Simulation of Glycemic Variability in Critically Ill Burn Patients

Abstract

AcknowledgementWithout the patient, thoughtful guidance of my advisor, Dr. Stephen Patek, I could not have come this far or look forward to so much more. Thank you, Steve.The faculty of the Center for Diabetes Technology, Drs. Boris Kovatchev, Marc Breton, Leon Farhi, and Stephen Patek, have created and nurtured a first-class learning environment, which I was extremely fortunate to have experienced. They employ and share their knowledge with a zest that I am sure comes from their love of the work they It has been a delight to be with all of you. AbstractTight glycemic control with insulin therapy protocols in the intensive care unit (ICU) can reduce mortality and morbidity from stress-induced hyperglycemia, but this control comes with the risk of hypoglycemia. Computer simulation can be an essential tool in evaluating protocols for insulin delivery in this setting, and to this end, it is necessary to have mathematical models that explain BG variability within this patient population.Current models of stress-induced hyperglycemia do not adequately incorporate the known physiology of stress hyperglycemia and are limited in their ability to account for the resistance to the actions of insulin found in these patients. In this thesis, we develop, validate, and illustrate applications for a new model of glucose variability. The new model is built from an existing model of glucose-insulin interactions for normal, pre-diabetic, and type II diabetic patients, with new features that account for the effects of trauma and physiological stress commonly experienced in the ICU. Hourly blood glucose, insulin, and feeding data from 154 burn-unit patients were input to our model. The in silico patient whose simulated BG most closely matched the BG of a burn-unit patient was determined with the method of least squares. For this in silico patient, a time-varying coefficient ("SA", stress action) was fitted to modify hepatic glucose production (HGP) and peripheral glucose uptake (PGU) to produce a simulated BG that matched a BG of a burn-unit patient. HGP was limited to a literature-derived maximum of 4.25 mg/kg/min. From the data of the 154 unique burn-unit patients, 212 SA vectors of at least 24 hours each and 86 unique in silico patients were identified. The simulator incorporating this model is validated by comparing cumulative distributions of simulated BGs with the cumulative distribution of real burn-unit BGs under the same intensive insulin therapy protocol used in the original data collection. This simulator, coded into a MATLAB Simulink simulation model, allows for testing insulin protocols in silico, before use in patients. As an illustrative application, the simulation model is used to optimize process control thresholds for an insulin protocol used in the burn unit.

Simulation-based methodology for improving the safety and efficacy of insulin therapy in critically ill patients

“…The in silico patients were formed using clinical electronic medical records (EMR) data from 154 burn victim patients obtained from the U.S. army institute of surgical research. The simulator pairs each in silico patient with two components (i) a non-stressed in silico patient derived from the burn-victim dataset and (ii) a time varying curve that accounts for stress-related variability in hepatic glucose production [12]. Therefore, this simulator creates a unique in silico patient population based on an existing population.…”

Population-specific models of glycemic control in intensive care: Towards a simulation-based methodology for protocol optimization

“…In this work we make use of a recently developed ICU BG Simulator [38], which is based on a reduced version of the oral glucose minimal model of [32] where the gut compartments of the model have been replaced with a model appropriate to distal enteral feeding. The effectiveness of the simulator derives from the fact that each associated in silico subject is a pairing of two elements: (i) a non-stressed in silico patient derived from the same data set used to develop the oral-glucose meal model [32] and (ii) a time-varying stress-action curve SA ( t ) ∈ [0,1] that accounts for stress-related variability in hepatic glucose production and the uptake of glucose by muscle and fat, as shown in Eqs.…”

“…(This is done numerically, taking advantage of the fact that simulated BG increases monotonically with increasing stress action.) From the in silico clones, it is possible to create an in silico population that is representative of the patient population at hand by “mixing and matching” the corresponding parameters of the underlying meal-model subjects and the SA curves [38].…”