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01.12.2015 | Research article | Ausgabe 1/2015 Open Access

BMC Endocrine Disorders 1/2015

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

Zeitschrift:
BMC Endocrine Disorders > Ausgabe 1/2015
Autoren:
Petra Augstein, Peter Heinke, Lutz Vogt, Roberto Vogt, Christine Rackow, Klaus-Dieter Kohnert, Eckhard Salzsieder
Wichtige Hinweise

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.
Petra Augstein and Peter Heinke contributed equally to this work.

Competing interests

PA, LV, PH and ES are currently applying for a patent for the Q-Score that is the subject of this manuscript. LV is CEO of the Diabetes Service Centre. KDK, RV and CR declare that they have no competing interests. All authors declare that they have no non-financial competing interests.

Authors’ contributions

PA designed the study and drafted the manuscript. PH participated in the design of the study, performed the statistical analysis and helped to draft the manuscript. LV constructed a database for the study. LV and KDK participated in analysis of data. PH, RV and CR carried out the categorisation of CGM profiles. ES conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.

Abstract

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.
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