References of "Signal, Matthew"
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See detailDoes the achievement of an intermediate glycemic target reduce organ failure and mortality? A post-hoc analysis of the Glucontrol Trial
Penning, Sophie ULg; Chase, J. Geoffrey; Preiser, Jean-Charles et al

in Journal of Critical Care (2014)

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See detailImpact of sensor and measurement timing errors on model-based insulin sensitivity
Pretty, Christopher ULg; Signal, Matthew; Fisk, Liam et al

in Computer Methods & Programs in Biomedicine (2013)

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See detailCumulative time in band (cTIB): glycemic level, variability and patient outcome all in one
Penning, Sophie ULg; Signal, Matthew; Preiser, Jean-Charles et al

Conference (2012, October 15)

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See detailCumulative time in band: glycemic level, variability and patient outcome vs. mortality
Penning, Sophie ULg; Signal, Matthew; Preiser, Jean-Charles et al

Poster (2012, October)

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See detailCumulative Time in Band (cTIB): Glycemic Level, Variability and Patient Outcome All in 1
Penning, Sophie ULg; Signal, Matthew; Preiser, Jean-Charles et al

in Intensive Care Medicine (2012, October), 38 (Suppl 1)

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See detailDoes Tight Glycemic Control positively impact on patient mortality?
Penning, Sophie ULg; Le Compte, Aaron J.; Signal, Matthew et al

Poster (2012, March 20)

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See detailDoes Tight Glycemic Control positively impact on patient mortality?
Penning, Sophie ULg; Le Compte, Aaron J.; Signal, Matthew et al

in Critical Care (2012, March 20)

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See detailPilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control.
Evans, Alicia; Shaw, Geoffrey M; Le Compte, Aaron et al

in Annals of intensive care (2011), 1

ABSTRACT: INTRODUCTION: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly ... [more ▼]

ABSTRACT: INTRODUCTION: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials. METHODS: Seven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay >/=3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. RESULTS: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. CONCLUSIONS: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT. [less ▲]

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