Journal of diabetes science and technology
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J Diabetes Sci Technol · Nov 2009
Is the drugstore safe? Counterfeit diabetes products on the shelves.
It is no longer possible to identify counterfeit medical products, including medications and devices, by simply checking packaging and labeling. Improvements in technology have made it cheaper and easier to produce fake packaging and labels, making it nearly impossible for consumers and authorities to detect counterfeits without conducting tests on the products themselves, as illustrated by the sale of over one million counterfeit blood glucose test strips sold to unsuspecting U. S. consumers at drugstores in more than 35 states and in other countries around the world in the fall of 2006. The pricier the drugs, the more counterfeiters seek to mimic them to maximize returns, victimizing those patients at highest risk who rely on life-saving medications.
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J Diabetes Sci Technol · Nov 2009
Glycemic variability and mortality in critically ill patients: the impact of diabetes.
Glycemic variability (GV) has recently been associated with mortality in critically ill patients. The impact of diabetes or its absence on GV as a risk factor for mortality is unknown. ⋯ Low GV during ICU stay was associated with increased survival among NON, and high GV was associated with increased mortality, even after adjustment for severity of illness. There was no independent association of GV with mortality among DM. Attempts to minimize GV may have a significant beneficial impact on outcomes of critically ill patients without diabetes.
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J Diabetes Sci Technol · Sep 2009
Clinical TrialBlood glucose controller for neonatal intensive care: virtual trials development and first clinical trials.
Premature neonates often experience hyperglycemia, which has been linked to worsened outcomes. Insulin therapy can assist in controlling blood glucose (BG) levels. However, a reliable, robust control protocol is required to avoid hypoglycemia and to ensure that clinically important nutrition goals are met. ⋯ A controller was developed that made optimum use of the very limited available BG measurements in the neonatal intensive care unit and provided robustness against BG sensor error and longer BG measurement intervals. It used more insulin than typical sliding scale approaches or retrospective hospital control. The potential advantages of a model-based approach demonstrated in simulation were applied to initial clinical trials.
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J Diabetes Sci Technol · Sep 2009
Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience.
Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented. ⋯ Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This "artificial pancreas" controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.
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J Diabetes Sci Technol · Sep 2009
Experimental evaluation of a recursive model identification technique for type 1 diabetes.
A model-based controller for an artificial beta cell requires an accurate model of the glucose-insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able to adapt to the new conditions by means of a recursive parameter estimation technique. Such an adaptive strategy will ensure that the most accurate model is used for the current conditions, and thus the most accurate model predictions are used in model-based control calculations. ⋯ In this experimental study, the recursively identified ARX models resulted in predictions of test data that were similar, but not superior, to the batch models. Even for the test data characteristic of reduced insulin sensitivity, the batch and recursive models demonstrated similar prediction accuracy. The predictions of the identified ARX models were only marginally more accurate than the model-free ZOH predictions. Given the simplicity of the ARX models and the computational ease with which they are identified, however, even modest improvements may justify the use of these models in a model-based controller for an artificial beta cell.