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.
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.
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.
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.
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.
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’.
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.
Additional file 1: Figure S1. Relationship between mean glucose and time that blood glucose remained above 8.9 mmol/L. The function indicates that, for a given mean glucose, the time spent in the hyperglycaemic range can vary for several hours. Even at a mean of 7 mmol/L the range is from 0 to 6 hours. At 9 mmol/L it is about 8–14 hours. Data are from 1562 profiles. Figure S2. Components of the Q-Score. Schematic illustration of the Q-Score components: MBG (mean glucose), MODD (mean of daily differences), thyper (time in hyperglycaemia), thypo (time in hypoglycaemia), Range, (min-max-difference on one day). Figure S3. Representative examples of CGMs in different Q-Score categories. The Q-Score is given for each example in the upper right corner. The green region indicates the target range for glucose (3.9–8.9 mmol/L). Figure S4. Improvement potential increases for CGM profiles categorised from very good to poor. Number of parameters with improvement potential in five Q-Score categories. The highest number of parameters with improvement potential was found in CGM profiles categorised as poor. Data are from 1562 profiles. Table S1. Association of the Q-Score with CGM quality parameters. Table S2. Limits for the improvement potential categories given for all parameters of the Q-Score.12902_2015_19_MOESM1_ESM.docx
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- Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
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