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See detailSafety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis
Stewart, K. W.; Pretty, C. G.; Tomlinson, H. et al

in Annals of Intensive Care (2016), 6(1),

Background: The changes in metabolic pathways and metabolites due to critical illness result in a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia and ... [more ▼]

Background: The changes in metabolic pathways and metabolites due to critical illness result in a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia and hypoglycemia difficult. In addition, clinical practices can vary significantly, thus making GC protocols difficult to generalize across units.The aim of this study was to provide a retrospective analysis of the safety, performance and workload of the stochastic targeted (STAR) glycemic control (GC) protocol to demonstrate that patient-specific, safe, effective GC is possible with the STAR protocol and that it is also generalizable across/over different units and clinical practices. Methods: Retrospective analysis of STAR GC in the Christchurch Hospital Intensive Care Unit (ICU), New Zealand (267 patients), and the Gyula Hospital, Hungary (47 patients), is analyzed (2011–2015). STAR Christchurch (BG target 4.4–8.0 mmol/L) is also compared to the Specialized Relative Insulin and Nutrition Tables (SPRINT) protocol (BG target 4.4–6.1 mmol/L) implemented in the Christchurch Hospital ICU, New Zealand (292 patients, 2005–2007). Cohort mortality, effectiveness and safety of glycemic control and nutrition delivered are compared using nonparametric statistics. Results: Both STAR implementations and SPRINT resulted in over 86 % of time per episode in the blood glucose (BG) band of 4.4–8.0 mmol/L. Patients treated using STAR in Christchurch ICU spent 36.7 % less time on protocol and were fed significantly more than those treated with SPRINT (73 vs. 86 % of caloric target). The results from STAR in both Christchurch and Gyula were very similar, with the BG distributions being almost identical. STAR provided safe GC with very few patients experiencing severe hypoglycemia (BG < 2.2 mmol/L, <5 patients, 1.5 %). Conclusions: STAR outperformed its predecessor, SPRINT, by providing higher nutrition and equally safe, effective control for all the days of patient stay, while lowering the number of measurements and interventions required. The STAR protocol has the ability to deliver high performance and high safety across patient types, time, clinical practice culture (Christchurch and Gyula) and clinical resources. © 2016, Stewart et al. [less ▲]

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See detailAccuracy and performance of continuous glucose monitors in athletes
Thomas, Felicity Louise ULg; Pretty, C. G.; Signal, M. et al

in IFAC Proceedings Volumes (IFAC-PapersOnline) (2015)

Continuous glucose monitoring (CGM) devices, with their 1-5 minute measurement interval, allow blood glucose dynamics to be captured more frequently and less invasively than traditional measures of blood ... [more ▼]

Continuous glucose monitoring (CGM) devices, with their 1-5 minute measurement interval, allow blood glucose dynamics to be captured more frequently and less invasively than traditional measures of blood glucose concentration (BG). These devices are primarily designed for the use in type 1 and type 2 diabetic patients to aid BG regulation. However, because of their increased measurement frequency and reduced invasiveness CGM devices have been recently applied to other subject cohorts, such as intensive care patients and neonates. One unexamined cohort is athletes. Continuous monitoring of an athlete's BG has the potential to increase race performance, speed recovery, and aid training, as BG can reflect metabolic and inflammatory conditions. However, before these benefits can be realized the accuracy and performance of CGM devices in active athletes must be evaluated. Two Ipro2 CGM devices (Medtronic Minimed, Northridge, CA, USA) were inserted into an athlete (resting HR 50 beats per minute (bpm), training 10-17hrs per week). Two fasting exercise tests were carried out 3 days apart, involving 2 hours of continuous exercise and a glucose bolus at the end of the 2 hours. Reference BG measurements were taken regularly. These tests were then repeated while the athlete was sedentary, HR < 80bmp. CGM devices agree well with each other and reference measurements during rigorous exercise with a median [IQR] MARD of 7.3 [5.4-10.9] %. During sedentary periods the accuracy of the CGM trace compared to reference measurements was reduced, 25.1 [16.9 35.4] %. However the good agreement between the sensors is maintained. This decrease in accuracy is likely related to the fact interstitial fluid is not actively pumped like blood. It relies on muscle movement to circulate and mix. Thus, it can be expected that during exercise more accurate results are seen as the rigorous movement allows rapid mixing and equilibrium between the blood and interstitial fluid. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. [less ▲]

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See detailTight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis.
Chase, J. G.; Le Compte, A. J.; Suhaimi, F. et al

in Computer Methods & Programs in Biomedicine (2011)

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 ... [more ▼]

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 75,000h 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. [less ▲]

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See detailTight Glycemic Control in Intensive Care: From engineering to clinical practice change
Chase, J. G.; Le Compte, A. J.; Evans, A. et al

in 5th European Conference of the International Federation for Medical and Biological Engineering (2011)

Tight glycemic control (TGC) is prevalent in critical care. Providing safe, effective TGC has proven very difficult to achieve with clinically derived protocols. The prob-lem is exacerbated by extreme ... [more ▼]

Tight glycemic control (TGC) is prevalent in critical care. Providing safe, effective TGC has proven very difficult to achieve with clinically derived protocols. The prob-lem 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 opti-mised solutions. This paper presents the development, clinical-ly validated virtual trials optimisation, and initial clinical implementation of a stochastic targeted (STAR) TGC method and framework. It is compared to a prior successful, model-derived, less flexible and dynamic TGC protocol (SPRINT). The use of stochastic models to safely forecast a range of glu-cose 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 hypo-glycemia, 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 ongo-ing clinical practice change. [less ▲]

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See detailReduced organ failure with effective glycemic control
PREISER, Jean-Charles ULg; Chase, J. G.; Pretty, C. G. et al

in Intensive Care Medicine (2010), 36(2), 173-173

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