Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

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.

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data

“…The pattern classification algorithm by Jensen et al 11 provides a unique approach to optimize the identification of hypoglycemic events in PCGM data. Several new calibration algorithms have been developed, and the algorithm by Mahmoudi et al 12 shows very good results.…”

“…This work evaluated the use of a pattern classification algorithm by Jensen et al 11 compared to a new PCGM calibration algorithm developed by Mahmoudi et al 12 We compared the algorithms' abilities to identify hypoglycemia in PCGM data from people with T1D. The performance measures were sample-based sensitivity and specificity and event-based sensitivity and number of FPs in the total sampling period.…”

“…More thorough descriptions of the algorithms are published elsewhere. 11,12,17 The performances of the algorithms' hypoglycemia identification are presented as sample-based (ie, based on the 5-minute readings) and event-based (ie, based on the number of hypoglycemic events) sensitivity and specificity. The output of the algorithm by Jensen et al is a decision as to whether each PCGM reading is above or below 70 mg/dl, despite the actual reading.…”

“…For calibration approaches, the BG-IG dynamics have been linearly modeled in several attempts, [6][7][8][9][10] but the true underlying dynamics are complex and inaccurate when simplified linearly. A recent algorithm by Jensen et al 11 based on pattern classification shows very promising results in the optimization of PCGM identification of hypoglycemia. Due to the nature of the pattern classification model, the approach does not consider the complex BG-IG dynamics.…”

Electrochemical Glucose Biosensors for Diabetes Care

“…The settings can be broadly divided between inpatient and outpatient. The most common outpatient situation is an endocrinologist, diabetes nurse educator (DNE), or general practitioner (GP) managing the diabetes, and perhaps other conditions, of a patient during regular visits [234][235][236]. The workflow of the office visit demands efficient evaluation of the patient status, needs, and effective delivery of required care.…”