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 & Aims: Hypoglycaemia is a constant threat for all patients with congenital hyperinsulinism (CHI) and, left untreated, can lead to neurological damage and impaired development. To improve glycaemic monitoring, self-monitoring-blood-glucose (SMBG) is increasingly being replaced by Continuous Glucose Monitoring (CGM) with potential to identify illness patterns and treatment responses although with unproven benefit for patients and families. Explor...

Introduction: Congenital hyperinsulinism (CHI) is the commonest cause of severe hypoglycaemia in early childhood but glycaemic characterisation remains scarce. Continuous glucose monitoring (CGM) offers a deep understanding of glycaemic control to understand disease burden, individualise patient care and inform therapeutic trials in CHI. Preliminary studies suggest inadequate accuracy and no efficacy of standalone CGM to reduce hypoglycaemia. Provision is hist...