Enhancing glucose sensor models: Modeling the drop-outs

Diabetes Technology and Therapeutics

Volumn 17, Issue 6, 2015, Pages 420-426

  Emami, Ali ^a,b   Rabasa Lhoret, Remi ^a,c,d,e   Haidar, Ahmad ^a,c  

^a CLIN RESEARCH INSTITUTE OF MONTREAL   (Canada)

^b MCGILL UNIVERSITY   (Canada)

^c MCGILL UNIVERSITY   (Canada)

^d UNIVERSITY OF MONTREAL   (Canada)

^e Montreal Diabetes Research Center   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

GLUCAGON; GLUCOSE; INSULIN; GLUCOSE BLOOD LEVEL;

ANALYZER; ARTICLE; ARTIFICIAL PANCREAS; BAYES THEOREM; BLOOD GLUCOSE MONITORING; CALIBRATION; CLINICAL ARTICLE; COMPARTMENT MODEL; COMPUTER SIMULATION; DROP OUTS; GLUCOSE BLOOD LEVEL; GLUCOSE SENSOR; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; MEASUREMENT ERROR; MONTE CARLO METHOD; PRIORITY JOURNAL; PROBABILITY; ANALYSIS; BIOLOGICAL MODEL; BLOOD; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

BAYES THEOREM; BLOOD GLUCOSE; COMPUTER SIMULATION; DIABETES MELLITUS, TYPE 1; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PANCREAS, ARTIFICIAL;

EID: 84929251225     PISSN: 15209156     EISSN: 15578593     Source Type: Journal    
DOI: 10.1089/dia.2014.0309     Document Type: Article

References (22)

1. Todd JA: Etiology of type 1 diabetes. Immunity 2010;32: 457-467.
2. Diabetes Control and Complications Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1995; 329:977-986.
3. American Diabetes Association: Standards of medical care in diabetes-2010. Diabetes Care 2010;33(Suppl 1):S11-S61.
4. Cryer PE: The barrier of hypoglycemia in diabetes. Diabetes 2008;57:3169-3176.
5. Heinemann L: Variability of insulin absorption and insulin action. Diabetes Technol Ther 2002;4:673-682.
6. Klonoff DC: Continuous glucose monitoring roadmap for 21st century diabetes therapy. Diabetes Care 2005;28: 1231-1239.
7. Rebrin K, Steil GM, van Antwerp WP, et al.: Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. Am J Physiol 1999; 277:E561-E571.
8. Hovorka R: Closed-loop insulin delivery: from bench to clinical practice. Nat Rev Endocrinol 2011;7:385-395.
9. Haidar A, Legault L, Messier V, Mitre RM, Leroux C, Rabasa-Lhoret R: Comparison of dual-hormone artificial pancreas, single-hormone artificial pancreas, and conventional insulin pump therapy for glycaemic control in patients with type 1 diabetes: an open-label randomised controlled crossover trial. Lancet Diabetes Endocrinol 2015; 3:17-26.
10. Wilinska ME, Hovorka R: Simulation models for in silico testing of closed-loop glucose controllers in type 1 diabetes. Drug Discov Today 2009;5:289-298.
11. Breton M, Kovatchev B: Analysis, modeling, and simulation of the accuracy of continuous glucose sensors. J Diabetes Sci Technol 2008;2:853-862.
12. Lunn DJ, Wei C, Hovorka R: Fitting dynamic models with forcing functions: application to continuous glucose monitoring in insulin therapy. Stat Med 2011;30:2234-2250.
13. Facchinetti A, Del Favero S, Sparacino G, et al.: Modeling the glucose sensor error. IEEE Trans Biomed Eng 2014;61: 620-629.
14. Helton KL, Ratner BD, Wisniewski NA: Biomechanics of the sensor-tissue interface-effects of motion, pressure, and design on sensor performance and the foreign body response-Part I: theoretical framework. J Diabetes Sci Technol 2011;5:632-646.
15. Helton KL, Ratner BD, Wisniewski NA: Biomechanics of the sensor-tissue interface-effects of motion, pressure, and design on sensor performance and the foreign body response-Part II: examples and application. J Diabetes Sci Technol 2011;5:647-656.
16. Baysal N, Cameron F, Buckingham BA, et al.: A novel method to detect pressure-induced sensor attenuations (PISA) in an artificial pancreas. J Diabetes Sci Technol 2014;8:1091-1096.
17. Bequette BW: Fault detection and safety in closed-loop artificial pancreas systems. J Diabetes Sci Technol 2014;8: 1204-1214.
18. Haidar A, Legault L, Dallaire M, et al.: Glucose-responsive insulin and glucagon delivery (dual-hormone artificial pancreas) in adults with type 1 diabetes: a randomized crossover controlled trial. CMAJ 2013;185:297-305.
19. Lunn DJ, Thomas A, Best N, et al.: WinBUGS-A Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 2000;10:325-337.
20. Lunn D: WinBUGS differential interface-worked examples. London: Department of Epidemiology and Public Health, Imperial College School of Medicine, 2004.
21. Spiegelhalter DJ, Best NG, Carlin BP, et al.: Bayesian measures of model complexity and fit. J R Stat Soc Ser B 2002;64:583-639.
22. Wilinska ME, Chassin LJ, Acerini CL, et al.: Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes. J Diabetes Sci Technol 2010;4: 132-144.