Hypoglycemia Detection and Carbohydrate Suggestion in an Artificial Pancreas

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...

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

“…The closed-loop protocol consists of the integrated multivariable adaptive AP (IMA-AP) with a generalized predictive controller that uses, in addition to the CGM signal, real-time measurements of biometric variables provided by the BodyMedia SenseWear armband and the Zephyr chest-band (Bioharness-3; Zephyr Technology, Annapolis, MD). 1,32 The additional bio-signals are employed in auxiliary modules, for tasks such as hypoglycemia prediction, detection of different physiological states (such as sleep or physical activity) that affect glucose dynamics, and providing additional information for the control algorithm. The multivariable AP system does not employ feedforward meal bolusing based on manual meal announcement, and the clinical experiment data do not include any insulin boluses at meal time that are proportional to the size of the consumed meal.…”

“…The presence of a hypoglycemia alarm within the last 30 min also initiates the safety criteria. The hypoglycemia module alerts users to consume rescue CHOs, 32,33 and the safety rule discontinues insulin infusion to avert the risks of low glucose levels. During the sampling instances where any of these events are detected, the safety criteria are initiated and the amount of insulin suggestion is reduced, even though the meal flag may be active, to ensure patient safety.…”

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

“…For instance, typical a DSS consist of alerts notifying the user of potential future adverse events, such as hypoglycaemia and hyperglycaemia [8]. They might also suggest the administration of meal insulin boluses or corrective insulin boluses to mitigate hyperglycaemia [9,10,11], recommend the intake of carbohydrates (CHO) to tackle hypoglycaemia (rescue CHO) [12,13,14], or provide suggestions to prevent exercise-induced hypoglycaemia [15].…”

Model-fusion-based online glucose concentration predictions in people with type 1 diabetes

“…The recent development of accurate continuous glucose monitoring (CGM) systems have increased interest in the predictive modeling of glucose concentrations, which is useful in hypo- and hyperglycemic early warning alarms (Chico et al 2003) and model-based predictive control in advanced AP systems (Hovorka et al 2004, Cobelli et al 2009, Ellingsen et al 2009, Kovatchev et al 2009, Dassau et al 2010, Pappada et al 2011, Bequette 2012, Cobelli et al 2012, Eren-Oruklu et al 2012, Haidar et al 2013, Jacobs et al 2014, Kirchsteiger et al 2015, Kovatchev et al 2016, Haidar et al 2017, Turksoy et al 2017, Wang et al 2017). Nevertheless, accurately predicting the future glucose trajectories is a challenging problem as BGC is influenced by several factors including meals, administered insulin, exercise (Diabetes Research in Children Network Study Group 2005, Breton et al 2014, Peyser et al 2014, DeBoer et al 2016, Jacobs et al 2016, Turksoy et al 2016a, Turksoy et al 2016b, Pasieka et al 2017, Turksoy et al 2017) and emotional state (related to the concentration of certain hormones) (Nomura et al 2000). Moreover, different physiological phenomena and the diverse lifestyles of individuals result in significant variability in glucose dynamics over time and among patients (Brazeau et al 2008).…”

“…Daily adaptation of physiological model was proposed recently (Dalla Man et al 2016, Messori et al 2016, Piccinini et al 2016, Visentin et al 2016, Toffanin et al 2017). Alternatively, data-driven models offer a simpler structure that is sufficient for online prediction yet computationally tractable for online and adaptive estimation, thus able to capture the time-varying relationships among the system variables (Cherkassky and Mulier 2007, Araghinejad 2013, Wang et al 2013, Turksoy et al 2014, Cinar et al 2016, Turksoy et al 2016a, Turksoy et al 2016b, Turksoy et al 2017). Once such relationships are identified, they can be used to train models that complement or replace physiological models.…”