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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Title: Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability
Authors: Herrero, P
Bondia, J
Adewuyi, O
Pesl, P
El-Sharkawy, M
Reddy, M
Toumazou, C
Oliver, N
Georgiou, P
Item 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.
Issue Date: 1-Jul-2017
Date of Acceptance: 25-May-2017
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
Sponsor/Funder: Wellcome Trust
Wellcome Trust
DexCom Inc.
Medical Research Council (MRC)
National Institute for Health Research
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: WT 100921/Z/13/Z
Imperial IIS-2015-021
RDA11 79560
Keywords: Science & Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Engineering, Biomedical
Medical Informatics
Computer Science
Artificial pancreas
Case-based reasoning
Run-to-Run control
Blood Glucose
Blood Glucose Self-Monitoring
Diabetes Mellitus, Type 1
Hypoglycemic Agents
Insulin Infusion Systems
Pancreas, Artificial
0903 Biomedical Engineering
Publication Status: Published
Online Publication Date: 2017-06-01
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Department of Medicine
Faculty of Medicine

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