Journal of diabetes science and technology
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Soon after the discovery that insulin regulates blood glucose by Banting and Best in 1922, the symptoms and risks associated with hypoglycemia became widely recognized. This article reviews devices to warn individuals of impending hypo- and hyperglycemia; biosignals used by these devices include electroencephalography, electrocardiography, skin galvanic resistance, diabetes alert dogs, and continuous glucose monitors (CGMs). While systems based on other technology are increasing in performance and decreasing in size, CGM technology remains the best method for both reactive and predictive alarming of hypo- or hyperglycemia.
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J Diabetes Sci Technol · Mar 2015
The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation.
The role of blood glucose (BG) measurement frequency on the domains of glycemic control is not well defined. This Monte Carlo mathematical simulation of glycemic control in a cohort of critically ill patients modeled sets of 100 patients with simulated BG-measuring devices having 5 levels of measurement imprecision, using 2 published insulin infusion protocols, for 200 hours, with 3 different BG-measurement intervals-15 minutes (Q15'), 1 hour (Q1h), and 2 hours (Q2h)-resulting in 1,100,000 BG measurements for 3000 simulated patients. The model varied insulin sensitivity, initial BG value and rate of gluconeogenesis. ⋯ Higher measurement frequency mitigated the deleterious effect of high measurement imprecision, defined as CV ≥ 15%. This Monte Carlo simulation suggests that glycemic control in critically ill patients is more optimal with a BG measurement interval no longer than 1h, with further benefit obtained with use of measurement interval of 15'. These findings have important implications for the development of glycemic control standards.
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J Diabetes Sci Technol · Jan 2015
Treating severe hypoglycemia: rapid mixing of lyophilized glucagon and diluent at point of care with the Enject GlucaPen.
Severe hypoglycemia (SH) is a common problem in type 1 diabetes (T1D). Annually, nearly 1 of 5 persons with long-standing T1D will have SH. Though injections of glucagon are effective in treating SH, liquid formulations of glucagon are biochemically very unstable. ⋯ Coupled with the emotional stress of the caregiver, errors in glucagon delivery are very common. For these reasons, workers at Enject, Inc are in the process of developing a device that addresses the shortcomings of this currently approved method of glucagon delivery. The Enject device will store the glucagon powder and the diluent in separate compartments and will rapidly mix and inject the components only upon activation of the pen at the point of care.
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J Diabetes Sci Technol · Jan 2015
Hypoglycemia prediction using machine learning models for patients with type 2 diabetes.
Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day. We trained a probabilistic model using machine learning algorithms and SMBG values from real patients. Hypoglycemia was defined as a SMBG value < 70 mg/dL. ⋯ In the model that incorporated medication information, the prediction window was for the hour of hypoglycemia, and the specificity improved to 90%. Our machine learning models can predict hypoglycemia events with a high degree of sensitivity and specificity. These models-which have been validated retrospectively and if implemented in real time-could be useful tools for reducing hypoglycemia in vulnerable patients.
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J Diabetes Sci Technol · Sep 2014
Accuracy of point-of-care blood glucose measurements in critically ill patients in shock.
A widely used method in monitoring glycemic status of ICU patients is point-of-care (POC) monitoring devices. A possible limitation to this method is altered peripheral blood flow in patients in shock, which may result in over/underestimations of their true glycemic status. This study aims to determine the accuracy of blood glucose measurements with a POC meter compared to laboratory methods in critically ill patients in shock. ⋯ POC blood glucose measurements were significantly less accurate in the hypotensive subgroup of ICU patients compared to the normotensive group. We recommend a lower threshold in confirming POC blood glucose with a central laboratory method if clinically incompatible. In light of recently updated accuracy standards, we also recommend alternative methods of glucose monitoring for the ICU population as a whole regardless of blood pressure status.