scholarly journals In-silico Testing of a Generalized Predictive Control Based Artificial Pancreas for Child Patients with Type 1 Diabetes

2020 ◽  
Keyword(s):  
Type 1 Diabetes ◽  
Predictive Control ◽  
In Silico ◽  
Artificial Pancreas ◽  
Generalized Predictive Control ◽  
Child Patients

scholarly journals Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial

2009 ◽  
Vol 3 (5) ◽  
pp. 1091-1098 ◽  
Author(s):  
Lalo Magni ◽  
Marco Forgione ◽  
Chiara Toffanin ◽  
Chiara Dalla Man ◽  
Boris Kovatchev ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Sensitivity ◽  
Model Predictive Control ◽  
Predictive Control ◽  
In Silico ◽  
Insulin Pump ◽  
Artificial Pancreas ◽  
Arx Model ◽  
Starting Point

Background: The technological advancements in subcutaneous continuous glucose monitoring and insulin pump delivery systems have paved the way to clinical testing of artificial pancreas devices. The experience derived by clinical trials poses technological challenges to the automatic control expert, the most notable being the large interpatient and intrapatient variability and the inherent uncertainty of patient information. Methods: A new model predictive control (MPC) glucose control system is proposed. The starting point is an MPC algorithm applied in 20 type 1 diabetes mellitus (T1DM) subjects. Three main changes are introduced: individualization of the ARX model used for prediction; synthesis of the MPC law on top of the open-loop basal/bolus therapy; and a run-to-run approach for implementing day-by-day tuning of the algorithm. In order to individualize the ARX model, a sufficiently exciting insulin profile is imposed by splitting the premeal bolus into two smaller boluses (40% and 60%) injected 30 min before and 30 min after the meal. Results: The proposed algorithm was tested on 100 virtual subjects extracted from an in silico T1DM population. The trial simulates 44 consecutive days, during which the patient receives breakfast, lunch, and dinner each day. For 10 days, meals are multiplied by a random variable uniformly distributed in [0.5, 1.5], while insulin delivery is based on nominal meals. Moreover, for 10 days, either a linear increase or decrease of insulin sensitivity (±25% of nominal value) is introduced. Conclusions: The ARX model identification procedure offers an automatic tool for patient model individualization. The run-to-run approach is an effective way to auto-tune the aggressiveness of the closed-loop control law, is robust to meal variation, and is also capable of adapting the regulator to slow parameter variations, e.g., on insulin sensitivity.

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.

scholarly journals Glucagon sensitivity and clearance in type 1 diabetes: insights from in vivo and in silico experiments

2015 ◽  
Vol 309 (5) ◽  
pp. E474-E486 ◽  
Author(s):  
Ling Hinshaw ◽  
Ashwini Mallad ◽  
Chiara Dalla Man ◽  
Rita Basu ◽  
Claudio Cobelli ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Artificial Pancreas ◽  
Glucose Production ◽  
Endogenous Glucose Production ◽  
Action Model ◽  
The Difference ◽  
Hormone Control

Glucagon use in artificial pancreas for type 1 diabetes (T1D) is being explored for prevention and rescue from hypoglycemia. However, the relationship between glucagon stimulation of endogenous glucose production (EGP) viz., hepatic glucagon sensitivity, and prevailing glucose concentrations has not been examined. To test the hypothesis that glucagon sensitivity is increased at hypoglycemia vs. euglycemia, we studied 29 subjects with T1D randomized to a hypoglycemia or euglycemia clamp. Each subject was studied at three glucagon doses at euglycemia or hypoglycemia, with EGP measured by isotope dilution technique. The peak EGP increments and the integrated EGP response increased with increasing glucagon dose during euglycemia and hypoglycemia. However, the difference in dose response based on glycemia was not significant despite higher catecholamine concentrations in the hypoglycemia group. Knowledge of glucagon's effects on EGP was used to develop an in silico glucagon action model. The model-derived output fitted the obtained data at both euglycemia and hypoglycemia for all glucagon doses tested. Glucagon clearance did not differ between glucagon doses studied in both groups. Therefore, the glucagon controller of a dual hormone control system may not need to adjust glucagon sensitivity, and hence glucagon dosing, based on glucose concentrations during euglycemia and hypoglycemia.

Model Predictive Control of glucose concentration in subjects with type 1 diabetes: an in silico trial

2008 ◽  
Vol 41 (2) ◽  
pp. 4246-4251 ◽  
Author(s):  
L. Magni ◽  
D.M. Raimondo ◽  
C. Dalla Man ◽  
G. De Nicolao ◽  
B. Kovatchev ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Glucose Concentration ◽  
In Silico

scholarly journals Control of Blood Glucose Level for Type 1 Diabetes Mellitus using Improved Hovorka Equations: Comparison between Clinical and In-Silico Works

2020 ◽  
Author(s):  
Nur’Amanina Mohd Sohadi ◽  
Ayub Md Som ◽  
Noor Shafina Mohd Nor ◽  
Nur Farhana Mohd Yusof ◽  
Sherif Abdulbari Ali ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
In Silico ◽  
Artificial Pancreas ◽  
The Body

AbstractBackgroundType 1 diabetes mellitus (T1DM) occurs due to inability of the body to produce sufficient amount of insulin to regulate blood glucose level (BGL) at normoglycemic range between 4.0 to 7.0 mmol/L. Thus, T1DM patients require to do self-monitoring blood glucose (SMBG) via finger pricks and depend on exogenous insulin injection to maintain their BGL which is very painful and exasperating. Ongoing works on artificial pancreas device nowadays focus primarily on a computer algorithm which is programmed into the controller device. This study aims to simulate so-called improved equations from the Hovorka model using actual patients’ data through in-silico works and compare its findings with the clinical works.MethodsThe study mainly focuses on computer simulation in MATLAB using improved Hovorka equations in order to control the BGL in T1DM. The improved equations can be found in three subsystems namely; glucose, insulin and insulin action subsystems. CHO intakes were varied during breakfast, lunch and dinner times for three consecutive days. Simulated data are compared with the actual patients’ data from the clinical works.ResultsResult revealed that when the patient took 36.0g CHO during breakfast and lunch, the insulin administered was 0.1U/min in order to maintain the blood glucose level (BGL) in the safe range after meal; while during dinner time, 0.083U/min to 0.1 U/min of insulins were administered in order to regulate 45.0g CHO taken during meal. The basal insulin was also injected at 0.066U/min upon waking up time in the early morning. The BGL was able to remain at normal range after each meal during in-silico works compared to clinical works.ConclusionsThis study proved that the improved Hovorka equations via in-silico works can be employed to model the effect of meal disruptions on T1DM patients, as it demonstrated better control as compared to the clinical works.

scholarly journals Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience

2009 ◽  
Vol 3 (5) ◽  
pp. 1031-1038 ◽  
Author(s):  
William L. Clarke ◽  
Stacey Anderson ◽  
Marc Breton ◽  
Stephen Patek ◽  
Laurissa Kashmer ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Predictive Control ◽  
Insulin Infusion ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Open Loop ◽  
Closed Loop Control ◽  
Open Loop Control ◽  
Loop Control

Background: Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented. Methods: Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70–140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared. Results: With closed-loop control, once BG levels achieved the target range (70–140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced ( p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control Conclusion: Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This “artificial pancreas” controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.

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