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...

In economically developed countries, mortality increases distinctly during winter. Many causes have been suggested, including light-dark cycles, temperature/weather, and infectious agents. The authors analyzed monthly mortality in the United States during the period 1959-1999 for four major disease classes. The authors isolated the seasonal component of mortality by removing trends and standardizing the time series. They evaluated four properties: coincidence in mortality peaks, autocorrelation structure and autoregressive integrated moving average (ARIMA) models, magnitude, and age distribution. Peak months of mortality for ischemic heart disease, cerebrovascular disease, and diabetes mellitus coincided appropriately with peaks in pneumonia and influenza, and coefficients of autocorrelation and ARIMA models were essentially indistinguishable. The magnitude of the seasonal component was highly correlated with traditional measures of excess mortality and was significantly larger in seasons dominated by influenza A(H2N2) and A(H3N2) viruses than in seasons dominated by A(H1N1) or B viruses. There was an age shift in mortality during and after the 1968/69 pandemic in each disease class, with features specific to influenza A(H3N2). These findings suggest that the cause of the winter increase in US mortality is singular and probably influenza. Weather and other factors may determine the timing and modulate the magnitude of the winter-season increase in mortality, but the primary determinant appears to be the influenza virus.

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.

The purpose of this study was to assess the performance and acceptability of a blood glucose meter coupled with a gaming system for children, adolescents, and young adults with type 1 diabetes. During an in-clinic visit, duplicate blood samples were tested by subjects (N = 147; aged 5-24 yr) and health care providers (HCPs) to evaluate the accuracy and precision of the Didget® system. Subjects' meter results were compared against Yellow Springs Instruments (YSI) reference results and HCP results using least squares regression and error grid analyses. Precision was measured by average within-subject and within-HCP coefficient of variation (CV). During the home-use component of this study, subjects (n = 58) tested their blood glucose at least two to three times daily for 3-5 d to evaluate routine use of the system. Subjects' meter results showed significant correlations with both YSI (r(2) = 0.94; p < 0.001 for regression slope) and HCP results (r(2) = 0.96; p < 0.001). Average within-subject and within-HCP CVs were 5.9 and 7.2%, respectively. Overall satisfaction was assessed by subjects, their parents or guardians, and HCP surveys. Subject satisfaction with the Didget® system was good to excellent; most subjects found the system easy to use, motivating, and helpful for building good blood glucose monitoring habits. Most HCPs agreed that the system fulfilled a need in diabetes management. In conclusion, the Didget® system was precise and clinically accurate in the hands of children, adolescents, and young adults with type 1 diabetes.

When compared with other BGMSs, CN demonstrated the lowest mean deviation from the reference value (by MAD and MARD) across multiple glucose ranges.