AS01 Closed-loop System and Algorithm

471 - SIMPLIFYING THE MEAL-TIME BOLUS CALCULATION: ACCURACY OF A PERSONALIZED SMALL/MEDIUM/LARGE (S/M/L) OPTION

Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM

Abstract

Background and Aims

To simplify the complexity of carb counting, a Meal Simplification (MS) algorithm was developed that allows one to choose a user-specific small, medium, or large carb-entry size (S/M/L), instead of entering estimated carbs to the nearest gram.

Methods

The MS system works in two phases (Figure). Phase-1 is the learning phase where traditional carb-counting and entry are carried out via insulin pump or app. Once enough data are collected and uploaded to the cloud, the system enters Phase-2 where it uses machine-learning techniques to identify clusters of meals based on meal size. A set of common S/M/L meal sizes with a corresponding range of carb values and a centroid carb value, on which a meal bolus amount is calculated, is determined. The user has the option to override the centroid carb value to adjust for the actual size of a specific meal. The algorithm updates the S/M/L carb-entry sizes to accommodate for these changes. An in-silico experiment was conducted with 2087 virtual Type 1 Diabetes subjects using the Medtronic Advanced Hybrid Closed-loop system.

Results

For mealtime boluses, the Control group involved the traditional carb-counting method. The Intervention group involved personalized S/M/L meal-clustering. Time spent between 70-180 mg/dL, <70 mg/dL, >180 mg/dL, and >250 mg/dL for the Control versus Intervention arm were 85.1±6.8% versus 84.5±6.6%, 2.0±2.6% versus 2.1±2.5%, 12.9±7.1% versus 13.4±7.1%, and 2.1±2.1% versus 2.3±2.6%, respectively.

Conclusions

Personalized Meal Simplification algorithms for pump users are expected to reduce the burden of carb counting to the nearest gram, without increasing hypoglycemia/hyperglycemia exposure.

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