• J Geoffrey Chase, Aaron J Le Compte, Fatanah Suhaimi, Geoffrey M Shaw, Adrienne Lynn, Jessica Lin, Christopher G Pretty, Normy Razak, Jacquelyn D Parente, Christopher E Hann, Jean-Charles Preiser, and Thomas Desaive.
• University of Canterbury, Dept of Mechanical Engineering, Centre for Bio-Engineering, Private Bag 4800, Christchurch, New Zealand. geoff.chase@canterbury.ac.nz
• Comput Methods Programs Biomed. 2011 May 1; 102 (2): 156-71.

AbstractTight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S(I)). Variation in S(I) provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S(I)) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.Copyright © 2010. Published by Elsevier Ireland Ltd.

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