Background: Most standalone real-time continuous glucose monitoring (RT-CGM) systems provide predictive low and high sensor glucose (SG) threshold alerts. The durations and risk of low and high SG excursions following Guardian™ Connect CGM system predictive threshold alerts were evaluated. Methods: Continuous glucose monitoring system data uploaded between January 2, 2017 and May 22, 2018 by 3133 individuals using multiple daily injections (MDIs) or continuous subcutaneous insulin infusion (CSII) therapy were deidentified and retrospectively analyzed. Glucose excursions were defined as SG values that went beyond a preset low or high SG threshold for ≥15 minutes. For a control group, thresholds were based on the median of the low SG threshold limit (70 mg/dL) and the high SG threshold limit (210 mg/dL) preset by all system users. During periods when alerts were not enabled, timestamps were identified when a predictive alert would have been triggered. The time before low horizon was 17.5 minutes and the time before high horizon was 15 minutes, of all users who enabled alerts. Excursions occurring after a low SG or high SG predictive alert were segmented into prevented, ≤20, 20-60, and >60 minutes. Results: Excursions were prevented after 59% and 39% of low and high SG predictive alerts, respectively. The risk of a low or high excursion occurring was 1.9 ( P < 0.001, 95% CI, 1.88-1.93) and 3.3 ( P < 0.001, 95% CI, 3.20-3.30) times greater, respectively, when alerts were not enabled. Conclusions: The predictive alerts of the RT-CGM system under study can help individuals living with diabetes prevent some real-world low and high SG excursions. This can be especially important for those unable to reach or maintain glycemic control with basic RT-CGM or CSII therapy.

The Medtronic GuardianTM Connect continuous glucose monitoring (CGM) system, with predictive high and low glucose alerts, allows users to view sensor glucose (SG) data on a smartphone that notifies them 10-60 minutes before an excursion. The rates of alerts and outcomes of users on the GuardianTM Connect system were evaluated. We identified 2,541 users with &gt;5 days of SG data in the CareLinkTM database from Jan 2, 2017-Sep 5, 2017 (data collected Jan 2, 2017-Dec 14, 2017). Excursions were identified when SG values were beyond the users’ preset SG threshold limit for ≥15 min. As a control, alerts were simulated during user alert-disabled periods to compare excursion frequency. Excursion durations following the alert times were segmented into avoided, ≤20min, 20-60 min, and &gt;60min. Simulated SG limits were 202mg/dL (11.2mmol/L) for high and 70mg/dL (3.9mmol/L) for low. Simulated predictive times before excursions were 12.5min for high and 17.5min for low. The Table shows percentages of each alert resulting in an excursion. Users who enabled predictive alerts avoided 60% of low and 39% of high events. The percentage point improvement for excursions avoided was 28% and 31% following predicted low and high alerts vs. control. Stand alone CGM technology, like the GuardianTM Connect system, with predictive alerts are useful for tracking SG and enabling timely actions that help avoid high and low excursions. Disclosure O. Cohen: Employee; Self; Medtronic. S. Abraham: None. C.M. McMahon: Employee; Self; Medtronic MiniMed, Inc.. P. Agrawal: None. R. Vigersky: Employee; Self; Medtronic MiniMed, Inc..

Background: The Guardian Connect continuous glucose monitoring (CGM) system displays current and trending sensor glucose (SG) via smartphone; records insulin, carbohydrate and exercise; and sends predictive high and low SG alerts up to one hour in advance. When used with the Sugar.IQ diabetes assistant application, personalized insights based on behavior patterns (e.g., food log or insulin dose entry) and glycemic outcomes can be tracked with the Glycemic Assist feature. Methods: System data uploaded between June-November of 2018, by 1765 individuals with diabetes were analyzed. Time in target glucose range (TIR, 70-180 mg/dL) and the Glucose Management Indicator (GMI) were compared between those who used (N=530) or did not use Sugar.IQ. Both groups had ≥5 days of SG data and similar demographic and initial glucose profiles. Results: System users had a mean GMI of 7.1%, mean±SD SG of 157.0±49.1mg/dL (8.7±2.7mmol/L), and a mean TIR of 64.5%, over the data upload period. When predictive alerts were enabled, excursions were avoided after 31% of high SG alerts and 62% of low SG alerts. Those who accessed Sugar.IQ experienced a TIR increase of 2.7% (p=0.006) and a mean SG decrease of -3.0% (p&lt;0.001), with reductions in SD of SG (47.9mg/dL to 41.5mg/dL, p&lt;0.001) and coefficient of variation of SG (0.32 to 0.29, p&lt;0.001), versus those who did not. Their GMI was also 6.8% versus 6.9% (p=0.007). Those who tracked food at least once/day increased TIR by 5% (p&lt;0.001). Users considered 88% of insights helpful. Discussion: The Guardian Connect CGM system with Sugar.IQ may advance patient understanding of glucose trends, aid in behavioral change that improves therapy adherence, and lead to better glycemic outcomes. Disclosure S. Arunachalam: Employee; Self; Medtronic. Y. Zhong: None. S. Abraham: Employee; Self; Medtronic MiniMed, Inc. P. Agrawal: Employee; Self; Medtronic MiniMed, Inc. Stock/Shareholder; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic MiniMed, Inc. T.L. Cordero: Employee; Self; Medtronic. F.R. Kaufman: Employee; Self; Medtronic.

Background and Aims: Dawn phenomenon (DP) refers to the morning rise in glucose levels observed in individuals with diabetes. While continuous glucose monitoring (CGM) may detect rates of change (ROC) in sensor glucose (SG) levels, ensuring that DP is not due to unannounced carbs can be challenging. Hence, a combination of CGM and activity data were used to identify true DP. Method: MiniMed™ 530G system data were uploaded between 6/2017-10/2017 by 33 individuals. Corresponding heart rate, steps, and metabolic equivalents (METs) were acquired with Fitbit® Charge 2™. Nights without carb or bolus entry from 12-6AM were analyzed. Nights with SG ROC &gt;0.28mg/dL/min (ROC between peak and nadir) and no activity (METs &lt;1.5) ±30min of nadir were identified as true DP; those involving activity (METs &gt;1.5) ±30min of nadir were identified as involving unannounced carb (UC); and those with no nadir were identified as ideal. Results: Individuals had 26 nights with DP, 27 nights with UC, and 39 ideal nights and the Table shows differences in the features related to each. The ROC of SG rise due to DP was lower, took longer time to reach peak, and tended to have a higher SG at nadir when compared with UC. Conclusion: Dawn phenomenon can be identified and characterized in real-world conditions using CGM and fitness tracking. Detecting DP may help improve nocturnal and early morning glycemic management. Disclosure S. Abraham: Employee; Self; Medtronic. D. Kang: None. Y. Zhong: Employee; Self; Medtronic. P. Agrawal: None. T.L. Cordero: Employee; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic.