Model predictive glycaemic regulation in critical illness using insulin and nutrition input: A pilot study

Wong, Xing-Wei; Chase, J. Geoffrey; Shaw, Geoffrey M.; Hann, C.E.; Lotz, Thomas; Lin, Jessica; Singh-Levett, I.; Hollingsworth, Laura; Wong, OF; Andreassen, Steen

doi:10.1016/j.medengphy.2005.10.015

Cited by 71 publications

(89 citation statements)

References 53 publications

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“…For this analysis, a clinically validated model [35,[89][90][91][92][93][94][95][96][97][98][99] is used to identify patient-specific, timevarying (hourly) insulin sensitivity (S I ) every hour: [86,87]. In particular, they have been used in several clinical TGC studies, including the development of SPRINT [35,89,90,[95][96][97][98][99][100].…”

Section: Analysing Insulin Sensitivity In the Critically Illmentioning

confidence: 99%

“…In particular, they have been used in several clinical TGC studies, including the development of SPRINT [35,89,90,[95][96][97][98][99][100]. Hence, the insulin sensitivity metric (S I ) it is well validated metric and has also shown significant correlation to gold standard research assessments of insulin sensitivity [91][92][93][94], and in comparison to steady states achieved in these gold standard tests [101].…”

Section: Analysing Insulin Sensitivity In the Critically Illmentioning

confidence: 99%

“…Those two are SPRINT, including its model-based precursors [34,97,98,105,120], and the eMPC algorithm [108]. Only SPRINT specifies carbohydrate intake, formula and/or goal feed rates [35,99].…”

Section: The Impact Of Nutrition and Implications For Tgc Protocol Dementioning

confidence: 99%

See 2 more Smart Citations

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Chase

Compte

Suhaimi

et al. 2011

Computer Methods and Programs in Biomedicine

119

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Tight 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 75,000 hours 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.

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Section: Analysing Insulin Sensitivity In the Critically Illmentioning

confidence: 99%

Section: Analysing Insulin Sensitivity In the Critically Illmentioning

confidence: 99%

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Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Chase

Compte

Suhaimi

et al. 2011

Computer Methods and Programs in Biomedicine

119

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“…The insulin rate required to achieve this BG target is computed with a bisection method using a clinically validated insulin-glucose system model [23][24][25] and the median (50 th percentile)…”

Section: Star-liege Protocolmentioning

confidence: 99%

First pilot trial of the STAR-Liege protocol for tight glycemic control in critically ill patients

Penning

Compte

Moorhead

et al. 2012

Computer Methods and Programs in Biomedicine

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“…More recently, significant clinical outcomes have been achieved by table-based glucose control protocols [12][13][14] that mimic and are developed from computer-based protocols [15][16][17][18], but are much simpler to implement and more likely to be accepted by clinical staff [19]. Such model-derived or model-validated approaches are also increasing in prevalence [20,21].…”

Section: Introductionmentioning

confidence: 99%

A fast generalizable solution method for glucose control algorithms

Hann

Docherty

Chase

2010

Mathematical Biosciences

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Model predictive glycaemic regulation in critical illness using insulin and nutrition input: A pilot study

Cited by 71 publications

References 53 publications

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

First pilot trial of the STAR-Liege protocol for tight glycemic control in critically ill patients

A fast generalizable solution method for glucose control algorithms

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