ESPE Abstracts

Background and Aims: Hypoglycaemia is a life-threatening risk for many patients and prevention is individualised and complex. Continuous Glucose Monitoring (CGM) shows promise but current accuracy is insufficient for acute hypoglycaemia detection and data review services are complex and generic. Machine Learning is increasingly used but ignores weekly hypoglycaemia patterns and behaviour change and thus has demonstrated no real-world reduction in hypoglycaemia...

Background and Aims: In patients with congenital hyperinsulinism (CHI), recurrent hypoglycaemia can lead to longstanding neurological impairments. At present, glycaemic monitoring is with infrequent fingerprick tests; a practice which can miss hypoglycaemic episodes between tests. Continuous glucose monitoring (CGM) is a promising alternative method which has the utility to identify risk and patterns of hypoglycaemia. Although CGM is well established in type 1...

Background and Objective: Continuous Glucose Monitoring (CGM) is gaining in popularity for patients with paediatric hypoglycaemia disorders such as Congenital Hyperinsulinism (CHI), but no standard measures of accuracy or associated clinical risk are available. A small number of studies have shown suboptimal accuracy of CGM in CHI but assessments have been inconsistent, incomplete and offer no measure of clinical application. Error grids that categorise clinic...

Background: Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycaemia in childhood. High cerebral glucose utilisation in the early hours results in high risk of hypoglycaemia for people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycaemia is the cornerstone of management for HI but the risk of hypoglycaemia at night or indeed the timing of hypoglycae...