Evidence map›Paper›PMID 30659356›Full record

ArticleJournal of neurology2019

Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience.

Ilias Thomas, Moudud Alam, Filip Bergquist, Dongni Johansson, Mevludin Memedi, Dag Nyholm, Jerker Westin

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 15% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Ilias ThomasDepartment of Micro-data Analysis, Dalarna University, 79 131, Falun, Sweden. ith@du.se.ORCID http://orcid.org/0000-0002-5795-7677
Moudud AlamDepartment of Micro-data Analysis, Dalarna University, 79 131, Falun, Sweden.
Filip BergquistDepartment of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden.
Dongni JohanssonDepartment of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden.
Mevludin MemediSchool of Business, Orebro University, 702 81, Örebro, Sweden.
Dag NyholmDepartment of Neuroscience, Neurology, Uppsala University, 751 85, Uppsala, Sweden.
Jerker WestinDepartment of Micro-data Analysis, Dalarna University, 79 131, Falun, Sweden.
Dalarna University · SEUniversity of Gothenburg · SEÖrebro University · SEUppsala University · SE

Funding

VINNOVA 2014-03727
6 · The paper itself

Abstract

objectiveDosing schedules for oral levodopa in advanced stages of Parkinson's disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS). MATERIALS AND

methodsIn collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson's KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.

resultsThe SBDS maintenance and morning dosing suggestions had a Pearson's correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician's adjustments.

conclusionThis study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.

Indexed as

AlgorithmsWearable Electronic DevicesActigraphyAdministration, OralAgedAged, 80 and overAntiparkinson AgentsCarbidopaDrug CombinationsFeasibility StudiesFemaleHumansLevodopaMaleMiddle AgedParkinson DiseaseAntiparkinson AgentsCarbidopacarbidopa, levodopa drug combinationDrug CombinationsLevodopaAlgorithmic suggestionsLevodopaOral medicationParkinson’s diseaseSensor data

Identifiers

PMID30659356
PMCPMC6394802
OpenAlexW2910569248

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.