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In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70–180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future. [ABSTRACT FROM AUTHOR]

The artificial pancreas (AP) system or closed-loop blood glucose (BG) regulation is a technological advancement that aims to relieve diabetic subjects from their current decision-making burden while tightening their BG levels. However, large disturbances such as meals and exercise still pose great challenges to a fully closed-loop system. In this paper, the problem of BG regulation with unannounced physical activity for type 1 diabetic subjects is addressed. We use a coordinated control strategy with insulin infusion and extra carbohydrates (CHO) for hypoglycemia avoidance. The insulin algorithm is based on a proportional–derivative controller with insulin feedback and the so-called safety auxiliary feedback element (SAFE) layer, and the algorithm for CHO is based on a predictive, quantified proportional-derivative controller. The UVa/Padua simulator glucose-insulin model is modified to include the effects of physical activity and is used to test the new AP. We consider scenarios where the subject does not announce physical activity and with challenging meals. Then, we analyze the performance and robustness of the combined insulin and CHO recommender system and compare them to the insulin-only controller. The simulation results show that the new AP system is able to mitigate daily hypoglycemia episodes (0.9 versus 0.2, $p < 0.01$) and increase the time in range during day (91.5% versus 92.4%, $p < 0.01$) without increasing the time above 180 mg/dl (6.3% versus 6.4%, $p > 0.05$). [ABSTRACT FROM AUTHOR]

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients' safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called "dynamic rule-based algorithm," is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients' safety, which reveals the feasibility of the approach to be included in different control algorithms. [ABSTRACT FROM AUTHOR]

Preclinical testing and validation of therapeutic strategies developed for patients with type 1 diabetes (T1D) require a cohort of virtual patients (VPs). However, current simulators provide a limited number of VPs, lack real-life scenarios, and inadequately represent intra- and inter-day variability in insulin sensitivity and blood glucose (BG) profile. The generation of a realistic scenario was achieved by using the meal patterns, insulin profiles (basal and bolus), and exercise sessions estimated as disturbances using clinical data from a cohort of 14 T1D patients using the Medtronic 640G insulin pump provided by the Hospital Clínic de Barcelona. The UVa/Padova's cohort of adult patients was used for the generation of a new cohort of VPs. Insulin model parameters were optimized and adjusted in a day-by-day fashion to replicate the clinical data to create a cohort of 75 VPs. All primary and secondary outcomes reflecting the BG profile of a T1D patient were analyzed and compared to the clinical data. The mean BG 166.3 versus 162.2 mg/dL (p = 0.19), coefficient of variation 32% versus 33% (p = 0.54), and percent of time in range (70 to 180 mg/dL) 59.6% versus 66.8% (p = 0.35) were achieved. The proposed methodology for generating a cohort of VPs is capable of mimicking the BG metrics of a real cohort of T1D patients from the Hospital Clínic de Barcelona. It can adopt the inter-day variations in the BG profile, similar to the observed clinical data, and thus provide a benchmark for preclinical testing of control techniques and therapy strategies for T1D patients. [ABSTRACT FROM AUTHOR]