The Artificial Pancreas: Current Situation and Future Directions

Description

The Artificial Pancreas: Current Situation and Future Directions presents research on the top issues relating to the artificial pancreas (AP) and its application to diabetes. AP is a newer form of treatment to accurately and efficiently inject insulin, thereby significantly improving the patient's quality of life. By connecting a continuous glucose monitor (CGM) to a continuous subcutaneous insulin infusion using a control algorithm, AP delivers and regulates the most accurate amount of insulin to maintain normal glycemic values. Featured chapters in this book are written by world leaders in AP research, thus providing readers with the latest studies and results. Cover The Artificial Pancreas: Current Situation and Future Directions Copyright Dedication Contributors About the contributors Foreword References Preface 1 Feedback control algorithms for automated glucose management in T1DM: the state of the art 1.1 Introduction 1.2 Proportional-integral-derivative control 1.2.1 Insulin delivery using PID control 1.2.2 Glucagon delivery using PID control 1.3 Logic-based control 1.4 Model predictive control 1.4.1 Unconstrained MPC with safety checks 1.4.2 Multiple model adaptive MPC 1.4.3 Zone MPC 1.4.4 Set-point-based enhanced MPC design 1.4.5 Multiple model probabilistic predictive control 1.4.6 Adaptive generalized predictive control and MPC design 1.4.7 Bihormone adaptive generalized predictive control 1.4.8 Policy-based stochastic MPC 1.5 Switched linear parameter varying control 1.6 Personalization and adaptation 1.6.1 Run-to-run approaches 1.6.2 Iterative learning control 1.6.3 Moving average approach 1.7 Machine-learning-based control 1.7.1 Reinforcement-learning-based approach 1.7.2 Gaussian process MPC 1.7.3 Deep learning-assisted control 1.8 Summary Acknowledgments References 2 Getting IoT-ready 2.1 Introduction 2.2 IoT-enabled AP ecosystems 2.2.1 The changing face of AP systems 2.2.2 AP research platforms: past and present 2.3 Interfacing with additional signals from the IoT 2.3.1 Activity sensors 2.3.2 Location sensors 2.3.3 Multimedia 2.3.4 Electronic health records 2.3.5 Crowdsourcing 2.3.6 Calendar data 2.4 Rewiring controllers for an IoT-enabled AP 2.4.1 PID 2.4.2 MPC 2.5 Case study: efficient resource utilization in an MPC-based embedded AP 2.6 Case study: IoT-enabled autonomous bolus assist via deep learning based zone MPC 2.7 High-level adaptation from big IoT data 2.7.1 The interplay of cloud, fog, and edge computing 2.7.2 An AP that learns from big data 2.8 Conclusions Acknowledgments References 3 Multivariable AP with adaptive control 3.1 Introduction 3.2 Preliminaries 3.2.1 Adaptive and personalized PIC estimator 3.2.2 Recursive subspace-based system identification 3.3 Adaptive PIC cognizant MPC algorithm 3.3.1 Integrating insulin compartment models with subspace identification 3.3.1.1 Set-point modification during exercise and recovery period 3.3.1.2 Glycemic risk index 3.3.1.3 Plasma insulin risk index 3.3.1.4 Feature extraction for manipulating constraints 3.3.1.5 Plasma insulin concentration bounds 3.3.1.6 Hypoglycemia detection and carbohydrate suggestion 3.3.2 Adaptive MPC formulation 3.4 Results 3.5 Conclusions 3.A Acknowledgments References 4 The ARG algorithm: clinical trials in Argentina 4.1 Introduction 4.2 Control-oriented models 4.3 ARG algorithm 4.3.1 Switched LQG regulator 4.3.2 SAFE layer 4.3.3 Auxiliary modules Hypoglycemia-related module (Hypo-RM) Hyperglycemia-related module (Hyper-RM) 4.4 Simulations 4.5 Clinical trials 4.5.1 Hardware and software 4.5.2 Clinical procedures 4.5.3 Results 4.6 Conclusions Acknowledgments References 5 Use of intraperitoneal insulin delivery for artificial pancreas 5.1 Bedside artificial pancreas: the birth of a concept 5.2 Prioritization of subcutaneous insulin delivery in the development of a wearable artificial pancreas (AP) 5.3 Rationale for using intraperitoneal insulin delivery 5.4 Clinical experience with continuous intraperitoneal insulin infusion 5.5 Closed-loop experience with IP insulin delivery 5.5.1 AP with IV glucose sensing and IP insulin delivery 5.5.2 AP with SC glucose sensing and IP insulin delivery 5.5.2.1 IP insulin delivery: closed-loop vs. open-loop 5.5.2.2 Closed-loop control with IP vs. SC insulin delivery 5.6 Perspectives for IP insulin use in AP 5.7 Declaration of interests References 6 Physiological models for artificial pancreas development 6.1 Role of physiological models 6.2 The University of Virginia/Padova T1D simulator 6.2.1 A serendipitous beginning 6.2.2 Accelerating AP research: the FDA-accepted T1D simulator 6.2.3 Further developments of the UVA/Padova T1D simulator 6.3 The oral glucose minimal model 6.3.1 The model 6.3.2 Insulin sensitivity: diurnal pattern 6.3.3 Insulin sensitivity: simple vs. complex carbohydrates 6.4 Models of new molecules 6.4.1 Inhaled insulin 6.4.2 Subcutaneous UltraFast acting insulin analog 6.4.3 Modeling of pramlintide: in silico assessment of optimal pramlintide to insulin ratio 6.5 Modeling subcutaneous glucose sensor delay 6.6 The UVA/Padova T1D simulator for nonadjunctive use of glucose sensors 6.7 Adaptive AP algorithms 6.7.1 Run-to-Run strategy for adaptive MPC tuning 6.7.2 In silico testing 6.8 Conclusions 6.A 6.A.1 UVA/Padova T1D simulator model equation Glucose subsystem Insulin subsystem Glucose rate of appearance Endogenous glucose production Glucose utilization Renal excretion External insulin rate of appearance Subcutaneous insulin kinetics Intradermal insulin kinetics Inhaled insulin kinetics Subcutaneous glucose kinetics Glucagon kinetics and secretion Subcutaneous glucagon kinetics Acknowledgments References 7 Deployment of modular MPC for type 1 diabetes control: the Italian experience 2008-2016 7.1 Introduction 7.2 AP hardware 7.2.1 APS: the in-patient hardware platform 7.2.2 DiAs: the out-patient hardware platform Config. A: Omnipod and Dexcom SEVEN PLUS Config. B: t:slim and G4 Platinum Config. C: Accu-Chek Combo and G4 Platinum 7.3 Telemedicine 7.4 AP control algorithm 7.4.1 Safety layer 7.4.2 Control layer 7.4.2.1 Hyperglycaemia Mitigation System 7.4.2.2 Model Predictive Control Model Cost function Constraints Meal announcement and feed-forward action 7.4.3 Adaptation layer 7.4.3.1 Adaption rules 7.4.3.2 Real-life algorithm 7.5 In-patient studies 7.5.1 Study design 7.5.2 Data analysis 7.5.3 Results 7.5.4 H2MS and mMPC 7.5.5 Further inpatient studies testing the mMPC 7.5.6 Concluding remarks 7.6 Out-patient studies 7.6.1 Study design 7.6.2 Data analysis 7.6.3 Results 7.6.3.1 H2MS, DiAs configuration A [28] 7.6.3.2 mMPC, DiAs configuration A [29] 7.6.3.3 mMPC, DiAs configuration C [21] 7.6.4 Concluding remarks 7.7 Real-life testing 7.7.1 Evening & night use of mMPC 7.7.2 Day & night use of mMPC 7.7.3 Adaptive mMPC 7.8 Concluding remarks 7.9 Conclusions Acknowledgments References 8 Integrating the clinical and engineering aspects of closed-loop control: the Virginia experience 8.1 Introduction 8.2 Overview of the technology 8.3 Early outpatient AP studies (2011-16) 8.4 Pediatric studies 8.5 Ongoing clinical trials (2016-) 8.6 Conclusion References 9 Strategies to mitigate hypoglycaemia in the artificial pancreas 9.1 Introduction 9.2 Safety monitoring systems and hypoglycaemia prediction 9.3 Control strategies for insulin infusion limitation 9.4 Feedforward actions in exercise-informed systems 9.5 Carbohydrate intake suggestions as counterregulatory control action 9.6 Glucagon as counterregulatory control action 9.7 Conclusions Acknowledgments References 10 Multiple-signal artificial pancreas systems 10.1 Introduction 10.2 Improving control of physical activity and exercise 10.2.1 Quantitative models of exercise and T1D 10.2.2 Physical activity as a cue to modified artificial pancreas operation 10.2.3 Adapting AP operation by quantifying the effect of physical activity and exercise inputs 10.3 Improving control of meals and snacks 10.4 Improving control of blood glucose overnight 10.4.1 Detecting sleep 10.4.2 Detected sleep as a cue to modified artificial pancreas operation 10.5 Enhancing patient safety and security 10.5.1 Remote patient monitoring for emergency services 10.5.2 Context awareness: pros and cons 10.6 Conclusions References 11 Artificial pancreas in pediatrics 11.1 Introduction 11.2 Effect on children of hypoglycaemia and hyperglycaemia 11.3 Overview of AP development in pediatrics 11.4 Threshold suspend and predictive low glucose suspend in pediatrics 11.5 Managing expectations about AP use among families and diabetes providers 11.6 Hybrid closed-loop systems in pediatrics 11.7 Fully closed-loop systems 11.8 Understanding differences in AP tuning in pediatrics 11.9 Use of AP systems in young children 11.10 Skin care issues impacting device use 11.11 Psychological and social considerations for AP in pediatrics 11.12 Role of automated decision support in pediatrics and short-term AP use in the developing world 11.13 Long-term needs and future improvements 11.14 Conclusions and summary References Index Back Cover