Diabetes technology & therapeutics
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Diabetes Technol. Ther. · Mar 2011
Impact of an alerting clinical decision support system for glucose control on protocol compliance and glycemic control in the intensive cardiac care unit.
Glycemic control in patients with acute cardiac conditions is a clinical challenge but may substantially improve patient outcome. The aim of the current study was to evaluate the effect of implementing an automated version of an existing insulin protocol for glucose regulation in the Intensive Cardiac Care Unit (ICCU) on compliance with the protocol and achievement of glycemic targets. ⋯ The CDSS implementation of an insulin protocol in an ICCU improved compliance, identified targets for further improvement of the protocol, and resulted in improved glucose regulation after implementation.
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Diabetes Technol. Ther. · Mar 2011
The ability of different areas of the skin to absorb heat from a locally applied heat source: the impact of diabetes.
When heat is applied to the skin, heat is conducted away because of the latent heat transfer properties of the skin and an increase in skin circulation, but little attention has been paid to the heat transfer properties of skin in different areas of the body and in people with diabetes. research design: Thirty subjects in the age range of 20-75 years had a thermode (44°C) applied to the skin of their arm, leg, foot, and back for 6 min to assess the heat transfer characteristics of skin in these four areas of the body. Skin blood flow and skin temperature were monitored over the 6-min period. ⋯ Thus, specific areas of the body are damaged more by diabetes than other areas.
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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.