Development of a model-based clinical sepsis biomarker for critically ill patients.

Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (S(I)) is known to decrease with worsening condition and could thus be used to aid diagnosis.

A benchmark data set for model-based glycemic control in critical care.

Background: Hyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the "how" and "for whom" in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition.

Monte Carlo analysis of a new model-based method for insulin sensitivity testing.

Insulin resistance (IR), or low insulin sensitivity, is a major risk factor in the pathogenesis of type 2 diabetes and cardiovascular disease. A simple, high resolution assessment of IR would enable earlier diagnosis and more accurate monitoring of intervention effects. Current assessments are either too intensive for clinical settings (Euglycaemic Clamp, IVGTT) or have too low resolution (HOMA, fasting glucose/insulin).

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

Objective: Present a new model-based tight glycaemic control approach using variable insulin and nutrition administration.

Background: Hyperglycaemia is prevalent in critical care. Current published protocols use insulin alone to reduce blood glucose levels, require significant added clinical effort, and provide highly variable results.

Clinical validation of a model-based glycaemic control design approach and comparison to other clinical protocols.

Hyperglycaemia is prevalent in critical care and tight control can reduce mortality from 9-43% depending on the level of control and the cohort. This research presents a table-based method that varies both insulin dose and nutritional input to achieve tight control. The system mimics a previously validated model-based system, but can be used for long term, large patient number clinical evaluation.

Overview of glycemic control in critical care: relating performance and clinical results.

Background: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution.