A Machine Learning Framework to Quantify Postprandial Glucose Responses in Gestational Diabetes.

OBJECTIVE

To develop a machine learning (ML) framework to identify postprandial glucose responses (PPGR) automatically from continuous glucose monitoring (CGM) data in pregnant adults with gestational diabetes mellitus (GDM).

METHODS

Pregnant adults diagnosed with GDM or impaired glucose tolerance (IGT) wore blinded CGMs and logged mealtimes for up to three 14-day time periods after enrollment. A random forest ML algorithm was applied to identify morning PPGRs from daily CGM profiles, and its performance compared against PPGRs derived using self-reported mealtimes.

RESULTS

21 participants provided analyzable data. Relative to self-reported mealtime, the ML algorithm's predicted mealtimes had an absolute error of a median 30 [IQR: 20,45] minutes. Comparing 1-hour and 2-hour PPGR values from the CGM using self-reported and ML-predicted mealtimes showed a median difference of 8.7 [IQR: 0,22.7] mg/dL and 3.3 [IQR: 0,13.2] mg/dL respectively for the two timepoints.

CONCLUSION

A random forest ML algorithm accurately identified PPGRs from CGM data in persons with GDM, enabling an automated and convenient approach to monitoring postprandial dysglycemia in this population.