Resumen:
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[EN] 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 ...[+]
[EN] 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.
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