Reference Blood Glucose Values Research Articles

Fusion of various optimisation based feature smoothing methods for wearable and non-invasive blood glucose estimation.

The traditional blood glucose estimation method requires to take the invasive measurements several times a day. Therefore, it has a high infection risk and the users are suffered from the pain. Moreover, the long term consumable cost is high. Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. Moreover, different subjects have different responses of the infrared light to the blood glucose. To address this issue, a polynomial fitting approach to smooth the obtained features or the reference blood glucose values has been proposed. In particular, the design of the coefficients in the polynomial is formulated as the various optimisation problems. First, the blood glucose values are estimated based on the individual optimisation approaches. Second, the absolute difference values between the estimated blood glucose values and the actual blood glucose values based on each optimisation approach are computed. Third, these absolute difference values for each optimisation approach are sorted in the ascending order. Fourth, for each sorted blood glucose value, the optimisation method corresponding to the minimum absolute difference value is selected. Fifth, the accumulate probability of each selected optimisation method is computed. If the accumulate probability of any selected optimisation method at a point is greater than a threshold value, then the accumulate probabilities of these three selected optimisation methods at that point are reset to zero. A range of the sorted blood glucose values are defined as that with the corresponding boundaries points being the previous reset point and this reset point. Hence, after performing the above procedures for all the sorted reference blood glucose values in the validation set, the regions of the sorted reference blood glucose values and the corresponding optimisation methods in these regions are determined. It is worth noting that the conventional lowpass denoising method was performed in the signal domain (either in the time domain or in the frequency domain), while the authors' proposed method is performed in the feature space or the reference blood glucose space. Hence, the authors' proposed method can further improve the reliability of the obtained feature values or the reference blood glucose values so as to improve the accuracy of the blood glucose estimation. Moreover, the individual modelling regression method has been employed here to suppress the effects of different users having different responses of the infrared light to the blood glucose values. The computer numerical simulation results show that the authors' proposed method yields the mean absolute relative deviation (MARD) at 0.0930 and the percentage of the test data falling in the zone A of the Clarke error grid at 94.1176%.

Performance of the Dexcom G6 Continuous Glucose Monitoring System in Pregnant Women with Diabetes.

Background: The aim of this study was to determine the performance of the Dexcom G6 continuous glucose monitoring (CGM) system across three sensor wear sites in pregnant women with diabetes in the second or third trimesters.Methods: Participants with type 1 (T1D), type 2 (T2D), or gestational (GDM) diabetes mellitus were enrolled at three sites. Each wore two G6 sensors on the abdomen, upper buttock, and/or posterior upper arm for 10 days and underwent a 6-h clinic session between days 3 and 7 of sensor wear, during which YSI reference blood glucose values were obtained every 30 min. No intentional glucose manipulations were performed. Accuracy metrics included the proportion of CGM values that were within ±20% of paired reference values >100 mg/dL or ±20 mg/dL of YSI values ≤100 mg/dL (hereafter referred to as %20/20), as well as the analogous %15/15, %30/30, and %40/40. The mean absolute relative difference (MARD) between CGM-YSI pairs was also calculated.Results: Thirty-two participants with T1D (n = 20), T2D (n = 3), or GDM (n = 9) were enrolled: 19 were in the second trimester and 13 were in the third trimester of pregnancy. Compared with the reference, 92.5% of CGM values were within ±20%/20 mg/dL. The overall MARD and that of sensors worn on the abdomen, upper buttock, and posterior upper arm was 10.3%, 11.5%, 11.2%, and 8.7%, respectively. There were no device-related adverse events. Skin reactions at the insertion sites were absent or minor.Conclusions: The Dexcom G6 CGM system is accurate and safe in pregnant women with diabetes.

Computing the surveillance error grid analysis: procedure and examples.

The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation. The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software's 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo- or hyperglycemia, respectively. To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published data, which reflect the performance of BGM devices and test strips under various environmental conditions. We conclude that the SEG software is a useful addition to the SEG analysis presented in this journal, developed to assess the magnitude of clinical risk from analytically inaccurate data in a variety of high-impact situations such as intensive care and disaster settings.

The single-port concept: combining optical glucose measurement with insulin infusion

The aim of this study was to develop a single-port system that combines glucose monitoring and insulin infusion. In a preclinical proof-of-concept trial, the performance of the glucose measurement at the site of insulin infusion was assessed. Glucose levels were clamped from 40 to 250mg/dL by intravenous glucose infusion and subcutaneous insulin infusion via the glucose sensor. Sensor-glucose values correlated well with reference blood-glucose values, despite infusion at the site of glucose measurement. The average median ARE value was 21.6±5.7% for sensors used for insulin infusion, 18.1±5.8% for sensors used for NaCl infusion and 19.2±7.9% for sensors without infusion. These preclinical in vivo results demonstrate that single-port glucose monitoring is feasible at the site of insulin infusion.

Accuracy Performance of the Medtronic NexSensor® for 6 Days in an Inpatient Setting Using Abdomen and Buttocks Insertion Sites

Users of continuous glucose monitoring are concerned with product accuracy and choice of insertion site. The Medtronic NexSensor™ was evaluated for accuracy during 6 days of wear when inserted in the abdomen and buttocks areas. Adults (ages 18-75) with type 1 diabetes wore two sensors simultaneously for 6 days, one each inserted in the abdomen and buttocks. Subjects underwent a frequent blood sampling study for 12 hours, during which time reference blood glucose values were obtained every 15 minutes and compared to sensor values. Sixty-three subjects were enrolled, and 61 subjects completed the study. The mean agreement rate between sensor and blood glucose values was 75.5% [95% confidence interval (CI), 69.5, 81.4] at the abdomen site, 73.8% (95% CI, 68.8, 78.8) at the buttocks site, and 75.6% (95% CI, 70.8, 80.4) when sensor and reference data were combined between sites. Over 90% of paired sensor-reference values on Clarke error grids were within the A and B ranges. The mean absolute relative differences were 17.1% at the abdomen site, 16.5% at the buttocks site, and 16.8% when sites were combined. The NexSensor was accurate for inpatient, frequent-sample testing for 6 days when inserted into the abdomen and buttocks. The results of this study also provide evidence that both the abdomen and buttocks are suitable as sensor insertion sites.

Pilot studies of transdermal continuous glucose measurement in outpatient diabetic patients and in patients during and after cardiac surgery.

We tested the hypothesis that glucose can be measured continuously and reliably in patients in diverse settings using a transdermal biosensor coupled to a permeated skin site. In addition, we compared a novel, abrasion-based skin permeation method to an ultrasound-based method for transdermal continuous glucose monitoring. Transdermal continuous glucose monitors were applied to patients with diabetes (study I), patients undergoing cardiac surgery (study II), and healthy volunteers (study III). Reference blood glucose measurements were performed with glucometers or standard blood glucose analyzers. At the conclusion of the 24-hour study, data were postprocessed for comparison with the reference blood glucose values collected during the study period. Data were validated for 10 subjects for 12 hours in study I, 8 subjects for 24 hours in study II, and 6 subjects in study III. The transdermal continuous glucose monitors usually required 1 hour of warm up. Depending on the study setting, single or multiple calibrations were applied to the datasets. Comparing predicted glucose versus reference blood glucose values, we found that study I yielded 89.6% in zone A and 9.0% in zone B in the Clarke error grid (222 data points), study II yielded 86.4% in zone A and 13.6% in zone B (147 data points), and study III yielded 89.9% in zone A and 10.1% in zone B (378 data points). Continuous transdermal glucose monitoring was demonstrated successfully in diverse clinical settings. The performance of abrasion was equivalent to ultrasound skin permeation methodology for transdermal glucose monitoring.

Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring—A Study on Hypoglycemic Clamp Data

The fear of hypoglycemia remains an important limiting factor in the ability of an individual with type 1 diabetes to tightly regulate glycemia. Continuous glucose monitors provide important feedback to improve glycemic control, but there remains a need for these devices to better alarm of possible impending hypoglycemia, particularly overnight or other periods when the individual is engaged in activities that take their focus away from glucose monitoring. We have previously proposed an algorithm, based on the use of real-time glucose sensor signals and optimal estimation theory (Kalman filtering), to predict hypoglycemia; the algorithm was validated in simulation-based studies. In this article we further refine and validate the prediction algorithm based on the analysis of clinical hypoglycemic clamp data from 13 subjects. The sensitivity and specificity of the predictions are calculated with respect to reference blood glucose values obtained at the same sampling rate of the sensor. For a 30-minute prediction horizon and alarm threshold of 70 mg/dl, the sensitivity and specificity were 90 and 79%, respectively, indicating that a 21% false alarm rate must be tolerated to predict 90% of the hypoglycemic events 30 minutes ahead of time. Shorter prediction horizons yield a significant improvement in sensitivity and specificity. Sensitivity and specificity data as a function of prediction horizon and alarm threshold enable an individual to adjust the alarm to best meet their needs. Such decisions can be made depending on the subject's risk for hypoglycemia, for example.