Hypoglycaemia‐free artificial pancreas project

Magdelaine, Nicolas; Rivadeneira, Pablo S.; Chaillous, Lucy; Fournier-Guilloux, A.L.; Krempf, Michel; MohammadRidha, Taghreed; Aït‐Ahmed, Mourad; Moog, Claude H.

doi:10.1049/iet-syb.2018.5069

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…Several pump companies have considered the amount of IOB to avoid hypoglycemia and to keep the patient’s BG within the euglycemic range ( 28 ). IOB is a function of the duration of insulin activity (DIA) and the number of previous insulin amount ( 29 ). DIA is an individualized data that vary because of blood flow, injection site, temperature, and exercise ( 17 , 30 ).…”

Section: Methodsmentioning

confidence: 99%

In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes

et al. 2023

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Artificial pancreas (AP) is a useful tool for maintaining the blood glucose (BG) of patients with type 1 diabetes (T1D) within the euglycemic range. An intelligent controller has been developed based on general predictive control (GPC) for AP. This controller exhibits good performance with the UVA/Padova T1D mellitus simulator approved by the US Food and Drug Administration. In this work, the GPC controller was further evaluated under strict conditions, including a pump with noise and error, a CGM sensor with noise and error, a high carbohydrate intake, and a large population of 100 in-silico subjects. Test results showed that the subjects are in high risk for hypoglycemia. Thus, an insulin on board (IOB) calculator, as well as an adaptive control weighting parameter (AW) strategy, was introduced. The percentage of time spent in euglycemic range of the in-silico subjects was 86.0% ± 5.8%, and the patient group had a low risk of hypoglycemia with the GPC+IOB+AW controller. Moreover, the proposed AW strategy is more effective in hypoglycemia prevention and does not require any personalized data compared with the IOB calculator. Thus, the proposed controller realized an automatic control of the BG of patients with T1D without meal announcements and complex user interaction.

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Section: Methodsmentioning

confidence: 99%

In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes

et al. 2023

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“…According to the T1DMS User Manual, the mean LBGI and HBGI are minimal for all patients (see Table 1). Concerning the mean BG, most patients present levels below 140 mg/dl, which allow them to achieve the recommended HbA1c 3 target level of 6.5% 4 .…”

Section: Islet-based Closed-loop Therapy Assessmentmentioning

confidence: 99%

“…HbA1c, glycosylated hemoglobin A1c 4. The conversion was computed using the online eAG/A1C Conversion Calculator provided by the American Diabetes Association.…”

mentioning

confidence: 99%

Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes

et al. 2022

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In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients’ lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.

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“…Also ( 27 ), proposed an IOB decaying curve to set the input reference in the MPC cost function. The direct estimation of the IOB from a minimal physiological model was used in ( 28 ) where a control law was derived to avoid hypoglycemia. Additionally, a sliding-mode safety layer that runs outside the main control strategy was developed in ( 8 ) where the control action is modulated according to a surface computed as the difference between the estimation of the IOB and an IOB boundary.…”

Section: Introductionmentioning

confidence: 99%

Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability

et al. 2022

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The aim of control strategies for artificial pancreas systems is to calculate the insulin doses required by a subject with type 1 diabetes to regulate blood glucose levels by reducing hyperglycemia and avoiding the induction of hypoglycemia. Several control formulations developed for this end involve a safety constraint given by the insulin on board (IOB) estimation. This constraint has the purpose of reducing hypoglycemic episodes caused by insulin stacking. However, intrapatient variability constantly changes the patient’s response to insulin, and thus, an adaptive method is required to restrict the control action according to the current situation of the subject. In this work, the control action computed by an impulsive model predictive controller is modulated with a safety layer to satisfy an adaptive IOB constraint. This constraint is established with two main steps. First, upper and lower IOB bounds are generated with an interval model that accounts for parameter uncertainty, and thus, define the possible system responses. Second, the constraint is selected according to the current value of glycemia, an estimation of the plant-model mismatch, and their corresponding first and second time derivatives to anticipate the changes of both glucose levels and physiological variations. With this strategy satisfactory results were obtained in an adult cohort where random circadian variability and sensor noise were considered. A 92% time in normoglycemia was obtained, representing an increase of time in range compared to previous MPC strategies, and a reduction of time in hypoglycemia to 0% was achieved without dangerously increasing the time in hyperglycemia.

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Hypoglycaemia‐free artificial pancreas project

Cited by 12 publications

References 23 publications

In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes

In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes

Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes

Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability

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