Tight Glycemic Control in Critically Ill Adults

Finfer, Simon; Delaney, Anthony

doi:10.1001/jama.300.8.963

Cited by 56 publications

(31 citation statements)

References 20 publications

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“…Similar issues and results were seen for the Leuven, VISEP, NICE-SUGAR and Glucontrol trials [3,44,46,54,146]. As a result the fear of hypoglycemia has led to a raising of glycemic target bands [65].…”

Section: Summary With Respect To Some Recent Protocolssupporting

confidence: 52%

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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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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: Summary With Respect To Some Recent Protocolssupporting

confidence: 52%

“…Hence, overall, there is significant controversy around TGC and its application [53][54][55]. This paper posits that it is a lack of understanding of both the problem and the patient-specific dynamics that hinder clarity on all of these issues.…”

Section: Hyperglycemia Hypoglycemia Tgc and Patient Mortality Outcomementioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Hence, there is significant controversy around TGC and its application (Wilson et al, 2007, Mesotten, 2008, Finfer and Delaney, 2008, Devos and Preiser, 2007. This paper posits that it is a lack of understanding of both the problem and the patient dynamics that hinder clarity on all of these issues.…”

Section: Hyperglycaemia Tgc and Outcomementioning

confidence: 99%

Tight Glycemic Control - the Leading Role of Insulin Sensitivity in Determining Efficacy and Thus Outcome

Chase¹

2009

7th IFAC Symposium on Modelling and Control in Biomedical Systems

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Tight glycaemic control (TGC) has emerged as a major focus in critical care. However, repeating the initial successful reductions in reducing mortality and other outcomes via TGC has proven very difficult. Hence, there has been growing debate over the necessity of TGC, its goals, safety from hypoglycemia, and target cohorts. This article reviews existing knowledge and results to provide a new interpretation and explanation for the variable results in applying TGC. It then uses a validated metabolic system model to show how the root cause is the intra-and inter-patient variability, which makes TGC difficult over diverse cohorts and thus yields such variable results over many protocols.

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“…This view has gained increasing support [9][10][11][12][13]. A meta-analysis concluded that TGC was not associated with significantly reduced mortality but was associated with increased risk of hypoglycaemia [14].…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in the measurement of blood glucose in paediatric intensive care: implications for clinical trials of tight glycaemic control

et al. 2011

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Purpose: In preparation for a tight glycaemic control (TGC) clinical trial we assessed the agreement between methods used to measure blood glucose in critically ill children. Methods: Service evaluation comparing blood gas and main laboratory analysers with point-ofcare (POC) devices PCX, ACCUChek and Hemocue. Results: Two hundred forty-five samples from 157 children measured on 2-4 devices provided 790 values. Marked variation was evident in glucose values between devices, time between tests, sample (whole blood/plasma) and source; 39% of paired values had [20% difference. The decision to start insulin at 7 mmol/L differed depending on the device used for 33% of samples. At low glucose values (\4 mmol/L), differences up to 1.8 mmol/L were evident. The blood gas analyser read lower than all POC models and the laboratory analyser (less risk of undetected hypoglycaemia). An inverse relationship was evident between haematocrit (Hct) and glucose error using POC devices.PCX values for samples with Hct \30% were higher (85%), whereas those for Hct values [38% were lower (66%). Glycolysis occurred during transfer of samples to the laboratory. Using the PCX at the bedside resulted in 0.5 mmol/L mean difference higher than laboratory values; locating the PCX in the laboratory reduced this to 0.2 mmol/L. Conclusions: Discrepancies between measurements may mask hypoglycaemia, and the potential benefits of controlling hyperglycaemia may not be achieved. Variation introduced by different devices, sample or source may have led to misclassification of treatment decisions contributing to the conflicting results of TGC studies.

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Tight Glycemic Control in Critically Ill Adults

Cited by 56 publications

References 20 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

Tight Glycemic Control - the Leading Role of Insulin Sensitivity in Determining Efficacy and Thus Outcome

Uncertainties in the measurement of blood glucose in paediatric intensive care: implications for clinical trials of tight glycaemic control

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