Alarms for Continuous Glucose Monitors: Alarm Characterization for a Continuous Glucose Monitor That Replaces Traditional Blood Glucose Monitoring

Background

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG); all CGM alarms require SMBG confirmation before treatment. In this report, an analysis method is proposed to determine the CGM threshold alarm accuracy required to eliminate SMBG confirmation.

Method

The proposed method builds on the Clinical and Laboratory Standards Institute (CLSI) guideline for evaluating CGM threshold alarms using data from an in-clinic study of subjects with type 1 diabetes. The CLSI method proposes a maximum time limit of ±30 minutes for the detection of hypo- and hyperglycemic events but does not include limits for glucose measurement accuracy. The International Standards Organization (ISO) standard for SMBG glucose measurement accuracy (ISO 15197) is ±15 mg/dl for glucose <75 mg/dl and ±20% for glucose ≥75 mg/dl. This standard was combined with the CLSI method to more completely characterize the accuracy of CGM alarms.

Results

Incorporating the ISO 15197 accuracy margins, FreeStyle Navigator® CGM system alarms detected 70 mg/dl hypoglycemia within 30 minutes at a rate of 70.3%, with a false alarm rate of 11.4%. The device detected high glucose in the range of 140–300 mg/dl within 30 minutes at an average rate of 99.2%, with a false alarm rate of 2.1%.

Conclusion

Self-monitoring of blood glucose confirmation is necessary for detecting and treating hypoglycemia with the FreeStyle Navigator CGM system, but at high glucose levels, SMBG confirmation adds little incremental value to CGM alarms.     

Alarms for Continuous Glucose Monitors: Alarm Characterization for Continuous Glucose Monitors Used as Adjuncts to Self-Monitoring of Blood Glucose

Background

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG). Alarm evaluation in the Clinical and Laboratory Standards Institute (CLSI) guideline for CGM does not specifically address devices that employ both CGM and SMBG. In this report, an alarm evaluation method is proposed for these devices.

Method

The proposed method builds on the CLSI method using data from an in-clinic study of subjects with type 1 diabetes. CGM was used to detect glycemic events, and SMBG was used to determine treatment. To optimize detection of a single glucose level, such as 70 mg/dl, a range of alarm threshold settings was evaluated. The alarm characterization provides a choice of alarm settings that trade off detection and false alarms. Detection of a range of high glucose levels was similarly evaluated.

Results

Using low glucose alarms, detection of 70 mg/dl within 30 minutes increased from 64 to 97% as alarm settings increased from 70 to100 mg/dl, and alarms that did not require treatment (SMBG >85 mg/dl) increased from 18 to 52%. Using high glucose alarms, detection of 180 mg/dl within 30 minutes increased from 87 to 96% as alarm settings decreased from 180 to 165 mg/dl, and alarms that did not require treatment (SMBG <180 mg/dl) increased from 24 to 42%.

Conclusion

The proposed alarm evaluation method provides information for choosing appropriate alarm thresholds and reflects the clinical utility of CGM alarms.     

Alarms for Continuous Glucose Monitors: Hypoglycemia Detection in Critical Care Using Continuous Glucose Monitors: An in Silico Proof of Concept Analysis

Background

Tight glycemic control (TGC) in critical care has shown distinct benefits but has also been proven difficult to obtain. The risk of severe hypoglycemia (<40 mg/dl) has been increased significantly in several, but not all, studies, raising significant concerns for safety. Continuous glucose monitors (CGMs) offer frequent measurement and thus the possibility of using them for early detection alarms to prevent hypoglycemia.

Methods

This study used retrospective clinical data from the Specialized Relative Insulin Nutrition Titration TGC study covering seven patients who experienced severe hypoglycemic events. Clinically validated metabolic system models were used to recreate a continuous blood glucose profile. In silico analysis was enabled by using a conservative single Gaussian noise model based on reported CGM clinical data from a critical care study [mean absolute percent error (MAPE) 17.4%]. A novel median filter was implemented and further smoothed with a least mean squares-fitted polynomial to reduce sensor noise. Two alarm approaches were compared. An integral-based method is presented that examined the area between a preset threshold and filtered simulated CGM data. An alarm was raised when this value became too low. A simple glycemic threshold method was also used for comparison. To account for random noise skewing the results, each patient record was Monte Carlo simulated 100 times with a different random noise profile for a total of 700 runs. Different alarm thresholds were analyzed parametrically. Results are reported in terms of detection time before the clinically measured event and any false alarms. These retrospective clinical data were used with approval from the New Zealand South Island Regional Ethics Committee.

Results

The median filter reduced MAPE from 17.4% [standard deviation (SD) 13%] to 9.3% (SD 7%) over the cohort. For the integral-based alarm, median per-patient detection times ranged, t, from –35 minutes (before event) to –170 minutes, with zero to two false alarms per patient over the cohort and different alarm parameters. For a simple glycemic threshold alarm (three consecutive values below threshold), median per-patient alarm times were –10 to –75 minutes and false alarms were zero to seven; however, in one case, five of seven subjects never alarmed at all, despite the hypoglycemic event.

Conclusions

A retrospective study used clinical hypoglycemic events from a TGC study to develop and analyze an integral-based hypoglycemia alarm for use in critical care TGC studies. The integral-based approach was accurate, provided significant lead time before a hypoglycemic event, alarmed at higher glycemic levels, was robust to sensor noise, and had minimal false alarms. The approach is readily generalizable to similar scenarios, and results would justify a pilot clinical trial to verify this study.     

Alarms for Continuous Glucose Monitors: Hypoglycemia Alarm Enhancement Using Data Fusion

Background

The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon^®). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features.

Methods

Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of ± standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of ± SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions.

Results

The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels.

Conclusion

Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms.     

Performance Evaluations of a Novel Continuous Glucose Monitor: Overview of a Novel Sensor for Continuous Glucose Monitoring

The core element of a continuous glucose monitoring (CGM) system is the glucose sensor, which should enable reliable CGM readings in the interstitial fluid in subcutaneous tissue for a period of several days. The aim of this article is to describe the layout and constituents of a novel glucose sensor and the rationale behind the measures that were used to optimize its performance. In order to achieve a stable glucose sensor signal, special attention was paid to the sensor materials and architecture, i.e., biocompatible coating of the sensor, limitation of glucose flux into the working electrode, low oxidation potential by use of manganese dioxide, and a tissue-averaging sensor design. A series of in vitro and in vivo evaluations showed that the sensor enables stable and accurate glucose sensing in the subcutaneous tissue for up to 7 days. Parallel measurements with four sensors in a single patient showed a close agreement between these sensors. In summary, this high-performance needle-type glucose sensor is well suited for CGM in patients with diabetes.     

Continuous Glucose Monitoring System in a Rural Intensive Care Unit: A Pilot Study Evaluating Accuracy and Acceptance

Background

Glucose management in an intensive care unit (ICU) is labor-intensive. A continuous glucose monitoring system (CGMS) has the potential to improve efficiency and safety in this setting. The goal of this study was to determine if the Medtronic Guardian^® REAL-Time CGMS was accurate and tolerated by patients in a rural hospital ICU unit.

Method

Differences between individual finger stick blood glucose (FSBG) and CGMS values were compared to American Diabetes Association (ADA) and International Organization for Standardization (ISO) standards. Continuous glucose monitoring system accuracy was evaluated over four ranges: <75, 75–140, 140–200, and >200 mg/dl. Other accuracy measures [mean absolute deviation (MAD), mean absolute relative difference (MARD), and coefficient of linear regression of CGMS on FSBG] were calculated. Nursing staff and patients were surveyed regarding use of the CGMS in the ICU.

Results

Twenty-nine participants had 320 FSBG and corresponding CGMS readings. Sixty-two percent of participants were admitted with diabetic ketoacidosis (DKA). Two hundred and thirteen (66.6%) were accurate within the ISO standard, whereas only 70 out of 320 (21.9%) were within the 5% ADA standard. The CGMS was most accurate in euglycemia. Technical difficulties, such as adequate time for “wetting” and calibration of electrodes, arose with the sensors. The MAD was 28.3 mg/dl, the MRAD was 17.4%, and the linear regression coefficient of CGMS on FSBG was 0.834 (p < 0.001).

Conclusions

The CGMS is well tolerated by ICU patients but, at present, is not sufficiently accurate to be used for therapeutic decisions in the acute setting, particularly in patients with diabetic ketoacidosis. There is a need to find resolution to the technical issues regarding electrode “wetting” and calibration if CGMS use in the ICU setting is to provide an effective means of diabetes care and management.     

Practical Considerations in the Use of Real-Time Continuous Glucose Monitoring Alerts

Background

The safety and efficacy of real-time (RT) continuous glucose monitoring (CGM) systems in the management of type 1 diabetes are increasingly apparent. Clinical trials have demonstrated the utility of these systems in lowering hemoglobin A1c, minimizing hypoglycemia, and reducing glycemic variability. These RT systems allow patients to conveniently monitor their glucose levels by displaying concentration and trending information. Several of these RT systems provide preset alerts that sound when absolute glucose thresholds are reached. Additionally, some systems allow for predictive algorithm-based alerts that incorporate rates of change. However, clinical trials have identified significant noncompliance in the use of these devices, most notably in the pediatric and adolescent populations.A retrospective review of CGM reports shows that many patients set high and low alert thresholds at levels that result in frequent alerts, potentially resulting in patient nuisance, dismissal of consequential alerts, and eventual product abandonment. Therefore, setting the alert thresholds at appropriate high and low settings can determine the balance between either a perceived benefit by the patient and their long-term use of CGM systems or annoyance to the patient and discontinuation.

Conclusion

Care should be taken to set CGM alerts at levels that result in a manageable number of notifications per day. In some cases, providers should consider not using alerts at all or consider using broad targets when initiating CGM to maximize alert specificity. Real-time CGM is safe and generally well tolerated; however, individualization of alert settings is necessary maximize the system''s benefits and patient adherence.     

The Need for Performance Standards for Continuous Glucose Monitors

Self-monitoring blood glucose (SMBG) devices or glucose meters currently provide a means for patients to manage insulin dosing through intermittent monitoring of blood glucose levels several times a day, but newer continuous glucose monitor devices (CGM) offer the potential of real-time glucose monitoring with less pain and lower cost. Unlike SMBG devices that sample glucose levels in circulating blood, CGM samples from the interstitial fluid. CGM devices generate not only a single level, but through averaging with past glucose results, can predict future trends. While consensus guidelines exist for evaluating the agreement of SMBG devices to other glucose and laboratory methods, no guidelines currently exists for CGM devices. Standards are needed to define the performance of CGM devices, both in terms of spot accuracy and trend information, as well as the level of performance required for clinical management. The Diabetes Technology Society (DTS) has been in close communication on the development of CGM guidelines with the Food and Drug Administration (FDA) and the Clinical and Laboratory Standards Institute (CLSI). Development of standards for CGM devices if adopted by the FDA would set minimum requirements of performance for manufacturers to meet in producing CGM devices and define common terminology for the display and clinical utilization of CGM data. It is expected that CGM performance standards will advance over time as technology improves and consumer demands change. The development of CGM standards will also play an important role in accelerating the development of an artificial pancreas, which relies on CGM technology.     

Susceptibility of Interstitial Continuous Glucose Monitor Performance to Sleeping Position

Background

Developing a round-the-clock artificial pancreas requires accurate and stable continuous glucose monitoring. The most widely used continuous glucose monitors (CGMs) are percutaneous, with the sensor residing in the interstitial space. Inaccuracies in percutaneous CGM readings during periods of lying on the devices (e.g., in various sleeping positions) have been anecdotally reported but not systematically studied.

Methods

In order to assess the impact of sleep and sleep position on CGM performance, we conducted a study in human subjects in which we measured the variability of interstitial CGM data at night as a function of sleeping position. Commercially available sensors were placed for 4 days in the abdominal subcutaneous tissue in healthy, nondiabetic volunteers (four sensors per person, two per side). Nocturnal sleeping position was determined from video recordings and correlated to sensor data.

Results

We observed that, although the median of the four sensor readings was typically 70–110 mg/dl during sleep, individual sensors intermittently exhibited aberrant glucose readings (>25 mg/dl away from median) and that these aberrant readings were strongly correlated with subjects lying on the sensors. We expected and observed that most of these aberrant sleep-position-related CGM readings were sudden decreases in reported glucose values, presumably due to local blood-flow decreases caused by tissue compression. Curiously, in rare cases, the aberrant CGM readings were elevated values.

Conclusions

These findings highlight limitations in our understanding of interstitial fluid physiology in the subcutaneous space and have significant implications for the utilization of sensors in the construction of an artificial pancreas.     

Alarms for Continuous Glucose Monitors: Hypoglycemia Prediction with Subject-Specific Recursive Time-Series Models

Background

Avoiding hypoglycemia while keeping glucose within the narrow normoglycemic range (70–120 mg/dl) is a major challenge for patients with type 1 diabetes. Continuous glucose monitors can provide hypoglycemic alarms when the measured glucose decreases below a threshold. However, a better approach is to provide an early alarm that predicts a hypoglycemic episode before it occurs, allowing enough time for the patient to take the necessary precaution to avoid hypoglycemia.

Methods

We have previously proposed subject-specific recursive models for the prediction of future glucose concentrations and evaluated their prediction performance. In this work, our objective was to evaluate this algorithm further to predict hypoglycemia and provide early hypoglycemic alarms. Three different methods were proposed for alarm decision, where (A) absolute predicted glucose values, (B) cumulative-sum (CUSUM) control chart, and (C) exponentially weighted moving-average (EWMA) control chart were used. Each method was validated using data from the Diabetes Research in Children Network, which consist of measurements from a continuous glucose sensor during an insulin-induced hypoglycemia. Reference serum glucose measurements were used to determine the sensitivity to predict hypoglycemia and the false alarm rate.

Results

With the hypoglycemic threshold set to 60 mg/dl, sensitivity of 89, 87.5, and 89% and specificity of 67, 74, and 78% were reported for methods A, B, and C, respectively. Mean values for time to detection were 30 ± 5.51 (A), 25.8 ± 6.46 (B), and 27.7 ± 5.32 (C) minutes.

Conclusions

Compared to the absolute value method, both CUSUM and EWMA methods behaved more conservatively before raising an alarm (reduced time to detection), which significantly decreased the false alarm rate and increased the specificity.     

Challenges in Glycemic Control in Perioperative and Critically Ill Patients: Clinical Need for Continuous Glucose Monitoring in the Hospital

Automation and standardization of the glucose measurement process have the potential to greatly improve glycemic control, clinical outcome, and safety while reducing cost. The resources required to monitor glycemia in hospitalized patients have thus far limited the implementation of intensive glucose management to patients in critical care units. Numerous available and up-and-coming technologies are targeted for the hospital patient population. Advantages and limitations of these devices are discussed herewith in.     

An Analysis of 2019 FDA Adverse Events for Two Insulin Pumps and Two Continuous Glucose Monitors

US Food and Drug Administration adverse event data for 2019 were analyzed for two insulin pumps and two continuous glucose monitors (CGMs). The analyses were selective—they were guided by the text described in the adverse events. They included (1) percent using auto mode for the Medtronic 670G pump, (2) distributions of hyper and hypo glucose values for Medtronic and Tandem pumps, (3) a Parkes error grid for Dexcom CGM vs glucose meter when the complaint was inaccuracy, and (4) the most frequent events for Abbott Freestyle. We found that for the 670G pump, there were more hypo events when auto mode was on than when auto mode was off. With Dexcom CGMs, users complained about inaccurate result when most results were in the B zone. With the Abbott Freestyle, the most frequent adverse event was an allergic skin reaction.     

Continuous Glucose Monitors: Use of Waveform Versus Glycemic Values in the Improvements of Glucose Control,Quality of Life,and Fear of Hypoglycemia

How patients are benefitting from continuous glucose monitoring (CGM) remains poorly understood. The focus on numerical glucose values persists, even though access to the glucose waveform and rate of change may contribute more to improved control. This pilot study compared outcomes of patients using CGMs with or without access to the numerical values on their CGM. Ten persons with type 1 diabetes, naïve to CGM use, enrolled in a 12-week study. Subjects were randomly assigned to either unmodified CGM receivers, or to CGM receivers that had their numerical values obscured but otherwise functioned normally. HbA_1c, quality of life (QLI-D), and fear of hypoglycemia (HFS) were assessed, at baseline and at week 12. Baseline HbA_1c for the entire group was 7.46 ± 1.27%. At week 12 the experimental group HbA_1c reduction was 1.5 ± 0.9% (p < .05), the control group’s reduction was 0.06 ± 0.61% (p > .05). Repeated measures testing revealed no significant difference in HbA_1c reduction between groups. Both groups had reductions in HFS; these reductions were statistically significant within groups (p < .05), but not between groups. QLI-D indices demonstrated improvements (p < .05) in QLI-D total and the health and family subscales, but not between groups. The results of this pilot study suggest that benefits of CGM extend beyond reductions in HbA_1c to reductions in fear of hypoglycemia and improvements in quality of life. The display of a numerical glucose value did not improve control when compared to numerically blinded units.     

Performance of Cleared Blood Glucose Monitors

Cleared blood glucose monitor (BGM) systems do not always perform as accurately for users as they did to become cleared. We performed a literature review of recent publications between 2010 and 2014 that present data about the frequency of inaccurate performance using ISO 15197 2003 and ISO 15197 2013 as target standards. We performed an additional literature review of publications that present data about the clinical and economic risks of inaccurate BGMs for making treatment decisions or calibrating continuous glucose monitors (CGMs). We found 11 publications describing performance of 98 unique BGM systems. 53 of these 98 (54%) systems met ISO 15197 2003 and 31 of the 98 (32%) tested systems met ISO 15197 2013 analytical accuracy standards in all studies in which they were evaluated. Of the tested systems, 33 were identified by us as FDA-cleared. Among these FDA-cleared BGM systems, 24 out of 32 (75%) met ISO 15197 2003 and 15 out of 31 (48.3%) met ISO 15197 2013 in all studies in which they were evaluated. Among the non-FDA-cleared BGM systems, 29 of 65 (45%) met ISO 15197 2003 and 15 out of 65 (23%) met ISO 15197 2013 in all studies in which they were evaluated. It is more likely that an FDA-cleared BGM system, compared to a non-FDA-cleared BGM system, will perform according to ISO 15197 2003 (χ² = 6.2, df = 3, P = 0.04) and ISO 15197 2013 (χ² = 11.4, df = 3, P = 0.003). We identified 7 articles about clinical risks and 3 articles about economic risks of inaccurate BGMs. We conclude that a significant proportion of cleared BGMs do not perform at the level for which they were cleared or according to international standards of accuracy. Such poor performance leads to adverse clinical and economic consequences.     

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.     

Numerical Simulation of the Effect of Rate of Change of Glucose on Measurement Error of Continuous Glucose Monitors

Background

A 5-day in-patient study designed to assess the accuracy of the FreeStyle Navigator® Continuous Glucose Monitoring System revealed that the level of accuracy of the continuous sensor measurements was dependent on the rate of glucose change. When the absolute rate of change was less than 1 mg•dl⁻¹•min⁻¹ (75% of the time), the median absolute relative difference (ARD) was 8.5%, with 85% of all points falling within the A zone of the Clarke error grid. When the absolute rate of change was greater than 2 mg•dl⁻¹•min⁻¹ (8% of the time), the median ARD was 17.5%, with 59% of all points falling within the Clarke A zone.

Method

Numerical simulations were performed to investigate effects of the rate of change of glucose on sensor measurement error. This approach enabled physiologically relevant distributions of glucose values to be reordered to explore the effect of different glucose rate-of-change distributions on apparent sensor accuracy.

Results

The physiological lag between blood and interstitial fluid glucose levels is sufficient to account for the observed difference in sensor accuracy between periods of stable glucose and periods of rapidly changing glucose.

Conclusions

The role of physiological lag on the apparent decrease in sensor accuracy at high glucose rates of change has implications for clinical study design, regulatory review of continuous glucose sensors, and development of performance standards for this new technology. This work demonstrates the difficulty in comparing accuracy measures between different clinical studies and highlights the need for studies to include both relevant glucose distributions and relevant glucose rate-of-change distributions.     

Pediatric Medicaid Patients With Type 1 Diabetes Benefit From Continuous Glucose Monitor Technology

Background:We determined the uptake rate of continuous glucose monitors (CGMs) and examined associations of clinical and demographic characteristics with CGM use among patients with type 1 diabetes covered by Colorado Medicaid during the first two years of CGM coverage with no out-of-pocket cost.Method:We retrospectively reviewed data from 892 patients with type 1 diabetes insured by Colorado Medicaid (Colorado Health Program [CHP] and CHP+, Colorado Medicaid expansion). Demographics, insulin pump usage, CGM usage, and hemoglobin A1c (A1c) were extracted from the medical record. Data downloaded into CGM software at clinic appointments were reviewed to determine 30-day use prior to appointments. Subjects with some exposure to CGM were compared to subjects never exposed to CGM, and we examined the effect of CGM use on glycemic control.Results:Twenty percent of subjects had some exposure to CGM with a median of 22 [interquartile range 8, 29] days wear. Sixty one percent of CGM users had >85% sensor wear. Subjects using CGM were more likely to be younger (P < .001), have shorter diabetes duration (P < .001), and be non-Hispanic White (P < .001) than nonusers. After adjusting for age and diabetes duration, combined pump and CGM users had a lower A1c than those using neither technology (P = .006). Lower A1c was associated with greater CGM use (P = .002) and increased percent time in range (P < .001).Conclusion:Pediatric Medicaid patients successfully utilized CGM. Expansion of Medicaid coverage for CGM may help improve glycemic control and lessen disparities in clinical outcomes within this population.     

Vascular Glucose Sensor Symposium: Continuous Glucose Monitoring Systems (CGMS) for Hospitalized and Ambulatory Patients at Risk for Hyperglycemia,Hypoglycemia, and Glycemic Variability

Hyperglycemia, hypoglycemia, and glycemic variability have been associated with increased morbidity, mortality, length of stay, and cost in a variety of critical care and non–critical care patient populations in the hospital. The results from prospective randomized clinical trials designed to determine the risks and benefits of intensive insulin therapy and tight glycemic control have been confusing; and at times conflicting. The limitations of point-of-care blood glucose (BG) monitoring in the hospital highlight the great clinical need for an automated real-time continuous glucose monitoring system (CGMS) that can accurately measure the concentration of glucose every few minutes. Automation and standardization of the glucose measurement process have the potential to significantly improve BG control, clinical outcome, safety and cost.     

The Role of Reimbursement in the Adoption of Continuous Glucose Monitors

Continuous glucose monitors are a clinically meaningful addition to treatment plans for patients with diabetes who are actively managing their care. Since they first became commercially available, much progress has been made to ensure coverage of these devices for patients, but inadequate reimbursement of clinicians'' time continues to serve as a barrier to adoption.     

Delays in Minimally Invasive Continuous Glucose Monitoring Devices: A Review of Current Technology

Through the use of enzymatic sensors—inserted subcutaneously in the abdomen or ex vivo by means of microdialysis fluid extraction—real-time minimally invasive continuous glucose monitoring (CGM) devices estimate blood glucose by measuring a patient''s interstitial fluid (ISF) glucose concentration. Signals ac-quired from the interstitial space are subsequently calibrated with capillary blood glucose samples, a method that has raised certain questions regarding the effects of physiological time lags and of the duration of processing delays built into these devices. The time delay between a blood glucose reading and the value displayed by a continuous glucose monitor consists of the sum of the time lag between ISF and plasma glucose, in addition to the inherent electro-chemical sensor delay due to the reaction process and any front-end signal-processing delays required to produce smooth traces. Presented is a review of commercially available, minimally invasive continuous glucose monitors with manufacturer-reported device delays. The data acquisition process for the Medtronic MiniMed (Northridge, CA) continuous glucose monitoring system—CGMS® Gold—and the Guardian® RT monitor is described with associated delays incurred for each processing step. Filter responses for each algorithm are examined using in vitro hypoglycemic and hyperglycemic clamps, as well as with an analysis of fast glucose excursions from a typical meal response. Results demonstrate that the digital filters used by each algorithm do not cause adverse effects to fast physiol-ogic glucose excursions, although nonphysiologic signal characteristics can produce greater delays.