Tackling diabetes management in patients with dementia: the use of continuous glucose monitoring

2020 ◽  
Vol 22 (1) ◽  
pp. 38-42
Author(s):  
Sarah Jane Palmer
Keyword(s):  
Disease Management ◽  
Continuous Glucose Monitoring ◽  
Older Patients ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Potential Solution ◽  
Patients With Diabetes ◽  
Future Care ◽  
Positive Results

A study analysing the use of continuous glucose monitoring through sensors in older patients with diabetes and dementia has produced positive results, providing a potential solution for future care and disease management. Sarah Jane Palmer explains

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 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 Diabetes and Frail Older Patients: Glycemic Control and Prescription Profile in Real Life

Pharmacy ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 115
Author(s):  
Anne-Sophie Mangé ◽  
Arnaud Pagès ◽  
Sandrine Sourdet ◽  
Philippe Cestac ◽  
Cécile McCambridge
Keyword(s):  
Glycemic Control ◽  
Older Patients ◽  
Diabetes Management ◽  
Real Life ◽  
Cross Sectional ◽  
Glucose Lowering ◽  
Frailty Status ◽  
Frail Patients ◽  
Patients With Diabetes ◽  
Hba1c Target

(1) Background: The latest recommendations for diabetes management adapt the objectives of glycemic control to the frailty profile in older patients. The purpose of this study was to evaluate the proportion of older patients with diabetes whose treatment deviates from the recommendations. (2) Methods: This cross-sectional observational study was conducted in older adults with known diabetes who underwent an outpatient frailty assessment in 2016. Glycated hemoglobin (HbA1c) target is between 6% and 7% for nonfrail patients and between 7% and 8% for frail patients. Frailty was evaluated using the Fried criteria. Prescriptions of glucose-lowering drugs were analyzed based on explicit and implicit criteria. (3) Results: Of 110 people with diabetes with an average age of 81.7 years, 67.3% were frail. They had a mean HbA1c of 7.11%. Of these patients, 60.9% had at least one drug therapy problem in their diabetes management and 40.9% were potentially overtreated. The HbA1c distribution in relation to the targets varied depending on frailty status (p < 0.002), with overly strict control in frail patients (p < 0.001). (4) Conclusions: Glycemic control does not seem to be routinely adjusted to the health of frail patients. Several factors can lead to overtreatment of these patients.

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.

735-P: Effects of Insulin Degludec in Patients with Diabetes Using Continuous Glucose Monitoring: A Scoping Review

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 735-P
Author(s):  
CHAOFAN WANG ◽  
WEN XU ◽  
XUBIN YANG ◽  
JINHUA YAN ◽  
DAIZHI YANG ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Scoping Review ◽  
Glucose Monitoring ◽  
Insulin Degludec ◽  
Patients With Diabetes

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.

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