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Aims: Current continuous glucose monitoring (CGM) devices provide features that alert individuals with diabetes about their current and impending adverse glycaemic events. The use of these features has been associated with glycaemic improvements. However, how these features are utilised under real‐world conditions has not been well studied. We queried a large database to quantify utilisation of the Dexcom G6 system features and how utilisation impacted glycaemic outcomes within a cohort of European users. Methods: This 6‐month retrospective, observational, large database analysis utilised anonymised data from a sample of 47,784 Europe‐based G6 users. Primary outcome measures were associations between utilisation and customisation of High/Low threshold alerts, 'urgent low soon' (ULS) alert, and established CGM metrics. Results: Users in the Germany, Austria, Switzerland region (n = 20,257), the Nordic countries (n = 10,314), United Kingdom (n = 9006), Italy (n = 4747), France (n = 2130) and Spain (1330) were included. All alert features were utilised by >75% of the cohort across all regions/countries and age groups. Enabling the Low alert and ULS alert was associated with lower percentage of time below range compared to disabling the Low alert (p < 0.001). Enabling the High alert was associated with higher percentage of time in range (%TIR) and lower percentage of time above range (%TAR) %TAR compared to disabling the High alert (p < 0.001). Paediatric patients and older adults tended to set a higher threshold for High/Low alerts, while younger adults tended to use lower threshold values for High/Low alerts. Conclusions: Individuals who utilised the Dexcom G6 features showed better glycaemic control, particularly among those who utilised more sensitive High alert and Low alert settings, than users who did not utilise the system features. [ABSTRACT FROM AUTHOR]

Aims: Regional variations in the adoption of diabetes technology may be reflected in population‐level metrics of glycaemic control. In this observational study, we aimed to assess the glycaemic impacts of transitioning from the Dexcom G5 Real‐Time Continuous Glucose Monitoring (RT‐CGM) System to the Dexcom G6 in three European countries. Methods: Anonymised RT‐CGM data (uploaded to the Dexcom Clarity app) were from users in Germany, Sweden, and the United Kingdom (UK) who transitioned from G5 to G6 between 9‐12 months after G6 launched in 2018. Primary endpoints were percent time in hypoglycaemia, percent time in range (TIR), user retention rates, device utilisation, and urgent low soon (ULS) alert utilisation. Metrics were computed for 3‐month intervals in the 2‐year study window. Results: In all three countries, the transition from G5 to G6 was associated with a clear decrease in hypoglycaemia. In months 0‐3 after transitioning, the median percent time 〈3 mmol/L (54 mg/dL) and 〈3.9 mmol/L (70 mg/dL) decreased by [0.12‐0.28] and [0.40‐0.43] percentage points, respectively, with another [0.11‐0.21] and [0.34‐0.65] percentage point decrease in months 3‐6 in the three countries analysed. TIR and CGM utilisation were sustained or improved slightly across all countries. At the end of the study window, the retention rate was [88.8‐94.8%] and ULS utilization was [83.9‐86.9%] in the three countries analysed. Conclusions: Similar RT‐CGM trends were observed across Germany, Sweden, and the UK. Improvements in hypoglycaemia occurred in all countries. The high retention of users may lead to sustained glycaemic benefits associated with RT‐CGM use. [ABSTRACT FROM AUTHOR]

Objective: In most continuous glucose monitoring (CGM) devices used for diabetes management, the electrical signal measured by the sensor is transformed to glucose concentration by a calibration function whose parameters are estimated using self-monitoring of blood glucose (SMBG) samples. The calibration function is usually a linear model approximating the nonlinear relationship between electrical signal and glucose concentration in certain time intervals. Thus, CGM devices require frequent calibrations, usually twice a day. The aim here is to develop a new method able to reduce the frequency of calibrations. Methods: The algorithm is based on a multiple-day model of sensor time-variability with second-order statistical priors on its unknown parameters. In an online setting, these parameters are numerically determined by the Bayesian estimation exploiting SMBG sparsely collected by the patient. The method is assessed retrospectively on 108 CGM signals acquired for 7 days by the Dexcom G4 Platinum sensor, testing progressively less-calibration scenarios. Results: Despite the reduction of calibration frequency (on average from 2/day to 0.25/day), the method shows a statistically significant accuracy improvement compared to manufacturer calibration, e.g., mean absolute relative difference when compared to a laboratory reference decreases from 12.83% to 11.62% (p-value of 0.006). Conclusion: The methodology maintains (sometimes improves) CGM sensor accuracy compared to that of the original manufacturer, while reducing the frequency of calibrations. Significance: Reducing the need of calibrations facilitates the adoption of CGM technology both in terms of ease of use and cost, an obvious prerequisite for its use as replacement of traditional SMBG devices. [ABSTRACT FROM PUBLISHER]

Background: Continuous glucose monitoring (CGM) sensors need to be calibrated twice/day by using self-monitoring of blood glucose (SMBG) samples. Recently, to reduce the calibration frequency, we developed an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation. When applied to Dexcom G4 Platinum (DG4P) sensor data, the algorithm allowed the frequency of calibrations to be reduced to one-every-four-days without significant worsening of sensor accuracy. The aim of this study is to assess the same methodology on raw CGM data acquired by a next-generation Dexcom CGM sensor prototype and compare the results with that obtained on DG4P.Methods: The database consists of 55 diabetic subjects monitored for 10 days by a next-generation Dexcom CGM sensor prototype. The new calibration algorithm is assessed, retrospectively, by simulating an online procedure using progressively fewer SMBG samples until zero. Accuracy is evaluated with mean absolute relative differences (MARD) between blood glucose versus CGM values.Results: The one-per-day and one-every-two-days calibration scenarios in the next-generation CGM data have an accuracy of 8.5% MARD (vs. 11.59% of DG4P) and 8.4% MARD (vs. 11.63% of DG4P), respectively. Accuracy slightly worsens to 9.2% (vs. 11.62% of DG4P) when calibrations are reduced to one-every-four-days. The calibration-free scenario results in 9.3% MARD (vs. 12.97% of DG4P).Conclusions: In next-generation Dexcom CGM sensor data, the use of an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation aids in the shift toward a calibration-free scenario with even better results than those obtained in present-generation sensors. [ABSTRACT FROM AUTHOR]

Background: In the DexCom G4 Platinum (DG4P) continuous glucose monitoring (CGM) sensor, the raw current signal generated by glucose-oxidase is transformed to glucose concentration by a calibration function whose parameters are periodically updated by matching self-monitoring of blood glucose references, usually twice a day, to compensate for sensor variability in time. The aim of this work is to reduce DG4P calibration frequency to once a day by a recently proposed Bayesian calibration algorithm, which employs a time-varying calibration function and suitable day-specific priors.Methods: The database consists of 57 CGM signals that are collected by the DG4P for 7 days. The Bayesian calibration algorithm is used to calibrate the raw current signal following two different schedules, that is, two and one calibration per day. Calibrated glycemic profiles are compared with those originally acquired by the manufacturer, on days 1, 4, and 7, where frequent blood glucose references were available, by using standard metrics, that is, mean absolute relative difference (MARD), percentage of accurate glucose estimates, and percentage of data in the A-zone of Clarke Error Grid.Results: The one per day Bayesian calibration algorithm has accuracy similar to that of two per day (11.8% vs. 11.7% MARD, respectively), and it is statistically better (P-value of 0.0411) than the manufacturer calibration algorithm, which requires two calibrations per day (13.1% MARD).Conclusions: A Bayesian calibration algorithm employing a time-varying calibration function and suitable priors enables a reduction of the calibrations of DG4P sensor from two to one per day. [ABSTRACT FROM AUTHOR]

Symptomatic hypoglycemia is an incentive for carbohydrate intake which, in excess, can lead to rebound hyperglycemia (RH) and contribute to high glycemic variability. We examined whether an optional "urgent low soon" (ULS) alert triggered by impending hypoglycemia could mitigate RH in the real world. Sensor glucose values (SGVs) were from the G5 and G6 continuous glucose monitoring (CGM) systems (Dexcom). G5 does not offer the ULS feature; all G6 usage was with the ULS alert enabled. An RH event was defined as any series of SGVs >180 mg/dL preceded within 2 hours by a low SGV (<70 or <55 mg/dL). Event severity was defined as the area under the curve (AUC) for consecutive SGVs >180 mg/dL. Data from a convenience sample of 24,518 anonymized G5 users who transitioned to G6 and uploaded ≥30 days of data from each system in 2018 were analyzed. RH events preceded by an SGV <70 mg/dL and those preceded by an SGV <55 mg/dL were analyzed separately. For both types of RH events, the transition from G5 to G6 and its predictive alert was associated with favorable and statistically significant reductions in the mean (SD) frequency, severity, and duration RH events (Table). The ULS alert in the G6 CGM system, intended to reduce hypoglycemia, mitigates RH events in users who transition from a system without predictive alerts and may contribute to other metrics of improved glycemic control. Disclosure: G. Acciaroli: Employee; Self; Dexcom, Inc. A. Parker: Employee; Self; Dexcom, Inc. Stock/Shareholder; Self; Dexcom, Inc. J. Welsh: Employee; Self; CSL Behring. S. Puhr: Employee; Self; Dexcom, Inc. T.C. Walker: Employee; Self; Dexcom, Inc. H.K. Akturk: Advisory Panel; Self; Sanofi US. Research Support; Self; Eli Lilly and Company, REMD Biotherapeutics, Senseonics. [ABSTRACT FROM AUTHOR]

The coefficient of variation (CV) of CGM-derived sensor glucose values (SGVs) is the preferred metric for glycemic variability. However, a high CV is not directly actionable. We hypothesized that overtreatment of hyperglycemia (leading to iatrogenic lows) and overtreatment of hypoglycemia (leading to rebound highs) contribute to excessive CVs. An iatrogenic low was defined as any series of SGVs <70 mg/dL starting within 2 h of an SGV >180 mg/dL; a rebound high was any series of SGVs >180 mg/dL starting within 2 h of an SGV <70 mg/dL. We used data from a convenience sample of anonymized U.S.-based users of G6 CGM systems (Dexcom) who had uploaded >80% of possible SGVs over >28 days in 3Q2019. Users were stratified into quartiles by CV. Cohorts with CVs <31.2% ("Stable") or >39.1% ("Labile") were compared with respect to rebound highs, iatrogenic lows, the percentage of SGVs <70 or >180 mg/dL (TBR and TAR, respectively), and the fraction meeting TAR and TBR goals of <25% and <4%, respectively. Compared to "Stable" users, the iatrogenic lows and rebound highs of "Labile" users were more frequent and longer. Users in the "Stable" cohort also had lower TAR and TBR percentages than users in the "Labile" cohort and were more likely to meet consensus goals for these metrics (Table). Patients may lower CV and improve other CGM metrics by focusing on avoiding overtreatment of high and low glucose values. Disclosure: K.J. Leone: Employee; Self; Dexcom, Inc. G. Acciaroli: Employee; Self; Dexcom, Inc. A. Parker: Employee; Self; Dexcom, Inc. Stock/Shareholder; Self; Dexcom, Inc. J. Welsh: Employee; Self; CSL Behring. T.C. Walker: Employee; Self; Dexcom, Inc. D.A. Price: Employee; Self; Dexcom, Inc. [ABSTRACT FROM AUTHOR]

Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes. [ABSTRACT FROM AUTHOR]