Longitudinal analysis of users transitioning from the Dexcom G5 to the G6 RT-CGM system in Germany, Sweden and the United Kingdom (2018-2020).

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.

Sustainable Use of a Real-Time Continuous Glucose Monitoring System from 2018 to 2020.

We aimed to describe patterns of continuous glucose monitoring (CGM) system use and glycemic outcomes from 2018 to 2020 in a large real-world cohort by analyzing anonymized data from US-based CGM users who transitioned from the G5 to the G6 System (Dexcom) in 2018. The main end points were persistent use, within-day and between-day utilization, hypoglycemia, time in range (TIR, 70-180 mg/dL [3.9-10 mmol/L]), and use of the optional calibration feature in 2019 and 2020. In a cohort of 31,034 individuals, rates of persistent use were high, with 27,932 (90.0%) and 26,861 (86.6%) continuing to upload data in 2019 and 2020, respectively. Compared with G5 use, G6 use was associated with higher device utilization, less hypoglycemia, higher TIR (in 2020), and >80% fewer calibrations in both 2019 and 2020 (P's < 0.001). High persistence and utilization of the G6 system may contribute to sustainable glycemic outcomes and decreased user burden.

Real-Time Continuous Glucose Monitoring During the Coronavirus Disease 2019 Pandemic and Its Impact on Time in Range.

Background: The coronavirus disease 2019 (COVID-19) pandemic disrupted the lives of people with diabetes. Use of real-time continuous glucose monitoring (rtCGM) helped manage diabetes effectively. Some of these disruptions may be reflected in population-scale changes to metrics of glycemic control, such as time-in-range (TIR). Methods: We examined data from 65,067 U.S.-based users of the G6 rtCGM System (Dexcom, Inc., San Diego, CA) who had uploaded data before and during the COVID-19 pandemic. Users associated with three counties that included the cities of Los Angeles, Chicago, and New York or with five regions designated by the Centers for Disease Control and Prevention (CDC) were compared. Public data were used to associate regions with prepandemic and intrapandemic glycemic parameters, COVID-19 mortality, and median household income. Results: Compared with an 8-week prepandemic interval before stay-at-home orders (January 6, 2020, to March 1, 2020), overall mean (standard deviation) TIR improved from 59.0 (20.1)% to 61.0 (20.4)% during the early pandemic period (April 20, 2020 to June 14, 2020, P < 0.001). TIR improvements were noted in all three counties and in all five CDC-designated regions. Higher COVID-19 mortality was associated with higher proportions of individuals experiencing TIR improvements of ≥5 percentage points. Users in economically wealthier zip codes had higher pre- and intrapandemic TIR values and greater relative improvements in TIR. TIR and pandemic-related improvements in TIR varied across CDC-designated regions. Conclusions: Population-level rtCGM data may be used to monitor changes in glycemic control with temporal and geographic specificity. The COVID-19 pandemic is associated with improvements in TIR, which were not evenly distributed across the United States.

X-ray computed tomography images and network data of sands under compression.

Ottawa sand and Angular sand consist of particles with distinct shapes. The x-ray computed tomography (XCT) image stacks of their in-situ confined compressive testings are provided in this paper. For each image stack, a contact network, a thermal network and a network feature - edge betweenness centrality - of each edge in the networks are also provided. The readers can use the image data to construct digital sands with applications of (1) extracting microstructural parameters such as particle size, particle shape, coordination number and more network features; (2) analysing mechanical behaviour and transport processes such as fluid flow, heat transfer and electrical conduction using either traditional simulation tools such as finite element method and discrete element method or newly network models which could be built based on the network files available here.

Preferential flow pathways in a deforming granular material: self-organization into functional groups for optimized global transport.

Existing definitions of where and why preferential flow in porous media occurs, or will occur, assume a priori knowledge of the fluid flow and do not fully account for the connectivity of available flow paths in the system. Here we propose a method for identifying preferential pathways through a flow network, given its topology and finite link capacities. Using data from a deforming granular medium, we show that the preferential pathways form a set of percolating pathways that is optimized for global transport of interstitial pore fluid in alignment with the applied pressure gradient. Two functional subgroups emerge. The primary subgroup comprises the main arterial paths that transmit the greatest flow through shortest possible routes. The secondary subgroup comprises inter- and intra-connecting bridges that connect the primary paths, provide alternative flow routes, and distribute flow through the system to maximize throughput. We examine the multiscale relationship between functionality and subgroup structure as the sample dilates in the lead up to the failure regime where the global volume then remains constant. Preferential flow pathways chain together large, well-connected pores, reminiscent of force chain structures that transmit the majority of the load in the solid grain phase.

Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability.

We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.