In this work the real-time estimation of plasma insulin concentration (PIC) to quantify the insulin in the bloodstream in patients with type 1 diabetes mellitus (T1DM) is presented. To this end, Hovorka's model, a glucose−insulin dynamics model, is incorporated with various estimation techniques, including continuous-discrete extended Kalman filtering, unscented Kalman filtering, and moving horizon estimation, to provide an estimate of PIC. Furthermore, due to the considerable variability in the temporal dynamics of patients, some uncertain model parameters that have significant effects on PIC estimates are considered as additional states in Hovorka's model to be simultaneously estimated. Latent variable regression models are developed to individualize the PIC estimators by appropriately initializing the time-varying model parameters for improved convergence. The performance of the proposed methods is evaluated using clinical data sets from subjects with T1DM, and the results demonstrate the accurate estimation of PIC.

No abstract

People with type 1 diabetes (T1D) may experience hypoglycemia (blood glucose concentration [BGC] < 70 mg/dl) episodes that may be caused by insulin doses that are too large in relation to the BGC, reduced food intake, extensive physical activity, or slow absorption of currently available "fastacting" insulins.1 Fear of hypoglycemia is a major concern for many patients and affects patient decisions for use of an artificial pancreas (AP) system. Various strategies have been proposed for predicting BGC to be implemented in an AP system for prevention of hypoglycemia. 2-16Most of the developed AP systems are based on subcutaneous glucose measurement and subcutaneous insulin infusion. 17 Even though studies have shown that some hypoglycemia episodes can be prevented by suspension of insulin infusion, 2,5 insulin as the only manipulated variable in an AP system may not be sufficient to prevent all hypoglycemia episodes. Glucagon has been used as the second control action to prevent hypoglycemia. [18][19][20][21][22][23][24][25] Glucagon increases glucose by stimulating adenylate cyclase to produce increased cyclic AMP, promoting hepatic glycogenolysis and gluconeogenesis. This antihypoglycemic effect requires preexisting liver glycogen stores. 26 When hypoglycemia occurs, the predicted insulin infusion is suspended and glucagon is infused to elevate BGC. Although glucagon works well to prevent low glucose concentration, there have been occasions where administration of only glucagon was not sufficient to fully prevent hypoglycemia in almost all reported studies and additional rescue carbohydrates (CHO) were needed. Glucagon is more difficult to work with than insulin due to its inability to remain stable in solution, AbstractFear of hypoglycemia is a major concern for many patients with type 1 diabetes and affects patient decisions for use of an artificial pancreas system. We propose an alternative way for prevention of hypoglycemia by issuing predictive hypoglycemia alarms and encouraging patients to consume carbohydrates in a timely manner. The algorithm has been tested on 6 subjects (3 males and 3 females, age 24.2 ± 4.5 years, weight 79.2 ± 16.2 kg, height 172.7 ± 9.4 cm, HbA1C 7.3 ± 0.48%, duration of diabetes 209.2 ± 87.9 months) over 3-day closed-loop clinical experiments as part of a multivariable artificial pancreas control system. Over 6 three-day clinical experiments, there were only 5 real hypoglycemia episodes, of which only 1 hypoglycemia episode occurred due to being missed by the proposed algorithm. The average hypoglycemia alarms per day and per subject was 3. Average glucose value when the first alarms were triggered was recorded to be 117 ± 30.6 mg/dl. Average carbohydrate consumption per alarm was 14 ± 7.8 grams. Our results have shown that most low glucose concentrations can be predicted in advance and the glucose levels can be raised back to the desired levels by consuming an appropriate amount of carbohydrate. The proposed algorithm is able to prevent most hypoglycemic events by suggesting appr...

No abstract

No abstract