Spencer Frank, United States of America
Dexcom, Inc. Algorithm EngineeringPresenter of 1 Presentation
FEASIBILITY OF USING A FACTORY-CALIBRATED CGM SYSTEM TO DIAGNOSE TYPE 2 DIABETES
Abstract
Background and Aims
Type 2 diabetes (T2D) can be diagnosed with the oral glucose tolerance test or with hemoglobin A1C (HbA1c); however, the reproducibility and concurrence between these tests is suboptimal. Continuous glucose monitoring (CGM) may allow for convenient and accurate T2D diagnosis. We assessed whether a factory-calibrated CGM system (Dexcom G6), worn in blinded mode for a single wear period, can be used to diagnose T2D.
Methods
We developed a binary classification diagnostic CGM ("dCGM") algorithm based on CGM and HbA1c data using a dataset of 716 individual CGM sensor sessions with associated HbA1c measurements from seven clinical trials. Data from 623 sensor sessions were used for training and 93 subjects for testing (49 normals [HbA1c <5.7%], 27 prediabetes, and 17 T2D [HbA1c ≥6.5%] not using pharmacotherapy). dCGM performance was evaluated against the accompanying HbA1c measurement which was assumed to provide the correct diagnosis.
Results
The dCGM algorithm's overall sensitivity, specificity, positive predictive value, and negative predictive value were 71%, 93%, 71%, and 93%, respectively. At other clinically relevant HbA1c thresholds, dCGM specificity among normals was 98% (48/49 correctly classified) and for subjects with suboptimally-controlled diabetes (HbA1c ≥7%, above the ADA recommended HbA1c goal) the sensitivity was 100% (8/8 subjects correctly diagnosed with T2D).
Conclusions
We have shown in a small dataset that dCGM has good performance for the diagnosis of T2D. Thus a factory-calibrated CGM system with a dCGM algorithm is a feasible alternative for the diagnosis of T2D and warrants further investigation.