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Abstract and Figures

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.
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Author Accepted Manuscript
Final publication is available from Mary Ann Liebert, Inc.: http://dx.doi.org/10.1089/dia.2018.0401
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.1517
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.1820 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) = ayI(t)(b+ct)w(t)(1)
The term ayI(t)is referred to as the sensor gain, and the
term b+ctis 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) = s1α(t) + s2β(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.1618 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.2325 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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... Причина таких отличий в настоящее время не ясна. Несмотря на то что некоторые исследователи отмечают отсутствие различий в погрешности ФМГ в разные дни использования сенсора, у нашей пациентки точность показаний ФМГ и НМГ была ниже в первые сутки установки датчика или сенсора [17]. Именно поэтому в первые сутки непрерывного мониторирования необходим более частый контроль гликемии при помощи глюкометра -это позволит выявить индивидуальную реакцию на установку сенсора, которая может искажать результаты. ...
... При анализе данных ФМГ с использованием сетки ошибки Кларка многие исследователи показали: почти 100% измерений попадают в зоны А и В, что свидетельствует о безопасности метода в случае принятия решения о лечении на основании показаний ФМГ [12,15,17]. В нашем исследовании в качестве референсного метода использовались в одном случае показания глюкометра, а в другом НМГ, что могло повлиять на результат. ...
Article
Background : Continuous glucose monitoring (CGM) has shown its benefits in pregnant women with diabetes. Flash glucose monitoring (FGM), as one of the CGM types, has not been well assessed in this patient group. The interpretation of a big volume of information on glycaemia obtained with various CGM devices is possible with statistical analysis according to the algorithms proposed by manufacturers. While these algorithms cannot be comprehensive, evaluation of alternative approaches to the CGM data statistical analysis and comparison of the results obtained with different devices seem reasonable. No unified algorithm for modification of antidiabetic treatment according to the CGM results has been yet developed. This study was performed in a pregnant patient with type 1 diabetes mellitus (T1DM) to demonstrate the methods to individualized analysis of the data from various devices (CGM, FGM, glucometer) that could be used in routine clinical practice. Aim : To evaluate the individual advantages and disadvantages of the simultaneous use of FGM, CGM and SMBG in a pregnant woman with type 1 diabetes. Materials and methods : This was an observational case study with a retrospective assessment of the patient's data obtained with FGM, CGM and a glucometer in a 31-year female patient with T1DM of 6-year duration and 9 weeks of gestation, who had been on pump insulin therapy for one year and had an HbA1c level of 5.4%. During the study the patient continued her pump therapy and performed blood glucose self-monitoring (BGSM) and simultaneously used FGM and CGM. The following FGM data were compared with CGM and glucometer results: measurement numbers, time in range, mean daily glucose, mean absolute difference (MAD), and mean absolute relative difference (MARD). Results : The FGM-derived mean daily glucose was lower than that measured with the glucometer: 5.1±1.9 mmol/L vs 6.4±2.2 mmol/L (p<0.001). The number of measurements with FGM was 32.0±12.9 times daily and with a glucometer 15.1±5.5 times daily (p<0.001). MAD values were minimal in the hypoglycemic range (0.5±0.3 mmol/L) and maximal in the hyperglycemic range (1.6±1.2 mmol/L, р<0.001). The MARD values were significantly smaller in the hyperglycemic than in the normoglycemic (16.6±12.6% vs 21.3±14.0%, р=0.035). The highest MAD and MARD were observed on the Day 1 of the sensor installation. The comparison of FGM and the glucometer readings with the Clarke consensus error grid showed that 82% of the FGM readings were in zone A or B. The FGM accuracy was higher from Day 2 to Day 9 (72.5% of the FGM readings in zone A). MAD between FGM and CGM readings was not different from that between FGM and the glucometer: 1.3±1.0 mmol/L and 1.2±0.9 mmol/L, respectively (p=0.09). MARD for the FGM and CGM comparison was higher than that for FGM and glucometer comparison: 24.4±23.0% and 18.8±13.5%, respectively (р<0.001). The Pearson's correlation coefficient FGM and CGM seemed lower than that between FGM and the glucometer (0.837 and 0.889, respectively). FGM has identified more hypoglycemic events compared to CGM: time below range was 29.4% and 8.8%, respectively, p<0.001). Conclusion : The FGM readings highly correlate with the glucometer. The FGM difference with the glucometer was lower in the hypo- and hyperglycemic ranges. FGM shows higher values for time below range than CGM. It is necessary to continue the study of the clinical acceptability of FGM in pregnant women and determination of its optimal regimen for the treatment of this patient category, as well as to develop an algorithm for treatment modification based on the results of FGM.
... There have been several commercial subcutaneous glucose sensors for continuous monitoring of diabetes, such as G6 (Dexcom, San Diego, USA) 22 , Freestyle Libre (Abbott, Chicago, USA) 23 , and Guardian (Medtronic, Northridge, USA) 24 . These sensors usually rely on the subcutaneous implantation of a needle-type biosensor with a length of several to ten millimeters to monitor glucose levels continuously for approximately 1 week 25,26 . However, these commercial sensors have several disadvantages, including skin pain, acupuncture phobia, and allergies 10,27 . ...
Article
Full-text available
Diabetes is a prevalent chronic metabolic disease with multiple clinical manifestations and complications, and it is among the leading causes of death. Painless and continuous monitoring of interstitial glucose is highly desirable for diabetes management. Here we unprecedentedly show continuous monitoring of diabetes with an integrated microneedle biosensing device. The device was manufactured with a 3D printing process, a microfabrication process, an electroplating process, and an enzyme immobilization step. The device was inserted into the dermis layer of mouse skin and showed accurate sensing performance for monitoring subcutaneous glucose levels in normal or diabetic mice. The detection results were highly correlated with those obtained from a commercial blood glucose meter. We anticipate that the study could open exciting avenues for monitoring and managing diabetes, alongside fundamental studies of subcutaneous electronic devices.
... [1][2][3] Factorycalibrated CGM devices offer distinct advantages over the previous user-calibrated devices, including reduced burden, reduction in test-strip usage, and removal of inaccuracy imparted from user glucose testing. [4][5][6] The improvement in device accuracy has also resulted in approval for insulin dosing from CGM values (non-adjunctive use), further reducing user burden. 7,8 Direct dosing from CGM has since been demonstrated to be safe and effective in several randomized clinical trials. ...
Article
Background: In this study, we evaluated the analytical performance of the second-generation factory-calibrated FreeStyle Libre Flash Glucose Monitoring (FreeStyle Libre 2) System compared to plasma venous blood glucose reference, Yellow Springs Instrument 2300 (YSI). Methods: The study enrolled participants aged four and above with type 1 or type 2 diabetes at seven sites in the United States. Adult participants (18+ years) participated in three in-clinic sessions and pediatric participants (4-17 years) participated in up to two in-clinic sessions stratified to provide data for days 1, 2, 3, 7, 8, 9, 12, 13, or 14 of sensor wear. Participants aged 11+ underwent supervised glycemic manipulation during in-clinic sessions to achieve glucose levels across the measurement range of the System. Performance evaluation included accuracy measures such as the proportion of continuous glucose monitoring (CGM) values that were within ±20% or ±20 mg/dL of reference glucose values, and bias measures such as the mean absolute relative difference (MARD) between CGM and reference values. Results: Data from the 144 adults and 129 pediatric participants were analyzed. Percent of sensor results within ±20%/20 mg/dL of YSI reference were 93.2% and 92.1%, and MARD was 9.2% and 9.7% for the adults and pediatric participants, respectively. The System performed well in the hypoglycemic range, with 94.3% of the results for the adult population and 96.1% of the data for pediatric population being within 15 mg/dL of the YSI reference. The time lag was 2.4 ± 4.6 minutes for adults and 2.1 ± 5.0 minutes for pediatrics. Conclusions: The System demonstrated improved analytical accuracy performance across the dynamic range during the 14-day sensor wear period as compared to the previous-generation device.NCT#: NCT03607448 and NCT03820050.
Article
Objective The objective of this study was to define expert opinion on Continuous Glucose Monitoring (CGM) in persons with type 2 diabetes mellitus (PWDM2), including its advantages, barriers, and best clinical practices for initiation, patient-clinician communication, and data management. Methods A series of virtual discussions was held to recommend improvements to clinical practice and design clinical tools for primary care clinicians. Participants included endocrinologists, primary care physicians, physician assistants (PAs), advanced practice nurses (APNs), and diabetes care and education specialists. Results The expert panels recommended CGM as a supplement to blood glucose monitoring (BGM) and hemoglobin A1c for managing persons with diabetes (PWD). CGM can help predict potential pitfalls in glycemic management--including hypo and hyperglycemic excursions--which directly influence lifestyle changes, medication initiation, and dosing decisions. A toolkit was designed with practical guidance on integration of CGM into clinical practice, interpretation of results, clinical guidelines, a patient action plan, and other useful management tools. Conclusion This review summarizes findings from a roundtable discussion with endocrinology and primary care clinicians, a discussion of the advantages and challenges with CGM, and clinical approaches to improving care of persons with diabetes. CGM offers more detailed tracking of glucose than BGM or A1c and it can detect asymptomatic hypoglycemia. Specialized education of providers, cost to patient and providers, and data management remain barriers to widespread adoption of CGM for PWD.
Article
Background The delivery and administration of insulin has undergone many changes over the years. This research examines U.S. trends in insulin use among people with type 1 diabetes (T1D) or type 2 diabetes (T2D) in the U.S. from 2009 to 2018. Methods The IBM ® MarketScan ® Commercial and Medicare databases were used to identify trends in insulin use over 10 years. The study included people with T1D or T2D who filled a prescription for insulin in any calendar year from 2009 to 2018. The analyses examined insulin regimen and delivery and the use of glucose monitoring systems. Generalized estimating equations were used to test whether trends were statistically significant. Results Individuals with T1D were most commonly prescribed a basal and bolus insulin regimen or short/rapid insulin only, while for people with T2D the use of basal-only insulin increased significantly over the study period. In both groups there was a significant decline in the use of premix insulin from 2009 to 2018. Insulin pump use increased for individuals with T1D, while disposable pen use increased for people in both cohorts. In both cohorts, there was a statistically significant increase in the use of continuous glucose monitoring, although this increase was more pronounced and occurred earlier among individuals with T1D. Conclusions Results indicate significant changes in insulin regimens and delivery and glucose monitoring from 2009 to 2018. These findings suggest that insulin prescribing continues to change in response to the development of new therapeutics, advances in insulin delivery technology, and glucose monitoring systems.
Article
Background Continuous glucose monitoring-derived parameters are becoming increasingly important in the treatment of people with diabetes. The aim of this study was to assess whether these parameters, as calculated from different continuous glucose monitoring systems worn in parallel, are comparable. In addition, clinical relevance of differences was investigated. Methods A total of 24 subjects wore a FreeStyle Libre (A) and a Dexcom G5 (B) sensor in parallel for 7 days. Mean glucose, coefficient of variation, glucose management indicator and time spent in different glucose ranges were calculated for each system. Pairwise differences between the two different continuous glucose monitoring systems were computed for these metrics. Results On average, the two CGM systems indicated an identical time in range (67.9±10.2 vs. 67.9±11.5%) and a similar coefficient of variation; both categorized as unstable (38.1±5.9 vs. 36.0±4.8%). In contrast, the mean time spent below and above range, as well as the individual times spent below, in and above range differed substantially. System A indicated about twice the time spent below range than system B (7.7±7.2 vs. 3.8±2.7%, p=0.003). This could have led to different therapy recommendations in approximately half of the subjects. Discussion The differences in metrics found between the two continuous glucose monitoring systems may result in different therapy recommendations. In order to make adequate clinical decisions, measurement performance of CGM systems should be standardized and all available information, including the HbA1c, should be utilized.
Article
Technology can aid patient diabetes self-management education and support. Technology in diabetes self-management education can improve the quality of life and decrease stress levels of individuals living with diabetes. This article provides a review of the basic principles of glucose pattern management when using a continuous glucose monitor, available insulin pumps, and other novel technological applications. The goal of technology integration in diabetes self-management education is to help patients minimize time spent managing the disease while optimizing their overall diabetes management leading to better outcomes.
Article
Background: Pediatric patients undergoing hematopoietic stem cell transplant (HSCT) may be at risk for malglycemia and adverse outcomes including infection, prolonged hospital stays, organ dysfunction, graft-versus-host-disease, delayed hematopoietic recovery, and increased mortality. Continuous glucose monitoring (CGM) may aid in describing and treating malglycemia in this population. However, no studies have demonstrated safety, tolerability or accuracy of CGM in this uniquely immunocompromised population. Methods: A prospective observational study was conducted, employing the Abbott Freestyle Libre Pro, was conducted in patients age 2-30 undergoing HSCT at Children's Hospital Colorado to evaluate continuous glycemia in this population. CGM occurred up to 7 days prior to, and 60 days after, HSCT during hospitalization only. In a secondary analysis of this data, blood glucoses collected during routine HSCT care were compared with CGM values to evaluate accuracy. Adverse events and patient refusal to wear CGM device were monitored to assess safety and tolerability. Results: Participants (n=29; median age 13.1 years, [IQR] [4.7, 16.6] years) wore 84 sensors for an average of 25 [21.5, 30.0] days per participant. Paired serum-sensor values (n=893) demonstrated a mean absolute relative difference of 20±14% with Clarke Error Grid analysis showing 99% of pairs in the clinically acceptable zones (A+B). There were 4 episodes of self-limited bleeding (4.8% of sensors); no other adverse events occurred. Six patients (20.7%) refused subsequent CGM placements. Conclusions: CGM use appears safe and feasible though with suboptimal accuracy in the hospitalized pediatric HSCT population. Few adverse events occurred, all of which were low grade.
Article
Glucose monitoring is an essential component of type 1 diabetes (T1D) treatment. Continuous glucose monitoring (CGM) systems measure glucose levels every few minutes and provide valuable trend information about the direction and speed glucose levels are changing. Use of CGM is increasing rapidly in youth with T1D and consistent use of CGM is associated with improved glycemic control. School nurses are a vital part of the care team for a student with T1D, and therefore, must be comfortable using CGM to support their students at school. This is the first article in a three-part series on the use of technology in managing diabetes in youth. The purpose of this article is to describe CGM devices, including calibration requirements and interpretation of trend arrows and provide tips for school nurses in incorporating CGM into the student’s individualized healthcare plan. Part 2 in this series will focus on insulin pumps and Part 3 will focus on special considerations and problem solving when using diabetes technology in the school setting.
Article
Full-text available
Objective: As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)-based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively. Research design and methods: A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms. Results: AP improved percent time 70-140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70-180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes. Conclusions: Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.
Article
Full-text available
The use of commercially available continuous glucose monitors for diabetes management requires sensor calibrations, which until recently are exclusively performed by the patient. A new development is the implementation of factory calibration for subcutaneous glucose sensors, which eliminates the need for user calibrations and the associated blood glucose tests. Factory calibration means that the calibration process is part of the sensor manufacturing process and performed under controlled laboratory conditions. The ability to move from a user calibration to factory calibration is based on several technical requirements related to sensor stability and the robustness of the sensor manufacturing process. The main advantages of factory calibration over the conventional user calibration are: (a) more convenience for the user, since no more fingersticks are required for calibration and (b) elimination of use errors related to the execution of the calibration process, which can lead to sensor inaccuracies. The FreeStyle Libre™ and FreeStyle Libre Pro™ flash continuous glucose monitoring systems are the first commercially available sensor systems using factory-calibrated sensors. For these sensor systems, no user calibrations are required throughout the sensor wear duration.
Article
Background: This study evaluated the safety and performance of the Guardian™ continuous glucose monitoring (CGM) system in children and adolescents with type 1 diabetes (T1D). Materials and methods: Subjects 2-18 years of age (mean ± standard deviation [SD] 13.1 ± 3.9 years) with T1D and duration of diagnosis ≥1 year were enrolled at 11 sites in the United States and wore two Guardian Sensor 3 sensors in the abdomen and/or buttock. Sensors were connected to a transmitter paired with either a Guardian Connect system (i.e., mobile device with software application allowing display of sensor glucose [SG] values) or a Guardian Link 3 transmitter used as a Glucose Sensor Recorder (GSR). There were 145 participants who underwent a 6-h in-clinic frequent sample testing (FST) on day 1 (n = 54), day 3 (n = 48), or day 7 (n = 43) postsensor insertion. During FST, SG values were compared with a Yellow Springs Instrument (YSI) plasma reference every 5-15 min (n = 124, 7-18 years of age; n = 2, 2-6 years of age), or to a self-monitoring of blood glucose (SMBG) reference every 5-30 min (n = 19, 2-6 years of age). Results: The overall mean absolute relative difference (ARD ± SD) between SG and reference values (YSI or SMBG) when calibrating approximately every 12 h, was 10.9% ± 10.7% (3102 paired points) for sensors communicating with the Guardian Connect system and 11.1% ± 10.6% (2624 paired points) for sensors connected to the GSR. The overall percentage of SG values within ±20% of reference values >80 mg/dL or within 20 mg/dL of reference values ≤80 mg/dL was 87.8% for the Guardian Connect system and 86.7% for the GSR, respectively. There was one device-related adverse event of contact dermatitis, but no serious device-related adverse events. Conclusions: The Guardian CGM system demonstrated good accuracy in children and adolescents. These findings support its use in sensor-integrated insulin pump platforms, as well as a standalone technology, for managing diabetes in pediatric populations.
Article
Numerous milestones mark the advance of diabetes care since the discovery of insulin in 1921. Glucose monitoring has progressed from urine to blood to interstitial fluid measurements every 5‐15 minutes with continuous glucose monitors (CGM). Similarly, advances in insulin formulations and their delivery include rapid acting and basal insulins as well as insulin pumps to more effectively dose insulin. This article is protected by copyright. All rights reserved.
Article
Background: This study assessed the accuracy of a factory-calibrated 10-day real-time continuous glucose monitoring (CGM) system (G6), which includes an automated sensor applicator. Methods: Seventy-six participants with insulin-treated diabetes were enrolled at four U.S. sites as part of a larger study of G6 system performance. In-clinic visits for frequent comparative blood glucose measurements using a reference instrument (YSI) were conducted on days 1, 4-5, 7, and/or 10 of system use. Accuracy evaluation included the proportion of CGM values that were within ±20% of YSI reference value for glucose levels >100 mg/dL and ±20 mg/dL for YSI glucose levels ≤100 mg/dL (%20/20), the analogous %15/15 and %30/30, and the mean absolute relative difference (MARD) between temporally matched CGM and YSI values. Participants calibrated the systems once daily. Raw sensor data were reprocessed using assigned sensor codes and a factory-calibration algorithm. Results: Reprocessed data from 62 participants (25 adults and 37 children and adolescents of ages 6-17 years; 3532 YSI-CGM pairs) were analyzed. The G6 system's overall %20/20 was 93.9% (adults, 92.5%; children and adolescents, 96.2%), its %15/15 was 83.3% (adults, 78.3%; children and adolescents, 91.1%), and its MARD was 9.0% (adults, 9.8%; children and adolescents, 7.7%). Overall day-1 %20/20 accuracy was 92.2%, %15/15 was 81.5%, and MARD was 9.3%. Accuracy was maintained across 10 days of use and various glucose concentration ranges in both adults and children and adolescents. Conclusions: The G6 system utilizing an automated sensor applicator provides accurate glucose readings in adults and children and adolescents throughout the 10-day sensor life.
Article
Objective: Cleared blood glucose monitor systems (BGMs) for personal use may not always deliver levels of accuracy currently specified by international and U.S. regulatory bodies. This study's objective was to assess the accuracy of 18 such systems cleared by the U.S. Food and Drug Administration representing approximately 90% of commercially available systems used from 2013 to 2015. Research design and methods: A total of 1,035 subjects were recruited to have a capillary blood glucose (BG) level measured on six different systems and a reference capillary sample prepared for plasma testing at a reference laboratory. Products were obtained from consumer outlets and tested in three triple-blinded studies. Each of the three participating clinical sites tested a different set of six systems for each of the three studies in a round-robin. In each study, on average, a BGM was tested on 115 subjects. A compliant BG result was defined as within 15% of a reference plasma value (for BG ≥100 mg/dL [5.55 mmol/L]) or within 15 mg/dL (0.83 mmol/L) (for BG <100 mg/dL [5.55 mmol/L]). The proportion of compliant readings in each study was compared against a predetermined accuracy standard similar to, but more lenient than, current regulatory standards. Other metrics of accuracy included the overall compliance proportion; the proportion of extreme outlier readings differing from the reference value by >20%; modified Bland-Altman analysis including average bias, coefficient of variation, and 95% limits of agreement; and proportion of readings with no clinical risk as determined by the Surveillance Error Grid. Results: The different accuracy metrics produced almost identical BGM rankings. Six of the 18 systems met the predetermined accuracy standard in all three studies, 5 systems met it in two studies, and 3 met it in one study. Four BGMs did not meet the accuracy standard in any of the three studies. Conclusions: Cleared BGMs do not always meet the level of analytical accuracy currently required for regulatory clearance. This information could assist patients, professionals, and payers in choosing products and regulators in evaluating postclearance performance.
Article
Background: Frequent use of continuous glucose monitoring (CGM) systems is associated with improved glycemic outcomes in persons with diabetes, but the need for calibrations and sensor insertions are often barriers to adoption. In this study, we evaluated the performance of G6, a sixth-generation, factory-calibrated CGM system specified for 10-day wear. Methods: The study enrolled participants of ages 6 years and up with type 1 diabetes or insulin-treated type 2 diabetes at 11 sites in the United States. Participation involved one sensor wear period of up to 10 days. Adults wore the system on the abdomen; youth of ages 6-17 years could choose to wear it on the abdomen or upper buttocks. Clinic sessions for frequent comparison with reference blood glucose measurements took place on days 1, 4-5, 7, and/or 10. Participants of ages 13 years and up underwent purposeful supervised glucose manipulation during in-clinic sessions. During the study, participants calibrated the systems once daily. However, analysis was performed on glucose values that were derived from reprocessed raw sensor data, independently of self-monitored blood glucose values used for calibration. Reprocessing used assigned sensor codes and a factory-calibration algorithm. Performance evaluation included the proportion of CGM values that were within ±20% of reference glucose values >100 mg/dL or within ±20 mg/dL of reference glucose values ≤100 mg/dL (%20/20), the analogous %15/15, and the mean absolute relative difference (MARD, expressed as a percentage) between temporally matched CGM and reference values. Results: Data from 262 study participants (21,569 matched CGM reference pairs) were analyzed. The overall %15/15, %20/20, and MARD were 82.4%, 92.3%, and 10.0%, respectively. Matched pairs from 134 adults and 128 youth of ages 6-17 years were similar with respect to %20/20 (92.4% and 91.9%) and MARD (9.9% and 10.1%). Overall %20/20 values on days 1 and 10 of sensor wear were 88.6% and 90.6%, respectively. The system's "Urgent Low Soon" (predictive of hypoglycemia within 20 min) hypoglycemia alert was correctly provided 84% of the time within 30 min before impending biochemical hypoglycemia (<70 mg/dL). The 10-day sensor survival rate was 87%. Conclusion: The new factory-calibrated G6 real-time CGM system provides accurate readings for 10 days and removes several clinical barriers to broader CGM adoption.
Article
Background: Continuous glucose monitoring (CGM) sensors need to be calibrated twice/day by using self-monitoring of blood glucose (SMBG) samples. Recently, to reduce the calibration frequency, we developed an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation. When applied to Dexcom G4 Platinum (DG4P) sensor data, the algorithm allowed the frequency of calibrations to be reduced to one-every-four-days without significant worsening of sensor accuracy. The aim of this study is to assess the same methodology on raw CGM data acquired by a next-generation Dexcom CGM sensor prototype and compare the results with that obtained on DG4P. Methods: The database consists of 55 diabetic subjects monitored for 10 days by a next-generation Dexcom CGM sensor prototype. The new calibration algorithm is assessed, retrospectively, by simulating an online procedure using progressively fewer SMBG samples until zero. Accuracy is evaluated with mean absolute relative differences (MARD) between blood glucose versus CGM values. Results: The one-per-day and one-every-two-days calibration scenarios in the next-generation CGM data have an accuracy of 8.5% MARD (vs. 11.59% of DG4P) and 8.4% MARD (vs. 11.63% of DG4P), respectively. Accuracy slightly worsens to 9.2% (vs. 11.62% of DG4P) when calibrations are reduced to one-every-four-days. The calibration-free scenario results in 9.3% MARD (vs. 12.97% of DG4P). Conclusions: In next-generation Dexcom CGM sensor data, the use of an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation aids in the shift toward a calibration-free scenario with even better results than those obtained in present-generation sensors.
Article
Aims: This is a meta-synthesis of extant qualitative literature related to impact of continuous glucose monitoring (CGM). CGM has been available for a decade for the management of Type 1 diabetes and is the lynchpin of future artificial pancreas technologies. Clinical uptake of CGM is an important area of inquiry. The purpose of this meta-synthesis is to understand the impact of CGM on individuals with Type 1 diabetes and others (parents, significant others, providers) in order to design appropriate clinical interventions for adherence. Methods: Studies published in English between 2007 and 2017 were included, reflecting commercial CGM availability. PubMed, PsychINFO, CINALH, Web of Science and EMBASE databases were queried using search terms related to CGM, qualitative, experience and Type 1 diabetes. Included articles contained original qualitative or mixed-method research on CGM, sensor-augmented pump or closed-loop therapies. Articles underwent quality appraisal and thematic interpretive integration by a multidisciplinary team. Results: Nine articles (343 participants) met the inclusion criteria and were included in the synthesis. Six novel themes emerged: interacting with CGM, burden of living with CGM, feeling different from others, feeling empowered, interacting with glucose information and impact on relationships. Conclusion: CGM affects physical, emotional and relational aspects of life. Clinicians can help minimize the burden of CGM with carefully delivered education and expectation-setting with individuals. Empowerment and relational partnerships in diabetes care can be explored to maximize satisfaction with CGM. Systematic interpretive synthesis of qualitative studies provides a comprehensive, contextual understanding of the impact of CGM on daily life and relationships. This article is protected by copyright. All rights reserved.