Accuracy Of Continuous Glucose Monitoring Research Articles

Comparison of Glucose Monitoring Methods during Steady-State Exercise in Women

Data from Continuous Glucose Monitoring (CGM) systems may help improve overall daily glycemia; however, the accuracy of CGM during exercise remains questionable. The objective of this single group experimental study was to compare CGM-estimated values to venous plasma glucose (VPG) and capillary plasma glucose (CPG) during steady-state exercise. Twelve recreationally active females without diabetes (aged 21.8 ± 2.4 years), from Central Washington University completed the study. CGM is used by individuals with diabetes, however the purpose of this study was to first validate the use of this device during exercise for anyone. Data were collected between November 2009 and April 2010. Participants performed two identical 45-min steady-state cycling trials (~60% Pmax) on non-consecutive days. Glucose concentrations (CGM-estimated, VPG, and CPG values) were measured every 5 min. Two carbohydrate gel supplements along with 360 mL of water were consumed 15 min into exercise. A product-moment correlation was used to assess the relationship and a Bland-Altman analysis determined error between the three glucose measurement methods. It was found that the CGM system overestimated mean VPG (mean absolute difference 17.4 mg/dL (0.97 mmol/L)) and mean CPG (mean absolute difference 15.5 mg/dL (0.86 mmol/L)). Bland-Altman analysis displayed wide limits of agreement (95% confidence interval) of 44.3 mg/dL (2.46 mmol/L) (VPG compared with CGM) and 41.2 mg/dL (2.29 mmol/L) (CPG compared with CGM). Results from the current study support that data from CGM did not meet accuracy standards from the 15197 International Organization for Standardization (ISO).

Determinants of the Accuracy of Continuous Glucose Monitoring in Non-Critically Ill Patients with Heart Failure or Severe Hyperglycemia

The accuracy of continuous glucose monitoring (CGM) in non-critically ill hospitalized patients with heart failure or severe hyperglycemia (SH) is unknown. Hospitalized patients with congestive heart failure (CHF) exacerbation (receiving IV or subcutaneous insulin) or SH requiring insulin infusion were compared to outpatients referred for retrospective CGM. Forty-three patients with CHF, 15 patients with SH, and 88 outpatients yielded 470, 164, and 2150 meter-sensor pairs, respectively. Admission glucose differed (188 versus 509 mg/dl in CHF and SH, p < .001) but not the first sensor glucose (p = .35). In continuous glucose error grid analysis, 67-78% of pairs during hypoglycemia were in zones A+B (p = .63), compared with 98-100% in euglycemia (p < .001) and 98%, 92%, and 99% (p = .001) during hyperglycemia for the CHF, SH, and outpatient groups, respectively. Mean absolute relative difference (MARD) was lower in the CHF versus the SH group in glucose strata above 100 mg/dl, but there was no difference between the CHF and outpatient groups. Linear regression models showed that CHF versus outpatient, SH versus CHF, and coefficient of variation were significant predictors of higher MARD. Among subjects with CHF, MARD was not associated with brain natriuretic peptide or change in plasma volume, but it was significantly higher in subjects randomized to IV insulin (p = .04). The results suggest that SH and glycemic variability are more important determinants of CGM accuracy than known CHF status alone in hospitalized patients.

Influence of Time Point of Calibration on Accuracy of Continuous Glucose Monitoring in Individuals with Type 1 Diabetes

Data on the influence of calibration on accuracy of continuous glucose monitoring (CGM) are scarce. The aim of the present study was to investigate whether the time point of calibration has an influence on sensor accuracy and whether this effect differs according to glycemic level. Two CGM sensors were inserted simultaneously in the abdomen on either side of 20 individuals with type 1 diabetes. One sensor was calibrated predominantly using preprandial glucose (calibration(PRE)). The other sensor was calibrated predominantly using postprandial glucose (calibration(POST)). At minimum three additional glucose values per day were obtained for analysis of accuracy. Sensor readings were divided into four categories according to the glycemic range of the reference values (low, ≤4 mmol/L; euglycemic, 4.1-7 mmol/L; hyperglycemic I, 7.1-14 mmol/L; and hyperglycemic II, >14 mmol/L). The overall mean±SEM absolute relative difference (MARD) between capillary reference values and sensor readings was 18.3±0.8% for calibration(PRE) and 21.9±1.2% for calibration(POST) (P<0.001). MARD according to glycemic range was 47.4±6.5% (low), 17.4±1.3% (euglycemic), 15.0±0.8% (hyperglycemic I), and 17.7±1.9% (hyperglycemic II) for calibration(PRE) and 67.5±9.5% (low), 24.2±1.8% (euglycemic), 15.5±0.9% (hyperglycemic I), and 15.3±1.9% (hyperglycemic II) for calibration(POST). In the low and euglycemic ranges MARD was significantly lower in calibration(PRE) compared with calibration(POST) (P=0.007 and P<0.001, respectively). Sensor calibration predominantly based on preprandial glucose resulted in a significantly higher overall sensor accuracy compared with a predominantly postprandial calibration. The difference was most pronounced in the hypo- and euglycemic reference range, whereas both calibration patterns were comparable in the hyperglycemic range.

Accuracy of the Sof-Sensor Glucose Sensor with the iPro Calibration Algorithm

Performance characteristics of devices that measure interstitial fluid glucose concentrations continue to improve. The overall accuracy of continuous glucose monitoring (CGM) systems depends on the sensor, the reference blood glucose concentrations used for calibration, and the calibration algorithm. We examined the accuracy of the iPro calibration algorithm when applied retrospectively to data collected with Sof-sensor CGM sensors. A total of 768 sensors were worn by 71 adults (ages 19–72) and 61 children (ages 7–17) with type 1 diabetes. Sensor data from the first 3 days of wear, along with contemporaneous blood glucose (BG) concentrations measured by the Paradigm Link BG monitor, were supplied to the iPro algorithm to generate a data set of sensor glucose (SG) concentrations. The mean absolute relative difference (MARD) between SG and BG concentrations, the SG:BG agreement rate, and Clarke error grid analysis of paired SG and BG values were used to estimate the overall accuracy of the SG data. The MARD was 9.9% in adults and 10.1% in children, and were lowest in the 240–400 mg/dl range (6.8% in adults, 6.3% in children). The SG:BG agreement rate (i.e., the percentage of SG values within 20% of the reference BG value or the percentage within 20 mg/dl for reference values <80 mg/dl) was 89.1% for adults and 88.7% for children. The highest agreement rates were seen in the 240–400 mg/dl range (94.9% in adults, 95.2% in children). For values in the 40–80 mg/dl range, 88.7% of adult and 85.5% of pediatric SG values were within 20 mg/dl of the reference value, and for values in the 80–120 mg/dl range, 83.3% of adult and 81.2% of pediatric SG values were within 20% of the reference value. Clarke error grid analysis (Figure 1) showed that 99.0% (4849 of 4897) of adult and 98.4% (4116 of 4181) of pediatric-paired values were within zones A and B. Figure 1 Clarke error grid analysis of paired SG and BG values from adults and children. The iPro algorithm is at the heart of the iPro Professional CGM system, which is used by clinicians to gain insights that are not apparent with finger stick and hemoglobin A1c testing alone. A CGM sensor and an attached iPro recording device are worn by the patient for 3 days, and four BG values are measured each day. Data from the BG meter and recording device are then uploaded for report generation and analysis. Therapy adjustments based on retrospectively-generated glycemic profiles have been shown to reduce the duration of hypoglycemia compared with therapy adjustments guided by BG values alone.1 Retrospective CGM was found to be well tolerated and beneficial in children2 and has been used to demonstrate frequent hypoglycemia in adults with poor control.3 It may be especially useful for those with hypoglycemia unawareness, for those with postprandial hyperglycemia, or during therapy regimen adjustments.4 The iPro algorithm provides SG readings that are in good agreement with respect to home BG meter readings when used in conjunction with Sof-sensor CGM sensors. The accuracy is maintained in both adults and children for at least 3 days.

Closing the loop

Closing the loop

PS12 - 62. Continuous glucose monitoring accuracy assessed at home is seemingly better than when assessed at the clinical research centre

To investigate whether continuous glucose monitoring system accuracy (CGMS) determined at home differs from accuracy determined at the clinical research unit (CRC).

Enhanced Accuracy of Continuous Glucose Monitoring by Online Extended Kalman Filtering

Most continuous glucose monitoring (CGM) devices measure a current, proportional to the interstitial glucose (IG) concentration, which is converted into a glucose level by a standard device calibration step that exploits some blood glucose (BG) references. However, data show that deterioration of sensor gain may occur, which can affect CGM output by a systematic and possibly large (e.g., up to 15/20 mg/dL) error. Enhanced calibration algorithms for improving the accuracy of CGM are thus of critical importance, especially in real-time applications. In this work we present an enhanced Bayesian calibration method that can be implemented online by using the Extended Kalman Filter. The method takes into account the existence of BG-to-IG kinetics by incorporating a population convolution model and exploits only four BG reference samples per day. The new method is successfully applied on 10 simulated virtual patients. Its performance in improving the accuracy of CGM profiles is significantly better than that of other current calibration procedures. Furthermore, the new method is shown to be robust to changes in its parameters. Improvement in the accuracy of CGM is also shown on a representative subject. Realistic simulations show that the new enhanced calibration method significantly improves the accuracy of CGM signals, suggesting potential benefits by its inclusion in real-time applications of CGM devices.

The Accuracy of a New Real-Time Continuous Glucose Monitoring Algorithm: An Analysis

In this issue of Journal of Diabetes Science and Technology, Keenan and colleagues used archival data from the STAR 1 clinical trial (Medtronic Diabetes) to support the claim that the new Veo calibration algorithm improves the accuracy of continuous glucose monitoring, particularly in the critical hypoglycemic range. Extensive data analyses are presented to support this claim; the results are convincing, and the estimated improvement in hypoglycemic detection from 55% for the standard calibration to 82% for the Veo is particularly impressive. We can therefore conclude that the Veo algorithm has the potential to improve the accuracy of hypoglycemia alarms and ultimately contribute to closed-loop control. However, the presented results should be interpreted cautiously because they are based on retrospective analysis and are heavily dependent on the distribution of blood glucose levels observed in a particular data set.

Alarm Characterization for a Continuous Glucose Monitor That Replaces Traditional Blood Glucose Monitoring

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG); all CGM alarms require SMBG confirmation before treatment. In this report, an analysis method is proposed to determine the CGM threshold alarm accuracy required to eliminate SMBG confirmation. The proposed method builds on the Clinical and Laboratory Standards Institute (CLSI) guideline for evaluating CGM threshold alarms using data from an in-clinic study of subjects with type 1 diabetes. The CLSI method proposes a maximum time limit of +/-30 minutes for the detection of hypo- and hyperglycemic events but does not include limits for glucose measurement accuracy. The International Standards Organization (ISO) standard for SMBG glucose measurement accuracy (ISO 15197) is +/-15 mg/dl for glucose <75 mg/dl and +/-20% for glucose > or = 75 mg/dl. This standard was combined with the CLSI method to more completely characterize the accuracy of CGM alarms. Incorporating the ISO 15197 accuracy margins, FreeStyle Navigator CGM system alarms detected 70 mg/dl hypoglycemia within 30 minutes at a rate of 70.3%, with a false alarm rate of 11.4%. The device detected high glucose in the range of 140-300 mg/dl within 30 minutes at an average rate of 99.2%, with a false alarm rate of 2.1%. Self-monitoring of blood glucose confirmation is necessary for detecting and treating hypoglycemia with the FreeStyle Navigator CGM system, but at high glucose levels, SMBG confirmation adds little incremental value to CGM alarms.

Statistical Tools to Analyze Continuous Glucose Monitor Data

Continuous glucose monitors (CGMs) generate data streams that are both complex and voluminous. The analyses of these data require an understanding of the physical, biochemical, and mathematical properties involved in this technology. This article describes several methods that are pertinent to the analysis of CGM data, taking into account the specifics of the continuous monitoring data streams. These methods include: (1) evaluating the numerical and clinical accuracy of CGM. We distinguish two types of accuracy metrics-numerical and clinical-each having two subtypes measuring point and trend accuracy. The addition of trend accuracy, e.g., the ability of CGM to reflect the rate and direction of blood glucose (BG) change, is unique to CGM as these new devices are capable of capturing BG not only episodically, but also as a process in time. (2) Statistical approaches for interpreting CGM data. The importance of recognizing that the basic unit for most analyses is the glucose trace of an individual, i.e., a time-stamped series of glycemic data for each person, is stressed. We discuss the use of risk assessment, as well as graphical representation of the data of a person via glucose and risk traces and Poincaré plots, and at a group level via Control Variability-Grid Analysis. In summary, a review of methods specific to the analysis of CGM data series is presented, together with some new techniques. These methods should facilitate the extraction of information from, and the interpretation of, complex and voluminous CGM time series.

Realistic Expectations and Practical Use of Continuous Glucose Monitoring for the Endocrinologist

Real-time continuous glucose monitoring (CGM) has been available for type 1 diabetes for several years. This paper is a status report on our early experiences with this next technology. The two major sources of data acquisition included PubMed search strategies and personal experience of the author from clinical experience. Data assessing CGM accuracy, short-term outcomes (12 wk), and longer term outcomes (6 months) are reported. Potential strategies for successful and efficient use in an office or clinic setting are also discussed. Practical aspects of CGM use (alarm settings, using glycemic trending information) are also reviewed. Accuracy of this technology has improved in the short amount of time it has been available. Six-month data suggest that patient selection is a key for success. Patients who do not understand or practice the basics of intensive insulin therapy have the greatest challenges. Those who do best watch the receiver frequently, continue with frequent home blood glucose monitoring, use the trending information to make insulin adjustments, and understand the limitations of the technology. With insurance reimbursement improving, CGM is gaining acceptance as an important tool for the management of type 1 diabetes. Like home blood glucose monitoring and insulin pump therapy, this technology by itself is not a panacea for diabetes control. However, it further adds to our ability to improve the lives of people with diabetes. Long-term, the hope is that this technology will pave the way for a "closed-loop" device.

Relationship between glycated haemoglobin levels and mean glucose levels over time.

Aims/hypothesisHbA1c, expressed as the percentage of adult haemoglobin that is glycated, is the most widely used measure of chronic glycaemia. Achieving near-normal HbA1c levels has been shown to reduce long-term complications and the HbA1c assay is recommended to determine whether treatment is adequate and to guide adjustments. However, daily adjustments of therapy are guided by capillary glucose levels (mmol/l). We determined the relationship between an accurate measure of mean glucose levels over time and the HbA1c level, and whether HbA1c can be expressed in the same units as self-monitoring results.MethodsTwenty-two participants with diabetes and three non-diabetic participants were included in this longitudinal observational study. Mean glucose levels were measured by continuous glucose monitoring (CGM), which measures interstitial glucose levels every 5 min, for 12 weeks. Capillary measurements were obtained four times per day to confirm the accuracy of CGM. HbA1c was measured at baseline and every 4 weeks.ResultsThe HbA1c results at weeks 8 and 12 correlated strongly (r = 0.90) with the CGM results during the preceding 8 and 12 weeks. A curvilinear (exponential) relationship and a linear regression captured the relationship with similarly high correlations, which allowed transformation of HbA1c values to a calculated mean glucose level.Conclusions and interpretationHbA1c correlates closely with a complete measure of average glycaemia over the preceding 8–12 weeks. The translation of HbA1c to an average glucose level for reporting and management purposes is feasible.