Adaptive tuning of basal and bolus insulin to reduce postprandial hypoglycemia in a hybrid artificial pancreas. (August 2019)

• Record Type: Journal Article
• Authors: Resalat, Navid; El Youssef, Joseph; Reddy, Ravi; Castle, Jessica; Jacobs, Peter G.
• Is Part Of: Journal of process control. Volume 80(2019)
• Page Start: 247
• Page End: 254
• Publication Date: 2019-08
• Subjects: AP artificial pancreas -- ALPHA adaptive learning postprandial hypoglycemia-prevention algorithm -- BR basal rate -- ICR insulin to carbohydrate ratio -- T1D type 1 diabetes -- MDI multiple daily injection -- CSII continuous subcutaneous insulin infusion -- CGM continuous glucose monitor -- IIR insulin infusion rate -- CHO carbohydrate -- FMPD fading memory proportional derivative -- LBGI low blood glucose index -- HBGI high blood glucose index; Type 1 diabetes -- Adaptive control -- Hybrid artificial pancreas; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.05.018
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Adaptive basal insulin dosing after meals can help reduce postprandial hypoglycemia within a hybrid artificial pancreas. Adapting postprandial insulin is better than adapting pre-meal insulin boluses for preventing hypoglycaemia. The ALPHA algorithm can be used to tune postprandial dosing to help people with type 1 diabetes avoid hypoglycaemia. Abstract: Objective: We introduce an adaptive learning algorithm to better adjust postprandial basal and pre-meal bolus insulin for reducing postprandial hypoglycemia in a hybrid artificial pancreas (AP). An AP uses a control algorithm and sensed glucose to automate the delivery of insulin to people with type 1 diabetes (T1D). A hybrid AP requires the person to dose insulin in advance of a meal. Insulin sensitivity is dynamic in people with T1D, making it challenging for an AP to maintain euglycemia. Adaptive approaches to meal dosing can help prevent postprandial hypoglycemia. Methods: An adaptive learning postprandial hypoglycemia-prevention algorithm (ALPHA) is introduced. One implementation of ALPHA adjusts the rate of postprandial insulin (ALPHA-BR) proportionally in response to prior postprandial episodes. This is achieved by an adaptive aggressiveness factor applied to postprandial basal insulin. The second implementation adaptively updates the pre-meal bolus insulin by changing the insulin-to-carbohydrate ratio (ALPHA-ICR), also proportionally in response to prior postprandial hypoglycemia. Both implementations were evaluated within an AP on an in-silico T1D virtual population of 99 subjects with circadian insulin sensitivity variations and 30% errors on meal estimations. Twenty real-world 4-day meal scenarios were given and glycemic outcomes were compared with an AP with no adaptation. Results: Out of the 99 in-silico subjects, 23 of them experienced postprandial hypoglycemia leading to greater than 1% overall time in hypoglycemia. Of these 23 subjects, we evaluated the benefit of using ALPHA-BR and ALPHA-ICR to prevent postprandial hypoglycemia. ALPHA-BR yielded substantially fewer percent time in hypoglycemia compared to AP (0.54% vs 1.92%, p < 0.001) and fewer rescue carbs per day (0.36 vs. 1.29, p < 0.001). For the control algorithm evaluated, it yielded an average aggressiveness factor of 0.72 for reducing postprandial basal insulin. ALPHA-ICR slightly reduced time in hypoglycemia compared to AP (1.77% vs. 1.92%, p = 0.09). Conclusion: Incorporating adaptive meal dosing into an AP can help reduce postprandial hypoglycemia, and the reduction is primarily due to changes in postprandial insulin delivery rather than pre-meal bolus. Significance: Adapting postprandial insulin can lead to substantial reduction in postprandial hypoglycemia and the adaptive algorithm presented can be used both to tune an algorithm prior to a study and to adapt to individuals during real-time usage.