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  4. Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability
 
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Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability
File(s)
Comput Methods Programs Biomed_2017_146-125-131.pdf (712.79 KB)
Accepted version
Author(s)
Herrero, Pau
Bondia, Jorge
Adewuyi, Oloruntoba
Pesl, Peter
El-Sharkawy, Mohamed
more
Type
Journal Article
Abstract
Background and Objective
Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.

Methods
In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.

Results
Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; percentage time in target [70, 180] mg/dl, 82.0 ± 7.0 vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0 vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4 vs. 140.5 ± 13.0 mg/dl; percentage time in target, 65.9 ± 12.9 vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1 vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed.

Conclusion
Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.
Date Issued
2017-07-01
Date Acceptance
2017-05-25
Citation
Computer Methods and Programs in Biomedicine, 2017, 146, pp.125-131
URI
http://hdl.handle.net/10044/1/69802
DOI
https://www.dx.doi.org/10.1016/j.cmpb.2017.05.010
ISSN
0169-2607
Publisher
Elsevier
Start Page
125
End Page
131
Journal / Book Title
Computer Methods and Programs in Biomedicine
Volume
146
Copyright Statement
© 2017 Elsevier B.V. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Wellcome Trust
Wellcome Trust
DexCom Inc.
Medical Research Council (MRC)
National Institute for Health Research
Imperial College Healthcare NHS Trust- BRC Funding
Grant Number
WT 100921/Z/13/Z
089758/Z/09/Z
Imperial IIS-2015-021
MC_PC_12015
RDA11 79560
RDA29
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Engineering, Biomedical
Medical Informatics
Computer Science
Engineering
Artificial pancreas
Diabetes
Case-based reasoning
Run-to-Run control
TO-RUN CONTROL
PANCREAS
ADULTS
Publication Status
Published
Date Publish Online
2017-06-01
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