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Thomas Lotz

Researcher at University of Canterbury

Publications -  71
Citations -  2647

Thomas Lotz is an academic researcher from University of Canterbury. The author has contributed to research in topics: Insulin & Insulin resistance. The author has an hindex of 28, co-authored 71 publications receiving 2576 citations. Previous affiliations of Thomas Lotz include University of Otago & Christchurch Hospital.

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Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change.

TL;DR: Reductions in mortality were observed compared with a retrospective hyperglycaemic cohort and the SPRINT protocol achieved a high level of glycaemic control on a severely ill critical cohort population.
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Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model

TL;DR: The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters and is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods.
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Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

TL;DR: The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability, and adaptive control method incorporating S(I) variability is shown to produce improved gly glucose control in simulated trials compared to current clinical results.
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A simple insulin-nutrition protocol for tight glycemic control in critical illness: development and protocol comparison.

TL;DR: Tight control was achieved in simulation using a protocol that is easy to implement in an intensive care unit and effective in achieving and maintaining normoglycemia in critical illness.
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Model-based glycaemic control in critical care—A review of the state of the possible

TL;DR: There are many opportunities and unanswered questions remaining on which model-based control research can have significant clinical impact, and there is an emerging, strong need for the more rigorous analysis and methods that model- based control methods bring to this type of problem.