ACCURACY OF CONTOUR®NEXT ONE BLOOD GLUCOSE MONITORING SYSTEM IN LOW BLOOD GLUCOSE RANGE USING PROBABILITY METHODOLOGY

Session Name
GLUCOSE SENSORS
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:30 - 09:30
Presenter
  • Andreas Stuhr, United States of America
Authors
  • Andreas Stuhr, United States of America
  • James M. Richardson, Switzerland
  • Scott Pardo, United States of America
  • Rimma Shaginian, Switzerland

Abstract

Background and Aims

Data analyzing blood glucose monitoring system (BGMS) accuracy in the low blood glucose range (LBGR: ≤70 mg/dL) are lacking, warranting further research. This post hoc analysis utilizes previously presented probability methodology to estimate the likelihood of accurate BGMS performance in the LBGR.

Methods

Data were computed from capillary blood samples obtained by study staff from patients in two separate trials. Trial 1 (Christiansen M, et al. J Diabetes Sci Technol. 2017;11:567-573) was conducted in the US and included the CONTOUR®NEXT ONE (CNO) BGMS only. Trial 2 (Jendrike N, et al. Curr Med Res Opin. 2019;35:301-311), conducted in Europe, compared five systems including CNO BGMS (and is included here to corroborate results from Trial 1). To estimate likelihood of accurate BGMS performance (results ±15% of reference values) in entire range of BG concentrations (20–460 mg/dL), probability curves were computed based on a linear regression model with BGMS results expressed as a function of laboratory data.

Results

For CNO BGMS, the probability of accurate system performance at specific BG concentrations in the LBGR (40, 54, 60 and 70 mg/dL) was >95% in both trials.

Conclusions

In this analysis, CNO BGMS was highly accurate in the LBGR, which is important for safe and effective diabetes management, especially in insulin-treated patients, diabetes patients with history of severe hypoglycemia or hypoglycemia unawareness, diabetes during pregnancy, and/or patients using CGM when BGM monitoring is recommended. This analysis also further demonstrates the utility of probability methodology for assessing BGMS accuracy. Hypoglycemia

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