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Factory Calibrated Continuous Glucose Monitoring:
How and Why it Works, and The Dangers of Reuse
Beyond Approved Duration of Wear
Gregory P. Forlenza, MD1, Taisa Kushner, MS2, Laurel H. Messer, RN, CDE, MPH1,
R. Paul Wadwa, MD1, Sriram Sankaranarayanan, PhD2
1Barbara Davis Center, University of Colorado Denver, Aurora, CO
2Department of Computer Science, University of Colorado, Boulder, CO
Abstract—Continuous glucose monitors (CGM) display real-
time glucose values enabling greater glycemic awareness with
reduced management burden. Factory calibrated CGM systems
allow for glycemic assessment without the pain and inconvenience
of fingerstick glucose testing. Advances in sensor chemistry and
CGM algorithms have enabled factory calibrated systems to have
greater accuracy than previous generations of CGM technology.
Despite these advances many patients and providers are hesitant
about the idea of removing fingerstick testing from their diabetes
care. In this commentary, we aim to review the clinical trials on
factory calibrated CGM systems, present the algorithms which
facilitate factory calibrated CGMs to improve accuracy, discuss
clinical use of factory calibrated CGMs, and finally present
two cases demonstrating the dangers of utilizing exploits in
commercial systems to prolong sensor life.
Index Terms—continuous glucose monitoring, type 1 diabetes,
factory calibration
I. INTRODUCTION
Subcutaneous Continuous Glucose Monitoring (CGM) uti-
lizes a glucose-oxidase enzyme reaction to measure the glu-
cose concentration in interstitial fluid and estimate glucose
concentration in the blood.1,2The first FDA approved CGM,
the Minimed Continuous Glucose Monitoring System, was
approved in 2000 with a mean absolute relative difference
(MARD) between Yellow Springs Instruments Glucose An-
alyzer (YSI, Yellow Spring, OH) and sensor glucose of 25%
(23, 27%).3Over the past 18 years CGM systems have pro-
gressively improved with MARD values in the 12-16% range
with third generation systems, 13-14% range with fourth gen-
eration systems, and 9-11% with fifth generation systems.4,5
These systems all required user calibrations whereby self-
monitoring of blood glucose (SMBG) fingerstick values via
a home blood glucose meter (BGM) were used to corre-
late the sensor signal with a patients blood glucose value.6
On September 27, 2017 the United States Food and Drug
Administration (FDA) approved the Abbott Freestyle Libre
Flash Glucose Monitoring (FGM) System as the first factory-
calibrated glucose monitoring system with a published MARD
of 11.4%.7On March 27, 2018 the FDA approved the Dexcom
G6 as the first real-time factory-calibrated continuous glucose
monitoring system with a published MARD of 9.0%.8,9
Factory calibration enables a CGM system to be used
by patients without the need to periodically conduct SMBG
measurements. Such an advancement enables therapies for
type 1 diabetes (T1D) and type 2 diabetes (T2D) to move
to a realm of decreased patient burden long thought to be
impossible. The ability of a provider to tell a patient you
no longer need to poke your finger is truly transformative
for the field. This advancement has been achieved partly
through improvements in sensor chemistry and in device
manufacturing processes, however the major driver has been
advancement in the algorithms used within the CGM systems
to translate the sensor signal into a glucose value. In this
commentary, we aim to demystify this process to better aid
clinicians in understanding how factory calibration works, why
it is safe, and how various exploits to prolong sensor life may
be dangerous.
II. CLINICAL TRIALS OF FAC TORY CALIBRATED CGM
The Abbott Freestyle Libre FGM calculates a glucose value
every 15 minutes, though it does not report real-time values.
Instead, the past 8 hours of data are downloaded to the reader
when the user scans or flashes the Near Field Communication
(NFC) tag. Hoss and colleagues reported accuracy for a factory
calibrated version of the Libre FGM sensor in 2014.10 They
examined 33 subjects with T1D and T2D, each wearing 4
sensors with a 6.0% coefficient of variation between sensors
within a subject across the study. Factory calibration of the
sensors produced a MARD of 13.4% with 83.5% of values
falling within zone A of the Consensus Error Grid (CEG).11
In 2015 Bailey and colleagues reported the results of the
adult pivotal trial of the Libre FGM system.7They studied
72 adults with T1D or T2D across 4 clinical sites wearing the
FGM sensor for up to 14 days. Sensor values were compared
against SMBG values and reference YSI values.12 The factory
calibrated FGM sensor demonstrated an overall MARD of
11.4% with 85-89% of values in the CEG zone A, and an
average sensor lag time of 4.5±4.8minutes.7
The Dexcom G6 CGM calculates a glucose value every 5
minutes and then reports that value in real-time via Bluetooth
communication to a paired receiver, cell phone, or insulin
pump. The results of the pivotal trials of the Dexcom G6
Author Accepted Manuscript
have been recently reported by Shah and Wadwa.8,9In this
series of studies, the Dexcom G6 was actually used with once
daily calibration. After the completion of patient-use, the raw
signal data was reprocessed using the new factory calibra-
tion algorithm without additional patient-driven calibrations
to demonstrate the accuracy of the system with this build.
Wadwa and colleagues reported results for 262 patients with
T1D and T2D ages 6+ years old at 11 sites using the factory
calibration algorithm.9Subjects wore the Dexcom G6 for up to
10 days and underwent frequent sample testing on day 1, 4, 5,
7, or 10. The overall MARD was 10.0% with a similar MARD
reported for patients 18+ years old and patients 6-17 years old
(9.9 vs 10.1%). This analysis also looked at the performance
of a real-time predictive hypoglycemia alert which correctly
alerted patients 84% of the time within 30 minutes before
impending hypoglycemia <70 mg/dL. Overall 87% of the
sensors lasted for the full 10 day period. The average time lag
was 4.5±3.3minutes.9
Shah and colleagues reported results for 62 patients with
T1D and T2D ages 6+ years old at 4 sites using the factory-
calibrated algorithm.8The primary purpose of this study was
to evaluate the accuracy of the Dexcom G6 CGM with a
new automated sensor applicator which was hypothesized to
decrease pain and inflammation and improve sensor accuracy.
Participants wore the Dexcom G6 for up to 10 days and
underwent frequent sample testing on day 1, 4, 5, 7, or 10. The
overall MARD was 9.0% with better accuracy reported for the
adolescents than for adults (7.7 vs 9.8%). Accuracy was found
to be similar for day 1 compared to the other 9 days of sensor
use. The average time lag was 3.7±3.1 minutes.8
III. DISCUSSION OF FACTORY CALIBRATED CONTINUOUS
GLU CO SE MONITORING
A. How Multiple-Daily-Calibrated Sensors Have Been Work-
ing
Most CGMs, including the Medtronic Guardian 3, Abbott
Freestyle Libre, Dexcom G4 Platinum, the Dexcom G5, and
Dexcom G6, utilize the previously mentioned glucose-oxidase
reaction to estimate glucose in the interstitial subcutaneous
tissue based on a sensed electrical current.13,14 The measured
current is proportional to the concentration of interstitial
glucose at the insertionite, however the relationship between
current and glucose concentration changes over time. Thus,
in order to determine concentrations of blood glucose, the
current measured at the interstitial site is converted to blood
glucose via a calibration function. Due to factors such as
manufacturing variability, sensor drift, and biocompatibility
(such as changes over time in foreign body response to the
sensor), the calibration function needs to be updated based on
sensor batch and time-since-insertion.15−17
In previous generation sensors such as the G4 Platinum and
G5, the parameters of the calibration function are periodically
updated, usually every 12 hours, by matching output from
the calibration function to a reference SMBG measurement to
preserve sensor accuracy. Here we describe an overview of the
calibration method, for which specifics have been previously
published.18−20 We note that the methods described here
correspond to the most recently published algorithms, which
to the best of our knowledge form the basis of the commercial
products. However, it is possible that these algorithms may or
may not have been modified between publication and final
device manufacturing.
The G4 Platinum algorithm uses a linear function to convert
the raw electrical current measured at the interstitial site to a
measurement of interstitial glucose. This model assumes that
interstitial glucose at some time t,uI(t), is equal to some
multiple, a, of the interstitial current yI(t), added white noise
w(t), a linear correction factor, b, and some multiple c, of
time-since-insertion, ∆t.
uI(t) = a∗yI(t)−(b+c∗∆t)−w(t)(1)
The term a∗yI(t)is referred to as the sensor gain, and the
term b+c∗∆tis the offset. Then, in order to account for
the relation between interstitial glucose and blood glucose,
the measurement uI(t)is transformed to a measurement of
glucose, uB(t), based on the two-compartment model.15
This function assumes a linear relationship between the
measured interstitial current and the actual glucose value,
however, due to the changing environment around the sensor
and other factors affecting sensor drift, this linear function
is only accurate for about 24 hours.15 This results in the
parameters of the function needing to be adjusted every 12
hours using SMBG measurements to ensure accuracy at the
level needed for safe medical decision making.
In order to adjust the parameters a, b, c of the calibration
function, a calibration algorithm is used. In essence, this algo-
rithm begins with an average value for each parameter a, b, c,
which are specific to day-since-insertion. These averages were
identified by averaging best-fit-parameters for 72 patients of
previously collected data consisting of measured interstitial
current and glucose measurements obtained via YSI.12
These averages provide a good starting point, however due
to sensor variability between batches and individuals wearing
the sensor, the parameters are adjusted from the averages
in order to minimize the difference between the glucose
identified using the new calibration function, and the last
two SMBG measurements. This process enables the sensor
to check how well the calibration function is working by
comparing the sensor glucose value identified from passing the
interstitial current through the calibration function to a ground
truth glucose value from the SMBG. It has been documented
that SMBG values are imperfect reference standards, as is
discussed in a later section on fingerstick testing.
B. How Factory Calibrated G6 Works
In order to remove the need for SMBG calibrations, the
G6 uses a calibration function which corrects for sensor
drift over the 10-day wear period by keeping track of day-
since-insertion and adjusting the calibration function, which
converts interstitial current to glucose, based on the day.
The adjustments are hardcoded and based on how much an
Final publication is available from Mary Ann Liebert, Inc.: http://dx.doi.org/10.1089/dia.2018.0401
Author Accepted Manuscript
average sensor would drift. Additionally, rather than the linear
function used in previous models which accounts for drift over
24h, the G6 uses a calibration function that has time-varying
functions for sensor offset and gain which can account for
drift over 10 days.15,18 The new function used to transform
interstitial current, yI(t), to interstitial glucose, uI(t), utilizes
device-dependent functions α(t)and β(t)which describe the
sensor drift over time. Specifically, the DG6 uses the following
calibration function, rather than (1) which is used by the G4
Platinum:
yI(t)=[uI(t) + b]·s(t) + w(t)(2)
Here w(t)still represents additive noise, but the sensor offset
and gain are calculated using s(t), which represents sensor
sensitivity, and is device specific, combined with a term, b,
which is the baseline of the glucose profile. The sensitivity
function, which now utilizes the device-dependent nonlinear
functions α(t)and β(t), and model parameters s1, s2, s3, is
shown below:
s(t) = s1∗α(t) + s2∗β(t) + s3(3)
Incorporation of the potentially non-linear time-varying func-
tions and which account for sensor sensitivity and drift is what
enables the DG6 to maintain accuracy over the 10-day use
window without requiring user calibration.15
Rather than adjusting parameters of the calibration function
with every incoming SMBG, the parameters are identified
based on an initial factory calibration and average values of
best-fit parameters. The distribution of average values comes
from best-fit parameters which were identified for a data set
consisting of measured values of interstitial current from the
CGM and reference YSI measurements from a group of clin-
ical trial subjects.15 These average values provide the starting
point for the parameters, which are then adjusted slightly dur-
ing the warm-up period using the factory calibration in order to
account for manufacturing variability between batches. For the
G6 in particular, the factory calibration information is stored
in the sensor code.
Removing the necessity of SMBG calibrations provides a
large benefit to the patient in terms of ease of use and cost,
however, it heightens the dependence on the initial factory
calibration and on-label use of the sensor. In particular, in order
to maintain high accuracy, the calibration function inside the
G6 relies on accurate information of time-since-insertion since
the sensor never checks how well the calibration function is
performing for a specific individual during wear. For a given
sensor, the same level of electrical current could translate to
a significantly different BG value on day 1 versus day 10
due to sensor drift and biocompatibility.16−18 Hence, off-label
resetting of the calibration-free device during the initial 10-day
window could result in the dangerous condition of the CGM
translating a measured current to a glucose value of 160mg/dL
when the actual value is 60mg/dL, or vice versa, by using the
wrong gain. Case series illustrating this exact phenomenon are
presented in the final section of this commentary.
IV. WHY RE MOVING FINGERSTICKS IS BEN EFI CI AL
Although SMBG calibrations give CGM users a sense of
control over the accuracy of their CGM readings, it is impor-
tant to consider the literature on SMBG accuracy itself. A 2017
study by Ekhlaspour investigated the comparative accuracy of
17 commercially available BGMs using venous blood samples,
thereby eliminating error from skin contamination.21 These
BGMs were compared against YSI values for reference. This
investigation demonstrated a range in MARD from 5.6% to
20.8% across these commercial devices. Of the 17 devices
tested, 47% had a MARD<10%, while 53% had a MARD
13 −20.8%. A separate analysis by Klonoff and the Diabetes
Technology Society looked at the accuracy of 18 commercially
approved BGMs compared against YSI using capillary blood
glucose testing.22 This analysis looked at 3 different accuracy
studies. They found that 6 of the 18 systems met the prede-
termined accuracy standard in all 3 studies, 5 met it in 2 of 3
studies, 3 met it in 1 of 3 studies, and 4 of the commercially
available devices did not meet accuracy standards in any of the
3 studies. Taken together, these 2 studies highlight the wide
variability in accuracy among commercially approved BGM
devices and that many devices fall outside the recommended
range for accuracy.
Beyond research, real-world device use is important to con-
sider. Among pediatric patients in particular, unclean finger-
tips can produce significant pseudo-hyperglycemia by falsely
elevating the measured SMBG value.23−25 Several studies
conducted on fingerstick-assessed SMBG values after handling
fruit have demonstrated that contamination can falsely elevate
assessed values by >250 mg/dL.25 Equally important is that
cleaning the fingertip with 1 or even 5 alcohol swabs prior
to testing did not eliminate the contamination effect for most
fruits.25 This form of error is highly important to consider
when discussing SMBG testing for CGM calibration as enter-
ing a falsely high reference value will bias the CGM reading
falsely high, thus limiting the reporting of true hypoglycemia.
There are two main take away points from this discussion
of SMBG accuracy. First, even conventional SMBG testing
is subject to error and possibly seriously erroneous glycemic
assessment in the setting of fingertip contamination. Second,
SMBG values may be a highly error-based form of calibration
of CGM systems. By supplying erroneous forms of truth to
the CGM systems we limit their potential accuracy thereby
reducing rather than improving patient utility and safety.
V. FACTORY CALIBRATED CGM IN CLINICAL PRACTICE
The implications of factory-calibrated CGM intersects sig-
nificantly with the ability of CGM systems to replace SMBG
for diabetes decision making. Taken together, the near-removal
of SMBG from clinical care with CGM dramatically changes
patient and provider practices for diabetes management and
support. In the real-world, some individuals still chose to
manually calibrate factory calibrated devices. Further, as part
of routine diabetes care, many individuals have been in the
practice of dosing insulin off of CGM systems not approved
Final publication is available from Mary Ann Liebert, Inc.: http://dx.doi.org/10.1089/dia.2018.0401
Author Accepted Manuscript
for replacement SMBG, however there are no published data
quantifying the extent of this practice.
In order to implement factory calibrated CGM into standard
clinical practice, it is incumbent on healthcare providers and
diabetes educators to make two critical changes. The first is to
alter clinical practices to enhance uptake and use of CGM data
for clinical diabetes care. Technologies like CGM continue to
be a barrier for providers and diabetes educators who feel ill
equipped to handle the magnitude of data available for diabetes
care.26 In order to better manage it, practices will need to
be restructured to facilitate use of CGM as primary glucose
monitoring for patient care visits. This will involve training
staff on new downloading and data preparation practices, and
new methods for interpreting sensor glucose levels. Ongoing
integration of CGM with pump downloads and other diabetes
data may ease the transition as well.27
Secondly, providers and educators must retune their edu-
cation priorities for CGM utility and ensuing diabetes man-
agement. The most critical education point is explaining the
theoretical harm in restarting these factory calibrated CGMs,
in particular as it relates to previous systems. Although the vast
majority of individuals need not be exposed to the calculations
presented above, patients do need a clear understanding of why
factory calibrated devices, which rely on accurate knowledge
of time-since-insertion, will degrade in accuracy when used
beyond their commercial labeling, leading to the possibility
of real harm. The highest concerns for sensors being restarted
with inappropriate calibration schema include administering
inappropriate doses of insulin in response to erroneous sen-
sor glucose levels,inappropriately treating hypoglycemia or
pseudo-hypoglycemia with carbohydrates or glucagon, missed
alerts, and missed detection of hypoglycemia resulting in se-
vere hypoglycemia. More research is warranted to characterize
the frequency and severity of these risks.
Another significant educational priority is cautiously nor-
malizing the use of factory calibrated CGMs for medical
decision making. This runs counter to decades of entrenched
education on using CGM as supplementary diabetes informa-
tion, and thus is a difficult concept to change for both clinicians
and individuals with diabetes. Unambiguous education and
endorsement from healthcare providers on safe and efficient
use of factory calibrated CGMs for medical decision making
will be beneficial to individual patients and the diabetes com-
munity at large. Major paradigm shifts in glucose monitoring
have occurred before, with the last one being 4 decades ago
from urine testing to blood glucose testing. Similarly, uptake to
contemporary CGM technologies may be slow and difficult in
transition; but will likely decrease burden of diabetes manage-
ment for patients. Healthcare providers and diabetes educators
must be prepared to provide relevant information and support
to patients in order to transition safety and appropriately to
factory-calibrated CGM for medical decision making.
VI. PATIENT CAS ES O F RES TART IN G TH E DEXCOM G6
In an attempt to better understand the impact of restarting a
factory calibrated CGM, we reviewed data from two adults
with T1D who regularly restart their Dexcom G6 systems
without providing SMBG calibration values. As this practice
is contrary to FDA labeling and believed by our group to be
unsafe, we did not attempt to prospectively gather such data.
Neither CGM user performed SMBG testing on a routine basis
to provide adequate data to compare SMBG against CGM
values. This data were voluntarily provided by these patients
and has been deidentified to comply with HIPAA standards.
Case 1 is a 25 year old female with type 1 diabetes for over
Fig. 1: Distribution of sensor glucose values on days 1-2 of recommended wear compared to days 11-12 of extended wear
as well as days 3-10 of recommended wear. Days 11-12 represent a different distribution from Days 1-2 due to software
incorporated into the sensor that uses different calibration on what is presumed to be a newly inserted sensor than for days
3-10.
Final publication is available from Mary Ann Liebert, Inc.: http://dx.doi.org/10.1089/dia.2018.0401
Author Accepted Manuscript
Fig. 2: Ambulatory glucose profiles over times of the day, grouped by days of wear, with percentiles and mean blood glucose
values.
10 years and Case 2 is a 36 year old male with type 1 diabetes
for over 10 years.
For factory-calibrated CGMs worn beyond the recom-
mended 10 day period, we analyzed the average and distri-
bution of the sensor glucose values across all days, with focus
on comparing the true days 1-2 of wear (48 hours) with days
11-12 of wear (the first 48 hours after the restart) with each
comparison consisting of approximately 576 values per time
period. We focused on the first 2 days of wear and first 2
days after restart due to the known wound reaction effects
seen with initial sensor placement.28 The factory-calibration
function must account for this reaction on true days 1 and 2.
With a sensor restart, the calibration function is correcting
for a wound reaction which is in fact not occurring. Due
to this effect, we hypothesized that restarting the sensors
would produce higher average glucose values with greater
glycemic variability as the calibration function was being
falsely informed that the sensor was a day 1-2 sensor when in
fact it was being used beyond day 10.
The results of these case reports are shown in Figure 1,
Figure 2, and Table 1. Both Case 1 and Case 2 had significantly
higher average SG values, with higher SG variability after
the sensor restart than on true days 1-2, as well as days
3-10 of wear. Calculating Kullback-Leibler divergences of
the distributions showed that the restart distributions were
significantly different from the true day 10-2 distributions
with p-values <0.01 for all individuals, while distributions of
days 1-2 with days 3-10 are more similar to one another. The
Kullback-Leibler divergence is a distribution-wise asymmetric
measure which quantifies if one distribution is different from
a second.29 Looking at the day of sensor start by day of week
did not reveal any trend towards tending to start sensors on a
certain day of the week (e.g. always on the weekend) whereby
periodic differences in habits could contribute to this trend.
These data indicate that the distributions of SG values
appear to be different after a user restart compared to using
the device in the initial 10 day period. That both individuals
SG averages tended to be higher after the restart is particularly
concerning as falsely elevated CGM values may be more likely
to falsely reassure a patient that true hypoglycemia is not
present or increase the risk for overdosing a correction dose
of insulin. While this analysis is small, exploratory, and by
no means conclusive, it indicates that extending sensor life
beyond its recommended duration may be unsafe. It is notable
that changes in stress, illness, and lifestyle between the periods
of comparison could have contributed to the differences seen
with extended wear in such a small case sample. Larger
prospective studies of subjects wearing multiple sensors for
a prolonged duration, such as those previously performed
by Buckingham30,31, would certainly better address these
questions.
VII. COM ME NT F ROM DEXCOM ON RE STARTING THE G6
CGM
As due diligence for this article, we reached out to Dexcom
for commentary on restarting the G6 CGM. Dexcom asked
that we supply the following information and warning to
researchers and health care providers:
The Dexcom G6 CGM algorithm is designed for optimal
performance for up to 10 days of sensor wear. Intentionally
altering, modifying or hacking the G6 system to extend the
sensor usage beyond the labeled 10-day wear period may
Final publication is available from Mary Ann Liebert, Inc.: http://dx.doi.org/10.1089/dia.2018.0401
Author Accepted Manuscript
compromise the system. This can lead to inaccurate CGM
readings, resulting in missed hypoglycemia or hyperglycemia.
The Dexcom G6 CGM System was granted De Novo
clearance in March 2018 by the US FDA as the first of its kind
interoperable CGM (iCGM) System. The FDA also published
several special clinical performance metrics for an iCGM. One
of these special controls requires manufacturers to demonstrate
that The device must include appropriate measures to ensure
that disposable sensors cannot be used beyond its claimed
sensor wear period. Dexcom is obligated to take measures at
all times to prevent sensors from restarting and assuch must
continually evaluate design mitigations to ensure adherence to
this special control.
VIII. CONCLUSIONS
With this transition from SMBG to factory-calibrated CGM
for medical decision making, it is inevitable that there will be
resistance to abandoning the perception of certainty inherent
to SMBG measurements. Garg and Hirsch outlined many key
issues with SMBG in the 2018 Advanced Technologies and
Treatments for Diabetes yearbook SMBG chapter.32 The key
point they raised is that many areas of the world do not
have access or affordability for CGM and thus SMBG will
not be disappearing any time soon. This certainly argues for
retaining SMBG as a mainstay of diabetes therapy in some
circumstances, however should not dissuade factory-calibrated
CGM from becoming a new standard for diabetes management
when available.
IX. ACK NOWLEDGEMENTS
Work on this publication by TK, LM, and SS was supported
by NSF Grant 1815983 and by GPF was supported by NIH
K12 grant DK094712. GPF reports research support from the
NIH NIDDK, Medtronic, Tandem, Insulet, Dexcom, Abbott,
Novo Nordisk, Type Zero, and Beta Bionics. He has served
as an advisory board member for Dexcom, a paid consultant
for Medtronic and Abbott, and a speaker for Tandem, Dex-
com, and Medtronic. LM is a Contract Product Trainer for
Medtronic Diabetes and consults for Tandem Diabetes Care,
Capillary Biomedical, and Clinical Sensors. RPW reports
research support from Lexicon, Dexcom, Bigfoot Biomedical,
MannKind Corporation, Novo Nordisk, Helmsley Charitable
Trust and NIH/NIDDK, advisory board consulting fees from
Eli Lilly and Company, and consulting fees from Dexcom. TK
and SS report no conflicts of interest.
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Final publication is available from Mary Ann Liebert, Inc.: http://dx.doi.org/10.1089/dia.2018.0401
