No abstract

Background: Automated insulin management features of the MiniMed® 640G sensor-augmented pump system include suspension in response to predicted low sensor glucose (SG) values (“suspend before low”), suspension in response to existing low SG values (“suspend on low”), and automatic restarting of basal insulin delivery upon SG recovery. The effectiveness of these features was evaluated using CareLink® software data.Methods: Anonymized data from MiniMed 640G system users (n = 4818), MiniMed 530G system users (n = 39,219), and MiniMed Paradigm® Veo™ system users (n = 43,193) who voluntarily uploaded pump and sensor data were retrospectively analyzed. Comparisons were made between days in which system features were enabled at any time and those in which they were not. Comparisons were also made between pump suspension events for which insulin delivery was automatically or manually resumed and between glycemic parameters of users who switched from the MiniMed Paradigm Veo system to the MiniMed 640G system.Results: Days in which the MiniMed 640G “suspend before low” feature was enabled had lower percentages of SG readings ≤70 mg/dL (3.9 mmol/L) or ≥240 mg/dL (13.3 mmol/L) than days when it was not enabled (P < 0.001 for each). Users who switched from the MiniMed Paradigm Veo system to the MiniMed 640G system had fewer excursions below ≤70 mg/dL (P < 0.001) and ≥240 mg/dL (P < 0.001). SG values following automatically resumed pump suspension events recovered more rapidly and had a more stabilized endpoint than following manually resumed events.Conclusions: Automated insulin management features of the MiniMed 640G system can reduce the frequency of both high and low SG values and help stabilize SG after resumption of insulin delivery.

The CGP has the potential to enable health care providers, investigators and patients to better understand the components of glycemic control and the effect of various interventions on the individual elements of that control. This can be done on a daily, weekly, or monthly basis. It also allows direct comparison of the effects on different interventions among clinical trials which is not possible using A1C alone. This new composite metric approach requires validation to determine if it provides a better predictor of long-term outcomes than A1C and/or better predictor of severe hypoglycemia than the low blood glucose index (LBGI).

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.

Self-monitoring of blood glucose (SMBG) is the standard method by which the vast majority of patients assess their diabetes control. By virtue of the episodic nature, the limited number of times per day that it is actually performed, and the infrequent testing at night, SMBG can provide only a partial view of the frequency and severity of hypoglycemia. While multiple studies have used the nadir of the glucose level to differentiate between mild and severe hypoglycemia, 1-3 it is not possible to infer the intensity of hypoglycemia from SMBG because the duration of hypoglycemia is not known. This limits our ability to interpret the effect of various interventions for improving glycemic control as well as our understanding of the short-and long-term risks associated with hypoglycemia. On the other hand, continuous glucose monitoring (CGM) collects data on the frequency, duration and severity of hypoglycemia whether or not it is symptomatic. 4-6 The hypoglycemia triad (Hypo-Triad) consists of the three metrics that are usually reported in trials using CGM-area under the curve (AUC), time in hypoglycemia, and frequency of hypoglycemic excursions per day. However, it is unclear which individual metric or combination of metrics of the Hypo-Triad best characterizes the clinical and pathophysiologic impact of hypoglycemia. Figure 1 shows an example of a CGM tracing with two hypoglycemic episodes with different characteristics. Therefore, we developed two new 721242D STXXX10.