Literature DB >> 19865614

Intensive Care Unit Insulin Delivery Algorithms: Why So Many? How to Choose?

Garry M Steil1, Dorothee Deiss, Judy Shih, Bruce Buckingham, Stuart Weinzimer, Michael S D Agus.   

Abstract

OBJECTIVE: Studies showing improved outcomes with tight glycemic control in the intensive care unit (ICU) have resulted in a substantial number of new insulin delivery algorithms being proposed. The present study highlights mechanisms used in the better-known approaches, examines what might be critical differences among them, and uses systems theory to characterize the conditions under which each can be expected to perform best.
METHODS: Algorithm dose (DeltaI/DeltaG) and step (response to a persistent elevation in glucose) response curves were calculated for written instruction algorithms, developed at the Providence Heart and Vascular Institute (Portland [P] protocol), the University of Washington (UW), and Yale University (Y), together with similar curves for the Glucommander (GM) and proportional integral derivative (PID) computer algorithms. From the simulated curves, different mechanisms used to adjust insulin delivery were identified.
RESULTS: All algorithms increased insulin delivery in response to persistent hyperglycemia, but the mechanism used altered the algorithm's sensitivity to glucose, or gain, in the GM, UW, and Y protocols, while leaving it unchanged for the P protocol and PID algorithm.
CONCLUSIONS: The increase in insulin delivery in response to persistent hyperglycemia observed with all the algorithms can be expected to bring subjects who respond to insulin to targeted glucose ranges. However, because the PID and P protocols did not alter the insulin delivery response curves, these algorithms can be expected to take longer to achieve target glucose levels in individuals who are insulin resistant and/or are exposed to increased carbohydrate loads (e.g., glucose infusions). By contrast, the GM, UW, and Y algorithms can be expected to adapt to the insulin resistance such that the time to achieve target levels is unchanged if the time for insulin to act does not change. If the insulin resistance is accompanied by a longer time for insulin to act, the UW, Y, and GM algorithms may increase the risk of hypoglycemia. Under these conditions, the longer time required for the PID and P protocols to achieve a target glucose level may be a reasonable trade-off for no increase in the risk of hypoglycemia.

Entities:  

Year:  2009        PMID: 19865614      PMCID: PMC2768418          DOI: 10.1177/193229680900300114

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  22 in total

Review 1.  The intravenous route to blood glucose control.

Authors:  R S Parker; F J Doyle; N A Peppas
Journal:  IEEE Eng Med Biol Mag       Date:  2001 Jan-Feb

Review 2.  Intensive insulin therapy in critical care: a review of 12 protocols.

Authors:  Mark Wilson; Jane Weinreb; Guy W Soo Hoo
Journal:  Diabetes Care       Date:  2007-01-09       Impact factor: 19.112

3.  Performance of a dose-defining insulin infusion protocol among trauma service intensive care unit admissions.

Authors:  Susan S Braithwaite; Renee Edkins; Kathy L Macgregor; Edward S Sredzienski; Michael Houston; Ben Zarzaur; Preston B Rich; Bernard Benedetto; Edmund J Rutherford
Journal:  Diabetes Technol Ther       Date:  2006-08       Impact factor: 6.118

4.  Glucommander: a computer-directed intravenous insulin system shown to be safe, simple, and effective in 120,618 h of operation.

Authors:  Paul C Davidson; R Dennis Steed; Bruce W Bode
Journal:  Diabetes Care       Date:  2005-10       Impact factor: 19.112

5.  Potentiation of insulin secretory responses by plasma glucose levels in man: evidence that hyperglycemia in diabetes compensates for imparied glucose potentiation.

Authors:  J B Halter; R J Graf; D Porte
Journal:  J Clin Endocrinol Metab       Date:  1979-06       Impact factor: 5.958

6.  Improving hyperglycemia management in the intensive care unit: preliminary report of a nurse-driven quality improvement project using a redesigned insulin infusion algorithm.

Authors:  Robert C Osburne; Curtiss B Cook; Lawrence Stockton; Marianne Baird; Valerie Harmon; Annie Keddo; Teresa Pounds; Linda Lowey; Joyce Reid; Kathryn A McGowan; Paul C Davidson
Journal:  Diabetes Educ       Date:  2006 May-Jun       Impact factor: 2.140

7.  Modeling insulin action for development of a closed-loop artificial pancreas.

Authors:  G M Steil; Bud Clark; Sami Kanderian; K Rebrin
Journal:  Diabetes Technol Ther       Date:  2005-02       Impact factor: 6.118

8.  Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients.

Authors:  Christoph Pachler; Johannes Plank; Heinz Weinhandl; Ludovic J Chassin; Malgorzata E Wilinska; Roman Kulnik; Peter Kaufmann; Karl-Heinz Smolle; Ernst Pilger; Thomas R Pieber; Martin Ellmerer; Roman Hovorka
Journal:  Intensive Care Med       Date:  2008-02-23       Impact factor: 17.440

9.  A randomized study in diabetic patients undergoing cardiac surgery comparing computer-guided glucose management with a standard sliding scale protocol.

Authors:  Leif Saager; Gordon L Collins; Beth Burnside; Heidi Tymkew; Lini Zhang; Eric Jacobsohn; Michael Avidan
Journal:  J Cardiothorac Vasc Anesth       Date:  2007-12-03       Impact factor: 2.628

10.  Implementation of a safe and effective insulin infusion protocol in a medical intensive care unit.

Authors:  Philip A Goldberg; Mark D Siegel; Robert S Sherwin; Joshua I Halickman; Michelle Lee; Valerie A Bailey; Sandy L Lee; James D Dziura; Silvio E Inzucchi
Journal:  Diabetes Care       Date:  2004-02       Impact factor: 19.112

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  21 in total

1.  Impact of Glucose Meter Error on Glycemic Variability and Time in Target Range During Glycemic Control After Cardiovascular Surgery.

Authors:  Brad S Karon; Jeffrey W Meeusen; Sandra C Bryant
Journal:  J Diabetes Sci Technol       Date:  2015-08-25

2.  Value of continuous glucose monitoring for minimizing severe hypoglycemia during tight glycemic control.

Authors:  Garry M Steil; Monica Langer; Karen Jaeger; Jamin Alexander; Michael Gaies; Michael S D Agus
Journal:  Pediatr Crit Care Med       Date:  2011-11       Impact factor: 3.624

3.  The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation.

Authors:  James S Krinsley; David E Bruns; James C Boyd
Journal:  J Diabetes Sci Technol       Date:  2015-01-06

Review 4.  The future is now: software-guided intensive insulin therapy in the critically ill.

Authors:  Rishi Rattan; Stanley A Nasraway
Journal:  J Diabetes Sci Technol       Date:  2013-03-01

5.  Design and rationale of safe pediatric euglycemia after cardiac surgery: a randomized controlled trial of tight glycemic control after pediatric cardiac surgery.

Authors:  Michael G Gaies; Monica Langer; Jamin Alexander; Garry M Steil; Janice Ware; David Wypij; Peter C Laussen; Jane W Newburger; Caren S Goldberg; Frank A Pigula; Avinash C Shukla; Christopher P Duggan; Michael S D Agus
Journal:  Pediatr Crit Care Med       Date:  2013-02       Impact factor: 3.624

6.  A Pediatric Intensive Care Unit Bedside Computer Clinical Decision Support Protocol for Hyperglycemia Is Feasible, Safe and Offers Advantages.

Authors:  Eliotte L Hirshberg; Michael J Lanspa; Emily L Wilson; Katherine A Sward; Al Jephson; Gitte Y Larsen; Alan H Morris
Journal:  Diabetes Technol Ther       Date:  2017-03-01       Impact factor: 6.118

7.  Tight glycemic control after pediatric cardiac surgery in high-risk patient populations: a secondary analysis of the safe pediatric euglycemia after cardiac surgery trial.

Authors:  Michael S D Agus; Lisa A Asaro; Garry M Steil; Jamin L Alexander; Melanie Silverman; David Wypij; Michael G Gaies
Journal:  Circulation       Date:  2014-03-26       Impact factor: 29.690

Review 8.  Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion.

Authors:  Susan Shapiro Braithwaite; Lisa P Clark; Thaer Idrees; Faisal Qureshi; Oluwakemi T Soetan
Journal:  Curr Diab Rep       Date:  2018-03-26       Impact factor: 4.810

9.  Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.

Authors:  Stephen D Patek; E Andy Ortiz; Leon S Farhy; Jennifer Mason Lobo; James Isbell; Jennifer L Kirby; Anthony McCall
Journal:  Proc Am Control Conf       Date:  2015-07-30

Review 10.  Health technology assessment review: Computerized glucose regulation in the intensive care unit--how to create artificial control.

Authors:  Miriam Hoekstra; Mathijs Vogelzang; Evgeny Verbitskiy; Maarten W N Nijsten
Journal:  Crit Care       Date:  2009-10-16       Impact factor: 9.097

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