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Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies

BACKGROUND: Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic ‘weak points’. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored reco...

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Autores principales: Augstein, Petra, Heinke, Peter, Vogt, Lutz, Vogt, Roberto, Rackow, Christine, Kohnert, Klaus-Dieter, Salzsieder, Eckhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4447008/
https://www.ncbi.nlm.nih.gov/pubmed/25929322
http://dx.doi.org/10.1186/s12902-015-0019-0
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author Augstein, Petra
Heinke, Peter
Vogt, Lutz
Vogt, Roberto
Rackow, Christine
Kohnert, Klaus-Dieter
Salzsieder, Eckhard
author_facet Augstein, Petra
Heinke, Peter
Vogt, Lutz
Vogt, Roberto
Rackow, Christine
Kohnert, Klaus-Dieter
Salzsieder, Eckhard
author_sort Augstein, Petra
collection PubMed
description BACKGROUND: Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic ‘weak points’. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential. METHODS: Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles. RESULTS: We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the ‘Q-Score’). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of ‘very good’, ‘good’, ‘satisfactory’, ‘fair’, and ‘poor’ metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0–5.9, good; 6.0–8.4, satisfactory; 8.5–11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as ‘low’, ‘moderate’ and ‘high’. CONCLUSIONS: The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12902-015-0019-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-44470082015-05-29 Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies Augstein, Petra Heinke, Peter Vogt, Lutz Vogt, Roberto Rackow, Christine Kohnert, Klaus-Dieter Salzsieder, Eckhard BMC Endocr Disord Research Article BACKGROUND: Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic ‘weak points’. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential. METHODS: Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles. RESULTS: We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the ‘Q-Score’). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of ‘very good’, ‘good’, ‘satisfactory’, ‘fair’, and ‘poor’ metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0–5.9, good; 6.0–8.4, satisfactory; 8.5–11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as ‘low’, ‘moderate’ and ‘high’. CONCLUSIONS: The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12902-015-0019-0) contains supplementary material, which is available to authorized users. BioMed Central 2015-05-01 /pmc/articles/PMC4447008/ /pubmed/25929322 http://dx.doi.org/10.1186/s12902-015-0019-0 Text en © Augstein et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Augstein, Petra
Heinke, Peter
Vogt, Lutz
Vogt, Roberto
Rackow, Christine
Kohnert, Klaus-Dieter
Salzsieder, Eckhard
Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
title Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
title_full Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
title_fullStr Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
title_full_unstemmed Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
title_short Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
title_sort q-score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4447008/
https://www.ncbi.nlm.nih.gov/pubmed/25929322
http://dx.doi.org/10.1186/s12902-015-0019-0
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