Diabetes technology & therapeutics · 2026
Quality-Score (Q-Score) Can Be More Sensitive than %Time in Range and Several Other CGM Metrics in Detecting Responses to Therapeutic Interventions.
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Rodbard David, Augstein Petra, Heinke Peter, Salzsieder Eckhard, Thomas Andreas, Reindel Jörg
Аннотация
PURPOSE: To evaluate the relative sensitivity of several available CGM metrics for the detection of the effects of clinical interventions in people with type 1 diabetes (T1D) and type 2 diabetes (T2D).
METHODS: Real-world data from people with poor glycemic control (hemoglobin A1c 8.2 ± 1.3%) for 120 people with T1D and 92 people with T2D, using Libre 2 CGM. Analysis of CGM data from 3 days prior to admission and 2 days immediately before discharge from ∼8 days of in-hospital care with changes in therapy as prescribed by hospital-based diabetes specialists. CGM metrics included: quality-score (Q-Score), Time in Range (TIR) (3.9-10 mmol/L), Time Above Range (>10 mmol/L), Time Below Range (<3.9 mmol/L), Mean Sensor Glucose, Glucose Management Indicator, Glycemia Risk Index, Glucose Daily Range, and Mean of Absolute Daily Differences (MODD). We evaluated the paired differences in all metrics pre- and postintervention within subjects using classical paired Student's t tests.
RESULTS: The Q-Score showed the largest effects in terms of Student's t-values for T1D, for T2D, and for all (T1D and T2D) subjects after pooling, indicating better sensitivity for detection of an effect than TIR or seven other metrics. One of the five components of the Q-Score, MODD, a classical measure of stability of glucose patterns from day to day, showed the second-best sensitivity in evaluating changes within subjects specifically for people with T1D.
CONCLUSION: We observed consistent differences in sensitivity for the detection of the effects of therapeutic interventions, with Q-Score being superior to eight alternatives. This study needs replication using additional patient populations and multiple types of interventions to evaluate its generalizability and applicability to both randomized controlled clinical trials and real-world clinical data.
METHODS: Real-world data from people with poor glycemic control (hemoglobin A1c 8.2 ± 1.3%) for 120 people with T1D and 92 people with T2D, using Libre 2 CGM. Analysis of CGM data from 3 days prior to admission and 2 days immediately before discharge from ∼8 days of in-hospital care with changes in therapy as prescribed by hospital-based diabetes specialists. CGM metrics included: quality-score (Q-Score), Time in Range (TIR) (3.9-10 mmol/L), Time Above Range (>10 mmol/L), Time Below Range (<3.9 mmol/L), Mean Sensor Glucose, Glucose Management Indicator, Glycemia Risk Index, Glucose Daily Range, and Mean of Absolute Daily Differences (MODD). We evaluated the paired differences in all metrics pre- and postintervention within subjects using classical paired Student's t tests.
RESULTS: The Q-Score showed the largest effects in terms of Student's t-values for T1D, for T2D, and for all (T1D and T2D) subjects after pooling, indicating better sensitivity for detection of an effect than TIR or seven other metrics. One of the five components of the Q-Score, MODD, a classical measure of stability of glucose patterns from day to day, showed the second-best sensitivity in evaluating changes within subjects specifically for people with T1D.
CONCLUSION: We observed consistent differences in sensitivity for the detection of the effects of therapeutic interventions, with Q-Score being superior to eight alternatives. This study needs replication using additional patient populations and multiple types of interventions to evaluate its generalizability and applicability to both randomized controlled clinical trials and real-world clinical data.