A New Meal Absorption Model for Artificial Pancreas Systems

2021 ◽  
pp. 193229682199011
Author(s):  
Travis Diamond ◽  
Faye Cameron ◽  
B. Wayne Bequette
Keyword(s):  
Prediction Accuracy ◽  
Artificial Pancreas ◽  
Meal Size ◽  
Model Fit ◽  
The Other ◽  
Automated Control ◽  
Response Models ◽  
Fine Grain ◽  
Simple Implementation

Background: Artificial pancreas (AP) systems reduce the treatment burden of Type 1 Diabetes by automatically regulating blood glucose (BG) levels. While many disturbances stand in the way of fully closed-loop (automated) control, unannounced meals remain the greatest challenge. Furthermore, different types of meals can have significantly different glucose responses, further increasing the uncertainty surrounding the meal. Methods: Effective attenuation of a meal requires quick and accurate insulin delivery because of slow insulin action relative to meal effects on BG. The proposed Variable Hump (VH) model adapts to meals of varying compositions by inferring both meal size and shape. To appropriately address the uncertainty of meal size, the model divides meal absorption into two disjoint regions: a region with coarse meal size predictions followed by a fine-grain region where predictions are fine-tuned by adapting to the meal shape. Results: Using gold-standard triple tracer meal data, the proposed VH model is compared to three simpler second-order response models. The proposed VH model increased model fit capacity by 22% and prediction accuracy by 12% relative to the next best models. A 47% increase in the accuracy of uncertainty predictions was also found. In a simple control scenario, the controller governed by the proposed VH model provided insulin just as fast or faster than the controller governed by the other models in four out of the six meals. While the controllers governed by the other models all delivered at least a 25% excess of insulin at their worst, the VH model controller only delivered 9% excess at its worst. Conclusions: The VH Model performed best in accuracy metrics and succeeded over the other models in providing insulin quickly and accurately in a simple implementation. Use in an AP system may improve prediction accuracy and lead to better control around mealtimes.

Meal Detection and Meal Size Estimation for Type 1 Diabetes Treatment: A Variable State Dimension Approach

2015 ◽  
Author(s):  
Jinyu Xie ◽  
Qian Wang
Keyword(s):  
Type 1 Diabetes ◽  
Goodness Of Fit ◽  
Estimation Error ◽  
Artificial Pancreas ◽  
Young Patients ◽  
Estimation Algorithm ◽  
Glucose Variability ◽  
Meal Size ◽  
Size Estimation

To compensate the glucose variability caused by meals is essential in developing Artificial Pancreas for type 1 diabetes. Most existing algorithms rely on meal announcements and determine the insulin doses based on an Insulin-to-Carbohydrate ratio (I:C ratio). However, patients, especially young patients, often forget to provide meal information under natural living conditions. A Variable State Dimension (VSD) based algorithm is developed to detect meals which are unknown to the controller (unannounced meals). The algorithm is evaluated using an FDA-approved UVa/Padova simulator and has demonstrated to achieve 95% success rate in meal detection with less than 17% false alarm rate. In addition, the average meal size estimation error is no more than 13%. We then integrate the VSD-based meal detection and estimation algorithm with our previous published glucose dynamics model consisting of both insulin and carbohydrate inputs. The goodness of fit for 30min-ahead glucose predictions using meal information provided by the VSD-based algorithm has increased by 86% in average compared to the prediction using a model without meal input based on plasma blood glucose (BG) data. Simulation results also show that compared to several meal detection/estimation algorithms in the literature, the VSD-based algorithm has comparable or shorter detection time.

scholarly journals Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes

2019 ◽  
Vol 13 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Nicole Hobbs ◽  
Iman Hajizadeh ◽  
Mudassir Rashid ◽  
Kamuran Turksoy ◽  
Marc Breton ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Type 1 Diabetes ◽  
Prediction Accuracy ◽  
Artificial Pancreas ◽  
Metabolic State ◽  
Physiological Variables ◽  
Additional Input ◽  
Glucose Prediction

Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Methods: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. Results: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model ( P < .001) and the standard MSO ( P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Conclusions: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

The pseudomalachite-ludjibaite-reichenbachite conundrum: OD description

2019 ◽  
Vol 57 (5) ◽  
pp. 571-581
Author(s):  
Emil Makovicky
Keyword(s):  
Crystal Structures ◽  
Current Knowledge ◽  
The Other ◽  
Crystal Chemical ◽  
Layer System ◽  
Coordination Polyhedra

Abstract Crystal structures of the three polymorphs of Cu5(PO4)2(OH)4, namely pseudomalachite, ludjibaite, and reichenbachite, can be described as being composed of rods perpendicular to their crystal-chemical layering. Two different sorts of rods can be defined. Type 1 rods share rows of Cu coordination polyhedra, forming a series of slabs. Slab boundaries and slab interiors represent alternating geometric OD layers of two kinds, with layer symmetries close to P21/m and , which make up two different stacking schemes of geometric OD layers in the structures of ludjibaite and pseudomalachite. Such OD layers, however, are not developed in reichenbachite. Type 2 rods are defined as having columns of PO4 tetrahedra in the corners of the rods. In the Type 2 slabs composed of these rods, geometric Pg OD layers of glide-arrayed tetrahedra alternate with more complex OD layers; in ludjibaite this system of layers is oriented diagonally with respect to the Type 1 OD layer system. Two different OD stackings of Type 2 OD layers form the ludjibaite and reichenbachite structures but not that of pseudomalachite. Thus, ludjibaite might form disordered intergrowths with either of the other two members of the triplet but reichenbachite and pseudomalachite should not form oriented intergrowths. Current knowledge concerning formation of the three polymorphs is considered.

scholarly journals Longitudinal Changes of Tongue Thickness and Tongue Pressure in Neuromuscular Disorders

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
George Umemoto ◽  
Shinsuke Fujioka ◽  
Hajime Arahata ◽  
Nobutaka Sakae ◽  
Naokazu Sasagasako ◽  
...  
Keyword(s):  
Neuromuscular Disorders ◽  
The Other ◽  
Tongue Pressure ◽  
Swallowing Function ◽  
Longitudinal Changes ◽  
Swallowing Dysfunction ◽  
The Relationship ◽  
Lateral Sclerosis

Abstract Background Swallowing dysfunction is related to major cause of adverse events and an indicator of shorter survival among patients with neuromuscular disorders (NMD). It is critical to assess the swallowing function during disease progression, however, there are limited tools that can easily evaluate swallowing function without using videofluoroscopic or videoendoscopic examination. Here, we evaluated the longitudinal changes in tongue thickness (TT) and maximum tongue pressure (MTP) among patients with amyotrophic lateral sclerosis (ALS), myotonic dystrophy type 1 (DM1), and Duchenne muscular dystrophy (DMD). Methods Between 2010 and 2020, TT and MTP were measured from 21 ALS, 30 DM1, and 14 DMD patients (mean ages of 66.9, 44.5, and 21.4 years, respectively) at intervals of more than half a year. TT was measured, by ultrasonography, as the distance from the mylohyoid muscle raphe to the tongue dorsum, and MTP was determined by measuring the maximum compression on a small balloon when pressing the tongue against the palate. Then we examined the relationship between these evaluations and patient background and swallowing function. Results Mean follow-up periods were 24.0 months in the ALS group, 47.2 months in the DM1group, and 61.1 months in the DMD group. The DMD group demonstrated larger first TT than the other groups, while the DM1 group had lower first MTP than the ALS group. The ALS group showed a greater average monthly reduction in mean TT than the DM1 group and greater monthly reductions in mean body weight (BW) and MTP than the other groups. Significant differences between the first and last BW, TT, and MTP measures were found only in the ALS group. Conclusions This study suggests that ALS is associated with more rapid degeneration of tongue function over several years compared to DMD and DM1.

Dual‐hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions

2021 ◽  
Author(s):  
Marco Infante ◽  
David A. Baidal ◽  
Michael R. Rickels ◽  
Andrea Fabbri ◽  
Jay S. Skyler ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Recent Progress ◽  
Artificial Pancreas ◽  
Future Directions

scholarly journals The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

2018 ◽  
Vol 12 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Roberto Visentin ◽  
Enrique Campos-Náñez ◽  
Michele Schiavon ◽  
Dayu Lv ◽  
Martina Vettoretti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Artificial Pancreas ◽  
Real Life ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Self Monitoring ◽  
Inhaled Insulin ◽  
Single Meal

Background: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. Method: Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject’s basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of “dawn” phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. Results: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. Conclusions: The new modifications introduced in the T1D simulator allow to extend its domain of validity from “single-meal” to “single-day” scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

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