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Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-3183-3756
Visa övriga samt affilieringar
2019 (Engelska)Ingår i: Journal of Neurology, ISSN 0340-5354, E-ISSN 1432-1459, Vol. 266, nr 3, s. 651-658Artikel i tidskrift (Refereegranskat) 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 for PD patients.

Ort, förlag, år, upplaga, sidor
2019. Vol. 266, nr 3, s. 651-658
Nyckelord [en]
Algorithmic suggestions, Levodopa, Oral medication, Parkinson’s disease, Sensor data
Nationell ämneskategori
Sannolikhetsteori och statistik Annan medicin och hälsovetenskap
Forskningsämne
Komplexa system - mikrodataanalys; Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-29314DOI: 10.1007/s00415-019-09183-6ISI: 000459203400013PubMedID: 30659356Scopus ID: 2-s2.0-85060256040OAI: oai:DiVA.org:du-29314DiVA, id: diva2:1280774
Tillgänglig från: 2019-01-21 Skapad: 2019-01-21 Senast uppdaterad: 2019-03-14Bibliografiskt granskad
Ingår i avhandling
1. Automating levodopa dosing schedules for Parkinson’s disease
Öppna denna publikation i ny flik eller fönster >>Automating levodopa dosing schedules for Parkinson’s disease
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Levodopa is mainly used to manage the motor symptoms of PD. However, disease progression and long-term use of levodopa cause reduced medication efficacy and side effects. When that happens, precise individualized dosing schedules are required.

This doctoral thesis in the field of Micro-data analysis introduces an end-to-end solution for the automation of the pharmacological management of PD with levodopa, and offers some insight on levodopa pharmacodynamics. For that purpose, an algorithm that derives objective ratings for the patients’ motor function through wearable sensors is introduced, a method to construct individual patient profiles is developed, and two dosing algorithms for oral and intestinal administration of levodopa are presented. Data from five different sources were used to develop the methods and evaluate the performance of the proposed algorithms.

The dose automation algorithms can work both with clinical and objective ratings (through wearable devices), and their application was evaluated against dosing adjustments from movement disorders experts. Both dosing algorithms showed promise and their dosing suggestions were similar to those of the clinicians.

The objective ratings algorithm had good test-retest reliability and its application during a clinical study was successful. Furthermore, the method of fitting individual patient models was robust and worked well with the objective ratings algorithm. Finally, a study was carried out that showed that about half the patients on levodopa treatment show reduced response during the afternoon hours, pointing to the need for more precise modelling of levodopa pharmacodynamics.

Ort, förlag, år, upplaga, sidor
Borlänge: Dalarna University, 2019
Serie
Dalarna Doctoral Dissertations ; 9
Nyckelord
Parkinson’s disease, levodopa, symptom assessment, symptom management, dosing algorithms, wearable sensors, microtablets, continuous infusion
Nationell ämneskategori
Datavetenskap (datalogi) Datorsystem Systemvetenskap, informationssystem och informatik
Forskningsämne
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
urn:nbn:se:du-29435 (URN)978-91-85941-80-3 (ISBN)
Disputation
2019-04-05, sal Clas Ohlson, Borlänge, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-03-11 Skapad: 2019-02-06 Senast uppdaterad: 2020-01-10Bibliografiskt granskad

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