Insulinothérapie fonctionnelle : un modèle d’approche éducative pour les patients ayant un diabète de type 1

Mohn, Angelika; Kavan, Christine; Bourcelot, Emilie; Zimmermann, C.; Penfornis, A.

doi:10.1016/s1957-2557(12)70465-0

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…More specifically, the main drawback of the classical prediction models is—as it was shown in Reference 1—that they generate apparent equilibria during fasting periods, so that each value of blood glucose (BG) can be maintained constant, thanks to a different insulin infusion rate. This stands in contradiction with real‐life and the well‐accepted FIT, since patients display only one single insulin infusion rate, defined as the basal rate, which stabilizes the glycemia at any value as long as no meal is taken 16 . Opposite to what is done in the classical T1DM representations, the model developed in Reference 1 represents the patient realistically in a domain of validity, which mainly means that it has a critically stable equilibrium manifold (instead of a stable one).…”

Section: Introductionmentioning

confidence: 86%

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

González

Rivadeneira

Ferramosca

et al. 2020

Optim Control Appl Methods

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Summary In this work, the problem of regulating blood glucose (glycemia) in type I diabetic patients is studied by means of an impulsive zone model predictive control (iZMPC), which bases its predictions on a novel long‐term glucose‐insulin model. Taking advantage of the impulsive version of the model—which features real‐life properties of diabetes patients that some other popular models do not—the given control guarantees the stability under moderate‐to‐severe plant‐model mismatch and disturbances. Long‐term scenarios—including meals and physiological parameter variations—are simulated and the results are satisfactory as every hyperglycemic and hypoglycemic episodes are suitably controlled.

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Section: Introductionmentioning

confidence: 86%

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

González

Rivadeneira

Ferramosca

et al. 2020

Optim Control Appl Methods

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“…It defines tools such as the insulin sensitivity factor (ISF), also known as the CF, and the carbohydrate-to-insulin ratio (CIR). These tools, empirically estimated from clinical protocols [12], are used to compute insulin boluses depending on BG level, BG target, meal carbohydrates (CHO) content, and the insulin on board (IOB) still active from previous boluses.…”

Section: Difficulty Of Computing the Bolus Of Insulinmentioning

confidence: 99%

“…When fasting, the correct basal insulin rate is established when glycaemia is maintained constant [12]. The equilibrium values x 2 Bas and x 3 Bas are thus…”

Section: Iob At Time Tmentioning

confidence: 99%

“…It defines tools such as the insulin sensitivity factor (ISF), also known as the CF, and the carbohydrate‐to‐insulin ratio (CIR). These tools, empirically estimated from clinical protocols [12], are used to compute insulin boluses depending on BG level, BG target, meal carbohydrates (CHO) content, and the insulin on board (IOB) still active from previous boluses. The correction bolus

U_{BG}

, depending on the patient's CF, BG level and BG target, is

U_{BG} = \frac{B {normalG}_{level} - B {normalG}_{target}}{CF}

The meal bolus

U_{Carb}

depends on the patient's CIR and the amount of CHO in the meal

U_{Carb} = \frac{CHO}{CIR}

The IOB is the number of insulin units that are still active in the body.…”

Section: Hfs Control Lawmentioning

confidence: 99%

“…

u = U_{Bas} + u_{bol}

. Thus the states

x_{2}

and

x_{3}

can also be written as the corresponding sums

x_{3} = x_{3_{Bas}} + x_{3_{bol}} = x_{3_{Bas}} + {\tilde{x}}_{3},

x_{2} = x_{2_{Bas}} + x_{2_{bol}} = x_{2_{Bas}} + {\tilde{x}}_{2} .

When fasting, the correct basal insulin rate is established when glycaemia is maintained constant [12]. The equilibrium values

x_{2_{Bas}}

and

x_{3_{Bas}}

are thus

U_{Bas} = \frac{θ_{1}}{θ_{2}} \Rightarrow x_{3_{Bas}} = x_{2_{Bas}} = \frac{θ_{1}}{θ_{2}},

and from (4) and (9) the glycaemia dynamics is written as

\begin{matrix} right leftthickmathspace.5em \\ {\dot{x}}_{1} & = θ_{1} - θ_{2} x_{2} = θ_{1} - θ_{2} (x_{2_{Bas}} + {\tilde{x}}_{2}), \\ {\dot{x}}_{1} & = θ_{1} - θ_{2} (θ_{1}) \end{matrix}

…”

Section: Hfs Control Lawmentioning

confidence: 99%

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Hypoglycaemia‐free artificial pancreas project

Magdelaine

Rivadeneira

Chaillous

et al. 2020

IET Systems Biology

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Driving blood glycaemia from hyperglycaemia to euglycaemia as fast as possible while avoiding hypoglycaemia is a major problem for decades for type-1 diabetes and is solved in this study. A control algorithm is designed that guaranties hypoglycaemia avoidance for the first time both from the theory of positive systems point of view and from the most pragmatic clinical practice. The solution consists of a state feedback control law that computes the required hyperglycaemia correction bolus in real-time to safely steer glycaemia to the target. A rigorous proof is given that shows that the control-law respects the positivity of the control and of the glucose concentration error: as a result, no hypoglycaemic episode occurs. The so-called hypo-free strategy control is tested with all the UVA/Padova T1DM simulator patients (i.e. ten adults, ten adolescents, and ten children) during a fasting-night scenario and in a hybrid closed-loop scenario including three meals. The theoretical results are assessed by the simulations on a large cohort of virtual patients and encourage clinical trials.

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P108: Évaluation de l’éducation à l’insulinothérapie fonctionnelle chez le diabétique de type 1

Berriche

Gammoudi

Koubaa

et al. 2014

Nutrition Clinique et Métabolisme

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Insulinothérapie fonctionnelle : un modèle d’approche éducative pour les patients ayant un diabète de type 1

Cited by 8 publications

References 9 publications

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

Hypoglycaemia‐free artificial pancreas project

P108: Évaluation de l’éducation à l’insulinothérapie fonctionnelle chez le diabétique de type 1

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