Background and Aims

Incorporation of an insulin sensor may help to improve performance of future AP algorithms by reducing severe hypoglycemic events. Optimal insulin measurement intervals were identified for a feedback-based threshold suspend safety-layer.

Methods

Personalized Kalman filter-estimated plasma insulin concentration (EPIC) measurements were used to supplement a zone model predictive control algorithm. Insulin delivery was suspended when both CGM was <140 mg/dL and EPIC values were greater than a personalized threshold based on fasting basal insulin concentrations. EPIC measurements occurred at 5-, 30-, 60-, 120-, and 180-minute intervals. Using the UVA/Padova Simulator, the controller was evaluated across ten in-silico subjects for three 8-hour, 50-gram carbohydrate scenarios: 1) sixty-minute exercise, induced via increasing glucose uptake rates, one hour after an announced meal, 2) meal size overestimation by 35% with carbohydrate ratio underestimated by 25%, and 3) announced meal (baseline).

Results

Implementing the EPIC safety-layer, the mean percent time below 70 mg/dL decreased: from 8.09±9.08% to 2.47±5.24% for 5-minute, 7.07±7.75% for 30-minute, 7.57±8.28% for 60-minute, and 7.59±8.26% for 120- through 180-minute intervals (scenario 1); from 5.07±5.33% to 0.00±0.00% for 5- through 30-minute, 0.87±2.76% for 60-minute, 2.12±4.65% for 120-minute, and 3.16±5.38% for 180-minute intervals (scenario 2); and from 0.69±2.17% to 0.00±0.00% for 30- through 120-minute, while remaining at 0.69±2.17% for 180-minute intervals (scenario 3). Infrequent measurements of 30- to 120- minutes resulted in slight performance degradation with increasing sample time.

*Indicates p-value<0.05

Conclusions

The EPIC safety-layer in-silico prevented severe hypoglycemia during challenging scenarios without significant rebound hyperglycemia. Future insulin sensors could potentially be designed utilizing 30- to 120-minute measurement intervals.