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Sensor-based algori...
Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease : a first experience
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- Thomas, Ilias (författare)
- Högskolan Dalarna,Mikrodataanalys,Department of Micro-data Analysis, Dalarna University, Falun, Sweden
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- Alam, Moudud, 1976- (författare)
- Högskolan Dalarna,Statistik,Department of Micro-data Analysis, Dalarna University, Falun, Sweden
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- Bergquist, Filip, 1970 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för farmakologi,Institute of Neuroscience and Physiology, Department of Pharmacology
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- Johansson, Dongni, 1988 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience
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- Memedi, Mevludin, PhD, 1983- (författare)
- Örebro universitet,Handelshögskolan vid Örebro Universitet
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- Nyholm, Dag (författare)
- Uppsala universitet,Neurologi
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- Westin, Jerker (författare)
- Högskolan Dalarna,Datateknik,Department of Micro-data Analysis, Dalarna University, Falun, Sweden
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(creator_code:org_t)
- 2019-01-18
- 2019
- Engelska.
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Ingår i: Journal of Neurology. - : Springer Science and Business Media LLC. - 0340-5354 .- 1432-1459. ; 266:3, s. 651-658
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Annan medicin och hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Other Medical and Health Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Farmakologi och toxikologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Pharmacology and Toxicology (hsv//eng)
Nyckelord
- Algorithmic suggestions
- Levodopa
- Oral medication
- Parkinson’s disease
- Sensor data
- Complex Systems – Microdata Analysis
- Komplexa system - mikrodataanalys
- Complex Systems – Microdata Analysis
- Komplexa system - mikrodataanalys
- Informatics
- Algorithmic suggestions
- Levodopa
- Oral medication
- Parkinson’s disease
- Sensor data
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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