Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring

Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology

BACKGROUND

Frequent blood glucose level (BGL) monitoring is essential for effective diabetes management. Poor compliance is common due to the painful finger pricking or subcutaneous lancet implantation required from existing technologies. There are currently no commercially available non-invasive devices that can effectively measure BGL. In this real-world study, a prototype non-invasive continuous glucose monitoring system (NI-CGM) developed as a wearable ring was used to collect bioimpedance data. The aim was to develop a mathematical model that could use these bioimpedance data to estimate BGL in real time.

METHODS

The prototype NI-CGM was worn by 14 adult participants with type 2 diabetes for 14 days in an observational clinical study. Bioimpedance data were collected alongside paired BGL measurements taken with a Food and Drug Administration (FDA)-approved self-monitoring blood glucose (SMBG) meter and an FDA-approved CGM. The SMBG meter data were used to improve CGM accuracy, and CGM data to develop the mathematical model.

RESULTS

A gradient boosted model was developed using a randomized 80-20 training-test split of data. The estimated BGL from the model had a Mean Absolute Relative Difference (MARD) of 17.9%, with the Parkes error grid (PEG) analysis showing 99% of values in clinically acceptable zones A and B.

CONCLUSIONS

This study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a real-world scenario. These data were used to train a model that could successfully estimate BGL with a promising MARD and clinically relevant PEG result. These results will enable continued development of the prototype NI-CGM as a wearable ring.

Analysis of "Multicenter Evaluation Study Comparing a New Factory-Calibrated Real-Time Continuous Glucose Monitoring System to Existing Flash Glucose Monitoring System"

In an article in the Journal of Diabetes Science and Technology, Ji et al. report on the accuracy of a new factory calibrated continuous glucose monitoring system, the AiDEX CGM (Microtech Medical Company, Ltd., Hangzhou, China). This is the first report from a new manufacturer of a highly accurate factory calibrated CGM. The authors report that the accuracy of the AiDEX CGM is comparable to previous results from Abbott Diabetes Care and Dexcom. However, the study protocol was significantly different from previous studies. This study is the first of numerous studies likely from other manufacturers of CGM technology. It raises the question of how to evaluate sensor performance in the coming era of mass adoption of CGM and increased use of automated insulin delivery systems.

Time-Series Analysis of Continuous Glucose Monitoring Data to Predict Treatment Efficacy in Patients with T2DM

CONTEXT

There is a challenge to predict treatment effects in patients with type 2 diabetes mellitus (T2DM).

OBJECTIVE

To assess and predict treatment effects in patients with T2DM through time-series analysis of continuous glucose monitoring (CGM) measurements.

METHOD

We extracted and clustered the trend components of CGM measurements to generate representative time-series profiles, which were used as a predictor of treatment effects in groups of patients.

SETTING AND PARTICIPANTS

We recruited 111 outpatients with T2DM at Ningbo City First Hospital, China.

INTERVENTION

The patients underwent CGM measurement for 14 days at the beginning of glucose-lowering treatment.

MAIN OUTCOME MEASURES

Hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) were obtained at the beginning and after 6 months of treatment.

RESULTS

111 patients each had 960 to 1344 CGM measurements for 14 days at 96 measurements per day. The patients were classified into 3 groups according to the profiles of trend components of CGM observed values by time-series clustering method, including decreasing (47 patients), increasing (26 patients), and unchanged (38 patients) profiles. After 6 months of glucose-lowering treatment, FPG declined from 10.2 to 6.8 mmol/L (a decline of 3.4 mmol/L) in the decreasing group, from 8.9 to 9.2 mmol/L (a rise of 0.3 mmol/L) in the increasing group, and from 8.4 to 7.5 mmol/L (a decline of 0.9 mmol/L) in the unchanged group. The changes of HbA1c were 2.3%, 0.2%, and 0.9% for the 3 groups (P < 0.01), respectively.

CONCLUSIONS

Clustering of the trend components of CGM data generates representative CGM profiles that are predictive of 6-month therapeutic effects for T2DM.

Performance of the Intermittently Scanned Continuous Glucose Monitoring (isCGM) System during a High Oral Glucose Challenge in Adults with Type 1 Diabetes-A Prospective Secondary Outcome Analysis

To assess intermittently scanned continuous glucose monitoring (isCGM) performance for different rates of change in plasma glucose (RCPG) during glycemic challenges in type 1 diabetes (T1D). Nineteen people with T1D (7 females; age 35 ± 11 years; HbA_1c 7.3 ± 0.6% (56 ± 7 mmol/mol)) performing two glycemic challenges (OGTT) were included. During OGTTs, plasma glucose was compared against sensor glucose for timepoints 0 min (pre-OGTT), +15 min, +30 min, +60 min, +120 min, +180 min, and +240 min by means of median absolute (relative) difference (MARD and MAD) and Clarke Error Grid (CEG), then was stratified for RCPG and glycemic ranges. Overall, MARD was 8.3% (4.0–14.8) during hypoglycemia level 1 18.8% (15.8–22.0), euglycemia 9.5% (4.3–15.1), hyperglycemia level 1 9.4% (4.0–17.2), and hyperglycemia level 2 7.1% (3.3–11.9). The MARD was associated with the RCPG (p < 0.0001), detailing significant differences in comparison of low, moderate, high, and very high RCPG (p = 0.014). Overall, CEG resulted in 88% (212 values) of comparison points in zone A, 12% (29 values) in zone B, and 0.4% (1 value) in zone D. The isCGM system was accurate during OGTTs. Its performance was dependent on the RCPG and showed an overestimation of the actual reference glucose during hypoglycemia.

Review of the Long-Term Implantable Senseonics Continuous Glucose Monitoring System and Other Continuous Glucose Monitoring Systems

The article published by Kevin Cowart in this issue of the Journal of Diabetes Science and Technology (JDST) is a detailed overview of the clinical trial data and analysis used to demonstrate the safety and effectiveness of the Eversense continuous glucose monitoring (CGM) System for regulatory approval and clinical acceptance. The article describes the published study results for safety, accuracy, reliability, ease of insertion/removal, adverse events, and ease of diabetes patient-use for controlling their glucose levels short and long term. The author nicely compares Eversense CGM System safety and performance with the short-term subcutaneous tissue CGM systems being commercialized by Dexcom, Medtronic Diabetes, and Abbott Diabetes. This comparison may help the clinician define which type of patient with diabetes might benefit the most from the long-term implantable CGM system. The majority of studied patients describe a positive experience managing their diabetes with the Eversense CGM System and request implantation of a new sensor 90 or 180 days later.

Continuous glucose monitoring for hypoglycaemia in children: Perspectives in 2020

Hypoglycaemia in children is a major risk factor for adverse neurodevelopment with rates as high as 50% in hyperinsulinaemic hypoglycaemia (HH). A key part of management relies upon timely identification and treatment of hypoglycaemia. The current standard of care for glucose monitoring is by infrequent fingerprick plasma glucose testing but this carries a high risk of missed hypoglycaemia identification. High-frequency Continuous Glucose Monitoring (CGM) offers an attractive alternative for glucose trend monitoring and glycaemic phenotyping but its utility remains largely unestablished in disorders of hypoglycaemia. Attempts to determine accuracy through correlation with plasma glucose measurements using conventional methods such as Mean Absolute Relative Difference (MARD) overestimate accuracy at hypoglycaemia. The inaccuracy of CGM in true hypoglycaemia is amplified by calibration algorithms that prioritize hyperglycaemia over hypoglycaemia with minimal objective evidence of efficacy in HH. Conversely, alternative algorithm design has significant potential for predicting hypoglycaemia to prevent neuroglycopaenia and consequent brain dysfunction in childhood disorders. Delays in the detection of hypoglycaemia, alarm fatigue, device calibration and current high cost are all barriers to the wider adoption of CGM in disorders of hypoglycaemia. However, machine learning, artificial intelligence and other computer-generated algorithms now offer significant potential for further improvement in CGM device technology and widespread application in childhood hypoglycaemia.

Design and development of the Hypoglycaemia Symptom Rating Questionnaire (HypoSRQ)

AIMS

To evaluate the Hypoglycaemia Symptom Rating Questionnaire (HypoSRQ©) and relationships between self-reported hypoglycaemia and hypoglycaemia measured using blinded continuous glucose monitoring (CGM).

METHODS

Diabetes outpatients (n = 113) recruited from Ashford and St. Peter's Hospital completed the HypoSRQ (recent weeks version) and provided clinical information. Thirty participants used blinded CGM for six days and completed the HypoSRQ (24-hour version) for seven days, at the end of each week (7-day version), and after four weeks (recent weeks version).

RESULTS

The HypoSRQ had a single-factor structure and excellent internal consistency (α = 0.90). There was high correspondence in recalled symptoms, bother ratings and hypoglycaemic episodes across one week and four weeks (r = 0.84-0.98, p < 0.001). HypoSRQ-reported hypoglycaemia correlated significantly with CGM-measured hypoglycaemia (interstitial glucose ≤ 3.9 mmol/l) frequency (r = 0.72, p < 0.001) across six days. The magnitude of the correlation increased when the person's own threshold for detecting hypoglycaemia was used (r = 0.78, p < 0.001). The number of days (out of six) a person reported symptoms of hypoglycaemia was associated with the number of days CGM detected hypoglycaemia (interstitial glucose ≤ 3.9 mmol/l) (r = 0.83, p < 0.001) and remained significant after controlling for covariates.

CONCLUSIONS

Psychometric properties of the HypoSRQ make it attractive for use in people with insulin-treated diabetes. The HypoSRQ may be a less-invasive and more-economical alternative to CGM.

Automated closed-loop control of diabetes: the artificial pancreas

The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.

Role of continuous glucose monitoring in the management of glycogen storage disorders

Management of liver glycogen storage diseases (GSDs) primarily involves maintaining normoglycemia through dietary modifications and regular glucose monitoring. Self-monitoring of blood glucose is typically done 3-6 times per day, and may not sufficiently capture periods of asymptomatic hypoglycemia, particularly during sleep. Continuous glucose monitoring systems (CGMS) provide 24-h continuous glucose data and have been used effectively in diabetes mellitus to monitor metabolic control and optimize treatment. This is a relatively new approach in GSDs with only a handful of studies exploring this modality. In this study we used Dexcom CGMS to study the glycemic profile of 14 pediatric and six adult patients with GSD I, III, and IX. A total of 176 days of CGMS data were available. The CGMS was found to be a reliable tool in monitoring glucose levels and trends at all times of the day with good concordance with finger-stick glucose values. This study revealed that in addition to overnight hypoglycemia, CGMS can uncover previously undetected, subclinical, low glucose levels during daytime hours. Additionally, the CGMS detected daytime and overnight hyperglycemia, an often overlooked concern in liver GSDs. The CGMS with concurrent dietary adjustments made by a metabolic dietitian improved metabolic parameters and stabilized blood glucose levels. The CGMS was found to be a safe, effective, and reliable method for optimizing treatment in patients with GSD I, III, and IX.

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

BACKGROUND

Different reference methods are used for the accuracy assessment of continuous glucose monitoring (CGM) systems. The effect of using venous, arterialized-venous, or capillary reference measurements on CGM accuracy is unclear.

METHODS

We evaluated 21 individuals with type 1 diabetes using a capillary calibrated CGM system. Venous or arterialized-venous reference glucose samples were taken every 15 min at two separate visits and assessed per YSI 2300 STAT Plus. Arterialization was achieved by heated-hand technique. Capillary samples were collected hourly during the venous reference visit. The investigation sequence (venous or arterialized-venous) was randomized. Effectiveness of arterialization was measured by comparing free venous oxygen pressure (PO2) of both visit days. Primary endpoint was the median absolute relative difference (ARD).

RESULTS

Median ARD using arterialized-venous reference samples was not different from venous samples (point estimated difference 0.52%, P = 0.181). When comparing the three reference methods, median ARD was also not different over the full glycemic range (venous 9.0% [n = 681], arterialized-venous 8.3% [n = 684], and capillary 8.1% [n = 205], P = 0.216), nor over the separate glucose ranges. Arterialization was successful (PO2 venous 5.4 kPa vs. arterialized-venous 8.9 kPa, P < 0.001). Arterialized-venous glucose was significantly higher than venous glucose and numerically higher than capillary glucose (arterialized-venous 142 mg/dL vs. venous 129 mg/dL [P < 0.001] and vs. capillary 134 mg/dL [P = 0.231]). Inconvenience related to arterialization included transient mild edema and redness of the hand in 4 out of 21 (19%) patients.

CONCLUSIONS

The use of venous, arterialized-venous, or capillary reference measurements did not significantly impact CGM accuracy. Venous reference seems preferable due to its ease of operation.

Role of Continuous Glucose Monitoring in Clinical Trials: Recommendations on Reporting

Thanks to significant improvements in the precision, accuracy, and usability of continuous glucose monitoring (CGM), its relevance in both ambulatory diabetes care and clinical research is increasing. In this study, we address the latter perspective and derive provisional reporting recommendations. CGM systems have been available since around the year 2000 and used primarily in people with type 1 diabetes. In contrast to self-measured glucose, CGM can provide continuous real-time measurement of glucose levels, alerts for hypoglycemia and hyperglycemia, and a detailed assessment of glycemic variability. Through a broad spectrum of derived glucose data, CGM should be a useful tool for clinical evaluation of new glucose-lowering medications and strategies. It is the only technology that can measure hyperglycemic and hypoglycemic exposure in ambulatory care, or provide data for comprehensive assessment of glucose variability. Other advantages of current CGM systems include the opportunity for improved self-management of glycemic control, with particular relevance to those at higher risk of or from hypoglycemia. We therefore summarize the current status and limitations of CGM from the perspective of clinical trials and derive suggested recommendations for how these should facilitate optimal CGM use and reporting of data in clinical research.

Future of Automated Insulin Delivery Systems

Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.

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.

Predicting Insulin Treatment Scenarios with the Net Effect Method: Domain of Validity

BACKGROUND

A simulation methodology based on the net effect, a signal estimated from continuous glucose monitoring (CGM) and insulin data accounting for sources of glucose variability, for example, meals and exercise, has been proposed. This method has been recently used to "replay" real-life treatment scenarios and determine the minimal level of CGM sensor accuracy required for nonadjunctive use. Given the potential of the net effect method, it is important to assess its domain of validity.

METHODS

The UVA/Padova type 1 diabetes simulator is used to generate glucose and insulin data. The net effect signal is estimated and used to predict the glucose profiles resulting from the following therapy modifications: (1) basal insulin increase/decrease, (2) bolus reduction to prevent hypoglycemia, (3) bolus addition after CGM hyperalarms, (4) hypotreatment addition after CGM hypoalarms. Results of the net effect method are compared with the reference provided by the UVA/Padova simulator.

RESULTS

The net effect method (1) well predicts the effect of small basal insulin adjustments (±10%), but overestimates time in hypo/hyperglycemia for larger adjustments (±50%); (2) underestimates the bolus reduction required to prevent hypoglycemia; (3) underestimates time in hyperglycemia when introducing correction boluses; and (4) overestimates time in hypoglycemia when introducing hypotreatments.

CONCLUSIONS

The net effect method is reliable for small adjustments of basal insulin, while outside this domain of validity it can provide inaccurate results.

Assessing the Accuracy of Continuous Glucose Monitoring (CGM) Calibrated With Capillary Values Using Capillary or Venous Glucose Levels as a Reference

BACKGROUND

Using the standard venous reference for the evaluation of continuous glucose monitoring (CGM) systems could possibly negatively affect measured CGM accuracy since CGM are generally calibrated with capillary glucose and venous and capillary glucose concentrations differ. We therefore aimed to quantify the effect of using capillary versus venous glucose reference samples on estimated accuracy in capillary calibrated CGM.

METHODS

We evaluated 41 individuals with type 1 diabetes mellitus (T1DM) using the Dexcom G4 CGM system over 6 days. Patients calibrated their CGM devices with capillary glucose by means of the HemoCue system. During 2 visits, capillary and venous samples were simultaneously measured by HemoCue and compared to concomitantly obtained CGM readings. The mean absolute relative difference (MARD) was calculated using capillary and venous reference samples.

RESULTS

Venous glucose values were 0.83 mmol/L (15.0 mg/dl) lower than capillary values over all glycemic ranges, P < .0001. Below 4 mmol/l (72 mg/dl), the difference was 1.25 mmol/l (22.5 mg/dl), P = .0001, at 4-10 mmol/l (72-180 mg/dl), 0.67 mmol/l (12.0 mg/dl), P < .0001 and above 10 mmol/l (180 mg/dl), 0.95 mmol/l (17.1 mg/dl), P < .0001. MARD was 11.7% using capillary values as reference compared to 13.7% using venous samples, P = .037. Below 4 mmol/l (72 mg/dl) MARD was 16.6% and 31.8%, P = .048, at 4-10 mmol/l (72-180 mg/dl) 12.1% and 12.6%, P = .32, above 10 mmol/l (180 mg/dl) 8.7% and 9.2%, P = .82.

CONCLUSION

Using capillary glucose concentrations as reference to evaluate the accuracy of CGM calibrated with capillary samples is associated with a lower MARD than using venous glucose as the reference. Capillary glucose concentrations were significantly higher than venous in all glycemic ranges.

Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities

Continuous glucose monitoring (CGM) provides information unattainable by intermittent capillary blood glucose, including instantaneous real-time display of glucose level and rate of change of glucose, alerts and alarms for actual or impending hypo- and hyperglycemia, "24/7" coverage, and the ability to characterize glycemic variability. Progressively more accurate and precise, reasonably unobtrusive, small, comfortable, user-friendly devices connect to the Internet to share information and are sine qua non for a closed-loop artificial pancreas. CGM can inform, educate, motivate, and alert people with diabetes. CGM is medically indicated for patients with frequent, severe, or nocturnal hypoglycemia, especially in the presence of hypoglycemia unawareness. Surprisingly, despite tremendous advances, utilization of CGM has remained fairly limited to date. Barriers to use have included the following: (1) lack of Food and Drug Administration approval, to date, for insulin dosing ("nonadjuvant use") in the United States and for use in hospital and intensive care unit settings; (2) cost and variable reimbursement; (3) need for recalibrations; (4) periodic replacement of sensors; (5) day-to-day variability in glycemic patterns, which can limit the predictability of findings based on retrospective, masked "professional" use; (6) time, implicit costs, and inconvenience for uploading of data for retrospective analysis; (7) lack of fair and reasonable reimbursement for physician time; (8) inexperience and lack of training of physicians and other healthcare professionals regarding interpretation of CGM results; (9) lack of standardization of software methods for analysis of CGM data; and (10) need for professional medical organizations to develop and disseminate additional clinical practice guidelines regarding the role of CGM. Ongoing advances in technology and clinical research have addressed several of these barriers. Use of CGM in conjunction with an insulin pump with automated suspension of insulin infusion in response to actual observed or predicted hypoglycemia, as well as progressive refinement of closed-loop systems, is expected to dramatically enhance the clinical utility and utilization of CGM.

Are Systematic Reviews and Meta-Analyses Appropriate Tools for Assessing Evolving Medical Device Technologies?

Systematic reviews and meta-analyses (SRMAs) provide unique insights into comparative effectiveness of diabetes treatments. However, use of these analyses may be inappropriate for assessing the value and utility of technologies that involve significant behavioral interventions and encompass rapidly evolving technologies such as real-time continuous glucose monitoring (RT-CGM). The rapid evolution of RT-CGM, compared with the time required for publication of clinical studies used in SRMAs, may preclude differentiation between past and current generations of devices. In addition, the effect of performance and usability differences between the various commercial devices on possible clinical outcomes associated with the devices are often not clearly discussed, and many of the RT-CGM studies assessed in SRMAs do not provide adequate information regarding whether and/or to what degree study subjects and clinicians were trained to use the RT-CGM and utilize the data to adjust therapy. Although numerous clinical studies have shown that the glycemic benefit of RT-CGM is related to the frequency and duration of use, a disproportionate number of RT-CGM studies included in recent SRMAs are based on the results of the intention-to-treat analyses and do not consider this fundamental behavioral component in their conclusions. Given these limitations, the generalizability of SRMA conclusions may be limited, and findings from these reports may significantly underestimate the potential glycemic benefit of current and future devices, posing challenges for coverage and reimbursement. We reviewed the potential limitations of the recent Cochrane Collaboration report on CGM, focusing on the 12 studies that assessed RT-CGM use in adults, children/adolescents or both.