scholarly journals Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications

2018 ◽  
Vol 12 (5) ◽  
pp. 1064-1071 ◽  
Author(s):  
Martina Vettoretti ◽  
Giacomo Cappon ◽  
Giada Acciaroli ◽  
Andrea Facchinetti ◽  
Giovanni Sparacino
Keyword(s):  
Continuous Glucose Monitoring ◽  
Data Analytics ◽  
Diabetes Management ◽  
Low Cost ◽  
Glucose Monitoring ◽  
Relevant Information ◽  
Digital Data ◽  
Sensor Data ◽  
Health Records ◽  
Data Ecosystem

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.

scholarly journals An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

2021 ◽  
pp. 193229682098557
Author(s):  
Alysha M. De Livera ◽  
Jonathan E. Shaw ◽  
Neale Cohen ◽  
Anne Reutens ◽  
Agus Salim
Keyword(s):  
Continuous Glucose Monitoring ◽  
Web Application ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Epidemiological Studies ◽  
Interactive Software ◽  
Web Based ◽  
Software Applications ◽  
Visualization Of Data ◽  
User Friendly

Motivation: Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data. Results: Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

scholarly journals Accuracy of flash glucose monitoring and continuous glucose monitoring technologies: Implications for clinical practice

2018 ◽  
Vol 15 (3) ◽  
pp. 175-184 ◽  
Author(s):  
Ramzi A Ajjan ◽  
Michael H Cummings ◽  
Peter Jennings ◽  
Lalantha Leelarathna ◽  
Gerry Rayman ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Interstitial Fluid ◽  
Glucose Monitoring ◽  
Relative Difference ◽  
Glucose Sensing ◽  
Sensor Data ◽  
Flash Glucose Monitoring ◽  
Fluid Sensor ◽  
Monitoring Technologies

Continuous glucose monitoring and flash glucose monitoring technologies measure glucose in the interstitial fluid and are increasingly used in diabetes care. Their accuracy, key to effective glycaemic management, is usually measured using the mean absolute relative difference of the interstitial fluid sensor compared to reference blood glucose readings. However, mean absolute relative difference is not standardised and has limitations. This review aims to provide a consensus opinion on assessing accuracy of interstitial fluid glucose sensing technologies. Mean absolute relative difference is influenced by glucose distribution and rate of change; hence, we express caution on the reliability of comparing mean absolute relative difference data from different study systems and conditions. We also review the pitfalls associated with mean absolute relative difference at different glucose levels and explore additional ways of assessing accuracy of interstitial fluid devices. Importantly, much data indicate that current practice of assessing accuracy of different systems based on individualised mean absolute relative difference results has limitations, which have potential clinical implications. Healthcare professionals must understand the factors that influence mean absolute relative difference as a metric for accuracy and look at additional assessments, such as consensus error grid analysis, when evaluating continuous glucose monitoring and flash glucose monitoring systems in diabetes care. This in turn will ensure that management decisions based on interstitial fluid sensor data are both effective and safe.

scholarly journals Functional data analysis and prediction tools for continuous glucose-monitoring studies

2020 ◽  
pp. 1-10
Author(s):  
Emrah Gecili ◽  
Rui Huang ◽  
Jane C. Khoury ◽  
Eileen King ◽  
Mekibib Altaye ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Functional Data ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Patient Specific ◽  
Prediction Errors ◽  
Medical Monitoring

Abstract Introduction: To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes. Methods: A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions. Results: The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions. Conclusions: By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.

scholarly journals Accuracy of Dexcom G6 Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients with Diabetes

2021 ◽  
Author(s):  
Georgia M. Davis ◽  
Elias K. Spanakis ◽  
Alexandra L. Migdal ◽  
Lakshmi G. Singh ◽  
Bonnie Albury ◽  
...  
Keyword(s):  
Critically Ill ◽  
Continuous Glucose Monitoring ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Relative Difference ◽  
Hospitalized Patients ◽  
Matched Pair ◽  
Patients With Diabetes ◽  
Clarke Error Grid ◽  
Error Grid

<b>Background: </b>Advances in continuous glucose monitoring (CGM) have transformed ambulatory diabetes management. Until recently, inpatient use of CGM has remained investigational with limited data on its accuracy in the hospital setting. <p><b>Methods: </b>To analyze the accuracy of Dexcom G6 CGM,<b> </b>we compared retrospective matched-pair CGM and capillary point-of-care (POC) glucose data from three inpatient CGM studies (two interventional and one observational) in general medicine and surgery patients with diabetes treated with insulin. Analysis of accuracy metrics included mean absolute relative difference (MARD), median absolute relative difference (ARD), and proportion of CGM values within ±15, 20 and 30% or ±15, 20 and 30 mg/dL of POC reference values for blood glucose >100 mg/dL or ≤100 mg/dL, respectively (?/15, /20, 0/30). Clinical reliability was assessed using Clarke error grid analyses.</p> <p><b>Results: </b>A total of 218 patients were included (96% with type 2 diabetes) with a mean age of 60.6 ± 12 years. The overall MARD (n=4,067 matched glucose pairs) was 12.8% and median ARD was 10.1% [IQR 4.6, 17.6]. The proportion of readings meeting ?/15, /20 and 0/30 criteria were 68.7, 81.7, and 93.8%. Clarke error grid analysis showed 98.7% of all values in zones A+B. MARD and median ARD were higher in hypoglycemia (<70mg/dL) and severe anemia (hemoglobin <7g/dL).</p> <p><b>Conclusion: </b>Our results indicate that CGM technology is a reliable tool for hospital use and may help improve glucose monitoring in non-critically ill hospitalized patients with diabetes. </p>

scholarly journals Practical implementation of remote continuous glucose monitoring in hospitalized patients with diabetes

2021 ◽  
Author(s):  
Matt Baker ◽  
Megan E Musselman ◽  
Rachel Rogers ◽  
Richard Hellman
Keyword(s):  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Practical Implementation ◽  
Management Decisions ◽  
Grid Analysis ◽  
Inpatient Diabetes ◽  
Inpatient Diabetes Management ◽  
Error Grid

Abstract Disclaimer In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose Inpatient diabetes management involves frequent assessment of glucose levels for treatment decisions. Here we describe a program for inpatient real-time continuous glucose monitoring (rtCGM) at a community hospital and the accuracy of rtCGM-based glucose estimates. Methods Adult inpatients with preexisting diabetes managed with intensive insulin therapy and a diagnosis of coronavirus disease 2019 (COVID-19) were monitored via rtCGM for safety. An rtCGM system transmitted glucose concentration and trending information at 5-minute intervals to nearby smartphones, which relayed the data to a centralized monitoring station. Hypoglycemia alerts were triggered by rtCGM values of ≤85 mg/dL, but rtCGM data were otherwise not used in management decisions; insulin dosing adjustments were based on blood glucose values measured via blood sampling. Accuracy was evaluated retrospectively by comparing rtCGM values to contemporaneous point-of-care (POC) blood glucose values. Results A total of 238 pairs of rtCGM and POC data points from 10 patients showed an overall mean absolute relative difference (MARD) of 10.3%. Clarke error grid analysis showed 99.2% of points in the clinically acceptable range, and surveillance error grid analysis showed 89.1% of points in the lowest risk category. It was determined that for 25% of the rtCGM values, discordances in rtCGM and POC values would likely have resulted in different insulin doses. Insulin dose recommendations based on rtCGM values differed by 1 to 3 units from POC-based recommendations. Conclusion rtCGM for inpatient diabetes monitoring is feasible. Evaluation of individual rtCGM-POC paired values suggested that using rtCGM data for management decisions poses minimal risks to patients. Further studies to establish the safety and cost implications of using rtCGM data for inpatient diabetes management decisions are warranted.

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