Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems

• Published in: Computer methods and programs in biomedicine 2022-11, Vol.226, p.107061-107061, Article 107061
• Main Authors: Sala-Mira, Iván, Garcia, Pedro, Díez, José-Luis, Bondia, Jorge
• Format: Article
• Language: English
• Subjects: Artificial pancreas; Disturbance rejection; Hypoglycemia avoidance; Internal model control; Postprandial control; Type 1 diabetes
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.107061

•The proposed module releases patients from meal and exercise announcements.•Besides infusing insulin, the module suggests rescue carbohydrates.•The module preserves the time in range of a hybrid system with counting errors.•Rescues suggestions mitigate hypoglycemia after aerobic exercise. Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements. The add-on module consists of an internal-model controller that generates a “virtual” control action to compensate for disturbances. This “virtual” action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise. For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g). The proposed add-on module achieved promising results: it outperformed the meal-detector-