Journal of diabetes science and technology
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J Diabetes Sci Technol · Mar 2018
Artificial Intelligence Methodologies and Their Application to Diabetes.
In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. ⋯ In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.
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J Diabetes Sci Technol · Jan 2018
ReviewThe Long and Winding Road Toward Personalized Glycemic Control in the Critically Ill.
Hyperglycemia is very common in critically ill patients and interventional studies of intensive insulin therapy with the goal of returning ICU glycemia to normal levels have demonstrated mixed results. A large body of literature has demonstrated that diabetes, per se, is not independently associated with increased risk of mortality in this population and that the relationship of glucose metrics to mortality is different for patients with and without diabetes. Moreover, these relationships are confounded by preadmission glycemia; in this regard, patients with diabetes and good preadmission glucose control, as reflected by HbA1c levels obtained at the time of ICU admission, are similar to patients without diabetes. These data point the way toward an era when blood glucose targets in the ICU will be "personalized," based on assessment of preadmission glycemia.
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J Diabetes Sci Technol · Jan 2018
Risk of Hypoglycemia During Insulin Infusion Directed by Paper Protocol Versus Electronic Glycemic Management System in Critically Ill Patients at a Large Academic Medical Center.
Insulin infusions are commonly utilized to control hyperglycemia in critically ill patients and decrease hyperglycemia associated complications. Safety concerns have been raised in trials evaluating methods of glycemic control regarding the incidence of hypoglycemia and its relationship to increased mortality. Electronic glycemic management systems (eGMS) may result in less variable blood glucose (BG) control and less hypoglycemia. This study aimed to compare BG control, time in target BG range, and the rate of hypoglycemia when critically ill patients were managed with an insulin infusion guided by paper-based protocol (PBP) versus eGMS. ⋯ An eGMS has the potential to address many of the unmet needs of an optimal glycemic control strategy, minimizing hypoglycemia, and glycemic variability in a heterogeneous critically ill population.
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J Diabetes Sci Technol · Nov 2017
Comparative StudyComparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.
Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers. ⋯ The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.