A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BACKGROUND

A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.

METHODS

We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.

RESULTS

The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.

CONCLUSION

The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Expert Recommendations for Using Time-in-Range and Other Continuous Glucose Monitoring Metrics to Achieve Patient-Centered Glycemic Control in People With Diabetes

New metrics for assessing glycemic control beyond HbA1c have recently emerged due to the increasing use of continuous glucose monitoring (CGM) in diabetes clinical practice. Among them, time in range (TIR) has appeared as a simple and intuitive metric that correlates inversely with HbA1c and has also been newly linked to the risk of long-term diabetes complications. The International Consensus on Time in Range established a series of target glucose ranges (TIR, time below range and time above range) and recommendations for time spent within these ranges for different diabetes populations. These parameters should be evaluated together with the ambulatory glucose profile (AGP). Using standardized visual reporting may help people with diabetes and healthcare professionals in the evaluation of glucose control in frequent clinical situations. The objective of the present review is to provide practical insights to quick interpretation of patient-centered metrics based on flash glucose monitoring data, as well as showing some visual examples of common clinical situations and giving practical recommendations for their management.

Patient Perspectives on the Ambulatory Glucose Profile Report for Type 1 Diabetes Management in Adults: A National Online Survey

OBJECTIVES

Continuous and flash glucose monitoring devices produce data reports (e.g. ambulatory glucose profile [AGP]) that can be used by people with diabetes and health-care providers (HCPs). Clinical benefits of these reports have been published, but the patient perspective is underreported.

METHODS

We conducted an online survey for adults with type 1 diabetes (T1D) using continuous/flash glucose monitoring to understand their use and attitudes toward the AGP report. Barriers and facilitators related to digital health technology were explored.

RESULTS

The survey included 291 respondents: 63% were <40 years of age, and 65% had been living with T1D for >15 years. Nearly 80% reviewed their AGP report, with 50% often discussing it with their HCP. Support from family and HCPs was positively associated with the use of the AGP report, and there was a positive relationship between motivation and better understanding of the AGP report (odds ratio=2.61; 95% confidence interval, 1.45 to 4.71). Nearly all respondents (92%) indicated that the AGP report is important for their diabetes management, but most indicated dissatisfaction with the cost of the device. Open-ended responses suggested some concern with the complexity of information from the AGP report.

CONCLUSIONS

The online survey showed that there may be few barriers to the use of the AGP report by people with T1D, with the main barrier being its cost of the devices. Facilitators for the use of the AGP report included motivation and support from both family and the HCP. Facilitating discussion between HCPs and patients may be a strategy to improve the use and potential benefit of the AGP.

Relationship between blood glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics: Subanalysis of a prospective cohort study

AIM

To clarify the relationship between ambulatory glucose profile (AGP) indexes and standardized continuous glucose monitoring (CGM) metrics in patients with type 2 diabetes (T2D).

METHODS

This is an exploratory, cross-sectional analysis of baseline data collected from a prospective, multicentre, 5-year follow-up observational study conducted and published previously by our group. The study participants were 999 outpatients with T2D who used CGM at baseline, and had no apparent history of cardiovascular disease. We investigated the relationship between average interquartile range (IQR) and time in range (TIR). We also calculated, for the first time, the cutoff values to achieve the TIR target values.

RESULTS

In both the TIR more than 70% and TIR more than 90% achievement groups, the average IQR was notably small compared with the non-achievement groups. Particularly in comparison of the TIR quartiles, the average IQR became significantly smaller as the TIR became larger. The average IQR correlated negatively with TIR, and the cutoff values for TIR of more than 70% achievement and TIR of more than 90% achievement were an average IQR (>70%/>90%) of 2.13/1.85 mmol/L.

CONCLUSION

Our results showed a negative correlation between TIR and the range of blood glucose variations visually represented in AGP. The results also showed that the range of blood glucose variations in AGP is associated with indices of intraday and interday blood glucose variations and also with hypoglycaemia. Our results may provide new perspectives in the assessment and application of AGP in the clinical setting.

Expert Panel Recommendations for Use of Standardized Glucose Reporting System Based on Standardized Glucometrics Plus Visual Ambulatory Glucose Profile (AGP) Data in Clinical Practice

This expert panel of diabetes specialists aimed to provide guidance to healthcare providers on the best practice in the use of innovative continuous glucose monitoring (CGM) techniques through a practical and implementable document that specifically addresses the rationale for and also analysis and interpretation of the new standardized glucose reporting system based on standardized CGM metrics and visual ambulatory glucose profile (AGP) data. This guidance document presents recommendations and a useful algorithm for the use of a standardized glucose reporting system in the routine diabetes care setting.

Impact of COVID-19 Lockdown on Glycemic Control in Adults with Type 1 Diabetes Mellitus

Aim

To examine the impact of the lockdown caused by the COVID-19 pandemic on both the glycemic control and the daily habits of a group of patients with type 1 diabetes mellitus (T1DM) using flash continuous glucose monitoring devices (flash CGMs).

Methods

Retrospective analysis based on all the information gathered in virtual consultations from a cohort of 50 adult patients with T1DM with follow-up at our site. We compared their CGM metrics during lockdown with their own previous data before the pandemic occurred, as well as the potential psychological and therapeutic changes.

Results

We observed a reduction of average glucose values: 160.26 ± 22.55 mg/dL vs 150 ± 20.96 mg/dL, P = .0009; estimated glycosylated hemoglobin: 7.21 ± 0.78% vs 6.83 ± 0.71%, P = .0005; glucose management indicator 7.15 ± 0.57% vs 6.88 ± 0.49%; P = .0003, and glycemic variability: 40.74 ± 6.66 vs 36.43 ± 6.09 P < .0001. Time in range showed an improvement: 57.46 ± 11.85% vs a 65.76 ± 12.09%, P < .0001, without an increase in percentage of time in hypoglycemia.

Conclusions

COVID-19 lockdown was associated with an improvement in glycemic control in patients with T1DM using CGMs.

Critical Reappraisal of the Time-in-Range: Alternative or Useful Addition to Glycated Hemoglobin?

The HbA1c value is a well-established parameter used to characterize glucose control. Continuous glucose monitoring (CGM)-derived parameters calculated using daily glucose profiles such as Time-in-Range (TiR) have increasingly been gaining interest for assessing a patient's current therapy. The question has arisen as to whether TiR could replace HbA1c? Because TiR focuses on the current quality of glucose control during a minimum of 10 to 14 days of CGM use and reflects the variability of glucose concentrations. Time-in-Range could be considered an attractive option for improving diabetes control in patients with diabetes. Due to the lack of established standards for glucose measurements with CGM systems, results from different CGM systems can deviate from each other. Time-in-Range should not be viewed as a replacement for HbA1c, but should be used to deliver valuable additional information.

Clinical Recommendations for the Use of the Ambulatory Glucose Profile in Diabetes Care

BACKGROUND

The ambulatory glucose profile (AGP) uses the wealth of data that are generated by continuous glucose monitoring, including flash glucose monitoring technologies, to provide a visual representation of glucose levels over a typical standard day of usually the most recent two weeks for a person with diabetes and helps to identify patterns and trends in glucose control. The AGP allows certain patterns of glucose levels to be identified and analyzed, such that treatment adjustments can be made, and new individual treatment goals can be defined. This helps to ensure increased treatment satisfaction and adherence, quality of life, and an improvement in metabolic management for people with diabetes.

OBJECTIVE

To date, a range of approaches exists for interpreting the information contained in an AGP, with different priorities given to identifying and targeting patterns of hypoglycemia and the degree of variability and stability underlying the glucose levels. The objective of the present recommendation is to describe the steps for assessing an AGP in detail and to illustrate these steps using visual examples.

CONCLUSION

This paper describes the consensus recommendations from a group of German expert diabetologists on the necessary steps for assessing an AGP in a structured and detailed way and to explain these steps using practical clinical examples.

Clinical Approach to Flash Glucose Monitoring: An Expert Recommendation

The flash glucose monitoring (FGM) system FreeStyle Libre® is a device that measures interstitial glucose in a very simple way and indicates direction and speed of glucose change. This allows persons with diabetes to prevent hypoglycemic and hyperglycemic events. Scientific evidence indicates that the system can improve glycemic control and quality of life. To obtain the maximum benefit, it is necessary to properly handle glucose values and trends. Due to the generalization of the system use, the purpose of the document is to provide recommendations for the optimal use of the device, not only in the management of glucose values and trends but also in the prevention of hypoglycemia, actuation in exercise, special situations, and retrospective analysis of the glucose data, among others.

Continuous Glucose Monitoring Integration in Clinical Practice: A Stepped Guide to Data Review and Interpretation

BACKGROUND

The advent of continuous glucose monitoring (CGM) technology has transformed the approach to diabetes care. Multiple CGM systems are commercially available and increased accuracy has allowed development of hybrid and automated insulin delivery systems. Evidence of CGM clinical benefits has also increased exponentially in the last decade.

METHODS

Literature search, review of professional guidelines, and consensus statements were used to guide the preparation of this article. The clinical benefits of both professional and personal CGM in clinical practice as well as barriers to wider adotpion were explored. A stepped approach to review and interpretation of CGM data is suggested for use in the clinician's office regardless of the software used.

RESULTS

Although increasing, the use of CGM in patients with diabetes is still not widespread; multiple barriers are still in place, despite the approval of CGM systems for patients above the age of 2 years old, the extension of coverage for Medicare beneficiaries and the integration of CGM with multiple insulin pump systems. Integration of CGM technology in clinical practice presents various challenges, from concerns relative to time constraints during office visits to lack of systematic approach to interpretation of the data.

CONCLUSIONS

Understanding the usefulness of personal and professional CGM, appropriate patient selection as well as patient and provider training are crucial for the expansion of CGM therapy use in clinical practice. Utilizing the proposed stepped approach to CGM review and interpretation may allow wider adoption of CGM with more effective and efficient office visits.

Ambulatory glucose profile in diabetes-related dementia

AIM

Diabetes-related dementia (DrD), a dementia subgroup associated with specific diabetes mellitus (DM)-related metabolic abnormalities rather than Alzheimer's disease (AD) pathology or cerebrovascular disease, is characterized by less well-controlled glycemia. We investigated the glucose level, variability and stability, and risk of hypoglycemia in DrD to determine characteristic ambulatory glucose profiles (AGP).

METHODS

We obtained AGP for 14 days of 40 patients with AD associated with DM and 19 patients with DrD using a novel sensor-based flash glucose monitoring system (FreeStyle Libre Pro).

RESULTS

Despite similar mean glucose and estimated A_1c values, the DrD group showed significantly greater glucose variability and higher percentage of time spent in hypoglycemia than the AD associated with DM group. Glucose level and variability correlated significantly and negatively with Mini-Mental State Examination in DrD, but not in AD associated with DM The estimated A_1c levels calculated from the 14 days of AGP data significantly correlated with the HbA_1c levels measured within 2 months of the insertion of the sensor.

CONCLUSIONS

DrD has a distinctively different AGP from that of AD associated with DM. Glucose variability and hypoglycemia are more involved in the pathophysiology of DrD than in that of AD associated with DM. The AGP analysis using the flash glucose monitoring system might provide useful information undetected by HbA_1c values. Geriatr Gerontol Int 2019; 19: 282-286.

Ambulatory glucose profile analysis of the juvenile diabetes research foundation continuous glucose monitoring dataset-Applications to the pediatric diabetes population

BACKGROUND

Increased continuous glucose monitor (CGM) use presents both the benefit and burden of increased data for clinicians to rapidly analyze. The ambulatory glucose profile (AGP) is an evolving a universal software report for CGM data analysis.

OBJECTIVES/HYPOTHESES

We utilized the Juvenile Diabetes Research Foundation-CGM dataset to evaluate the AGP across a broad spectrum of patients to show how AGP can be used clinically to assist with CGM-related decision making. We hypothesized that AGP metrics would be different across age and HbA1c strata.

SUBJECTS

AGPs were generated from the JDRF-CGM trial dataset for all periods during which there were ≥10 days of CGM coverage in the 2 weeks adjacent to an HbA1c measurement yielding 1101 AGPs for 393 unique subjects.

METHODS

AGPs were stratified by age group (8-14, 15-24, and ≥25 years) and HbA1c (within or above target for age) and compared for between group differences in AGP metrics via two-factor ANOVA. Glycemic differences between time periods were analyzed via segmented regression analysis.

RESULTS

Glucose exposure (average and estimated A1c) and variability (standard deviation and interquartile range) were different between the low and high HbA1c levels. Within a given HbA1c level all age groups were significantly different from each other with older patients having lower averages with less variability than younger patients.

CONCLUSIONS

AGP analysis of the JDRF-CGM data highlights significant differences in glycemic profiles between pediatric and adult age groups and between well and less well-controlled patient populations.

First Clinical Experience with Retrospective Flash Glucose Monitoring (FGM) Analysis in South Africa: Characterizing Glycemic Control with Ambulatory Glucose Profile

BACKGROUND

In 2014, an innovative blinded continuous glucose monitoring system was introduced with automated ambulatory glucose profile (AGP) reporting. The clinical use and interpretation of this new technology has not previously been described. Therefore we wanted to understand its use in characterizing key factors related to glycemic control: glucose exposure, variability, and stability, and risk of hypoglycemia in clinical practice.

METHODS

Clinicians representing affiliated diabetes centers throughout South Africa were trained and subsequently were given flash glucose monitoring readers and 2-week glucose sensors to use at their discretion. After patient use, sensor data were collected and uploaded for AGP reporting.

RESULTS

Complete data (sensor AGP with corresponding clinical information) were obtained for 50 patients with type 1 (70%) and type 2 diabetes (30%), irrespective of therapy. Aggregated analysis of AGP data comparing patients with type 1 versus type 2 diabetes, revealed that despite similar HbA1c values between both groups (8.4 ± 2 vs 8.6 ± 1.7%, respectively), those with type 2 diabetes had lower mean glucose levels (9.2 ± 3 vs 10.3 mmol/l [166 ± 54 vs 185 mg/dl]) and lower indices of glucose variability (3.0 ± 1.5 vs 5.0 ± 1.9 mmol/l [54 ± 27 vs 90 ± 34.2 mg/dl]). This highlights key areas for future focus.

CONCLUSIONS

Using AGP, the characteristics of glucose exposure, variability, stability, and hypoglycemia risk and occurrence were obtained within a short time and with minimal provider and patient input. In a survey at the time of the follow-up visit, clinicians indicated that aggregated AGP data analysis provided important new clinical information and insights.

Evaluation of accuracy of ambulatory glucose profile in an outpatient setting in children with type 1 diabetes

BACKGROUND

In children with type 1 diabetes, intensive diabetes management has been demonstrated to reduce long-term microvascular complications. At present, self-monitoring of blood glucose (SMBG) by patients at home and glycated hemoglobin estimation every 3 months are used to monitor glycemic control in children. Recently, ambulatory glucose profile (AGP) is increasingly being used to study the glycemic patterns in adults. However, accuracy and reliability of AGP in children have not been evaluated yet.

OBJECTIVES

To assess the accuracy of AGP data in children with type 1 diabetes mellitus when compared with laboratory random blood sugar (RBS) levels, capillary blood glucose (CBG) measured by glucometer in the hospital, and SMBG monitored at home.

METHODS

Paired RBS, CBG, and AGP data were analyzed for 51 patients who wore AGP sensors for 2 weeks. Simultaneous venous and CBG samples were collected on day 1 and day 14. SMBG at home was checked and recorded by the patients for optimizing insulin doses. Accuracy measures (mean absolute deviation, mean absolute relative difference (MARD), and coefficient of linear regression of AGP on RBS, CBG, and home-monitored SMBG were calculated.

RESULTS

Seventy paired RBS, CBG, and AGP data and 362 paired home-monitored SMBG and AGP data were available. The MARD was 9.56% for AGP over RBS and 15.07% for AGP over CBG. The linear regression coefficient of AGP over RBS was 0.93 and that of AGP over CBG was 0.89 (P < 0.001). The accuracy of AGP over SMBG was evaluated over four ranges: <75, 76-140, 141-200, and >200 mg/dl.

CONCLUSION

In this study, AGP data significantly correlate with RBS and CBG data in children with type 1 diabetes. However, a large number of samples in a research setting would help to document reproducibility of our results.

Development of the Likelihood of Low Glucose (LLG) algorithm for evaluating risk of hypoglycemia: a new approach for using continuous glucose data to guide therapeutic decision making

The objective was to develop an analysis methodology for generating diabetes therapy decision guidance using continuous glucose (CG) data. The novel Likelihood of Low Glucose (LLG) methodology, which exploits the relationship between glucose median, glucose variability, and hypoglycemia risk, is mathematically based and can be implemented in computer software. Using JDRF Continuous Glucose Monitoring Clinical Trial data, CG values for all participants were divided into 4-week periods starting at the first available sensor reading. The safety and sensitivity performance regarding hypoglycemia guidance "stoplights" were compared between the LLG method and one based on 10th percentile (P10) values. Examining 13 932 hypoglycemia guidance outputs, the safety performance of the LLG method ranged from 0.5% to 5.4% incorrect "green" indicators, compared with 0.9% to 6.0% for P10 value of 110 mg/dL. Guidance with lower P10 values yielded higher rates of incorrect indicators, such as 11.7% to 38% at 80 mg/dL. When evaluated only for periods of higher glucose (median above 155 mg/dL), the safety performance of the LLG method was superior to the P10 method. Sensitivity performance of correct "red" indicators of the LLG method had an in sample rate of 88.3% and an out of sample rate of 59.6%, comparable with the P10 method up to about 80 mg/dL. To aid in therapeutic decision making, we developed an algorithm-supported report that graphically highlights low glucose risk and increased variability. When tested with clinical data, the proposed method demonstrated equivalent or superior safety and sensitivity performance.

Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the ambulatory glucose profile

Underutilization of glucose data and lack of easy and standardized glucose data collection, analysis, visualization, and guided clinical decision making are key contributors to poor glycemic control among individuals with type 1 diabetes mellitus. An expert panel of diabetes specialists, facilitated by the International Diabetes Center and sponsored by the Helmsley Charitable Trust, met in 2012 to discuss recommendations for standardizing the analysis and presentation of glucose monitoring data, with the initial focus on data derived from continuous glucose monitoring systems. The panel members were introduced to a universal software report, the Ambulatory Glucose Profile, and asked to provide feedback on its content and functionality, both as a research tool and in clinical settings. This article provides a summary of the topics and issues discussed during the meeting and presents recommendations from the expert panel regarding the need to standardize glucose profile summary metrics and the value of a uniform glucose report to aid clinicians, researchers, and patients.