scholarly journals The Hypoglycaemia-Hyperglycaemia Minimizer System in the Management of Type 1 Diabetes

2016 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Brian L Levy ◽  
◽  
Thomas W McCann ◽  
Jr and Daniel A Finan ◽  
◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Insulin Infusion ◽  
Closed Loop ◽  
Insulin Dose ◽  
Infusion Pump ◽  
Artificial Pancreas ◽  
Continuous Glucose Monitor ◽  
Glucose Levels

Living with type 1 diabetes (T1D) presents many challenges in terms of daily living. Insulin users need to frequently monitor their blood glucose levels and take multiple injections per day and/or multiple boluses through an insulin infusion pump, with the consequences of failing to match the insulin dose to the body’s needs resulting in hypoglycaemia and hyperglycaemia. The former can result in seizures, coma and even death; the latter can have both acute and long-term health implications. Many patients with T1D also fail to meet their treatment goals. In order to reduce the burdens of self-administering insulin, and improve efficacy and safety, there is a need to at least partially remove the patient from the loop via a closed-loop ‘artificial pancreas’ system. The Hypoglycaemia-Hyperglycaemia Minimizer (HHM) System, comprising a continuous, subcutaneous insulin infusion pump, continuous glucose monitor (CGM) and closed-loop insulin dosing algorithm, is able to predict changes in blood glucose and adjust insulin delivery accordingly to help keep the patient at normal glucose levels. Early clinical data indicate that this system is feasible, effective and safe, and has the potential to dramatically improve the therapeutic outcomes and quality of life for people with T1D.

Formal Verification of a Multi-Basal Insulin Infusion Control Model

10.29007/kcrp ◽  
2018 ◽  
Author(s):  
Xin Chen ◽  
Souradeep Dutta ◽  
Sriram Sankaranarayanan
Keyword(s):  
Blood Glucose ◽  
Insulin Infusion ◽  
Infusion Pump ◽  
Artificial Pancreas ◽  
Small Time ◽  
Time Interval ◽  
State Variables ◽  
Continuous Glucose Monitor ◽  
Glucose Levels ◽  
Blood Glucose Levels

The artificial pancreas concept automates the delivery of insulin to patients with type-1 diabetes, sensing the blood glucose levels through a continuous glucose monitor (CGM) and using an insulin infusion pump to deliver insulin. Formally verifying control algorithms against physiological models of the patient is an important challenge. In this paper, we present a case study of a simple hybrid multi-basal control system that switches to different preset insulin delivery rates over various ranges of blood glucose levels. We use the Dalla- Man model for modeling the physiology of the patient and a hybrid automaton model of the controller. First, we reduce the problem state space and replace nonpolynomial terms by approximations with very small errors in order to simplify the model. Nevertheless, the model still remains nonlinear with up to 9 state variables.Reachability analysis on this hybrid model is used to verify that the blood glucose levels remain within a safe range overnight. This poses challenges, including (a) the model exhibits many discrete jumps in a relatively small time interval, and (b) the entire time horizon corresponding to a full night is 720 minutes, wherein the controller time period is 5 minutes. To overcome these difficulties, we propose methods to effectively handle time- triggered jumps and merge flowpipes over the same time interval. The evaluation shows that the performance can be improved with the new techniques.

scholarly journals Hybrid Closed-Loop System Achieves Optimal Perioperative Glycemia in a Boy With Type 1 Diabetes: A Case Report

2021 ◽  
Vol 9 ◽  
Author(s):  
Jesus Dominguez-Riscart ◽  
Nuria Buero-Fernandez ◽  
Ana Garcia-Zarzuela ◽  
Fernando A. Marmolejo-Franco ◽  
Ana C. Perez-Guerrero ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Insulin Infusion ◽  
Closed Loop ◽  
Total Duration ◽  
Blood Glucose Monitoring ◽  
Medical Team ◽  
The Family ◽  
The Impact

The goal in type 1 diabetes (T1D) therapy is to maintain optimal glycemic control under any circumstance. Diabetes technology is in continuous development to achieve this goal. The most advanced Food and Drug Administration- and European Medicines Agency-approved devices are hybrid closed-loop (HCL) systems, which deliver insulin subcutaneously in response to glucose levels according to an automated algorithm. T1D is frequently encountered in the perioperative period. The latest international guidelines for the management of children with diabetes undergoing surgery include specific adjustments to the patient's insulin therapy, hourly blood glucose monitoring, and intravenous (IV) insulin infusion. However, these guidelines were published while the HCL systems were still marginal. We present a case of a 9-year-old boy with long-standing T1D, under HCL system therapy for the last 9 months, and needing surgery for an appendectomy. We agreed with the family, the surgical team, and the anesthesiologists to continue HCL insulin infusion, without further adjustments, hourly blood glucose checks or IV insulin, while monitoring closely. The HCL system was able to keep glycemia within range for the total duration of the overnight fast, the surgery, and the initial recovery, without any external intervention or correction bolus. This is, to the best of our knowledge, the first reported pediatric case to undergo major surgery using a HCL system, and the results were absolutely satisfactory for the patient, his family, and the medical team. We believe that technology is ripe enough to advocate for a “take your pump to surgery” message, minimizing the impact and our interventions. The medical team may discuss this possibility with the family and patients.

scholarly journals Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Arthur Bertachi ◽  
Lyvia Biagi ◽  
Aleix Beneyto ◽  
Josep Vehí
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Infusion ◽  
Artificial Pancreas ◽  
The Body ◽  
Control Algorithms ◽  
Rule Based ◽  
Control Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

scholarly journals The mathematician's control toolbox for management of type 1 diabetes

2014 ◽  
Vol 4 (5) ◽  
pp. 20140042 ◽  
Author(s):  
Marie Csete ◽  
John Doyle
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Feedback Loop ◽  
Artificial Pancreas ◽  
Glucagon Release ◽  
Control Engineering ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Physiological Feedback

Blood glucose levels are controlled by well-known physiological feedback loops: high glucose levels promote insulin release from the pancreas, which in turn stimulates cellular glucose uptake. Low blood glucose levels promote pancreatic glucagon release, stimulating glycogen breakdown to glucose in the liver. In healthy people, this control system is remarkably good at maintaining blood glucose in a tight range despite many perturbations to the system imposed by diet and fasting, exercise, medications and other stressors. Type 1 diabetes mellitus (T1DM) results from loss of the insulin-producing cells of the pancreas, the beta cells. These cells serve as both sensor (of glucose levels) and actuator (insulin/glucagon release) in a control physiological feedback loop. Although the idea of rebuilding this feedback loop seems intuitively easy, considerable control mathematics involving multiple types of control schema were necessary to develop an artificial pancreas that still does not function as well as evolved control mechanisms. Here, we highlight some tools from control engineering used to mimic normal glucose control in an artificial pancreas, and the constraints, trade-offs and clinical consequences inherent in various types of control schemes. T1DM can be viewed as a loss of normal physiologic controls, as can many other disease states. For this reason, we introduce basic concepts of control engineering applicable to understanding pathophysiology of disease and development of physiologically based control strategies for treatment.

Continuous Glucose Monitoring Versus Self-monitoring of Blood Glucose in Children with Type 1 Diabetes—The Pros and Cons

2012 ◽  
Vol 08 (01) ◽  
pp. 27 ◽  
Author(s):  
Susana R Patton ◽  
Mark A Clements ◽  
◽  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Insulin Dose ◽  
Healthcare Providers ◽  
Glucose Monitoring ◽  
Continuous Glucose Monitor ◽  
Self Monitoring ◽  
Advantages And Disadvantages ◽  
Pros And Cons

Glucose monitoring is essential for modern diabetes treatment and the achievement of near-normal glycemic levels. Monitoring of blood glucose provides the data necessary for patients to make daily management decisions related to food intake, insulin dose, and physical exercise and it can enable patients to avoid potentially dangerous episodes of hypo- and hyperglycemia. Additionally, monitoring can provide healthcare providers with the information needed to identify glycemic patterns, educate patients, and adjust insulin. Presently, youth with type 1 diabetes can self-monitor blood glucose via home blood glucose meters, or monitor glucose concentrations nearly continuously using a continuous glucose monitor. There are advantages and disadvantages to the use of either of these technologies. This article describes the two technologies and the research supporting their use in the management of youth with type 1 diabetes in order to weigh their relative pros and cons.

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.

scholarly journals LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3303
Author(s):  
Jeremy Beauchamp ◽  
Razvan Bunescu ◽  
Cindy Marling ◽  
Zhongen Li ◽  
Chang Liu
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Artificial Pancreas ◽  
Real Patient ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Novel Approach ◽  
The 1960S

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.

Clinical Usefulness of a Bolus Calculator in Maintaining Normoglycaemia in Active Professional Patients with Type 1 Diabetes Treated with Continuous Subcutaneous Insulin Infusion

2008 ◽  
Vol 36 (5) ◽  
pp. 1112-1116 ◽  
Author(s):  
T Klupa ◽  
T Benbenek-Klupa ◽  
M Malecki ◽  
M Szalecki ◽  
J Sieradzki
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Metabolic Control ◽  
Continuous Subcutaneous Insulin Infusion ◽  
Insulin Infusion ◽  
Target Range ◽  
Subcutaneous Insulin ◽  
Glucose Levels ◽  
Blood Glucose Levels

This observational study assessed metabolic control in young, active professionals with type 1 diabetes treated with continuous subcutaneous insulin infusion (CSII) with or without the use of a bolus calculator. Eighteen patients aged 19 − 51 years with diabetes duration of 6 − 22 years were included; eight patients used a bolus calculator and 10 did not. Metabolic control was assessed by glycosylated haemoglobin (HbA1c) measurements and blood glucose profiles. A continuous glucose monitoring system (CGMS) was also used by three patients from each group. Mean HbA1c and fasting blood glucose levels were not significantly different between the two groups, but mean post-prandial blood glucose was significantly lower in bolus calculator users than non-users. The CGMS showed more blood glucose levels within the target range in bolus calculator users than non-users, but statistical significance was not achieved. In conclusion, a bolus calculator may help to improve post-prandial blood glucose levels in active professional type 1 diabetes patients treated with CSII, but does not have a major impact on HbA1c levels.

scholarly journals In Silico Modeling of Minimal Effective Insulin Doses Using the UVA/PADOVA Type 1 Diabetes Simulator

2017 ◽  
Vol 12 (2) ◽  
pp. 376-380 ◽  
Author(s):  
Enrique Campos-Náñez ◽  
Jennifer E. Layne ◽  
Howard C. Zisser
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
In Silico ◽  
Basal Insulin ◽  
Insulin Infusion ◽  
Glucose Levels ◽  
In Silico Modeling ◽  
Adolescents And Adults ◽  
Bolus Insulin

Background: The objective of this study was to identify the minimum basal insulin infusion rates and bolus insulin doses that would result in clinically relevant changes in blood glucose levels in the most insulin sensitive subjects with type 1 diabetes. Methods: The UVA/PADOVA Type 1 Diabetes Simulator in silico population of children, adolescents, and adults was administered a basal insulin infusion rate to maintain blood glucose concentrations at 120 mg/dL (6.7 mmol/L). Two scenarios were modeled independently after 1 hour of simulated time: (1) basal insulin infusion rates in increments of 0.01 U/h were administered and (2) bolus doses in increments of 0.01 U were injected. Subjects were observed for 4 hours to determine insulin delivery required to change blood glucose by 12.5 mg/dL (0.7 mmol/L) and 25 mg/dL (1.4 mmol/L) in only 5% of the in silico population. Results: The basal insulin infusion rates required to change blood glucose by 12.5 mg/dL and 25 mg/dL in 5% of children, adolescents, and adults were 0.03, 0.11, and 0.10 U/h and 0.06, 0.21, and 0.19 U/h, respectively. The bolus insulin doses required to change blood glucose by the target amounts in the respective populations were 0.10, 0.28, and 0.30 U and 0.19, 0.55, and 0.60 U. Conclusions: In silico modeling suggests that only a very small percentage of individuals with type 1 diabetes, corresponding to children with high insulin sensitivity and low body weight, will exhibit a clinically relevant change in blood glucose with very low basal insulin rate changes or bolus doses.

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