OBJECTIVE -The objectives of this study were 1) to construct new error grids (EGs) for blood glucose (BG) self-monitoring by using the expertise of a large panel of clinicians and 2) to use the new EGs to evaluate the accuracy of BG measurements made by patients. RESEARCH DESIGN AND METHODS -To construct newEGs for type 1 and type 2 diabetic patients, a total of 100 experts of diabetes were asked to assign any error in BG measurement to 1 of 5 risk categories. We used these EGs to evaluate the accuracy of self-monitoring of blood glucose (SMBG) levels in 152 diabetic patients. The SMBG data were used to compare the new type 1 diabetes EG with a traditional EG.RESULTS -Both the type 1 and type 2 diabetes EGs divide the risk plane into 8 concentric zones with no discontinuities. The new EGs are similar to each other, but they differ from the traditional EG in several significant ways. When used to evaluate a data set of measurements made by a sample of patients experienced in SMBG, the new type 1 diabetes EG rated 98.6% of their measurements as clinically acceptable, compared with 95% for the traditional EG. CONCLUSIONS -The consensus EGs furnish a new tool for evaluating errors in the mea E m e r g i n g T r e a t m e n t s a n d T e c h n o l o g i e s 1144DIABETES CARE, VOLUME 23, NUMBER 8, AUGUST 2000 New error grid for blood glucose 152 patients who routinely monitor their own BG. The study was performed in a diabetes clinic with patients using their own meters. The distribution of errors in our sample is considered in light of both EGs and the latest recommendations of the American Diabetes Association (ADA). RESEARCH DESIGN AND METHODS Consensus EGWe surveyed 100 physicians at the 1994 ADA Annual Meeting to construct an unbiased tool to analyze the clinical significance of SMBG measurement errors. All of the respondents were clinicians who treated diabetic patients. Pursuant to constructing an EG in the fashion of Clarke et al. (6), each doctor was asked to assign any plausible error in BG measurement to 1 of 5 risk categories. The risk categories, in order of increasing severity, were defined as follows: A: no effect on clinical action; B: altered clinical action or little or no effect on clinical outcome; C: altered clinical action-likely to effect clinical outcome; D: altered clinical action-could have significant medical risk; and E: altered clinical action-could have dangerous consequences.The above definitions were intended to correspond to the definitions of the risk zones in the Clarke EG while allowing the respondents maximal freedom to set their own boundaries. For example, zone A of the Clarke EG is defined as Ͻ20% deviation or having both reference and measured BGs Ͻ70 mg/dl. In addition, the UVA authors (6) stated that "values falling within this range are clinically accurate in that they would lead to clinically correct treatment decisions." Our definition of zone A asks each respondent to define his or her own range of "clinically accurate measurements," which is clarified as having "no effe...

Introduction: Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. Methods:A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG.Results: SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. Discussion:The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs.

Cleared BGMs do not always meet the level of analytical accuracy currently required for regulatory clearance. This information could assist patients, professionals, and payers in choosing products and regulators in evaluating postclearance performance.

It remains to be seen by how much the new error grid, which is currently being developed by the Food and Drug Administration/Diabetes Technology Society/American Diabetes Association/The Endocrine Society/Association for Advancement of Medical Instrumentation, will deviate from the Parkers error grid.

Objective:The objective of this study was to determine inaccuracies of miscoded blood glucose (BG) meters and potential errors in insulin dose based on values from these meters. Research Design:Fasting diabetic subjects at three clinical centers participated in a 2-hour meal tolerance test. At various times subjects' blood was tested on five BG meters and on a Yellow Springs Instruments laboratory glucose analyzer. Some meters were purposely miscoded. Using the BG values from these meters, along with three insulin dose algorithms, Monte Carlo simulations were conducted to generate ideal and simulated-meter glucose values and subsequent probability of insulin dose errors based on normal and empirical distribution assumptions. Results:Maximal median percentage biases of miscoded meters were +29% and -37%, while maximal median percentage biases of correctly coded meters were only +0.64% and -10.45% (p = 0.000, c 2 test, df = 1). Using the low-dose algorithm and the normal distribution assumption, the combined data showed that the probability of insulin error of ±1U, ±2, ±3, ±4, and ±5U for miscoded meters could be as high as 49.6, 50.0, 22.3, 1.4, and 0.04%, respectively. This is compared to manually, correctly coded meters where the probability of error of ±1, ±2, and ±3U could be as high as 44.6, 7.1, and 0.49%, respectively. There was no instance of a ±4 or ±5U insulin dose error with a manually, correctly coded meter. For autocoded meters, the probability of ±1 and ±2U could be as high as 35.4 and 1.4%, respectively. For autocoded meters there were no calculated insulin dose errors above ±2U. The probability of insulin misdosing with either manually, correctly coded or autocoded meters was significantly lower than that with miscoded meters. Results using empirical distributions showed similar trends of insulin dose errors. Conclusions:Blood glucose meter coding errors may result in significant insulin dosing errors. To avoid error, patients should be instructed to code their meters correctly or be advised to use an autocoded meter that showed superior performance over manually, correctly coded meters in this study.