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

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.

[1]  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.

[2]  Fabian León-Vargas,et al.  Postprandial response improvement via safety layer in closed-loop blood glucose controllers , 2015, Biomed. Signal Process. Control..

[3]  M. Al-Tabakha.  Future prospect of insulin inhalation for diabetic patients: The case of Afrezza versus Exubera. , 2015, Journal of controlled release : official journal of the Controlled Release Society.

[4]  C. Toumazou,et al.  Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study. , 2016, Diabetes technology & therapeutics.

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

[6]  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.

[7]  Graham C. Goodwin,et al.  A fundamental control limitation for linear positive systems with application to Type 1 diabetes treatment , 2015, Autom..

[8]  Eyal Dassau,et al.  Dynamic Insulin on Board: Incorporation of Circadian Insulin Sensitivity Variation , 2013, Journal of diabetes science and technology.

[9]  Jorge Bondia,et al.  Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals , 2020, IEEE Journal of Biomedical and Health Informatics.

[10]  J. Shaw,et al.  Glycemic control and excess mortality in type 1 diabetes. , 2015, The New England journal of medicine.

[11]  Ali Cinar,et al.  Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems , 2018, Journal of diabetes science and technology.

[12]  Fabian León-Vargas,et al.  Postprandial blood glucose control using a hybrid adaptive PD controller with insulin-on-board limitation , 2013, Biomed. Signal Process. Control..

[13]  B. Frier,et al.  The risks of nocturnal hypoglycaemia in insulin-treated diabetes. , 2017, Diabetes research and clinical practice.

[14]  H. De Battista,et al.  Safety Auxiliary Feedback Element for the Artificial Pancreas in Type 1 Diabetes , 2013, IEEE Transactions on Biomedical Engineering.

[15]  Josep Vehí,et al.  Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. , 2017, Diabetes technology & therapeutics.

[16]  Jorge Bondia,et al.  Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing--the iBolus--in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial. , 2012, Diabetes technology & therapeutics.

[17]  Aesha Drozdowski,et al.  Standards of medical care in diabetes. , 2004, Diabetes care.

[18]  6. Glycemic Targets: Standards of Medical Care in Diabetes—2018 , 2017, Diabetes Care.

[19]  L. Magni,et al.  Evaluating the Efficacy of Closed-Loop Glucose Regulation via Control-Variability Grid Analysis , 2008, Journal of diabetes science and technology.

[20]  Hernán De Battista,et al.  Advanced Control for Constrained Processes and Systems , 2011 .

[21]  A. Brazeau,et al.  Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. , 2013, Diabetes research and clinical practice.

[22]  Josep Vehí,et al.  Assessment of Mitigation Methods to Reduce the Risk of Hypoglycemia for Announced Exercise in a Uni-hormonal Artificial Pancreas. , 2018, Diabetes technology & therapeutics.

[23]  R. Rabasa-Lhoret,et al.  The challenges of achieving postprandial glucose control using closed‐loop systems in patients with type 1 diabetes , 2018, Diabetes, obesity & metabolism.

[24]  Anirban Roy,et al.  The effect of insulin feedback on closed loop glucose control. , 2011, The Journal of clinical endocrinology and metabolism.

[25]  Chengwei Li,et al.  An Improved PID Algorithm Based on Insulin-on-Board Estimate for Blood Glucose Control with Type 1 Diabetes , 2015, Comput. Math. Methods Medicine.

[26]  David R Webb,et al.  Pathophysiology of type 1 and type 2 diabetes mellitus: a 90-year perspective , 2015, Postgraduate Medical Journal.

[27]  Christofer Toumazou,et al.  Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability , 2017, Comput. Methods Programs Biomed..

[28]  R. Hovorka,et al.  Coming of age: the artificial pancreas for type 1 diabetes , 2016, Diabetologia.

[29]  Link,et al.  UvA-DARE ( Digital Academic Repository ) Closing the loop , squaring the circle : Studies on insulin delivery , glucose monitoring and the artificial , 2013 .

[30]  José Luis Díez,et al.  Insulin limitation in the Artificial Pancreas by Sliding Mode Reference Conditioning and Insulin Feedback: an in silico comparison , 2017 .

[31]  R. Beck,et al.  Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring , 2018, Diabetes Care.

[32]  Ali Cinar,et al.  Plasma Insulin Estimation in People with Type 1 Diabetes Mellitus , 2017 .

[33]  Ezra Belay Ketema,et al.  Correlation of fasting and postprandial plasma glucose with HbA1c in assessing glycemic control; systematic review and meta-analysis , 2015, Archives of Public Health.

[34]  Ali Cinar,et al.  Real-time insulin bolusing for unannounced meals with artificial pancreas , 2017 .

[35]  D. Nathan,et al.  The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study at 30 Years: Overview , 2013, Diabetes Care.

[36]  C. Cobelli,et al.  The UVA/PADOVA Type 1 Diabetes Simulator , 2014, Journal of diabetes science and technology.

[37]  R. Hovorka,et al.  Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis , 2018, British Medical Journal.

[38]  Lauren M. Huyett,et al.  Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms , 2014, Diabetes Care.

[39]  Patricio Colmegna,et al.  Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses , 2018, Journal of diabetes science and technology.