Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?. (January 2016)

• Record Type: Journal Article
• Authors: Fabris, Chiara; Patek, Stephen D.; Breton, Marc D.
• Is Part Of: Journal of diabetes science and technology. Volume 10:Number 1(2016:Jan.)
• Page Start: 50
• Page End: 59
• Publication Date: 2016-01
• Subjects: continuous glucose monitoring -- risk indices -- high blood glucose index -- low blood glucose index -- self-monitoring blood glucose; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/1932296815599177
• Languages: English
• ISSNs: 1932-2968

Abstract

Background: The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series. Methods: Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data. Results: LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series. Conclusions: Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.