Detecting Unusual Continuous Glucose Monitor
Measurements: A Stochastic Model Approach
Matthew Signal, Deborah L. Harris, Philip J. Weston, Aaron Le Compte,
J. Geoffrey Chase, Jane E. Harding, on behalf of the CHYLD Study Group
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Background: Continuous glucose monitors (CGMs) have recently been used to
monitor blood glucose (BG) levels in critically ill adults and infants, where
hyperglycaemia and hypoglycaemia have been associated with negative outcomes.
However, there have been concerns regarding the accuracy and reliability of these
devices in critically ill patients.
Objective: Use CGM data from neonatal infants to develop a tool that will aid
clinicians in identifying unusual CGM behavior, retrospectively or in real-time.
BG (mmol/L)
INTRODUCTION
CGM
Ref BG
6
4
2
0
500
1000
1500
2000
2500
time (mins)
(www.medtronic.com)
RESULTS
Example Patient 1
 Stable, flat CGM trace with little variability over the
METHODS
~3.5 days of monitoring
 No measurements classified as being very unusual
1. CREATE STOCHASTIC MODEL
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3. CLASSIFY UNUSUAL CGM
MEASUREMENTS
12
Consider: CGMn-1 = 5.0 mmol/L and CGMn = 5.5 mmol/L. This would
be classified as follows:
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1. Using the model distribution at 5.0 mmol/L, locate 5.5 mmol/L
and determine the percentile:
n
CGM (mmol/L)
10
6
4
2
0
0
2
4
6
CGM
n-1
8
(mmol/L)
10
12
14
Example Patient 2
Figure 1: Poincare plot of empirical CGM data (~67,000 measurements)
 A slightly more variable CGM trace with several
sections of aqua and yellow
Stochastic model equations
 Of particular interest is the hypoglycaemic event at
~1 day, which is classified as very unusual and
consequently coloured red  interpret with care
80th percentile
4
5
6
2. Use the percentile to colour that section of CGM trace using the
following classifications:
Example Patient 3
10 - 90th Percentiles
5 - 95th Percentiles
0.5 -
99.5th
Percentiles
Figure 4: CGM colour classification
Figure 2: Stochastic model surface. Note the variations in model surface with
increasing CGMn-1, reinforcing that a single distribution is not applicable to the
entire range of CGMn-1
 This CGM trace appears to be fairly good until
~day 3, after which there are a lot of very unusual
measurements (potentially sensor degradation)
3. Repeat classification steps 1 and 2 until all paired CGM
measurements (CGMn-1, CGMn) have been classified
2. CHECK MODEL FIT
CONCLUSIONS
Figure 3: PDF’s from the model at different values of CGMn-1 compared to PDF’s
created using empirical data
Table 1: Results from a 5 fold validation, Monte Carlo simulated 25 times. Percent
of measurements in each confidence interval presented as Median [IQR]
80% CI
90% CI
99% CI
Variation 0.83 [0.79 - 0.86] 0.91 [0.89 - 0.93] 0.99 [0.98 - 0.99]
While BG measurements are required to make definitive conclusions
about glycaemic abnormalities, the stochastic classification provides
another level of information to aid users in interpretation and decision
making. Furthermore, in the real-time application, clinical protocols
might use stochastic information to justify an added BG measurement
to clarify a potentially significant event.