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A.J. LeCompte

Researcher at University of Canterbury

Publications -  21
Citations -  311

A.J. LeCompte is an academic researcher from University of Canterbury. The author has contributed to research in topics: Sepsis & Insulin. The author has an hindex of 7, co-authored 21 publications receiving 299 citations.

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Journal ArticleDOI

Model-based insulin and nutrition administration for tight glycaemic control in critical care.

TL;DR: Modulating both low dose insulin boluses and nutrition input rates challenges the current practice of using only insulin in larger doses to reduce hyperglycaemic levels and shows very tight control in safe glycaemic bands.
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Monte Carlo analysis of a new model-based method for insulin sensitivity testing

TL;DR: The proposed protocol is simple, cost effective, repeatable and highly correlated to the gold-standard clamp, and designed with physiological dosing, short duration, simple protocol, low cost and high repeatability.
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A Benchmark Data Set for Model-Based Glycemic Control in Critical Care

TL;DR: A benchmark data set based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies provides a benchmark for comparing and analyzing performance in model-based glycemic control in a medical intensive care unit (ICU).
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Overview of glycemic control in critical care: relating performance and clinical results.

TL;DR: Model-based methods provide tighter, more adaptable one method fits all solutions, using methods that enable patient-specific modeling and control, and correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines.
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Development of a model-based clinical sepsis biomarker for critically ill patients

TL;DR: The multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis and Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsi score shows potential avenues to improve the positive predictive value.