Literature DB >> 17459094

A method for assessing quality of control from glucose profiles.

N R Hill1, P C Hindmarsh, R J Stevens, I M Stratton, J C Levy, D R Matthews.   

Abstract

AIM: As the practice of multiple assessments of glucose concentration throughout the day increases for people with diabetes, there is a need for an assessment of glycaemic control weighted for the clinical risks of both hypoglycaemia and hyperglycaemia.
METHODS: We have developed a methodology to report the degree of risk which a glycaemic profile represents. Fifty diabetes professionals assigned risk values to a range of 40 blood glucose concentrations. Their responses were summarised and a generic function of glycaemic risk was derived. This function was applied to patient glucose profiles to generate an integrated risk score termed the Glycaemic Risk Assessment Diabetes Equation (GRADE). The GRADE score was then reported by use of the mean value and the relative percent contribution to the weighted risk score from the hypoglycaemic, euglycaemic, hyperglycaemic range, respectively, e.g. GRADE (hypoglycaemia%, euglycaemia%, hyperglycaemia%).
RESULTS: The GRADE scores of indicative glucose profiles were as follows: continuous glucose monitoring profile non-diabetic subjects GRADE = 1.1, Type 1 diabetes continuous glucose monitoring GRADE = 8.09 (20%, 8%, 72%), Type 2 diabetes home blood glucose monitoring GRADE = 9.97 (2%, 7%, 91%).
CONCLUSIONS: The GRADE score of a glucose profile summarises the degree of risk associated with a glucose profile. Values < 5 correspond to euglycaemia. The GRADE score is simple to generate from any blood glucose profile and can be used as an adjunct to HbA1c to report the degree of risk associated with glycaemic variability.

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Year:  2007        PMID: 17459094     DOI: 10.1111/j.1464-5491.2007.02119.x

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  43 in total

1.  Translating glucose variability metrics into the clinic via Continuous Glucose Monitoring: a Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©).

Authors:  Renata A Rawlings; Hang Shi; Lo-Hua Yuan; William Brehm; Rodica Pop-Busui; Patrick W Nelson
Journal:  Diabetes Technol Ther       Date:  2011-09-20       Impact factor: 6.118

2.  Prediction of the risk to develop diabetes-related late complications by means of the glucose pentagon model: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring study.

Authors:  Andreas Thomas; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2012-05-01

3.  Evaluating Glucose Control With a Novel Composite Continuous Glucose Monitoring Index.

Authors:  Lalantha Leelarathna; Hood Thabit; Malgorzata E Wilinska; Lia Bally; Julia K Mader; Thomas R Pieber; Carsten Benesch; Sabine Arnolds; Terri Johnson; Lutz Heinemann; Norbert Hermanns; Mark L Evans; Roman Hovorka
Journal:  J Diabetes Sci Technol       Date:  2019-03-31

4.  Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

Authors:  Enrico Longato; Giada Acciaroli; Andrea Facchinetti; Alberto Maran; Giovanni Sparacino
Journal:  J Diabetes Sci Technol       Date:  2019-03-31

5.  Hypoglycemia, but not glucose variability, relates to vascular function in children with type 1 diabetes.

Authors:  Alexia S Peña; Jennifer J Couper; Jennifer Harrington; Roger Gent; Jan Fairchild; Elaine Tham; Peter Baghurst
Journal:  Diabetes Technol Ther       Date:  2012-02-07       Impact factor: 6.118

6.  Assessment of Glucose Control Metrics by Discriminant Ratio.

Authors:  Vanessa Moscardó; Pau Herrero; Monika Reddy; Nathan R Hill; Pantelis Georgiou; Nick Oliver
Journal:  Diabetes Technol Ther       Date:  2020-10       Impact factor: 6.118

7.  A semilogarithmic scale for glucose provides a balanced view of hyperglycemia and hypoglycemia.

Authors:  David Rodbard
Journal:  J Diabetes Sci Technol       Date:  2009-11-01

Review 8.  Metrics Beyond Hemoglobin A1C in Diabetes Management: Time in Range, Hypoglycemia, and Other Parameters.

Authors:  Lorena Alarcon-Casas Wright; Irl B Hirsch
Journal:  Diabetes Technol Ther       Date:  2017-05       Impact factor: 6.118

9.  Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data.

Authors:  Giada Acciaroli; Giovanni Sparacino; Liisa Hakaste; Andrea Facchinetti; Giorgio Maria Di Nunzio; Alessandro Palombit; Tiinamaija Tuomi; Rafael Gabriel; Jaime Aranda; Saturio Vega; Claudio Cobelli
Journal:  J Diabetes Sci Technol       Date:  2017-06-01

10.  A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control.

Authors:  Michelle Nguyen; Julia Han; Elias K Spanakis; Boris P Kovatchev; David C Klonoff
Journal:  Diabetes Technol Ther       Date:  2020-03-04       Impact factor: 6.118

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