Professional continuous glucose monitoring in subjects with type 1 diabetes: retrospective hypoglycemia detection

An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, e...

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Bibliographic Details
Published in:Journal of diabetes science and technology Vol. 7; no. 1; p. 135
Main Authors: Jensen, Morten Hasselstrøm, Christensen, Toke Folke, Tarnow, Lise, Mahmoudi, Zeinab, Johansen, Mette Dencker, Hejlesen, Ole Kristian
Format: Journal Article
Language:English
Published: United States 01.01.2013
Subjects:
Adult
Algorithms
Automation
Blood Glucose - analysis
Blood Glucose Self-Monitoring
Diabetes Mellitus, Type 1 - blood
Humans
Hypoglycemia - blood
Hypoglycemia - diagnosis
Male
Middle Aged
Monitoring, Physiologic
Retrospective Studies
Sensitivity and Specificity
ISSN:1932-2968, 1932-3107
Online Access:Get more information
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Summary:An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.
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ISSN:1932-2968
1932-3107
DOI:10.1177/193229681300700116