Abstract
The objective was to evaluate the precision and accuracy of 6 handheld glucose meters, designed for human use [Accu-Chek Aviva Plus (AC), Roche Diabetes Care, Mannheim, Germany; Aga Matrix (AM), AgaMatrix Inc., Salem, NH; Contour Next (CT), Bayer HealthCare LLC, Leverkusen, Germany; FreeStyle Precision Neo (FS), Abbott Diabetes Care Ltd., Alameda, CA; Nova Max Plus (NM), Nova Biomedical Corporation, Waltham, MA; and Precision Xtra (PX), Abbott Diabetes Care Ltd., Witney, UK] to measure blood glucose concentration in dairy cows. Blood samples from Jersey and Jersey × Holstein crossbreed cows (n = 97 for all; except CT, n = 71) were collected and analyzed in triplicate using the 6 handheld glucose meters evaluated. Plasma glucose was also measured with the laboratory reference method (hexokinase glucose-6-phosphate dehydrogenase). Based on the intra-assay coefficient of variation (CV), precision varied across handheld glucose meters: AC (2.2%), CT (4.0%), PX (4.7%), FS (5.6%), AM (6.2%), and NM (6.7%). Lin's concordance correlation coefficients between handheld glucose meters and the reference method were 0.75 for FS, 0.74 for PX, 0.62 for AC, 0.55 for CT, 0.53 for NM, and 0.48 for AM. Based on Passing-Bablok regression, the AM and PX meters showed bias in the measurements of blood glucose. Bland-Altman plots indicated a negative bias (FS = -0.25 mmol/L; CT = -0.60 mmol/L) or a positive bias (AM = 0.29 mmol/L; PX = 0.33 mmol/L; NM = 0.52 mmol/L; AC = 0.65 mmol/L) between handheld glucose meters and the reference method. All handheld glucose meters evaluated had wide limits of agreement (LoA) ranging from -0.18 to 1.47 mmol/L (AC, narrowest LoA) to -1.25 to 1.82 mmol/L (AM, widest LoA). Bias was the major contributor to the total observed error (TEobs), accounting for 81.5% of the TEobs in AC, 72.0% in CT, 64.9% in AM, 61.1% in NM, 57.8% in PX, and 56.2% in FS. Overall, although some handheld meters (AC, CT, and PX) showed satisfactory precision, none were accurate measuring glucose. Future studies should evaluate whether incorporating algorithms designed for cattle can improve accuracy and precision of handheld glucose meters.
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