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

Morten Hasselstrøm Jensen, Toke Folke Christensen, Lise Tarnow, Zeinab Mahmoudi, Mette Dencker Johansen, Ole Kristian Hejlesen, Morten Hasselstrøm Jensen, Toke Folke Christensen, Lise Tarnow, Zeinab Mahmoudi, Mette Dencker Johansen, Ole Kristian Hejlesen

Abstract

Background: 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.

Method: 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.

Results: 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.

Conclusions: 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.

© 2013 Diabetes Technology Society.

Figures

Figure 1
Figure 1
The graph illustrates the ROC curve with the largest AUC from the forward selection.
Figure 2
Figure 2
The subject experiences one instance of hypoglycemia during this session. The algorithm detects this event, but it also detects an event around 280 min after insulin injection, which is not confirmed by the PG readings.

Source: PubMed

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