Derivative weighted active insulin control algorithms and intensive care unit trials

Abstract Critically ill-patients often experience stress-induced hyperglycemia. This research demonstrates the effectiveness of a simple automated insulin infusion for controlling the rise and duration of blood glucose excursion in critically ill-patients. Heavy derivative controllers derived from a simple, two-compartment model reduced blood glucose excursion 79–89% after a glucose input in proof-of-concept clinical trials. Modelled performance is very similar to clinical results, including a strong correlation between modelled and actual insulin consumed, validating the fundamental models and methods. However, the need for additional dynamics in the model employed is clearly illustrated despite capturing the essential dynamics for this problem.

[1]  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.

[2]  Miet Schetz,et al.  Outcome benefit of intensive insulin therapy in the critically ill: Insulin dose versus glycemic control* , 2003, Critical care medicine.

[3]  Yasser Ousman,et al.  Hyperglycemia in the Hospitalized Patient , 2002 .

[4]  C. Osmond,et al.  Understanding Oral Glucose Tolerance: Comparison of Glucose or Insulin Measurements During the Oral Glucose Tolerance Test with Specific Measurements of Insulin Resistance and Insulin Secretion , 1994, Diabetic medicine : a journal of the British Diabetic Association.

[5]  Piero Marchetti,et al.  Phasic insulin release and metabolic regulation in type 2 diabetes. , 2002, Diabetes.

[6]  John Pickup,et al.  Continuous subcutaneous insulin infusion at 25 years: evidence base for the expanding use of insulin pump therapy in type 1 diabetes. , 2002, Diabetes care.

[7]  R. Ollerton.  Application of optimal control theory to diabetes mellitus , 1989 .

[8]  Timothy W. Evans,et al.  Glucose Control and Mortality in Critically Ill Patients , 2003 .

[9]  A. Kitabchi,et al.  Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. , 2002, The Journal of clinical endocrinology and metabolism.

[10]  J. Geoffrey Chase,et al.  Active insulin infusion control of the blood glucose derivative , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[11]  G. Charpentier,et al.  Management of drugs affecting blood glucose in diabetic patients with renal failure. , 2000, Diabetes & metabolism.

[12]  E. Kraegen,et al.  Blood Glucose Control by Intermittent Loop Closure in the Basal Mode: Computer Simulation Studies with a Diabetic Model , 1985, Diabetes Care.

[13]  M. Fisher,et al.  A semiclosed-loop algorithm for the control of blood glucose levels in diabetics , 1991, IEEE Transactions on Biomedical Engineering.

[14]  T. Yoneyama,et al.  A robust controller for insulin pumps based on H-infinity theory , 1993, IEEE Transactions on Biomedical Engineering.

[15]  Geoffrey M. Shaw,et al.  Derivative weighted active insulin control algorithms and trials , 2003 .

[16]  G. V. Berghe,et al.  Intensive insulin therapy in critically ill patients. , 2001, The New England journal of medicine.

[17]  E. Kraegen,et al.  Insulin responses to varying profiles of subcutaneous insulin infusion: kinetic modelling studies , 1984, Diabetologia.

[18]  C Cobelli,et al.  Stable-Label Intravenous Glucose Tolerance Test Minimal Model , 1989, Diabetes.

[19]  P. V. van Heerden,et al.  Closed-loop Control of Blood Glucose Levels in Critically Ill Patients , 2002, Anaesthesia and intensive care.

[20]  D. Christensen.  Critical care: Sugar limit saves lives , 2001 .

[21]  R. Hovorka,et al.  Pancreatic beta-cell responsiveness during meal tolerance test: model assessment in normal subjects and subjects with newly diagnosed noninsulin-dependent diabetes mellitus. , 1998, The Journal of clinical endocrinology and metabolism.

[22]  Roman Hovorka,et al.  Pancreatic β-Cell Responsiveness during Meal Tolerance Test: Model Assessment in Normal Subjects and Subjects with Newly Diagnosed Noninsulin-Dependent Diabetes Mellitus1 , 1998 .

[23]  Claudio Cobelli,et al.  Overestimation of minimal model glucose effectiveness in presence of insulin response is due to undermodeling. , 1998, American journal of physiology. Endocrinology and metabolism.

[24]  R N Bergman,et al.  Assessment of insulin sensitivity in vivo. , 1985, Endocrine reviews.

[25]  Z. Bloomgarden,et al.  Inflammation and insulin resistance. , 2003, Diabetes care.

[26]  E D Lehmann,et al.  Computer assisted diabetes care: a 6-year retrospective. , 1996, Computer methods and programs in biomedicine.

[27]  Vic Hasselblad,et al.  A clinical approach for the diagnosis of diabetes mellitus: an analysis using glycosylated hemoglobin levels. Meta-analysis Research Group on the Diagnosis of Diabetes Using Glycated Hemoglobin Levels. , 1996, JAMA.

[28]  H. Gerstein,et al.  Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview , 2000, The Lancet.

[29]  Z-H Lam,et al.  Active insulin infusion using optimal and derivative-weighted control. , 2002, Medical engineering & physics.