BACKGROUND: Two independent studies reported that 16% of people who self-monitor blood glucose used incorrectly coded meters. The degree of analytical error, however, was not characterized. Our study objectives were to demonstrate that miscoding can cause analytical errors and to characterize the potential amount of bias that can occur. The impact of calibration error with three selfblood glucose monitoring systems (BGMSs), one of which has an autocoding feature, is reported. METHODS: Fresh capillary fingerstick blood from 50 subjects, 18 men and 32 women ranging in age from 23 to 82 years, was used to measure glucose with three BGMSs. Two BGMSs required manual coding and were purposely miscoded using numbers different from the one recommended for the reagent lot used. Two properly coded meters of each BGMS were included to assess within-system variability. Different reagent lots were used to challenge a third system that had autocoding capability and could not be miscoded. RESULTS: Some within-system comparisons showed deviations of greater than +/-30% when results obtained with miscoded meters were compared with data obtained with ones programmed using the correct code number. Similar erroneous results were found when the miscoded meter results were compared with those obtained with a glucose analyzer. For some miscoded meter and test strip combinations, error grid analysis showed that 90% of results fell into zones indicating altered clinical action. Such inaccuracies were not found with the BGMS having the autocoding feature. CONCLUSIONS: When certain meter code number settings of two BGMSs were used in conjunction with test strips having code numbers that did not match, statistically and clinically inaccurate results were obtained. Coding errors resulted in analytical errors of greater than +/-30% (-31.6 to +60.9%). These results confirm the value of a BGMS with an automatic coding feature.
BACKGROUND: Two independent studies reported that 16% of people who self-monitor blood glucose used incorrectly coded meters. The degree of analytical error, however, was not characterized. Our study objectives were to demonstrate that miscoding can cause analytical errors and to characterize the potential amount of bias that can occur. The impact of calibration error with three selfblood glucose monitoring systems (BGMSs), one of which has an autocoding feature, is reported. METHODS: Fresh capillary fingerstick blood from 50 subjects, 18 men and 32 women ranging in age from 23 to 82 years, was used to measure glucose with three BGMSs. Two BGMSs required manual coding and were purposely miscoded using numbers different from the one recommended for the reagent lot used. Two properly coded meters of each BGMS were included to assess within-system variability. Different reagent lots were used to challenge a third system that had autocoding capability and could not be miscoded. RESULTS: Some within-system comparisons showed deviations of greater than +/-30% when results obtained with miscoded meters were compared with data obtained with ones programmed using the correct code number. Similar erroneous results were found when the miscoded meter results were compared with those obtained with a glucose analyzer. For some miscoded meter and test strip combinations, error grid analysis showed that 90% of results fell into zones indicating altered clinical action. Such inaccuracies were not found with the BGMS having the autocoding feature. CONCLUSIONS: When certain meter code number settings of two BGMSs were used in conjunction with test strips having code numbers that did not match, statistically and clinically inaccurate results were obtained. Coding errors resulted in analytical errors of greater than +/-30% (-31.6 to +60.9%). These results confirm the value of a BGMS with an automatic coding feature.
Authors: Charles H Raine; Linda E Schrock; Steven V Edelman; Sunder Raj D Mudaliar; Weiping Zhong; Lois J Proud; Joan Lee Parkes Journal: J Diabetes Sci Technol Date: 2007-03