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P. 237
CHAPTER
Modeling the CGM
measurement error 12
1
Chiara Fabris, PhD , Marc D. Breton, PhD 2
1
Assistant Professor, Center for Diabetes Technology, Department of Psychiatry and
2
Neurobehavioral Sciences, University of Virginia, Charlottesville, VA, United States; Assistant
Professor, Center for Diabetes Technology, Department of Psychiatry and Neurobehavioral
Sciences, University of Virginia, Charlottesville, VA, United States
Introduction
Continuous glucose monitors (CGMs) provide detailed time series of consecutive
observations on the underlying process of glucose fluctuations. The feedback of
such detailed information to patients with diabetes has been shown to have a
positive influence on their glycemic control, including a reduction in glucose
variability, time spent in nocturnal hypoglycemia, time spent in hyperglycemia,
and levels of glycosylated hemoglobin [1e4]. However, some CGM technology
continues to face challenges in terms of sensitivity, stability, calibration, and the
physiological time lag between blood glucose (BG) and interstitial glucose (IG)
concentration [5e11]. Thus it is frequently concluded that the abundance of
information about glucose fluctuations carried by the CGM data stream is to
some extent offset by the possibility of sensor errors that exceed in magnitude
the errors of the traditional self-monitoring blood glucose (SMBG) devices.
Such a conclusion, however, is only partially accurate: while the observed error
in an isolated CGM data point is indeed generally larger than the error observed
in an SMBG data point, the additional informationprovidedbyCGM time series
allows the application of error-reduction techniques that are unavailable in SMBG
devices. For example, deconvolution and other modeling techniques allow for
the mitigation of certain sensor deviations due to blood-to-interstitial time
delay [12,13].
The key to CGM error mitigation is a detailed analysis and subsequent math-
ematical modeling, which allow for the understanding of the sources and magni-
tude of sensor errors. The analytical approach proposed in this manuscript is the
one originally described in Ref. [14] and is based on two principles: (i) CGMs
assess BG fluctuations indirectlydby measuring the concentration of IGdbut
are calibrated via SMBG to approximate BG; and (ii) CGM data reflect an
underlying process in time and therefore are time series consisting of ordered,
in-time highly interdependent data points. The first principle stipulates that cali-
bration errors would be responsible for a portion of the sensor deviation from
Glucose Monitoring Devices. https://doi.org/10.1016/B978-0-12-816714-4.00012-0 241
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