Individualized multiple-day simulation model of type 1 diabetic patient decision-making for developing, testing and optimizing insulin therapies driven by glucose sensors
A mathematical model of type 1 diabetes (T1D) patient decision-making can be used to simulate, in silico, realistic glucose/insulin dynamics, for several days, in a variety of subjects who take therapeutic actions (e.g. insulin dosing) driven by either self-monitoring blood glucose (SMBG) or continuous glucose monitoring (CGM). The decision-making (DM) model can simulate real-life situations and everyday patient behaviors, Accurate submodels of SMBG and CGM measurement errors are incorporated in the comprehensive DM model. The DM model accounts for common errors the patients are used to doing in their diabetes management, such as miscalculations of meal carbohydrate content, early/delayed insulin administrations and missed insulin boluses. The DM model can be used to assess in silico if/when CGM can safely substitute SMBG in T1D management, to develop and test guidelines for CGM driven insulin dosing, to optimize and individualize off-line insulin therapies and to develop and test decision support systems.
1. A method for delivering a therapeutic recommendation for insulin delivery, comprising:
collecting data from a patient over a period of time, the data including continuous glucose monitoring data obtained from a glucose sensor and insulin pump data collected for the patient, the insulin pump data including an insulin infusion rate;
generating, based on the collected data, a model comprising estimated parameters of the patient's physiology that reproduces the patient's glucose-insulin dynamics;
inputting the estimated parameters into a type 1 diabetes patient glucose-insulin model, whereby an individualized patient model is obtained;
utilizing the individualized patient model in patient-specific simulations to estimate optimal insulin therapy parameters or individualized guidelines for continuous glucose monitoring-driven insulin dosing; and
outputting the optimal insulin therapy parameters.
2. The method of claim 1 , wherein outputting the optimal insulin therapy parameters comprises displaying the therapeutic recommendation to a patient on a display.
3. The method of claim 2 , wherein the display is a smartphone display.
4. The method of claim 1 , wherein outputting the optimal insulin therapy parameters comprises issuing instructions to an insulin pump for delivering an insulin dose.
5. The method of claim 1 , wherein utilizing the individualized patient model in patient-specific simulations comprises iteratively running simulations, wherein at the first iteration, the patient's current insulin therapy is implemented, then, at each subsequent iteration, the therapy parameters are updated until it is determined that the therapy parameters satisfy predetermined criteria.
6. The method of claim 1 , wherein it is determined if the therapy parameters need to be updated by assessing a level of glycemic control.