Improving glycemic control in the cardiothoracic intensive care unit: Clinical experience in two hospital settings

Goldberg, Philip A.; Sakharova, Olga V.; Barrett, Peter; Falko, Lillian N.; Roussel, Maureen G.; Bak, Leigh B.; Blake-Holmes, Dawn; Marieb, Norman J.; Inzucchi, Silvio E.

doi:10.1053/j.jvca.2004.08.003

Cited by 75 publications

(46 citation statements)

References 29 publications

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“…However, only a few trials have reported per patient results [34,39,68]. More importantly, Table 1 summarises the cohort results from selected trials in terms of median and (lognormal multiplicative) variance, where enough data were reported to clearly identify these values.…”

Section: The Interrelationship Of Glycemia Tgc Patients and 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

123

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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: The Interrelationship Of Glycemia Tgc Patients and 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

123

View full text Add to dashboard Cite

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“…Only a few trials have reported per patient results (Chase et al, 2008b, Goldberg et al, 2004. More importantly, Table 1 summarises the cohort results from several trials in terms of median and (lognormal multiplicative) variance.…”

Section: Known Facts and How Tgc Really Workmentioning

confidence: 99%

“…Currently, only a very few protocols either directly or indirectly adapt their intervention based on patient insulin sensitivity (Chase et al, 2008b, Plank et al, 2006, Braithwaite et al, 2006, Goldberg et al, 2004). Most are model-based.…”

Section: Summary and Implications For Tgc Protocol Designmentioning

confidence: 99%

Tight Glycemic Control - The leading role of insulin sensitivity in determining efficacy and thus outcome

Chase

Compte

Lin

et al. 2009

IFAC Proceedings Volumes

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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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“…Studies have shown reduced rates of infection in the acute postoperative period with this BG target. 5,40,41 Although multiple BG protocols have been developed, 5,[46][47][48][49] there is a paucity of clinical evidence on the effect of BG control in stressinduced hyperglycemia on diabetes and nondiabetes patients. Defined parameters for intraoperative BG management are still lacking, 5,50,51 while there is also controversy over the safety and efficacy of IIT in acute care settings.…”

Section: It Is Not Clear What Level Of Glycemic Control Is Associatedmentioning

confidence: 99%

Perioperative Blood Glucose Monitoring in the General Surgical Population

Raju

Torjman

Goldberg

2009

J Diabetes Sci Technol

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Several studies have shown a relationship between poor outcome and uncontrolled blood glucose (BG) in cardiac, neurosurgical, critical care, and general surgical patients. A major study showed that tight glycemic control (80–110mg/dl) was related to increased mortality. Based on evidence from controlled studies, the American Diabetes Association, and the Society of Thoracic Surgeons, maintaining intraoperative BG levels in the 140–180 mg/dl range seems appropriate. Optimization of the patient's preoperative medications and the use of insulin infusions, as well as surgical and anesthetic technique, are important factors for achieving desirable perioperative BG control. Minimizing BG variability during surgery should be part of the glycemic control strategy. Advances in real-time glucose monitoring may soon benefit hospitalized diabetes and nondiabetes patients.

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Improving glycemic control in the cardiothoracic intensive care unit: Clinical experience in two hospital settings

Cited by 75 publications

References 29 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

Perioperative Blood Glucose Monitoring in the General Surgical Population

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