Combining Technology with Treatment

Type 1 diabetes mellitus (T1DM) is characterized by the destruction of the insulin-producing b-cells in the pancreas. Exogenous insulin administration is therefore required to regulate the blood glucose concentration. The goal of diabetes management is to maintain homeostasis and blood glucose near normal levels (80–140 mg/dL) and thus to avoid immediate life-threatening situations, such as severe hypoglycemia and ketoacidosis, and long-term complications, such as cardiovascular disease, nephropathy, neuropathy, and retinopathy. Treatment of T1DM requires either multiple daily insulin injections or continuous subcutaneous (SC) insulin infusion (CSII) delivered via an insulin infusion pump. Both treatment modes necessitate frequent blood glucose measurements (eight to ten times/day, including fasting, pre- and postprandial, before bedtime, and in the middle of the night) to determine the daily insulin requirements for maintaining near-normal blood glucose levels [1]–[3]. With the advent of continuous glucose sensing, which reports interstitial glucose concentrations (that reflect the blood glucose) approximately every minute, and the development of hardware and algorithms to communicate with and control insulin pumps, the vision of closed-loop control of blood glucose is approaching a reality. In individuals without diabetes, blood glucose is controlled by various neural and hormonal inputs from the brain, gut, liver, and pancreas that respond to various situations such as meals, stress, and exercise. A closed-loop system for

[1]  Efstratios N. Pistikopoulos,et al.  Model-based blood glucose control for type 1 diabetes via parametric programming , 2006, IEEE Transactions on Biomedical Engineering.

[2]  Lawrence A Leiter,et al.  Hyperglycemia After Intense Exercise in IDDM Subjects During Continuous Subcutaneous Insulin Infusion , 1988, Diabetes Care.

[3]  F. Doyle,et al.  Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell , 2010 .

[4]  Howard Zisser,et al.  Practical issues in the identification of empirical models from simulated type 1 diabetes data. , 2007, Diabetes technology & therapeutics.

[5]  Daniel Aaron Finan Modeling and monitoring strategies for type 1 diabetes , 2008 .

[6]  Eyal Dassau,et al.  Enhanced 911/Global Position System Wizard: A Telemedicine Application for the Prevention of Severe Hypoglycemia—Monitor, Alert, and Locate , 2009, Journal of diabetes science and technology.

[7]  Roman Hovorka,et al.  The future of continuous glucose monitoring: closed loop. , 2008, Current diabetes reviews.

[8]  L. Jovanovic,et al.  Insulin and glucose requirements during the first stage of labor in insulin-dependent diabetic women. , 1983, The American journal of medicine.

[9]  Howard Zisser,et al.  Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic β Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers , 2008, Journal of diabetes science and technology.

[10]  F. Doyle,et al.  Detection of a Meal Using Continuous Glucose Monitoring , 2008, Diabetes Care.

[11]  Eyal Dassau,et al.  Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring , 2010, Diabetes Care.

[12]  Eyal Dassau,et al.  Safety Constraints in an Artificial Pancreatic β Cell: An Implementation of Model Predictive Control with Insulin on Board , 2009, Journal of diabetes science and technology.

[13]  Marc D Breton,et al.  Physical Activity—The Major Unaccounted Impediment to Closed Loop Control , 2008, Journal of diabetes science and technology.

[14]  Howard C. Zisser,et al.  Prandial Insulin Dosing Using Run-to-Run Control , 2007, Diabetes Care.

[15]  Francis J Doyle,et al.  Experimental Evaluation of a Recursive Model Identification Technique for Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[16]  Francis J. Doyle,et al.  Survey on iterative learning control, repetitive control, and run-to-run control , 2009 .

[17]  Efstratios N. Pistikopoulos,et al.  Multi-objective blood glucose control for type 1 diabetes , 2009, Medical & Biological Engineering & Computing.

[18]  Eyal Dassau,et al.  In silico evaluation platform for artificial pancreatic beta-cell development--a dynamic simulator for closed-loop control with hardware-in-the-loop. , 2009, Diabetes technology & therapeutics.

[19]  C. Cobelli,et al.  In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[20]  Eyal Dassau,et al.  Bolus calculator: a review of four "smart" insulin pumps. , 2008, Diabetes technology & therapeutics.

[21]  Francis J. Doyle,et al.  Indirect iterative learning control: Application on artificial pancreatic β-cell , 2009, 2009 Chinese Control and Decision Conference.

[22]  W. Schenk,et al.  Does Physiological Blood Glucose Control Require an Adaptive Control Strategy? , 1987, IEEE Transactions on Biomedical Engineering.

[23]  R. Hovorka,et al.  Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. , 2004, Physiological measurement.

[24]  R. Bergman,et al.  Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.

[25]  L. Jovanovic,et al.  Randomized trial of computer-assisted insulin delivery in patients with type I diabetes beginning pump therapy. , 1986, The American journal of medicine.

[26]  Claudio Cobelli,et al.  Meal Simulation Model of the Glucose-Insulin System , 2007, IEEE Transactions on Biomedical Engineering.

[27]  Howard C. Zisser,et al.  Clinical Update on Optimal Prandial Insulin Dosing Using a Refined Run-to-Run Control Algorithm , 2009, Journal of diabetes science and technology.

[28]  R. Hovorka Continuous glucose monitoring and closed‐loop systems , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[29]  R. Bellazzi Telemedicine and Diabetes Management: Current Challenges and Future Research Directions , 2008, Journal of diabetes science and technology.

[30]  Darrell M. Wilson,et al.  Duration of Nocturnal Hypoglycemia Before Seizures , 2008, Diabetes Care.

[31]  M. Morari,et al.  On-line optimization via off-line parametric optimization tools , 2000 .

[32]  Eyal Dassau,et al.  Modular Artificial β-Cell System: A Prototype for Clinical Research , 2008 .

[33]  Eyal Dassau,et al.  Closed-Loop Control of Artificial Pancreatic $\beta$ -Cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control , 2010, IEEE Transactions on Biomedical Engineering.

[34]  Malgorzata E. Wilinska,et al.  Insulin kinetics in type-1 diabetes: continuous and bolus delivery of rapid acting insulin , 2005, IEEE Transactions on Biomedical Engineering.

[35]  H. Zisser,et al.  Run-to-run control of meal-related insulin dosing. , 2005, Diabetes technology & therapeutics.

[36]  A. Ertl,et al.  Evidence for a vicious cycle of exercise and hypoglycemia in type 1 diabetes mellitus , 2004, Diabetes/metabolism research and reviews.

[37]  Eyal Dassau,et al.  Implications of Meal Library & Meal Detection to Glycemic Control of Type 1 Diabetes Mellitus through MPC Control , 2008 .