Background and Aims
Functional insulin therapy (FIT), i.e., adjusting meal insulin to estimated meal carbohydrate (CHOmeal) and pre-meal blood glucose (BGmeal), is the recommended approach for prandial insulin dosing. However, the choice of FIT parameters (i.e., insulin-to-carbohydrate ratio [CR] and correction factor [CF]) remains challenging. Here, we propose a novel data-driven method for the online adaptation of FIT parameters.
A multi-step method was developed. A linear model of postprandial minimum glycemia (PMBG) is built, with BGmeal, CHOmeal, CR and CF as model predictors. At every meal, the model is identified using recursive least squares; simultaneously, distributions of CHOmeal, BGmeal and model error (all assumed normal) are updated. At every weekly adaptation run, optimal CR and CF are derived by minimizing the variance of PMBG, imposing 5% of PMBG values <70mg/dl. To test the method, a 10-week in-silico study was built using the UVA/Padova simulator. One-hundred virtual adults received 3 meals/day of random size, with 50% carbohydrate counting error, and meal boluses delayed up to 60min. Hypoglycemia treatments were administered at glycemia <70mg/dl. The method (initialized with random error on CR and CF) was compared to the subjects’ original FIT parameters.
The evolution of glycemic outcomes is shown in the figure. At week 10, average time in 70-180mg/dl, <70mg/dl, and >180mg/dl, and number of hypoglycemia treatments/day were (original–adapted parameters): 82.6%–83.7%, 2.8%–0.4%, 14.7%–16%, and 2.3–0.3.
The novel approach to optimize FIT parameters corrects for suboptimal CR/CF and improves glycemic outcomes in a simulation environment; further studies are needed to assess its robustness for real-life applications.