Beyond HbA1c: using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision‐making. Issue 6 (5th April 2019) - Brown, S. A.; Basu, A.; Kovatchev, B. P.
Abstract: Assessment of glycaemic outcomes in the management of Type 1 and Type 2 diabetes has been revolutionized in the past decade with the increasing availability of accurate, user‐friendly continuous glucose monitoring (CGM). This advancement has brought a need for new techniques to appropriately analyse and understand the voluminous and complex CGM data for application in research‐related goals and clinical guidance for individuals. Traditionally, HbA1c was established using the Diabetes Control and Complications Trial (DCCT) and other trials as the ultimate measure of glycaemic control in terms of efficacy and, by default, risk of microvascular complications of diabetes. However, it is acknowledged that HbA1c alone is inadequate at describing an individual's daily glycaemic variation and risks for hypo‐ and hyperglycaemia, and it does not provide the guidance needed to decrease those risks. CGM data provide means by which to characterize an individual's daily glycaemic excursions on a different time scale measured in minutes rather than months. As a consequence, clinical reports, such as the ambulatory glucose profile, increasingly include summary statistics related to averages (mean glucose, time in range) as well as markers related to glycaemic variability (coefficient of variation, standard deviation). However, there is a need to translate those metrics into specific risks that can be addressed in an actionable plan by individuals with diabetes and providers. This review presents several clinical scenarios of glycaemic outcomes from CGM data that can be analysed to describe glycaemic variability and its attendant risks of hyperglycaemia and hypoglycaemia, moving towards relevant interpretation of the complex CGM data streams. What's new?: Continuous glucose monitoring (CGM) captures individual variation in glucose control and provides complex data streams that require appropriate analysis. Time in range is increasingly a focus for outcomes in clinical trials and is accurately measured by CGM. Measures of glycaemic variability are important elements of CGM analysis and are responsive to different therapeutic interventions Risk of hyperglycaemia and hypoglycaemia need to be considered in addition to time in range.