Type 1 Diabetes Mellitus
Conditions
Keywords
type 1 diabetes mellitus, automated insulin delivery, unannounced meals, proportional integral derivative control, model predictive control
Brief summary
The development of advanced hybrid closed-loop (a-HCL) systems represents a significant step toward in improving glucose control and reducing user-dependent variability, especially In pediatric patients. Systems can automatically deliver correction boluses and modulate insulin delivery based on CGM feedback, thereby compensating for some of the consequences of human error. Current evidence suggests that a-HCL systems can tolerate unannounced carbohydrate loads up to approximately 20 g without compromising time in range (TIR) or safety. However, the metabolic response to larger or compositionally complex meals remains variable and highly dependent on the specific algorithm governing insulin delivery. Currently a variety of AID are commercially available: all of them present similarities and differences. The Medtronic MiniMed™ 780G uses a proportional-integral-derivative (PID) algorithm, a mathematical model that adjusts in real time insulin delivery rate based on 3 elements obtained from CGM reading values: the difference between the actual value and the chosen glucose target (proportional action), past values (integral action) and the glucose's rate of change (derivative action), In contrast, the Tandem t:slim X2™ with Control-IQ employs a model predictive control (MPC) algorithm, which aims, through a complex mathematical model, to predict glucose trends up to half-an-hour in the future, takin, also, in consideration actual and past glucose values. Despite sharing the same objective, said algorithms have different approaches, the former one being "reactive" and the latter "predictive". Therefore, their difference could result in different performances while facing mixed-nutrient meal or unannounced meals, defined as the consumptions of a meal with any prior insulin administration. Pediatric patients represent certainly a unique subgroup in which therapeutic adherence is a relevant issue, due to cognitive, developmental and behavioral factors. Understanding how different AID algorithms respond to unannounced meals in this age group is therefore crucial for optimizing safety and personalization of diabetes management. This study was designed to evaluate the strengths and limitations of two a-HCL systems, the Medtronic 780G (PID algorithm) and the Tandem t:slim X2 (MPC algorithm), in managing unannounced meals with different macronutrient compositions in children and adolescents with T1D. We also aim to better understand physiological and technological unannounced meal implications as to provide additional insight useful for the development of new fully closed loop algorithms, capable of minimizing glucose excursions and patient's burden.
Interventions
OTHERUnannounced meal
Participants consumed both the "CHO meal" and the "mixed meal" three times each, without announcing their carbohydrate intake to the device. Consequently, no user-initiated boluses were administered during this period, and all insulin delivery adjustments were determined exclusively by the algorithm.
Sponsors
University of Catania
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
11 Years to 18 Years
Healthy volunteers
No
Inclusion criteria
* Age between 11 and 18 years; * Type 1 diabetes diagnosed for at least 12 months; * Use of an A-HCL device for at least 3 months; * Proficiency in carbohydrate counting.
Exclusion criteria
* Presence of malabsorption disorders; * Presence of chronic diabetes complications.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in blood glucose concentration from pre-meal to two hours post-meal (2h-ΔBG), as measured by CGM in mg/dl | from pre-meal to two hours post-meal | Change in blood glucose concentration from pre-meal to two hours post-meal (2h-ΔBG), as measured by CGM |
Secondary
| Measure | Time frame |
|---|---|
| Pre- and post-meal four-hour glucose difference (4h-ΔBG), as measured by CGM in mg/dl | from pre-meal to four hour postprandial |
| Peak CGM value in mg/dl | from pre-meal to four hour postprandial |
| Time to peak CGM value | from pre-meal to four hour postprandial |
| Percentage of spent time in range (TIR, 70-180 mg/dL), as measured by CGM | from pre-meal to four hour postprandial |
| Percentage of spent time in tight range (TITR, 70-140 mg/dL), as measured by CGM | from pre-meal to four hour postprandial |
| Percentage of spent time below range (TBR, < 70 mg/dL), as measured by CGM | from pre-meal to four hour postprandial |
| Percentage of spent time above range (TAR, > 180 mg/dL), as measured by CGM | from pre-meal to four hour postprandial |
| Percentage of spent time in second-level TAR (> 250 mg/dL), as measured by CGM | from pre-meal to four hour postprandial |
Countries
Italy
Outcome results
None listed