Automatic identification of unreported meals from continuous glucose monitoring data in individuals after bariatric surgery using a template matching algorithm - Elisa Pellizzari; Francesco Prendin; Giacomo Cappon; Elena Idi; Simone Del Favero; David Herzig; Lia Bally; Andrea Facchinetti (Scientific Reports)
Abstract Post-bariatric hypoglycemia (PBH) is a metabolic complication of individuals with obesity who have undergone bariatric surgery, characterized by rapid glycemic excursions followed by hypoglycemic events usually occurring 1–3 h post-meal. Without an approved pharmacotherapy, dietary modifications are essential for managing PBH, with continuous glucose monitoring (CGM) devices emerging as crucial tools for capturing postprandial glucose responses that can guide intervention strategies to prevent PBH. The effectiveness of such interventions is based on the availability of rich datasets, containing both CGM and meal data. However, meal information is often incomplete, being its manual recording burdensome and prone to user-related errors. In response, we proposed a template match algorithm (TMA) for the retrospective identification of unreported meals using CGM data only. TMA relies on a similarity score calculated between a post-prandial glycemic curve template and the glycemic trace of interest. Our study demonstrates promising results: TMA correctly identifies 1237 out of 1340 meals, generating 208 false positives within a dataset of 20 PBH subjects monitored in free-living conditions for nearly 50 days, yielding a median F1-score of 0.90. The effectiveness of TMA enables its use to enhance data quality in long-term studies involving PBH patients, facilitating the development of new approaches to manage PBH.