Hypoglycemia impairs quality of blood glucose simulation in a clinical decision support system

Background

Clinical decision support systems allow for decisions based on blood glucose simulations. The DiasNet simulation tool is based on accepted principles of physiology and simulates blood glucose concentrations accurately in type 1 diabetes mellitus (T1DM) patients during periods without hypoglycemia, but deviations appear after hypoglycemia, possibly because of the long-term glucose counter-regulation to hypoglycemia. The purpose of this study was to evaluate the impact of hypoglycemia on blood glucose simulations.

Method

Continuous glucose monitoring (CGM) data and diary data (meals, insulin, self-monitored blood glucose) were collected for 2 to 5 days from 17 T1DM patients with poor glycemic control. Hypoglycemic episodes [CGM glucose <63 mg/dl (3.5 mmol/liter) for ≥20 min] were identified in valid (well-calibrated) CGM data. For 24 hours after each hypoglycemic episode, a simulated (DiasNet) glucose profile was compared to the CGM glucose.

Results

A total of 52 episodes of hypoglycemia were identified in valid data. All subjects had at least one hypoglycemic episode. Ten episodes of hypoglycemia from nine subjects were eligible for analysis. The CGM glucose was significantly (p < .05) higher than simulated blood glucose for a period of 13 h, beginning 8 h after hypoglycemia onset.

Conclusions

The present data show that hypoglycemia introduces substantial and systematic simulation errors for up to 24 h after hypoglycemia. This underlines the need for further evaluation of mechanisms behind this putative long-term glucose counter-regulation to hypoglycemia. When using blood glucose simulations in decision support systems, the results indicate that simulations for several hours following a hypoglycemic event may underestimate glucose levels by 100 mg/dl (5.6 mmol/liter) or more.     

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

Background

An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM.

Methods

Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures.

Results

With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively.

Conclusions

The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient’s glycemic control and in initiating necessary measures for improving glycemic control.     

Detection of asymptomatic drug-induced hypoglycemia using continuous glucose monitoring in older people – Systematic review

Aim

We conducted a systematic review of the use of continuous glucose monitoring (CGM) in older patients, in order to consolidate the growing evidence base in this area.

Continuous Glucose Monitors and the Burden of Tight Glycemic Control in Critical Care: Can They Cure the Time Cost?

Background

Tight glycemic control (TGC) in critical care has shown distinct benefits but has also proven to be difficult to obtain. The risk of severe hypoglycemia (<40 mg/dl) raises significant concerns for safety. Added clinical burden has also been an issue. Continuous glucose monitors (CGMs) offer frequent automated measurement and thus the possibility of using them for early detection and intervention of hypoglycemic events. Additionally, regular measurement by CGM may also be able to reduce clinical burden.

Aim

An in silico study investigates the potential of CGM devices to reduce clinical effort in a published TGC protocol.

Methods

This study uses retrospective clinical data from the Specialized Relative Insulin Nutrition Titration (SPRINT) TGC study covering 20 patients from a benchmark cohort. Clinically validated metabolic system models are used to generate a blood glucose (BG) profile for each patient, resulting in 33 continuous, separate BG episodes (6881 patient hours). The in silico analysis is performed with three different stochastic noise models: two Gaussian and one first-order autoregressive. The noisy, virtual CGM BG values are filtered and used to drive the SPRINT TGC protocol. A simple threshold alarm is used to trigger glucose interventions to avert potential hypoglycemia. The Monte Carlo method was used to get robust results from the stochastic noise models.

Results

Using SPRINT with simulated CGM noise, the BG time in an 80–110 mg/dl band was reduced no more than 4.4% to 45.2% compared to glucometer sensors. Antihypoglycemic interventions had negligible effect on time in band but eliminated all recorded hypoglycemic episodes in these simulations. Assuming 4–6 calibration measurements per day, the nonautomated clinical measurements are reduced from an average of 16 per day to as low as 4. At 2.5 min per glucometer measurement, a daily saving of ∼25–30 min per patient could potentially be achieved.

Conclusions

This paper has analyzed in silico the use of CGM sensors to provide BG input data to the SPRINT TGC protocol. A very simple algorithm was used for early hypoglycemic detection and prevention and tested with four different-sized intravenous glucose boluses. Although a small decrease in time in band (still clinically acceptable) was experienced with the addition of CGM noise, the number of hypoglycemic events was reduced. The reduction to time in band depends on the specific CGM sensor error characteristics and is thus a trade-off for reduced nursing workload. These results justify a pilot clinical trial to verify this study.     

The Accuracy and Efficacy of the Dexcom G4 Platinum Continuous Glucose Monitoring System

Background:

The purpose of this study was to evaluate the accuracy and efficacy of Dexcom G4 Platinum CGM System.

Methods:

Seventy-two subjects enrolled at 4 US centers; 61% were male; 83% had T1DM and17% had T2DM. Subjects wore at least 1 system for up to 7 days. Subjects participated in a total of 36 hours in the clinic to contribute YSI reference glucose measurements with venous blood draws every 15 minutes on study Day 1, Day 4, and Day 7.

Results:

The overall mean absolute relative difference (ARD) versus YSI was 13% with a median of 10%. Precision ARD was 9% ± 4% between 2 sensors with a 7% coefficient of variation. The mean ARD versus SMBG was 14% with a median of 11%. One hundred two (94%) sensors lasted 7 days and the systems displayed 97% of their expected glucose readings in average. The time spent in low CGM readings during nighttime hours decreased from the first night use to the 6th night (P < .001) with a small difference in average CGM glucose from 147 ± 40 mg/dL to 166 ± 62 mg/dL. There were no serious adverse events or infectious complications reported.

Conclusions:

The study showed the Dexcom G4 Platinum CGM System is one of the most accurate CGMs. The significant reduction in nocturnal time spent in a hypoglycemic state observed during this study suggests that a longer term study of CGM use, especially nocturnal use, could be beneficial for patients with hypoglycemia unawareness.     

A Semilogarithmic Scale for Glucose Provides a Balanced View of Hyperglycemia and Hypoglycemia

Objective

It would be desirable to improve the ability of physicians and patients to identify hypoglycemic episodes when viewing displays of glucose by date, time of day, or day of the week.

Research Design and Methods

A logarithmic scale is utilized for display of glucose versus date and time of day using a range of 40 to 400 mg/dl. Several plausible alternatives are considered for transformation of the glucose data.

Result

Use of a semilogarithmic plot triples the percentage of the vertical axis allocated to hypoglycemia (e.g., 40–80 mg/dl) from 10% to 30.1% while compressing the hyperglycemic region. The log scale improves the symmetry of the glucose distribution. Transformations were evaluated corresponding to the Schlichtkrull M₁₀₀ value, the high blood glucose index/low blood glucose index of Kovatchev and associates, an index of glycemic control developed by the present author, and the GRADE score of Hill and coworkers. Results are similar for all four transformations. This approach is applicable both to self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM). Based on preliminary results, it is proposed that the log transform could potentially facilitate analysis of glucose patterns and may facilitate rapid and consistent detection and appreciation of the severity and consistency of hypoglycemic episodes, even in the presence of complex overlapping patterns commonly observed in both SMBG and CGM glucose profiles.

Conclusion

Display of glucose on a logarithmic scale can potentially improve the accuracy of analysis and interpretation of popular methods for graphic display of glucose values. Device manufacturers should consider including options for semilogarithmic display of glucose on SMBG meters, CGM sensors, and software for retrospective analyses of glucose data.     

Time Delay of CGM Sensors: Relevance,Causes, and Countermeasures

Background:

Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these.

Methods:

CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay.

Results:

Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average.

Conclusions:

Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.     

Hypoglycemia in Type 2 Diabetes - More Common Than You Think: A Continuous Glucose Monitoring Study

Background:

Hypoglycemia is often the limiting factor for intensive glucose control in diabetes management, however its actual prevalence in type 2 diabetes (T2DM) is not well documented.

Methodology:

A total of 108 patients with T2DM wore a continuous glucose monitoring system (CGMS) for 5 days. Rates and patterns of hypoglycemia and glycemic variability (GV) were calculated. Patient and medication factors were correlated with rates, timing, and severity of hypoglycemia.

Results:

Of the patients, 49.1% had at least 1 hypoglycemic episode (mean 1.74 episodes/patient/ 5 days of CGMS) and 75% of those patients experienced at least 1 asymptomatic hypoglycemic episode. There was no significant difference in the frequency of daytime versus nocturnal hypoglycemia. Hypoglycemia was more frequent in individuals on insulin (alone or in combination) (P = .02) and those on oral hypoglycemic agents (P < .001) compared to noninsulin secretagogues. CGMS analysis resulted in treatment modifications in 64% of the patients. T2DM patients on insulin exhibited higher glycemic variability (GV) scores (2.3 ± 0.6) as compared to those on oral medications (1.8 ± 0.7, P = .017).

Conclusions:

CGMS can provide rich data that show glucose excursions in diabetes patients throughout the day. Consequently, unwarranted onset of hypo- and hyperglycemic events can be detected, intervened, and prevented by using CGMS. Hypoglycemia was frequently unrecognized by the patients in this study (75%), which increases their potential risk of significant adverse events. Incorporation of CGMS into the routine management of T2DM would increase the detection and self-awareness of hypoglycemia resulting in safer and potentially better overall control.     

Multisite Study of an Implanted Continuous Glucose Sensor Over 90 Days in Patients With Diabetes Mellitus

Background:

Continuous glucose monitoring (CGM), which enables real-time glucose display and trend information as well as real-time alarms, can improve glycemic control and quality of life in patients with diabetes mellitus. Previous reports have described strategies to extend the useable lifetime of a single sensor from 1-2 weeks to 28 days. The present multisite study describes the characterization of a sensing platform achieving 90 days of continuous use for a single, fully implanted sensor.

Method:

The Senseonics CGM system is composed of a long-term implantable glucose sensor and a wearable smart transmitter. Study subjects underwent subcutaneous implantation of sensors in the upper arm. Eight-hour clinic sessions were performed every 14 days, during which sensor glucose values were compared against venous blood lab reference measurements collected every 15 minutes using mean absolute relative differences (MARDs).

Results:

All subjects (mean ± standard deviation age: 43.5 ± 11.0 years; with 10 sensors inserted in men and 14 in women) had type 1 diabetes mellitus. Most (22 of 24) sensors reported glucose values for the entire 90 days. The MARD value was 11.4 ± 2.7% (range, 8.1-19.5%) for reference glucose values between 40-400 mg/dl. There was no significant difference in MARD throughout the 90-day study (P = .31). No serious adverse events were noted.

Conclusions:

The Senseonics CGM, composed of an implantable sensor, external smart transmitter, and smartphone app, is the first system that uses a single sensor for continuous display of accurate glucose values for 3 months.     

Performance Evaluations of a Novel Continuous Glucose Monitor: Performance Evaluation of a Continuous Glucose Monitoring System under Conditions Similar to Daily Life

Background

This study aimed at evaluating and comparing the performance of a new generation of continuous glucose monitoring (CGM) system versus other CGM systems, under daily lifelike conditions.

Methods

A total of 10 subjects (7 female) were enrolled in this study. Each subject wore two Dexcom G4™ CGM systems in parallel for the sensor lifetime specified by the manufacturer (7 days) to allow assessment of sensor-to-sensor precision. Capillary blood glucose (BG) measurements were performed at least once per hour during daytime and once at night. Glucose excursions were induced on two occasions. Performance was assessed by calculating the mean absolute relative difference (MARD) between CGM readings and paired capillary BG readings and precision absolute relative difference (PARD), i.e., differences between paired CGM readings.

Results

Overall aggregate MARD was 11.0% (n = 2392). Aggregate MARD for BG <70 mg/dl was 13.7%; for BG between 70 and 180 mg/dl, MARD was 11.4%; and for BG >180 mg/dl, MARD was 8.5%. Aggregate PARD was 7.3%, improving from 11.6% on day 1 to 5.2% on day 7.

Conclusions

The Dexcom G4 CGM system showed good overall MARD compared with results reported for other commercially available CGM systems. In the hypoglycemic range, where CGM performance is often reported to be low, the Dexcom G4 CGM system achieved better MARD than that reported for other CGM systems in the hypoglycemic range. In the hyperglycemic range, the MARD was comparable to that reported for other CGM systems, whereas during induced glucose excursions, the MARD was similar or slightly worse than that reported for other CGM systems. Overall PARD was 7.3%, improving markedly with sensor life time.     

Comparative Performance Assessment of Point-of-Care Testing Devices for Measuring Glucose and Ketones at the Patient Bedside

Background:

Point-of-care (POC) testing devices for monitoring glucose and ketones can play a key role in the management of dysglycemia in hospitalized diabetes patients. The accuracy of glucose devices can be influenced by biochemical changes that commonly occur in critically ill hospital patients and by the medication prescribed. Little is known about the influence of these factors on ketone POC measurements. The aim of this study was to assess the analytical performance of POC hospital whole-blood glucose and ketone meters and the extent of glucose interference factors on the design and accuracy of ketone results.

Methods:

StatStrip glucose/ketone, Optium FreeStyle glucose/ketone, and Accu-Chek Performa glucose were also assessed and results compared to a central laboratory reference method. The analytical evaluation was performed according to Clinical and Laboratory Standards Institute (CLSI) protocols for precision, linearity, method comparison, and interference.

Results:

The interferences assessed included acetoacetate, acetaminophen, ascorbic acid, galactose, maltose, uric acid, and sodium. The accuracies of both Optium ketone and glucose measurements were significantly influenced by varying levels of hematocrit and ascorbic acid. StatStrip ketone and glucose measurements were unaffected by the interferences tested with exception of ascorbic acid, which reduced the higher level ketone value. The accuracy of Accu-Chek glucose measurements was affected by hematocrit, by ascorbic acid, and significantly by galactose. The method correlation assessment indicated differences between the meters in compliance to ISO 15197 and CLSI 12-A3 performance criteria.

Conclusions:

Combined POC glucose/ketone methods are now available. The use of these devices in a hospital setting requires careful consideration with regard to the selection of instruments not sensitive to hematocrit variation and presence of interfering substances.     

Statistical Hypoglycemia Prediction

Background

Hypoglycemia presents a significant risk for patients with insulin-dependent diabetes mellitus. We propose a predictive hypoglycemia detection algorithm that uses continuous glucose monitor (CGM) data with explicit certainty measures to enable early corrective action.

Method

The algorithm uses multiple statistical linear predictions with regression windows between 5 and 75 minutes and prediction horizons of 0 to 20 minutes. The regressions provide standard deviations, which are mapped to predictive error distributions using their averaged statistical correlation. These error distributions give confidence levels that the CGM reading will drop below a hypoglycemic threshold. An alarm is generated if the resultant probability of hypoglycemia from our predictions rises above an appropriate, user-settable value. This level trades off the positive predictive value against lead time and missed events.

Results

The algorithm was evaluated using data from 26 inpatient admissions of Navigator® 1-minute readings obtained as part of a DirecNet study. CGM readings were postprocessed to remove dropouts and calibrate against finger stick measurements. With a confidence threshold set to provide alarms that correspond to hypoglycemic events 60% of the time, our results were (1) a 23-minute mean lead time, (2) false positives averaging a lowest blood glucose value of 97 mg/dl, and (3) no missed hypoglycemic events, as defined by CGM readings. Using linearly interpolated FreeStyle capillary glucose readings to define hypoglycemic events provided (1) the lead time was 17 minutes, (2) the lowest mean glucose with false alarms was 100 mg/dl, and (3) no hypoglycemic events were missed.

Conclusion

Statistical linear prediction gives significant lead time before hypoglycemic events with an explicit, tunable trade-off between longer lead times and fewer missed events versus fewer false alarms.     

Effect on glycemic control by short- and long-term use of continuous glucose monitoring in clinical practice

Background

In Sweden, patients with diabetes mellitus frequently receive short-term (<3 months) continuous glucose monitoring (CGM) to study glucose patterns or long-term CGM to treat poor glycemic control or severe hypoglycemia. The effects of CGM on glycemic control in clinical practice in relation to indication and duration of use has not been completely studied.

Methods

Patients with diabetes, among which 99% were diagnosed as type 1, receiving CGM at 10 outpatient clinics in Sweden were studied retrospectively. Long-term use of CGM was defined as ≥3 months use of CGM and short-term as <3 months. A control group matched on start date and date of latest value 3 months after the start was selected for both long- and short-term groups.

Results

In 34 long-term users of CGM, over a mean follow-up of 1.1 years, the adjusted mean difference of hemoglobin A1c (HbA1c) compared with controls (n = 408) was -0.76 (95% confidence interval -1.17; -0.33, p < .001). Long-term users with indications for high HbA1c (n = 15) had a reduction of 1.2% in HbA1c from 10.1 to 8.9% (p = .003), whereas patients with hypoglycemia as their indication (n = 16) decreased by 0.3% (p = .17). Nonsevere hypoglycemic events decreased in long-term users within the same follow-up period (p = .004). Short-term users showed no statistically significant improvement in HbA1c compared with controls at 1.1 years (n = 41), p = .85 or at 2.6 years (n = 43), p = .19.

Conclusion

Long-term CGM use was associated with improved glycemic control in clinical practice and a reduction in nonsevere hypoglycemic events, whereas short-term use had no effect on HbA1c. The effect on glycemic control varied by indication.     

Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

Background:

The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series.

Methods:

Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data.

Results:

LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series.

Conclusions:

Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.     

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

Introduction:

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.

Methods:

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.

Results:

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.

Conclusions:

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.     

Performance Evaluations of a Novel Continuous Glucose Monitor: Performance Evaluation of Three Continuous Glucose Monitoring Systems: Comparison of Six Sensors per Subject in Parallel

Background:

This study is aimed at comparing the performance of three continuous glucose monitoring (CGM) systems following the Clinical and Laboratory Standards Institute’s POCT05-A guideline, which provides recommendations for performance evaluation of CGM systems.

Methods:

A total of 12 subjects with type 1 diabetes were enrolled in this study. Each subject wore six CGM systems in parallel, two sensors of each CGM system [FreeStyle Navigator™ (Navigator), MiniMed Guardian® REAL-Time with Enlite sensor (Guardian), DexCom™ Seven® Plus 3rd generation (Seven Plus)]. Each sensor was used for the lifetime specified by the manufacturer. To follow POCT05-A recommendations, glucose excursions were induced on two separate occasions, and venous and capillary blood glucose (BG) concentrations were obtained every 15 min for five consecutive hours. Capillary BG concentrations were measured at least once per hour during the day and once at night. Parameters investigated were CGM-to-BG differences [mean absolute relative difference (MARD)] and sensor-to-sensor differences [precision absolute relative difference (PARD)].

Results:

Compared with capillary BG reference readings, the Navigator showed the lowest MARD, with 12.1% overall and 24.6% in the hypoglycemic range; for the Guardian and the Seven Plus, MARD was 16.2%/34.9% and 16.3%/32.7%, respectively. PARD also was lowest for the Navigator (9.6%/9.8%), followed by the Seven Plus (16.7%/25.5%) and the Guardian (18.1%/20.2%). During induced glucose excursions, MARD between CGM and BG was, again, lowest for the Navigator (14.3%), followed by the Seven Plus (15.8%) and the Guardian (19.2%).

Conclusions:

In this study, two sensors of each of the three CGM systems were compared in a setting following POCT05-A recommendations. The Navigator CGM system achieved more accurate results than the Guardian or the Seven Plus with respect to MARD and PARD. Performance in the hypoglycemic range was markedly worse for all CGM systems when compared with BG results.     

Clinical Accuracy of a Continuous Glucose Monitoring System With an Advanced Algorithm

Background:

We assessed the performance of a modified Dexcom G4 Platinum system with an advanced algorithm, in comparison with frequent venous samples measured on a laboratory reference (YSI) during a clinic session and in comparison to self-monitored blood glucose (SMBG) during home use.

Methods:

Fifty-one subjects with diabetes were enrolled in a prospective multicenter study. Subjects wore 1 sensor for 7-day use and participated in one 12-hour in-clinic session on day 1, 4, or 7 to collect YSI reference venous glucose every 15 minutes and capillary SMBG test every 30 minutes. Carbohydrate consumption and insulin dosing and timing were manipulated to obtain data in low and high glucose ranges.

Results:

In comparison with the laboratory reference method (n = 2,263) the system provided a mean and median absolute relative differences (ARD) of 9.0% and 7.0%, respectively. The mean absolute difference for CGM was 6.4 mg/dL when the YSIs were within hypoglycemia ranges (≤ 70 mg/dL). The percentage in the clinically accurate Clarke error grid A zone was 92.4% and in the benign error B zone was 7.1%. Majority of the sensors (73%) had an aggregated MARD in reference to YSI ≤ 10%. The MARD of CGM-SMBG for home use was 11.3%.

Conclusions:

The study showed that the point and rate accuracy, clinical accuracy, reliability, and consistency over the duration of wear and across glycemic ranges were superior to current commercial real-time CGM systems. The performance of this CGM is reaching that of a self-monitoring blood glucose meter in real use environment.     

Continuous Monitoring of Glucose in Subcutaneous Tissue Using Microfabricated Differential Affinity Sensors

Objective

We describe miniaturized differential glucose sensors based on affinity binding between glucose and a synthetic polymer. The sensors possess excellent resistance to environmental disturbances and can potentially allow wireless measurements of glucose concentrations within interstitial fluid in subcutaneous tissue for long-term, stable continuous glucose monitoring (CGM).

Methods

The sensors are constructed using microelectromechanical systems (MEMS) technology and exploit poly(N-hydroxy-ethyl acrylamide-ran-3-acrylamidophenylboronic acid) (PHEAA-ran-PAAPBA), a glucose-binding polymer with excellent specificity, reversibility, and stability. Two sensing approaches have been investigated, which respectively, use a pair of magnetically actuated diaphragms and perforated electrodes to differentially measure the glucose-binding-induced changes in the viscosity and permittivity of the PHEAA-ran-PAAPBA solution with respect to a reference, glucose-unresponsive polymer solution.

Results

In vivo characterization of the MEMS affinity sensors were performed by controlling blood glucose concentrations of laboratory mice by exogenous glucose and insulin administration. The sensors experienced an 8–30 min initialization period after implantation and then closely tracked commercial capillary glucose meter readings with time lags ranging from 0–15 min during rapid glucose concentration changes. Clarke error grid plots obtained from sensor calibration suggest that, for the viscometric and dielectric sensors, respectively, approximately 95% (in the hyperglycemic range) and 84% (ranging from hypoglycemic to hyperglycemic glucose concentrations) of measurement points were clinically accurate, while 5% and 16% of the points were clinically acceptable.

Conclusions

The miniaturized MEMS sensors explore differential measurements of affinity glucose recognition. In vivo testing demonstrated excellent accuracy and stability, suggesting that the devices hold the potential to enable long-term and reliable CGM in clinical applications.     

Optimum Subcutaneous Glucose Sampling and Fourier Analysis of Continuous Glucose Monitors

Background

The objective of this article was to focus on the application of harmonic decomposition to continuous glucose monitor (CGM) measurements. We show evidence of an attenuation of fast variations of interstitial glucose when compared to blood in type 1 diabetes mellitus (T1DM) and, using information theory, propose optimal sampling schedules associated with the use and study of CGMs.

Method

Using a cohort of 26 T1DM subjects, wearing two Navigator™ sensors for 1 to 3 days, we analyzed the frequency content of each glucose signal and derived across subject frequency cutoffs using discrete Fourier transform and common signal processing techniques.

Results

We observed a significant difference in the frequency content of blood glucose compared to interstitial glucose in T1DM, providing evidence toward the existence of a diffusion process between blood and interstitial glucose, acting as a low-pass filter. Furthermore, we obtained a 15-minutes sampling schedule for optimal comparison of CGM values to blood reference.

Conclusion

Blood glucose and interstitial glucose have different dynamics, as shown by harmonic analysis, and these differences have consequences on advisable schedules for accuracy studies of CGMS.     

Determinants of the accuracy of continuous glucose monitoring in non-critically ill patients with heart failure or severe hyperglycemia

Background

The accuracy of continuous glucose monitoring (CGM) in non-critically ill hospitalized patients with heart failure or severe hyperglycemia (SH) is unknown.

Methods

Hospitalized patients with congestive heart failure (CHF) exacerbation (receiving IV or subcutaneous insulin) or SH requiring insulin infusion were compared to outpatients referred for retrospective CGM.

Results

Forty-three patients with CHF, 15 patients with SH, and 88 outpatients yielded 470, 164, and 2150 meter–sensor pairs, respectively. Admission glucose differed (188 versus 509 mg/dl in CHF and SH, p < .001) but not the first sensor glucose (p = .35). In continuous glucose error grid analysis, 67–78% of pairs during hypoglycemia were in zones A+B (p = .63), compared with 98–100% in euglycemia (p < .001) and 98%, 92%, and 99% (p = .001) during hyperglycemia for the CHF, SH, and outpatient groups, respectively. Mean absolute relative difference (MARD) was lower in the CHF versus the SH group in glucose strata above 100 mg/dl, but there was no difference between the CHF and outpatient groups. Linear regression models showed that CHF versus outpatient, SH versus CHF, and coefficient of variation were significant predictors of higher MARD. Among subjects with CHF, MARD was not associated with brain natriuretic peptide or change in plasma volume, but it was significantly higher in subjects randomized to IV insulin (p = .04).

Conclusions

The results suggest that SH and glycemic variability are more important determinants of CGM accuracy than known CHF status alone in hospitalized patients.