The Q-Score for Continuous Glucose Monitoring in Diabetes
The Q-Score for Continuous Glucose Monitoring in Diabetes
CGM profiles and self-control data were recorded in earlier studies, which were approved by the Regional Ethics Review Board of the University of Greifswald (Germany). All included subjects provided informed consent to participate in CGM and data analysis. Data from 1,562 subjects (females/males; 499/1,063) with type 1 and type 2 diabetes (n = 48 and n = 1514, respectively) were analysed (Table 1). The mean age was 65.8 ± 9.0 years (range 39–89); duration of diabetes 10 ± 9.1 years (range 1–51); body mass index (BMI) 30.9 ± 5.4 kg/m (range 18.5–55.4). Subjects received diet-based diabetes therapy (n = 120), oral hypoglycaemic agents (OHA; n = 513), a combination of OHA and insulin (n = 439), or insulin alone (n = 490). The carbohydrate intake was 142.8 ± 37.2 (range 48–336) g carbohydrates/d. The mean HbA1c was 7.0 ± 0.9% (range 4.5–13.4; 53 ± 10 mmol/mol [range 26–123]).
CGM profiles (72 h) were acquired with the Medtronic Gold system (Medtronic Diabetes, Northridge, CA, USA) according to the manufacturer's instructions. CGM was performed in an outpatient setting under daily-life conditions. The quality of CGM profiles was assessed on three subsequent days, and the measures were averaged for analyses. All CGM profiles were assessed using the following parameters: MBG, median glucose level (median), SD, range, MAGE, CONGA over a 6-h period, MODD, interquartile range (IQR), tG and AUCG above or below the target range from 3.9 to 8.9 mmol/l, risk scores for LBGI and HBGI, and GRADE.
The factor analysis was conducted with the FACTOR procedure available in PASW Statistics 17 (SPSS Inc., Chicago, IL, USA). Initially, all included parameters were normalised using the z-score and the correlation between all variables was determined. The number of components to be retained was first based on a scree plot. A calculation with an additional factor provided a further independent and interpretable factor.
The calculation of the Kaiser–Meyer–Olkin (KMO) measure resulted in a KMO of 0.821, which indicated that the factor model was appropriate and the sampling was highly adequate. A varimax (orthogonal) rotation was used to obtain a set of independent, interpretable factors. The resulting factor pattern was interpreted with the use of factor loadings >0.5.
A randomly selected subset from all CGM profiles (n = 766) was independently categorised into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control, by three diabetes specialists. The specialists had access to both the CGM profiles and the patient records that indicated the diabetes type, diabetes duration, and types of therapy associated with each CGM.
All analyses were performed with PASW Statistics 17. Results are expressed as mean ± SD or as medians and IQR. Analysis of variance was used to assess differences between groups. The strength of the dependence between two continuous variables was assessed with the Pearson's correlation coefficient and between ordinal variables with Kendall's tau-b correlation. The weighted Cohen's kappa score was used to assess the inter-rater reliability of the categorisation of the CGM profiles between diabetes specialists and between Q-Score and diabetes specialists. The reliability (concordance of assessments) was measured using the method proposed by Landis and Koch. A P-value <0.05 was considered to indicate statistical significance.
Methods
Patient Data
CGM profiles and self-control data were recorded in earlier studies, which were approved by the Regional Ethics Review Board of the University of Greifswald (Germany). All included subjects provided informed consent to participate in CGM and data analysis. Data from 1,562 subjects (females/males; 499/1,063) with type 1 and type 2 diabetes (n = 48 and n = 1514, respectively) were analysed (Table 1). The mean age was 65.8 ± 9.0 years (range 39–89); duration of diabetes 10 ± 9.1 years (range 1–51); body mass index (BMI) 30.9 ± 5.4 kg/m (range 18.5–55.4). Subjects received diet-based diabetes therapy (n = 120), oral hypoglycaemic agents (OHA; n = 513), a combination of OHA and insulin (n = 439), or insulin alone (n = 490). The carbohydrate intake was 142.8 ± 37.2 (range 48–336) g carbohydrates/d. The mean HbA1c was 7.0 ± 0.9% (range 4.5–13.4; 53 ± 10 mmol/mol [range 26–123]).
Continuous Glucose Monitoring
CGM profiles (72 h) were acquired with the Medtronic Gold system (Medtronic Diabetes, Northridge, CA, USA) according to the manufacturer's instructions. CGM was performed in an outpatient setting under daily-life conditions. The quality of CGM profiles was assessed on three subsequent days, and the measures were averaged for analyses. All CGM profiles were assessed using the following parameters: MBG, median glucose level (median), SD, range, MAGE, CONGA over a 6-h period, MODD, interquartile range (IQR), tG and AUCG above or below the target range from 3.9 to 8.9 mmol/l, risk scores for LBGI and HBGI, and GRADE.
Factor Analysis
The factor analysis was conducted with the FACTOR procedure available in PASW Statistics 17 (SPSS Inc., Chicago, IL, USA). Initially, all included parameters were normalised using the z-score and the correlation between all variables was determined. The number of components to be retained was first based on a scree plot. A calculation with an additional factor provided a further independent and interpretable factor.
The calculation of the Kaiser–Meyer–Olkin (KMO) measure resulted in a KMO of 0.821, which indicated that the factor model was appropriate and the sampling was highly adequate. A varimax (orthogonal) rotation was used to obtain a set of independent, interpretable factors. The resulting factor pattern was interpreted with the use of factor loadings >0.5.
Categorisation of CGM Profiles
A randomly selected subset from all CGM profiles (n = 766) was independently categorised into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control, by three diabetes specialists. The specialists had access to both the CGM profiles and the patient records that indicated the diabetes type, diabetes duration, and types of therapy associated with each CGM.
Statistical Methods
All analyses were performed with PASW Statistics 17. Results are expressed as mean ± SD or as medians and IQR. Analysis of variance was used to assess differences between groups. The strength of the dependence between two continuous variables was assessed with the Pearson's correlation coefficient and between ordinal variables with Kendall's tau-b correlation. The weighted Cohen's kappa score was used to assess the inter-rater reliability of the categorisation of the CGM profiles between diabetes specialists and between Q-Score and diabetes specialists. The reliability (concordance of assessments) was measured using the method proposed by Landis and Koch. A P-value <0.05 was considered to indicate statistical significance.