Diabetes technology & therapeutics
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Diabetes Technol. Ther. · Oct 2013
Randomized Controlled Trial Comparative StudyA comparative study of the effects of a dipeptidyl peptidase-IV inhibitor and sulfonylurea on glucose variability in patients with type 2 diabetes with inadequate glycemic control on metformin.
This study aimed to compare the effects of sitagliptin on glycemic change and 24-h blood glucose variability with those of the sulfonylurea glimepiride. ⋯ When sitagliptin was combined with metformin, the patients showed much more efficient blood glucose controlling effects, not only the three indexes of fasting blood glucose, postprandial blood glucose, and glycated hemoglobin, but also MAGE.
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Diabetes Technol. Ther. · Oct 2013
Multicenter Study Comparative StudyA new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system.
Use of continuous glucose monitoring (CGM) systems can improve glycemic control, but widespread adoption of CGM utilization has been limited, in part because of real and perceived problems with accuracy and reliability. This study compared accuracy and performance metrics for a new-generation CGM system with those of a previous-generation device. ⋯ The performance of DG4P was significantly improved compared with that of DSP, which may increase routine clinical use of CGM and improve patient outcomes.
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Diabetes Technol. Ther. · Aug 2013
A glucose model based on support vector regression for the prediction of hypoglycemic events under free-living conditions.
The prevention of hypoglycemic events is of paramount importance in the daily management of insulin-treated diabetes. The use of short-term prediction algorithms of the subcutaneous (s.c.) glucose concentration may contribute significantly toward this direction. The literature suggests that, although the recent glucose profile is a prominent predictor of hypoglycemia, the overall patient's context greatly impacts its accurate estimation. The objective of this study is to evaluate the performance of a support vector for regression (SVR) s.c. glucose method on hypoglycemia prediction. ⋯ Results suggest that hypoglycemia prediction using SVR can be accurate and performs better in most diurnal and nocturnal cases compared with other techniques. It is advised that the problem of hypoglycemia prediction should be handled differently for nocturnal and diurnal periods as regards input variables and interpretation of results.
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Diabetes Technol. Ther. · Jul 2013
Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes.
Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. ⋯ This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.