Literature DB >> 14511411

Hypoglycemia warning signal and glucose sensors: requirements and concepts.

Tim Heise1, Theodor Koschinsky, Lutz Heinemann, Volker Lodwig.   

Abstract

Hypoglycemia is the most feared side effect of diabetes therapy with blood glucose-lowering agents. The fear of hypoglycemia often contributes to poor metabolic control of patients with diabetes. Therefore, integration of a hypoglycemia warning signal into continuous glucose monitoring systems represents an important additional help for patients with diabetes. The warning signal can be triggered at a preset level based on the current glucose values (as provided with the presently available glucose monitoring systems) or on prospective trend analysis offering the possibility to predict the risk of a hypoglycemic event in an anticipatory manner. Using the approach of a "Finite State Machine," such a more advanced warning system can completely be described as a finite collection of four states and possible transitions in-between. Most of the currently available glucose monitoring systems measure glucose in the interstitial fluid (ISF) of the dermal or subcutaneous tissue but are calibrated to blood glucose levels. This requires a number of factors to be taken into account: precision and accuracy of the glucose measurements, physiological and physical lag time, and calibration of the glucose monitoring system. From our point of view, the analytical performance of the system should be such that the majority of all hypoglycemic episodes are correctly diagnosed (>75%). Inconsistent findings regarding physiological discrepancies between blood and ISF glucose, which usually are described as a physiological lag time, range from some seconds up to 15 min. They can be observed especially during dynamic blood glucose changes (>3 mg/dL/min) and may represent major challenges for the development of a reliable hypoglycemia warning signal. In addition to possible physiological time lags, device-inherent physical lag times must be considered when selecting the threshold for the warning signal. Despite these problems, most probably all patients with diabetes who are treated with blood glucose-lowering agents will benefit from such a system since their safety and quality of life can be greatly improved, including an optimized metabolic control and lowered diabetes-related mortality. The benefit will be greatest for patients with hypoglycemia unawareness or impaired perception of hypoglycemic symptoms. The risks related to the use of a hypoglycemia warning signal seem to be low if certain precautionary measures are taken. In any case, additional clinical-experimental studies in healthy subjects as well as long-term clinical studies in diabetic patients are necessary to further evaluate the efficacy, benefits, and risks of different hypoglycemia warning concepts implemented in the different continuous glucose monitoring systems.

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Year:  2003        PMID: 14511411     DOI: 10.1089/152091503322250587

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  10 in total

1.  Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms.

Authors:  B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2010-03-01

2.  Numerical simulation of the effect of rate of change of glucose on measurement error of continuous glucose monitors.

Authors:  Marc B Taub; Thomas A Peyser; J Erik Rosenquist
Journal:  J Diabetes Sci Technol       Date:  2007-09

3.  Self-Cleaning, Thermoresponsive P (NIPAAm-co-AMPS) Double Network Membranes for Implanted Glucose Biosensors.

Authors:  Ruochong Fei; A Kristen Means; Alexander A Abraham; Andrea K Locke; Gerard L Coté; Melissa A Grunlan
Journal:  Macromol Mater Eng       Date:  2016-05-04       Impact factor: 4.367

4.  Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring.

Authors:  Boris P Kovatchev; Stephen D Patek; Edward Andrew Ortiz; Marc D Breton
Journal:  Diabetes Technol Ther       Date:  2014-12-01       Impact factor: 6.118

5.  Diabetes: Models, Signals, and Control.

Authors:  Claudio Cobelli; Chiara Dalla Man; Giovanni Sparacino; Lalo Magni; Giuseppe De Nicolao; Boris P Kovatchev
Journal:  IEEE Rev Biomed Eng       Date:  2009-01-01

6.  Hypoglycemia detection and prediction using continuous glucose monitoring-a study on hypoglycemic clamp data.

Authors:  Cesar C Palerm; B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2007-09

7.  Self-cleaning membrane to extend the lifetime of an implanted glucose biosensor.

Authors:  Alexander A Abraham; Ruochong Fei; Gerard L Coté; Melissa A Grunlan
Journal:  ACS Appl Mater Interfaces       Date:  2013-12-11       Impact factor: 9.229

Review 8.  Artificial pancreas: past, present, future.

Authors:  Claudio Cobelli; Eric Renard; Boris Kovatchev
Journal:  Diabetes       Date:  2011-11       Impact factor: 9.461

Review 9.  "Smart" continuous glucose monitoring sensors: on-line signal processing issues.

Authors:  Giovanni Sparacino; Andrea Facchinetti; Claudio Cobelli
Journal:  Sensors (Basel)       Date:  2010-07-12       Impact factor: 3.576

Review 10.  Diabetes technology: markers, monitoring, assessment, and control of blood glucose fluctuations in diabetes.

Authors:  Boris P Kovatchev
Journal:  Scientifica (Cairo)       Date:  2012-10-17
  10 in total

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