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    Blood glucose (BG) concentrations of patients with diabetes mellitus (DM) are monitored during surgery to prevent hypo- and hyperglycemia. Access to point-of-care test (POCT) glucose meters at an operating room will usually provide monitoring at shorter intervals and may improve glycemic control. However, these meters are not validated for patients under general anesthesia. This cross-sectional study included 75 arterial BG measurements from 75 patients (71 with DM, mostly insulin dependent) who underwent elective non-cardiac surgery under general anesthesia. Arterial blood samples were taken at least 60 minutes after induction. One drop of blood was used for Accu Chek Inform II (ACI II) POCT BG meter and the residual blood was sent to the clinical laboratory for a Hexokinase Plasma reference method. A Bland-Altman plot was used to visualize the differences between both methods, and correlation was assessed using the intra-class correlation coefficient (ICC). The results showed an estimated mean difference of 0.8 mmol/L between ACI II and the reference method, with limits of agreement equal to -0.6 and 2.2 mmol/L. In general, the reference method produced higher values than ACI II. ICC was 0.955 (95% CI 0.634-0.986), P < 0.001, and concordance correlation coefficient (CCC) was 0.955 (95% CI 0.933-0.970). Arterial BG measurements during surgery in patients with DM under general anesthesia using POCT BG meter are in general lower than laboratory measurements, but the ICC and CCC show a clinically acceptable correlation. We conclude that POCT measurements conducted on arterial specimens using the ACI II provide sufficiently accurate results for glucose measurement during surgery under general anesthesia. © 2021. The Author(s).

    Citation

    B Lascaris, H G Freling, M A Edens, M J Fokkert, C G Olthof, R J Slingerland. Comparison of Accu Chek Inform II point-of-care test blood glucose meter with Hexokinase Plasma method for a diabetes mellitus population during surgery under general anesthesia. Journal of clinical monitoring and computing. 2022 Apr;36(2):355-361

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    PMID: 33496941

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