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.

The clinical efficacy of real-time continuous glucose monitoring

Background: Excess carbohydrate intake during hypoglycemia can lead to rebound hyperglycemia (RH). We investigated associations between RH and use of real-time continuous glucose monitoring (rtCGM) and an rtCGM system’s predictive alert. Methods: RH events were series of sensor glucose values (SGVs) >180 mg/dL starting within two hours of an antecedent SGV <70 mg/dL. Events were characterized by their frequency, duration (consecutive SGVs >180 mg/dL × five minutes), and severity (area under the glucose concentration-time curve). To assess the impact of rtCGM, data gathered during the four-week baseline phase (without rtCGM) and four-week follow-up phase (with rtCGM) from 75 participants in the HypoDE clinical trial (NCT02671968) of hypoglycemia-unaware individuals were compared. To assess the impact of predictive alerts, we identified a convenience sample of 24 518 users of an rtCGM system without predictive alerts who transitioned to a system whose predictive alert signals an SGV ≤55 mg/dL within 20 minutes (Dexcom G5 and G6, respectively). RH events from periods of blinded versus unblinded rtCGM wear and from periods of G5 and G6 wear were compared with paired t tests. Results: Compared to RH events in the HypoDE baseline phase, the mean frequency, duration, and severity of events fell by 14%, 12%, and 23%, respectively, in the follow-up phase (all P < .05). Compared to RH events during G5 use, the mean frequency, duration, and severity of events fell by 7%, 8%, and 13%, respectively, during G6 use (all P < .001). Conclusions: Rebound hypreglycemia can be objectively quantified and mitigated with rtCGM and rtCGM-based predictive alerts.

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.

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.

Most of the commercially available continuous glucose monitoring devices have a wired-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be converted in real time to glucose concentration through a calibration process that relies on a mathematical model having several unknown parameters. This chapter will first give a formal description of the calibration problem with special emphasis on the most critical aspects that mathematically characterize calibration. Then, it will review the most interesting calibration approaches proposed in the literature, and it will end with an example of the implementation of a recently proposed Bayesian calibration approach.

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.

Background: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices. Methods: We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods. Results: The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%). Conclusion: The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.

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.

Continuous blood pressure (BP) monitoring can help in preventing hypertension and other cardiovascular diseases. In principle, an indirect non-invasive continuous-time measurement of BP is possible by exploiting the photoplethysmography (PPG) signal, which can be obtained through wearable optical sensor devices. However, a model of the PPG-to-BP dynamical system is needed. In this study, we investigate if autoregressive with exogenous input (ARX) models with kernel-based regularization are suited for the scope. We analyzed 10 PPG time-series acquired on different individuals by a wearable optical sensor and correspondent BP reference values to evaluate feasibility of continuous BP estimation from a single PPG source. This first proof-of-concept study shows promising results in continuous BP estimation during resting states.

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a “raw” current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient’s discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.

Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters-age, sex, BMI, and waist circumference-with an accuracy of 87.1%.

Some of commercial continuous glucose monitoring (CGM) devices, i.e., minimally-invasive sensors able to measure almost continuously glucose concentration in the subcutaneous tissue, recently received the regulatory approval to be used for making therapeutic decisions in diabetes management. A fundamental requirement for its safe and effective use is represented by the accuracy of CGM measurements. However, despite recent advances in sensors accuracy and reliability, CGM still suffers from inaccuracy problems in presence of pharmacologic interferences, e.g., the common orally administered acetaminophen (APAP), which artificially raises CGM glucose readings for several hours. A model of the artifact induced by APAP on CGM measurements would be useful to design algorithms to compensate such a distortion. The aim of this work is to exploit the data published by previous literature studies to design a model of oral APAP pharmacokinetics and its effect on glucose concentration measured by CGM sensors. Specifically, the developed model was identified on average data of both plasma APAP concentration and the APAP effect on CGM profiles after an oral administration of 1000 mg of APAP. The APAP effect on CGM readings was estimated from the difference observed, in the same study, between the glucose profile measured by a Dexcom G4 Platinum sensor and the plasma glucose concentration. The model was validated by comparing the simulated effect of mealtime APAP administration in CGM measurements of 100 virtual subjects generated by the UVA/Padova Type 1 Diabetes (TID) Simulator vs. the effect observed in a clinical study by Maahs et al. (Diabetes Care, 2015) in 40 TID subjects taking APAP at breakfast. Results suggest that the proposed model is able to reliably describe the mean APAP effect on CGM measurements.