Diabetes technology & therapeutics
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Diabetes Technol. Ther. · Mar 2011
Calculating the mean amplitude of glycemic excursion from continuous glucose monitoring data: an automated algorithm.
Glycemic variability is currently under scrutiny as a possible predictor of the complications of diabetes. The manual process for estimating a now classical measure of glycemic variability, the mean amplitude of glycemic excursion (MAGE), is both tedious and prone to error, and there is a special need for an automated method to calculate the MAGE from continuous glucose monitoring (CGM) data. ⋯ The proposed algorithm eliminates the tedium and/or errors of manually identifying and measuring countable excursions in CGM data in order to estimate the MAGE. It can also be used to calculate the MAGE from "sparse" blood glucose measurements, such as those collected in home blood glucose monitoring.
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Diabetes Technol. Ther. · Feb 2011
Randomized Controlled Trial Multicenter Study Comparative StudyConversion from intravenous insulin to subcutaneous insulin after cardiovascular surgery: transition to target study.
No study of transition from intravenous to subcutaneous insulin after cardiac surgery with dose based on percentage of intravenous total daily insulin (TDI) has reported a clearly superior regimen for achieving target blood glucose. We compared three first-dose transition strategies for insulin glargine: two based on TDI alone and one that also took body weight into account. ⋯ No subcutaneous insulin regimen implemented approximately 1 day after cardiac surgery showed significantly better control of blood glucose over the 3-day study period. Further studies are needed to determine optimal formulae for effecting an early transition to subcutaneous insulin after cardiac surgery or whether it is preferable and/or necessary to continue intravenous insulin therapy for an additional period of time.
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Diabetes Technol. Ther. · Feb 2011
Comparative StudyA new index to optimally design and compare continuous glucose monitoring glucose prediction algorithms.
Continuous glucose monitoring (CGM) data can be exploited to prevent hypo-/hyperglycemic events in real time by forecasting future glucose levels. In the last few years, several glucose prediction algorithms have been proposed, but how to compare them (e.g., methods based on polynomial rather than autoregressive time-series models) and even how to determine the optimal parameter set for a given method (e.g., prediction horizon and forgetting) are open problems. ⋯ The new index can be used to compare different prediction strategies and to optimally design their parameters.
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Diabetes Technol. Ther. · Feb 2011
Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.
Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose. ⋯ Real-time prediction of glucose via the proposed NNM may provide a means of intelligent therapeutic guidance and direction.
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Diabetes Technol. Ther. · Feb 2011
Clinical TrialPrediction of glucose concentration in post-cardiothoracic surgery patients using continuous glucose monitoring.
This study evaluated the predictive capability of simple linear extrapolation of continuous glucose data in postsurgical patients undergoing intensive care. ⋯ Our data provide evidence that simple linear extrapolation of glucose trend information obtained by continuous glucose monitoring can be used to predict the course of glycemia in critically ill patients for up to 20-30 min. This "glimpse into the future" can be used to proactively prevent the occurrence of adverse events.