Measurement Performance of Two Continuous Tissue Glucose Monitoring Systems Intended for Replacement of Blood Glucose Monitoring

Accuracy of FreeStyle Libre flash glucose monitoring in patients with type 2 diabetes who migrated from highlands to plains

BACKGROUND

The FreeStyle Libre flash glucose monitoring (FGM) system entered the Chinese market in 2017 to complement the self-monitoring of blood glucose. Due to its increased usage in clinics, the number of studies investigating its accuracy has increased. However, its accuracy has not been investigated in highland popu-lations in China.

AIM

To evaluate measurements recorded using the FreeStyle Libre FGM system compared with capillary blood glucose measured using the enzyme electrode method in patients with type 2 diabetes (T2D) who had migrated within 3 mo from highlands to plains.

METHODS

Overall, 68 patients with T2D, selected from those who had recently migrated from highlands to plains (within 3 mo), were hospitalized at the Department of Endocrinology from August to October 2017 and underwent continuous glucose monitoring (CGM) with the FreeStyle Libre FGM system for 14 d. Throughout the study period, fingertip capillary blood glucose was measured daily using the enzyme electrode method (Super GL, China), and blood glucose levels were read from the scanning probe during fasting and 2 h after all three meals. Moreover, the time interval between reading the data from the scanning probe and collecting fingertip capillary blood was controlled to < 5 min. The accuracy of the FGM system was evaluated according to the CGM guidelines. Subsequently, the factors influencing the mean absolute relative difference (MARD) of this system were analyzed by a multiple linear regression method.

RESULTS

Pearson's correlation analysis showed that the fingertip and scanned glucose levels were positively correlated (R = 0.86, P = 0.00). The aggregated MARD of scanned glucose was 14.28 ± 13.40%. Parker's error analysis showed that 99.30% of the data pairs were located in areas A and B. According to the probe wear time of the FreeStyle Libre FGM system, MARD1 d and MARD2-14 d were 16.55% and 14.35%, respectively (t = 1.23, P = 0.22). Multiple stepwise regression analysis showed that MARD did not correlate with blood glucose when the largest amplitude of glycemic excursion (LAGE) was < 5.80 mmol/L but negatively correlated with blood glucose when the LAGE was ≥ 5.80 mmol/L.

CONCLUSION

The FreeStyle Libre FGM system has good accuracy in patients with T2D who had recently migrated from highlands to plains. This system might be ideal for avoiding the effects of high hematocrit on blood glucose monitoring in populations that recently migrated to plains. MARD is mainly influenced by glucose levels and fluctuations, and the accuracy of the system is higher when the blood glucose fluctuation is small. In case of higher blood glucose level fluctuations, deviation in the scanned glucose levels is the highest at extremely low blood glucose levels.

Differences in glucose readings between the continuous glucose monitoring calibration free interstitial sensors versus capillary blood glucose monitoring by glucometer: An analysis of two cases

Aim

To assess the differences in glucose readings between the continuous glucose monitoring calibration-free interstitial sensors versus capillary blood glucose monitoring by glucometer.

Study design

Two healthy non-pregnant volunteers participated in the study, and wore simultaneously both the calibration-free Freestyle Libre and the Dexcom G6 sensor. Glucose values were recorded before and after meals during breakfast, lunch, and dinner on three separate days by either scanning the Freestyle Libre CGM sensor with a smartphone, or obtaining glucose readings real-time through the Dexcom G6 CLARITY mobile application. Blood glucose values were recorded using the Accu-Chek Active glucose meter. The Wilcoxon signed-rank test was used for paired non-parametric data to compare glucose readings between groups.

Results

The average glucose values obtained from the Dexcom G6 CGM consistently registered higher (6.54 ± 0.80 mmol/L) and those from the Freestyle Libre (5.49 ± 0.65 mmol/L) consistently lower, from the glucometer (6.17 ± 0.55 mmol/L), with p-value <0.05 between groups. In the three-way comparison, the Dexcom G6 CGM sensor yielded the highest values, followed by the glucose meter, and finally the Freestyle Libre CGM sensor.

Conclusion

Both CGM systems exhibited discrepancies from blood glucose (BG) measurements, and variations were observed among the different CGM systems themselves.

Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Effectiveness of Using the FreeStyle Libre® System for Monitoring Blood Glucose during the COVID-19 Pandemic in Diabetic Individuals: Systematic Review

BACKGROUND

Artificial Intelligence (AI) is an area of computer science/engineering that is aiming to spread technological systems. The COVID-19 pandemic caused economic and public health turbulence around the world. Among the many possibilities for using AI in the medical field is FreeStyle Libre^® (FSL), which uses a disposable sensor inserted into the user's arm, and a touchscreen device/reader is used to scan and retrieve other continuous monitoring of glucose (CMG) readings. The aim of this systematic review is to summarize the effectiveness of FSL blood glucose monitoring during the COVID-19 pandemic.

METHODS

This systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) and was registered in the international prospective register of systematic reviews (PROSPERO: CRD42022340562). The inclusion criteria considered studies involving the use of the FSL device during the COVID-19 pandemic and published in English. No publication date restrictions were set. The exclusion criteria were abstracts, systematic reviews, studies with patients with other diseases, monitoring with other equipment, patients with COVID-19, and bariatrics patients. Seven databases were searched (PubMed, Scopus, Embase, Web of Science, Scielo, PEDro and Cochrane Library). The ACROBAT-NRSI tool (A Cochrane Risk of Bias Assessment Tool for Non-Randomized Studies) was used to evaluate the risk of bias in the selected articles.

RESULTS

A total of 113 articles were found. Sixty-four were excluded because they were duplicates, 39 were excluded after reading the titles and abstracts, and twenty articles were considered for full reading. Of the 10 articles analyzed, four articles were excluded because they did not meet the inclusion criteria. Thus, six articles were included in the current systematic review. It was observed that among the selected articles, only two were classified as having serious risk of bias. It was shown that FSL had a positive impact on glycemic control and on reducing the number of individuals with hypoglycemia.

CONCLUSION

The findings suggest that the implementation of FSL during COVID-19 confinement in this population can be confidently stated to have been effective in diabetes mellitus patients.

Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus

AIMS

Glucose and insulin metabolism are altered in hemodialysis patients, and diabetes management is difficult in these patients. We aimed to validate flash glucose monitoring (FGM) in hemodialysis patients with and without diabetes mellitus as an attractive option for glucose monitoring not requiring regular self-punctures.

METHODS

We measured interstitial glucose using a FreeStyle Libre device in eight hemodialysis patients with and seven without diabetes mellitus over 14 days and compared the results to simultaneously performed self-monitoring of capillary blood glucose (SMBG).

RESULTS

In 720 paired measurements, mean flash glucose values were significantly lower than self-measured capillary values (6.17±2.52 vs. 7.15±2.41 mmol/L, p=1.3 E-86). Overall, the mean absolute relative difference was 17.4%, and the mean absolute difference was 1.20 mmol/L. The systematic error was significantly larger in patients without vs. with diabetes (- 1.17 vs. - 0.82 mmol/L) and on dialysis vs. interdialytic days (-1.09 vs. -0.90 mmol/L). Compared to venous blood glucose (72 paired measurements), the systematic error of FGM was even larger (5.89±2.44 mmol/L vs. 7.78±7.25 mmol/L, p=3.74E-22). Several strategies to reduce the systematic error were evaluated, including the addition of +1.0 mmol/L as a correction term to all FGM values, which significantly improved accuracy.

CONCLUSIONS

FGM systematically underestimates blood glucose in hemodialysis patients but, taking this systematic error into account, the system may be useful for glucose monitoring in hemodialysis patients with or without diabetes.

Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol

Purpose

Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device.

Methods

This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices.

Results

Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm.

Conclusion

This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions.

Trial registration

ClinicalTrials.gov identifier: NCT03936634. Registered on 11 March 2022, retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1.

Supplementary information

The online version contains supplementary material available at 10.1007/s12553-022-00719-x.

Silicon-photonics-based waveguide Bragg grating sensor for blood glucose monitoring

We demonstrated the design of two different structures, a two-sided structure and a top-surface structure, of glucose waveguide Bragg grating (WBG) sensors in a single-mode silicon-on-insulator (SOI) chip. A two-sided WBG structure was fabricated, and chip preparation was realized by lithography and other processes. A photonic platform for testing the two-sided WBG using glucose was built and completed. When the blood glucose concentration changed by 1 mg/mL, the two-sided WBG had a wavelength offset of 78 pm. The experimental results show that the two structures can achieve the sensing of different blood glucose concentrations. The two-sided WBG had better sensing performance and thus has a wide range of application prospects.

A head-to-head comparison between Guardian Connect and FreeStyle Libre systems and an evaluation of user acceptability of sensors in patients with type 1 diabetes

AIMS

A user-calibrated real-time continuous glucose monitoring (rt-CGM) system is compared to a factory-calibrated flash glucose monitoring (FGM) system and assessed in terms of accuracy and acceptability in patients with type 1 diabetes (T1D).

METHODS

Ten participants with T1D were enroled from a specialist diabetes centre in Singapore and provided with the Guardian Connect with Enlite Sensor (Medtronic, Northridge, CA, USA) and first-generation Freestyle Libre System (Abbott Diabetes Care, Witney, UK), worn simultaneously. Participants had to check capillary blood glucose four times per day. At the end of week 1 and week 2, participants returned for data download and were given a user evaluation survey.

RESULTS

Accuracy evaluation between Guardian Connect and Freestyle Libre includes the overall mean absolute relative difference value (9.7 ± 11.0% vs. 17.5 ± 10.9%), Clarke Error Grid zones A + B (98.6% vs. 98.1%), sensitivity (78.9% vs. 63.4%), and specificity (93.4% vs. 81.0%). Notably, time below range (<3.9 mmol/L) was 10.5% for FGM versus 2% for rt-CGM. From the evaluation survey, 90% of participants perceived rt-CGM to be accurate versus 40% for FGM, although the majority found both devices to be easy to use, educational, and useful in improving glycaemic control. However, due to the cost of sensors, only 30% were keen to use either device for continuous monitoring.

CONCLUSIONS

Although rt-CGM was superior to FGM in terms of accuracy, the value of glucose trends in both devices is still useful in diabetes self-management. Patients and clinicians may consider either technology depending on their requirements.

Technologies for Diabetes Self-Monitoring: A Scoping Review and Assessment Using the REASSURED Criteria

BACKGROUND

Self-management is an important pillar for diabetes control and to achieve it, glucose self-monitoring devices are needed. Currently, there exist several different devices in the market and many others are being developed. However, whether these devices are suitable to be used in resource constrained settings is yet to be evaluated.

AIMS

To assess existing glucose monitoring tools and also those in development against the REASSURED which have been previously used to evaluate diagnostic tools for communicable diseases.

METHODS

We conducted a scoping review by searching PubMed for peer-review articles published in either English, Spanish or Portuguese in the last 5 years. We selected papers including information about devices used for self-monitoring and tested on humans with diabetes; then, the REASSURED criteria were used to assess them.

RESULTS

We found a total of 7 continuous glucose monitoring device groups, 6 non-continuous, and 6 devices in development. Accuracy varied between devices and most of them were either invasive or minimally invasive. Little to no evidence is published around robustness, affordability and delivery to those in need. However, when reviewing publicly available prices, none of the devices would be affordable for people living in low- and middle-income countries.

CONCLUSIONS

Available devices cannot be considered adapted for use in self-monitoring in resource constraints settings. Further studies should aim to develop less-invasive devices that do not require a large set of components. Additionally, we suggest some improvement in the REASSURED criteria such as the inclusion of patient-important outcomes to increase its appropriateness to assess non-communicable diseases devices.

Variation of Mean Absolute Relative Differences of Continuous Glucose Monitoring Systems Throughout the Day

BACKGROUND

There is an increasing use of continuous glucose monitoring (CGM) by people with diabetes. Measurement performance is often characterized by the mean absolute relative difference (MARD). However, MARD is influenced by a number of factors and little is known about whether MARD is stable throughout the day.

MATERIAL AND METHODS

A total of 24 participants with type 1 diabetes were enrolled in the study. The study was performed for seven in-patient days. Participants wore two CGM systems in parallel and performed additional frequent blood glucose (BG) measurements. On two days, glucose excursions were induced.MARD was calculated between pairs of CGM and BG values, with BG values serving as reference values. ARD values calculated from CGM-BG pairs were grouped by hour of the day. Results were analyzed separately for glucose excursion days and for regular days.

RESULTS

Total MARDs for the complete study duration were 12.5% ± 3.6% and 13.2% ± 2.4% (n = 24). Throughout the day marked variability of MARD was observed (8.0% ± 1.3%-16.3% ± 2.9% (G5); 9.1% ± 1.4%-16.3% ± 5.3% (FL), up to n = 157 each). Low(est) MARD values were observed before breakfast and dinner, when subjects were in or near a fasting state. Especially after breakfast and lunch, MARD values were higher than average.

CONCLUSIONS

Analytical performance of the two CGM systems, assessed by MARD, was found to vary markedly throughout the day. Activities of daily life likely triggered these variations. An increasing number of CGM users base therapeutic decisions on CGM values, and they should be aware of these variations of performance throughout the day.

HbA1c and Glucose Management Indicator Discordance Associated with Obesity and Type 2 Diabetes in Intermittent Scanning Glucose Monitoring System

Glucose management indicator (GMI) is frequently used as a substitute for HbA1c, especially when using telemedicine. Discordances between GMI and HbA1c were previously mostly reported in populations with type 1 diabetes (T1DM) using real-time CGM. Our aim was to investigate the accordance between GMI and HbA1c in patients with diabetes using intermittent scanning CGM (isCGM). In this retrospective cross-sectional study, patients with diabetes who used isCGM >70% of the time of the investigated time periods were included. GMI of four different time spans (between 14 and 30 days), covering a period of 3 months, reflected by the HbA1c, were investigated. The influence of clinical- and isCGM-derived parameters on the discordance was assessed. We included 278 patients (55% T1DM; 33% type 2 diabetes (T2DM)) with a mean HbA1c of 7.63%. The mean GMI of the four time periods was between 7.19% and 7.25%. On average, the absolute deviation between the four calculated GMIs and HbA1c ranged from 0.6% to 0.65%. The discordance was greater with increased BMI, a diagnosis of T2DM, and a greater difference between the most recent GMI and GMI assessed 8 to 10 weeks prior to HbA1c assessment. Our data shows that, especially in patients with increased BMI and T2DM, this difference is more pronounced and should therefore be considered when making therapeutic decisions.

Performance of intermittently scanned continuous glucose monitoring systems in people with type 1 diabetes: A pooled analysis

AIMS

To conduct a pooled analysis to assess the performance of intermittently scanned continuous glucose monitoring (isCGM) in association with the rate of change in sensor glucose in a cohort of children, adolescents, and adults with type 1 diabetes.

MATERIAL AND METHODS

In this pooled analysis, isCGM system accuracy was assessed depending on the rate of change in sensor glucose. Clinical studies that have been investigating isCGM accuracy against blood glucose, accompanied with collection time points were included in this analysis. isCGM performance was assessed by means of median absolute relative difference (MedARD), Parkes error grid (PEG) and Bland-Altman plot analyses.

RESULTS

Twelve studies comprising 311 participants were included, with a total of 15 837 paired measurements. The overall MedARD (interquartile range) was 12.7% (5.9-23.5) and MedARD differed significantly based on the rate of change in glucose (P < 0.001). An absolute difference of -22 mg/dL (-1.2 mmol/L) (95% limits of agreement [LoA] 60 mg/dL (3.3 mmol/L), -103 mg/dL (-5.7 mmol/L)) was found when glucose was rapidly increasing (isCGM glucose minus reference blood glucose), while a -32 mg/dL (1.8 mmol/L) (95% LoA 116 mg/dL (6.4 mmol/L), -51 mg/dL (-2.8 mmol/L)) absolute difference was observed in periods of rapidly decreasing glucose.

CONCLUSIONS

The performance of isCGM was good when compared to reference blood glucose measurements. The rate of change in glucose for both increasing and decreasing glucose levels diminished isCGM performance, showing lower accuracy during high rates of glucose change.

Glycemic Metrics Derived From Intermittently Scanned Continuous Glucose Monitoring

BACKGROUND

Glucose data from intermittently scanned continuous glucose monitoring (isCGM) is a combination of scanned and imported glucose values. The present knowledge of glycemic metrics originate mostly from glucose data from real-time CGM sampled every five minutes with a lack of information derived from isCGM.

METHODS

Glucose data obtained with isCGM and hemoglobin A1c (HbA1c) were obtained from 169 patients with type 1 diabetes. Sixty-one patients had two observations with an interval of more than three months.

RESULTS

The best regression line of HbA1c against mean glucose was observed from 60 days prior to HbA_1c measurement as compared to 14, 30, and 90 days. The difference between HbA1c and estimated HbA1c (=glucose management indicator [GMI]) first observed correlated with the second observation (R2 0.61, P < .001). Time in range (TIR, glucose between 3.9 and 10 mmol/L) was significantly related to GMI (R2 0.87, P < .001). A TIR of 70% corresponded to a GMI of 6.8% (95% confidence interval, 6.3-7.4). The fraction of patients with the optimal combination of TIR >70% and time below range (TBR) <4% was 3.6%. The fraction of patients with TBR>4% was four times higher for those with high glycemic variability (coefficient of variation [CV] >36%) than for those with lower CV.

CONCLUSION

The individual difference between HbA1c and GMI was reproducible. High glycemic variability was related to increased TBR. A combination of TIR and TBR is suggested as a new composite quality indicator.

Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

BACKGROUND

International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system.

METHOD

A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics.

RESULTS

Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%).

CONCLUSION

Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

Extraction With Sweat-Sebum Emulsion as a New Test Method for Leachables in Patch-Based Medical Devices, Illustrated by Assessment of Isobornylacrylate (IBOA) in Diabetes Products

BACKGROUND

The increasing offering of patch-based medical devices is accompanied by growing numbers of reported adverse skin reactions. Procedures for testing leachables according to ISO 10993 may not be optimal for lipophilic substances that can be mobilized on skin by sweat and sebum. We propose an improved extraction method for targeted analysis of leachables using low volumes of a sweat-sebum emulsion. The approach is illustrated by the analysis of isobornylacrylate (IBOA), a compound found in some devices and suspected for allergenic potential.

METHOD

Three patch-based products were tested: an implantable device for continuous glucose monitoring (CGM), an intermittently scanned CGM (isCGM) device, and a micro-insulin pump. Quantification of IBOA was performed by gas chromatography and allergenic potential of IBOA levels was assessed by the KeratinoSens cell assay. Different combinations were used for extraction solvent (isopropanol, 5% ethanol-water solution, and sweat-sebum emulsion), extraction volumes (complete immersion vs partial immersion in 2 mm of solvent), and extraction time (3, 5, and 14 days).

RESULTS

Isobornylacrylate was only found in the isCGM device. About 20 mg/L IBOA were eluted after 3 days in isopropanol but only about 1 mg/L in ethanol-water. Sweat-sebum emulsion dissolves IBOA better and gives a more stable solution than ethanol-water. Decomposition of IBOA solutions requires adjusted extraction timing or correction of results. In the sweat-sebum extract, IBOA levels were about 20 mg/L after 3 days and about 30 mg/L after 5 days, clearly above the threshold found in the KerationSens assay for keratinocyte activation (10 mg/L).

CONCLUSION

Extraction by low volumes of sweat-sebum emulsion can be a superior alternative for the targeted simulating-use assessment of leachables in patch-based medical devices.

Diabetes Technology Meeting 2020

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

Glycemic Variability in Type 1 Diabetes Mellitus Saudis Using Ambulatory Glucose Profile

Background

Glucose variability (GV) is a common and challenging clinical entity in the management of people with type 1 diabetes (T1DM). The magnitude of GV in Saudi people with T1DM was not addressed before. Therefore, we aimed to study GV in a consecutive cohort of Saudis with T1DM.

Methods

We prospectively assessed interstitial glucose using FreeStyle^® Libre flash glucose monitoring in people with TIDM who attended follow-up in the diabetes clinics at King Fahad Medical City between March and June 2017. Glycemia profile, standard deviation (SD), coefficient of variation (CV), mean of daily differences (MODD), and mean amplitude of glucose excursion (MAGE) were measured using the standard equations over a period of 2 weeks.

Results

Fifty T1DM subjects (20 males) with mean age 20.2 ± 6.1 years and mean fortnight glucose 192 ± 42.3 mg/dl were included. The mean SD of 2-week glucose readings was 100.4 ± 36.3 mg/dl and CV was 52.1% ± 13%. Higher levels of glucose excursions were also observed. MODD and MAGE were recorded as 104.5 ± 51.7 and 189 ± 54.9 mg/dl, respectively which is 2 to 4 times higher than the international standards. Higher MODD and MAGE were observed on weekends compared to weekdays (111.3 ± 62.1 vs 98.6 ± 56.2 mg/dl and 196.4 ± 64.6 vs 181.7 ± 52.4 mg/dl, respectively; P ⩽ .001).

Conclusion

Higher degree of glycemic variability was observed in this cohort of TIDM Saudis. Weekends were associated with higher glucose swings than weekdays. More studies are needed to explore these findings further.

Antioxidants other than vitamin C may be detected by glucose meters: Immediate relevance for patients with disorders targeted by antioxidant therapies

Owing to their ease of use, glucose meters are frequently used in research and medicine. However, little is known of whether other non-glucose molecules, besides vitamin C, interfere with glucometry. Therefore, we sought to determine whether other antioxidants might behave like vitamin C in causing falsely elevated blood glucose levels, potentially exposing patients to glycemic mismanagement by being administered harmful doses of glucose-lowering drugs. To determine whether various antioxidants can be detected by seven commercial glucose meters, human blood samples were spiked with various antioxidants ex vivo and their effect on the glucose results were assessed by Parkes error grid analysis. Several of the glucose meters demonstrated a positive bias in the glucose measurement of blood samples spiked with vitamin C, N-acetylcysteine, and glutathione. With the most interference-sensitive glucose meter, non-blood solutions of 1 mmol/L N-acetylcysteine, glutathione, cysteine, vitamin C, dihydrolipoate, and dithiothreitol mimicked the results seen on that glucose meter for 0.7, 1.0, 1.2, 2.6, 3.7 and 5.5 mmol/L glucose solutions, respectively. Glucose meter users should be alerted that some of these devices might produce spurious glucose results not only in patients on vitamin C therapy but also in those being administered other antioxidants. As discussed herein, the clinical relevance of the data is immediate in view of the current use of antioxidant therapies for disorders such as the metabolic syndrome, diabetes, cardiovascular diseases, and coronavirus disease 2019.

Flash Continuous Glucose Monitoring: A Practical Guide and Call to Action for Pharmacists

Despite advances in diabetes technology, the proportion of patients with type 2 diabetes achieving recommended glycemic goals remains suboptimal. There is a growing interest in flash continuous glucose monitoring (CGM) among patients, pharmacists and providers. Pharmacists are well positioned to collaborate with patients and providers in ambulatory care or community-based settings to allow a greater number of patients with diabetes to harness the benefits of flash CGM. The purpose of this narrative review is to provide pharmacists with a background on flash CGM technology, review the data supporting pharmacist-driven flash CGM services, and address common questions that arise in pharmacy practice surrounding flash CGM.

Comparative Accuracy Analysis of a Real-time and an Intermittent-Scanning Continuous Glucose Monitoring System

BACKGROUND

Currently, two different types of continuous glucose monitoring (CGM) systems are available: real time (rt) CGM systems that continuously provide glucose values and intermittent-scanning (is) CGM systems. This study compared accuracy of an rtCGM and an isCGM system when worn in parallel.

METHODS

Dexcom G5 Mobile (DG5) and FreeStyle Libre (FL) were worn in parallel by 27 subjects for 14 days including two clinic sessions with induced glucose excursions. The percentage of CGM values within ±20% or ±20 mg/dL of the laboratory comparison method results (YSI 2300 STAT Plus, YSI Inc., Yellow Springs, OH, United States; glucose oxidase based) or blood glucose meter values and mean absolute relative difference (MARD) were calculated. Consensus error grid and continuous glucose error grid analyses were performed to assess clinical accuracy.

RESULTS

Both systems displayed clinically accurate readings. Compared to laboratory comparison method results during clinic sessions, DG5 had 91.5% of values within ±20%/20 mg/dL and a MARD of 9.5%; FL had 82.5% of scanned values within ±20%/20 mg/dL and an MARD of 13.6%. Both systems showed a lower level of performance during the home phase and when using the blood glucose meter as reference.

CONCLUSION

The two systems tested in this study represent two different principles of CGM. DG5 generally provided higher accordance with laboratory comparison method results than FL.

Blood glucose and subcutaneous continuous glucose monitoring in critically ill horses: A pilot study

This pilot prospective study reports the feasibility, management and cost of the use of a continuous glucose monitoring (CGM) system in critically ill adult horses and foals. We compared the glucose measurements obtained by the CGM device with blood glucose (BG) concentrations. Neonatal foals (0–2 weeks of age) and adult horses (> 1 year old) admitted in the period of March-May 2016 with clinical and laboratory parameters compatible with systemic inflammatory response syndrome (SIRS) were included. Glucose concentration was monitored every 4 hours on blood samples with a point-of-care (POC) glucometer and with a blood gas analyzer. A CGM system was also placed on six adults and four foals but recordings were successfully obtained only in four adults and one foal. Glucose concentrations corresponded fairly well between BG and CGM, however, there appeared to be a lag time for interstitial glucose levels. Fluctuations of glucose in the interstitial fluid did not always follow the same trend as BG. CGM identified peaks and drops that would have been missed with conventional glucose monitoring. The use of CGM system is feasible in ill horses and may provide clinically relevant information on glucose levels, but there are several challenges that need to be resolved for the system to gain more widespread usability.

Clinical approaches to treat impaired awareness of hypoglycaemia

Impaired awareness of hypoglycaemia (IAH) affects between 25% and 30% of all people with type 1 diabetes (T1D) and markedly increases risk of severe hypoglycaemia. This greatly feared complication of T1D impairs quality of life and has a recognised morbidity. People with T1D have an increased propensity to hypoglycaemia as a result of fundamental physiological defects in their ability to respond appropriately to a fall in blood glucose levels. With repeated exposure to low glucose, many then develop a condition referred to as IAH, where there is a reduced ability to perceive the onset of hypoglycaemia and take appropriate corrective action. The management of individuals with IAH relies initially on its identification in the clinic through a detailed exploration of the frequency of hypoglycaemia and an assessment of the individual's ability to recognise these episodes. In this review article, we will address the clinical strategies that may help in the management of the patient with IAH once identified, who may or may not also suffer from problematic hypoglycaemia. The initial focus is on how to identify such patients and then on the variety of approaches involving educational programmes and technological approaches that may be taken to minimise hypoglycaemia risk. No single approach can be advocated for all patients, and it is the role of the health care professional to identify the clinical strategy that best enables their patient to achieve this goal.

Proof of Concept for a New Raman-Based Prototype for Noninvasive Glucose Monitoring

BACKGROUND

Noninvasive glucose monitoring (NIGM) in diabetes is a long-sought-for technology. Among the many attempts Raman spectroscopy was considered as the most promising because of its glucose specificity. In this study, a recently developed prototype (GlucoBeam, RSP Systems A/S, Denmark) was tested in patients with type 1 diabetes to establish calibration models and to demonstrate proof of concept for this device in real use.

METHODS

The NIGM table-top prototype was used by 15 adult subjects with type 1 diabetes for up to 25 days at home and in an in-clinic setting. On each day, the subjects performed at least six measurement units throughout the day. Each measurement unit comprised two capillary blood glucose measurements, two scans with an intermittent scanning continuous glucose monitoring (CGM) system, and two NIGM measurements using the thenar of the subject's right hand.

RESULTS

Calibration models were established using data from 19 to 24 days. The remaining 3-8 days were used for independent validation. The mean absolute relative difference of the NIGM prototype was 23.6% ± 13.1% for the outpatient days, 28.2% ± 9.9% for the in-clinic day, and 26.3% ± 10.8% for the complete study. Consensus error grid analysis of the NIGM prototype for the complete study showed 93.6% of values in clinically acceptable zones A and B.

CONCLUSIONS

This proof of concept study demonstrated a practical realization of a Raman-based NIGM device, with performance on par with early-generation CGM systems. The findings will assist in further performance improvements of the device.

A Three-Way Accuracy Comparison of the Dexcom G5, Abbott Freestyle Libre Pro, and Senseonics Eversense Continuous Glucose Monitoring Devices in a Home-Use Study of Subjects with Type 1 Diabetes

Background: There is a dearth of comparative accuracy studies of continuous glucose monitoring (CGM) devices in the home-use setting, and none with the Eversense implantable CGM. Methods: We evaluated the accuracy of the Dexcom G5, Abbott Freestyle Libre Pro, and Senseonics Eversense during a 6-week free-living home-use bionic pancreas study involving 23 subjects with type 1 diabetes who wore all three devices concurrently. The primary outcome was the mean absolute relative difference (MARD) between CGM readings and point-of-care (POC) plasma-glucose (PG) values obtained approximately twice daily by the subjects. We compared PG values with CGM readings when available from all three CGMs in the 5 min preceding the PG values (n = 829 sets). Since the Libre Pro records readings every 15 min, we also did a two-way comparison between the G5 and the Eversense with a higher number of comparisons (n = 2277 sets). Results: All three CGM systems produced higher average MARDs than during in-clinic studies. However, since all three CGM systems were worn by the same individuals and used the same meter for comparator PG measurements, we could directly compare their performances. In the three-way comparison, Eversense achieved the lowest nominal MARD (14.8%) followed by Dexcom G5 (16.3%) and Libre Pro (18.0%) (Eversense vs. Libre Pro P = 0.004, other comparisons P = NS). There was a statistically significant difference (P = 0.008) in the two-way comparison of the MARDs for Eversense (15.1%) and G5 (16.9%). Conclusions: The point accuracy of the Eversense was better than two other CGMs when compared with POC PG values.

Basics and use of continuous glucose monitoring (CGM) in diabetes therapy

Background

For a long time, self-monitoring of blood glucose (SMBG) was widely viewed as the essential glucose measurement procedure in the therapy of insulin-treated people with diabetes. With increasing accuracy and simplified handling of continuous glucose monitoring (CGM) systems, this evolving technology challenges and at least partly replaces SMBG systems.

Content

Sensors of all currently available CGM systems measure glucose levels in the subcutaneous interstitial fluid for 6–14 days. The only available implantable sensor facilitates a measurement span of up to 6 months. Depending on the used system, glucose levels are either shown in real time (rtCGM systems) or after scanning (iscCGM systems). Functions such as alerts, alarms and trend arrows and data presentation encourage independent self-management of diabetes therapy. The high frequency of glucose data and the multitude of existing functions require an extensive training of people with diabetes and their caregivers.

Summary

CGM systems provide a much more detailed picture of glycemia in people with diabetes. Educated patients can use these data to react adequately to their glucose levels and therefore avoid hypoglycemic and hyperglycemic events. Studies showed that glycated hemoglobin (HbA1c) levels and hypoglycemic events can be significantly reduced by frequent use of CGM systems.

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.

Benefits and limitations of continuous glucose monitoring in type 1 diabetes

Introduction: Type 1 diabetes (T1D) is a chronic condition characterized by a complete deficiency in insulin production. Optimal management requires constant knowledge of glucose levels for safe and effective insulin administration. Self-monitoring of blood glucose (SMBG) using capillary blood glucose meters is cumbersome and provides limited information to guide management. Continuous glucose monitoring (CGM) technology addresses many of these gaps, but itself has limitations which have prevented people with diabetes and their clinicians from fully embracing this technology. This review covers the benefits and limitations of CGM use, and looks toward future application of this technology in the management of T1D.Areas covered: Impact of CGM on physical and psychosocial outcomes in people with T1D. Barriers to CGM uptake. Integration with insulin pumps and other technologies. Opportunities for future application.Expert opinion: CGM technology will be utilized by the majority of people with T1D and increasing numbers of people with type 2 diabetes due to improved insurance coverage and easier-to-use systems. Its use as part of artificial pancreas systems will add further utility, as it will help to protect from both hypoglycemia and hyperglycemia. People with diabetes will spend more time in range and experience fewer acute and chronic complications.

The Accuracy and Precision of the Continuously Stored Data from Flash Glucose Monitoring System in Type 2 Diabetes Patients during Standard Meal Tolerance Test

BACKGROUND

The purpose of this study was to investigate the accuracy of the continuously stored data from the Abbott FreeStyle Libre flash glucose monitoring (FGM) system in Chinese diabetes patients during standard meal tests when glucose concentrations were rapidly changing. Subjects and Methods. Interstitial glucose levels were monitored for 14 days in 26 insulin-treated patients with type 2 diabetes using the FGM system. Standard meal tests were conducted to induce large glucose swings. Venous blood glucose (VBG) was tested at 0, 30, 60, and 120 min after standard meal tests in one middle day of the first and second weeks, respectively. The corresponding sensor glucose values were obtained from interpolating continuously stored data points. Assessment of accuracy was according to recent consensus recommendations with median absolute relative difference (MARD) and Clarke and Parkes error grid analysis (CEG and PEG).

RESULTS

Among 208 paired sensor-reference values, 100% were falling within zones A and B of the Clarke and Parkes error grid analysis. The overall MARD was 10.7% (SD, 7.8%). Weighted least squares regression analysis resulted in high agreement between the FGM sensor glucose and VBG readings. The overall MTT results showed that FGM was lower than actual VBG, with MAD of 22.1 mg/dL (1.2 mmol/L). At VBG rates of change of -1 to 0, 0 to 1, 1 to 2, and 2 to 3 mg/dl/min, MARD results were 11.4% (SD, 8.7%), 9.4% (SD, 6.5%), 9.9% (SD, 7.5%), and 9.5% (SD, 7.7%). At rapidly changing VBG concentrations (>3 mg/dl/min), MARD increased to 19.0%, which was significantly higher than slow changing BG groups.

CONCLUSIONS

Continuously stored interstitial glucose measurements with the FGM system were found to be acceptable to evaluate VBG in terms of clinical decision during standard meal tests. The continuously stored data from the FGM system appeared to underestimate venous glucose and performed less well during rapid glucose changes.

Estimating plasma glucose with the FreeStyle Libre Pro continuous glucose monitor during oral glucose tolerance tests in youth without diabetes

Few studies have assessed the accuracy of the FreeStyle Libre Pro (FLP) continuous glucose monitor for estimating plasma glucose (PG) in non-diabetic children.

OBJECTIVE

Determine the accuracy of FLP compared to PG during OGTT in healthy children.

SUBJECTS

Children (7-11.99 years) with healthy weight and overweight/obesity (n = 33; 52% male).

METHODS

Participants wore the FLP before and during a 2-hour OGTT; PG was measured at 30 minutes intervals. Potential systematic- and magnitude-related biases for FLP vs PG were examined.

RESULTS

FLP 15-minute averages and PG were correlated at most timepoints during OGTT (r2 = 0.35-0.69, P's < .001 for time point 30-120 minutes) and for PG area under the curve (AUC) (r2 = 0.65, P < .0001). There were no systematic biases as assessed by Bland-Altman analyses for FLP AUC or for FLP at each OGTT timepoint. However, for fasting glucose, a significant magnitude bias was noted (r2 = 0.38, P < .001), such that lower PG was underestimated, and higher PG was overestimated by FLP readings; further, there was poor correlation between fasting PG and FLP (r2 = 0.06, P = .22). BMIz was also associated with FLP accuracy: FLP overestimated PG in children with low BMIz and underestimated PG in those with overweight/obesity for OGTT AUC and OGTT PG at baseline, 60, and 120 minutes (all P's ≤ .015). No adverse events occurred with FLP.

CONCLUSIONS

Among children without diabetes, the FLP was well tolerated and correlated with post-OGTT glucose, but had magnitude bias affecting fasting glucose and appeared to underestimate plasma glucose in those with overweight/obesity. These results suggest potential limitations for the utility of the FLP for research.

Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes

BACKGROUND

For medical tests that have a central role in clinical decision-making, current guidelines advocate outcome-based analytical performance specifications. Given that empirical (clinical trial-style) analyses are often impractical or unfeasible in this context, the ability to set such specifications is expected to rely on indirect studies to calculate the impact of test measurement uncertainty on downstream clinical, operational, and economic outcomes. Currently, however, a lack of awareness and guidance concerning available alternative indirect methods is limiting the production of outcome-based specifications. Therefore, our aim was to review available indirect methods and present an analytical framework to inform future outcome-based performance goals.

CONTENT

A methodology review consisting of database searches and extensive citation tracking was conducted to identify studies using indirect methods to incorporate or evaluate the impact of test measurement uncertainty on downstream outcomes (including clinical accuracy, clinical utility, and/or costs). Eighty-two studies were identified, most of which evaluated the impact of imprecision and/or bias on clinical accuracy. A common analytical framework underpinning the various methods was identified, consisting of 3 key steps: (a) calculation of "true" test values; (b) calculation of measured test values (incorporating uncertainty); and (c) calculation of the impact of discrepancies between (a) and (b) on specified outcomes. A summary of the methods adopted is provided, and key considerations are discussed.

CONCLUSIONS

Various approaches are available for conducting indirect assessments to inform outcome-based performance specifications. This study provides an overview of methods and key considerations to inform future studies and research in this area.

Measures of Accuracy for Continuous Glucose Monitoring and Blood Glucose Monitoring Devices

Currently, patients with diabetes may choose between two major types of system for glucose measurement: blood glucose monitoring (BGM) systems measuring glucose within capillary blood and continuous glucose monitoring (CGM) systems measuring glucose within interstitial fluid. Although BGM and CGM systems offer different functionality, both types of system are intended to help users achieve improved glucose control. Another area in which BGM and CGM systems differ is measurement accuracy. In the literature, BGM system accuracy is assessed mainly according to ISO 15197:2013 accuracy requirements, whereas CGM accuracy has hitherto mainly been assessed by MARD, although often results from additional analyses such as bias analysis or error grid analysis are provided. The intention of this review is to provide a comparison of different approaches used to determine the accuracy of BGM and CGM systems and factors that should be considered when using these different measures of accuracy to make comparisons between the analytical performance (ie, accuracy) of BGM and CGM systems. In addition, real-world implications of accuracy and its relevance are discussed.

Freestyle Libre trend arrows for the management of adults with insulin-treated diabetes: A practical approach

Freestyle Libre (FSL) system is a new method to detect glucose enabling a new paradigm in glucose monitoring and self-management. The sensor, reading the interstitial fluid glucose concentration, provides a numerical data of glucose level and a trend arrow that add context to static measurement of glucose level. Therefore, patients could easily follow the progression of their glucose levels over the time, allowing early detection and timely treatment of deviations from targeted glucose level range, thus preventing extreme fluctuations. In order to take full advantage of the system both the caregiver and the person with diabetes must appreciate the need of careful interpretation of the data generated by the FSL. To this purpose we have generated recommendations that are based on methods suggested for CGM, our clinical experience and discussion with experienced patients using FSL, to provide a pragmatic approach to use FSL trend arrow data for managing diabetes in adults.

Switching from Flash Glucose Monitoring to Continuous Glucose Monitoring on Hypoglycemia in Adults with Type 1 Diabetes at High Hypoglycemia Risk: The Extension Phase of the I HART CGM Study

BACKGROUND

The I HART CGM study showed that real-time continuous glucose monitoring (RT-CGM) has greater beneficial impact on hypoglycemia than intermittent flash glucose monitoring (flash) in adults with type 1 diabetes (T1D) at high risk. The impact of continuing RT-CGM or switching from flash to RT-CGM for another 8 weeks was then evaluated.

METHODS

Prospective randomized parallel group study with an extension phase. After a 2-week run-in with blinded CGM, participants were randomized to either RT-CGM or flash for 8 weeks. All participants were then given the option to continue with RT-CGM for another 8 weeks. Glycemic outcomes at 8 weeks are compared with the 16-week endpoint.

RESULTS

Forty adults with T1D on intensified multiple daily insulin injections and with impaired awareness of hypoglycemia or a recent episode of severe hypoglycemia were included (40% female, median [IQR] age 49.5 [37.5-63.5] years, diabetes duration 30.0 [21.0-36.5] years, HbA1c 56 [48-63] mmol/mol, and Gold Score 5 [4-5]), of whom 36 completed the final 16-week extension. There was a significant reduction in percentage time in hypoglycemia (<3.0 mmol/L) in the group switching from flash to RT-CGM (from 5.0 [3.7-8.6]% to 0.8 [0.4-1.9]%, P = 0.0001), whereas no change was observed in the RT-CGM group continuing with the additional 8 weeks of RT-CGM (1.3 [0.4-2.8] vs. 1.3 [0.8-2.5], P = 0.82). Time in target (3.9-10 mmol/L) increased in the flash group after switching to RT-CGM (60.0 [54.5-67.8] vs. 67.4 [56.3-72.4], P = 0.02) and remained the same in the RT-CGM group that continued with RT-CGM (65.9 [54.1-74.8] vs. 64.9 [49.2-73.9], P = 0.64).

CONCLUSIONS

Our data suggest that switching from flash to RT-CGM has a significant beneficial impact on hypoglycemia outcomes and that continued use of RT-CGM maintains hypoglycemia risk benefit in this high-risk population.

Differences Between Flash Glucose Monitor and Fingerprick Measurements

Freestyle Libre (FL) is a factory calibrated Flash Glucose Monitor (FGM). We investigated Mean Absolute Relative Difference (MARD) between Self Monitoring of Blood Glucose (SMBG) and FL measurements in the first day of sensor wear in 39 subjects with Type 1 diabetes. The overall MARD was 12.3%, while the individual MARDs ranged from 4% to 25%. Five participants had a MARD ≥ 20%. We estimated bias and lag between the FL and SMBG measurements. The estimated biases range from -1.8 mmol / L to 1.4 mmol / L , and lags range from 2 min to 24 min . Bias is identified as a main cause of poor individual MARDs. The biases seem to persist in days 2⁻7 of sensor usage. All cases of MARD ≥ 20% in the first day are eliminated by bias correction, and overall MARD is reduced from 12.3% to 9.2%, indicating that adding support for voluntary user-supplied bias correction in the FL could improve its performance.