scholarly journals Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management

2019 ◽  
Vol 14 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Tong Sheng ◽  
Reid Offringa ◽  
David Kerr ◽  
Mark Clements ◽  
Jerome Fischer ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Healthcare Professionals ◽  
Rank Order ◽  
Glucose Monitoring ◽  
Absolute Error ◽  
Multiple Data ◽  
Time In Range ◽  
Glucose Management ◽  
Diabetes Educators ◽  
Glycemic Management

Background: Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. Methods: We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from “best” to “worst”). We also asked HCPs to endorse features of CGM data that were important in making such assessments. Results: In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall’s W = .52, P < .001). Rankings were similar between endocrinologists and educators ( R2 = .90, Cohen’s kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). Conclusions: Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

scholarly journals The Importance of Time in Range (TIR) for Continuous Glucose Monitoring (CGM) in the Clinical Practice for Diabetes

2021 ◽  
pp. 12-13
Author(s):  
Bando Hiroshi
Keyword(s):  
Clinical Practice ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Coefficient Of Variation ◽  
Hba1c Level ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Glucose Profile ◽  
Time In Range ◽  
Glucose Management

As to the development of treatment for diabetes, Continuous Glucose Monitoring (CGM) has been recently prevalent rapidly. By the analysis of real-time CGM, Ambulatory Glucose Profile (AGP) has been used. It includes time in range (TIR, 70-180 mg/dL), time above range (TAR, >181mg/dL), time below range (TBR, <69 mg/dL), Glycemic Variability (GV), Glucose Management Indicator (GMI), Glycemic variability, Coefficient Of Variation (CV%) and so on. TIR value indicating approximately 70% seems to correlate closely with the HbA1c level of 6.77.0%. Marked discordance of HbA1c values has been found between laboratory HbA1c and estimated HbA1c (eA1c) using GMI from CGM.

Glycemic Metrics Derived From Intermittently Scanned Continuous Glucose Monitoring

2020 ◽  
pp. 193229682097582
Author(s):  
Klavs Würgler Hansen ◽  
Bo Martin Bibby
Keyword(s):  
Continuous Glucose Monitoring ◽  
Regression Line ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Lack Of Information ◽  
Time In Range ◽  
The Difference ◽  
The Individual ◽  
Glucose Management ◽  
Mean Glucose

Background: Glucose data from intermittently scanned continuous glucose monitoring (isCGM) is a combination of scanned and imported glucose values. The present knowledge of glycemic metrics originate mostly from glucose data from real-time CGM sampled every five minutes with a lack of information derived from isCGM. Methods: Glucose data obtained with isCGM and hemoglobin A1c (HbA1c) were obtained from 169 patients with type 1 diabetes. Sixty-one patients had two observations with an interval of more than three months. Results: The best regression line of HbA1c against mean glucose was observed from 60 days prior to HbA1c measurement as compared to 14, 30, and 90 days. The difference between HbA1c and estimated HbA1c (=glucose management indicator [GMI]) first observed correlated with the second observation (R2 0.61, P < .001). Time in range (TIR, glucose between 3.9 and 10 mmol/L) was significantly related to GMI (R2 0.87, P < .001). A TIR of 70% corresponded to a GMI of 6.8% (95% confidence interval, 6.3-7.4). The fraction of patients with the optimal combination of TIR >70% and time below range (TBR) <4% was 3.6%. The fraction of patients with TBR>4% was four times higher for those with high glycemic variability (coefficient of variation [CV] >36%) than for those with lower CV. Conclusion: The individual difference between HbA1c and GMI was reproducible. High glycemic variability was related to increased TBR. A combination of TIR and TBR is suggested as a new composite quality indicator.

Increased Time-in-Range with Sotagliflozin as Adjunct Therapy to Insulin in Adults with Type 1 Diabetes as Demonstrated by 24-Week Continuous Glucose Monitoring (inTandem1, inTandem2)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1179-P ◽  
Author(s):  
THOMAS DANNE ◽  
BERTRAND CARIOU ◽  
JOHN B. BUSE ◽  
SATISH K. GARG ◽  
JULIO ROSENSTOCK ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Adjunct Therapy ◽  
Time In Range

Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care

2021 ◽  
pp. 193229682199872
Author(s):  
Gregg D. Simonson ◽  
Richard M. Bergenstal ◽  
Mary L. Johnson ◽  
Janet L. Davidson ◽  
Thomas W. Martens
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Glucose Monitoring ◽  
Care Model ◽  
Time In Range ◽  
Glucose Management ◽  
The Impact ◽  
Primary Care Management

Background: Little data exists regarding the impact of continuous glucose monitoring (CGM) in the primary care management of type 2 diabetes (T2D). We initiated a quality improvement (QI) project in a large healthcare system to determine the effect of professional CGM (pCGM) on glucose management. We evaluated both an MD and RN/Certified Diabetes Care and Education Specialist (CDCES) Care Model. Methods: Participants with T2D for >1 yr., A1C ≥7.0% to <11.0%, managed with any T2D regimen and willing to use pCGM were included. Baseline A1C was collected and participants wore a pCGM (Libre Pro) for up to 2 weeks, followed by a visit with an MD or RN/CDCES to review CGM data including Ambulatory Glucose Profile (AGP) Report. Shared-decision making was used to modify lifestyle and medications. Clinic follow-up in 3 to 6 months included an A1C and, in a subset, a repeat pCGM. Results: Sixty-eight participants average age 61.6 years, average duration of T2D 15 years, mean A1C 8.8%, were identified. Pre to post pCGM lowered A1C from 8.8% ± 1.2% to 8.2% ± 1.3% (n=68, P=0.006). The time in range (TIR) and time in hyperglycemia improved along with more hypoglycemia in the subset of 37 participants who wore a second pCGM. Glycemic improvement was due to lifestyle counseling (68% of participants) and intensification of therapy (65% of participants), rather than addition of medications. Conclusions: Using pCGM in primary care, with an MD or RN/CDCES Care Model, is effective at lowering A1C, increasing TIR and reducing time in hyperglycemia without necessarily requiring additional medications.

scholarly journals Enlarged glycemic variability in sulfonylurea-treated well-controlled type 2 diabetics identified using continuous glucose monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fumi Uemura ◽  
Yosuke Okada ◽  
Keiichi Torimoto ◽  
Yoshiya Tanaka
Keyword(s):  
Glucose Level ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
High Dose ◽  
Type 2 Diabetics ◽  
Time In Range ◽  
The Difference ◽  
Glycated Hemoglobin Level

AbstractTime in range (TIR) is an index of glycemic control obtained from continuous glucose monitoring (CGM). The aim was to compare the glycemic variability of treatment with sulfonylureas (SUs) in type 2 diabetes mellitus (T2DM) with well-controlled glucose level (TIR > 70%). The study subjects were 123 patients selected T2DM who underwent CGM more than 24 h on admission without changing treatment. The primary endpoint was the difference in glycemic variability, while the secondary endpoint was the difference in time below range < 54 mg/dL; TBR < 54, between the SU (n = 63) and non-SU (n = 60) groups. The standard deviation, percentage coefficient of variation (%CV), and maximum glucose level were higher in the SU group than in the non-SU group, and TBR < 54 was longer in the high-dose SU patients. SU treatment was identified as a significant factor that affected %CV (β: 2.678, p = 0.034). High-dose SU use contributed to prolonged TBR < 54 (β: 0.487, p = 0.028). Our study identified enlarged glycemic variability in sulfonylurea-treated well-controlled T2DM patients and high-dose SU use was associated with TBR < 54. The results highlight the need for careful adjustment of the SU dose, irrespective of glycated hemoglobin level or TIR value.

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