Accuracy Of Continuous Glucose Monitoring Research Articles

Continuous Glucose Monitoring: A Consensus Conference of the American Association of Clinical Endocrinologists and American College of Endocrinology

Barriers to continuous glucose monitoring (CGM) use continue to hamper adoption of this valuable technology for the management of diabetes. The American Association of Clinical Endocrinologists and the American College of Endocrinology convened a public consensus conference February 20, 2016, to review available CGM data and propose strategies for expanding CGM access. Conference participants agreed that evidence supports the benefits of CGM in type 1 diabetes and that these benefits are likely to apply whenever intensive insulin therapy is used, regardless of diabetes type. CGM is likely to reduce healthcare resource utilization for acute and chronic complications, although real-world analyses are needed to confirm potential cost savings and quality of life improvements. Ongoing technological advances have improved CGM accuracy and usability, but more innovations in human factors, data delivery, reporting, and interpretation are needed to foster expanded use. The development of a standardized data report using similar metrics across all devices would facilitate clinician and patient understanding and utilization of CGM. Expanded CGM coverage by government and private payers is an urgent need. CGM improves glycemic control, reduces hypoglycemia, and may reduce overall costs of diabetes management. Expanding CGM coverage and utilization is likely to improve the health outcomes of people with diabetes. A1C = glycated hemoglobin AACE = American Association of Clinical Endocrinologists ACE = American College of Endocrinology ASPIRE = Automation to Simulate Pancreatic Insulin Response CGM = continuous glucose monitoring HRQOL = health-related quality of life ICER = incremental cost-effectiveness ratio JDRF = Juvenile Diabetes Research Foundation MARD = mean absolute relative difference MDI = multiple daily injections QALY = quality-adjusted life years RCT = randomized, controlled trial SAP = sensor-augmented pump SMBG = self-monitoring of blood glucose STAR = Sensor-Augmented Pump Therapy for A1C Reduction T1D = type 1 diabetes T2D = type 2 diabetes.

Nonadjunctive Use of Continuous Glucose Monitoring for Diabetes Treatment Decisions.

While self-monitoring of blood glucose (SMBG) is the current standard used by people with diabetes to manage glucose levels, recent improvements in accuracy of continuous glucose monitoring (CGM) technology are making it very likely that diabetes-related treatment decisions will soon be made based on CGM values alone. Nonadjunctive use of CGM will lead to a paradigm shift in how patients manage their glucose levels and will require substantial changes in how care providers educate their patients, monitor their progress, and provide feedback to help them manage their diabetes. The approval to use CGM nonadjunctively is also a critical step in the pathway toward FDA approval of an artificial pancreas system, which is further expected to transform diabetes care for people with type 1 diabetes. In this article, we discuss how nonadjunctive CGM is expected to soon replace routine SMBG and how this new usage scenario is expected to transform health outcomes and patient care.

Accuracy of Continuous Glucose Monitoring in Patients After Total Pancreatectomy with Islet Autotransplantation.

Among postsurgical and critically ill patients, malglycemia is associated with increased complications. Continuous glucose monitoring (CGM) in the inpatient population may enhance glycemic control. CGM reliability may be compromised by postsurgical complications such as edema or vascular changes. We utilized Clarke Error Grid (CEG) and Surveillance Error Grid (SEG) analysis to evaluate CGM performance after total pancreatectomy with islet autotransplantation. This subanalysis evaluated Medtronic Enlite 2 CGM values against YSI serum glucose in seven post-transplant patients (86% female; 38.6 ± 9.4 years) on artificial pancreas for 72 h at transition from intravenous to subcutaneous insulin. Sensor recalibration occurred for absolute relative difference (ARD) ≥20% x2, ≥30% x1, or by investigator discretion based on trend. Sensor analysis showed mean absolute relative difference (MARD) of 11.0% ± 11.5%. The sensors were recalibrated 8.3 times/day; active sensor was switched 1.4 times/day. Calibration factor was 7.692 ± 3.786 mg/nA·dL (target = 1.5-20 mg/nA·dL). CEG analysis showed 86.1% of pairs in Zone A (clinically accurate zone) and 99.4% of pairs in Zones A + B (low risk of error). SEG analysis of hypoglycemia/hyperglycemia risk showed 92.22% of pairs in the "no risk" zone, 5.96% of pairs in the "slight lower" risk zone, 1.01% of pairs in the "slight higher" risk zone, and only 0.81% of pairs in the "moderate lower" risk zone. Overall performance of the Medtronic Enlite 2 CGM in the post-transplant population was reasonably good with "no risk" or "slight lower" risk by SEG analysis and high CGM-YSI agreement by CEG analysis; however, frequent recalibrations were required in this intensive care population.

Accuracy of continuous glucose monitoring during differing exercise conditions

AimDepending on intensity, exercise may induce a strong hormonal and metabolic response, including acid-base imbalances and changes in microcirculation, potentially interfering with the accuracy of continuous glucose monitoring (CGM). The present study aimed at comparing the accuracy of the Dexcom G4 Platinum (DG4P) CGM during continuous moderate and intermittent high-intensity exercise (IHE) in adults with type 1 diabetes (T1DM). MethodsTen male individuals with well-controlled T1DM (HbA1c 7.0±0.6% [54±6mmol/mol]) inserted the DG4P sensor 2 days prior to a 90min cycling session (50% VO2peak) either with (IHE) or without (CONT) a 10s all-out sprint every 10min. Venous blood samples for reference glucose measurement were drawn every 10min and euglycemia (target 7mmol/l) was maintained using an oral glucose solution. Additionally, lactate and venous blood gas variables were determined. ResultsMean reference blood glucose was 7.6±0.2mmol/l during IHE and 6.7±0.2mmol/l during CONT (p<0.001). IHE resulted in significantly higher levels of lactate (7.3±0.5mmol/l vs. 2.6±0.3mmol/l, p<0.001), while pH values were significantly lower in the IHE group (7.27 vs. 7.38, p=0.001). Mean absolute relative difference (MARD) was 13.3±2.2% for IHE and 13.6±2.8% for CONT suggesting comparable accuracy (p=0.90). Using Clarke Error Grid Analysis, 100% of CGM values during both IHE and CONT were in zones A and B (IHE: 77% and 23%; CONT: 78% and 22%). ConclusionsThe present study revealed good and comparable accuracy of the DG4P CGM system during intermittent high intensity and continuous moderate intensity exercise, despite marked differences in metabolic conditions. This corroborates the clinical robustness of CGM under differing exercise conditions.Clinical trial registration number: ClinicalTrials.gov NCT02068638

Continuous Glucose Monitoring Versus Capillary Point-of-Care Testing for Inpatient Glycemic Control in Type 2 Diabetes Patients Hospitalized in the General Ward and Treated With a Basal Bolus Insulin Regimen.

Continuous glucose monitoring (CGM) may improve the management of patients with type 2 diabetes hospitalized in the general ward by facilitating the detection of hyper- and hypoglycemic episodes. However, the lack of data on the accuracy and safety of CGM have limited its application. A prospective pilot study was conducted including 38 patients hospitalized in the general ward with a known diagnosis of type 2 diabetes mellitus (DM) and hyperglycemic individuals without a history of DM with a blood sugar of 140-400 mg on admission treated with a basal bolus insulin regimen. Inpatient glycemic control and the incidence of hypoglycemic episodes were compared between detection by CGM of interstitial fluid for up to 6 days and point-of-care (POC) capillary blood glucose monitoring performed pre- and postprandially, before bedtime and at 3 am. No differences in average daily glucose levels were observed between CGM and POC (176.2 ± 33.9 vs 176.6 ± 33.7 mg/dl, P = .828). However, CGM detected a higher number of hypoglycemic episodes than POC (55 vs 12, P < .01). Glucose measurements were clinically valid, with 91.9% of patients falling within the Clarke error grid A and B zones. Our preliminary results indicate that the use of CGM in type 2 patients hospitalized in the general ward provides accurate estimation of blood sugar levels and is more effective than POC for the detection of hypoglycemic episodes and asymptomatic hypoglycemia.

Randomized Evaluation of Glycemic Control in the Medical Intensive Care Unit Using Real-Time Continuous Glucose Monitoring (REGIMEN Trial).

Hyperglycemia occurs commonly in patients admitted to medical intensive care units (MICUs). Whether real-time (RT) continuous glucose monitoring (CGM) improves glycemic control and variability and reduces hypoglycemia in severely ill MICU patients with an Acute Physiology and Chronic Health Evaluation II (APACHE-II) score of ≥20 has not been studied. Thirty-five patients (66 ± 10 years of age; APACHE-II score, 28 ± 6) were randomly assigned to RT-CGM (n = 16) using the GlucoDay(®)S (A. Menarini Diagnostics, Florence, Italy) device or to blinded CGM. Insulin was infused using a modified Yale protocol targeting a blood glucose level between 80 and 120 mg/dL. Outcome measures were percentage of time in normoglycemia (80-110 mg/dL) and in hypoglycemia (<60 mg/dL), glycemic variability (SD, coefficient of variation, mean amplitude of glucose excursions, and mean of daily differences), and CGM accuracy (error grid analyses, Bland-Altman bias plot, and mean absolute relative deviation). During 96 h of monitoring, glycemia reached target (80-110 mg/dL) in 37 ± 15%, was between 70 and 180 mg/dL in 91 ± 10%, and <60 mg/dL in 2 ± 2% of the time. In the RT-CGM group glycemia averaged 119 ± 17 mg/dL versus 122 ± 11 mg/dL in the control group. Parameters of glucose variability and percentages of time at target glycemia and in hypoglycemia were similar between groups. GlucoDayS values and arterial glycemia correlated well, with 98.6% of data falling in Zones A and B of the error grid analysis. Mean absolute relative devation was 11.2%. RT-CGM did not ameliorate glucose control or variability; neither did it reduce the number of hypoglycemic events, but our insulin infusion protocol led to overall good glucose control without a significant hypoglycemia risk, making further improvement difficult.

Effect of Continuous Glucose Monitoring Accuracy on Clinicians' Retrospective Decision Making in Diabetes: A Pilot Study.

The use of continuous glucose monitoring (CGM) in clinical decision making in diabetes could be limited by the inaccuracy of CGM data when compared to plasma glucose measurements. The aim of the present study is to investigate the impact of CGM numerical accuracy on the precision of diabetes treatment adjustments. CGM profiles with maximum 5-day duration from 12 patients with type 1 diabetes treated with a basal-bolus insulin regimen were processed by 2 CGM algorithms, with the accuracy of algorithm 2 being higher than the accuracy of algorithm 1, using the median absolute relative difference (MARD) as the measure of accuracy. During 2 separate and similar occasions over a 1-month interval, 3 clinicians reviewed the processed CGM profiles, and adjusted the dose level of basal and prandial insulin. The precision of the dosage adjustments were defined in terms of the interclinician agreement and the intraclinician reproducibility of the decisions. The Cohen's kappa coefficient was used to assess the precision of the decisions. The study was based on retrospective and blind CGM data. For the interclinician agreement, in the first occasion, the kappa of algorithm 1 was .32, and that of algorithm 2 was .36. For the interclinician agreement, in the second occasion, the kappas of algorithms 1 and 2 were .17 and .22, respectively. For the intraclinician reproducibility of the decisions, the kappas of algorithm 1 were .35, .22, and .80 and the kappas of algorithm 2 were .44, .52, and .32, for the 3 clinicians, respectively. For the interclinician agreement, the relative kappa change from algorithm 1 to algorithm 2 was 86.06%, and for the intraclinician reproducibility, the relative kappa change from algorithm 1 to algorithm 2 was 53.99%. Results indicated that the accuracy of CGM algorithms might potentially affect the precision of the CGM-based insulin adjustments for type 1 diabetes patients. However, a larger study with several clinical centers, with higher number of clinicians and patients is required to validate the impact of CGM accuracy on decisions precision.

Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring.

The article by Peyser et al.1 published in this issue of Diabetes Technology & Therapeutics reports accuracy results in the hypoglycemic range for the Dexcom (San Diego, CA) G4 Platinum continuous glucose monitoring (CGM) system (software 505), which uses algorithmic signal processing to convert the raw electrochemical sensor data into calibrated blood glucose (BG) values. In this commentary, we match these results to previously published in silico experiments linking accuracy of CGM to frequency of hypoglycemia. The in silico experiments indicate that, compared with the original Dexcom G4 Platinum sensor, software 505 would result in (1) an approximately 25% reduction in biochemical severe hypoglycemia (defined as reference BG ≤39 mg/dL) during direct point-of-care use of CGM values for insulin dosing and (2) 45% reduction in biochemical severe hypoglycemia if all hypoglycemia alerts issued by the sensor are adequately attended to. In addition, the reported error levels of Dexcom G4 Platinum (software 505) are below the thresholds that would allow CGM use for insulin dosing decisions (i.e., below the thresholds for nonadjunct CGM use) determined by our in silico experiments.

Hypoglycemic Accuracy and Improved Low Glucose Alerts of the Latest Dexcom G4 Platinum Continuous Glucose Monitoring System

Accuracy of continuous glucose monitoring (CGM) devices in hypoglycemia has been a widely reported shortcoming of this technology. We report the accuracy in hypoglycemia of a new version of the Dexcom (San Diego, CA) G4 Platinum CGM system (software 505) and present results regarding the optimum setting of CGM hypoglycemic alerts. CGM values were compared with YSI analyzer (YSI Life Sciences, Yellow Springs, OH) measurements every 15 min. We reviewed the accuracy of the CGM system in the hypoglycemic range using standard metrics. We analyzed the time required for the CGM system to detect biochemical hypoglycemia (70 mg/dL) compared with the YSI with alert settings at 70 mg/dL and 80 mg/dL. We also analyzed the time between the YSI value crossing 55 mg/dL, defined as the threshold for cognitive impairment due to hypoglycemia, and when the CGM system alerted for hypoglycemia. The mean absolute difference for a glucose level of less than 70 mg/dL was 6 mg/dL. Ninety-six percent of CGM values were within 20 mg/dL of the YSI values between 40 and 80 mg/dL. When the CGM hypoglycemic alert was set at 80 mg/dL, the device provided an alert for biochemical hypoglycemia within 10 min in 95% of instances and at least a 10-min advance warning before the cognitive impairment threshold in 91% of instances in the study. Use of an 80 mg/dL threshold setting for hypoglycemic alerts on the G4 Platinum (software 505) may provide patients with timely warning of hypoglycemia before the onset of cognitive impairment, enabling them to treat themselves for hypoglycemia with fast-acting carbohydrates and prevent neuroglycopenia associated with very low glucose levels.

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors.

Minimally invasive continuous glucose monitoring (CGM) sensors measure in the subcutis a current signal, which is converted into interstitial glucose (IG) concentration by a calibration process periodically updated using fingerstick blood glucose (BG) references. Though important in diabetes management, CGM sensors still suffer from accuracy problems. Here, we propose a new online calibration method improving accuracy of CGM glucose profiles with respect to manufacturer calibration. The proposed method fits CGM current signal against the BG references collected twice a day for calibration purposes, by a time-varying calibration function whose parameters are identified in a Bayesian framework using a priori second-order statistical knowledge. The distortion introduced by BG-to-IG kinetics is compensated before parameter identification via nonparametric deconvolution. The method was tested on a database where 108 CGM signals were collected for 7days by the Dexcom G4 Platinum sensor. Results show the new method drives to a statistically significant accuracy improvement as measured by three commonly used metrics: mean absolute relative difference reduced from 12.73% to 11.47%; percentage of accurate glucose estimates increased from 82.00% to 89.19%; and percentage of values falling in the "A" zone of the Clark error grid increased from 82.22% to 88.86%. The new calibration method significantly improves CGM glucose profiles accuracy with respect to manufacturer calibration. The proposed algorithm provides a real-time improvement of CGM accuracy, which can be crucial in several CGM-based applications, including the artificial pancreas, thus providing a potential great impact in the diabetes technology research community.

Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings

The accuracy of continuous glucose monitoring (CGM) systems is often assessed with respect to blood glucose (BG) readings. CGM readings are affected by a physiological and a technical time delay when compared to BG readings. In this analysis, the dependence of CGM performance parameters on the BG rate of change was investigated for 2 CGM systems. Data from a previously published study were retrospectively analyzed. An established CGM system (Dexcom G4, Dexcom, San Diego, CA; system A) and a prototype system (Roche Diagnostics GmbH, Mannheim, Germany; system B) with 2 sensors each were worn by 10 subjects in parallel. Glucose swings were induced to achieve rapidly changing BG concentrations. Mean absolute relative differences (MARD) were calculated in different BG rate-of-change categories. In addition, sensor-to-sensor precision was assessed. At BG rates of change of -1 mg/dl/min to 0 mg/dl/min and 0 mg/dl/min to +1 mg/dl/min, MARD results were 12.6% and 11.3% for system A and 8.2% and 10.0% for system B. At rapidly changing BG concentrations (<-3 mg/dl/min and ≥+3 mg/dl/min), higher MARD results were found for both systems, but system B was less affected (system A: 24.9% and 29.6%, system B: 10.6% and 16.3%). The impact of rate of change on sensor-to-sensor precision was less pronounced. Both systems were affected by rapidly changing BG concentrations to some degree, although system B was mostly unaffected by decreasing BG concentrations. It would seem that technological advancements in CGM systems might allow for a more precise tracking of BG concentrations even at rapidly changing BG concentrations.

Effects of pH, lactate, hematocrit and potassium level on the accuracy of continuous glucose monitoring (CGM) in pediatric intensive care unit.

BackgroundContinuous glucose monitoring (CGM) originally was developed for diabetic patients and it may be a useful tool for monitoring glucose changes in pediatric intensive care unit (PICU). Its use is, however, limited by the lack of sufficient data on its reliability at insufficient peripheral perfusion. We aimed to correlate the accuracy of CGM with laboratory markers relevant to disturbed tissue perfusion.Patients and MethodsIn 38 pediatric patients (age range, 0–18 years) requiring intensive care we tested the effect of pH, lactate, hematocrit and serum potassium on the difference between CGM and meter glucose measurements. Guardian® (Medtronic®) CGM results were compared to GEM 3000 (Instrumentation laboratory®) and point-of-care measurements. The clinical accuracy of CGM was evaluated by Clarke Error Grid -, Bland-Altman analysis and Pearson’s correlation. We used Friedman test for statistical analysis (statistical significance was established as a p < 0.05).ResultsCGM values exhibited a considerable variability without any correlation with the examined laboratory parameters. Clarke, Bland-Altman analysis and Pearson’s correlation coefficient demonstrated a good clinical accuracy of CGM (zone A and B = 96%; the mean difference between reference and CGM glucose was 1,3 mg/dL, 48 from the 780 calibration pairs overrunning the 2 standard deviation; Pearson’s correlation coefficient: 0.83).ConclusionsThe accuracy of CGM measurements is independent of laboratory parameters relevant to tissue hypoperfusion. CGM may prove a reliable tool for continuous monitoring of glucose changes in PICUs, not much influenced by tissue perfusion, but still not appropriate for being the base for clinical decisions.

Point and trend accuracy of continuous glucose monitoring using intravenous microdialysis in critically ill patients

Insulin infusion in critically ill patients mandates frequent measurements of the blood glucose level [1]. Microdialysis is a well-established technology that offers the opportunity to sample blood analytes with high accuracy, without the need for drawing blood samples [2,3]. We aimed to determine point and trend accuracy of microdialysis-based continuous glucose monitoring (CGM) (EIRUS®; Maquet Critical Care AB, Solna, Sweden).

Glucose control in the intensive care unit by use of continuous glucose monitoring: what level of measurement error is acceptable?

Accuracy and frequency of glucose measurement is essential to achieve safe and efficacious glucose control in the intensive care unit. Emerging continuous glucose monitors provide frequent measurements, trending information, and alarms. The objective of this study was to establish the level of accuracy of continuous glucose monitoring (CGM) associated with safe and efficacious glucose control in the intensive care unit. We evaluated 3 established glucose control protocols [Yale, University of Washington, and Normoglycemia in Intensive Care Evaluation and Surviving Using Glucose Algorithm Regulation (NICE-SUGAR)] by use of computer simulations. Insulin delivery was informed by intermittent blood glucose (BG) measurements or CGM levels with an increasing level of measurement error. Measures of glucose control included mean glucose, glucose variability, proportion of time glucose was in target range, and hypoglycemia episodes. Apart from the Washington protocol, CGM with mean absolute relative deviation (MARD) ≤ 15% resulted in similar mean glucose as with the use of intermittent BG measurements. Glucose variability was also similar between CGM and BG-informed protocols. Frequency and duration of hypoglycemia were not worse by use of CGM with MARD ≤ 10%. Measures of glucose control varied more between protocols than at different levels of the CGM error. The efficacy of CGM-informed and BG-informed commonly used glucose protocols is similar, but the risk of hypoglycemia may be reduced by use of CGM with MARD ≤ 10%. Protocol choice has greater influence on glucose control measures than the glucose measurement method.

Performance Comparison of the Medtronic Sof-Sensor and Enlite Glucose Sensors in Inpatient Studies of Individuals with Type 1 Diabetes

Knowledge of the accuracy of continuous glucose monitoring (CGM) devices is important for its use as a management tool for individuals with diabetes and for its use to assess outcomes in clinical studies. Using data from several inpatient studies, we compared the accuracy of two sensors, the Medtronic Enlite™ using MiniMed Paradigm(®) Veo™ calibration and the Sof-Sensor(®) glucose sensor using Guardian(®) REAL-Time CGM calibration (all from Medtronic Diabetes, Northridge, CA). Nocturnal data were analyzed from eight inpatient studies in which both CGM and reference glucose measurements were available. The analyses included 1,666 CGM-reference paired glucose values for the Enlite in 54 participants over 69 nights and 3,627 paired values for the Sof-Sensor in 66 participants over 91 nights. The Enlite sensor tended to report glucose levels lower than the reference over the entire range of glucose values, whereas the Sof-Sensor values tended to be higher than reference values in the hypoglycemic range and lower than reference values in the hyperglycemic range. The overall median sensor-reference difference was -15 mg/dL for the Enlite and -1 mg/dL for the Sof-Sensor (P<0.001). The median relative absolute difference was 15% for the Enlite versus 12% for the Sof-Sensor (P=0.06); 66% of Enlite values and 73% of Sof-Sensor values met International Organization for Standardization criteria. We found that the Enlite tended to be biased low over the entire glucose range, whereas the Sof-Sensor showed the more typical sensor pattern of being biased high in the hypoglycemic range and biased low in the hyperglycemic range.

Prognostic Accuracy of Continuous Glucose Monitoring in the Prediction of Diabetes Mellitus in Children with Incidental Hyperglycemia: Receiver Operating Characteristic Analysis

The purpose of this study is to evaluate continuous glucose monitoring (CGM) in predicting diabetes mellitus in children with incident hyperglycemia (IH) and negativity for some insular autoantibodies. Thirty-one autoantibody-negative children who presented at our center with IH underwent a baseline assessment and were followed up for 23.8 months (range, 6-48 months). At the end of the follow-up, we compared the receiver operating characteristic (ROC) areas under the curve (AUCs) of metabolic markers from 17 children who developed diabetes (Group A; n=17) and 14 children who did not develop diabetes (Group B; n=14). Only two oral glucose tolerance test (OGTT)-derived markers and three CGM-derived markers showed a good prognostic performance, with ROC AUCs indicating significant results (P<0.0001) for the following markers: 2-h glucose, OGTT (0.813; 95% confidence interval [CI] 0.621-0.954); AUC glucose, OGTT (0.832; 95% CI 0.611-0.950); CGM glucose measurement peak (0.803; 95% CI 0.621-0.923); percentage of CGM glucose measurements inside the range 70-125 mg/dL (0.866; 95% CI 0.695-0.961); and percentage of CGM measurements ≥126 mg/dL (0.889; 95% CI 0.724-0.973). The combination of the OGTT-derived markers did not increase the predictive value, but the combination of CGM markers with each other or with the OGTT markers yielded higher ROC AUCs (ranging from 0.828 to 0.945). This is the first study showing that CGM is useful in predicting diabetes mellitus in children with IH.

Accuracy of continuous glucose monitoring during exercise in type 1 diabetes pregnancy.

Performance of continuous glucose monitors (CGMs) may be lower when glucose levels are changing rapidly, such as occurs during physical activity. Our aim was to evaluate accuracy of a current-generation CGM during moderate-intensity exercise in type 1 diabetes (T1D) pregnancy. As part of a study of 24-h closed-loop insulin delivery in 12 women with T1D (disease duration, 17.6 years; glycosylated hemoglobin, 6.4%) during pregnancy (gestation, 21 weeks), we evaluated the Freestyle Navigator(®) sensor (Abbott Diabetes Care, Alameda, CA) during afternoon (15:00-18:00 h) and morning (09:30-12:30 h) exercise (55 min of brisk walking on a treadmill followed by a 2-h recovery), compared with sedentary conditions (18:00-09:00 h). Plasma (reference) glucose, measured at regular 15-30-min intervals with the YSI Ltd. (Fleet, United Kingdom) model YSI 2300 analyzer, was used to assess CGM performance. Sensor accuracy, as indicated by the larger relative absolute difference (RAD) between paired sensor and reference glucose values, was lower during exercise compared with rest (median RAD, 11.8% vs. 18.4%; P<0.001). These differences remained significant when correcting for plasma glucose relative rate of change (P<0.001). Analysis by glucose range showed lower accuracy during hypoglycemia for both sedentary (median RAD, 24.4%) and exercise (median RAD, 32.1%) conditions. Using Clarke error grid analysis, 96% of CGM values were clinically safe under resting conditions compared with only 87% during exercise. Compared with sedentary conditions, accuracy of the Freestyle Navigator CGM was lower during moderate-intensity exercise in pregnant women with T1D. This difference was particularly marked in hypoglycemia and could not be solely explained by the glucose rate of change associated with physical activity.

The Use of Continuous Glucose Monitoring Combined with Computer-Based eMPC Algorithm for Tight Glucose Control in Cardiosurgical ICU

Aim. In postcardiac surgery patients, we assessed the performance of a system for intensive intravenous insulin therapy using continuous glucose monitoring (CGM) and enhanced model predictive control (eMPC) algorithm. Methods. Glucose control in eMPC-CGM group (n = 12) was compared with a control (C) group (n = 12) treated by intravenous insulin infusion adjusted according to eMPC protocol with a variable sampling interval alone. In the eMPC-CGM group glucose measured with a REAL-Time CGM system (Guardian RT) served as input for the eMPC adjusting insulin infusion every 15 minutes. The accuracy of CGM was evaluated hourly using reference arterial glucose and Clarke error-grid analysis (C-EGA). Target glucose range was 4.4–6.1 mmol/L. Results. Of the 277 paired CGM-reference glycemic values, 270 (97.5%) were in clinically acceptable zones of C-EGA and only 7 (2.5%) were in unacceptable D zone. Glucose control in eMPC-CGM group was comparable to C group in all measured values (average glycemia, percentage of time above, within, and below target range,). No episode of hypoglycemia (<2.9 mmol) occurred in eMPC-CGM group compared to 2 in C group. Conclusion. Our data show that the combination of eMPC algorithm with CGM is reliable and accurate enough to test this approach in a larger study population.

Obtaining surrogate consent for a minimal-risk research study in the intensive care unit setting

Obtaining surrogate consent for clinical research studies conducted in the intensive care unit (ICU) setting is logistically challenging. To determine whether differences in proportions consenting to trial enrollment existed among patients eligible to consent directly versus those requiring surrogate decision makers in a minimal-risk study to evaluate the accuracy of continuous glucose monitoring in the ICU setting. Low initial enrollment rates prompted a detailed tracking of the screening and consent process. We analyzed the subset of eligible patients identified during a single year to document whether they were approached about trial enrollment, whether they consented or declined, the reasons for declining, and the method of consent (self or surrogate). The proportion of participants who consented and the reasons for declining were compared for self-consenting and surrogate-consenting participants. Of the 3041 patients screened, one-third (n = 982) were eligible; 119 of the 982 were approached regarding enrollment. Absence of a surrogate accounted for the majority of eligible patients (726; 84%) not approached. The most common reasons for refusal in the self versus surrogate groups included feeling overwhelmed (13% vs 24%), fear of discomfort (22% vs 12%), and fear of risk (7% vs 4%). Of the 57 eligible patients capable of consenting directly, 11 (19%) enrolled versus 12 (19%) of the 62 who required surrogate consent. When recruitment hours were expanded to include evening time, more eligible patients or their surrogates could be approached than during the day-shift hours alone. Consent was obtained for a larger proportion of potential participants with a history of diabetes (40%) than for those without a history of diabetes (14%). The findings are from a subset of the entire study sample; data were available only for participants who could be approached, who may have differed from those who could not be approached. Surrogate and self-consent rates were similar. Surrogate unavailability was a major barrier to enrollment; overlap of staffing with usual visiting hours should be considered when planning trials in the ICU.

Minimizing the Impact of Time Lag Variability on Accuracy Evaluation of Continuous Glucose Monitoring Systems

Despite all commercially available continuous glucose monitoring (CGM) systems being designed to operate in the extracellular interstitial fluid, and even though there is a well-recognized time lag between the interstitial and the venous compartments, the accuracy of the CGM device readings is still evaluated against the glucose concentration in venous blood (VB) samples, thus resulting in a perceived decrease in accuracy. This article explains how different time lag compensation methods (no compensation, compensation with a fixed delay, compensation with a variable delay based on an intercompartmental diffusional model) have an impact on how CGM accuracy is evaluated. The data set used consisted of 210 CGM/blood glucose data pairs from 18 diabetes subjects (15 type 1 and 3 type 2) selected from a data base collected during two independent clinical trials. All CGM measurements were performed using the GlucoMen ®Day CGM system (A. Menarini Diagnostics, Italy), and the reference VB glucose measurements by means of a standard laboratory instrument. For each applied time lag compensation method, the CGM accuracy evaluation was performed as recommended by the POCT05-A consensus guideline. The perceived accuracy of the CGM device significantly improved when applying both the fixed or the variable delay compensation method. However, it is worth noting how the variable delay method, which relies on a closer description of the intercompartmental diffusion processes, provided the best perception of the clinical accuracy of the device. When assessing the accuracy of a CGM system, a crucial step in data analysis is to account for time lag, which enables minimization of the apparent decline in system accuracy.