Assessing the Accuracy of Continuous Glucose Monitoring (CGM) Calibrated With Capillary Values Using Capillary or Venous Glucose Levels as a Reference

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Concordance in COVID-19 serology, bone mineralization, and inflammatory analytes between venous and self-collected capillary blood samples exposed to various pre-analytical conditions

BACKGROUND

The COVID-19 has led to a significant increase in demand for remote blood sampling in clinical trials. This study aims to ascertain the concordance between venous versus capillary samples, processed immediately or exposed to various pre-analytical conditions.

METHODS

Participants (≥12 years old) provided a venous blood sample (processed immediately) and capillary samples allocated to one of the following conditions: processed immediately or exposed to 12-, 24-, or 36-h delays at room temperature or 36-h delays with a freeze-thaw cycle. The analytes of interest included SARS-CoV-2 IgG, 25-hydroxy vitamin D (25(OH)D), alkaline phosphate (ALP), calcium (Ca), phosphate (Ph), and c-reactive protein (CRP). Paired samples were considered interchangeable if they met three criteria: minimal within-subject mean difference, 95% of values within desirable total errors, and inter-class correlation (ICC) > 0.90.

RESULTS

90 participants (44.1% male) were enrolled. When comparing rapidly processed venous with capillary samples, 25(OH)D, ALP, and CRP met all three criteria; SARS-CoV-2 IgG met two criteria (mean difference and ICC); and Ca and Ph met one criterion (mean difference). When considering all three criteria, concentrations of 25(OH)D, CRP, and ALP remained unchanged after delays of up to 36 h; SARS-CoV-2 IgG met two criteria (mean difference and ICC); Ca and Ph met one criterion (mean difference).

CONCLUSION

These findings suggest that remote blood collection devices can be used to measure anti-SARS-CoV-2 IgG, 25(OH)D, CRP, and ALP. Further analysis is required to evaluate the interchangeability between venous and capillary testing in Ca and Ph levels, which are more sensitive to pre-analytical conditions.

Performance of Dexcom G5 and FreeStyle Libre sensors tested simultaneously in people with type 1 or 2 diabetes and advanced chronic kidney disease

BACKGROUND

Advanced chronic kidney disease (CKD) is a common complication for people with type 1 and 2 diabetes and can often lead to glucose instability. Continuous glucose monitoring (CGM) helps users monitor and stabilize their glucose levels. To date, CGM and intermittent scanning CGM are only approved for people with diabetes but not for those with advanced CKD.

AIM

To compare the performance of Dexcom G5 and FreeStyle Libre sensors in adults with type 1 or 2 diabetes and advanced CKD.

METHODS

This was a non-randomized clinical trial that took place in two outpatient clinics in western Sweden. All patients with type 1 or 2 diabetes and an estimated glomerular filtration rate (eGFR) of < 30 mL/min per 1.73 m² were invited to participate. Forty patients (full analysis set = 33) carried the Dexcom G5 sensor for 7 d and FreeStyle Libre sensor for 14 d simultaneously. For referencing capillary blood glucose (SMBG) was measured with a high accuracy glucose meter (HemoCue^®) during the study period. At the end of the study, all patients were asked to answer a questionnaire on their experience using the sensors.

RESULTS

The mean age was 64.1 (range 41-77) years, hemoglobin A1c was 7.0% [standard deviation (SD) 3.2], and diabetes duration was 28.5 (SD 14.7) years. A total of 27.5% of the study population was on hemodialysis and 22.5% on peritoneal dialysis. The mean absolute relative difference for Dexcom G5 vs SMBG was significantly lower than that for FreeStyle Libre vs SMBG [15.2% (SD 12.2) vs 20.9% (SD 8.6)], with a mean difference of 5.72 [95% confidence interval (CI): 2.11-9.32; P = 0.0036]. The mean absolute difference was also significantly lower for Dexcom G5 than for FreeStyle Libre, 1.21 mmol/L (SD 0.78) and 1.76 mmol/L (SD 0.78), with a mean diffrenec of 0.55 (95%CI: 0.27-0.83; P = 0.0004).The mean difference (MD) was -0.107 mmol/L and -1.10 mmol/L (P = 0.0002), respectively. In all, 66% of FreeStyle Libre values were in the no risk zone on the surveillance error grid compared to 82% of Dexcom G5 values.

CONCLUSION

Dexcom G5 produces more accurate sensor values than FreeStyle Libre in people with diabetes and advanced CKD and is likely safe to be used by those with advanced CKD.

Technologies for Diabetes Self-Monitoring: A Scoping Review and Assessment Using the REASSURED Criteria

BACKGROUND

Self-management is an important pillar for diabetes control and to achieve it, glucose self-monitoring devices are needed. Currently, there exist several different devices in the market and many others are being developed. However, whether these devices are suitable to be used in resource constrained settings is yet to be evaluated.

AIMS

To assess existing glucose monitoring tools and also those in development against the REASSURED which have been previously used to evaluate diagnostic tools for communicable diseases.

METHODS

We conducted a scoping review by searching PubMed for peer-review articles published in either English, Spanish or Portuguese in the last 5 years. We selected papers including information about devices used for self-monitoring and tested on humans with diabetes; then, the REASSURED criteria were used to assess them.

RESULTS

We found a total of 7 continuous glucose monitoring device groups, 6 non-continuous, and 6 devices in development. Accuracy varied between devices and most of them were either invasive or minimally invasive. Little to no evidence is published around robustness, affordability and delivery to those in need. However, when reviewing publicly available prices, none of the devices would be affordable for people living in low- and middle-income countries.

CONCLUSIONS

Available devices cannot be considered adapted for use in self-monitoring in resource constraints settings. Further studies should aim to develop less-invasive devices that do not require a large set of components. Additionally, we suggest some improvement in the REASSURED criteria such as the inclusion of patient-important outcomes to increase its appropriateness to assess non-communicable diseases devices.

Evaluation of Reference Metrics for Continuous Glucose Monitoring in Persons Without Diabetes and Prediabetes

BACKGROUND

Recent guidelines have been developed for continuous glucose monitoring (CGM) metrics in persons with diabetes. To understand what glucose profiles should be judged as normal in clinical practice and glucose-lowering trials, we examined the glucose profile of healthy individuals using CGM.

METHODS

Persons without diabetes or prediabetes were included after passing a normal oral glucose tolerance test, two-hour value <8.9 mmol/L (160 mg/dL), fasting glucose <6.1 mmol/L (110 mg/dL), and HbA1c <6.0% (<42 mmol/mol). CGM metrics were evaluated using the Dexcom G4 Platinum.

RESULTS

In total, 60 persons were included, mean age was 43.0 years, 70.0% were women, mean HbA1c was 5.3% (34 mmol/mol), and mean body mass index was 25.7 kg/m². Median and mean percent times in hypoglycemia <3.9 mmol/L (70 mg/dL) were 1.6% (IQR 0.6-3.2), and 3.2% (95% CI 2.0; 4.3), respectively. For glucose levels <3.0 mmol/L (54 mg/dL), the corresponding estimates were 0.0% (IQR 0.0-0.4) and 0.5% (95% CI 0.2; 0.8). Median and mean time-in-range (3.9-10.0 mmol/L [70-180 mg/dL]) was 97.3% (IQR 95.4-98.7) and 95.4% (95% CI 94.0; 96.8), respectively. Median and mean standard deviations were 1.04 mmol/L (IQR 0.92-1.29) and 1.15 mmol/L (95% CI 1.05; 1.24), respectively. Measures of glycemic variability (standard deviation, coefficient of variation, mean amplitude of glycemic excursions) were significantly greater during daytime compared with nighttime, whereas others did not differ.

CONCLUSIONS

People without prediabetes or diabetes show a non-negligible % time in hypoglycemia, median 1.6% and mean 3.2%, which needs to be accounted for in clinical practice and glucose-lowering trials. Glycemic variability measures differ day and night in this population.

Glucose Concentrations from Continuous Glucose Monitoring Devices Compared to Those from Blood Plasma during an Oral Glucose Tolerance Test in Healthy Young Adults

Continuous glucose monitoring devices measure glucose in interstitial fluid. The devices are effective when used by patients with type 1 and 2 diabetes but are increasingly being used by researchers who are interested in the effects of various behaviours of glucose concentrations in healthy participants. Despite their more frequent application in this setting, the devices have not yet been validated for use under such conditions. A total of 124 healthy participants were recruited to a ten-day laboratory study. Each participant underwent four oral glucose tolerance tests, and a total of 3315 out of a possible 4960 paired samples were included in the final analysis. Bland-Altman plots and mean absolute relative differences were used to determine the agreement between the two methods. Bland-Altman analyses revealed that the continuous glucose monitoring devices had proportional bias (R = 0.028, p < 0.001) and a mean bias of -0.048 mmol/L, and device measurements were more variable as glucose concentrations increased. Ninety-nine per cent of paired values were in Zones A and B of the Parkes Error Grid plot, and there was an overall mean absolute relative difference of 16.2% (±15.8%). There was variability in the continuous glucose monitoring devices, and this variability was higher when glucose concentrations were higher. If researchers were to use continuous glucose monitoring devices to measure glucose concentrations during an oral glucose tolerance test in healthy participants, this variability would need to be considered.

Modeling continuous glucose monitoring with fractional differential equations subject to shocks

Continuous Glucose Monitoring (CGM) produces long time-series of noisy observations of a single variable (tissue glucose concentration), whose evolution may be explained by a dynamical model. In order to represent the unknown mixture of possible control mechanisms of different orders affecting the measured variable, a fractional differential approach seems justified. In any case, variations in food intake and/or physical activity ought to be taken into account if a plausible interpretation of the dynamics is to be obtained. In the present work, the mathematical construction and the numerical implementation of a Fractional Differential Equations (FDE) initial value problem are systematically reviewed, with the intent of offering the reader a concise and mathematically rigorous description of this approach. An FDE model for CGM is formulated: the model includes compartments for stomach and intestinal glucose contents and for blood and tissue (subcutaneous) glucose concentrations, as well as the shock effects of food ingestion and of increased glucose consumption due to physical activity. The model parameters, including the (non-integer) order of differentiation, are estimated from CGM observations on six Type 1 diabetic patients. The best-fit fractional orders for the six subjects range from 1.59 to 2.13. For comparison, best fits have also been computed for all subjects using an average fractional order of 1.9 and integer orders of 1 and 2.The results indicate that in the case of CGM the fractional differential model, which should be physiologically more appropriate, in fact fits the data much better than the first-order model and also better than the 2nd-order model.

Review of the Long-Term Implantable Senseonics Continuous Glucose Monitoring System and Other Continuous Glucose Monitoring Systems

The article published by Kevin Cowart in this issue of the Journal of Diabetes Science and Technology (JDST) is a detailed overview of the clinical trial data and analysis used to demonstrate the safety and effectiveness of the Eversense continuous glucose monitoring (CGM) System for regulatory approval and clinical acceptance. The article describes the published study results for safety, accuracy, reliability, ease of insertion/removal, adverse events, and ease of diabetes patient-use for controlling their glucose levels short and long term. The author nicely compares Eversense CGM System safety and performance with the short-term subcutaneous tissue CGM systems being commercialized by Dexcom, Medtronic Diabetes, and Abbott Diabetes. This comparison may help the clinician define which type of patient with diabetes might benefit the most from the long-term implantable CGM system. The majority of studied patients describe a positive experience managing their diabetes with the Eversense CGM System and request implantation of a new sensor 90 or 180 days later.

Clinically Accurate Prediction of Glucose Levels in Patients with Type 1 Diabetes

Background and Aims: Accurate prediction of glucose levels in patients with type 1 diabetes mellitus (T1DM) is critical both for their glycemic control and for the development of closed-loop systems. Methods: In this study, we utilized real-life, retrospective, continuous glucose monitoring data from 141 T1DM patients (9,083 connection days, 1,592,506 glucose measurements) and in silico data generated by the UVA/Padova T1DM simulator to evaluate different computational methods for glucose prediction. We evaluated the performance of the models using both measures of numerical accuracy, measured by the root mean square error, and clinical accuracy, measured by the percentage of time in each of the Clarke error grid (CEG) zones, and compared the predictions done by autoregressive (AR) models, tree-based methods, artificial neural networks, and a novel model that we devised and optimized for this task. Results: Our novel model, constructed on real-life data, achieved clinical accuracy of 99.3% and 95.8% in predicting the glucose level 30 and 60 min ahead, respectively, and reduced the percentage of glucose predictions in zones C-E of the CEG by 60.6% and 38.4% in these prediction horizons, compared with a standard AR model. The model was superior to all other models across all age groups and achieved higher clinical accuracy in subgroups of patients with high glucose variability and greater time spent in hypoglycemia. Compared with real-life data, when evaluated on in silico data, the model had a higher clinical and numerical accuracy. Conclusions: A model that optimizes for CEG zones may significantly improve clinical accuracy and clinical outcomes of treatment decisions in T1DM patients. Results obtained from simulated data may overestimate the performance of models on real-life data.

Association between reduced kidney function and incident hypoglycaemia in people with diabetes: The Stockholm Creatinine Measurements (SCREAM) project

AIM

To evaluate possible associations between estimated glomerular filtration rate (eGFR) and hypoglycaemia in adults with diabetes.

METHODS

We conducted an observational study in adults with diabetes from the Stockholm Creatinine Measurement (SCREAM) project, a Swedish healthcare utilization cohort during 2007 to 2011. We evaluated diagnoses and outpatient glucose tests for incidence rate ratios (IRRs) of hypoglycaemia (overall and by severity) in outpatient care by eGFR strata using zero-inflated negative binomial regression. We identified clinical predictors through ordinal logistic regression and assessed 7-day and 30-day mortality from hypoglycaemia in relation to eGFR with Cox proportional hazard models.

RESULTS

We identified 29 434 people with diabetes (13% with type 1 diabetes). Their mean age was 66 years, 43% were women and the median eGFR was 80 mL/min/1.73 m² . During 2 years of follow-up, 1812 patients (6.2%) had hypoglycaemia registered at least once. The risk of hypoglycaemia increased linearly with lower eGFR, with an IRR of 1.2 (95% confidence interval [CI] 1.0-1.4) for eGFR 60-89 mL/min/1.73 m² and 5.8 (95% CI 3.8-9.0) for eGFR <15 mL/min/1.73 m² compared to eGFR 90 to 104 mL/min/1.73 m² . This trend was observed for both mild and severe hypoglycaemia. Both 7-day and 30-day post-hypoglycaemia mortality increased with lower eGFR, peaking in those with eGFR <15 mL/min/1.73 m² (hazard ratio 21.2, 95% CI 5.1-87.9) as compared to those with eGFR 90 to 104 mL/min/1.73 m² . Lower eGFR categories, type 1 diabetes, previous hypoglycaemia, liver disease, presence of diabetic complications and use of insulin and sulphonylureas increased the odds of hypoglycaemia.

CONCLUSION

In this large, observational study, low eGFR was strongly associated with the occurrence, severity and fatality of hypoglycaemia in people with diabetes.

Benefits and Limitations of MARD as a Performance Parameter for Continuous Glucose Monitoring in the Interstitial Space

High-quality performance of medical devices for glucose monitoring is important for a safe and efficient usage of this diagnostic option by patients with diabetes. The mean absolute relative difference (MARD) parameter is used most often to characterize the measurement performance of systems for continuous glucose monitoring (CGM). Calculation of this parameter is relatively easy and comparison of the MARD numbers between different CGM systems appears to be straightforward on the first glance. However, a closer look reveals that a number of complex aspects make interpretation of the MARD numbers provided by the manufacturer for their CGM systems difficult. In this review, these aspects are discussed and considerations are made for a systematic and appropriate evaluation of the MARD in clinical trials. The MARD should not be used as the sole parameter to characterize CGM systems, especially when it comes to nonadjunctive usage of such systems.

Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes

Background: Real-time continuous glucose monitoring (CGM) devices help detect glycemic excursions associated with exercise, meals, and insulin dosing in patients with type 1 diabetes (T1D). However, the delay between interstitial and blood glucose may result in CGM underestimating the true change in glycemia during activity. The purpose of this study was to examine CGM discrepancies during exercise and the meal postexercise versus self-monitoring of blood glucose (SMBG). Methods: Seventeen adults with T1D using insulin pump therapy and CGM completed 60 min of aerobic exercise on three occasions. A standardized meal was given 30 min postexercise. SMBG was measured during exercise and in recovery using OmniPod® Personal Diabetes Manager (PDM; Insulet, Billerica, MA) with built-in glucose meter (FreeStyle; Abbott Laboratories, Abbott Park, IL), while CGM was measured with Dexcom G4® with 505 algorithm (n = 4) or G5® (n = 13), which were calibrated with subjects' own PDM. Results: SMBG showed a large drop in glycemia during exercise, while CGM showed a lag of 12 ± 11 (mean ± standard deviation) minutes and bias of -7 ± 19 mg/dL/min during activity. Mean absolute relative difference (MARD) for CGM versus SMBG was 13 (6-22)% [median (interquartile range)] during exercise and 8 (5-14)% during mealtime. Clarke error grids showed CGM values were in zones A and B 94%-99% of the time for SMBG. Conclusion: In summary, the drop in CGM lags behind the drop in blood glucose during prolonged aerobic exercise by 12 ± 11 min, and MARD increases to 13 (6-22)% during exercise as well. Therefore, if hypoglycemia is suspected during exercise, individuals should confirm glucose levels with a capillary glucose measurement.

Evaluation of the FreeStyle® Libre Flash Glucose Monitoring System in Children and Adolescents with Type 1 Diabetes

BACKGROUND/AIMS

The FreeStyle® Libre Flash Glucose Monitoring System (FGM, Abbott) measures glucose concentrations in the interstitial fluid for up to 14 days. It has been approved for use in children aged > 4 years in January 2016. Experience in children is still limited. We evaluated the accuracy and usability of the FGM in children with type 1 diabetes mellitus (DM).

METHODS

67 children with type 1 DM (35 girls), aged 4-18 years, were included. Subjects wore a sensor on the back of their upper arm. For the first 14 days, they regularly measured capillary blood glucose (BG) with their usual BG meter (Accu-Chek® Mobile [ACM], Roche [n = 24]; Contour® Next Link [CNL], Bayer [n = 26]; OneTouch® Verio® IQ [OTV], LifeScan [n = 17]) followed by a sensor glucose (SG) scanning. SG readings were compared to BG measurements by consensus error grid (CEG) analysis; the mean difference (MD), the mean relative difference (MRD), the mean absolute difference (MAD), and the mean absolute relative difference (MARD) were calculated. After 14 days, subjects were asked to fill in a questionnaire on the usability of the FGM.

RESULTS

2,626 SG readings were paired with BG results. FGM readings were highly correlated with BG (r = 0.926, p < 0.001). 80.3% of the data pairs were in zone A (= no effect on clinical action) and 18.4% were in zone B (= altered clinical action with little or no effect on the clinical outcome) of the CEG. Overall MD was +7.5 mg/dL; MD varied with the BG meter: ACM +10.4 mg/dL, CNL +14.2 mg/dL, OTV -3.6 mg/dL (p < 0.001). Overall, MARD was 16.7%. We observed a large interindividual variability in the accuracy parameters. MD and MRD were inversely related to BMI (r = -0.261 [p < 0.05]; r = -0.266 [p < 0.05], respectively). MARD was inversely related to age (r = -0.266 [p < 0.05]). Twenty-nine patients (43.3%) reported sensor problems, mainly early detachment of the sensor. Nonetheless, the usability questionnaire indicated high levels of satisfaction.

CONCLUSIONS

Our results showed a reasonable agreement between the FGM SG readings and capillary BG measurements in children. There was, however, a large interindividual variability. The wearing of the sensor requires special attention. Further studies in children are imperative in order to document the accuracy and safety of the FGM in the paediatric population.

Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials

Systems for continuous glucose monitoring (CGM) are evolving quickly, and the data obtained are expected to become the basis for clinical decisions for many patients with diabetes in the near future. However, this requires that their analytical accuracy is sufficient. This accuracy is usually determined with clinical studies by comparing the data obtained by the given CGM system with blood glucose (BG) point measurements made with a so-called reference method. The latter is assumed to indicate the correct value of the target quantity. Unfortunately, due to the nature of the clinical trials and the approach used, such a comparison is subject to several effects which may lead to misleading results. While some reasons for the differences between the values obtained with CGM and BG point measurements are relatively well-known (e.g., measurement in different body compartments), others related to the clinical study protocols are less visible, but also quite important. In this review, we present a general picture of the topic as well as tools which allow to correct or at least to estimate the uncertainty of measures of CGM system performance.

A randomized controlled pilot study of continuous glucose monitoring and flash glucose monitoring in people with Type 1 diabetes and impaired awareness of hypoglycaemia

AIM

Hypoglycaemia in Type 1 diabetes is associated with mortality and morbidity, especially where awareness of hypoglycaemia is impaired. Clinical pathways for access to continuous glucose monitoring (CGM) and flash glucose monitoring technologies are unclear. We assessed the impact of CGM and flash glucose monitoring in a high-risk group of people with Type 1 diabetes.

METHODS

A randomized, non-masked parallel group study was undertaken. Adults with Type 1 diabetes using a multiple-dose insulin-injection regimen with a Gold score of ≥ 4 or recent severe hypoglycaemia were recruited. Following 2 weeks of blinded CGM, they were randomly assigned to CGM (Dexcom G5) or flash glucose monitoring (Abbott Freestyle Libre) for 8 weeks. The primary outcome was the difference in time spent in hypoglycaemia (below 3.3 mmol/l) from baseline to endpoint with CGM versus flash glucose monitoring.

RESULTS

Some 40 participants were randomized to CGM (n = 20) or flash glucose monitoring (n = 20). The participants (24 men, 16 women) had a median (IQR) age of 49.6 (37.5-63.5) years, duration of diabetes of 30.0 (21.0-36.5) years and HbA_1c of 56 (48-63) mmol/mol [7.3 (6.5-7.8)%]. The baseline median percentage time < 3.3 mmol/l was 4.5% in the CGM group and 6.7% in the flash glucose monitoring. At the end-point the percentage time < 3.3 mmol/l was 2.4%, and 6.8% respectively (median between group difference -4.3%, P = 0.006). Time spent in hypoglycaemia at all thresholds, and hypoglycaemia fear, were different between groups, favouring CGM.

CONCLUSION

CGM more effectively reduces time spent in hypoglycaemia in people with Type 1 diabetes and impaired awareness of hypoglycaemia compared with flash glucose monitoring. (Clinical Trial Registry No: NCT03028220).

Venous, Arterialized-Venous, or Capillary Glucose Reference Measurements for the Accuracy Assessment of a Continuous Glucose Monitoring System

BACKGROUND

Different reference methods are used for the accuracy assessment of continuous glucose monitoring (CGM) systems. The effect of using venous, arterialized-venous, or capillary reference measurements on CGM accuracy is unclear.

METHODS

We evaluated 21 individuals with type 1 diabetes using a capillary calibrated CGM system. Venous or arterialized-venous reference glucose samples were taken every 15 min at two separate visits and assessed per YSI 2300 STAT Plus. Arterialization was achieved by heated-hand technique. Capillary samples were collected hourly during the venous reference visit. The investigation sequence (venous or arterialized-venous) was randomized. Effectiveness of arterialization was measured by comparing free venous oxygen pressure (PO2) of both visit days. Primary endpoint was the median absolute relative difference (ARD).

RESULTS

Median ARD using arterialized-venous reference samples was not different from venous samples (point estimated difference 0.52%, P = 0.181). When comparing the three reference methods, median ARD was also not different over the full glycemic range (venous 9.0% [n = 681], arterialized-venous 8.3% [n = 684], and capillary 8.1% [n = 205], P = 0.216), nor over the separate glucose ranges. Arterialization was successful (PO2 venous 5.4 kPa vs. arterialized-venous 8.9 kPa, P < 0.001). Arterialized-venous glucose was significantly higher than venous glucose and numerically higher than capillary glucose (arterialized-venous 142 mg/dL vs. venous 129 mg/dL [P < 0.001] and vs. capillary 134 mg/dL [P = 0.231]). Inconvenience related to arterialization included transient mild edema and redness of the hand in 4 out of 21 (19%) patients.

CONCLUSIONS

The use of venous, arterialized-venous, or capillary reference measurements did not significantly impact CGM accuracy. Venous reference seems preferable due to its ease of operation.

Discrepancies Between Blood Glucose and Interstitial Glucose-Technological Artifacts or Physiology: Implications for Selection of the Appropriate Therapeutic Target

BACKGROUND

For decades, the major source of information used to make therapeutic decisions by patients with diabetes has been glucose measurements using capillary blood samples. Knowledge gained from clinical studies, for example, on the impact of metabolic control on diabetes-related complications, is based on such measurements. Different to traditional blood glucose measurement systems, systems for continuous glucose monitoring (CGM) measure glucose in interstitial fluid (ISF). The assumption is that glucose levels in blood and ISF are practically the same and that the information provided can be used interchangeably. Thus, therapeutic decisions, that is, the selection of insulin doses, are based on CGM system results interpreted as though they were blood glucose values.

METHODS

We performed a more detailed analysis and interpretation of glucose profiles obtained with CGM in situations with high glucose dynamics to evaluate this potentially misleading assumption.

RESULTS

Considering physical activity, hypoglycemic episodes, and meal-related differences between glucose levels in blood and ISF uncover clinically relevant differences that can make it risky from a therapeutic point of view to use blood glucose for therapeutic decisions.

CONCLUSIONS

Further systematic and structured evaluation as to whether the use of ISF glucose is more safe and efficient when it comes to acute therapeutic decisions is necessary. These data might also have a higher prognostic relevance when it comes to long-term metabolic consequences of diabetes. In the long run, it may be reasonable to abandon blood glucose measurements as the basis for diabetes management and switch to using ISF glucose as the appropriate therapeutic target.

Future of Automated Insulin Delivery Systems

Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.

A Clinical Trial of the Accuracy and Treatment Experience of the Flash Glucose Monitor FreeStyle Libre in Adults with Type 1 Diabetes

BACKGROUND

In Sweden, FreeStyle Libre a flash glucose monitoring system came onto the market in 2014 as a complement to self-monitoring of blood glucose. The aim of this study was to evaluate the accuracy and treatment experience of the FreeStyle Libre system.

METHODS

Fifty-eight adults with type 1 diabetes used FreeStyle Libre for 10-14 days and measured capillary blood glucose levels with the HemoCue blood glucose measurement system at least six times a day simultaneously.

RESULTS

For the entire study period, the mean absolute relative difference (MARD) was 13.2% (95% confidence interval [CI] 12.0%-14.4%). MARD was 13.6% (95% CI 12.1%-15.4%) during week 1 and 12.7% (95% CI 11.5%-13.9%) during week 2. The mean absolute difference (MAD) for the whole study period was 19.8 mg/dL (1.1 mmol/L) (95% CI 17.8-21.8 mg/dL), including 20.5 mg/dL (1.14 mmol/L) during week 1 and 19.0 mg/dL (1.05 mmol/L) during week 2. The overall correlation coefficient was 0.96. For glucose values <72, 72-180, and >180 mg/dL (<4, 4-10, and >10 mmol/L), the MARD was 20.3% (95% CI 17.7%-23.1%), 14.7% (95% CI 13.4%-16%), and 9.6% (95% CI 8.5%-10.8%), respectively, and respective MAD values were 12.3, 17.8, and 23.6 mg/dL (0.69, 0.99, and 1.31 mmol/L). Using the 10-item visual analog scale, patients rated their experience with FreeStyle Libre as generally positive, with mean values ranging from 8.22 to 9.8.

CONCLUSIONS

FreeStyle Libre had a similar overall MARD as continuous blood glucose monitoring systems in earlier studies when studied in similar at-home conditions. The overall patient satisfaction was high.

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.