Classification of gastroparesis from glycemic variability in type 1 diabetes: a proof-of-concept study

Background and Objective:
Delayed gastric emptying is a substantial challenge for people with diabetes, affecting quality of life and blood glucose regulation. The complication is underdiagnosed, and current diagnostic tests are expensive or time consuming or have modest accuracy. The assessment of glycemic variations has potential use in gastroparesis screening. The aim of this study was to investigate the differences in glycemic variability between type 1 diabetes patients with gastroparesis and without a diagnosis of gastroparesis and the potential for using a classification model to differentiate between groups.

Methods:
Continuous glucose monitoring (CGM) from 425 patients with diabetes was included in the analytic cohort, including 16 patients with a diagnosis of gastroparesis and 409 without a known gastroparesis diagnosis. Sixteen features (9 daytime features and 7 nighttime features) describing glucose dynamics were extracted to assess differences between patients with and without a diagnosis of gastroparesis. A logistic regression model was trained using forward selection and cross-validation.

Results:
In total, 3 features were included in the model utilizing forward selection of features and cross-validation: mean absolute glucose (MAG), span, and standard deviation during the night. The Receiver operating characteristic (ROC) AUC for the classification model was 0.76.

Conclusions:
Gastroparesis seems to have an impact on glucose variability, especially during the night. Moreover, CGM could possibly be used as a part of the screening process for delayed gastric emptying, but more studies are needed to determine a realistic accuracy.