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Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience.
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2019 (English)In: Journal of Neurology, ISSN 0340-5354, E-ISSN 1432-1459, Vol. 266, no 3, p. 651-658Article in journal (Refereed) Published
Abstract [en]

Objective: Dosing 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 methods: In 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.

Results: The 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.

Conclusion: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication forPD patients.

Place, publisher, year, edition, pages
2019. Vol. 266, no 3, p. 651-658
National Category
Neurology
Identifiers
URN: urn:nbn:se:uu:diva-378390DOI: 10.1007/s00415-019-09183-6ISI: 000459203400013PubMedID: 30659356OAI: oai:DiVA.org:uu-378390DiVA, id: diva2:1293692
Funder
Knowledge FoundationVinnovaAvailable from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-06-12Bibliographically approved

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