Accuracy Of Continuous Glucose Monitoring Research Articles

Evaluation of Accuracy of Continuous Glucose Monitoring (CGM) in Patients With End Stage Renal Disease (ESRD) on Intermittent Hemodialysis (iHD).

Evaluation of Accuracy of Continuous Glucose Monitoring (CGM) in Patients With End Stage Renal Disease (ESRD) on Intermittent Hemodialysis (iHD).

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration.

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.

95-LB: Utility of Continuous Glucose Monitoring in Pediatric Diabetic Ketoacidosis

Continuous glucose monitoring (CGM) commonly used in the outpatient setting is yet to be implemented in the pediatric intensive care unit (PICU) setting. The purpose of this study was to evaluate the feasibility, reliability and accuracy of the system in pediatric patients with DKA. Objective: Determine the accuracy of CGM Dexcom G6™ in the setting of pediatric DKA compared to point of care capillary (POC) and serum glucose testing. Secondly, determine if the degree of acidosis impacts CGM when compared to POC and serum glucose testing. Methods: Single institution IRB approved prospective trial. PICU patients that met inclusion criteria receiving standard DKA therapy were enrolled. CGM Dexcom G6™ sensors was applied to the patient’s abdomen on admission. Hourly POC and every 6 hour serum basic metabolic panels were obtained. CGM data was obtained every 5 minutes and matched to the nearest POC capillary and serum glucose levels ± 3 minutes. Reliability and feasibility analysis included frequency of data display, data gaps and any reasons for sensor removal. Results: 35 pediatric patients, 11.9 ± 4.1 years old were enrolled. 51.4% were known diabetics and were older compared to the new onset diabetics, mean age 13.9 vs. 9.7 years respectively (p= 0.001). Clark Error grid of serum glucose versus CGM reveals 95.6% of readings within zones A and B, the Clark Error grid for POC versus CGM reveals 95.4% of readings within A and B zones. There was a negative correlation (-0.27) between the serum glucose and CGM delta when compared to serum bicarbonate level (p=0.007), while a positive correlation (0.24) between the POC and CGM delta when compared to serum bicarbonate level (p = 0.056). There were no sensors removed or adverse events that occurred during the study. Conclusion: CGM use in patients with DKA seems both feasible and accurate. Serum bicarbonate level does not appear to impact the accuracy of CGM in the setting of DKA. Further studies examining the use and cost effectiveness of CGM in treatment decisions in DKA are warranted. Disclosure T.R. Pott: None. J.M. Jimenez Vega: Consultant; Self; Dexcom, Inc. J.L. Parker: None. R. Fitzgerald: None.

103-LB: Intergenerational Improvements of Continuous Glucose Monitoring Systems in Youth

Standard methods and metrics to describe the accuracy of continuous glucose monitoring (CGM) systems in children and adolescents are subject to change. We reanalyzed data from youth ages 6-17 from clinical trials evaluating G4 PLATINUM (NCT01667185), G5 Mobile (NCT02280577), and G6 (NCT02880267) CGM systems (all from Dexcom, Inc., San Diego, CA). Subjects participated in 1-2 clinic sessions of varying duration, depending on their age, throughout the 7-day wear of G4 and G5 and 10-day wear of G6 for comparison of CGM values with temporally matched YSI measurements. Reference measurements were obtained every 15±5 minutes and paired with the CGM measurement that immediately followed (within 5 minutes). Percent accuracy was defined as the proportion of CGM values within ±20% of paired YSI values for CGM values &amp;gt;100 mg/dL or within ±20 mg/dL of paired YSI values for CGM values ≤100 mg/dL. Accuracy was also evaluated using the mean absolute difference (MAD) and mean absolute relative difference (MARD) between CGM and YSI pairs, as well as the proportion of pairs in Clarke Error Grid (CEG) Zone A. Accuracy improved dramatically from G4 to G6, regardless of the metric evaluated (Table). Improved device performance may drive increased uptake and adherence, especially among children and adolescents. Disclosure S. Puhr: Employee; Self; Dexcom, Inc. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. J. Welsh: Employee; Self; Dexcom, Inc. T.C. Walker: Employee; Self; Dexcom, Inc. D.A. Price: Employee; Self; Dexcom, Inc. Stock/Shareholder; Self; Johnson &amp; Johnson.

Changes in Accuracy of Continuous Glucose Monitoring Using Dexcom G4 Platinum Over the Course of Moderate Intensity Aerobic Exercise in Type 1 Diabetes.

Continuous glucose monitoring (CGM) systems help diabetes management in patients with type 1 diabetes (T1D) but could have lower accuracy during exercise. We aim to evaluate the dynamics of CGM accuracy during exercise in patients with T1D. Secondary analysis of data was carried out on 22 patients with T1D (glycated hemoglobin [HbA1c]: 7.3% ± 1.0%, diabetes duration: 23 ± 13 years), who did three exercise sessions (45 min at 60% VO2max on an ergocycle, 3 h postmeal) with paired Dexcom G4 Platinum, and capillary glucose values that were collected every 5 min. Dexcom accuracy was evaluated using sensor bias (SB) and absolute relative difference (ARD). For dynamics of SB analysis, data pairs following hypoglycemia correction were excluded. The analyzed data included 792 pairs (594 during 66 exercise sessions, 198 at rest before exercise). Median ARD was 8.44 (5.35-12.13)% at rest and increased to 16.77 (10.75-26.72)% during exercise (P < 0.001). During exercise, mean SB values evolved from T0 minutes = 5.95 ± 16.04 mg/dL (exercise start); T5 = 9.55 ± 16.40; T10 = 13.51 ± 18.02; T15 = 15.32 ± 20.36; T20 = 17.30 ± 18.92; T25 = 19.46 ± 17.48; T30 = 21.08 ± 19.64; T35 = 19.10 ± 20.36; T40 = 19.82 ± 20.18; and T45 = 18.02 ± 20.90 (exercise end). CGM overestimated capillary at a mean SB of 14.23 ± 16.76 mg/dL over the whole exercise session. CGM accuracy decreased during moderate aerobic exercise as previously described. However, the trend to overestimate capillary glucose was maintained at relatively stable values within 15 min of exercise initiation, which could help patients in their clinical decisions. Similar analyses would be needed for other types of exercise.

Time Lag and Accuracy of Continuous Glucose Monitoring During High Intensity Interval Training in Adults with Type 1 Diabetes.

Background: This study investigated the accuracy of real-time continuous glucose monitoring (rtCGM) during high intensity interval training (HIIT) in patients with type 1 diabetes (T1D). Methods: Seventeen participants with T1D, using multiple daily injections (MDI) with basal insulin glargine 300 U/mL (Gla-300), completed four fasted HIIT sessions over 4 weeks while wearing a Dexcom rtCGM G4 Platinum system. Each exercise consisted of high intensity interval cycling and multimodal training over 25 min. Reference venous plasma glucose (PG) was measured at 60- and 10-min before exercise (Stage 1), every 10 min during exercise and then every 15 min until 180 min after the end of exercise (Stage 2: during exercise and 45-min early recovery; Stage 3: 45 min to 3 h after the end of exercise); and at 6-, 10-, and 13-h postexercise (Stage 4). Results: In the 64 HIIT sessions that resulted in hyperglycemia, PG increased 90.0 ± 32.4 mg/dL (mean ± standard deviation), peaking at 68.0 ± 18.4 min from the start of HIIT. Mean absolute relative difference was highest during exercise and early recovery (Stage 2) at 17.8%, versus Stage 1 (10.4%), Stage 3 (10.6%), and Stage 4 (11.5%) (P < 0.001). During Stage 2, rtCGM showed a significant negative bias of 35.3 mg/dL (P < 0.001) compared to reference glucose. Lag time to reach the half-maximal glucose rise was 35 min in rtCGM versus PG. The Surveillance Error Grid found that in Stage 2, only 65.5% of paired values were in the no-risk zone and the %15/15 was 50%, significantly lower than the other stages (P < 0.001). Conclusions: During HIIT and early recovery, there is an increase in lag time and a related decline in accuracy of Dexcom rtCGM G4, compared to pre-exercise and later recovery, in patients with T1D using MDI.

SAT-127 Hypoglycemic Episodes in Type 2 Diabetes Mellitus : Continuous Glucose Monitoring Versus Self Monitoring of Blood Glucose

Introduction: Strict glycemic control with lifestyle modifications has been emphasized to prevent microvascular and macrovascular diabetic complications. However patients treated with intensive treatment often experienced more frequent episodes of hypoglycemia than did those treated with the standard treatment. Moreover, resent trials revealed that episodes of severe hypoglycemia were associated with an increased risk of subsequent mortality and morbidity. The use of CGM can detect both symptomatic and silent hypoglycaemia and can therefore prevent hypoglycaemia related morbidities and mortality. Aim: To detect occurrence of hypoglycaemic episodes by continuous glucose monitoring (CGM) and self monitoring of blood glucose (SMBG) in type 2 diabetic patients Methods: Ninety one type 2 diabetes mellitus patients who have Hb A1C ≤ 7.5 % and treated with insulin or insulin secretagogue (sulphonylureas and meglitinides) were included in this study. After making history taking and physical examination, they were instructed to record their food diary, medication history, to perform SMBG 4 times per day and to wear CGM for 72 hours period. At the end of 72 hours, CGM was removed from patient's body and data were submitted to Medtronic database. The report on continuous monitoring of patients’ blood glucose were downloaded from the database and interpreted for hypoglycemic episodes. Results: During study period, 16 patients (17.6 %) experienced symptomatic and 14 patients (15.4%) experienced silent hypoglycaemias which were detected by SMBG. In comparison, 16 patients (17.6%) experienced symptomatic whereas 17 patients (18.7%) experienced silent hypoglycaemiaby using CGM system. The accuracy of CGM had 93.4% sensitivity and 96.7% specificity in detection of hypoglycemia in study population. So it had overall accuracy of 95.6%. However, the numbers of hypoglycemic episodes detected by CGM were higher compared to SMBG (43 versus 107) within 72 hours of observation.Most of the hypoglycaemic episodes occurred in AM1 (00:00 AM to 6:00AM) ie,nocturnal followed by PM2 (18:01 PM 23:59 PM) ie, post dinner to bed time. Conclusion: We concluded that hypoglycemia is relatively common in fairly controlled type 2 diabetic patients. The accuracy of CGM was 95.6% in overall which is very good accuracy. With regards to the detection of number of hypoglycemic episodes, CGM is more reliable.

Impact of physical exercise on sensor performance of the FreeStyle Libre intermittently viewed continuous glucose monitoring system in people with Type 1 diabetes: a randomized crossover trial.

To evaluate the sensor performance of the FreeStyle Libre intermittently viewed continuous glucose monitoring system using reference blood glucose levels during moderate-intensity exercise while on either full or reduced basal insulin dose in people with Type 1 diabetes. Ten participants with Type 1 diabetes [four women, mean ± sd age 31.4 ± 9.0 years, BMI 25.5±3.8 kg/m2 , HbA1c 55±7 mmol/mol (7.2±0.6%)] exercised on a cycle ergometer for 55 min at a moderate intensity for 5 consecutive days at the clinical research facility, while receiving either their usual or a 75% basal insulin dose. After a 4-week washout period, participants performed the second exercise period having switched to the alternative basal insulin dose. During exercise, reference capillary blood glucose values were analysed using the fully enzymatic-amperometric method and compared with the interstitial glucose values obtained. Intermittently viewed continuous glucose monitoring accuracy was analysed according to median (interquartile range) absolute relative difference, and Clarke error grid and Bland-Altman analysis for overall glucose levels during exercise, stratified by glycaemic range and basal insulin dosing scheme (P<0.05). A total of 845 glucose values were available during exercise to evaluate intermittently viewed continuous glucose monitoring sensor performance. The median (interquartile range) absolute relative difference between the reference values and those obtained by the sensor across the glycaemic range overall was 22 (13.9-29.7)%, and was 36.3 (24.2-45.2)% during hypoglycaemia, 22.8 (14.6-30.6)% during euglycaemia and 15.4 (9-21)% during hyperglycaemia. Usual basal insulin dose was associated with a worse sensor performance during exercise compared with the reduced (75%) basal insulin dose [median (interquartile range) absolute relative difference: 23.7 (17.2-30.7)% vs 20.5 (12-28.1)%; P<0.001). The intermittently viewed continuous glucose monitoring sensor showed diminished accuracy during exercise. Absolute glucose readings derived from the sensor should be used cautiously and need confirmation by additional finger-prick blood glucose measurements.

Measures of Accuracy for Continuous Glucose Monitoring and Blood Glucose Monitoring Devices

Currently, patients with diabetes may choose between two major types of system for glucose measurement: blood glucose monitoring (BGM) systems measuring glucose within capillary blood and continuous glucose monitoring (CGM) systems measuring glucose within interstitial fluid. Although BGM and CGM systems offer different functionality, both types of system are intended to help users achieve improved glucose control. Another area in which BGM and CGM systems differ is measurement accuracy. In the literature, BGM system accuracy is assessed mainly according to ISO 15197:2013 accuracy requirements, whereas CGM accuracy has hitherto mainly been assessed by MARD, although often results from additional analyses such as bias analysis or error grid analysis are provided. The intention of this review is to provide a comparison of different approaches used to determine the accuracy of BGM and CGM systems and factors that should be considered when using these different measures of accuracy to make comparisons between the analytical performance (ie, accuracy) of BGM and CGM systems. In addition, real-world implications of accuracy and its relevance are discussed.

The Accuracy of Continuous Glucose Monitoring and Flash Glucose Monitoring During Aerobic Exercise in Type 1 Diabetes

The Accuracy of Continuous Glucose Monitoring and Flash Glucose Monitoring During Aerobic Exercise in Type 1 Diabetes

A Three-Way Accuracy Comparison of the Dexcom G5, Abbott Freestyle Libre Pro, and Senseonics Eversense CGM Devices in an Outpatient Study of Subjects with Type 1 Diabetes

There are no published studies directly comparing the accuracy of continuous glucose monitoring (CGM) devices in the outpatient setting. We tested the performance of the Dexcom G5, Abbot Freestyle Libre Pro, and Senseonics Eversense (an implantable CGM approved in Europe) during a 6-week, free-living, outpatient bionic pancreas study involving 23 subjects with type 1 diabetes who wore all 3 devices concomitantly. The primary outcome was the mean absolute relative difference (MARD) vs. plasma glucose (PG) values measured with the Nova Biomedical StatStrip Xpress meter that was also used for calibrations according to the manufacturer's instruction (except for Libre Pro that is not calibrated). We compared PG values with CGM readings when they were available from all 3 CGMS in the 5 minutes preceding the PG values (n=829 sets). Since the Libre Pro records readings every 15 minutes, we also did a two-way comparison between the G5 and the Eversense that allowed a higher number of comparisons (n=2277 sets). Statistical significance was determined using a repeated measurements model fitted with the generalized estimating equation method. All 3 CGM systems produced higher average MARDs than during in-clinic studies. However, since all three CGM systems were worn by the same individuals and used the same meter for calibration and as comparator, we were able to directly compare their performance under real-world conditions. In the 3-way comparison Eversense achieved the lowest nominal MARD (14.8%) followed by Dexcom G5 (16.3%) and Libre Pro (18.0%) (Eversense vs. Libre Pro p=0.004, other comparisons p=NS). In the 2-way comparison the MARD difference between Eversense (15.1%) and G5 (16.9%) was statistically significant (p=0.008). We found that the point accuracy of the Eversense was significantly better than two other CGM systems. The Eversense CGM system may be useful to provide glucose values to artificial pancreas devices. Disclosure R.Z. Jafri: None. C.A. Balliro: None. F. El-Khatib: Stock/Shareholder; Self; Beta Bionics. Employee; Self; Beta Bionics. M. Maheno: None. M.A. Hillard: None. A.J. O'Donovan: None. R. Selagamsetty: None. H. Zheng: None. E. Damiano: Other Relationship; Self; Beta Bionics. S.J. Russell: Other Relationship; Self; Beta Bionics, Novo Nordisk Inc.. Advisory Panel; Self; Companion Medical, Tandem Diabetes Care, Inc., Unomedical a/s. Research Support; Self; Beta Bionics, Zealand Pharma A/S, MITRE Corporation.

Continuous Glucose Monitoring (CGM) or Blood Glucose Monitoring (BGM): Interactions and Implications.

At the 2017 10th annual International Conference on Advanced Technologies and Treatments for Diabetes (ATTD) in Paris, France, four speakers presented their perspectives on the roles of continuous glucose monitoring (CGM) and of blood glucose monitoring (BGM) in patient management within one symposium. These presentations included discussions of the differences in the accuracy of CGM and BGM, a clinical perspective on the physiological reasons behind differences in CGM and BGM values, and an overview of the impact of variations in device accuracy on patients with diabetes. Subsequently a short summary of these presentations is given, highlighting the value of good accuracy of BGM or CGM systems and the ongoing need for standardization. The important role of both BGM and CGM in patient management was a theme across all presentations.

Accuracy of Continuous Glucose Monitoring before, during, and after Aerobic and Anaerobic Exercise in Patients with Type 1 Diabetes Mellitus.

Continuous glucose monitoring (CGM) plays an important role in treatment decisions for patients with type 1 diabetes under conventional or closed-loop therapy. Physical activity represents a great challenge for diabetes management as well as for CGM systems. In this work, the accuracy of CGM in the context of exercise is addressed. Six adults performed aerobic and anaerobic exercise sessions and used two Medtronic Paradigm Enlite-2 sensors under closed-loop therapy. CGM readings were compared with plasma glucose during different periods: one hour before exercise, during exercise, and four hours after the end of exercise. In aerobic sessions, the median absolute relative difference (MARD) increased from 9.5% before the beginning of exercise to 16.5% during exercise (p < 0.001), and then decreased to 9.3% in the first hour after the end of exercise (p < 0.001). For the anaerobic sessions, the MARD before exercise was 15.5% and increased without statistical significance to 16.8% during exercise realisation (p = 0.993), and then decreased to 12.7% in the first hour after the cessation of anaerobic activities (p = 0.095). Results indicate that CGM might present lower accuracy during aerobic exercise, but return to regular operation a few hours after exercise cessation. No significant impact for anaerobic exercise was found.

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

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. 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). 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. 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.

Cost-effectiveness of G5 Mobile continuous glucose monitoring device compared to self-monitoring of blood glucose alone for people with type 1 diabetes from the Canadian societal perspective

Aims: To evaluate the cost-effectiveness of real-time continuous glucose monitoring (CGM) compared to self-monitoring of blood glucose (SMBG) alone in people with type 1 diabetes (T1DM) using multiple daily injections (MDI) from the Canadian societal perspective.Methods: The IMS CORE Diabetes Model (v.9.0) was used to assess the long-term (50 years) cost-effectiveness of real-time CGM (G5 Mobile CGM System; Dexcom, Inc., San Diego, CA) compared with SMBG alone for a cohort of adults with poorly-controlled T1DM. Treatment effects and baseline characteristics of patients were derived from the DIAMOND randomized controlled clinical trial; all other assumptions and costs were sourced from published research. The accuracy and clinical effectiveness of G5 Mobile CGM is the same as the G4 Platinum CGM used in the DIAMOND randomized clinical trial. Base case assumptions included (a) baseline HbA1c of 8.6%, (b) change in HbA1c of –1.0% for CGM users vs –0.4% for SMBG users, and (c) disutilities of –0.0142 for non-severe hypoglycemic events (NSHEs) and severe hypoglycemic events (SHEs) not requiring medical intervention, and –0.047 for SHEs requiring medical resources. Treatment costs and outcomes were discounted at 1.5% per year.Results: The incremental cost-effectiveness ratio for the base case G5 Mobile CGM vs SMBG was $33,789 CAD/quality-adjusted life-year (QALY). Sensitivity analyses showed that base case results were most sensitive to changes in percentage reduction in hypoglycemic events and disutilities associated with hypoglycemic events. The base case results were minimally impacted by changes in baseline HbA1c level, incorporation of indirect costs, changes in the discount rate, and baseline utility of patients.Conclusions: The results of this analysis demonstrate that G5 Mobile CGM is cost-effective within the population of adults with T1DM using MDI, assuming a Canadian willingness-to-pay threshold of $50,000 CAD per QALY.

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.

Inpatient Continuous Glucose Monitoring and Glycemic Outcomes.

Continuous glucose monitoring (CGM) is commonly used in the outpatient setting to improve diabetes management. CGM can provide real-time glucose trends, detecting hyperglycemia and hypoglycemia before the onset of clinical symptoms. In 2011, at the time the Endocrine Society CGM guidelines were published, the society did not recommend inpatient CGM as its efficacy and safety were unknown. While many studies have subsequently evaluated inpatient CGM accuracy and reliability, glycemic outcome studies have not been widely published. In the non-ICU setting, investigational CGM studies have commonly blinded providers and patients to glucose data. Retrospective review of the glucose data reflects increased hypoglycemia detection with CGM. In the ICU setting, data are inconsistent whether CGM can improve glycemic outcomes. Studies have not focused on hospitalized patients with type 1 diabetes mellitus, the population most likely to benefit from inpatient CGM. This article reviews inpatient CGM glycemic outcomes in the non-ICU and ICU setting.

Retrofitting Real-Life Dexcom G5 Data

We proposed in 2014 a retrofitting algorithm to retrospectively increase the accuracy of continuous glucose monitoring (CGM) data by using some blood glucose (BG) measurements. The method proved effective on Dexcom SEVEN Plus when about 10 highly accurate YSI measurements/session were available. In this study, we test the method on Dexcom G5 sensor in a more realistic setup, where only five capillary BG measurements (self-monitoring blood glucose [SMBG]) per 12 h-session are available. Furthermore, we investigate how accuracy is affected by the number of BG measurements. The algorithm was tested in 51 adults and 46 adolescents studied for 7 days with Dexcom G5. Each patient also underwent an ∼12-h hospital admission where frequent SMBG and YSI measurements were collected. First, five SMBGs per 12-h session were used to retrofit the CGM. Then, we varied the number of SMBGs provided to the method from 2 to 10 per 12-h session. Retrofitted CGM traces with five SMBGs per 12-h session have lower mean absolute difference than original CGM, reduced from 16.2 to 10.7 mg/dL (P < 0.001) in adults and from 17.6 to 11.5 mg/dL (P < 0.001) in adolescents, and mean absolute relative difference is reduced from 9.0% to 6.4% (P < 0.001) in adults and from 10.3% to 6.8% (P < 0.001) in adolescents. Reducing the number of BG measurements reduces improvement in the accuracy from >30% with 10 SMBGs per 12-h session to <16% with 2 SMBGs/day. The retrofitting method retrospectively improves the accuracy of CGM data, even if applied to one of the most accurate CGM sensors currently available on the market.

Accuracy and Longevity of an Implantable Continuous Glucose Sensor in the PRECISE Study: A 180-Day, Prospective, Multicenter, Pivotal Trial

It is known that continuous glucose monitoring (CGM) systems can lower mean glucose compared with episodic self-monitoring of blood glucose. Implantable CGM systems may provide additional benefits. We studied the Eversense (Senseonics Inc.) implantable CGM sensor in 71 participants aged 18 years and older with type 1 and type 2 diabetes in a 180-day multinational, multicenter pivotal trial. Participants used the CGM system at home and in the clinic. CGM accuracy was assessed during eight in-clinic visits with the mean absolute relative difference (MARD) for venous reference glucose values >4.2 mmol/L as the primary end point. Secondary end points included Clarke Error Grid Analysis and alarm performance. The primary safety outcome was device-related serious adverse events. This trial is registered with ClinicalTrials.gov, number NCT02154126. The MARD value against reference glucose values >4.2 mmol/L was 11.1% (95% CI 10.5, 11.7). Clarke Error Grid Analysis showed 99.2% of samples in the clinically acceptable error zones A and B. Eighty-one percent of hypoglycemic events were detected by the CGM system within 30 min. No device-related serious adverse events occurred during the study. Our results indicate the safety and accuracy of this new type of implantable CGM system and support it as an alternative for transcutaneous CGM.

Significance and Reliability of MARD for the Accuracy of CGM Systems.

There is a need to assess the accuracy of continuous glucose monitoring (CGM) systems for several uses. Mean absolute relative difference (MARD) is the measure of choice for this. Unfortunately, it is frequently overlooked that MARD values computed with data acquired during clinical studies do not reflect the accuracy of the CGM system only, but are strongly influenced by the design of the study. Thus, published MARD values must be understood not as precise values but as indications with some uncertainty. Data from a recent clinical trial, Monte Carlo simulations, and assumptions about the error distribution of the reference measurements have been used to determine the confidence region of MARD as a function of the number and the accuracy of the reference measurements. The uncertainty of the computed MARD values can be quantified by a newly introduced MARD reliability index (MRI), which independently mirrors the reliability of the evaluation. Thus MARD conveys information on the accuracy of the CGM system, while MRI conveys information on the uncertainty of the computed MARD values. MARD values from clinical studies should not be used blindly but the reliability of the evaluation should be considered as well. Furthermore, it should not be ignored that MARD does not take into account the key feature of CGM sensors, the frequency of the measurements. Additional metrics, such as precision absolute relative difference (PARD) should be used as well to obtain a better evaluation of the CGM performance for specific uses, for example, for artificial pancreas.