References of "Le Compte, Aaron"
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See detailInterstitial insulin kinetic parameters for a 2-compartment insulin model with saturable clearance
Pretty, Christopher G.; Le Compte, Aaron; Penning, Sophie ULg et al

in Computer Methods & Programs in Biomedicine (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: 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 detailInterface Design and Human Factors Consideration for Model-Based Tight Glycemic Control in Critical Care
Ward, Logan; Steel, James; Le Compte, Aaron et al

in Journal of Diabetes Science and Technology (2012)

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See detailStochastic Targeted (STAR) Glycemic Control - Design, Safety and Performance
Evans, Alicia; Le Compte, Aaron; Tan, Chian-Siong et al

in Journal of Diabetes Science and Technology (2012)

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See detailData Entry Errors and Design for Model-Based Tight Glycemic Control in Critical Care
Ward, Logan; Steel, James; Le Compte, Aaron et al

in Journal of Diabetes Science and Technology (2012)

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See detailEnhanced insulin sensitivity variability in the first 3 days of ICU stay: Implications for TGC
Chase, J. Geoffrey; Le Compte, Aaron; Penning, Sophie ULg et al

in Critical Care (2011, March)

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See detailValidation of a virtual patient and virtual trials method for accurate prediction of TGC protocol performance
Suhaimi, Fatanah; Le Compte, Aaron; Penning, Sophie ULg et al

in Critical Care (2011, March)

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See detailValidation of a Virtual Patient and Virtual Trials Method for Accurate Prediction of TGC Protocol Performance
Suhaimi, Fatanah; Le Compte, Aaron; Penning, Sophie ULg et al

Poster (2011, March)

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See detailEnhanced insulin sensitivity variability in the first 3 days of ICU stay: Implications for TGC
Chase, Geoffrey; Le Compte, Aaron; Penning, Sophie ULg et al

Poster (2011, March)

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See detailSafety and Performance of Stochastic Targeted (STAR) TGC of Insulin and Nutrition
Shaw, GM; Le Compte, Aaron; Evans, A et al

in Proceedings of SQAO 2011 (2011)

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See detailPilot Trials of STAR Target to Range Glycemic Control
Penning, Sophie ULg; Le Compte, Aaron; Massion, Paul et al

in Intensive Care Medicine (2011), 37 (Suppl 1)

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See detailVariability of insulin sensitivity for diabetics and non-diabetics during the first 3 days of ICU stay
Pretty, Christopher G.; Le Compte, Aaron; Preiser, Jean-Charles et al

in Intensive Care Medicine (2011), 37 (Suppl 1)

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See detailVariability of insulin sensitivity for diabetics and non-diabetics during the first 3 days of ICU stay
Pretty, Christopher G.; Le Compte, Aaron; Preiser, Jean-Charles et al

Poster (2011)

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See detailSafety and Performance of Stochastic Targeted (STAR) Glycemic Control of Insulin and Nutrition - First Pilot Results
Shaw, Geoffrey M.; Le Compte, Aaron; Evans, Alicia et al

Poster (2011)

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See detailSafety and Performance of Stochastic Targeted (STAR) Glycemic Control of Insulin and Nutrition – First Pilot Results
Shaw, Geoffrey M.; Le Compte, Aaron; Evans, Alicia et al

in Intensive Care Medicine (2011)

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See detailPilot Trials of STAR Target to Range Glycemic Control
Penning, Sophie ULg; Le Compte, Aaron; Massion, Paul et al

Poster (2011)

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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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See detailTight Glycemic Control Models for Critically Ill Patients in Intensive Care Units
Penning, Sophie ULg; Le Compte, Aaron; Desaive, Thomas ULg et al

Poster (2010, November 26)

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See detailTight Glycemic Control Models for Critically Ill Patients in Intensive Care Units
Penning, Sophie ULg; Le Compte, Aaron; Moorhead, Katherine ULg et al

in 9th Belgian Day on Biomedical Engineering, "Bridging the gap between medicine and engineering', Friday November 26th 2010 in the Academy Palace, Hertogstraat 1, 1000 Brussels (2010, November 26)

Critically ill patients often present stress-induced hyperglycemia and low insulin sensitivity. Recent studies have shown that high blood glucose (BG) levels are linked to worsened patient outcomes and ... [more ▼]

Critically ill patients often present stress-induced hyperglycemia and low insulin sensitivity. Recent studies have shown that high blood glucose (BG) levels are linked to worsened patient outcomes and increased mortality. Tight glycemic control (TGC) aims at reducing BG levels taking into account inter-patient variability, evolving physiological patient conditions and minimizing hypoglycemic risks. Clinical protocols are used to specify insulin and nutrition rates and BG measurement time interval during control. This research compares different protocols to determine the best one to use at the CHU of Liege. [less ▲]

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