A
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.
Papers
More filters
Journal ArticleDOI
Model-based insulin and nutrition administration for tight glycaemic control in critical care.
J.G. Chase,Geoffrey M. Shaw,Thomas Lotz,A.J. LeCompte,Jason Wong,J. Lin,Timothy Lonergan,Michael Willacy,Christopher E. Hann +8 more
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.
Journal ArticleDOI
Monte Carlo analysis of a new model-based method for insulin sensitivity testing
Thomas Lotz,J. Geoffrey Chase,Kirsten A. McAuley,Geoffrey M. Shaw,Xing-Wei Wong,Jessica Lin,A.J. LeCompte,Christopher E. Hann,Jim Mann +8 more
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.
Journal ArticleDOI
A Benchmark Data Set for Model-Based Glycemic Control in Critical Care
J. Geoffrey Chase,A.J. LeCompte,Geoffrey M. Shaw,Amy Blakemore,Jason Wong,Jessica Lin,Christopher E. Hann +6 more
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).
Journal ArticleDOI
Overview of glycemic control in critical care: relating performance and clinical results.
J. Geoffrey Chase,Christopher E. Hann,Geoffrey M. Shaw,Jason Wong,Jessica Lin,Thomas Lotz,A.J. LeCompte,Timothy Lonergan +7 more
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.
Journal ArticleDOI
Development of a model-based clinical sepsis biomarker for critically ill patients
Jessica Lin,Jacquelyn D. Parente,J. Geoffrey Chase,Geoffrey M. Shaw,Amy Blakemore,A.J. LeCompte,Christopher G. Pretty,Normy Norfiza Abdul Razak,Dominic S. Lee,Christopher E. Hann,Sheng-Hui Wang +10 more
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.