دورية أكاديمية
Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient.
| العنوان: | Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient. |
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| المؤلفون: | Mansell EJ; Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand . Electronic address: erin.mansell@pg.canterbury.ac.nz., Docherty PD; Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand . Electronic address: paul.docherty@canterbury.ac.nz., Fisk LM; Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand., Chase JG; Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand. |
| المصدر: | Mathematical biosciences [Math Biosci] 2015 Aug; Vol. 266, pp. 108-17. Date of Electronic Publication: 2015 Jun 17. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Elsevier Country of Publication: United States NLM ID: 0103146 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-3134 (Electronic) Linking ISSN: 00255564 NLM ISO Abbreviation: Math Biosci Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York, American Elsevier. |
| مواضيع طبية MeSH: | Models, Theoretical*, Blood Glucose/*metabolism , Diabetes Mellitus, Type 1/*metabolism, Humans |
| مستخلص: | Some individuals with type 1 diabetes mellitus find self-managed glycaemic control difficult due to the confounding influence of secondary effects. Stress and sleep deprivation temporarily lower insulin sensitivity (SI), often resulting in hyperglycaemia, while aerobic exercise depletes glucose, leading to hypoglycaemia if treatment is unchanged. This study tests the estimation of these factors and circadian rhythms of SI in noisy data. Sparse, irregular and noisy virtual blood glucose data, mimicking the glycaemic dynamics of an individual with type 1 diabetes, was created via adapted pharmacokinetic-pharmacodynamic models of glucose and insulin that included the impact of the secondary effects. A Gauss-Newton algorithm was used to recover the original model parameters for SI, stress, fatigue and exercise. During longer identification periods, compensation was made for drift in SI. Monte Carlo analyses were undertaken to validate the methods. The coefficient of variation (CV) in all parameters decreased as the data accumulated in proportion to the 1/n rule (R(2) > 99.9%). Relatively small biases from the original parameter values occurred (<1%). Long term drift trends in SI were captured and did not obscure estimation of the secondary effects (biases < 1%, CV approximately equivalent to drift free outcomes). Adherence to the 1/n trend indicates a robust identification method and the ability of accumulating data to override the effect of measurement error. Compensation for SI drift allows viable observation of secondary effects and SI rhythms over longer time periods. Collectively, these outcomes indicate that quality results for identified parameters could be obtained during in vivo studies. (Copyright © 2015 Elsevier Inc. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Gauss–Newton; Glycaemic control; Monte Carlo; Parameter identification; Stress; Type 1 diabetes |
| المشرفين على المادة: | 0 (Blood Glucose) |
| تواريخ الأحداث: | Date Created: 20150621 Date Completed: 20161214 Latest Revision: 20161230 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.mbs.2015.06.002 |
| PMID: | 26092607 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1879-3134 |
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| DOI: | 10.1016/j.mbs.2015.06.002 |