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Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study

Medical & Biological Engineering & Computing volume 58, pages 2325–2337 (2020)Cite this article

Abstract

Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, an algorithm based on switched control and time-varying IOB constraints is presented. The proposed control strategy is evaluated in silico using the FDA-approved UVA/ Padova simulator and its performance contrasted with the previously introduced Automatic Regulation of Glucose (ARG) algorithm in the pediatric population. The effect of unannounced meals is also explored. Results indicate that the proposed strategy achieves lower hypo- and hyperglycemia than the ARG for both announced and unannounced meals.

Block diagram and illustrative example of insulin and glucose evolution over time for the proposed algorithm (ARGAE)

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Funding

Research in this area is supported by the Argentine government (PICT 2017-3211 Agencia Nacional de Promoción Científica y Tecnológica, PIP 112-201501-00837 CONICET, and UNLP 11/I216).

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Authors and Affiliations

  1. Grupo de Control Aplicado (GCA), Instituto LEICI, Facultad de Ingeniería, UNLP-CONICET, 48 y 116 CC, 91 (1900) La Plata, Buenos Aires, Argentina

    Emilia Fushimi, María Cecilia Serafini, Hernán De Battista & Fabricio Garelli

Authors
  1. Emilia Fushimi
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  2. María Cecilia Serafini
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  3. Hernán De Battista
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  4. Fabricio Garelli
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Correspondence to Emilia Fushimi.

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Appendix

Appendix

Here, in detail, tables are shown in order to provide a more accurate representation of the results from the in silico study performed in this paper. All tables show median [Q1–Q3]. Statistical significance is shown in superscripts (∗ for p value < 0.05, ⋆ for p value < 0.01, † for p value < 0.005).

Table 1 Results (% of time) for the 10 adolescents of the UVA/Padova simulator using SAP, the ARG, ARG80%, and ARGAE with different values of β
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Table 2 Results (% of time) for the 10 children of the UVA/Padova simulator using SAP, the ARG, ARG80%, and ARGAE with different values of β
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Table 3 Results (% of time) for the 10 adolescents and the 10 children of the UVA/Padova simulator using SAP, the ARG, and ARGAE with β = 1.40 (adolescents) and β = 1.25 (children)
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Table 4 Results (% of time) for the 10 adolescents an 10 children of the UVA/Padova simulator using SAP, the ARG, and ARGAE with β = 1.40 (adolescents) and β = 1.25 (children)
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Table 5 Results (% of time) for the 10 adolescents of the UVA/Padova Simulator using the ARG, ARG80%, and ARGAE with different values of β
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Table 6 Results (% of time) for the 10 children of the UVA/Padova simulator using the ARG, ARG80%, and ARGAE with different values of β
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Table 7 Results (% of time) for the 10 adolescents and 10 children of the UVA/Padova simulator using the ARG and ARGAE with β = 1.15 (adolescents) and β = 1.05 (children)
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Table 8 Results (% of time) for the 10 adolescents and 10 children of the UVA/Padova simulator using the ARG and ARGAE with β = 1.15 (adolescents) and β = 1.05 (children)
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Fushimi, E., Serafini, M.C., De Battista, H. et al. Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study. Med Biol Eng Comput 58, 2325–2337 (2020). https://doi.org/10.1007/s11517-020-02213-w

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  • DOI: https://doi.org/10.1007/s11517-020-02213-w

Keywords

  • Artificial pancreas
  • Switched control
  • Insulin on board
  • Constrained control