Organ failure and tight glycemic control in the SPRINT study

Chase, J. Geoffrey; Pretty, Christopher G.; Pfeifer, Leesa; Shaw, Geoffrey M.; Preiser, Jean‐Charles; Compte, Aaron J. Le; Lin, Jessica; Hewett, D.; Moorhead, Katherine T.; Desaive, Thomas

doi:10.1186/cc9224

Cited by 115 publications

(88 citation statements)

References 58 publications

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“…This goal will also have lesser impact on immune response to infection, thus reducing the potential for sepsis, multi-organ dysfunction and failure, and thus death [36,[71][72][73][74].…”

Section: Discussionmentioning

confidence: 99%

“…In particular, studies of glycemia and mortality show increasing risk of death average for glycemia of 7.0 mmol/L or greater, but no change in risk of death below 6.0 mmol/L [2,6,75]. In addition, a recent study of organ failure and SPRINT showed that cumulative time in the 4.4-7.0 mmol/L band greater than 50% was associated with faster reduction of organ failure [36]. Thus, within this band there are no current limitations or guidelines on clinical practice or choice.…”

Section: Discussionmentioning

confidence: 99%

“…They were matched by equally impressive reductions in cost per patient treated [37,38], and in reduced clinical incidence of sepsis, polyneuropathy and organ failure [36,39].…”

Section: Hyperglycemia Hypoglycemia Tgc and Patient Mortality Outcomementioning

confidence: 99%

“…A later study by Van den Berghe et al [33] was less successful with a more dynamic medical ICU cohort. The SPRINT (Specialised Relative Insulin and Nutrition Titration) study obtained significant mortality reductions for a medical ICU cohort controlling both nutrition and insulin inputs [34,35], which is a unique approach in the field [36]. These studies showed reductions of 17-42% in mortality for patients whose length of ICU stay was 3-5 days or longer.…”

Section: Hyperglycemia Hypoglycemia Tgc and Patient Mortality Outcomementioning

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

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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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“…This goal will also have lesser impact on immune response to infection, thus reducing the potential for sepsis, multi-organ dysfunction and failure, and thus death [36,[71][72][73][74].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…They were matched by equally impressive reductions in cost per patient treated [37,38], and in reduced clinical incidence of sepsis, polyneuropathy and organ failure [36,39].…”

Section: Hyperglycemia Hypoglycemia Tgc and Patient Mortality Outcomementioning

confidence: 99%

Section: Hyperglycemia Hypoglycemia Tgc and Patient Mortality Outcomementioning

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

Self Cite

119

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“…SPRINT is currently employed in the Christchurch ICU and effectively reduced mortality by 20-45% and per patient costs by $1000-2000 per-patient [3]. It has also been shown to reduce the rate and severity of organ failure by 10% [8]. Unlike all other published protocols with similar glycaemic targets [5], it also reduced the incidence of hypoglycaemia by 50%.…”

Section: Introductionmentioning

confidence: 99%

Tight Glycemic Control in Intensive Care: From Engineering to Clinical Practice Change

et al. 2011

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Abstract-Tight glycemic control (TGC) is prevalent in critical care. Providing safe, effective TGC has proven very difficult to achieve with clinically derived protocols. The problem is exacerbated by extreme patient variability and the need to minimize clinical effort and burden. These ingredients make an ideal scenario for model-based methods to provide optimised solutions. This paper presents the development, clinically validated virtual trials optimisation, and initial clinical implementation of a stochastic targeted (STAR) TGC method and framework. It is compared to a prior successful, modelderived, less flexible and dynamic TGC protocol (SPRINT). The use of stochastic models to safely forecast a range of glucose outcomes over 1-3 hours ensures better performance, more dynamic use of the range of insulin and nutrition inputs and thus better glycemic performance and safety from hypoglycemia, the latter of which was reduced by 3.0x times. Hence, the paper presents an overall engineering approach to TGC from engineering models to clinical implementation and ongoing clinical practice change.

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Safety and Efficacy of Antiviral Therapy for Prevention of Cytomegalovirus Reactivation in Immunocompetent Critically Ill Patients

et al. 2017

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Organ failure and tight glycemic control in the SPRINT study

Cited by 115 publications

References 58 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 in Intensive Care: From Engineering to Clinical Practice Change

Safety and Efficacy of Antiviral Therapy for Prevention of Cytomegalovirus Reactivation in Immunocompetent Critically Ill Patients

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