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

Journal article
Authors Ilias Thomas
Moudud Alam
Filip Bergquist
Dongni Johansson
Mevludin Memedi
Dag Nyholm
Jerker Westin
Published in Journal of Neurology
Volume 266
Issue 3
Pages 651–658
ISSN 0340-5354
Publication year 2019
Published at Institute of Neuroscience and Physiology, Department of Clinical Neuroscience
Institute of Neuroscience and Physiology, Department of Pharmacology
Pages 651–658
Language en
Links doi.org/10.1007/s00415-019-09183-6
Keywords Algorithmic suggestions, Levodopa, Oral medication, Parkinson’s disease, Sensor data
Subject categories Pharmacology, Neurology

Abstract

© 2019, The Author(s). 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 for PD patients.

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