (MDS-UPDRS) Total Score. In one arm, objective measurement assisted doctors to alter therapy over successive visits until objective measurement scores were in target. Patients in the other arm were conventionally assessed and therapies were changed until judged optimal. There were 75 subjects in the objective measurement arm and 79 in the arm with conventional assessment and treatment. There were statistically significant improvements in the moderate clinically meaningful range in the MDS-UPDRS Total, III, IV scales in the arm using objective measurement, but not in the conventionally treated arm. These findings show that global motor and non-motor disability is improved when management of Parkinson’s disease is assisted by objective measurement.

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“…Khodakarami et al [51] examined the role of wearable sensors, specifically the personal kinetigraph, in an on/off stimulation classification problem. Their linear regression models determined classification with an AUC of 0.92.…”

Section: Programming Optimizationmentioning

confidence: 99%

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

et al. 2020

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Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson’s disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information’s Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers’ (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

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

Cited by 41 publications

References 53 publications

A Method for Measuring Time Spent in Bradykinesia and Dyskinesia in People With Parkinson’s Disease Using an Ambulatory Monitor

A Method for Measuring Time Spent in Bradykinesia and Dyskinesia in People With Parkinson’s Disease Using an Ambulatory Monitor

A blinded, controlled trial of objective measurement in Parkinson’s disease

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

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