The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Background: Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability. Methods: Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS ^® Gold device. Glycemic variability was calculated using EasyGV ^© software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG). Results: Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0–3.0; CONGA, 3.6–5.5; LI, 0.0–4.7; J-Index, 4.7–23.6; LBGI, 0.0–6.9; HBGI, 0.0–7.7; GRADE, 0.0–4.7; MODD, 0.0–3.5; MAGE-CGM, 0.0–2.8; ADDR, 0.0–8.7; M-value, 0.0–12.5; and MAG, 0.5–2.2. Conclusions: We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Problematic hypoglycemia, defined as two or more episodes per year of severe hypoglycemia or as one episode associated with impaired awareness of hypoglycemia, extreme glycemic lability, or major fear and maladaptive behavior, is a challenge, especially for patients with long-standing type 1 diabetes. Individualized therapy for such patients should include a composite target: optimal glucose control without problematic hypoglycemia. Therefore, we propose a tiered, four-stage algorithm based on evidence of efficacy given the limitations of educational, technological, and transplant interventions. All patients with problematic hypoglycemia should undergo structured or hypoglycemia-specific education programs (stage 1). Glycemic and hypoglycemia treatment targets should be individualized and reassessed every 3–6 months. If targets are not met, one diabetes technology—continuous subcutaneous insulin infusion or continuous glucose monitoring—should be added (stage 2). For patients with continued problematic hypoglycemia despite education (stage 1) and one diabetes technology (stage 2), sensor-augmented insulin pumps preferably with an automated low-glucose suspend feature and/or very frequent contact with a specialized hypoglycemia service can reduce hypoglycemia (stage 3). For patients whose problematic hypoglycemia persists, islet or pancreas transplant should be considered (stage 4). This algorithm provides an evidence-informed approach to resolving problematic hypoglycemia; it should be used as a guide, with individual patient circumstances directing suitability and acceptability to ensure the prudent use of technology and scarce transplant resources. Standardized reporting of hypoglycemia outcomes and inclusion of patients with problematic hypoglycemia in studies of new interventions may help to guide future therapeutic strategies.

OBJECTIVE: It is known that continuous glucose monitoring (CGM) systems can lower mean glucose compared with episodic self-monitoring of blood glucose. Implantable CGM systems may provide additional benefits. RESEARCH DESIGN AND METHODS: We studied the Eversense (Senseonics Inc.) implantable CGM sensor in 71 participants aged 18 years and older with type 1 and type 2 diabetes in a 180-day multinational, multicenter pivotal trial. Participants used the CGM system at home and in the clinic. CGM accuracy was assessed during eight in-clinic visits with the mean absolute relative difference (MARD) for venous reference glucose values >4.2 mmol/L as the primary end point. Secondary end points included Clarke Error Grid Analysis and alarm performance. The primary safety outcome was device-related serious adverse events. This trial is registered with ClinicalTrials.gov, number NCT02154126. RESULTS: The MARD value against reference glucose values >4.2 mmol/L was 11.1% (95% CI 10.5, 11.7). Clarke Error Grid Analysis showed 99.2% of samples in the clinically acceptable error zones A and B. Eighty-one percent of hypoglycemic events were detected by the CGM system within 30 min. No device-related serious adverse events occurred during the study. CONCLUSIONS: Our results indicate the safety and accuracy of this new type of implantable CGM system and support it as an alternative for transcutaneous CGM.