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Individual dose-response models for levodopa infusion dose optimization
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-3183-3756
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2018 (English)In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 112, p. 137-142Article in journal (Refereed) Published
Abstract [en]

Background and Objective

To achieve optimal effect with continuous infusion treatment in Parkinson’s disease (PD), the individual doses (morning dose and continuous infusion rate) are titrated by trained medical personnel. This study describes an algorithmic method to derive optimized dosing suggestions for infusion treatment of PD, by fitting individual dose-response models. The feasibility of the proposed method was investigated using patient chart data.

Methods

Patient records were collected at Uppsala University hospital which provided dosing information and dose-response evaluations. Mathematical optimization was used to fit individual patient models using the records’ information, by minimizing an objective function. The individual models were passed to a dose optimization algorithm, which derived an optimized dosing suggestion for each patient model.

Results

Using data from a single day’s admission the algorithm showed great ability to fit appropriate individual patient models and derive optimized doses. The infusion rate dosing suggestions had 0.88 correlation and 10% absolute mean relative error compared to the optimal doses as determined by the hospital’s treating team. The morning dose suggestions were consistency lower that the optimal morning doses, which could be attributed to different dosing strategies and/or lack of on-off evaluations in the morning.

Conclusion

The proposed method showed promise and could be applied in clinical practice, to provide the hospital personnel with additional information when making dose adjustment decisions.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 112, p. 137-142
Keywords [en]
Levodopa infusion; Algorithmic dosing suggestions; Patient-specific models; Parkinson’s disease
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-27065DOI: 10.1016/j.ijmedinf.2018.01.018ISI: 000426130900018PubMedID: 29500011OAI: oai:DiVA.org:du-27065DiVA, id: diva2:1179812
Available from: 2018-02-02 Created: 2018-02-02 Last updated: 2019-02-06Bibliographically approved
In thesis
1. Automating levodopa dosing schedules for Parkinson’s disease
Open this publication in new window or tab >>Automating levodopa dosing schedules for Parkinson’s disease
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2019
Series
Dalarna Doctoral Dissertations in Microdata Analysis ; 9
Keywords
Parkinson’s disease, levodopa, symptom assessment, symptom management, dosing algorithms, wearable sensors, microtablets, continuous infusion
National Category
Computer Sciences Computer Systems Information Systems
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
Identifiers
urn:nbn:se:du-29435 (URN)978-91-85941-80-3 (ISBN)
Public defence
2019-04-05, sal Clas Ohlson, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2019-03-11 Created: 2019-02-06 Last updated: 2019-06-17Bibliographically approved

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Thomas, IliasAlam, MoududWestin, Jerker

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