Performance comparison of the medtronic sof-sensor and enlite glucose sensors in inpatient studies of individuals with type 1 diabetes

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.

Insight into continuous glucose monitoring: from medical basics to commercialized devices

According to the latest statistics, more than 537 million people around the world struggle with diabetes and its adverse consequences. As well as acute risks of hypo- or hyper- glycemia, long-term vascular complications may occur, including coronary heart disease or stroke, as well as diabetic nephropathy leading to end-stage disease, neuropathy or retinopathy. Therefore, there is an urgent need to improve diabetes management to reduce the risk of complications but also to improve patient's quality life. The impact of continuous glucose monitoring (CGM) is well recognized, in this regard. The current review aims at introducing the basic principles of glucose sensing, including electrochemical and optical detection, summarizing CGM technology, its requirements, advantages, and disadvantages. The role of CGM systems in the clinical diagnostics/personal testing, difficulties in their utilization, and recommendations are also discussed. In the end, challenges and prospects in future CGM systems are discussed and non-invasive, wearable glucose biosensors are introduced. Though the scope of this review is CGMs and provides information about medical issues and analytical principles, consideration of broader use will be critical in future if the right systems are to be selected for effective diabetes management.

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.

Electrochemical glucose sensors in diabetes management: an updated review (2010-2020)

While over half a century has passed since the introduction of enzyme glucose biosensors by Clark and Lyons, this important field has continued to be the focus of immense research activity. Extensive efforts during the past decade have led to major scientific and technological innovations towards tight monitoring of diabetes. Such continued progress toward advanced continuous glucose monitoring platforms, either minimal- or non-invasive, holds considerable promise for addressing the limitations of finger-prick blood testing toward tracking glucose trends over time, optimal therapeutic interventions, and improving the life of diabetes patients. However, despite these major developments, the field of glucose biosensors is still facing major challenges. The scope of this review is to present the key scientific and technological advances in electrochemical glucose biosensing over the past decade (2010-present), along with current obstacles and prospects towards the ultimate goal of highly stable and reliable real-time minimally-invasive or non-invasive glucose monitoring. After an introduction to electrochemical glucose biosensors, we highlight recent progress based on using advanced nanomaterials at the electrode-enzyme interface of three generations of glucose sensors. Subsequently, we cover recent activity and challenges towards next-generation wearable non-invasive glucose monitoring devices based on innovative sensing principles, alternative body fluids, advanced flexible materials, and novel platforms. This is followed by highlighting the latest progress in the field of minimally-invasive continuous glucose monitoring (CGM) which offers real-time information about interstitial glucose levels, by focusing on the challenges toward developing biocompatible membrane coatings to protect electrochemical glucose sensors against surface biofouling. Subsequent sections cover new analytical concepts of self-powered glucose sensors, paper-based glucose sensing and multiplexed detection of diabetes-related biomarkers. Finally, we will cover the latest advances in commercially available devices along with the upcoming future technologies.

Diet-induced differences in estimated plasma glucose concentrations in healthy, non-diabetic adults are detected by continuous glucose monitoring-a randomized crossover trial

Continuous glucose monitors (CGMs) have been developed for diabetic patients for estimating and controlling plasma glucose changes throughout the day. However, elevated postprandial glucose concentrations may also be detrimental for non-diabetic subjects by increasing the risk of developing vascular complications and type 2 diabetes. Therefore, CGMs may also be valuable in clinical research and we hypothesized that diet-induced differences in estimated plasma glucose concentrations in healthy, non-diabetic adults could be detected by the Abbott FreeStyle Libre Pro CGM. In this single-blind randomized cross-over trial, 23 healthy but overweight or obese men and women therefore consumed two diets differing in glycemic load in randomized order for three consecutive days. Based on the CGM measurements, two-hour total areas under the curve (tAUCs) after breakfast, lunch and dinner were calculated. Additionally, postprandial glucose was measured with the CGM and in plasma during a rice meal challenge. The average tAUC was significantly lower on the low GL diet compared to the high GL diet (P < .0001). The same conclusions were drawn when tAUCs for breakfast (P < .0001), lunch (P < .0001) and dinner (P < .0001) were analyzed separately. During the rice meal challenge, significantly higher glucose responses were observed after the low GL period, as monitored by both the CGM device (P < .0001) and the plasma glucose analysis (P < .0001). The difference between the means of both methods was 0.11 mmol/L (1.78%) with a higher glucose value in plasma. The absolute mean difference was 0.66 mmol/L (10.5%). We conclude that the CGM detected diet-induced differences in estimated plasma glucose concentrations, which supports its use not only in clinical practice, but also for research purposes during dietary interventions in non-diabetic participants.

The Impact of Physical Activity on Glycemic Variability Assessed by Continuous Glucose Monitoring in Patients With Type 2 Diabetes Mellitus: A Systematic Review

Aim: Patients with Type 2 Diabetes Mellitus (T2DM) have increased risk of developing vascular complications due to chronic hyperglycemia. Glycemic variability (GV) has been suggested to play an even more important role in the risk of developing diabetic complications than sustained hyperglycemia. Physical activity (PA) has shown reducing effects on mean plasma glucose; however, the effect on GV in T2DM needs further description. The objective of this review is to evaluate the effect of PA on GV, assessed by continuous glucose monitoring (CGM) in people with T2DM. Methods: A systematic literature search was conducted on MEDLINE and Embase to find randomized controlled trials (RCTs) covering the aspects T2DM, PA, and CGM. Following eligibility screening, variables of population characteristics, PA interventions, and GV outcomes were extracted and processed through qualitative synthesis. Risk of bias (ROB) was assessed using Cochrane ROB tool v2.0. Results: Of 1,825 identified articles, 40 full texts were screened. In the ten included RCTs matching the eligibility criteria, sample sizes ranged from nine to 63, mean age from 51 (SD 11) to 65 (SD 2) years and mean T2DM duration from four (SD 3) to ten (SD 6) years. Eight RCTs examined GV following single bouts of exercise, while two RCTs examined GV following training interventions. One RCT applied parallel group design, while nine RCTs applied crossover design. Numeric reductions in GV following acute exercise were seen, with four RCTs reaching statistical significance. Numeric reductions in GV were seen following training interventions, with one RCT reaching statistical significance. Numeric reductions of GV after PA appeared independently of intensity and T2DM progression but higher in participants with high baseline HbA1c and GV than with low. 80% of the trials were evaluated as uncertain/high ROB. Conclusion: The systematic literature search revealed limited and biased evidence showing that acute PA numerically reduced GV in patients with T2DM. PA reduced GV independently of PA intensity and T2DM progression. Prolonged RCTs with low ROB are needed to confirm reducing effects of PA on GV and to assess the influence of patient- and intervention characteristics on the effect of PA on GV.

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.

Faster Compared With Standard Insulin Aspart During Day-and-Night Fully Closed-Loop Insulin Therapy in Type 1 Diabetes: A Double-Blind Randomized Crossover Trial

OBJECTIVE

We evaluated the safety and efficacy of day-and-night fully closed-loop insulin therapy using faster (Faster-CL) compared with standard insulin aspart (Standard-CL) in young adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS

In a double-blind, randomized, crossover trial, 20 participants with type 1 diabetes on insulin pump therapy (11 females, aged 21.3 ± 2.3 years, HbA_1c 7.5 ± 0.5% [58.5 ± 5.5 mmol/mol]) underwent two 27-h inpatient periods with unannounced afternoon moderate-vigorous exercise and unannounced/uncovered meals. We compared Faster-CL and Standard-CL in random order. During both interventions, the fuzzy-logic control algorithm DreaMed GlucoSitter was used. Glucose sensor data were analyzed by intention-to-treat principle with the difference (between Faster-CL and Standard-CL) in proportion of time in range 70-180 mg/dL (TIR) over 27 h as the primary end point.

RESULTS

The proportion of TIR was similar for both arms: 53.3% (83% overnight) in Faster-CL and 57.9% (88% overnight) in Standard-CL (P = 0.170). The proportion of time in hypoglycemia <70 mg/dL was 0.0% for both groups. Baseline-adjusted interstitial prandial glucose increments 1 h after meals were greater in Faster-CL compared with Standard-CL (P = 0.017). The gaps between measured plasma insulin and estimated insulin-on-board levels at the beginning, at the end, and 2 h after the exercise were smaller in the Standard-CL group (P = 0.029, P = 0.003, and P = 0.004, respectively). No severe adverse events occurred.

CONCLUSIONS

Fully closed-loop insulin delivery using either faster or standard insulin aspart was safe and efficient in achieving near-normal glucose concentrations outside postprandial periods. The closed-loop algorithm was better adjusted to the standard insulin aspart.

Advances in Type 1 Diabetes Technology Over the Last Decade

The past 10 years have witnessed rapid advances in the technology used to treat patients with type 1 diabetes (T1D). While the disease burden is still high, these advances have contributed to improvements in both glycaemic control and quality of life for many of those affected. New technologies allow for individualisation of care, as patients are able to work with their providers to determine which systems best fit their lifestyle and needs. In addition, thanks to improved glucose monitoring technologies, patients can now simultaneously improve glycaemic control and reduce hypoglycaemia, thereby mitigating risk for acute and chronic complications. Technological advances in T1D care are rapidly moving us toward increasingly automated devices, which offer the promise of reduced disease burden. In this article, we review advances in glucose monitoring, insulin and glucagon delivery, and the applications and algorithms seeking to integrate novel technologies.

Technological advances shaping diabetes care

PURPOSE OF REVIEW

To emphasize the current unmet needs for patients with diabetes and evaluate the recent technological advances in the diabetes field and summarize upcoming technologies in diabetes care. This review highlights emerging diabetes technologies and patient-centered diabetes management.

RECENT FINDINGS

A review of the literature showed that there is a clear benefit of using diabetes technologies in diabetes care. Recently, the US Food and Drug Administration (FDA) created a new category of Class II integrated continuous glucose monitoring (iCGM) devices and announced new guidelines to accelerate the approval of future products. With the first-generation hybrid-closed loop, a new era opened in automated insulin delivery systems. Diabetes coaching, apps, and remote monitoring technologies eased access to the providers and increased patient's self-confidence for diabetes management.

SUMMARY

Improvements in diabetes technologies will hopefully overcome unmet needs for patients with diabetes and improve health outcomes. Patients will benefit from the upcoming technologies in their day-to-day diabetes management while providers may monitor patients remotely with ease and efficiently. These developments will decrease diabetes burden, improve quality of life, and open a new era of personalized diabetes care.

The Hybrid Closed-Loop System: Evolution and Practical Applications

Achievement of well-controlled blood glucose is essential for preventing complications in patients with type 1 diabetes. Since the inception of continuous subcutaneous insulin infusion, the aim has been to develop an artificial pancreas, with the ability to use an automated algorithm to deliver one or more hormones in response to blood glucose with the intent to keep blood sugar as close to a prespecified target as possible. Development and rapid improvement of continuous glucose sensor technology has recently allowed swift progress toward a fully closed-loop insulin delivery system. In 2017, Medtronic began marketing the 670G insulin pump with Guardian 3 sensor. When in auto mode, this is a hybrid closed-loop insulin delivery system that automatically adjusts basal insulin delivery every 5 min based on sensor glucose to maintain blood glucose levels as close to a specific target as possible. Patients receive prandial insulin by entering carbohydrate amount into the bolus calculator. Early studies show improvement in HbA1c in both adults and adolescents with this technology. Initial safety trials showed no occurrence of diabetic ketoacidosis or hypoglycemia. The utility of this device is limited by blood glucose targets of 120 and 150 mg/dL that are unacceptably high for some patients. Notwithstanding recent advances, we are far from a system that is able to replicate islet function in the form of a fully automated, multihormonal blood glucose control device.

Comparison of the pharmacodynamics of protamine zinc insulin and insulin degludec and validation of the continuous glucose monitoring system iPro2 in healthy cats

With the aim to improve current therapeutic and monitoring options for diabetic cats, the present study compared pharmacodynamic parameters of protamine zinc insulin (PZI) and insulin degludec and validated the continuous glucose monitoring system (CGMS) iPro2 with Sof-sensor and Enlite-sensor focusing on the low glycemic range. Three doses (0.1, 0.2 and 0.3IU/kg) of the two insulin preparations and the CGMS iPro2 with two different sensors were tested in six healthy cats. After each insulin administration, onset of action, time to glucose nadir and duration of action were calculated by measuring glucose concentrations with a portable blood glucose meter (PBGM). After sensor placement, paired PBGM and sensor glucose measurements were done and analytical and clinical accuracy were calculated according to the ISO 15197:2013 criteria. Onset of action, time to glucose nadir and glucose nadir were similar for both insulin formulations. Duration of action of insulin degludec was significantly longer than those of PZI at 0.1IU/kg (P=0.043) and 0.2IU/kg (P=0.043). Overall, 166/191 (87%) Sof-sensor measurements and 106/121 (88%) Enlite-sensor measurements met ISO criteria for analytical accuracy, and all sensor measurements fulfilled ISO criteria for clinical accuracy. Insulin degludec was well tolerated in healthy cats and showed longer duration of action than PZI. Further studies on the use of insulin degludec in diabetic cats might be recommended. Both sensors had good clinical accuracy, when used with the CGMS iPro2, but the analytical accuracy was below the minimum set by ISO 15197:2013.

Evaluation of subcutaneous glucose monitoring systems under routine environmental conditions in patients with type 1 diabetes

Continuous and flash glucose monitoring (GM) systems have been established in diabetes care. We compared the sensor performance of 3 commercially available GM systems. A total of 12 patients with type 1 diabetes were included in a single-centre, open-label study in which the sensor performance of the Abbott FreeStyle libre (Abbott), Dexcom G4 Platinum (Dexcom) and Medtronic MiniMed 640G (Medtronic) systems over 12 hours was compared during mimicked real-life conditions (meals, exercise, hypo- and hyperglycaemia). Sensor performance was determined by fulfilment of ISO 15197:2013 criteria, calculating mean absolute relative difference (MARD), and was also illustrated using Parkes error grid and Bland-Altman plots. Sensor performance during changes in metabolic variables (lactate, betahydroxybutyrate, glucagon, non-esterified-fatty-acids) was determined by Spearman's rank correlation coefficient testing. The systems fulfilled ISO 15197:2013 criteria by 73.2% (Abbott), 56.1% (Dexcom) and 52.0% (Medtronic). The MARDs ± standard deviation in the entire glycaemic range were 13.2% ± 10.9% (Abbott), 16.8% ± 12.3% (Dexcom) and 21.4% ± 17.6% (Medtronic), respectively. All sensors performed less accurately during hypoglycaemia and best during hyperglycaemia. We did not observe an influence of metabolic variables on sensor performance.

Sensor Life and Overnight Closed Loop: A Randomized Clinical Trial

BACKGROUND

Closed-loop (CL) systems direct insulin delivery based on continuous glucose monitor (CGM) sensor values. CGM accuracy varies with sensor life, being least accurate on day 1 of sensor insertion. We evaluated the effect of sensor life (enhanced Enlite, Medtronic MiniMed, Northridge, CA) on overnight CL.

METHODS

In an open-label, randomized, 2-period, inpatient crossover pilot study, 12 adolescents on insulin pump (age 16.7 ± 1.9 years; HbA1c 66 ± 10 mmol/mol) attended a clinical research facility on 2 overnight occasions. In random order, participants received CL on day 1 or on day 3-4 after sensor insertion. During both periods, glucose was automatically controlled by a model predictive control algorithm informed by sensor glucose. Plasma glucose was measured every 30 to 60 min.

RESULTS

During overnight CL (22:30 to 07:30), the proportion of time with plasma glucose readings in the target range (3.9-8.0 mmol/l, primary endpoint) when initiated on day 1 of sensor insertion vs day 3-4 were comparable (58 ± 32% day 1 vs 56 ± 36% day 3-4; P = .34), and there were no significant differences between interventions in terms of mean plasma glucose ( P = .26), percentage time above 8.0 mmol/l ( P = .49), and time spent below 3.9 mmol/l ( P = .93). Sensor accuracy varied with sensor life (mean absolute relative difference 19.8 ± 15.0% on day 1 and 13.7 ± 10.2% on day 3 to 4). Sensor glucose tended to under-read plasma glucose inflating benefits of CL on glucose control.

CONCLUSIONS

In spite of differences in sensor accuracy, overnight CL glucose control informed by sensor glucose on day 1 or day 3-4 after sensor insertion was comparable. The model predictive controller appears to mitigate against sensor inaccuracies.

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator

BACKGROUND

Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia.

METHOD

We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events.

RESULTS

The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min).

CONCLUSION

The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.

Continuous glucose monitoring for patients with type 1 diabetes and impaired awareness of hypoglycaemia (IN CONTROL): a randomised, open-label, crossover trial

BACKGROUND

Patients with type 1 diabetes who have impaired awareness of hypoglycaemia have a three to six times increased risk of severe hypoglycaemia. We aimed to assess whether continuous glucose monitoring (CGM) improves glycaemia and prevents severe hypoglycaemia compared with self-monitoring of blood glucose (SMBG) in this high-risk population.

METHODS

We did a randomised, open-label, crossover trial (IN CONTROL) at two medical centres in the Netherlands. Eligible participants were patients diagnosed with type 1 diabetes according to American Diabetes Association criteria, aged 18-75 years, with impaired awareness of hypoglycaemia as confirmed by a Gold score of at least 4, and treated with either continuous subcutaneous insulin infusion or multiple daily insulin injections and doing at least three SMBG measurements per day. After screening, re-education about diabetes management, and a 6-week run-in phase (to obtain baseline CGM data), we randomly assigned patients (1:1) with a computer-generated allocation sequence (block size of four) to either 16 weeks of CGM followed by 12 weeks of washout and 16 weeks of SMBG, or 16 weeks of SMBG followed by 12 weeks of washout and 16 weeks of CGM (where the SMBG phase was the control). During the CGM phase, patients used a real-time CGM system consisting of a Paradigm Veo system with a MiniLink transmitter and an Enlite glucose sensor (Medtronic, CA, USA). During the SMBG phase, patients were equipped with a masked CGM device, consisting of an iPro 2 continuous glucose monitor and an Enlite glucose sensor, which does not display real-time glucose values. The number of SMBG measurements per day and SMBG systems were not standardised between patients, to mimic real-life conditions. During both intervention periods, patients attended follow-up visits at the centres each month and had telephone consultations 2 weeks after each visit inquiring about adverse events, episodes of hypoglycaemia, etc. The primary endpoint was the mean difference in percentage of time spent in normoglycaemia (4-10 mmol/L) over the total intervention periods, analysed on an intention-to-treat basis. Severe hypoglycaemia (requiring third party assistance) was a secondary endpoint. This trial is registered with ClinicalTrials.gov, number NCT01787903.

FINDINGS

Between March 4, 2013, and Feb 9, 2015, we recruited and randomly assigned 52 patients to either the CGM-SMBG sequence (n=26) or the SMBG-CGM sequence (n=26). The last patient visit was on March 21, 2016. Time spent in normoglycaemia was higher during CGM than during SMBG: 65·0% (95% CI 62·8-67·3) versus 55·4% (53·1-57·7; mean difference 9·6%, 95% CI 8·0-11·2; p<0·0001), with reductions in both time spent in hypoglycaemia (ie, blood glucose ≤3·9 mmol/L [6·8% vs 11·4%, mean difference 4·7%, 3·4-5·9; p<0·0001]) and time spent in hyperglycaemia (ie, blood glucose >10 mmol/L [28·2% vs 33·2%, mean difference 5·0%, 3·1-6·9; p<0·0001]). During CGM, the number of severe hypoglycaemic events was lower (14 events vs 34 events, p=0·033). Five serious adverse events other than severe hypoglycaemia occurred during the trial, but all were deemed unrelated to the trial intervention. Additionally, no mild to moderate adverse events were related to the trial intervention.

INTERPRETATION

CGM increased time spent in normoglycaemia and reduced severe hypoglycaemia in patients with type 1 diabetes and impaired awareness of hypoglycaemia, compared with SMBG. Our results support the concept of using CGM in this high-risk population.

FUNDING

Eli Lilly and Sanofi.

Continuous Glucose Monitoring: Impact on Hypoglycemia

The necessity of strict glycemic control is unquestionable. However, hypoglycemia remains a major limiting factor in achieving satisfactory glucose control, and evidence is mounting to show that hypoglycemia is not benign. Over the past decade, evidence has consistently shown that real-time continuous glucose monitoring improves glycemic control in terms of lowering glycated hemoglobin levels. However, real-time continuous glucose monitoring has not met the expectations of the diabetes community with regard to hypoglycemia prevention. The earlier trials did not demonstrate any effect on either mild or severe hypoglycemia and the effect of real-time continuous glucose monitoring on nocturnal hypoglycemia was often not reported. However, trials specifically designed to reduce hypoglycemia in patients with a high hypoglycemia risk have demonstrated a reduction in hypoglycemia, suggesting that real-time continuous glucose monitoring can prevent hypoglycemia when it is specifically used for that purpose. Moreover, the newest generation of diabetes technology currently available commercially, namely sensor-augmented pump therapy with a (predictive) low glucose suspend feature, has provided more convincing evidence for hypoglycemia prevention. This article provides an overview of the hypoglycemia outcomes of randomized controlled trials that investigate the effect of real-time continuous glucose monitoring alone or sensor-augmented pump therapy with a (predictive) low glucose suspend feature. Furthermore, several possible explanations are provided why trials have not shown a reduction in severe hypoglycemia. In addition, existing evidence is presented of real-time continuous glucose monitoring in patients with impaired awareness of hypoglycemia who have the highest risk of severe hypoglycemia.

HBA1C AND MEAN GLUCOSE DERIVED FROM SHORT-TERM CONTINUOUS GLUCOSE MONITORING ASSESSMENT DO NOT CORRELATE IN PATIENTS WITH HBA1C >8

OBJECTIVE

Optimum therapy for patients with diabetes depends on both acute and long-term changes in plasma glucose, generally assessed by glycated hemoglobin (HbA1c) levels. However, the correlation between HbA1c and circulating glucose has not been fully determined. Therefore, we carefully examined this correlation when glucose levels were assessed by continuous glucose monitoring (CGM).

METHODS

Fifty-one patients (70% female, 30% male) were examined; among them were 28 with type 1 diabetes and 23 with type 2 diabetes. Clinically determined HbA1c levels were compared with blood glucose determined by CGM during a short time period.

RESULTS

Changes in HbA1c levels up to 8.0% showed a clear and statistically strong correlation (R = 0.6713; P<.0001) with mean blood glucose levels measured by CGM, similar to that observed in the A1c-derived Average Glucose study in which patients were monitored for a longer period. However, we found no statistical correlation (R = 0.0498; P = .83) between HbA1c and CGM-assessed glucose levels in our patient population when HbA1c was >8.0%.

CONCLUSION

Short-term CGM appears to be a good clinical indicator of long-term glucose control (HbA1c levels); however, cautions should be taken while interpreting CGM data from patients with HbA1c levels >8.0%. Over- or underestimation of the actual mean glucose from CGM data could potentially increase the risks of inappropriate treatment. As such, our results indicate that a more accurate analysis of CGM data might be useful to adequately tailor clinical treatments.

ABBREVIATIONS

ADAG = A1c-Derived Average Glucose CGM = continuous glucose monitoring %CV = percent coefficient of variation HbA1c = glycated hemoglobin.

Comparison of Two Continuous Glucose Monitoring Systems, Dexcom G4 Platinum and Medtronic Paradigm Veo Enlite System, at Rest and During Exercise

BACKGROUND

Despite technological advances, the accuracy of continuous glucose monitoring (CGM) systems may not always be satisfactory with rapidly changing glucose levels, as is notable during exercise. We compare the performance of two current and widely used CGM systems, Dexcom G4 Platinum (Dexcom) and Medtronic Paradigm Veo Enlite system (Enlite), during both rest and exercise in adults with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Paired sensor and plasma glucose (PG) values (total of 431 data pairs for Dexcom and 425 for Enlite) were collected from 17 adults (37.3 ± 13.6 years) with T1D. To evaluate and compare the accuracy of sensor readings, criteria involving sensor bias (sensor minus PG levels), absolute relative difference (ARD), and percentage of readings meeting International Organization for Standardization (ISO) criteria were considered.

RESULTS

Both Dexcom and Enlite performed equally well during the rest period, with respective mean/median biases of -0.12/-0.02 mmol/L versus -0.18/-0.40 (P = 0.78, P = 0.66) mmol/L and ARDs of 13.77/13.34% versus 12.38/11.95% (P = 0.53, P = 0.70). During exercise, sensor bias means/medians were -0.40/-0.21 mmol versus -0.26/-0.24 mmol/L (P = 0.67, P = 0.62) and ARDs were 22.53/15.13% versus 20.44/14.11% (P = 0.58, P = 0.68) for Dexcom and Enlite, respectively. Both sensors demonstrated significantly lower performance during exercise; median ARD comparison at rest versus exercise for both Dexcom and Enlite showed a P = 0.02. More data pairs met the ISO criteria for Dexcom and Enlite at rest, 73.6% and 76.9% compared with exercise 48.2% and 53.9%.

CONCLUSION

Dexcom and Enlite demonstrated comparable overall performances during rest and physical activity. However, a lower accuracy was observed during exercise for both sensors, necessitating a fine-tuning of their performance with physical activity.

The long-term treatment of a patient with type 1 diabetes mellitus and glutaric aciduria type 1: the effect of insulin

The coexistence of two diseases associated with different metabolic disorders is a very rare event. Some associations, although sporadic, can be particularly challenging both in terms of diagnostic and therapeutic management and in terms of theoretical perspective. Here, we report a child affected by type 1 diabetes mellitus (T1DM) and glutaric aciduria type 1 (GA1). The child was diagnosed with classical T1DM at 15 months of age, with a tendency toward hypoglycemia. A few months later, during an acute intercurrent infective episode, the child displayed acute hypotonia of the lower limbs and limbs dystonia. A brain MRI showed bilateral striatal necrosis, suggesting GA1 diagnosis. Treatment with a low-lysine dietary regimen and carnitine supplementation was started and resulted in an improvement in metabolic control and a reduction of hypoglycemic episodes along with an increasing in insulin daily dose. After 2 years, the neurological outcome consisted of a reduction in dystonic movements and a metabolic stability of both diseases.

CONCLUSION

This case provides some insight into the reciprocal interconnections between the two metabolic disorders. Similar pathogenic mechanisms responsible for the neuronal injury might have impacted each other, and a strict relationship between a specific aspect of GA1-impaired metabolism and glucose homeostasis might explain how the tailored management of GA1 was not only effective in controlling the disease, but it also resulted in an improvement in the control of the glycemic profile. What in known: • Glutaric aciduria type 1 (GA1) usually presents in childhood with severe and possibly irreversible neuronal damage, triggered by a catabolic stress • The association of GA1 with other diseases, including type 1 diabetes mellitus (T1DM), is a rare event, complicating the treatment management What is new: • Insulin treatment has a role in preventing GA1 metabolic decompensation, even in the catabolic condition of hypoglycemia • Promoting GA1 metabolic equilibrium by tailoring drug and dietary treatment in our patient affect by T1DM has a positive impact also in improving glycemic balance.

Taking a Closer Look--Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients with Type 2 Diabetes Mellitus Under Basal-Bolus Insulin Therapy

BACKGROUND

Inpatient glucose management is based on four daily capillary blood glucose (BG) measurements. The aim was to test the capability of continuous glucose monitoring (CGM) for assessing the clinical impact and safety of basal-bolus insulin therapy in non-critically ill hospitalized patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

Eighty-four patients with T2DM (age, 68±10 years; glycosylated hemoglobin, 72±28 mmol/mol; body mass index, 31±7 kg/m(2)) were treated with basal-bolus insulin. CGM was performed with the iPro(®)2 system (Medtronic MiniMed, Northridge, CA) and calibrated retrospectively.

RESULTS

A remarkable consistency between CGM and BG measurements and therapy improvement was shown over the study period of 501 patient-days. The number of CGM and BG measurements (CGM/BG) in the range from 3.9-10 mmol/L increased from 67.7%/67.2% (on Day 1) to 77.5%/78.6% (on the last day) (P<0.04). The number of low glycemic episodes (3.3 to <3.9 mmol/L) during nighttime detected by CGM was 15-fold higher, and the number of episodes >13.9 mmol/L detected by CGM during night was 12.5-fold higher than the values from the BG measurements. Ninety-nine percent of data points were in the clinically accurate or acceptable Clarke Error Grid Zones A+B, and the relative numbers of correctly identified episodes of <3.9 and >13.9 mmol/L detected by CGM (sensitivity) were 47.3% and 81.5%, respectively.

CONCLUSIONS

Our data exhibit a good agreement between overall CGM and BG measurements, but there were a high number of missed hypo- and hyperglycemic episodes with BG measurements, particularly during nighttime. Overall assessment of glycemic control using CGM is feasible, whereas the use of CGM for individualized therapy decisions needs further improvement.

Predictive Low-Glucose Insulin Suspension Reduces Duration of Nocturnal Hypoglycemia in Children Without Increasing Ketosis

OBJECTIVE

Nocturnal hypoglycemia can cause seizures and is a major impediment to tight glycemic control, especially in young children with type 1 diabetes. We conducted an in-home randomized trial to assess the efficacy and safety of a continuous glucose monitor-based overnight predictive low-glucose suspend (PLGS) system.

RESEARCH DESIGN AND METHODS

In two age-groups of children with type 1 diabetes (11-14 and 4-10 years of age), a 42-night trial for each child was conducted wherein each night was assigned randomly to either having the PLGS system active (intervention night) or inactive (control night). The primary outcome was percent time <70 mg/dL overnight.

RESULTS

Median time at <70 mg/dL was reduced by 54% from 10.1% on control nights to 4.6% on intervention nights (P < 0.001) in 11-14-year-olds (n = 45) and by 50% from 6.2% to 3.1% (P < 0.001) in 4-10-year-olds (n = 36). Mean overnight glucose was lower on control versus intervention nights in both age-groups (144 ± 18 vs. 152 ± 19 mg/dL [P < 0.001] and 153 ± 14 vs. 160 ± 16 mg/dL [P = 0.004], respectively). Mean morning blood glucose was 159 ± 29 vs. 176 ± 28 mg/dL (P < 0.001) in the 11-14-year-olds and 154 ± 25 vs. 158 ± 22 mg/dL (P = 0.11) in the 4-10-year-olds, respectively. No differences were found between intervention and control in either age-group in morning blood ketosis.

CONCLUSIONS

In 4-14-year-olds, use of a nocturnal PLGS system can substantially reduce overnight hypoglycemia without an increase in morning ketosis, although overnight mean glucose is slightly higher.

CGM-measured glucose values have a strong correlation with C-peptide, HbA1c and IDAAC, but do poorly in predicting C-peptide levels in the two years following onset of diabetes

AIMS/HYPOTHESIS

The aim of this work was to assess the association between continuous glucose monitoring (CGM) data, HbA1c, insulin-dose-adjusted HbA1c (IDAA1c) and C-peptide responses during the first 2 years following diagnosis of type 1 diabetes.

METHODS

A secondary analysis was conducted of data collected from a randomised trial assessing the effect of intensive management initiated within 1 week of diagnosis of type 1 diabetes, in which mixed-meal tolerance tests were performed at baseline and at eight additional time points through 24 months. CGM data were collected at each visit.

RESULTS

Among 67 study participants (mean age [± SD] 13.3 ± 5.7 years), HbA1c was inversely correlated with C-peptide at each time point (p < 0.001), as were changes in each measure between time points (p < 0.001). However, C-peptide at one visit did not predict the change in HbA1c at the next visit and vice versa. Higher C-peptide levels correlated with increased proportion of CGM glucose values between 3.9 and 7.8 mmol/l and lower CV (p = 0.001 and p = 0.02, respectively) but not with CGM glucose levels <3.9 mmol/l. Virtually all participants with IDAA1c < 9 retained substantial insulin secretion but when evaluated together with CGM, time in the range of 3.9-7.8 mmol/l and CV did not provide additional value in predicting C-peptide levels.

CONCLUSIONS/INTERPRETATION

In the first 2 years after diagnosis of type 1 diabetes, higher C-peptide levels are associated with increased sensor glucose levels in the target range and with lower glucose variability but not hypoglycaemia. CGM metrics do not provide added value over the IDAA1c in predicting C-peptide levels.

Factors associated with nocturnal hypoglycemia in at-risk adolescents and young adults with type 1 diabetes

BACKGROUND

Hypoglycemia remains an impediment to good glycemic control, with nocturnal hypoglycemia being particularly dangerous. Information on major contributors to nocturnal hypoglycemia remains critical for understanding and mitigating risk.

MATERIALS AND METHODS

Continuous glucose monitoring (CGM) data for 855 nights were studied, generated by 45 subjects 15-45 years of age with hemoglobin A1c (HbA1c) levels of ≤8.0% who participated in a larger randomized study. Factors assessed for potential association with nocturnal hypoglycemia (CGM measurement of <60 mg/dL for ≥30 min) included bedtime blood glucose (BG), exercise intensity, bedtime snack, insulin on board, day of the week, previous daytime hypoglycemia, age, gender, HbA1c level, diabetes duration, daily basal insulin, and daily insulin dose.

RESULTS

Hypoglycemia occurred during 221 of 885 (25%) nights and was more frequent with younger age (P<0.001), lower HbA1c levels (P=0.006), medium/high-intensity exercise during the preceding day (P=0.003), and the occurrence of antecedent daytime hypoglycemia (P=0.001). There was a trend for lower bedtime BG levels to be associated with more frequent nocturnal hypoglycemia (P=0.10). Bedtime snack, before bedtime insulin bolus, weekend versus weekday, gender, and daily basal and bolus insulin were not associated with nocturnal hypoglycemia.

CONCLUSIONS

Awareness that HbA1c level, exercise, bedtime BG level, and daytime hypoglycemia are all modifiable factors associated with nocturnal hypoglycemia may help patients and providers decrease the risk of hypoglycemia at night. Risk for nocturnal hypoglycemia increased in a linear fashion across the range of variables, with no clear-cut thresholds to guide clinicians or patients for any particular night.

Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring

BACKGROUND

The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes.

MATERIALS AND METHODS

A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to "replay" real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows.

RESULTS

In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels ≤50 mg/dL and BG levels ≤39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD =10%. Similarly, hyperglycemia (frequency of BG levels ≥250 mg/dL and BG levels ≥400 mg/dL) increased and displayed an abrupt slope change at MARD=10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively.

CONCLUSIONS

Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD=10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

Real-time continuous glucose monitoring reduces the duration of hypoglycemia episodes: a randomized trial in very low birth weight neonates

OBJECTIVES

Hypoglycemia is frequent in very low birth weight (VLBW) neonates and compromises their neurological outcome. The aim of this study was to compare real-time continuous glucose monitoring system (RT-CGMS) to standard methods by intermittent capillary blood glucose testing in detecting and managing hypoglycemia.

STUDY DESIGN

Forty-eight VLBW neonates were enrolled in this prospective study. During their 3 first days of life, their glucose level was monitored either by RT-CGMS (CGM-group), or by intermittent capillary glucose testing (IGM-group) associated with a blind-CGMS to detect retrospectively missed hypoglycemia. Outcomes were the number and duration of hypoglycemic (≤ 50 mg/dl) episodes per patient detected by CGMS.

RESULTS

Forty-three monitorings were analyzed (IGM n = 21, CGM n = 22), with a median recording time of 72 hours. In the IGM group, blind-CGMS revealed a significantly higher number of hypoglycemia episodes than capillary blood glucose testing (1.2 ± 0.4 vs 0.4 ± 0.2 episode/patient, p<0.01). In the CGM-group, the use of RT-CGMS made it possible (i) to detect the same number of hypoglycemia episodes as blind-CGMS (1.2 ± 0.4 episode/patient), (ii) to adapt the glucose supply in neonates with hypoglycemia (increased supply during days 1 and 2), and (iii) to significantly reduce the duration of hypoglycemia episodes per patient (CGM 44[10-140] min versus IGM 95[15-520] min, p<0.05). Furthermore, it reduced the number of blood samples (CGM 16.9 ± 1.0 vs IGM 21.9 ± 1.0 blood sample/patient, p<0.001).

CONCLUSION

RT-CGMS played a beneficial role in managing hypoglycemia in VLBW neonates by adjusting the carbohydrate supply to the individual needs and by reducing the duration of hypoglycemia episodes. The clinical significance of the biological differences observed in our study need to be explored.

Experience with the Enlite sensor in a multicenter pediatric study

PURPOSE

The purpose of this article is to outline the experience of certified diabetes educators (CDEs) using the Enlite continuous glucose monitoring sensor system in a pediatric multicenter randomized controlled trial. Continuous glucose monitoring (CGM) is becoming popular as a tool for educators and families to improve glycemic control. CGM can be a valuable educational tool to demonstrate to the user the impact of insulin dosing and effects of physical activity, food intake, and other life events such as work, illness, and stress on their glycemic control. The authors will share education tips and practical applications for diabetes educators to facilitate education and sustained use of Enlite glucose sensors in children and adolescents using insulin pump therapy.

CONCLUSIONS

The Enlite glucose sensor is a comfortable and user-friendly device. Improvements to both the insertion device and the Enlite glucose sensor have resulted in improved level of comfort on insertion and with ongoing wear, which may translate into greater adherence and effectiveness.

A clinical trial of the accuracy and treatment experience of the Dexcom G4 sensor (Dexcom G4 system) and Enlite sensor (guardian REAL-time system) tested simultaneously in ambulatory patients with type 1 diabetes

BACKGROUND

Continuous glucose monitoring (CGM) is a tool widely used in the treatment of patients with type 1 diabetes. The purpose of the current study was to evaluate whether accuracy and patient treatment satisfaction differ between the Enlite™ (Medtronic MiniMed, Inc., Northridge, CA) and Dexcom(®) (San Diego, CA) G4 PLATINUM CGM sensors.

SUBJECTS AND METHODS

Thirty-eight ambulatory patients with type 1 diabetes used the Dexcom G4 and Enlite sensors simultaneously for a minimum of 4 and maximum of 6 days. Patients measured capillary glucose levels with a HemoCue(®) (Ängelholm, Sweden) system six to 10 times a day. In addition, two inpatient studies were performed between Days 1-3 and 4-6.

RESULTS

The mean absolute relative difference (MARD) in blood glucose for the Dexcom G4 was significantly lower (13.9%) than for the Enlite sensor (17.8%) (P<0.0001). The corresponding MARDs for Days 1-3 were 15.0% versus 19.4% (P=0.0027) and 13.6% versus 15.9% (P=0.026) for Days 4-6. For glucose levels in the hypoglycemic range (<4.0 mmol/L), the MARD for the Dexcom G4 was 20.0% compared with 34.7% for the Enlite (P=0.0041). On a visual analog scale (VAS) (0-100), patients rated the Dexcom G4 more favorably than the Enlite in 12 out of the 13 user experience questions. For example, more patients rated their experience with the Dexcom G4 as positive (VAS, 79.7 vs. 46.6; P<0.0001) and preferred to use it in their daily lives (VAS, 79.1 vs. 42.1; P<0.0001).

CONCLUSIONS

The Dexcom G4 sensor was associated with greater overall accuracy than the Enlite sensor during initial (Days 1-3) and later (Days 4-6) use and for glucose levels in the hypoglycemic range. Patients reported a significantly more positive experience using the Dexcom G4 than the Enlite.

Safety of closed-loop therapy during reduction or omission of meal boluses in adolescents with type 1 diabetes: a randomized clinical trial

We evaluated the safety and efficacy of closed-loop therapy with meal announcement during reduction and omission of meal insulin boluses in adolescents with type 1 diabetes (T1D). Twelve adolescents with T1D [six male; mean (s.d.) age 15.9 (1.8) years; mean (s.d.) glycated haemoglobin (HbA1c) 77 (27) mmol/mol] were studied in a randomized crossover study comparing closed-loop therapy with meal announcement with conventional pump therapy over two 24-h stays at a clinical research facility. Identical meals were given on both occasions. The evening meal insulin bolus was calculated to cover half of the carbohydrate content of the meal and no bolus was delivered for lunch. Plasma glucose levels were in the target range of 3.9-10 mmol/l for a median [interquartile range (IQR)] of 74 (55,86)% of the time during closed-loop therapy with meal announcement and for 62 (49,75)% of the time during conventional therapy (p = 0.26). Median (IQR) time spent with plasma glucose levels > 10 mmol/l [23 (13,39) vs. 27 (10,50)%; p = 0.88] or < 3.9 mmol/l [1(0,4) vs. 5 (1,10)%; p = 0.24] and mean [standard deviation (SD)] glucose levels [8.0 (7.6,9.3) vs. 7.7 (6.6,10.1) mmol/l, p = 0.79] were also similar. In conclusion, these results assist home testing of closed-loop delivery with meal announcement in adolescents with poorly controlled T1D who miscalculate or miss meal insulin boluses.

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia

BACKGROUND

The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian(®) REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia.

SUBJECTS AND METHODS

CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics.

RESULTS

The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm.

CONCLUSIONS

The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms.

Accuracy and acceptability of the 6-day Enlite continuous subcutaneous glucose sensor

OBJECTIVE

This study evaluated the performance and acceptability of the Enlite(®) glucose sensor (Medtronic MiniMed, Inc., Northridge, CA).

SUBJECTS AND METHODS

Ninety adults with type 1 or type 2 diabetes wore two Enlite sensors on the abdomen and/or buttock for 6 days and calibrated them at different frequencies. On Days 1, 3, and 6, accuracy was evaluated by comparison of sensor glucose values with frequently sampled plasma glucose values collected over a 12-h period. Accuracy was assessed at different reference glucose concentrations and during times when absolute glucose concentration rates of change were <1, 1-2, and >2 mg/dL/min. The sensor's ability to detect hypoglycemia or hyperglycemia was evaluated with simulated alerts. Subject satisfaction was evaluated with a 7-point Likert-type questionnaire, with a score of 7 indicating strong agreement.

RESULTS

With abdomen sensors under actual-use calibration (mean, 2.8 ± 0.9 times/day), the overall mean (median) absolute relative difference (ARD) values between sensor and reference values were 13.6% (10.1%); the corresponding buttock sensor ARD values were 15.5% (10.5%). With abdomen sensors under minimal calibration (mean, 1.2 ± 0.9 times/day), the mean (median) ARD values were 14.7% (10.8%). Mean ARD values of abdomen sensors at rates of change of <1, 1-2, and >2 mg/dL/min were 13.6%, 12.9%, and 16.3%, respectively. With abdomen sensors, 79.5% and 94.1% of hypoglycemic and hyperglycemic events, respectively, were correctly detected; 81.9% and 94.9% of hypoglycemic and hyperglycemic alerts, respectively, were confirmed. The failure rates for abdomen and buttock sensors were 19.7% and 13.9%, respectively. Mean responses to survey questions for all subjects related to comfort and ease of use were favorable.

CONCLUSIONS

The Enlite sensor provided accurate data at different glucose concentrations and rates of change. Subjects found the sensor comfortable and easy to use.