Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia

Background: Postbariatric hypoglycemia (PBH) is a complication of bariatric surgery with limited therapeutic options. We developed an event-based system to predict and detect hypoglycemia based on continuous glucose monitor (CGM) data and recommend delivery of minidose liquid glucagon. Methods: We performed an iterative development clinical study employing a novel glucagon delivery system: a Dexcom CGM connected to a Windows tablet running a hypoglycemia prediction algorithm and an Omnipod pump filled with an investigational stable liquid glucagon formulation. Meal tolerance testing was performed in seven participants with PBH and history of neuroglycopenia. Glucagon was administered when hypoglycemia was predicted. Primary outcome measures included the safety and feasibility of this system to predict and prevent severe hypoglycemia. Secondary outcomes included hypoglycemia prediction by the prediction algorithm, minimization of time below hypoglycemia threshold using glucagon, and prevention of rebound hyperglycemia. Results: The hypoglycemia prediction algorithm alerted for impending hypoglycemia in the postmeal state, prompting delivery of glucagon (150g). After observations of initial incomplete efficacy to prevent hypoglycemia in the first two participants, system modifications were implemented: addition of PBH-specific detection algorithm, increased glucagon dose (300g), and a second glucagon dose if needed. These modifications, together with rescue carbohydrates provided to some participants, contributed to progressive improvements in glucose time above the hypoglycemia threshold (75mg/dL). Conclusions: Preliminary results indicate that our event-based automatic monitoring algorithm successfully predicted likely hypoglycemia. Minidose glucagon therapy was well tolerated, without prolonged or severe hypoglycemia, and without rebound hyperglycemia.