Flash glucose monitoring improves glycemia in higher risk patients: a longitudinal, observational study under real-life settings.

Objective: To assess the role of flash glucose monitoring in early and late changes in glycemic markers under real-life conditions. Research design and methods: Deidentified glucose results from 6802 flash glucose monitors were analyzed after dividing into high, medium and low-risk groups based on tertiles of time spent in hypoglycemia (min/day <70 mg/dL) or hyperglycemia (hours/day >240 mg/dL). Groups were further subdivided into tertiles of glucose scanning frequency and glycemic measures analyzed in the first 14 days and over 6 months. Results: Improvement in dysglycemia mainly occurred in the first month of device use. Comparing first and last 14 study days, high-hyperglycemic-risk individuals showed reduced time >240 mg/dL (mean±SEM) from 6.07±0.06 to 5.73±0.09 hours/day (p<0.0001). High-frequency scanners showed 0.82 hours/day reduction in hyperglycemia (p<0.0001) whereas low-frequency scanners failed to demonstrate a benefit. High-hypoglycemic-risk individuals showed reduction in time ≤54 mg/dL from 90±1 to 69±2 min/day (p<0.0001) comparing first and last 14 study days. This reduction was evident in both low and high-frequency scanners but with reduced hyperglycemic exposure in the latter group. Conclusions: Under real-world conditions, flash monitoring is associated with rapid and sustained reduction in dysglycemia with high-frequency scanners demonstrating more significant reduction in hyperglycemia.

Evaluating a Glucose-Sensor-Based Tool to Help Clinicians and Adults With Type 1 Diabetes Improve Self-Management Skills.

BACKGROUND: The goal of this uncontrolled pilot study was to assess the feasibility of a self-care management mobile app, called Sugar Sleuth, which incorporates the FreeStyle Libre™ glucose sensor, to help clinicians and people with type 1 diabetes (PWD) identify and mitigate self-care behaviors that contribute to glucose variability. METHODS: PWDs with a baseline A1c between 7.5 and 9.0% used the mobile app for 14 weeks. The app prompted the PWD to enter the suspected cause of detected glycemic excursions, and to record food and insulin information. PWDs met with clinicians to collaboratively review data, identify challenges, and devise a specific self-care plan. Outcome measures included a single glycemic outcome score (SGOS) and attitude rating scales to better understand how participant attitudes could affect glycemic outcome. RESULTS: Thirty enrolled PWDs had a mean age of 55 ± 2.6 years, and a mean diabetes duration of 32 ± 2.9 years. A significant average reduction in A1c of 0.5 ± 0.07% ( P < .01) and in mean daily carbohydrate intake of 43 ± 21 grams ( P = .05) was found. No statistically significant change in glycemic metrics, body weight, or total daily insulin dose was found. A significant negative association occurred between SGOS and "hypoglycemia tolerance" ( P = .04), and a positive correlation occurred that approached significance with "motivation to change behavior" ( P = .06). CONCLUSIONS: These findings suggest that this mobile app system, in conjunction with CGM, provides a useful platform for helping clinicians and adults with T1D improve self-management skills to improve glycemic control.

Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: A European analysis of over 60 million glucose tests.

AIMS: Randomised controlled trials demonstrate that using flash glucose monitoring improves glycaemic control but it is unclear whether this applies outside trial conditions. We investigated glucose testing patterns in users worldwide under real life settings to establish testing frequency and association with glycaemic parameters. METHODS: Glucose results were de-identified and uploaded onto a dedicated database once readers were connected to an internet-ready computer. Data between September 2014 and May 2016, comprising 50,831 readers and 279,446 sensors worldwide, were analysed. Scan rate per reader was determined and each reader was sorted into twenty equally-sized rank-ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated HbA1c, time above, below and within range identified as 3.9-10.0 mmol/L. RESULTS: Users performed a mean of 16.3 scans/day [median (IQR): 14 (10-20)] with 86.4 million hours of readings and 63.8 million scans. Estimated HbA1c gradually reduced from 8.0% to 6.7% (64 to 50 mmol/mol) as scan rate increased from lowest to highest scan groups (4.4 and 48.1 scans/day, respectively; p < .001). Simultaneously, time below 3.9, 3.1 and 2.5 mmol/L decreased by 15%, 40% and 49%, respectively (all p < .001). Time above 10.0 mmol/L decreased from 10.4 to 5.7 h/day (44%, p < .001) while time in range increased from 12.0 to 16.8 h/day (40%, p < .001). These patterns were consistent across different countries. CONCLUSIONS: In real-world conditions, flash glucose monitoring allows frequent glucose checks with higher rates of scanning linked to improved glycaemic markers, including increased time in range and reduced time in hyper and hypoglycaemia.

Individuals with Type 1 and Type 2 Diabetes Mellitus Trade Increased Hyperglycemia for Decreased Hypoglycemia When Glycemic Variability is not Improved.

INTRODUCTION: Glycemic variability refers to oscillations in blood glucose within a day and differences in blood glucose at the same time on different days. Glycemic variability is linked to hypoglycemia and hyperglycemia. The relationship among these three important metrics is examined here, specifically to show how reduction in both hypo- and hyperglycemia risk is dependent on changes in variability. METHODS: To understand the importance of glycemic variability in the simultaneous reduction of hypoglycemia and hyperglycemia risk, we introduce the glycemic risk plot-estimated HbA1c % (eA1c) vs. minutes below 70 mg/dl (MB70) with constant variability contours for predicting post-intervention risks in the absence of a change in glycemic variability. RESULTS: The glycemic risk plot illustrates that individuals who do not reduce glycemic variability improve one of the two metrics (hypoglycemia risk or hyperglycemia risk) at the cost of the other. It is important to reduce variability to improve both risks. These results were confirmed by data collected in a randomized controlled trial consisting of individuals with type 1 and type 2 diabetes on insulin therapy. For type 1, a total of 28 individuals out of 35 (80%) showed improvement in at least one of the risks (hypo and/or hyper) during the 100-day course of the study. Seven individuals (20%) showed improvement in both. Similar data were observed for type 2 where a total of 36 individuals out of 43 (84%) showed improvement in at least one risk and 8 individuals (19%) showed improvement in both. All individuals in the study who showed improvement in both hypoglycemia and hyperglycemia risk also showed a reduction in variability. CONCLUSION: Therapy changes intended to improve an individual's hypoglycemia or hyperglycemia risk often result in the reduction of one risk at the expense of another. It is important to improve glucose variability to reduce both risks or at least maintain one risk while reducing the other. FUNDING: Abbott Diabetes Care.

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.

Impact of glucose measurement processing delays on clinical accuracy and relevance.

BACKGROUND: In a hospital setting, glucose is often measured from venous blood in the clinical laboratory. However, laboratory glucose measurements are typically not available in real time. In practice, turn-around times for laboratory measurements can be minutes to hours. This analysis assesses the impact of turn-around time on the effective clinical accuracy of laboratory measurements. METHODS: Data obtained from an earlier study with 58 subjects with type 1 diabetes mellitus (T1DM) were used for this analysis. In the study, glucose measurements using a YSI glucose analyzer were obtained from venous blood samples every 15 min while the subjects were at the health care facility. To simulate delayed laboratory results, each YSI glucose value from a subject was paired with one from a later time point (from the same subject) separated by 15, 30, 45, and 60 min. To assess the clinical accuracy of a delayed YSI result relative to a real-time result, the percentage of YSI pairs that meet the International Organization for Standardization (ISO) 15197:2003(E) standard for glucose measurement accuracy (±15 mg/dl for blood glucose < 75 mg/dl, ±20% for blood glucose ≥ 75 mg/dl) was calculated. RESULTS: It was observed that delays of 15 min or more reduce clinical accuracy below the ISO 15197:2003(E) recommendation of 95%. The accuracy was less than 65% for delays of 60 min. CONCLUSION: This analysis suggests that processing delays in glucose measurements reduce the clinical relevance of results in patients with T1DM and may similarly degrade the clinical value of measurements in other patient populations.