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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]

A perennial challenge for the characterization and modelling of phenomena involving granular media is that the internal connectivity of, and interactions between, the pores and the particles exhibit hallmarks of complexity: multi-scale and nonlinear interactions that lead to a plethora of patterns at the mesoscale, including fluid flow patterns that ultimately render a permeability of the granular media at the macroscale. A multitude of physical parameters exist to characterize geometry and structure, including pore/particle shape, volume and surface area, while a rich class of complex network parameters quantifies internal connectivity of the pore and particles in the material. A large collection of such variables is likely to exhibit a high degree of redundancy. Here we demonstrate how to use feature selection in machine learning theory to identify the most informative and non-redundant, yet parsimonious set of features that optimally characterizes the interstitial flow properties of porous, granular media, e.g., permeability, from high resolution data.