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Background: Although tight glycemic control has been associated with improved outcomes in the intensive care unit (ICU), glycemic variability may be the influential factor in mortality. The main goal of the study was to relate blood glucose (BG) variability of burn ICU patients to outcomes using a sensitive measure of glycemic variability, the average daily risk range (ADRR). Method: Data from patients admitted to a burn ICU were used. Patients were matched by total body surface area (TBSA) and injury severity score (ISS) to test whether increased BG variability measured by ADRR was associated with higher mortality risk and whether we could identify ADRR-based classifications associated with the degree of risk. Results: Four ADRR classifications were identified: Low risk, medium-low, medium-high, and high. Mortality progressively increased from 25% in the low-risk group to over 60% in the high-risk group ( p < .001). In a post hoc analysis, age also contributed to outcome. Younger (age < 43 years) survivors and nonsurvivors matched by TBSA and ISS had no significant difference in age, mean BG or standard deviation of BG; however, nonsurvivors had higher ADRR ( p < .01). Conclusions: Independent of injury severity, glycemic variability measured by the ADRR was significantly associated with mortality in the ICU. When age was considered, ADRR was the only measure of glycemia significantly associated with mortality in younger patients with burns.

El principal objetivo del presente estudio es caracterizar el emprendimiento sostenible a partir de un diseño de caso múltiple. Con base al análisis de cinco casos, se describió el proceso de emprendimiento sostenible. Las respuestas de los entrevistados se analizaron a partir de un conjunto de pasos: 1) codificación de las respuestas, 2) categorización en temáticas comunes y 3) establecimiento de conclusiones que permitan una explicación general agrupada en los temas comunes. Los resultados identificados para el emprendimiento sostenible son: 1) el reconocimiento de una problemática social o ambiental, 2) el reconocimiento de una oportunidad social o ambiental, 3) el desarrollo de una solución de doble resultado, 4) el desarrollo de una solución de triple resultado, 5) la financiación y formación de una organización sostenible, y 6) la creación o ingreso a un mercado sostenible. Se concluye que estos hallazgos permiten realizar contribuciones teóricas y prácticas de emprendimiento sostenible.

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.

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