Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.

Measurement of glycated hemoglobin (HbA 1c ) has been the traditional method for assessing glycemic control. However, it does not reflect intra-and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA 1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA 1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.Glucose measurements are critical to effective diabetes management. Although measurement of glycated hemoglobin (HbA 1c ) has been the traditional method for assessing glycemic control, it does not reflect intra-and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Moreover, although self-monitoring of blood glucose (SMBG) has been shown to improve glycemic control and quality of life in both insulin-treated and noninsulin-treated diabetes when used within a structured testing regimen (1-4) [C,C,C,C], it cannot predict impending hypoglycemia or alert for hypoglycemia (5,6) [C,C] (7).Real-time continuous glucose monitoring (rtCGM) and intermittently viewed CGM (iCGM) address many of the limitations inherent in HbA 1c testing and SMBG. rtCGM uniformly tracks the glucose concentrations in the body's interstitial fluid, providing near real-time glucose data; iCGM uses similar methodology to show continuous glucose measurements retrospectively at the time of checking. Both rtCGM and iCGM facilitate monitoring of time spent in the target glucose range ("time in range"). However, only rtCGM can warn users if glucose is trending toward hypoglycemia or hyperglycemia. With iCGM, these trends can only be viewed after physically scanning the sensor. It is often difficult to distinguish between technologies regarding issues such as calibrations, alarms/alerts, human factors of applying and wearing sensors, and the cost, which are device specific. As these technological details are subject to constant change, the term CGM is used for all issues related to the device class unless indicated otherwis...

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Background: The safety and effectiveness of the in-home use of a hybrid closed-loop (HCL) system that automatically increases, decreases, and suspends insulin delivery in response to continuous glucose monitoring were investigated.Methods: Adolescents (n = 30, ages 14–21 years) and adults (n = 94, ages 22–75 years) with type 1 diabetes participated in a multicenter (nine sites in the United States, one site in Israel) pivotal trial. The Medtronic MiniMed® 670G system was used during a 2-week run-in phase without HCL control, or Auto Mode, enabled (Manual Mode) and, thereafter, with Auto Mode enabled during a 3-month study phase. A supervised hotel stay (6 days/5 nights) that included a 24-h frequent blood sample testing with a reference measurement (i-STAT) occurred during the study phase.Results: Adolescents (mean ± standard deviation [SD] 16.5 ± 2.29 years of age and 7.7 ± 4.15 years of diabetes) used the system for a median 75.8% (interquartile range [IQR] 68.0%–88.4%) of the time (2977 patient-days). Adults (mean ± SD 44.6 ± 12.79 years of age and 26.4 ± 12.43 years of diabetes) used the system for a median 88.0% (IQR 77.6%–92.7%) of the time (9412 patient-days). From baseline run-in to the end of study phase, adolescent and adult HbA1c levels decreased from 7.7% ± 0.8% to 7.1% ± 0.6% (P < 0.001) and from 7.3% ± 0.9% to 6.8% ± 0.6% (P < 0.001, Wilcoxon signed-rank test), respectively. The proportion of overall in-target (71–180 mg/dL) sensor glucose (SG) values increased from 60.4% ± 10.9% to 67.2% ± 8.2% (P < 0.001) in adolescents and from 68.8% ± 11.9% to 73.8% ± 8.4% (P < 0.001) in adults. During the hotel stay, the proportion of in-target i-STAT® blood glucose values was 67.4% ± 27.7% compared to SG values of 72.0% ± 11.6% for adolescents and 74.2% ± 17.5% compared to 76.9% ± 8.3% for adults. There were no severe hypoglycemic or diabetic ketoacidosis events in either cohort.Conclusions: HCL therapy was safe during in-home use by adolescents and adults and the study phase demonstrated increased time in target, and reductions in HbA1c, hyperglycemia and hypoglycemia, compared to baseline. Trial Registration: identifier: NCT02463097.

Continuous glucose monitoring can be associated with improved glycemic control in adults with type 1 diabetes. Further work is needed to identify barriers to effectiveness of continuous monitoring in children and adolescents. (ClinicalTrials.gov number, NCT00406133.)

Closed-loop artificial pancreas technology uses a control algorithm to automatically adjust insulin delivery based on subcutaneous sensor data to improve diabetes management. Currently available systems stop insulin in response to existing 1 or predicted 2 low sensor glucose values, whereas hybrid closedloop systems combine user-delivered premeal boluses with automatic interprandial insulin delivery. 3 This study investigated the safety of a hybrid closed-loop system in patients with type 1 diabetes. Methods | Patients aged 14 to 75 years with type 1 diabetes for at least 2 years, glycated hemoglobin (HbA 1c) less than 10%, and more than 6 months of insulin pump use were recruited from 10 centers (9 in the United States, 1 in Israel) between June 2, 2015, and November 11, 2015. This before and after study had a 2-week run-in period (baseline) for patients to learn the devices without the automated features followed by a 3-month study period with the initial 6 days used to collect insulin and sensor glucose data for the hybrid closed-loop algorithm. In the study period, there was a 6-day hotel stay during which 1 day was used for frequent sampling of venous blood glucose to verify the accuracy of the system. The last patient visit was March 7, 2016. Two central and 4 local institutional review boards approved the study. Written informed consent was obtained from adults and parents, and written assent from minors. The system included investigational continuous glucose monitoring sensors with transmitters, insulin pumps displaying real-time glucose data, a proprietary algorithm, and blood glucose meters. 4 Patients were required to periodically calibrate sensors and enter carbohydrate estimates for meal boluses. Every midnight, multiple parameters were automatically adjusted by the algorithm. Safety end points obtained during the run-in and study periods (including the hotel stay) were the incidence of severe hypoglycemia and diabetic ketoacidosis, serious adverse events, and device-related serious and unanticipated adverse events. Prespecified descriptive end points included time in open vs closed-loop systems; the percentage of sensor glucose values below, within, and above target range (71-180 mg/dL), including at night time; changes in HbA 1c , insulin requirements and body weight; and measures of glycemic variability. End points were collected during both periods and analyzed with SAS (SAS Institute), version 9.4.

A1C reduction was no different between the two groups. Subjects in the CG group had increased hypoglycemia AUC and number of events during blinded CGM use; however, there was no increase in hypoglycemia AUC or number of events in the SG group. Subjects with greater sensor utilization showed a greater improvement in A1C levels.

Hyperglycemia and hypoglycemia were prevalent in the PICU. Hypoglycemia, hyperglycemia, and, in particular, increased glucose variability were associated with increased morbidity (length of stay) and mortality rates.