A comparison of bolus insulin dose errors based on results of a clinical trial of five blood glucose monitoring systems.

Aim: Inaccurate blood glucose monitoring system (BGMS) results may lead to insulin dosing errors and adverse clinical outcomes. Results & methodology: This post-hoc analysis used a model to estimate the bolus insulin dose error associated with each of the five BGMSs, for a hypothetical person with diabetes (assuming a standardized meal and target blood glucose of 100 mg/dl). Differences in dose-error distribution between BGMSs were statistically tested. The 95% dose-error range for each BGMS was (insulin units): CONTOUR®PLUS, -1.1-0.7; Accu-Chek® Active, -2.4-0.7; Accu-Chek® Performa, -2.9-0.8; FreeStyle Freedom, from -5.5 to -0.5; OneTouch® SelectSimple™, -4.1-3.0. Conclusion: The CONTOUR®PLUS BGMS was associated with a statistically significantly smaller model-estimated median bolus insulin dose-error and dosing error range, compared with the other BGMSs.

Use of radar plots to compare the analytical performance of five blood glucose monitoring systems.

Aim: The radar plot is a relatively new way of communicating blood glucose monitoring system (BGMS) accuracy and precision: data points positioned within concentric circles represent the magnitude (increasing with distance from center) and direction (relative to horizontal) of BGMS-error (center = equivalency with reference instrument measurement). This manuscript aims to demonstrate the utility of radar plots as visual tools for interpretation of BGMS analytical performance. Results & methodology: Radar plots were constructed for five BGMSs, to compare BGMS blood glucose results with reference instrument measurements. Conclusion: Radar plots are a useful tool for the visualization of BGMS analytical performance, communicating accuracy, precision and the satisfaction of certain regulatory criteria at a glance.

Accuracy Beyond ISO: Introducing a New Method for Distinguishing Differences Between Blood Glucose Monitoring Systems Meeting ISO 15197:2013 Accuracy Requirements.

BACKGROUND: Diabetes treatment is intended to maintain near-normal glycemic levels. Self-monitoring of blood glucose (SMBG) allows patients to track their BG levels compared with glycemic targets and is associated with improved health outcomes. Because of the importance of SMBG, it is essential that results are accurate to prevent errors in nutritional intake and drug dosing. This study presents a new methodology to evaluate the accuracy of BG monitoring systems (BGMSs). METHODS: Sensitivity analyses were performed using real and simulated BGMS data to compute probabilities that, for any BG value, the BGMS result would be within prescribed error bounds and confidence limits compared with laboratory reference values. Multiple BG value ranges were used. RESULTS: Probability curves were created using data from 3 simulated BGMSs and anonymized data from 3 real-world BGMSs. Accuracy probability curves from capillary fingertip blood samples (actual clinical data) showed that all 3 real-world BGMSs met EN ISO 15197:2015 accuracy criteria, since 99.63%, 99.63%, and 99.81% of results from the 3 BGMSs were within ±15 mg/dL or ±15% of reference for BG <100 mg/dL and ≥100 mg/dL, respectively. However, there was identifiable variability between BGMSs if BG was <70 mg/dL; one BGMS showed further reductions in accuracy if BG was <50 mg/dL. CONCLUSIONS: Probability curves highlight the importance of BGMS accuracy to help achieve optimal glycemic control while avoiding hypoglycemia or hyperglycemia. This may be especially significant in very low BG ranges where small errors in BGMS measurements can have substantial impacts on patient-related outcomes, including hypoglycemia risk.

Bolus Insulin Dose Error Distributions Based on Results From Two Clinical Trials Comparing Blood Glucose Monitoring Systems.

BACKGROUND: In 2 previous clinical trials, fingertip capillary blood samples were evaluated using prespecified blood glucose monitoring systems (BGMSs) and a reference YSI glucose analyzer. In post hoc analyses, hypothetical insulin doses were calculated using these blood glucose measurements; dosing errors were compared for each trial. METHOD: For each blood glucose measurement, premeal bolus insulin dosing was determined for a hypothetical person, assuming a 60-g carbohydrate meal and 100-mg/dL target blood glucose level (adjusting 1/25 insulin sensitivity and 1/15 insulin:carbohydrate ratio inputs to account for BGMS measurement error). Dosing error was the difference between doses calculated using the BGMS and YSI results. RESULTS: In Clinical Trial 1, 95% dose error ranges (in units of insulin) were: CONTOUR®NEXT EZ BGMS (EZ), -0.9 to 0.5; Accu-Chek® Aviva BGMS (ACA), -0.5 to 1.8; FreeStyle Freedom Lite® BGMS (FFL), -3.2 to -0.3; OneTouch® Ultra®2 BGMS (OTU2), -4.1 to 0.3; and Truetrack® BGMS (TT), -3.9 to 2.2. In Clinical Trial 2, these ranges were: CONTOUR®NEXT BGMS (CN), -0.7 to 1.7; Accu-Chek® Aviva Nano BGMS (ACAN), -1.3 to 1.8; FreeStyle Lite® BGMS (FSL), -5.1 to 0.2; OTU2, -1.9 to 1.2; OneTouch® Verio® Pro BGMS (OTVP), -1.0 to 1.9; and TT, -5.1 to 1.7. Within each trial, EZ and CN had statistically significantly smaller insulin dose error ranges than other BGMSs ( P <0.0001). CONCLUSIONS: The ranges of insulin dose errors were statistically significantly smaller with EZ and CN than with all other BGMSs in this post hoc analysis. Differences in BGMS accuracy could result in clinically important differences in insulin dosing.

Using Radar Plots to Demonstrate the Accuracy and Precision of 6 Blood Glucose Monitoring Systems.

BACKGROUND: Previously, fingertip capillary blood glucose measurements from the CONTOUR®NEXT (CN) blood glucose monitoring system (BGMS) and 5 other BGMSs were evaluated in comparison with measurements from a reference YSI glucose analyzer. Here, we use Radar Plots to graphically represent the accuracy and precision results from the previous study, including whether they met ISO 15197:2013 accuracy criteria. METHOD: A Radar Plot, a new method for capturing a distinct, single visualization of BGMS analytical performance, is a collection of concentric circles, each representing a particular magnitude of error. The center of the plot represents zero error (BGMS result is equivalent to reference result); as points are more distant from the center, the error increases, expressed in units of mg/dL or percentage for YSI values <100 and ≥100 mg/dL, respectively. The position of the data point above or below the horizontal line bisecting the plot indicates whether the BGMS measurement error was positive (BGMS result > YSI result) or negative (BGMS result < YSI result). Points within the "15-15 Zone," representing ±15 mg/dL or ±15% error, satisfy ISO 15197:2013 accuracy criteria. RESULTS: The percentage of results within the 15-15 Zone ranged from 83.6% to 99.8% for the 6 BGMSs (99.6% for CN). CONCLUSIONS: Radar Plots provide a different method for visually comparing the analytical performance of multiple BGMSs. The tight clustering of data points at the center of the CN Radar Plot illustrates the analytical performance of CN compared with 5 other BGMSs.

The Quantitative Relationship Between ISO 15197 Accuracy Criteria and Mean Absolute Relative Difference (MARD) in the Evaluation of Analytical Performance of Self-Monitoring of Blood Glucose (SMBG) Systems.

The relationship between International Organization for Standardization (ISO) accuracy criteria and mean absolute relative difference (MARD), 2 methods for assessing the accuracy of blood glucose meters, is complex. While lower MARD values are generally better than higher MARD values, it is not possible to define a particular MARD value that ensures a blood glucose meter will satisfy the ISO accuracy criteria. The MARD value that ensures passing the ISO accuracy test can be described only as a probabilistic range. In this work, a Bayesian model is presented to represent the relationship between ISO accuracy criteria and MARD. Under the assumptions made in this work, there is nearly a 100% chance of satisfying ISO 15197:2013 accuracy requirements if the MARD value is between 3.25% and 5.25%.

Fundamental Importance of Reference Glucose Analyzer Accuracy for Evaluating the Performance of Blood Glucose Monitoring Systems (BGMSs).

BACKGROUND: As blood glucose monitoring system (BGMS) accuracy is based on comparison of BGMS and laboratory reference glucose analyzer results, reference instrument accuracy is important to discriminate small differences between BGMS and reference glucose analyzer results. Here, we demonstrate the important role of reference glucose analyzer accuracy in BGMS accuracy evaluations. METHODS: Two clinical studies assessed the performance of a new BGMS, using different reference instrument procedures. BGMS and YSI analyzer results were compared for fingertip blood that was obtained by untrained subjects' self-testing and study staff testing, respectively. YSI analyzer accuracy was monitored using traceable serum controls. RESULTS: In study 1 (N = 136), 94.1% of BGMS results were within International Organization for Standardization (ISO) 15197:2013 accuracy criteria; YSI analyzer serum control results showed a negative bias (-0.64% to -2.48%) at the first site and a positive bias (3.36% to 6.91%) at the other site. In study 2 (N = 329), 97.8% of BGMS results were within accuracy criteria; serum controls showed minimal bias (<0.92%) at both sites. CONCLUSIONS: These findings suggest that the ability to demonstrate that a BGMS meets accuracy guidelines is influenced by reference instrument accuracy.

How Should Blood Glucose Meter System Analytical Performance Be Assessed?

Blood glucose meter system analytical performance is assessed by comparing pairs of meter system and reference instrument blood glucose measurements measured over time and across a broad array of glucose values. Consequently, no single, complete, and ideal parameter can fully describe the difference between meter system and reference results. Instead, a number of assessment tools, both graphical (eg, regression plots, modified Bland-Altman plots, and error grid analysis) and tabular (eg, International Organization for Standardization guidelines, mean absolute difference, and mean absolute relative difference) have been developed to evaluate meter system performance. The strengths and weaknesses of these methods of presenting meter system performance data, including a new method known as Radar Plots, are described here.

Analytic characteristics of three Bayer contour blood glucose monitoring systems in neonates.

Hypoglycemia in infants is common, is difficult to recognize, and may lead to permanent neurologic impairment. Low glucose concentrations and high hematocrits in newborns pose significant analytic challenges for whole blood glucose meters. Three Bayer glucose monitoring systems were evaluated using 211 blood samples from 162 neonates (age range 5 hours to 29 days, median age 3 days). Hematocrit and whole blood glucose were determined in heparinized whole blood, and plasma glucose was determined using the Roche Cobas 6000. Accuracy was evaluated against plasma concentrations using ISO 15197:2013 and CLSI POCT 12-A3 criteria. Glucose imprecision on the Cobas system was 1.8-2.6% (CV) from 26-610 mg/dL. Imprecision across all meter systems was 2.8% (CV) at 130 mg/dL. Glucose concentrations, hematocrit, and total bilirubin ranged from 20-150 mg/dL, 18 -75%, and 0.5-19.6 mg/dL, respectively. Linear regression analysis of whole blood versus plasma for the 3 combined systems yielded an average slope of 1.06 and correlation coefficient greater than 0.980. Bias between the Contour and Cobas was not significantly correlated with hematocrit. Greater than 99% of meter results were within 15 mg/dL and 20% of plasma results at glucose concentrations ≤ 75 and > 75 mg/dL, respectively. Of meter results, 97% were within 12.5 mg/dL of plasma results at concentrations ≤ 100 mg/dL, while 96% of meter results were within 12.5% of plasma at concentrations > 100 mg/dL. The Bayer CONTOUR Blood Glucose Monitoring Systems exceed ISO 15197:2013 and CLSI criteria in neonatal blood samples.

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 PlusTM 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 new test strip technology platform for self-monitoring of blood glucose.

In the management of diabetes, accuracy of devices used for self-monitoring of blood glucose (SMBG) is critical because SMBG results can affect patient diabetes-related health outcomes. A new blood glucose monitoring system (BGMS) platform has been developed that is based on the new CONTOUR® NEXT (CN) test strip. This BGMS platform uses a proprietary electron mediator and algorithm to minimize errors at different steps in the testing process, thus minimizing outliers and significantly improving accuracy from prior-generation blood glucose meter systems. As demonstrated by questionnaire results from clinical studies with the new BGMS platform, accuracy and ease of use are important considerations for people with diabetes and their health care professionals when selecting an SMBG device. This article provides an overview of laboratory studies and clinical trials in the hands of lay users involving the performance of the portfolio of blood glucose meters that uses the new test strip. Each BGMS in the platform, which includes the CONTOUR XT (CONTOUR NEXT EZ in the United States), CONTOUR NEXT LINK, CONTOUR NEXT USB, and CN systems, demonstrated advanced accuracy both in the laboratory and in the hands of subjects (people with diabetes) and trained health care professionals. All systems met and exceeded International Organization for Standardization accuracy criteria (both ISO 15197:2003 and ISO 15197:2013). Each system in the new BGMS platform delivers advanced accuracy, which is essential to people who utilize SMBG for improved management.

Performance of a new blood glucose monitoring system in the hands of intended users.

BACKGROUND: This study assessed the performance of a blood glucose monitoring system (BGMS) in development that uses a new generation of blood glucose test strips with capillary and venous blood in the hands of its intended users, people with diabetes and healthcare professionals (HCPs). SUBJECTS AND METHODS: In total, 93 subjects ≥ 18 years old (median age, 33 years) with type 1 (78%) or type 2 (22%) diabetes participated. Untrained subjects performed self-test fingersticks using a Microlet(®)2 lancing device (Bayer HealthCare LLC, Diabetes Care, Tarrytown, NY) followed by testing of their own capillary blood on the BGMS. HCPs performed fingersticks (using a Tenderlett(®) lancing device [International Technidyne Corp., Edison, NJ]) and venipunctures on subjects and tested both capillary and venous samples from subjects on the BGMS. All BGMS results were compared with Yellow Springs Instruments (YSI) (YSI Life Sciences, Inc., Yellow Springs, OH) laboratory results. Analytical accuracy was assessed according to International Organization for Standardization (ISO) 15197:2003 guidelines (i.e., within ± 15 mg/dL or ± 20% of the YSI results for samples with glucose concentrations < 75 mg/dL and ≥ 75 mg/dL, respectively) and more stringent criteria (i.e., within ± 15 mg/dL or ± 15% of the YSI results for samples with glucose concentrations < 100 mg/dL and ≥ 100 mg/dL, respectively). RESULTS: Overall, 98.9% (180/182) of subject Microlet2 capillary fingerstick results, 99.5% (182/183) of HCP Tenderlett capillary fingerstick results, and 100% (186/186) of venous results met current ISO criteria and more stringent criteria. The average hematocrit was 44%, with values ranging from 32% to 52%. CONCLUSIONS: Test results from both capillary fingerstick and venous samples with a new BGMS in development met current accuracy guidelines as well as proposed tighter criteria.

Multitaxonomic diversity patterns along a desert riparian-upland gradient.

Riparian areas are noted for their high biodiversity, but this has rarely been tested across a wide range of taxonomic groups. We set out to describe species richness, species abundance, and community similarity patterns for 11 taxonomic groups (forbs & grasses, shrubs, trees, solpugids, spiders, scarab beetles, butterflies, lizards, birds, rodents, and mammalian carnivores) individually and for all groups combined along a riparian-upland gradient in semiarid southeastern Arizona, USA. Additionally, we assessed whether biological characteristics could explain variation in diversity along the gradient using five traits (trophic level, body size, life span, thermoregulatory mechanism, and taxonomic affiliation). At the level of individual groups diversity patterns varied along the gradient, with some having greater richness and/or abundance in riparian zones whereas others were more diverse and/or abundant in upland zones. Across all taxa combined, riparian zones contained significantly more species than the uplands. Community similarity between riparian and upland zones was low, and beta diversity was significantly greater than expected for most taxonomic groups, though biological traits explained little variance in diversity along the gradient. These results indicate heterogeneity amongst taxa in how they respond to the factors that structure ecological communities in riparian landscapes. Nevertheless, across taxonomic groups the overall pattern is one of greater species richness and abundance in riparian zones, coupled with a distinct suite of species.

Evaluation of a novel continuous glucose measurement device in patients with diabetes mellitus across the glycemic range.

BACKGROUND: This glucose clamp study assessed the performance of an electrochemical continuous glucose monitoring (CGM) system for monitoring levels of interstitial glucose. This novel system does not require use of a trocar or needle for sensor insertion. METHOD: Continuous glucose monitoring sensors were inserted subcutaneously into the abdominal tissue of 14 adults with type 1 or type 2 diabetes. Subjects underwent an automated glucose clamp procedure with four consecutive post-steady-state glucose plateau periods (40 min each): (a) hypoglycemic (50 mg/dl), (b) hyperglycemic (250 mg/dl), (c) second hypoglycemic (50 mg/dl), and (d) euglycemic (90 mg/dl). Plasma glucose results obtained with YSI glucose analyzers were used for sensor calibration. Accuracy was assessed retrospectively for plateau periods and transition states, when glucose levels were changing rapidly (approximately 2 mg/dl/min). RESULTS: Mean absolute percent difference (APD) was lowest during hypoglycemic plateaus (11.68%, 14.15%) and the euglycemic-to-hypoglycemic transition (14.21%). Mean APD during the hyperglycemic plateau was 17.11%; mean APDs were 18.12% and 19.25% during the hypoglycemic-to-hyperglycemic and hyperglycemic-to-hypoglycemic transitions, respectively. Parkes (consensus) error grid analysis (EGA) and rate EGA of the plateaus and transition periods, respectively, yielded 86.8% and 68.6% accurate results (zone A) and 12.1% and 20.0% benign errors (zone B). Continuous EGA yielded 88.5%, 75.4%, and 79.3% accurate results and 8.3%, 14.3%, and 2.4% benign errors for the euglycemic, hyperglycemic, and hypoglycemic transition periods, respectively. Adverse events were mild and unlikely to be device related. CONCLUSION: This novel CGM system was safe and accurate across the clinically relevant glucose range.

Evaluation of an over-the-counter glycated hemoglobin (A1C) test kit.

BACKGROUND: Glycated hemoglobin (A1C) monitoring is an integral component of diabetes management. This study was conducted to evaluate the performance of the A1CNow® SELFCHECK device when used by lay users and health care professionals (HCPs) to measure A1C. METHODS: Subjects performed two A1CNow SELFCHECK finger-stick self-tests followed by a finger-stick test of the subject's blood by a HCP. The primary endpoint assessed accuracy of the subject and HCP A1CNow SELFCHECK readings. Secondary endpoints included precision, comprehension of instructional material (written material±DVD), and product satisfaction. For accuracy comparison, a venous blood sample was drawn from each subject and tested by laboratory (TOSOH) analysis. Subject comprehension of product instructional material was evaluated via first-time failure (FTF) rate as recorded by the HCP, and subject satisfaction was assessed through written survey. RESULTS: A total of 110 subjects with (n=93) and without (n=17) diabetes participated. Of 177 subject A1C values, 165 (93.2%) were within the acceptable range of ±13.5% of the laboratory reference value and considered accurate. Regression analysis showed good correlation of subject values to laboratory and HCP results (R2=0.93 for both). The average within-subject coefficient of variation was 4.57% (n=74). The FTF rates with and without instructional DVD were 11.3% (n=56) and 39.6% (n=54), respectively. Subjects with diabetes/prediabetes overwhelmingly indicated that they were "very" to "extremely" likely (93.5%) to discuss their home A1C results with their HCP. CONCLUSIONS: Lay users found the A1CNow SELFCHECK easy to use, and both lay users and HCPs were able to measure A1C accurately.