A comparison of blood glucose meters in Australia

Cohen, Matthew; Boyle, Erin C.; Delaney, Carol A.; Shaw, Jonathan

doi:10.1016/j.diabres.2005.05.013

Cited by 61 publications

(47 citation statements)

References 10 publications

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“…Similar to that reported by Moses et al [4] , we found a similar degree of underestimation of glucose level in Elite although our readings were obtained on capillary instead of venous plasma samples. We also confirmed the finding of a low bias in CareSens as previously reported [12] .…”

Section: Discussionsupporting

confidence: 81%

Comparison in the Performance of Glucose Meters in Blood Glucose Monitoring during Pregnancy

Kong¹,

Tam²,

Chan³

et al. 2010

Gynecol Obstet Invest

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Aims: To compare the performance of the four latest models of glucose meters in capillary blood glucose monitoring during pregnancy. Methods: 208 pregnant women with gestational diabetes were recruited. Each subject had simultaneous capillary glucose monitoring by two study glucose meters and venous plasma glucose assay. The performance of four glucose meters was compared using error grid analysis (EGA) and the agreement between the meter readings and plasma glucose by Bland-Altman plot analysis. Results: Elite, Advantage II and CareSens had more than 90% of readings in the acceptable target range of EGA. CareSens had the lowest mean bias by Bland-Altman analysis while Advantage II had the highest proportion of readings within 5% difference from plasma glucose. Readings from all glucose meters except Optium were not influenced by the change in maternal hematocrit levels. Conclusions: The performance of four study glucose meters appeared very similar.

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Section: Discussionsupporting

confidence: 81%

Comparison in the Performance of Glucose Meters in Blood Glucose Monitoring during Pregnancy

Kong¹,

Tam²,

Chan³

et al. 2010

Gynecol Obstet Invest

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“…In particular, the portable glucometer is capable of measuring glucose levels in less than 5 sec by using a very small amount of capillary blood (less than 1 mL). Its accuracy and precision has been previously studied in comparison with 4 other latest device models (AccuCheck Go and Accu-Check Advantage manufactured by Roche; Optimum, Abbott; and GlucoMan PC, Menarini), which confirmed its precision and accuracy with a mini-mum bias level of <5% [17].…”

Section: Devicesmentioning

confidence: 87%

Comparison of Blood Glucose Measurements Using Samples Obtained from the Forearm, Finger Skin Puncture, and Venous Serum

Park

Joo

et al. 2010

Ann Lab Med

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“…8 In the United States, over 50% of diagnosed diabetics aged 20-64 are deemed "out of control. 9 " The higher accuracy of bedside capillary blood glucose meters 10,11 and the latest insulin analogues for MDI therapy, 12 coupled with better control methods, have the potential to provide better care to the majority of outpatient or ambulatory type 1 diabetics than currently observed. Such techniques must necessarily be simple to implement to ensure broad clinical uptake by the diabetes population.…”

Section: Introductionmentioning

confidence: 99%

Development of a Clinical Type 1 Diabetes Metabolic System Model andin SilicoSimulation Tool

Wong

Chase

Hann

et al. 2008

J Diabetes Sci Technol

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Abbreviations: (CGM) continuous glucose measurement, (CSII) continuous subcutaneous insulin infusion, (HGP) hepatic glucose production, (IDDM) insulin-dependent diabetes mellitus, (MDI) multiple daily injection, (SMBG) self-monitoring blood glucose, (TBGU) total body glucose uptake Keywords: blood glucose, compartmental models, decision support, diabetes, hyperglycemia, insulin, simulation, subcutaneous injection SYMPOSIUM Abstract Objectives:The goal of this study was to develop a system model of type 1 diabetes for the purpose of in silico simulation for the prediction of long-term glycemic control outcomes. Methods:The system model was created and identified on a physiological cohort of virtual type 1 diabetes patients (n = 40). Integral-based identification was used to develop (n = 40) insulin sensitivity profiles. Results:The n = 40 insulin sensitivity profiles provide a driving input for virtual patient trials using the models developed. The identified models have a median (90% range) absolute percentage error of 1.33% (0.08-7.20%). The median (90% range) absolute error was 0.12 mmol/liter (0.01-0.56 mmol/liter). The model and integralbased identification of S I captured all patient dynamics with low error, which would lead to more physiological behavior simulation. Conclusions:A simulation tool incorporating n = 40 virtual patient data sets to predict long-term glycemic control outcomes from clinical interventions was developed based on a physiological type 1 diabetes metabolic system model. The overall goal is to utilize this model and insulin sensitivity profiles to develop and optimize self-monitoring blood glucose and multiple daily injection therapy.

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A comparison of blood glucose meters in Australia

Cited by 61 publications

References 10 publications

Comparison in the Performance of Glucose Meters in Blood Glucose Monitoring during Pregnancy

Comparison in the Performance of Glucose Meters in Blood Glucose Monitoring during Pregnancy

Comparison of Blood Glucose Measurements Using Samples Obtained from the Forearm, Finger Skin Puncture, and Venous Serum

Development of a Clinical Type 1 Diabetes Metabolic System Model andin SilicoSimulation Tool

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