Literature DB >> 17979649

Model-based insulin and nutrition administration for tight glycaemic control in critical care.

J Geoffrey Chase1, Geoffrey M Shaw, Thomas Lotz, Aaron LeCompte, Jason Wong, Jessica Lin, Timothy Lonergan, Michael Willacy, Christopher E Hann.   

Abstract

OBJECTIVE: Present a new model-based tight glycaemic control approach using variable insulin and nutrition administration.
BACKGROUND: Hyperglycaemia is prevalent in critical care. Current published protocols use insulin alone to reduce blood glucose levels, require significant added clinical effort, and provide highly variable results. None directly address both the practical clinical difficulties and significant patient variation seen in general critical care, while also providing tight control.
METHODS: The approach presented manages both nutritional inputs and exogenous insulin infusions using tables simplified from a model-based, computerised protocol. Unique delivery aspects include bolus insulin delivery for safety and variable enteral nutrition rates. Unique development aspects include the use of simulated virtual patient trials created from retrospective data. The model, protocol development, and first 50 clinical case results are presented.
RESULTS: High qualitative correlation to within +/-10% between simulated virtual trials and published clinical results validates the overall approach. Pilot tests covering 7358 patient hours produced an average glucose of 5.9 +/- 1.1 mmol/L. Time in the 4-6.1 mmol/L band was 59%, with 84% in 4.0-7.0 mmol/L, and 92% in 4.0-7.75 mmol/L. The average feed rate was 63% of patient specific goal feed and the average insulin dose was 2.6U/hour. There was one hypoglycaemic measurement of 2.1 mmol/L. No departures from protocol or clinical interventions were required at any time.
SUMMARY: Modulating both low dose insulin boluses and nutrition input rates challenges the current practice of using only insulin in larger doses to reduce hyperglycaemic levels. Clinical results show very tight control in safe glycaemic bands. The approach could be readily adopted in any typical ICU.

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Year:  2007        PMID: 17979649     DOI: 10.2174/156720107782151223

Source DB:  PubMed          Journal:  Curr Drug Deliv        ISSN: 1567-2018            Impact factor:   2.565


  25 in total

1.  Stochastic targeted (STAR) glycemic control: design, safety, and performance.

Authors:  Alicia Evans; Aaron Le Compte; Chia-Siong Tan; Logan Ward; James Steel; Christopher G Pretty; Sophie Penning; Fatanah Suhaimi; Geoffrey M Shaw; Thomas Desaive; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2012-01-01

2.  Development of a clinical type 1 diabetes metabolic system model and in silico simulation tool.

Authors:  Xing-Wei Wong; J Geoffrey Chase; Christopher E Hann; Thomas F Lotz; Jessica Lin; Aaron J Le; Geoffrey M Shaw
Journal:  J Diabetes Sci Technol       Date:  2008-05

3.  Blood glucose controller for neonatal intensive care: virtual trials development and first clinical trials.

Authors:  Aaron Le Compte; J Geoffrey Chase; Adrienne Lynn; Chris Hann; Geoffrey Shaw; Xing-Wei Wong; Jessica Lin
Journal:  J Diabetes Sci Technol       Date:  2009-09-01

4.  Identification of intraday metabolic profiles during closed-loop glucose control in individuals with type 1 diabetes.

Authors:  Sami S Kanderian; Stu Weinzimer; Gayane Voskanyan; Garry M Steil
Journal:  J Diabetes Sci Technol       Date:  2009-09-01

5.  Organ failure and tight glycemic control in the SPRINT study.

Authors:  J Geoffrey Chase; Christopher G Pretty; Leesa Pfeifer; Geoffrey M Shaw; Jean-Charles Preiser; Aaron J Le Compte; Jessica Lin; Darren Hewett; Katherine T Moorhead; Thomas Desaive
Journal:  Crit Care       Date:  2010-08-12       Impact factor: 9.097

6.  Impact of human factors on clinical protocol performance: a proposed assessment framework and case examples.

Authors:  J Geoffrey Chase; Steen Andreassen; Karsten Jensen; Geoffrey M Shaw
Journal:  J Diabetes Sci Technol       Date:  2008-05

7.  A benchmark data set for model-based glycemic control in critical care.

Authors:  J Geoffrey Chase; Aaron LeCompte; Geoffrey M Shaw; Amy Blakemore; Jason Wong; Jessica Lin; Christopher E Hann
Journal:  J Diabetes Sci Technol       Date:  2008-07

8.  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

9.  Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study.

Authors:  Tony Zhou; Jennifer L Dickson; Geoffrey M Shaw; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2017-11-06

10.  The impact of parameter identification methods on drug therapy control in an intensive care unit.

Authors:  Christopher E Hann; J Geoffrey Chase; Michael F Ypma; Jos Elfring; Noorhafiz Mohd Nor; Piers Lawrence; Geoffrey M Shaw
Journal:  Open Med Inform J       Date:  2008-05-27
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