Glucose Monitoring Devices: Measuring Blood Glucose to Manage and Control Diabetes

Description

Glucose Monitoring Devices: Measuring Blood Glucose to Manage and Control Diabetes presents the state-of-the-art regarding glucose monitoring devices and the clinical use of monitoring data for the improvement of diabetes management and control. Chapters cover the two most common approaches to glucose monitoring–self-monitoring blood glucose and continuous glucose monitoring–discussing their components, accuracy, the impact of use on quality of glycemic control as documented by landmark clinical trials, and mathematical approaches. Other sections cover how data obtained from these monitoring devices is deployed within diabetes management systems and new approaches to glucose monitoring. This book provides a comprehensive treatment on glucose monitoring devices not otherwise found in a single manuscript. Its comprehensive variety of topics makes it an excellent reference book for doctoral and postdoctoral students working in the field of diabetes technology, both in academia and industry. Glucose Monitoring Devices: Measuring Blood Glucose to Manage and Control Diabetes Copyright Contributors About the Authors 1. Introduction to SMBG Historical perspective and principles of blood glucose control The evidence base for SMBG in type 1 diabetes The evidence base for SMBG in type 2 diabetes Guidelines for SMBG The shortcomings of SMBG and future perspective References 2. Analytical performance of SMBG systems Introduction The process for premarket approval of SMBG devices Analytical performance according to ISO 15197 Precision Interference evaluation Accuracy Analytical performance according to FDA Postmarket analytical performance Advances in analytical performance of SMBG devices Conclusion List of authors References 3. Clinical evaluation of SMBG systems References 4. Consequences of SMBG systems inaccuracy Introduction Quantifying the effect of inaccurate BGM systems A complex system Patient behavior is the main driver Effects spread over time and space Clinical outcomes Short term Short term Long term Long term Quality of life Financial outcomes Short term Short term Long term Long term Requirements Physiological variability Behavioral variability Device and lot variability Therapy modes Time span Accuracy and its consequences Modeling and simulation Metabolic models and simulators (metabolic variability) Behavioral modeling and simulation (behavioral variability and therapy modes) Integrated metabolic/behavioral simulation Modeling glucose monitoring devices (device and lot variability) In silico accuracy studies From in silico results to short-term clinical outcomes Long-term health and complications From in silico results to long-term complications From clinical performance to financial outcomes An extended illustration The in silico study Meter models Behavioral models Clinical and financial outcomes Results: clinical outcomes Accuracy and clinical outcomes: a regression model Determining financial impact Cost compared to an ideal Worst-case costing Observations from the illustrative example Limitations Conclusions and future work References 5. Modeling the SMBG measurement error SMBG measurement error Why modeling the SMBG measurement error? Literature models of SMBG measurement error The state-of-the-art modeling method by Vettoretti et al. Definition of training and test sets Constant-SD zones identification Maximum-likelihood fitting Model validation Derivation of a model of SMBG error distribution for two commercial devices Case study 1: modeling the One Touch Ultra 2 measurement error Dataset Preprocessing of YSI and SMBG-YSI matching Model development Model validation Case study 2: modeling the Bayer Contour Next measurement error Dataset Preprocessing of YSI and SMBG-YSI matching Model development Model validation Remark Applications of the SMBG measurement error models Conclusion References 6. CGM sensor technology Introduction Glucose transduction technologies Current technologies Transduction technologies used in commercially approved CGMs Enzymatic, electrochemical-based sensors Enzymatic, electrochemical-based sensors Nonenzymatic, optical-based sensors Nonenzymatic, optical-based sensors Transduction technologies in development Tissue interface for transcutaneous and subcutaneous transduction Noninvasive technologies Sensor interface and system connectivity Sensor front end electronics Transmitter software and sensor calibration Skin interface for the wearable transmitter System user interface and connectivity Connected systems Artificial pancreas Connected pens Commercial systems Overview Abbott Freestyle Libre flash glucose monitor Dexcom G6 CGM Medtronic Guardian Connect CGM Eversense CGM system Summary References 7. Clinical impact of CGM use Introduction History and general rationale for glucose monitoring Parameters of glucose control and risk association HbA1c Hypoglycemia Time in range and glucose variability Glucose monitoring Limitations of SMBG Benefits of CGM Operational advantage Direction, pattern and trends, investigative tool Alerts Clinical application of CGM CGM efficacy Retrospective CGM studies Observational trials Randomized controlled trials Real-time CGM studies Observational Randomized controlled trials CGM pregnancy data CGM quality of life data CGM limitations User dependent Healthcare provider dependent Device dependent Available CGM systems Dexcom Medtronic Medtrum Senseonics Flash glucose monitoring Further utility of CGM Combining technology Inpatient CGM Summary References 8. Accuracy of CGM systems Introduction Clinical accuracy Numerical (statistical) accuracy Conclusions References 9. Calibration of CGM systems Calibration of minimally invasive CGM sensors Problem statement Critical aspects affecting calibration State-of-art calibration algorithms and today's challenges Simple heuristic to deal with the BG-IG system Kalman filter-based approaches Methods relying on autoregressive models A calibration method integrating several local dynamics models Two approaches to optimize the computational complexity Deconvolution-based Bayesian approach Recursive approaches exploiting past CGM data Today's challenges for CGM calibration algorithms The Bayesian approach applied to the calibration problem Description of a Bayesian calibration algorithm Estimation of model parameters Step 0: parameter initialization Step 0: parameter initialization Step 1: use of calibration model Step 1: use of calibration model Step 2: compensation of BG-to-IG kinetics Step 2: compensation of BG-to-IG kinetics Step 3: match between estimated BG and available SMBG Step 3: match between estimated BG and available SMBG Step 4: parameter update Step 4: parameter update Calibration of the current signal Example of implementation Dataset description Implementation Prior derivation Prior derivation Calibration scenarios Calibration scenarios Performance assessment Performance assessment Results Conclusions References 10. CGM filtering and denoising techniques Introduction The denoising problem Possible approaches to CGM denoising CGM denoising by Kalman filter Overview of the Kalman filter Formulation as online self-tunable approach A priori model for u(t) Determination of λ2 and σ2 Step 1 Step 2 In silico assessment Accuracy in SNR determination Importance of filter parameters accuracy Importance of filter parameters accuracy Comparison with MA Comparison with MA Assessment on data Dealing with SNR intraindividual variability Conclusions References 11. Retrofitting CGM traces Introduction Chapter organization The retrofitting algorithm Problem formulation Notation Algorithm description Step A: retrospective Bayesian CGM recalibration Step B: constrained regularized deconvolution Retrofitting outpatient study data Original dataset Outpatient-like dataset Accuracy outcomes metrics and statistical analysis Statistical analysis Results Retrofitting real-life adjunctive data Original datasets Real-life-like datasets Accuracy outcomes metrics and statistical analysis Results Accuracy of retrofitted CGM versus number of references available Conclusions Appendix: data preprocessing References 12. Modeling the CGM measurement error Introduction Methods Datasets A posteriori recalibration Reference-sensor density and delay estimation Estimation and modeling of the sensor error distribution Estimation and modeling of sensor error time dependency Results Effect of rate of change on sensor error and delay estimation Characterization of recalibrated synchronized sensor errors Modeling of subcutaneous sensors Conclusions References 13. Low glucose suspend systems Introduction Low glucose suspend system Clinical studies with LGS system Real-life evidence with TS system Cost-effectiveness The limitations of the low glucose suspend system Future direction References 14. Predictive low glucose suspend systems Introduction Algorithm development PLGS clinical studies Commercial devices Keys to clinical use Summary and conclusions References 15. Automated closed-loop insulin delivery: system components, performance, and limitations Introduction Closed-loop glycemic control algorithms Proportional-integral-derivative control Fuzzy logic control Model predictive control Zone model predictive control Adaptive control Recursive modeling Subspace-based state-space system identification Recursive system identification Recursive system identification Adaptive generalized predictive control Run-to-run control Iterative learning control Adaptive weights through glycemic risk index Quantifying plasma insulin concentrations Modulating insulin infusion Closed-loop glycemic control results Future directions Conclusions References 16. The dawn of automated insulin delivery: from promise to product Introduction Continuous subcutaneous insulin infusion therapy: the first building block in developing a closed-loop system Continuous glucose monitors: the second step in the construction of a closed-loop system The way forward: the JDRF roadmap to an artificial pancreas Making the dive less deep and shorter: low glucose suspend systems Stopping the plunge: suspend before low systems The algorithms: the final piece of the puzzle Speeding up the process: the creation of an FDA approved simulator Early studies aimed at closing the loop Control in clinic: the first closed-loop studies in rigorous research environments From transitional environments to tests at home From prototype to product: the MiniMed 670G system Exploring the equipment: components and characteristics of the 670G Guardian sensor 3 The Medtronic 670G insulin pump Auto mode Auto mode exits: what are they and why do they happen The highs and lows of real-world use of the 670G system Challenges that remain A bright future Patient considerations Conclusion Short biography References Index A B C D E F G H I J K L M N P R S T U V W Z