Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management

Diabetes is a complex metabolic condition that demands tailored, individualized approaches for effective management. Real-time continuous glucose monitoring (rtCGM) systems have improved in terms of design, usability and accuracy over the years and play a pivotal role in the delivery of integrated personalized diabetes management (iPDM). iPDM is a comprehensive multidisciplinary approach that combines individualized care strategies utilizing technologies and interventions and encourages the active involvement of the person with diabetes in the care provided. The use of stand-alone rtCGM and its integration with other diabetes technologies, such as hybrid automated insulin delivery, have enabled improved glycaemic and quality of life outcomes for people with diabetes. As the uptake of rtCGM and associated technologies is increasing and becoming the standard of care for people with diabetes, it is important that efforts are focused on associated goals such as reducing health inequalities in terms of access, aligning structured education with rtCGM usage, choosing the right technology based on needs and preferences, and minimizing burden while aiming for optimal glucose outcomes. Utilizing rtCGM in other settings than outpatients and in diabetes cohorts beyond type 1 and type 2 diabetes needs further exploration. This review aims to provide an overview of the role of rtCGM and how best to link rtCGM to iPDM, highlighting its role in enhancing personalized treatment strategies.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

A systematic bibliometric analysis on the clinical practice of CGM in diabetes mellitus from 2012 to 2022

Background

Continuous glucose monitoring (CGM) has revolutionized diabetes management, but a comprehensive analysis of its clinical implementation is lacking. This study aims to explore CGM in diabetes practice over the past decade using bibliometric analysis. It will identify trends, research focal points, and provide a framework for future investigations.

Materials and methods

The Web of Science Core Collection (WOSCC) was utilized to acquire literature pertaining to the employment of continuous glucose monitoring (CGM) in diabetes that was published between the years 2012 and 2022, and to conduct a comprehensive analysis of the associated citation data. To achieve bibliometric visualization and analysis of the collated data, the bibliography package in the Rstudio(v.4.2.2), Citespace 6.2.R4, and VOS viewer were employed.

Results

A total of 3024 eligible publications were extracted from 91 countries, with the United States being the leading country in terms of the number of issued articles. Furthermore, the annual publication rate has shown a gradual increase during the past decade. Among the various journals in this field, DIABETES TECHNOLOGY & THERAPEUTICS was identified as the most highly cited one. Keyword clustering analysis of the extracted publications indicates that the research hotspots in the past decade have primarily focused on "continuous glucose monitoring", "glycemic variability", "type 1 diabetes", "hypoglycemia", and "glycemic control". Moreover, the analysis of keyword emergence reveals that "Time In Range" and "Young Adult" represent the current research frontiers for the years 2012-2022.

Conclusion

The concept of Time in Range (TIR) has garnered considerable attention as a significant area of inquiry and an emerging research trend in the clinical practice of Continuous Glucose Monitoring (CGM) for Diabetes Mellitus. Moreover, recent investigations have demonstrated a growing focus on young adults with type 1 diabetes as the research population of interest. In the foreseeable future, research endeavors will persist in the pursuit of improving glycemic management among young adults through the utilization of continuous glucose monitoring (CGM) technology, while also delving into the examination of the Time in Range metric via supplementary clinical investigations.