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.

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: Advances in continuous glucose monitoring (CGM) have transformed ambulatory diabetes management. Until recently, inpatient use of CGM has remained investigational, with limited data on its accuracy in the hospital setting. RESEARCH DESIGN AND METHODS: To analyze the accuracy of Dexcom G6, we compared retrospective matched-pair CGM and capillary point-of-care (POC) glucose data from three inpatient CGM studies (two interventional and one observational) in general medicine and surgery patients with diabetes treated with insulin. Analysis of accuracy metrics included mean absolute relative difference (MARD), median absolute relative difference (ARD), and proportion of CGM values within 15, 20, and 30% or 15, 20, and 30 mg/dL of POC reference values for blood glucose >100 mg/dL or ≤100 mg/dL, respectively (% 15/15, % 20/20, % 30/30). Clinical reliability was assessed with Clarke error grid (CEG) analyses. RESULTS: A total of 218 patients were included (96% with type 2 diabetes) with a mean age of 60.6 ± 12 years. The overall MARD (n = 4,067 matched glucose pairs) was 12.8%, and median ARD was 10.1% (interquartile range 4.6, 17.6]. The proportions of readings meeting % 15/15, % 20/20, and % 30/30 criteria were 68.7, 81.7, and 93.8%, respectively. CEG analysis showed 98.7% of all values in zones A and B. MARD and median ARD were higher in the case of hypoglycemia (<70 mg/dL) and severe anemia (hemoglobin <7 g/dL). CONCLUSIONS: Our results indicate that CGM technology is a reliable tool for hospital use and may help improve glucose monitoring in non–critically ill hospitalized patients with diabetes.

Continuous glucose monitoring (CGM) has become a widely used tool in the ambulatory setting for monitoring glucose levels, as well as detecting uncontrolled hyperglycemia, hypoglycemia, and glycemic variability. The accuracy of some CGM systems has recently improved to the point of manufacture with factory calibration and Food and Drug Administration clearance for nonadjunctive use to dose insulin. In this commentary, we analyze the answers to six questions about what is needed to bring CGM into the hospital as a reliable, safe, and effective tool. The evidence to date indicates that CGM offers promise as an effective tool for monitoring hospitalized patients. During the current coronavirus disease 2019 crisis, we hope to provide guidance to healthcare professionals, who are seeking to reduce exposure to SARS-Cov-2, as well as preserve invaluable personal protective equipment. In this commentary, we address who, what, where, when, why, and how CGM can be adopted for inpatient use.

OBJECTIVE Use of real-time continuous glucose monitoring (RT-CGM) systems in the inpatient setting is considered investigational. The objective of this study was to evaluate whether RT-CGM, using the glucose telemetry system (GTS), can prevent hypoglycemia in the general wards. RESEARCH DESIGN AND METHODS In a randomized clinical trial, insulin-treated patients with type 2 diabetes at high risk for hypoglycemia were recruited. Participants were randomized to RT-CGM/GTS or point-of-care (POC) blood glucose testing. The primary outcome was difference in inpatient hypoglycemia. RESULTS Seventy-two participants were included in this interim analysis, 36 in the RT-CGM/GTS group and 36 in the POC group. The RT-CGM/GTS group experienced fewer hypoglycemic events (<70 mg/dL) per patient (0.67 [95% CI 0.34–1.30] vs. 1.69 [1.11–2.58], P = 0.024), fewer clinically significant hypoglycemic events (<54 mg/dL) per patient (0.08 [0.03–0.26] vs. 0.75 [0.51–1.09], P = 0.003), and a lower percentage of time spent below range <70 mg/dL (0.40% [0.18–0.92%] vs. 1.88% [1.26–2.81%], P = 0.002) and 180–250 mg/dL and >250 mg/dL were found between the groups. The RT-CGM/GTS group had no prolonged hypoglycemia compared with 0.20 episodes <54 mg/dL and 0.40 episodes <70 mg/dL per patient in the POC group. CONCLUSIONS RT-CGM/GTS can decrease hypoglycemia among hospitalized high-risk insulin-treated patients with type 2 diabetes.

OBJECTIVE: The efficacy and safety of continuous glucose monitoring (CGM) in adjusting inpatient insulin therapy have not been evaluated. RESEARCH DESIGN AND METHODS: This randomized trial included 185 general medicine and surgery patients with type 1 and type 2 diabetes treated with a basal-bolus insulin regimen. All subjects underwent point-of-care (POC) capillary glucose testing before meals and bedtime. Patients in the standard of care (POC group) wore a blinded Dexcom G6 CGM with insulin dose adjusted based on POC results, while in the CGM group, insulin adjustment was based on daily CGM profile. Primary end points were differences in time in range (TIR; 70–180 mg/dL) and hypoglycemia (<70 mg/dL and <54 mg/dL). RESULTS: There were no significant differences in TIR (54.51% ± 27.72 vs. 48.64% ± 24.25; P = 0.14), mean daily glucose (183.2 ± 40 vs. 186.8 ± 39 mg/dL; P = 0.36), or percent of patients with CGM values <70 mg/dL (36% vs. 39%; P = 0.68) or <54 mg/dL (14 vs. 24%; P = 0.12) between the CGM-guided and POC groups. Among patients with one or more hypoglycemic events, compared with POC, the CGM group experienced a significant reduction in hypoglycemia reoccurrence (1.80 ± 1.54 vs. 2.94 ± 2.76 events/patient; P = 0.03), lower percentage of time below range <70 mg/dL (1.89% ± 3.27 vs. 5.47% ± 8.49; P = 0.02), and lower incidence rate ratio <70 mg/dL (0.53 [95% CI 0.31–0.92]) and <54 mg/dL (0.37 [95% CI 0.17–0.83]). CONCLUSIONS: The inpatient use of real-time Dexcom G6 CGM is safe and effective in guiding insulin therapy, resulting in a similar improvement in glycemic control and a significant reduction of recurrent hypoglycemic events compared with POC-guided insulin adjustment.