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.

The use of continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) systems has gained wide acceptance in diabetes care. These devices have been demonstrated to be clinically valuable, improving glycemic control and reducing risks of hypoglycemia in ambulatory patients with type 1 diabetes and type 2 diabetes. Approximately 30–40% of patients with type 1 diabetes and an increasing number of insulin-requiring patients with type 2 diabetes are using pump and sensor technology. As the popularity of these devices increases, it becomes very likely that hospital health care providers will face the need to manage the inpatient care of patients under insulin pump therapy and CGM. The American Diabetes Association advocates allowing patients who are physically and mentally able to continue to use their pumps when hospitalized. Health care institutions must have clear policies and procedures to allow the patient to continue to receive CSII treatment to maximize safety and to comply with existing regulations related to self-management of medication. Randomized controlled trials are needed to determine whether CSII therapy and CGM systems in the hospital are associated with improved clinical outcomes compared with intermittent monitoring and conventional insulin treatment or with a favorable cost-benefit ratio.

Introduction: Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. Methods: A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG. Results: SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. Discussion: The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs.

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

OBJECTIVE: Cleared blood glucose monitors (BGMs) for personal use may not always deliver levels of accuracy currently specified by international and U.S. regulatory bodies. This study’s objective was to assess the accuracy of 18 such systems cleared by the U.S. Food and Drug Administration representing approximately 90% of commercially available systems used from 2013 to 2015. RESEARCH DESIGN AND METHODS: A total of 1,035 subjects were recruited to have a capillary blood glucose (BG) level measured on six different systems and a reference capillary sample prepared for plasma testing at a reference laboratory. Products were obtained from consumer outlets and tested in three triple-blinded studies. Each of the three participating clinical sites tested a different set of six systems for each of the three studies in a round-robin. In each study, on average, a BGM was tested on 115 subjects. A compliant BG result was defined as within 15% of a reference plasma value (for BG ≥100 mg/dL [5.55 mmol/L]) or within 15 mg/dL (0.83 mmol/L) (for BG 20%; modified Bland-Altman analysis including average bias, coefficient of variation, and 95% limits of agreement; and proportion of readings with no clinical risk as determined by the Surveillance Error Grid. RESULTS: The different accuracy metrics produced almost identical BGM rankings. Six of the 18 systems met the predetermined accuracy standard in all three studies, 5 systems met it in two studies, and 3 met it in one study. Four BGMs did not meet the accuracy standard in any of the three studies. CONCLUSIONS: Cleared BGMs do not always meet the level of analytical accuracy currently required for regulatory clearance. This information could assist patients, professionals, and payers in choosing products and regulators in evaluating postclearance performance.