Changes in glucometric parameters in people living with diabetes users of the free-style libre 2 system before and after the update possibility to real-time glucose readings in real world practice

In Spain, from October 10th, 2023, the FreeStyle Libre 2 system offers the possibility to automatically changed from isCGM to rtCGM with a system update. Our study aimed to evaluate the glucometric before and after that date. We didn't find significant changes in TIR, however time of use increased and TBR decreased.

Glucometrics: Where Are We Now?

PURPOSE OF REVIEW

Inpatient glucose data analysis, or glucometrics, has developed alongside the growing emphasis on glycemic control in the hospital. Shortcomings in the initial capabilities for glucometrics have pushed advancements in defining meaningful units of measurement and methods for capturing glucose data. This review addresses the growth in glucometrics and ends with its promising new state.

RECENT FINDINGS

Standardization, allowing for benchmarking and purposeful comparison, has been a goal of the field. The National Quality Foundation glycemic measures and recently enacted Center for Medicare and Medicaid Services (CMS) electronic quality measures for hypo- and hyperglycemia have allowed for improved integration and consistency. Prior systems have culminated in an upcoming measure from the Center for Disease Control and Prevention's National Healthcare Safety Network. It is poised to create a new gold standard for glucometrics by expanding and refining the CMS metrics, which should empower both local improvement and benchmarking as the program matures.

Role of continuous glucose monitoring in diabetic patients at high cardiovascular risk: an expert-based multidisciplinary Delphi consensus

Background

Continuous glucose monitoring (CGM) shows in more detail the glycaemic pattern of diabetic subjects and provides several new parameters (“glucometrics”) to assess patients’ glycaemia and consensually guide treatment. A better control of glucose levels might result in improvement of clinical outcome and reduce disease complications. This study aimed to gather an expert consensus on the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk or with heart disease.

Methods

A list of 22 statements concerning type of patients who can benefit from CGM, prognostic impact of CGM in diabetic patients with heart disease, CGM use during acute cardiovascular events and educational issues of CGM were developed. Using a two-round Delphi methodology, the survey was distributed online to 42 Italian experts (21 diabetologists and 21 cardiologists) who rated their level of agreement with each statement on a 5-point Likert scale. Consensus was predefined as more than 66% of the panel agreeing/disagreeing with any given statement.

Results

Forty experts (95%) answered the survey. Every statement achieved a positive consensus. In particular, the panel expressed the feeling that CGM can be prognostically relevant for every diabetic patient (70%) and that is clinically useful also in the management of those with type 2 diabetes not treated with insulin (87.5%). The assessment of time in range (TIR), glycaemic variability (GV) and hypoglycaemic/hyperglycaemic episodes were considered relevant in the management of diabetic patients with heart disease (92.5% for TIR, 95% for GV, 97.5% for time spent in hypoglycaemia) and can improve the prognosis of those with ischaemic heart disease (100% for hypoglycaemia, 90% for hyperglycaemia) or with heart failure (87.5% for hypoglycaemia, 85% for TIR, 87.5% for GV). The experts retained that CGM can be used and can impact the short- and long-term prognosis during an acute cardiovascular event. Lastly, CGM has a recognized educational role for diabetic subjects.

Conclusions

According to this Delphi consensus, the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk is promising and deserves dedicated studies to confirm the experts’ feelings.

Longitudinal Analysis of Real-World Performance of an Implantable Continuous Glucose Sensor over Multiple Sensor Insertion and Removal Cycles

The Eversense® Continuous Glucose Monitoring (CGM) System, with the first long-term, implantable glucose sensor, has been commercially available in Europe and South Africa since 2016 for adults with diabetes. The performance of the sensor over multiple, sequential 90- or 180-day cycles from either real-world experience or clinical studies has not been previously published. The Eversense Data Management System (DMS) was used to evaluate the accuracy of General Data Protection Regulation (GDPR)-compliant sensor glucose (SG) values against self-monitoring of blood glucose (SMBG) from June 2016 through August 2019 among patients with at least four sensor cycles from European and South African health care practices. Mean SG and associated measures of variability, glucose management indicator (GMI), and percent and time in various hypoglycemic, euglycemic, and hyperglycemic ranges were calculated for the 24-h time period over each cycle. In addition, transmitter wear time was evaluated across each sensor wear cycle. Among the 945 users included in the analysis, the mean absolute relative difference (standard deviation [SD]) using 152,206, 174,645, 206,024, and 172,587 calibration matched pairs against SMBG was 11.9% (3.6%), 11.5% (4.0%), 11.8% (4.7%), and 11.5% (4.1%) during the first four sensor cycles, respectively. Mean values of the CGM metrics over the first sensor cycle were 156.5 mg/dL for SG, 54.7 mg/dL for SD, 0.35 for coefficient of variation, and 7.04% for GMI. Percent SG at different glycemic ranges was as follows: <54 mg/dL was 1.1% (16 min), <70 mg/dL was 4.6% (66 min), ≥70-180 mg/dL (time in range) was 64.5% (929 min), >180-250 mg/dL was 22.8% (328 min), and >250 mg/dL was 8.1% (117 min). The median transmitter wear time over the first cycle was 83.2%. CGM metrics and wear time were similar over the subsequent three cycles. This real-world evaluation of adult patients with diabetes using the Eversense CGM System in the home setting demonstrated that the implantable sensor provides consistent stable accuracy and CGM metrics over multiple, sequential sensor cycles with no indication of degradation of sensor performance.

Real-World Data from the First U.S. Commercial Users of an Implantable Continuous Glucose Sensor

Background: The Eversense^® Continuous Glucose Monitoring (CGM) System, with the first 90-day implantable sensor, received FDA (Food and Drug Administration) approval in June 2018. No real-world experience has been published. Methods: Deidentified sensor glucose (SG) data from August 1, 2018 to May 11, 2019 in the Eversense Data Management System (DMS) were analyzed for the first 205 patients who reached a 90-day wear period on the Eversense CGM system. The mean SG, standard deviation (SD), median interquartile range, coefficient of variation (CV), glucose measurement index (GMI), and percent and time in minutes across glucose ranges were computed for the 24-h time period, the nighttime (00:00-06:00), and by 30-day wear periods. Sensor accuracy, sensor reinsertion rate, transmitter wear time, and safety data were assessed. Results: Of the 205 patients, 129 identified as type 1, 18 as type 2, and 58 were unreported. Fifty were CGM naive, 112 had prior CGM experience, and 43 were unreported. The mean SG was 161.8 mg/dL, SD was 57.4 mg/dL, CV was 0.35, and GMI was 7.18%. Percent SG at <54 mg/dL was 1.2% (18 min), <70 mg/dL was 4.1% (59.7 min), time in range (≥70-180 mg/dL) was 62.3% (897.7 min), >180-250 mg/dL was 21.9% (315.8 min), and >250 mg/dL was 11.6% (166.7 min). Nighttime values were similar. The glucometric values were similar over 30-day time periods of the sensor wear. The mean absolute relative difference (SD) using 27,708 calibration paired points against home blood glucose meters was 11.2% (11.3%). The sensor reinsertion rate was 78.5%. The median transmitter wear time was 83.6%. There were no related serious adverse events. Conclusion: The Eversense real-world data showed promising glycemic results, sensor accuracy, and safety. These data suggest that the Eversense CGM system is a valuable tool for diabetes management.

Glucometrics and Insulinometrics

PURPOSE OF REVIEW

Glucometrics is the systematic analysis of inpatient glucose data and is of key interest as hospitals strive to improve inpatient glycemic control. Insulinometrics is the systematic analysis and reporting of inpatient insulin therapy. This paper reviews some of the questions to be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines.

RECENT FINDINGS

There remains a lack of standardization on how glucometircs should be measured and reported. Before hospitals can commit resources to compiling and extracting data, consensus must be reached on such questions as which measures to report, definitions of glycemic targets, and how data should be obtained. Examples are provided on how insulin administration can be measured and reported. Hospitals should begin assessment of glucometrics and insulinometrics. However, consensus and standardization must first occur to allow for a national benchmarking process.

Computerized Insulin Order Sets and Glycemic Control in Hospitalized Patients

BACKGROUND

The purpose of this study was to evaluate the impact of computerized provider order entry subcutaneous insulin order sets on inpatient glycemic control and ordering behavior.

METHODS

This was an interrupted time series study of non-intensive care patients at an urban teaching hospital. The primary outcome was proportion of capillary blood glucose in optimal range (4.0-10.0 mmol/L [72-180 mg/dL]) during the 6 months before and after a change to a computerized provider order entry-integrated insulin order set. Secondary outcomes included other measures of glycemia (hyperglycemia [>13.9mmol/L (250 mg/dL)], hypoglycemia [<4.0 mmol/L (72 mg/dL)], severe hypoglycemia [<2.2 mmol/L (40 mg/dL)]) and ordering behavior (use of basal-bolus-correctional insulin regimens). Comparisons of sensitivity-based versus generic correctional scale were also conducted.

RESULTS

A total of 63,393 measurements were obtained from June 2011 to June 2012. Order set usage was limited (51.5%). The weekly proportion of capillary blood glucose within the optimal range was not significantly different after the switch to computerized provider order entry order sets (pre-period: 64.9% vs post-period: 65.3%, P = .996). There were no differences in the proportions of moderate or severe hyperglycemia (pre-period: 10.9% vs post-period: 12.0%, P = .061) and hypoglycemia (pre-period: 1.9% vs post-period: 1.6%, P = .144). However, an increased proportion within the optimal range was seen in those with an order set featuring a sensitivity-based correctional scale versus orders without (65.3% vs 55.0%, P <.001). Increased basal-bolus-correctional ordering was observed after protocol implementation (20.3% vs 23.6%, P <.0001).

CONCLUSIONS

With low institutional uptake, computerized insulin order sets did not improve inpatient glycemic control.