Diabetes and hyperglycemia: Strict glycemic control

Turina, Matthias; Christ‐Crain, Mirjam; Polk, Hiram C.

doi:10.1097/01.ccm.0000231887.84751.04

Cited by 47 publications

(34 citation statements)

References 86 publications

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“…The metabolic response to stress is characterised by highly variable changes in insulin sensitivity and glucose metabolism. The overall result is hyperglycemia, the pathogenesis of which is becoming well described [1,5]. Tight glucose control has been shown to reduce Intensive Care Unit (ICU) patient mortality up to 45%.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

Lin

Lee

Chase

et al. 2008

Computer Methods and Programs in Biomedicine

146

177

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Targeted, tight model-based glycemic control in critical care patients that can reduce mortality 18 -45% is enabled by prediction of insulin sensitivity, S I . However, this parameter can vary significantly over a given hour in the critically ill as their condition evolves. A stochastic model of S I variability is constructed using data from 165 critical care patients. Given S I for an hour, the stochastic model returns the probability density function of S I for the next hour. Consequently, the glycemic distribution following a known intervention can be derived, enabling pre-determined likelihoods of the result and more accurate control.Cross validation of the S I variability model shows that 86.6% of the blood glucose measurements are within the 0.90 probability interval, and 54.0% are within the interquartile interval. "Virtual Patients" with S I behaving to the overall S I variability model achieved similar predictive performance in simulated trials (86.8% and 45.7%).Finally, adaptive control method incorporating S I variability is shown to produce improved glycemic control in simulated trials compared to current clinical results. The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability.

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Section: Introductionmentioning

confidence: 99%

“…More specifically, "bumpy" areas of S I probability density function in Figure 3 may be a sign of S I absorbing physiology unmodelled or undermodelled in Equations (1)- (5). However, this unmodelled physiology represents uncommon sudden, or extreme, dynamics, evident by the low overall joint probability density in these areas in Figure 1.…”

mentioning

confidence: 98%

Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

Lin

Lee

Chase

et al. 2008

Computer Methods and Programs in Biomedicine

146

177

View full text Add to dashboard Cite

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“…Age-adjusted mortality is twice that of patients without diabetes (Turina et al 2006). Patients who are not diabetic but who present during acute illness with raised blood sugar have a poorer prognosis than patients who are normoglycaemic.…”

Section: Why Do Critically-ill Patients Become Hyperglycaemic?mentioning

confidence: 99%

Glucose control in the intensive care unit: how it is done

Harper

2007

Proc. Nutr. Soc.

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Hyperglycaemia occurs in the majority of critically-ill patients, partly because patients are hypercatabolic and consequently have increased glucose levels and partly because of insulin resistance. Hyperglycaemia is associated with increased mortality in critical illness. In 2001 it was shown that mortality and other complications of critical illness can be decreased by adopting ‘tight’ glycaemic control (4·1–6·4 mmol/l). The critical care world adopted tight glycaemic control enthusiastically, until it became apparent that profound life-threatening hypoglycaemia could result. Most clinicians, currently, have adopted regimens aiming to control glucose between 4 and 8 mmol/l. Practising this regimen safely requires attention to detail. Patients must be provided with energy as well as insulin; preferably via the enteral route, but parenterally if necessary. Insulin is administered according to a relatively simple scale that is adjustable by nursing staff according to patients' glucose results. Frequent glucose measurement is essential to success, along with using visual charting that makes sudden changes in blood glucose levels obvious. There are several ‘champions’ of safe implementation of glucose control in the intensive care unit at the Royal Liverpool University Hospital who are educators and who feed results back to staff regularly. Further studies will clarify the ultimate role of tight glycaemic control, but it can be done safely with meticulous attention to detail.

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“…5,6 These include identifying potential mechanisms for the deleterious effects of hyperglycemia and the protective role of insulin in glycemic control. 7,8 The risk for hypoglycemia has also prompted concerns that one must identify patients who are most likely to benefit from insulin therapy. 9 Emerging evidence raises intriguing questions on the role of the kidney in this process and provides an opportunity to learn from these observations.…”

mentioning

confidence: 99%

Glycemic Control and Critical Illness

Mehta¹

2007

Journal of the American Society of Nephrology

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Diabetes and hyperglycemia: Strict glycemic control

Cited by 47 publications

References 86 publications

Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

Glucose control in the intensive care unit: how it is done

Glycemic Control and Critical Illness

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