Mean Absolute Relative Difference Research Articles

Noninvasive Real-Time Glucose Monitoring Is in the Near Future.

Objective: Since the introduction of continuous glucose monitoring (CGM) technology, developers have rigorously researched the feasibility of creating a noninvasive glucose monitoring device. In a recent pilot study, investigators reported a strong correlation between glucose values obtained from novel noninvasive monitoring device (GWave) values to venous and capillary glucose measurements. Research Design and Methods: We investigated whether the level of accuracy observed in the pilot study could be reproduced in a larger cohort, using a smaller third-generation manufacturable device (Gen III GWave) containing a standardized sensor chip that can be mass produced for commercial use. The evaluation assessed concordance with capillary blood glucose, reproducibility between two Gen III devices, and accuracy during insulin-induced hypoglycemia. Results: Assessment of samples from 75 subjects (type 2 diabetes, n = 6; type 1 diabetes, n = 28; nondiabetic pregnant subjects, n = 10; and nondiabetic, n = 31) showed that 97% of values were in Zone A with 3% in Zone B of the Clarke Error Grid, with a mean absolute relative difference of 6.7% from reference blood glucose. Comparison between two independent Gen III GWave devices demonstrated reproducibility between the sensors (R2 = 0.95), with 100% of values within Zone A. In the hypoglycemia assessment, measurements from the Gen III sensor tightly followed the capillary glucose measurements down to 42 mg/dL (2.3 mmol/L), whereas the CGM measurements from two different CGM only converged with the GWave and capillary glucose readings after 90 min of decreasing glucose levels. Conclusion: Our results show promise as potentially the first noninvasive technology. Future studies will focus on larger number of people in all glucose ranges. Real-time noninvasive blood glucose monitoring is possible using GWave technology.

Early feasibility study with an implantable near-infrared spectroscopy sensor for glucose, ketones, lactate and ethanol.

To evaluate the safety and performance of an implantable near-infrared (NIR) spectroscopy sensor for multi-metabolite monitoring of glucose, ketones, lactate, and ethanol. This is an early feasibility study (GLOW, NCT04782934) including 7 participants (4 with type 1 diabetes (T1D), 3 healthy volunteers) in whom the YANG NIR spectroscopy sensor (Indigo) was implanted for 28 days. Metabolic challenges were used to vary glucose levels (40-400 mg/dL, 2.2-22.2 mmol/L) and/or induce increases in ketones (ketone drink, up to 3.5 mM), lactate (exercise bike, up to 13 mM) and ethanol (4-8 alcoholic beverages, 40-80g). NIR spectra for glucose, ketones, lactate, and ethanol levels analyzed with partial least squares regression were compared with blood values for glucose (Biosen EKF), ketones and lactate (GlucoMen LX Plus), and breath ethanol levels (ACE II Breathalyzer). The effect of potential confounders on glucose measurements (paracetamol, aspartame, acetylsalicylic acid, ibuprofen, sorbitol, caffeine, fructose, vitamin C) was investigated in T1D participants. The implanted YANG sensor was safe and well tolerated and did not cause any infectious or wound healing complications. Six out 7 sensors remained fully operational over the entire study period. Glucose measurements were sufficiently accurate (overall mean absolute (relative) difference MARD of 7.4%, MAD 8.8 mg/dl) without significant impact of confounders. MAD values were 0.12 mM for ketones, 0.16 mM for lactate, and 0.18 mM for ethanol. The first implantable multi-biomarker sensor was shown to be well tolerated and produce accurate measurements of glucose, ketones, lactate, and ethanol. Clinical trial identifier: NCT04782934.

Non-invasive Blood Glucose Monitoring Using a Radiofrequency Sensor and a Machine Learning Model

Diabetes Mellitus (DM) affects over 530 million people globally and is a condition characterized by high blood glucose (BG) that can result in severe long-term health consequences if poorly managed. To mitigate the risks associated with DM, it is crucial to regulate BG levels through regular monitoring. Current methods of monitoring BG come with drawbacks in the form of pain and expense. With the rapid increase in prevalence of DM, there is an increasing need for economical, accurate, and non-invasive continuous measures of BG. Here we present a validation for a novel sensor that rapidly scans through a wide range of radio frequencies (RF) and uses machine learning (ML) techniques for the purpose of non-invasive BG measurement. After model training, the RF sensor and ML techniques employed in this study can predict BG at a level significantly better than chance, as shown below. Data were collected from 13 healthy participants over a series of tests lasting 2 – 3 hours. During each test, participants placed their forearm on an RF sensor that measured their BG levels using sweeps across the 500 MHz – 1500 MHz range at 0.1 MHz intervals. Participants ingested 37.5 grams of glucose solution to generate BG readings across normoglycemic and hyperglycemic ranges. Each sweep took approximately 22 seconds, including a one second pause between sweeps. Across 110 tests, we collected 3,311 BG observations. Concurrent measurements from a Continuous Glucose Monitor (CGM) were taken as reference. Data were divided using a 60-20-20 (training-validation-test) split and trained on a Light Gradient-Boosting Machine (lightGBM) model to predict BG values. A ‘blind’ evaluation of model performance was determined using the held-out test dataset. We predicted BG values in the held-out test dataset with a Mean Absolute Relative Difference (MARD) of 10.8% in the normoglycemic range and 15.9% in the hyperglycemic range (11.27% overall MARD). These results were significantly better than a chance model (two-sample t-test, p <0.01). In a Surveillance Error Grid analysis of model accuracy, 89.0% of predictions fell in Risk Level 0, 10.1% in Risk Level 1, and 0.9% in Risk Level 2. These results demonstrate that the novel RF sensor and ML techniques described can predict a reference BG value in the population under study. More research is underway to refine and expand these methods using a gold-standard blood glucose reference device and among a broader participant population with an expanded glucose range. DK and VS are consultants for and own stock in Know Labs. JA and KC are employed by and have stock options in Know Labs. CW and KP are consultants for Know Labs. NA. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Continuous Glucose Monitoring Using the Dexcom G6 in Cardiac Surgery During the Postoperative Period

ObjectiveCardiac surgery is associated with hyperglycemia, which in turn is associated with adverse postsurgical outcomes such as wound infections, acute renal failure, and mortality. This pilot study seeks to determine if Dexcom G6Pro continuous glucose monitor (Dexcom G6Pro CGM) is accurate during the postoperative cardiac surgery period when fluid shifts, systemic inflammatory response syndrome, and vasoactive medications are frequently encountered, compared to standard glucose monitoring techniques. MethodsThis study received institutional review board approval. In this prospective study, correlation between clinical and Dexcom glucose readings was evaluated. Clinical glucose (blood gas, metabolic panel, and point of care) data set included 1428 readings from 29 patients, while the Dexcom G6Pro CGM data included 45 645 data points following placement to upper arm. Additionally, average clinical measurements of day and overnight temperatures and hemodynamics were evaluated.Clinical and Dexcom data were restricted to being at least 1 hour after prior clinical reading Matching Dexcom G6Pro CGM data were required within 5 minutes of clinical measure. Data included only if taken at least 2 hours after Dexcom G6Pro CGM insertion (warm-up time) and analyzed only following intensive care unit (ICU) admission. Finally, a data set excluding the first 24 hours after ICU admission was created to explore stability of the device. Patients remained on Dexcom G6Pro CGM until discharge or 10 days postoperatively. ResultsThe population was 71% male, 14% with known diabetes; 66% required intravenous insulin infusion. The Clarke error grid plot of all measures post-ICU admission showed 53.5% in zone A, 45.9% in zone B, and 0.6% (n = 5) in zones D or E. The restricted dataset that excluded the first 24 hours post-ICU admission showed 55.9% in zone A, 43.9% in zone B, and 0.2% in zone D. Mean absolute relative difference between clinical and Dexcom G6Pro CGM measures was 20.6% and 21.6% in the entire post-ICU admission data set, and the data set excluding the first 24 hours after ICU admission, respectively. In the subanalysis of the 12 patients who did not have more than a 5-minute tap in the operating room, a consensus error grid, demonstrated that after ICU admission, percentage in zone A was 53.9%, zone B 45.4%, and zone C 0.7%. Similar percentages were obtained removing the first 24 hours post-ICU admission. These numbers are very similar to the entire cohort.A consensus error grid created post-ICU admission demonstrated: (zone A) 54%, (zone B) 45%, (zone C) 0.9%, and the following for the dataset created excluding the first 24 hours: (zone A) 56%, (zone B) 44%, (zone C) 0.4%, which demonstrated very close agreement with the original Clarke error grid. No adverse events were reported. ConclusionsAlmost 100% of Dexcom G6Pro CGM and clinical data matching points fell within areas considered as giving clinically correct decisions (zone A) and clinically uncritical decisions (zone B). However, the relatively high mean absolute relative difference precludes its use for both monitoring and treatment in the clinical context. As technology evolves, interstitial glucose monitoring may become an important tool to limit iatrogenic anemia and mitigate glycemic fluctuations.

Accurate blood glucose level monitoring using microwave imaging

Painless and non-invasive detection techniques are needed to replace finger-prick blood collection for people with diabetes. A first-of-its-kind, noninvasive, and continuous blood glucose level (BGL) detection method based on microwave imaging is introduced in this paper. This method avoids the complex task of frequency choice for the design of electromagnetic sensors. A radar-based microwave imaging technology combined with an improved very-deep super-resolution (VDSR-BL) method is presented to obtain high-resolution (HR) microwave images. After image super-resolution reconstruction by VDSR-BL, the peak signal-to-noise ratio and structural similarity index of HR images reach 35.4461 dB and 0.9761, respectively. Then, an ensemble learning strategy based on support vector regression and random forest algorithms is proposed to identify HR microwave images for BGL estimation. The developed detection system has been verified on the medium under tests with different glucose solutions. The final detection results obtain a root mean squared error of 0.1394 mg ml−1 and a mean absolute relative difference of 8.02%, which show good accuracy with clinical acceptance. Meanwhile, we also conducted human trials. A high correlation coefficient (R) of 0.9254 was achieved between the results of microwave imaging and invasive BGL. Together, these results show that microwave imaging offers a promising new approach for noninvasive BGL monitoring.

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes.

Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting. Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day. Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6. Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

First Accuracy and User-Experience Evaluation of New Continuous Glucose Monitoring System for Hypoglycemia Due to Hyperinsulinism.

Patients with congenital hyperinsulinism (HI) require constant glucose monitoring to detect and treat recurrent and severe hypoglycemia. Historically, this has been achieved with intermittent self-monitoring blood glucose (SMBG), but patients are increasingly using continuous glucose monitoring (CGM). Given the rapidity of CGM device development, and increasing calls for CGM use from HI families, it is vital that new devices are evaluated early. We provided two months of supplies for the new Dexcom G7 CGM device to 10 patients with HI who had recently finished using the Dexcom G6. Self-monitoring blood glucose was performed concurrently with paired readings providing accuracy calculations. Patients and families completed questionnaires about device use at the end of the two-month study period. Compared to the G6, the G7 showed a significant reduction in mean absolute relative difference (25%-18%, P < .001) and in the over-read error (Bland Altman +1.96 SD; 3.54 mmol/L to 2.95 mmol/L). This resulted in an improvement in hypoglycemia detection from 42% to 62% (P < .001). Families reported an overall preference for the G7 but highlighted concerns about high sensor failure rates. The reduction in mean absolute relative difference and over-read error and the improvement in hypoglycemia detection implies that the G7 is a safer and more useful device in the management of hypoglycemia for patients with HI. Accuracy, while improved from previous devices, remains suboptimal with 40% of hypoglycemia episodes not detected.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body’s cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Concordance of Blood Glucose and CGM During a Pilot Trial of Automated Insulin Delivery in Type 1 Diabetes Pregnancies.

Customized and standard automated insulin delivery (AID) systems for use in pregnancies of women with preexisting type 1 diabetes (T1D) are being developed and tested to achieve pregnancy appropriate continuous glucose monitoring (CGM) targets. Guidance on the use of CGM for treatment decisions during pregnancy in the United States is limited. Ten pregnant women with preexisting T1D participated in a trial evaluating at-home use of a pregnancy-specific AID system. Seven-point self-monitoring of blood glucose (SMBG) was compared to the closest sensor glucose (Dexcom G6 CGM) value biweekly to assess safety and reliability based on the 20%/20 mg/dL criteria. All participants completed the study with 7 participants satisfying the safety and reliability criteria with a mean absolute relative difference of 10.3%. Three participants did not fulfill the criteria, mainly because the frequency of SMBG did not meet the requirements. Dexcom G6 CGM is safe and accurate in the real-world setting for use in pregnant women with preexisting T1D with reduced SMBG testing as part of a pregnancy-specific AID system.

Clinical application of real-time continuous glucose monitoring system during postoperative enteral nutrition therapy in esophageal cancer patients.

Enteral nutrition (EN) support therapy increases the risk of abnormal blood glucose (BG). The aim of this study is to evaluate the clinical value of a real-time continuous glucose monitoring (rt-CGM) system in BG monitoring during postoperative EN support therapy in patients with esophageal cancer. Patients without diabetes mellitus (DM) with esophageal cancer who planned to receive postoperative EN were enrolled. With the self-monitoring of BG value as the reference BG, the accuracy of rt-CGM was evaluated by the mean absolute relative difference (MARD) value, correlation efficient, agreement analysis, and Parkes and Clarke error grid plot. Finally, paired t tests were used to compare the differences in glucose fluctuations between EN and non-EN days and slow and fast days. The total MARD value of the rt-CGM system was 13.53%. There was a high correlation between interstitial glucose and fingertip capillary BG (consistency correlation efficient = 0.884 [95% confidence interval, 0.874-0.894]). Results of 15/15%, 20/20%, 30/30% agreement analysis were 58.51%, 84.71%, and 99.65%, respectively. The Parkes and Clarke error grid showed that the proportion of the A and B regions were 100% and 99.94%, respectively. The glucose fluctuations on EN days vs non-EN days and on fast days vs slow days were large, and the difference was statistically significant (P < 0.001). The rt-CGM system achieved clinical accuracy and can be used as a new option for glucose monitoring during postoperative EN therapy. The magnitude of glucose fluctuation during EN therapy remains large, even in the postoperative population without DM.

Accuracy of a Real-Time Continuous Glucose Monitor in Pediatric Diabetic Ketoacidosis Admissions.

Objective: Continuous glucose monitoring (CGM) devices are integral in the outpatient care of people with type 1 diabetes, although they lack inpatient labeling. Food and Drug Administration began allowing inpatient use during the coronavirus disease 2019 (COVID-19) pandemic, with some accuracy data now available, primarily from adult hospitals. Pediatric inpatient data remain limited, particularly during diabetic ketoacidosis (DKA) admissions and for patients receiving intravenous (IV) insulin. Design and Methods: This retrospective chart review compared point-of-care glucose values to personal Dexcom G6 sensor data during pediatric hospitalizations. Accuracy was assessed using mean absolute relative difference (MARD), Clarke Error Grids, and the percentage of values within 15/20/30% if glucose value >100 mg/dL and 15/20/30 mg/dL if glucose value ≤100 mg/dL. Results: Matched paired glucose values (N = 612) from 36 patients (median age 14 years, 58.3% non-Hispanic White, 47.2% male) and 42 inpatient encounters were included in this subanalysis of DKA admissions. The MARDs for DKA and non-DKA admissions (N = 503) were 11.8% and 11.7%, with 97.6% and 98.6% of pairs falling within A and B zones of the Clarke Error Grid, respectively. Severe DKA admissions (pH <7.15 and/or bicarbonate <5 mmol/L) had a MARD of 8.9% compared to 14.3% for nonsevere DKA admissions. The MARD during administration of IV insulin (N = 266) was 13.4%. Conclusions: CGM accuracy is similar between DKA and non-DKA admissions and is maintained in severe DKA and during IV insulin administration, suggesting potential usability in pediatric hospitalizations. Further study on the feasibility of implementation of CGM in the hospital is needed.

Combining an Electrochemical Continuous Glucose Sensor With an Insulin Delivery Cannula: A Feasibility Study.

Combining a continuous glucose monitor with an insulin delivery cannula (CGM-IS) could benefit clinical outcomes. We evaluated the feasibility of a single-needle insertion electrochemical investigational CGM-IS (Pacific Diabetes Technologies, Portland, Oregon) in type 1 diabetes adults. Following 48 hours run-in using a Medtronic 780G in manual mode with a commercial insulin set, 12 participants commenced insulin delivery using the CGM-IS. A standardized test meal was eaten on the mornings of days 1 and 4. Venous samples were collected every 10 minutes one hour prior to and 15 minutes post-meal for four hours. CGM-IS glucose measurements were post-processed with a single capillary blood calibration during warm-up and benchmarked against YSI. A Dexcom G6 sensor was worn post-consent to study end. Mean absolute relative difference (MARD) for the CGM-IS glucose measurements was 9.2% (484 paired data points). Consensus error grid revealed 88.6% within zone A and 100% in A + B. Mean (SD) % bias was -3.5 (11.7) %. There were 35 paired YSI readings <100 mg/dL cutoff and 449 ≥100 mg/dL with 81.4% within ±15 mg/dL or ±15%, and 89.9% within ±20 mg/dL or ±20%. Two cannula occlusions required discontinuation of insulin delivery: one at 70 hours post insertion and another during the day 4 meal test. Mean (SD) Dexcom glucose measurements during run-in and between meal tests was respectively 161.3 ± 27.3 mg/dL versus 158.0 ± 25.6 mg/dL; P = .39 and corresponding mean total daily insulin delivered by the pump was 58.0 ± 25.4 Units versus 57.1 ± 28.8 Units; P = .47. Insulin delivery and glucose sensing with the investigational CGM-IS was feasible. Longer duration studies are needed.

Accuracy and Feasibility of Real-time Continuous Glucose Monitoring in Critically Ill Patients After Abdominal Surgery and Solid Organ Transplantation.

Glycemia management in critical care is posing a challenge in frequent measuring and adequate insulin dose adjustment. In recent years, continuous glucose measurement has gained accuracy and reliability in outpatient and inpatient settings. The aim of this study was to assess the feasibility and accuracy of real-time continuous glucose monitoring (CGM) in ICU patients after major abdominal surgery. We included patients undergoing pancreatic surgery and solid organ transplantation (liver, pancreas, islets of Langerhans, kidney) requiring an ICU stay after surgery. We used a Dexcom G6 sensor, placed in the infraclavicular region, for real-time CGM. Arterial blood glucose measured by the amperometric principle (ABL 800; Radiometer, Copenhagen, Denmark) served as a reference value and for calibration. Blood glucose was also routinely monitored by a StatStrip bedside glucose meter. Sensor accuracy was assessed by mean absolute relative difference (MARD), bias, modified Bland-Altman plot, and surveillance error grid for paired samples of glucose values from CGM and acid-base analyzer (ABL). We analyzed data from 61 patients and obtained 1,546 paired glucose values from CGM and ABL. Active sensor use was 95.1%. MARD was 9.4%, relative bias was 1.4%, and 92.8% of values fell in zone A, 6.1% fell in zone B, and 1.2% fell in zone C of the surveillance error grid. Median time in range was 78%, with minimum (<1%) time spent in hypoglycemia. StatStrip glucose meter MARD compared with ABL was 5.8%. Our study shows clinically applicable accuracy and reliability of Dexcom G6 CGM in postoperative ICU patients and a feasible alternative sensor placement site.

Performance of an interstitial glucose monitoring device in patients with type 1 diabetes during haemodialysis.

The use of interstitial glucose monitoring devices such as flash glucose monitoring has been shown to be beneficial in patients with type 1 diabetes mellitus (T1DM). However, these devices have been little studied in patients with diabetes treated by chronic haemodialysis (HD). The goal of this prospective, observational, multicentric study was to evaluate the analytical performance of the FreeStyle Libre 2 (FSL2) sensor in T1DM patients during HD sessions. During three HD sessions, interstitial fluid glucose (ISFG) concentrations given by the FSL2 were compared every 15minutes with blood glucose (BG) concentrations obtained simultaneously. BG concentrations were measured by two different glucometers: the Accu-Chek Guide and StatStrip meters. Twelve HD patients were included, with a mean age of 54±11years and a mean diabetes duration of 36.5±11.6years. Dialysis vintage was 35±22months. A total of 565 pairs of ISFG/BG values were available for analysis. The mean absolute relative difference, defined as the mean of the absolute relative differences between the ISFG and BG measurements, was 17.4% and 20.9% when the ISFG was compared with the StatStrip meter or Accu-Chek Guide, respectively. Interstitial results tend to underestimate blood results, but all values were classified as having clinically acceptable error. The differences observed remained stable during the dialysis session and were not associated with the ultrafiltration rate. Use of the FSL2 interstitial glucose monitoring device in HD patients with T1DM is clinically acceptable, even though the accuracy of the device is generally poorer than in studies including non-dialysis patients.

Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System.

Background: We evaluated the accuracy and safety of the CareSens Air, a novel real-time continuous glucose monitoring system (CGMS), during 15 days of use in adults with diabetes. Methods: Adults with either type 1 diabetes or type 2 diabetes requiring intensive insulin therapy participated at four sites in South Korea. All participants wore the sensor for 15 days. Participants were scheduled for four 8-h clinic sessions on Day 1, 5 ± 1, 10 ± 1, and 15. Accuracy was evaluated based on the proportion of continuous glucose monitoring (CGM) values within 15% of YSI values ≥100 mg/dL or within 15 mg/dL of YSI values <100 mg/dL (%15/15), along with the %20/20, %30/30, and %40/40 agreement rates. The mean absolute relative difference (MARD) between the CGM and YSI values was calculated. Results: Data from 83 participants (83 sensors, 10,029 CGM-YSI matched pairs) were analyzed. The overall MARD was 10.42%, and the overall %15/15, %20/20, %30/30, and %40/40 accuracy were 78.55%, 89.04%, 96.47%, and 98.87%, respectively. The consensus error grid analysis showed that 99.92% of CGM values fell into Zone A or B (Zone A: 89.83%, Zone B: 10.09%). The %20/20 accuracy of CGMS was 88.11% on Day 1, 90.11% on Day 3-5, 92.09% on Day 8-10, and 85.63% on Day 15. No serious adverse events were reported. Conclusions: The CareSens Air demonstrated accurate performance across the wide glycemic range and was well tolerated during the 15-day sensor use period.

Breaking through diabetes management barriers: Assessing the accuracy of Freestyle Libre Pro flash continuous glucose monitoring in diabetes patients with myocardial infarction.

The objective of this study was to assess the accuracy of FreeStyle Libre Pro (FSL-Pro) flash continuous glucose monitoring (CGM) in patients with type 2 diabetes mellitus (T2DM) and acute myocardial infarction (AMI). A single-arm, single-center prospective study was conducted in the cardiac care unit from January 2021 to September 2023. Patients underwent finger-prick blood glucose (FPBG) testing before breakfast (6:00 am) and after meals (at 9:00, 13:00, 19:00 pm), along with CGM during their hospitalization. Statistical analyses included mean differences (MDs), mean absolute relative difference (MARDs) of blood glucose levels, and hypoglycemia occurrences. A Bland-Altman plot analysis and Pearson correlation were performed. Ninety-seven T2DM and AMI patients underwent CGM for up to 72 h (1142 monitoring point). Mean daily BG, Fasting plasma glucose (FPG) and mean postprandial plasma glucose (PPG) were significantly lower by CGM than by FPBG with an estimated MD of -0.89 mmol/L in BG, -0.88 mmol/L in FPG, and -0.90 mmol/L in PPG, respectively. The maximum effect was mainly in the first day and then the difference was gradually declined (falling range, Day1, -1.24; Day 2, -0.70; Day 3, -0.68, mmol/L, respectively). The incidence rates of hypoglycemia and potential hypoglycemia was 1.57% and 8.5% higher, respectively, in CGM than in FBPG. A Bland-Altman Plot revealed some variability and bias between the two methods of measurement of glucose monitoring (p < .001). Pearson's correlation coefficient demonstrated a significant correlation between the mean BG, FPG, and PPG of CGM and FBPG (Pearson's coefficient: 0.92, 0.87, 0.92, respectively, p < .001). Compared with FPBG, FSL Pro-CGM showed lower mean glucose and higher hypoglycemia detection in T2DM and AMI patients, especially in the first 24 h.

Identification of individuals with diabetes who are eligible for continuous glucose monitoring forecasting

Background and objectivesPredicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting. MethodsParticipants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min. ResultsA strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661–0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two. ConclusionsThe identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.

High-accuracy noninvasive continuous glucose monitoring using OCT angiography-purified blood scattering signals in human skin.

The accuracy of noninvasive continuous glucose monitoring (CGM) through near-infrared scattering is challenged by mixed scattering signals from different compartments, where glucose has a positive correlation with a blood scattering coefficient but a negative correlation with a tissue scattering coefficient. In this study, we developed a high-accuracy noninvasive CGM based on OCT angiography (OCTA)-purified blood scattering signals. The blood optical scattering coefficient (BOC) was initially extracted from the depth attenuation of backscattered light in OCT and then purified by eliminating the scattering signals from the surrounding tissues under the guidance of a 3D OCTA vascular map in human skin. The purified BOC was used to estimate the optical blood glucose concentration (BGC) through a linear calibration. The optical and reference BGC measurements were highly correlated (R = 0.94) without apparent time delay. The mean absolute relative difference was 6.09%. All optical BGC measurements were within the clinically acceptable Zones A + B, with 96.69% falling in Zone A on Parke's error grids. The blood glucose response during OGTT was mapped with a high spatiotemporal resolution of the single vessel and 5 seconds. This noninvasive OCTA-based CGM shows promising accuracy for clinical use. Future research will involve larger sample sizes and diabetic participants to confirm these preliminary findings.

Accuracy of a Continuous Glucose Monitor During Pediatric Type 1 Diabetes Inpatient Admissions.

Objective: Continuous glucose monitors (CGMs) used for type 1 diabetes management are associated with lower hemoglobin A1c. CGMs are not approved for inpatient use, when close glucose monitoring and intensive insulin management are essential for optimal health. Accuracy data from adult hospitalizations have been published, but pediatric data are limited. Design and Methods: This retrospective review of Dexcom G6 data from youth with type 1 diabetes during hospitalization assessed CGMs and matched (within 5 min) point-of-care (POC) and laboratory glucose values. Glucose values >400 and <40 mg/dL were excluded due to sensor reporting capabilities. Standard methods for CGM accuracy were used including mean absolute relative difference (MARD), Clarke Error Grids, and percentage of CGM values within 15%/20%/30% if glucose value is >100 mg/dL and 15/20/30 mg/dL if value is ≤100 mg/dL. Results: A total of 1120 POC and 288 laboratory-matched pairs were collected from 83 unique patients (median age 12.0 years, 68.7% non-Hispanic white, 54.2% male) during 100 admissions. For POC values, overall, MARD was 11.8%, that on the medical floor was 13.5%, and that in the intensive care unit was 7.9%. The MARD for all laboratory values was 6.5%. In total, 98% of matched pairs were within Clarke Error Grid A and B zones. Conclusions: Findings from our pediatric population were similar to accuracy reported in hospitalized adults, indicating the potential role for CGM use during pediatric hospitalizations. Additional research is needed to assess accuracy under various conditions, including medication use, as well as development of safe hospital protocols for successful CGM implementation for routine inpatient care.

Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes.

Background: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. Methods: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. Results: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. Conclusions: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).