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Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement.

Emilia Fushimi1,2, Patricio Colmegna2,3,4, Hernán De Battista1,2

  • 1Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina.

Journal of Diabetes Science and Technology
|July 25, 2019
PubMed
Summary
This summary is machine-generated.

A new artificial pancreas algorithm, automatic regulation of glucose with unannounced meals (ARGum), removes the need for manual mealtime announcements. This innovation improves time in euglycemic range for slow-absorbing meals, offering a less burdensome approach for type 1 diabetes management.

Keywords:
artificial pancreascarbohydrate countingsliding mode controlswitched control

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Area of Science:

  • Biomedical Engineering
  • Endocrinology
  • Diabetes Technology

Background:

  • Carbohydrate counting is a burdensome and error-prone requirement for type 1 diabetes management under standard or hybrid closed-loop control.
  • Existing artificial pancreas algorithms, like automatic regulation of glucose (ARG), reduce but do not eliminate manual input, still requiring mealtime and size declarations.

Purpose of the Study:

  • To introduce an automatic switching signal generator (SSG) to eliminate manual mealtime announcements in the ARG algorithm.
  • To evaluate the performance of the ARG algorithm with unannounced meals (ARGum) compared to announced meals (ARGam) in silico.

Main Methods:

  • Development of an SSG utilizing a Kalman filter and continuous glucose monitoring data.
  • In silico testing of the ARGum algorithm against the ARGam algorithm using diverse mixed meals and simulating intrapatient variability.

Main Results:

  • The ARGum strategy significantly increased time in the euglycemic range (70-180 mg/dL) for slow-absorbing meals compared to ARGam (87.8% vs. 78.1%).
  • Comparable glycemic control was observed between ARGum and ARGam for fast-absorbing meals and when accounting for intrapatient variability.
  • Simulations demonstrated comparable time in euglycemia between the two strategies under intrapatient variability (80.9% vs. 81.4%).

Conclusions:

  • The developed SSG effectively removes the need for manual mealtime announcements in the ARG algorithm.
  • In silico evaluations suggest the feasibility of in vivo testing for the ARG algorithm with unannounced meals.
  • The ARGum algorithm shows promise for a more automated and user-friendly artificial pancreas system for type 1 diabetes.