Do We Need the Replacement of YSI 2300? A View from the Clinical Laboratory

In this issue of Journal of Diabetes Science and Technology, Baumstark et al. evaluated the analytical performance of a bench-top laboratory glucose analyzer (SUPER-GL) intended for replacement for the YSI2300-STAT analyzer, that served for several decades as a comparator method in clinical and analytical studies of blood glucose monitoring systems (BGMS). The authors concluded that the SUPER-GL’s overall performance is comparable to that of YSI2300-STAT, and has the potential to be a candidate comparator analyzer. However, the question is if we need to recommend as a “comparator method,” a specific device, that measure glucose using the same analytical method with most BGMS. In this analysis we present our point of view hoping to generate a discussion on the necessity for such a replacement.     

Analysis of “Accuracy Evaluation of Five Blood Glucose Monitoring Systems: The North American Comparator Trial”

In an article in Journal of Diabetes Science and Technology, Halldorsdottir and coauthors examined the accuracy of five blood glucose monitoring systems (BGMSs) in a study sponsored by the manufacturer of the BGMS CONTOUR NEXT EZ (EZ) and found that this BGMS was the most accurate one. However, their findings must be viewed critically given that one of the BGMSs (ACCU-CHEK Aviva) was not compared against the reference measurement specified by its manufacturer, thus making it likely that it performed suboptimally. Also, the accuracy of the glucose-oxidase-based ONE TOUCH Ultra2 and TRUEtrack BGMS is likely to have been underestimated because of the expected low oxygen level in the glycolysed blood samples used to test the performance of these BGMSs under hypoglycemic conditions. In conclusion, although this study shows that EZ is an accurate BGMS, comparisons between this and other BGMSs should be interpreted with caution.     

Accuracy and User Performance of a New Blood Glucose Monitoring System

Introduction:Self-monitoring of blood glucose (BG) is important in diabetes management, allowing people with diabetes (PWD) to assess responses to diabetes therapy and to inform if they are attaining their glycemic targets. This study assessed the accuracy and user performance (UP) of a new blood glucose monitoring system (BGMS), CONTOUR®PLUS ELITE, according to International Organization for Standardization (ISO) 15197:2013 criteria and also more stringent criteria.Methods:In laboratory Study 1, capillary fingertip blood samples from 100 PWD were evaluated using the new BGMS. In clinical Study 2, 130 PWD had Yellow Springs Instrument (YSI) analyzer reference measurements against subject-obtained fingertip and palm blood, and trial staff-obtained venous blood. The new BGMS was tested with test strips from three different lots. A UP questionnaire assessed ease of use.Results:Study 1: 100% of combined accuracy results fulfilled ISO criteria (±15 mg/dL at BG <100 mg/dL; ±15% at BG ≥100 mg/dL); 99.8% fulfilled more stringent criteria (±10 mg/dL at BG <100 mg/dL; ±10% at BG ≥100 mg/dL). Error grid analysis showed that 100% of results were within zone A. Study 2: >98% of subject- and 100% of trial staff-obtained performance results met ISO criteria. Most subjects (>96%) found the BGMS easy to use.Conclusion:The new BGMS exceeded minimum ISO 15197:2013-specified standards for both accuracy and UP criteria, along with the more stringent accuracy criteria. These data show that this new BGMS can be a useful tool in managing glycemic control for PWD.     

Evaluation of a Blood Glucose Monitoring System with Automatic High- and Low-Pattern Recognition Software in Insulin-Using Patients: Pattern Detection and Patient-Reported Insights

Background

This study aimed to evaluate the performance of a glucose pattern recognition tool incorporated in a blood glucose monitoring system (BGMS) and its association with clinical measures, and to assess user perception and understanding of the pattern messages they receive.

Methods

Participants had type 1 or type 2 diabetes mellitus and were self-adjusting insulin doses for ≥1 year. During a 4-week home testing period, participants performed ≥6 daily self-tests, adjusted their insulin regimen based on BGMS results, and recorded pattern messages in the logbook. Participants reflected on usability of the pattern tool in a questionnaire.

Results

Study participants (n = 101) received a mean ± standard deviation of 4.5 ± 1.9 pattern messages per week (3.6 ± 1.8 high glucose patterns and 0.9 ± 1.3 low glucose patterns). Most received ≥1 high (96.5%) and/or ≥1 low (46.0%) pattern message per week. The average number of high- and low-pattern messages per week was associated with higher and lower, respectively, baseline hemoglobin A1c (p < .01) and fasting plasma glucose (p < .05). Participants found high- and low-pattern messages clear and easy to understand (84.2% and 83.2%, respectively) and considered the frequency of low (82.0%) and high (63.4%) pattern messages about right. Overall, 71.3% of participants indicated they preferred to use a meter with pattern messages.

Conclusions

The on-device Pattern tool identified meaningful blood glucose patterns, highlighting potential opportunities for improving glycemic control in patients who self-adjust their insulin.     

Accuracy Evaluation of Five Blood Glucose Monitoring Systems: The North American Comparator Trial

Background

This study evaluated differences in accuracy between the CONTOUR^® NEXT EZ (EZ) blood glucose monitoring system (BGMS) and four other BGMSs [ACCU-CHEK^® Aviva (ACAP), FreeStyle Freedom Lite^® (FFL), ONE TOUCH^® Ultra^®2 (OTU2), and TRUEtrack^® (TT)].

Methods

Up to three capillary blood samples (N = 393) were collected from 146 subjects with and without diabetes. One sample per subject was tested with fresh (natural) blood; the other samples were glycolyzed to lower blood glucose to <70 mg/dl. Meter results were compared with results from plasma from the same sample tested on a Yellow Springs Instruments (YSI) 2300 STAT Plus™ glucose analyzer. Blood glucose monitoring system accuracy was compared using mean absolute relative difference (MARD; from laboratory reference method results) and other analyses. Separate analyses on fresh (natural) samples only were conducted to determine potential effects of glycolysis on MARD values of systems utilizing glucose-oxidase-based test strip chemistry.

Results

Across the tested glucose range, the EZ had the lowest MARD of 4.7%; the ACAP, FFL, OTU2, and TT had MARD values of 6.3%, 18.3%, 23.4%, and 26.2%, respectively. For samples with glucose concentrations <70 mg/dl, the EZ had the lowest MARD (0.65%), compared with the ACAP (2.5%), FFL (18.3%), OTU2 (22.4%), and TT (33.2%) systems.

Conclusions

The EZ had the lowest MARD across the tested glucose ranges when compared with four other BGMSs when all samples were analyzed as well as when natural samples only were analyzed.     

A Cause-and-Effect-Based Mathematical Curvilinear Model That Predicts the Effects of Self-Monitoring of Blood Glucose Frequency on Hemoglobin A1c and Is Suitable for Statistical Correlations

Background

Previous studies have shown an association between the frequency of self-monitored blood glucose (SMBG) and hemoglobin A1c. Randomized controlled trials (RCTs) have shown this to be a causal correlation for insulin-using patients. Several studies have used linear regression, but a straight line will descend into negative hemoglobin A1c values (an impossibility). This study developed a cause-and-effect-based nonlinear model to predict the outcome of RCTs on this subject, tested this model with clinical data, and offered this model in place of linear regression, especially for the still-debated case of noninsulin-using patients.

Methods

The model was developed from cause-and-effect principles. The clinical study utilized retrospective data from patient histories of a large endocrine practice. Data sets were obtained for five treatment regimens: continuous subcutaneous insulin infusion (CSII), subcutaneous insulin (SC), no insulin (NI), oral medication (OM), and no medication (NM). OM and NM are subgroups of NI. The model was fitted to each group using nonlinear leastsquares methods. Each group was ordered by SMBG tests per day (BGpd) and was divided in half; t tests were run between the A1C''s of the two halves.

Results

Self-monitored blood glucose readings from 1255 subjects were analyzed (CSII, N = 417; SC, N = 286; NI, N = 552; OM, N = 505; NM, N = 47). The CSII, SC, NI, and OM groups showed the expected declining statistically fitted curve and a significant association of BGpd with hemoglobin A1c (P < 0.004). The NM group showed insignificant results.

Conclusions

The nonlinear model is based on cause-and-effect principles and mathematics. It yields a prediction that RCTs will be able to reveal that higher SMBG frequency causes lower hemoglobin A1c.     

Impact of low-starch high-fiber pasta on postprandial blood glucose

Background and aimsAlmost all of the energy in noodle dishes is derived from carbohydrates, particularly starch. Recently, we invented a pasta with reduced starch content to about 50% and increased dietary fiber content, designated low-starch high-fiber pasta (LSHFP). In this study, we investigated the ingestion of LSHFP on the postprandial glucose response as a breakfast meal.Methods and resultThis was a randomized, single-blinded, crossover study. The postprandial glucose area under the curve for 4 h (4h-gluAUC), as the primary outcome, and the extent of postprandial glucose elevation (maxΔBG) were evaluated using a continuous glucose monitoring system in healthy volunteers and patients with type 2 diabetes (T2DM) after intake of LSHFP, standard pasta (SP), and rice. The amount of total carbohydrate was matched between LSHFP and SP. Ten individuals with T2DM and 10 individuals who did not have T2DM and were otherwise healthy were enrolled in this crossover study. The 4h-gluAUC for LSHFP (137.6 ± 42.2 mg/dL?h) was significantly smaller than the 4h-gluAUC for rice (201.7 ± 38.7 mg/dL?h) (p = 0.001) and SP (178.5 ± 59.2 mg/dL?h) (p = 0.020). The maxΔBG for rice (118.6 ± 24.2 mg/dL) was significantly higher than those for SP (87.5 ± 19.9 mg/dL) (p < 0.001) and LSHFP (72.7 ± 26.2 mg/dL) (p = 0.001), while the maxΔBG for LSHFP (p = 0.047) was significantly lower than that for SP, in T2DM patients as well as in healthy participants.ConclusionsThis study demonstrated that LSHFP can reduce postprandial glucose elevation compared with SP in both healthy participants and patients with T2DM.     

Automated Inpatient Blood Sampling for Glucose Measurement: Evaluation of the VIA? Blood Chemistry Monitor for Glucose in Healthy and Diabetic Volunteers

Background

Manual methods of blood glucose monitoring are labor-intensive, costly, prone to error, and expose the caregiver to blood. The VIA® blood chemistry monitor for glucose can automatically measure plasma glucose (PG) every 5 minutes for 72 hours using blood sampled from a peripheral vein/artery or a central vein.

Methods

VIA performance was evaluated in eight normal and five type 1 diabetic (T1DM) subjects in 15 separate experiments. The VIA device was connected to a peripheral vein and reported a PG value every 5 minutes during each 510-minute experiment. Blood samples were collected manually every 10 minutes and assayed using a HemoCue® β-glucose analyzer (HC). Whole blood HC measurements were corrected to PG values. Paired HC/VIA measurements (n = 717) were analyzed.

Results

Mean PG was 90 ± 14 and 96 ± 12 mg/dl in normal subjects and 194 ± 64 and 173 ± 48 mg/dl in T1DM subject as measured by the HC and VIA, respectively. Clark error grid analysis revealed 86% points in zone A, 11% points in zone B, and 2% points in zone D. Linear regression analysis yielded the following equation: VIA = 0.732 × HC + 30.5 (r² = 0.954). Residual analysis revealed a glucose-dependent bias between the HC and the VIA. VIA data were transformed using the linear regression equation to correct for bias. After the correction, the mean absolute relative difference between the VIA and the HC was less than 10%, and 99.6% of data were in zones A and B. The VIA was able to sample blood automatically every 5 minutes for more than 8 hours in the laboratory setting. On average, the VIA reported glucose values for 94% of the samples it attempted to obtain.

Conclusions

This study demonstrated that the VIA blood chemistry monitor for glucose can reliably sample blood frequently for a prolonged period of time safely and effectively in diabetic and nondiabetic volunteers. Agreement between the two devices was the closest at normal glucose concentrations. After correcting for a glucose-dependent bias between the devices, the MARD was consistently less than 10% for all glucose ranges.     

Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

Background:

The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series.

Methods:

Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data.

Results:

LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series.

Conclusions:

Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.     

Historical achievements of self-monitoring of blood glucose technology development in Japan

Japanese companies were the first in the world to achieve a colorimetric glucose measurement meter back in 1973. Over the following 40 or so years, they succeeded in achieving a much greater level of user-friendliness and performance and in so doing, have contributed to the spread of self-monitoring of blood glucose. This article aims to unravel the history of blood glucose measurement's technological developments; to look at the direction and features of the development path Japan is taking; as well as to introduce some Japanese products that are on the market.