U.S. patent application number 17/398628 was filed with the patent office on 2021-12-02 for advanced analyte sensor calibration and error detection.
The applicant listed for this patent is DexCom, Inc.. Invention is credited to Sebastian Bohm, Daiting Rong, Peter C. Simpson.
Application Number | 20210369149 17/398628 |
Document ID | / |
Family ID | 1000005769926 |
Filed Date | 2021-12-02 |
United States Patent
Application |
20210369149 |
Kind Code |
A1 |
Bohm; Sebastian ; et
al. |
December 2, 2021 |
ADVANCED ANALYTE SENSOR CALIBRATION AND ERROR DETECTION
Abstract
Systems and methods for processing sensor data and
self-calibration are provided. In some embodiments, systems and
methods are provided which are capable of calibrating a continuous
analyte sensor based on an initial sensitivity, and then
continuously performing self-calibration without using, or with
reduced use of, reference measurements. In certain embodiments, a
sensitivity of the analyte sensor is determined by applying an
estimative algorithm that is a function of certain parameters. Also
described herein are systems and methods for determining a property
of an analyte sensor using a stimulus signal. The sensor property
can be used to compensate sensor data for sensitivity drift, or
determine another property associated with the sensor, such as
temperature, sensor membrane damage, moisture ingress in sensor
electronics, and scaling factors.
Inventors: |
Bohm; Sebastian; (San Diego,
CA) ; Rong; Daiting; (San Diego, CA) ;
Simpson; Peter C.; (Cardiff, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DexCom, Inc. |
San Diego |
CA |
US |
|
|
Family ID: |
1000005769926 |
Appl. No.: |
17/398628 |
Filed: |
August 10, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16951759 |
Nov 18, 2020 |
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17398628 |
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16586434 |
Sep 27, 2019 |
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16951759 |
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16460943 |
Jul 2, 2019 |
10561354 |
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16586434 |
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16405887 |
May 7, 2019 |
10682084 |
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16460943 |
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15994905 |
May 31, 2018 |
10327688 |
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16405887 |
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14860392 |
Sep 21, 2015 |
10004442 |
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15994905 |
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13446977 |
Apr 13, 2012 |
9149220 |
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14860392 |
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13446848 |
Apr 13, 2012 |
9801575 |
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13446977 |
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61476145 |
Apr 15, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 27/026 20130101;
A61B 5/14532 20130101; A61B 5/7257 20130101; A61B 5/14546 20130101;
A61B 5/1486 20130101; G01N 33/49 20130101; G01D 18/00 20130101;
A61B 2560/0276 20130101; A61B 5/1473 20130101; H05K 999/99
20130101; A61B 5/14517 20130101; A61B 5/14503 20130101; G01N
27/3274 20130101; A61B 5/1495 20130101; A61B 5/1451 20130101; H05K
999/00 20130101; A61B 5/7267 20130101 |
International
Class: |
A61B 5/1495 20060101
A61B005/1495; G01N 27/327 20060101 G01N027/327; A61B 5/145 20060101
A61B005/145; A61B 5/1486 20060101 A61B005/1486; A61B 5/00 20060101
A61B005/00; G01D 18/00 20060101 G01D018/00; G01N 27/02 20060101
G01N027/02; G01N 33/49 20060101 G01N033/49; A61B 5/1473 20060101
A61B005/1473 |
Claims
1. A method for calibrating an analyte sensor, the method
comprising: applying a time-varying signal to the analyte sensor;
measuring a signal response to the applied signal; determining,
using sensor electronics, a sensitivity of the analyte sensor, the
determining comprising correlating at least one property of the
signal response to a predetermined sensor sensitivity profile; and
generating, using sensor electronics, estimated analyte
concentration values using the determined sensitivity and sensor
data generated by the analyte sensor.
2. The method of claim 1, wherein the sensitivity profile comprises
varying sensitivity values over time since implantation of the
sensor.
3. The method of claim 1, wherein the predetermined sensitivity
profile comprises a plurality of sensitivity values.
4. The method of claim 1, wherein the predetermined sensitivity
profile is based on sensor sensitivity data generated from studying
sensitivity changes of analyte sensors similar to the analyte
sensor.
5. The method of claim 1, further comprising applying a bias
voltage to the sensor, wherein the time-varying signal comprises a
step voltage above the bias voltage or a sine wave overlaying a
voltage bias voltage.
6. The method of claim 1, wherein the determining further comprises
calculating an impedance value based on the measured signal
response and correlating the impedance value to a sensitivity value
of the predetermined sensitivity profile.
7. The method of claim 1, further comprising applying a DC bias
voltage to the sensor to generate sensor data, wherein the
estimating analyte concentration values includes generating
corrected sensor data using the determined sensitivity.
8. The method of claim 7, further comprising applying a conversion
function to the corrected sensor data to generate the estimated
analyte concentration values.
9. The method of claim 7, further comprising forming a conversion
function based at least in part of the determined sensitivity, and
wherein the conversion function is applied to the sensor data to
generate the estimated analyte concentration values.
10. The method of claim 1, wherein the property is a peak current
value of the signal response.
11. The method of claim 1, wherein the determining further
comprises using at least one of performing a Fast Fourier Transform
on the signal response data, integrating at least a portion of a
curve of the signal response, and determining a peak current of the
signal response.
12. The method of claim 11, further comprising generating estimated
analyte concentration values using the selected sensitivity
profile.
13. The method of claim 1, wherein the determining further
comprises selecting the predetermined sensitivity profile based on
the determined sensor property from a plurality of different
predetermined sensitivity profiles.
14. The method of claim 13, wherein the selecting includes
performing a data association analysis to determine a correlation
between the determined sensor property and each of the plurality of
different predetermined sensitivity profiles and wherein the
selected predetermined sensitivity profile has the highest
correlation.
15. The method of claim 13, further comprising generating estimated
analyte concentration values using the selected sensitivity
profile.
16. The method of claim 15, further comprising determining a second
sensitivity value using the selected sensitivity profile, wherein a
first set of estimated analyte concentration values is generated
using the determined sensitivity value and sensor data associated
with a first time period, and wherein a second set of concentration
values is generated using the second sensitivity value and sensor
data associated with a second time period.
17. A system for measuring an analyte, the system comprising sensor
electronics configured to be operably connected to a continuous
analyte sensor, the sensor electronics configured to: apply a
time-varying signal to the analyte sensor; measure a signal
response to the applied signal; determine a sensitivity of the
analyte sensor, the determining comprising correlating at least one
property of the signal response to a predetermined sensor
sensitivity profile; and generate estimated analyte concentration
values using the determined sensitivity and sensor data generated
by the analyte sensor.
18. The system of claim 17, wherein the sensitivity profile
comprises varying sensitivity values over time since implantation
of the sensor.
19. The system of claim 17, wherein the predetermined sensitivity
profile comprises a plurality of sensitivity values.
20. The system of claim 17, wherein the predetermined sensitivity
profile is based on sensor sensitivity data generated from studying
sensitivity changes of analyte sensors similar to the analyte
sensor.
Description
INCORPORATION BY REFERENCE TO RELATED APPLICATIONS
[0001] Any and all priority claims identified in the Application
Data Sheet, or any correction thereto, are hereby incorporated by
reference under 37 CFR 1.57. This application is a continuation of
U.S. application Ser. No. 16/951,759, filed Nov. 18, 2020, which is
a continuation of U.S. application Ser. No. 16/586,434, filed Sep.
27, 2019, now abandoned, which is a continuation of U.S.
application Ser. No. 16/460,943, filed Jul. 2, 2019, now U.S. Pat.
No. 10,561,354, which is a continuation of U.S. application Ser.
No. 16/405,887, filed May 7, 2019, now U.S. Pat. No. 10,682,084,
which is a continuation of U.S. application Ser. No. 15/994,905,
filed May 31, 2018, now U.S. Pat. No. 10,327,688, which is a
continuation of U.S. application Ser. No. 14/860,392, filed Sep.
21, 2015, now U.S. Pat. No. 10,004,442, which is a continuation of
U.S. application Ser. No. 13/446,977, filed Apr. 13, 2012, now U.S.
Pat. No. 9,149,220, which is a continuation of U.S. application
Ser. No. 13/446,848, filed Apr. 13, 2012, now U.S. Pat. No.
9,801,575, which claims the benefit of U.S. Provisional Application
No. 61/476,145, filed Apr. 15, 2011. Each of the aforementioned
applications is incorporated by reference herein in its entirety,
and each is hereby expressly made a part of this specification.
TECHNICAL FIELD
[0002] The embodiments described herein relate generally to systems
and methods for processing sensor data from continuous analyte
sensors and for self-calibration.
BACKGROUND
[0003] Diabetes mellitus is a chronic disease, which occurs when
the pancreas does not produce enough insulin (Type I), or when the
body cannot effectively use the insulin it produces (Type II). This
condition typically leads to an increased concentration of glucose
in the blood (hyperglycemia), which can cause an array of
physiological derangements (e.g., kidney failure, skin ulcers, or
bleeding into the vitreous of the eye) associated with the
deterioration of small blood vessels. Sometimes, a hypoglycemic
reaction (low blood sugar) is induced by an inadvertent overdose of
insulin, or after a normal dose of insulin or glucose-lowering
agent accompanied by extraordinary exercise or insufficient food
intake.
[0004] A variety of sensor devices have been developed for
continuously measuring blood glucose concentrations.
Conventionally, a diabetic person carries a self-monitoring blood
glucose (SMBG) monitor, which typically involves uncomfortable
finger pricking methods. Due to a lack of comfort and convenience,
a diabetic will often only measure his or her glucose levels two to
four times per day. Unfortunately, these measurements can be spread
far apart, such that a diabetic may sometimes learn too late of a
hypoglycemic or hyperglycemic event, thereby potentially incurring
dangerous side effects. In fact, not only is it unlikely that a
diabetic will take a timely SMBG measurement, but even if the
diabetic is able to obtain a timely SMBG value, the diabetic may
not know whether his or her blood glucose value is increasing or
decreasing, based on the SMBG alone.
[0005] Heretofore, a variety of glucose sensors have been developed
for continuously measuring glucose values. Many implantable glucose
sensors suffer from complications within the body and provide only
short-term and less-than-accurate sensing of blood glucose.
Similarly, transdermal sensors have run into problems in accurately
sensing and reporting back glucose values continuously over
extended periods of time. Some efforts have been made to obtain
blood glucose data from implantable devices and retrospectively
determine blood glucose trends for analysis; however these efforts
do not aid the diabetic in determining real-time blood glucose
information. Some efforts have also been made to obtain blood
glucose data from transdermal devices for prospective data
analysis, however similar problems have occurred.
SUMMARY OF THE INVENTION
[0006] In a first aspect, a method is provided for calibrating
sensor data generated by a continuous analyte sensor, comprising:
generating sensor data using a continuous analyte sensor;
iteratively determining, with an electronic device, a sensitivity
value of the continuous analyte sensor as a function of time by
applying a priori information comprising sensor sensitivity
information; and calibrating the sensor data based at least in part
on the determined sensitivity value.
[0007] In an embodiment of the first aspect or any other embodiment
thereof, calibrating the sensor data is performed iteratively
throughout a substantially entire sensor session.
[0008] In an embodiment of the first aspect or any other embodiment
thereof, iteratively determining a sensitivity value is performed
at regular intervals or performed at irregular intervals, as
determined by the a priori information.
[0009] In an embodiment of the first aspect or any other embodiment
thereof, iteratively determining a sensitivity value is performed
throughout a substantially entire sensor session.
[0010] In an embodiment of the first aspect or any other embodiment
thereof, determining a sensitivity value is performed in
substantially real time.
[0011] In an embodiment of the first aspect or any other embodiment
thereof, the a priori information is associated with at least one
predetermined sensitivity value that is associated with a
predetermined time after start of a sensor session.
[0012] In an embodiment of the first aspect or any other embodiment
thereof, at least one predetermined sensitivity value is associated
with a correlation between a sensitivity determined from in vitro
analyte concentration measurements and a sensitivity determined
from in vivo analyte concentration measurements at the
predetermined time.
[0013] In an embodiment of the first aspect or any other embodiment
thereof, the a priori information is associated with a
predetermined sensitivity function that uses time as input.
[0014] In an embodiment of the first aspect or any other embodiment
thereof, time corresponds to time after start of a sensor
session.
[0015] In an embodiment of the first aspect or any other embodiment
thereof, time corresponds to at least one of time of manufacture or
time since manufacture.
[0016] In an embodiment of the first aspect or any other embodiment
thereof, the sensitivity value of the continuous analyte sensor is
also a function of at least one other parameter.
[0017] In an embodiment of the first aspect or any other embodiment
thereof, the at least one other parameter is selected from the
group consisting of: temperature, pH, level or duration of
hydration, curing condition, an analyte concentration of a fluid
surrounding the continuous analyte sensor during startup of the
sensor, and combinations thereof.
[0018] In an embodiment of the first aspect or any other embodiment
thereof, calibrating the sensor data is performed without using
reference blood glucose data.
[0019] In an embodiment of the first aspect or any other embodiment
thereof, the electronic device is configured to provide a level of
accuracy corresponding to a mean absolute relative difference of no
more than about 10% over a sensor session of at least about 3 days,
and wherein reference measurements associated with calculation of
the mean absolute relative difference are determined by analysis of
blood.
[0020] In an embodiment of the first aspect or any other embodiment
thereof, the sensor session is at least about 4 days.
[0021] In an embodiment of the first aspect or any other embodiment
thereof, the sensor session is at least about 5 days.
[0022] In an embodiment of the first aspect or any other embodiment
thereof, the sensor session is at least about 6 days.
[0023] In an embodiment of the first aspect or any other embodiment
thereof, the sensor session is at least about 7 days.
[0024] In an embodiment of the first aspect or any other embodiment
thereof, the sensor session is at least about 10 days.
[0025] In an embodiment of the first aspect or any other embodiment
thereof, the mean absolute relative difference is no more than
about 7% over the sensor session.
[0026] In an embodiment of the first aspect or any other embodiment
thereof, the mean absolute relative difference is no more than
about 5% over the sensor session.
[0027] In an embodiment of the first aspect or any other embodiment
thereof, the mean absolute relative difference is no more than
about 3% over the sensor session.
[0028] In an embodiment of the first aspect or any other embodiment
thereof, the a priori information is associated with a calibration
code.
[0029] In an embodiment of the first aspect or any other embodiment
thereof, the a priori sensitivity information is stored in the
sensor electronics prior to use of the sensor.
[0030] In a second aspect, a system is provided for implementing
the method of the first aspect or any embodiments thereof.
[0031] In a third aspect, a method is provided for calibrating
sensor data generated by a continuous analyte sensor, the method
comprising: generating sensor data using a continuous analyte
sensor; determining, with an electronic device, a plurality of
different sensitivity values of the continuous analyte sensor as a
function of time and of sensitivity information associated with a
priori information; and calibrating the sensor data based at least
in part on at least one of the plurality of different sensitivity
values.
[0032] In an embodiment of the third aspect or any other embodiment
thereof, calibrating the continuous analyte sensor is performed
iteratively throughout a substantially entire sensor session.
[0033] In an embodiment of the third aspect or any other embodiment
thereof, the plurality of different sensitivity values are stored
in a lookup table in computer memory.
[0034] In an embodiment of the third aspect or any other embodiment
thereof, determining a plurality of different sensitivity values is
performed once throughout a substantially entire sensor
session.
[0035] In an embodiment of the third aspect or any other embodiment
thereof, the a priori information is associated with at least one
predetermined sensitivity value that is associated with a
predetermined time after start of a sensor session.
[0036] In an embodiment of the third aspect or any other embodiment
thereof, the at least one predetermined sensitivity value is
associated with a correlation between a sensitivity determined from
in vitro analyte concentration measurements and a sensitivity
determined from in vivo analyte concentration measurements at the
predetermined time.
[0037] In an embodiment of the third aspect or any other embodiment
thereof, the a priori information is associated with a
predetermined sensitivity function that uses time as input.
[0038] In an embodiment of the third aspect or any other embodiment
thereof, time corresponds to time after start of a sensor
session.
[0039] In an embodiment of the third aspect or any other embodiment
thereof, time corresponds to time of manufacture or time since
manufacture.
[0040] In an embodiment of the third aspect or any other embodiment
thereof, the plurality of sensitivity values are also a function of
at least one parameter other than time.
[0041] In an embodiment of the third aspect or any other embodiment
thereof, the at least one other parameter is selected from the
group consisting of: temperature, pH, level or duration of
hydration, curing condition, an analyte concentration of a fluid
surrounding the continuous analyte sensor during startup of the
sensor, and combinations thereof.
[0042] In an embodiment of the third aspect or any other embodiment
thereof, calibrating the continuous analyte sensor is performed
without using reference blood glucose data.
[0043] In an embodiment of the third aspect or any other embodiment
thereof, the electronic device is configured to provide a level of
accuracy corresponding to a mean absolute relative difference of no
more than about 10% over a sensor session of at least about 3 days;
and wherein reference measurements associated with calculation of
the mean absolute relative difference are determined by analysis of
blood.
[0044] In an embodiment of the third aspect or any other embodiment
thereof, the sensor session is at least about 4 days.
[0045] In an embodiment of the third aspect or any other embodiment
thereof, the sensor session is at least about 5 days.
[0046] In an embodiment of the third aspect or any other embodiment
thereof, the sensor session is at least about 6 days.
[0047] In an embodiment of the third aspect or any other embodiment
thereof, the sensor session is at least about 7 days.
[0048] In an embodiment of the third aspect or any other embodiment
thereof, the sensor session is at least about 10 days.
[0049] In an embodiment of the third aspect or any other embodiment
thereof, the mean absolute relative difference is no more than
about 7% over the sensor session.
[0050] In an embodiment of the third aspect or any other embodiment
thereof, the mean absolute relative difference is no more than
about 5% over the sensor session.
[0051] In an embodiment of the third aspect or any other embodiment
thereof, the mean absolute relative difference is no more than
about 3% over the sensor session.
[0052] In an embodiment of the third aspect or any other embodiment
thereof, the a priori information is associated with a calibration
code.
[0053] In a fourth aspect, a system is provided for implementing
the method of the third aspect or any embodiments thereof.
[0054] In a fifth aspect, a method is provided for processing data
from a continuous analyte sensor, the method comprising: receiving,
with an electronic device, sensor data from a continuous analyte
sensor, the sensor data comprising at least one sensor data point;
iteratively determining a sensitivity value of the continuous
analyte sensor as a function of time and of an at least one
predetermined sensitivity value associated with a predetermined
time after start of a sensor session; forming a conversion function
based at least in part on the sensitivity value; and determining an
analyte output value by applying the conversion function to the at
least one sensor data point.
[0055] In an embodiment of the fifth aspect or any other embodiment
thereof, the iteratively determining a sensitivity of the
continuous analyte sensor is performed continuously.
[0056] In an embodiment of the fifth aspect or any other embodiment
thereof, iteratively determining a sensitivity is performed in
substantially real time.
[0057] In an embodiment of the fifth aspect or any other embodiment
thereof, the method further comprises determining a baseline of the
continuous analyte sensor, and wherein the conversion function is
based at least in part on the baseline.
[0058] In an embodiment of the fifth aspect or any other embodiment
thereof, determining a baseline of the continuous analyte sensor is
performed continuously.
[0059] In an embodiment of the fifth aspect or any other embodiment
thereof, determining a sensitivity of the continuous analyte sensor
and determining a baseline of the analyte sensor are performed at
substantially the same time.
[0060] In an embodiment of the fifth aspect or any other embodiment
thereof, the at least one predetermined sensitivity value is set at
a manufacturing facility for the continuous analyte sensor.
[0061] In an embodiment of the fifth aspect or any other embodiment
thereof, the method further comprises receiving at least one
calibration code; and applying the at least one calibration code to
the electronic device at a predetermined time after start of the
sensor session.
[0062] In an embodiment of the fifth aspect or any other embodiment
thereof, iteratively determining a sensitivity is performed at
regular intervals or performed at irregular intervals, as
determined by the at least one calibration code.
[0063] In an embodiment of the fifth aspect or any other embodiment
thereof, the at least one calibration code is associated with the
at least one predetermined sensitivity.
[0064] In an embodiment of the fifth aspect or any other embodiment
thereof, the at least one calibration code is associated with a
predetermined sensitivity function that uses time of the function
of time as input.
[0065] In an embodiment of the fifth aspect or any other embodiment
thereof, time corresponds to time after start of the sensor
session.
[0066] In an embodiment of the fifth aspect or any other embodiment
thereof, time corresponds to time of manufacture or time since
manufacture.
[0067] In an embodiment of the fifth aspect or any other embodiment
thereof, the sensitivity value of the continuous analyte sensor is
also a function of at least one other parameter.
[0068] In an embodiment of the fifth aspect or any other embodiment
thereof, the at least one other parameter is selected from the
group consisting of: temperature, pH, level or duration of
hydration, curing condition, an analyte concentration of a fluid
surrounding the continuous analyte sensor during startup of the
sensor, and combinations thereof.
[0069] In a sixth aspect, a system is provided for implementing the
method of the fifth aspect or any embodiments thereof.
[0070] In a seventh aspect, a method is provided for calibrating a
continuous analyte sensor, the method comprising: receiving sensor
data from a continuous analyte sensor; forming or receiving a
predetermined sensitivity profile corresponding to a change in
sensor sensitivity to an analyte over a substantially entire sensor
session, wherein the predetermined sensitivity profile is a
function of at least one predetermined sensitivity value associated
with a predetermined time after start of the sensor session; and
applying, with an electronic device, the sensitivity profile in
real-time calibrations.
[0071] In an embodiment of the seventh aspect or any other
embodiment thereof, the at least one predetermined sensitivity
value, the predetermined sensitivity profile, or both are set at a
manufacturing facility for the continuous analyte sensor.
[0072] In an embodiment of the seventh aspect or any other
embodiment thereof, the method further comprises receiving at least
one calibration code; and applying the at least one calibration
code to the electronic device at a predetermined time after start
of the sensor session.
[0073] In an embodiment of the seventh aspect or any other
embodiment thereof, the at least one calibration code is associated
with the at least one predetermined sensitivity.
[0074] In an embodiment of the seventh aspect or any other
embodiment thereof, the at least one calibration code is associated
with a predetermined sensitivity function that uses time as
input.
[0075] In an embodiment of the seventh aspect or any other
embodiment thereof, the sensitivity profile is a function of
time.
[0076] In an embodiment of the seventh aspect or any other
embodiment thereof, time corresponds to time after start of the
sensor session.
[0077] In an embodiment of the seventh aspect or any other
embodiment thereof, time corresponds to time of manufacture or time
since manufacture.
[0078] In an embodiment of the seventh aspect or any other
embodiment thereof, the sensitivity value is a function of time,
the predetermined sensitivity value, and at least one parameter
selected from the group consisting of: temperature, pH, level or
duration of hydration, curing condition, an analyte concentration
of a fluid surrounding the continuous analyte sensor during startup
of the sensor, and combinations thereof.
[0079] In an eighth aspect, a system is provided for implementing
the method of the seventh aspect or any embodiments thereof.
[0080] In a ninth aspect, a method is provided for processing data
from a continuous analyte sensor, the method comprising: receiving,
with an electronic device, sensor data from a continuous analyte
sensor, the sensor data comprising at least one sensor data point;
receiving or forming a sensitivity profile corresponding to a
change in sensor sensitivity over a substantially entire sensor
session; forming a conversion function based at least in part on
the sensitivity profile; and determining an analyte output value by
applying the conversion function to the at least one sensor data
point.
[0081] In an embodiment of the ninth aspect or any other embodiment
thereof, the sensitivity profile is set at a manufacturing facility
for the continuous analyte sensor.
[0082] In an embodiment of the ninth aspect or any other embodiment
thereof, the method comprises receiving at least one calibration
code; and applying the at least one calibration code to the
electronic device at a predetermined time after start of the sensor
session.
[0083] In an embodiment of the ninth aspect or any other embodiment
thereof, the at least one calibration code is associated with the
at least one predetermined sensitivity.
[0084] In an embodiment of the ninth aspect or any other embodiment
thereof, the at least one calibration code is associated with the
sensitivity profile.
[0085] In an embodiment of the ninth aspect or any other embodiment
thereof, the sensitivity profile is a function of time.
[0086] In an embodiment of the ninth aspect or any other embodiment
thereof, time corresponds to time after start of the sensor
session.
[0087] In an embodiment of the ninth aspect or any other embodiment
thereof, time corresponds to time of manufacture or time since
manufacture.
[0088] In an embodiment of the ninth aspect or any other embodiment
thereof, the sensitivity is a function of time and at least one
parameter is selected from the group consisting of: temperature,
pH, level or duration of hydration, curing condition, an analyte
concentration of a fluid surrounding the continuous analyte sensor
during startup of the sensor, and combinations thereof.
[0089] In a tenth aspect, a system is provided for implementing the
method of the ninth aspect or any embodiments thereof.
[0090] In an eleventh aspect, a system is provided for monitoring
analyte concentration in a host, the system comprising: a
continuous analyte sensor configured to measure analyte
concentration in a host and to provide factory-calibrated sensor
data, the factory-calibrated sensor data being calibrated without
reference blood glucose data; wherein the system is configured to
provide a level of accuracy corresponding to a mean absolute
relative difference of no more than about 10% over a sensor session
of at least about 3 days, wherein reference measurements associated
with calculation of the mean absolute relative difference are
determined by analysis of blood.
[0091] In an embodiment of the eleventh aspect or any other
embodiment thereof, the sensor session is at least about 4
days.
[0092] In an embodiment of the eleventh aspect or any other
embodiment thereof, the sensor session is at least about 5
days.
[0093] In an embodiment of the eleventh aspect or any other
embodiment thereof, the sensor session is at least about 6
days.
[0094] In an embodiment of the eleventh aspect or any other
embodiment thereof, the sensor session is at least about 7
days.
[0095] In an embodiment of the eleventh aspect or any other
embodiment thereof, the sensor session is at least about 10
days.
[0096] In an embodiment of the eleventh aspect or any other
embodiment thereof, the mean absolute relative difference is no
more than about 7% over the sensor session.
[0097] In an embodiment of the eleventh aspect or any other
embodiment thereof, the mean absolute relative difference is no
more than about 5% over the sensor session.
[0098] In an embodiment of the eleventh aspect or any other
embodiment thereof, the mean absolute relative difference is no
more than about 3% over the sensor session.
[0099] In a twelfth aspect, a method for determining a property of
a continuous analyte sensor, the method comprising: applying a bias
voltage to an analyte sensor; applying a voltage step above the
bias voltage to the analyte sensor; measuring, using sensor
electronics, a signal response of the voltage step; determining,
using the sensor electronics, a peak current of the signal
response; determining, using the sensor electronics, a property of
the sensor by correlating the peak current to a predetermined
relationship.
[0100] In an embodiment of the twelfth aspect or any other
embodiment thereof, correlating the peak current to the
predetermined relationship comprises calculating an impedance of
the sensor based on the peak current and correlating the sensor
impedance to the predetermined relationship.
[0101] In an embodiment of the twelfth aspect or any other
embodiment thereof, the property of the sensor is a sensitivity of
the sensor or a temperature of the sensor.
[0102] In an embodiment of the twelfth aspect or any other
embodiment thereof, the peak current is a difference between a
magnitude of the response prior to the voltage step and a magnitude
of the largest measured response resulting from the voltage
step.
[0103] In an embodiment of the twelfth aspect or any other
embodiment thereof, the predetermined relationship is an
impedance-to-sensor sensitivity relationship, and wherein the
property of the sensor is a sensitivity of the sensor.
[0104] In an embodiment of the twelfth aspect or any other
embodiment thereof, the method further comprises compensating
sensor data using the determined property of the sensor.
[0105] In an embodiment of the twelfth aspect or any other
embodiment thereof, the compensating comprises correlating a
predetermined relationship of the peak current to sensor
sensitivity or change in sensor sensitivity and modifying a value
or values of the sensor data responsive to the correlated sensor
sensitivity or change in sensor sensitivity.
[0106] In an embodiment of the twelfth aspect or any other
embodiment thereof, predetermined relationship is a linear
relationship over time of use of the analyte sensor.
[0107] In an embodiment of the twelfth aspect or any other
embodiment thereof, the predetermined relationship is a non-linear
relationship over time of use of the analyte sensor.
[0108] In an embodiment of the twelfth aspect or any other
embodiment thereof, wherein the predetermined relationship is
determined by prior testing of sensors similar to the analyte
sensor.
[0109] In an embodiment of the thirteenth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processor of the sensor system, cause the sensor system to
implement the method of the twelfth aspect or any embodiment
thereof.
[0110] In a fourteenth aspect, a method is provided for calibrating
an analyte sensor, the method comprising: applying a time-varying
signal to the analyte sensor; measuring a signal response to the
applied signal; determining, using sensor electronics, a
sensitivity of the analyte sensor, the determining comprising
correlating at least one property of the signal response to a
predetermined sensor sensitivity profile; and generating, using
sensor electronics, estimated analyte concentration values using
the determined sensitivity and sensor data generated by the analyte
sensor.
[0111] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the sensitivity profile comprises varying
sensitivity values over time since implantation of the sensor.
[0112] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the predetermined sensitivity profile comprises
a plurality of sensitivity values.
[0113] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the predetermined sensitivity profile is based
on sensor sensitivity data generated from studying sensitivity
changes of analyte sensors similar to the analyte sensor.
[0114] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the method further comprises applying a bias
voltage to the sensor, wherein the time-varying signal comprises a
step voltage above the bias voltage or a sine wave overlaying a
voltage bias voltage.
[0115] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the determining further comprises calculating
an impedance value based on the measured signal response and
correlating the impedance value to a sensitivity value of the
predetermined sensitivity profile.
[0116] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the method further comprises applying a DC bias
voltage to the sensor to generate sensor data, wherein the
estimating analyte concentration values includes generating
corrected sensor data using the determined sensitivity.
[0117] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the method further comprises applying a
conversion function to the corrected sensor data to generate the
estimated analyte concentration values.
[0118] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the method further comprises forming a
conversion function based at least in part of the determined
sensitivity, and wherein the conversion function is applied to the
sensor data to generate the estimated analyte concentration
values.
[0119] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the property is a peak current value of the
signal response.
[0120] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the determining further comprises using at
least one of performing a Fast Fourier Transform on the signal
response data, integrating at least a portion of a curve of the
signal response, and determining a peak current of the signal
response.
[0121] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the determining further comprises selecting the
predetermined sensitivity profile based on the determined sensor
property from a plurality of different predetermined sensitivity
profiles.
[0122] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the selecting includes performing a data
association analysis to determine a correlation between the
determined sensor property and each of the plurality of different
predetermined sensitivity profiles and wherein the selected
predetermined sensitivity profile has the highest correlation.
[0123] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the method further comprises generating
estimated analyte concentration values using the selected
sensitivity profile.
[0124] In an embodiment of the fourteenth aspect or any other
embodiment thereof, the method further comprises determining a
second sensitivity value using the selected sensitivity profile,
wherein a first set of estimated analyte concentration values is
generated using the determined sensitivity value and sensor data
associated with a first time period, and wherein a second set of
concentration values is generated using the second sensitivity
value and sensor data associated with a second time period.
[0125] In a fifteenth aspect, a sensor system is provided for
implementing the method of the fourteenth aspect or any embodiments
thereof.
[0126] In an embodiment of the fifteenth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the fourteenth aspect or any embodiment
thereof.
[0127] In a sixteenth aspect, a method is provided for determining
whether an analyte sensor system is functioning properly, the
method comprising: applying a stimulus signal to the analyte
sensor; measuring a response to the stimulus signal; estimating a
value of a sensor property based on the signal response;
correlating the sensor property value with a predetermined
relationship of the sensor property and a predetermined sensor
sensitivity profile; and initiating an error routine if the
correlation does not exceed a predetermined correlation
threshold.
[0128] In an embodiment of the sixteenth aspect or any other
embodiment thereof, correlating includes performing a data
association analysis.
[0129] In an embodiment of the sixteenth aspect or any other
embodiment thereof, the error routine comprises displaying a
message to a user indicating that the analyte sensor is not
functioning properly.
[0130] In an embodiment of the sixteenth aspect or any other
embodiment thereof, the sensor property is an impedance of the
sensor membrane.
[0131] In a seventeenth aspect, a sensor system is provided
configured to implement the method of the sixteenth aspect or any
embodiment thereof.
[0132] In an embodiment of the seventeenth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the sixteenth aspect or any embodiment
thereof.
[0133] In an eighteenth aspect, a method is provided for
determining a temperature associated with an analyte sensor, the
method comprising: applying a stimulus signal to the analyte
sensor; measuring a signal response of the signal; and determining
a temperature associated with of the analyte sensor, the
determining comprising correlating at least one property of the
signal response to a predetermined relationship of the sensor
property to temperature.
[0134] In an embodiment of the eighteenth aspect or any other
embodiment thereof, the method further comprises generating
estimated analyte concentration values using the determined
temperature and sensor data generated from the analyst sensor.
[0135] In an embodiment of the eighteenth aspect or any other
embodiment thereof, the generating includes compensating the sensor
data using the determined temperature and converting the
compensated sensor data to the generated estimated analyte values
using a conversion function.
[0136] In an embodiment of the eighteenth aspect or any other
embodiment thereof, the generating includes forming or modifying a
conversion function using the determined temperature and converting
the sensor data to the generated estimated analyte values using the
formed or modified conversion function.
[0137] In an embodiment of the eighteenth aspect or any other
embodiment thereof, the method further comprises measuring a
temperature using a second sensor, wherein the determining further
comprises using the measured temperature to determine the
temperature associated with the analyte sensor.
[0138] In an embodiment of the eighteenth aspect or any other
embodiment thereof, the second sensor is a thermistor.
[0139] In a nineteenth aspect, a sensor system is provided
configured to implement the methods of the eighteenth aspect or any
embodiment thereof.
[0140] In an embodiment of the nineteenth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the eighteenth aspect or any embodiment
thereof.
[0141] In a twentieth aspect, a method is provided for determining
moisture ingress in an electronic sensor system, comprising:
applying a stimulus signal having a particular frequency or a
signal comprising a spectrum of frequencies to an analyte sensor;
measuring a response to the stimulus signal; calculating, using
sensor electronics, an impedance based on the measured signal
response; determining, using sensor electronics, whether the
impedance falls within a predefined level corresponding to moisture
ingress; initiating, using sensor electronics, an error routine if
the impedance exceeds one or both of the respective predefined
levels
[0142] In an embodiment of the twentieth aspect or any other
embodiment thereof, the method further comprises the error routine
includes one or more of triggering an audible alarm and a visual
alarm on a display screen to alert a user that the sensor system
may not be functioning properly.
[0143] In an embodiment of the twentieth aspect or any other
embodiment thereof, the stimulus signal has a predetermined
frequency.
[0144] In an embodiment of the twentieth aspect or any other
embodiment thereof, the stimulus signal comprises a spectrum of
frequencies.
[0145] In an embodiment of the twentieth aspect or any other
embodiment thereof, the calculated impedance comprises a magnitude
value and a phase value, and wherein the determination comprises
comparing the impedance magnitude value to a predefined impedance
magnitude threshold and the phase value to a predefined phase
threshold.
[0146] In an embodiment of the twentieth aspect or any other
embodiment thereof, the calculated impedance is a complex impedance
value.
[0147] In a twenty-first aspect, a sensor system is provided
configured to implement the methods of the twentieth aspect or any
embodiment thereof.
[0148] In an embodiment of the twenty-first aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the twentieth aspect or any embodiment
thereof.
[0149] In an twenty-second aspect, a method is provided for
determining membrane damage of an analyte sensor using a sensor
system, comprising: applying a stimulus signal to an analyte
sensor; measuring a response to the stimulus signal; calculating,
using sensor electronics, an impedance based on the signal
response; determining, using the sensor electronics, whether the
impedance falls within a predefined level corresponding to membrane
damage; and initiating, using the sensor electronics, an error
routine if the impedance exceeds the predefined level.
[0150] In an embodiment of the twenty-second aspect or any other
embodiment thereof, the error routine includes triggering one or
more of an audible alarm and a visual alarm on a display
screen.
[0151] In an embodiment of the twenty-second aspect or any other
embodiment thereof, the stimulus signal has a predetermined
frequency.
[0152] In an embodiment of the twenty-second aspect or any other
embodiment thereof, the stimulus signal comprises a spectrum of
frequencies.
[0153] In an embodiment of the twenty-second aspect or any other
embodiment thereof, the calculated impedance comprises a magnitude
value and a phase value, and wherein the determination comprises
comparing the impedance magnitude value to a predefined impedance
magnitude threshold and the phase value to a predefined phase
threshold.
[0154] In an embodiment of the twenty-second aspect or any other
embodiment thereof, the calculated impedance is a complex impedance
value.
[0155] In a twenty-third aspect, a sensor system is provided
configured to implement the methods of the twenty-second aspect or
any embodiment thereof.
[0156] In an embodiment of the twenty-third aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the twenty-second aspect or any embodiment
thereof.
[0157] In a twenty-fourth aspect, a method for determining reuse of
an analyte sensor, comprising, applying a stimulus signal to an
analyte sensor; measuring a response of the stimulus signal;
calculating an impedance response based on the response; comparing
the calculated impedance to a predetermined threshold; initiating a
sensor reuse routine if it is determined that the impedance exceeds
the threshold.
[0158] In an embodiment of the twenty-fourth aspect or any other
embodiment thereof, the sensor reuse routine includes triggering an
audible and/or visual alarm notifying the user of improper sensor
reuse.
[0159] In an embodiment of the twenty-fourth aspect or any other
embodiment thereof, the sensor reuse routing includes causing a
sensor system to fully or partially shut down and/or cease display
of sensor data on a user interface of the sensor system.
[0160] In a twenty-fifth aspect, a sensor system is provided
configured to implement the methods of the twenty-fourth aspect or
any embodiments thereof.
[0161] In an embodiment of the twenty-fifth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the twenty-fourth aspect or any embodiments
thereof.
[0162] In a twenty-sixth aspect, a system is provided for
determining reuse of an analyte sensor, comprising, applying a
stimulus signal to an analyte sensor; measuring a response of the
stimulus signal; calculating an impedance based on the response;
using a data association function to determine a correlation of the
calculated impedance to one or more recorded impedance values; and
initiating a sensor reuse routine if it is determined that the
correlation is above a predetermined threshold.
[0163] In an embodiment of the twenty-sixth aspect or any other
embodiment thereof, the sensor reuse routine includes triggering an
audible and/or visual alarm notifying the user of improper sensor
reuse.
[0164] In an embodiment of the twenty-sixth aspect or any other
embodiment thereof, the sensor reuse routing includes causing a
sensor system to fully or partially shut down and/or cease display
of sensor data on a user interface of the sensor system.
[0165] In an embodiment of the twenty-sixth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the twenty-fifth aspect.
[0166] In a twenty-seventh aspect, a method is provided for
applying an overpotential to an analyte sensor, comprising,
applying a stimulus signal to an analyte sensor; measuring a
response of the stimulus signal; determining a sensor sensitivity
or change in sensor sensitivity based on the response; and applying
an over potential to the sensor based on the determined sensitivity
or sensitivity change.
[0167] In an embodiment of the twenty-seventh aspect or any other
embodiment thereof, the determining further comprises calculating
an impedance based on the response and determining the sensitivity
or sensitivity change based on the impedance.
[0168] In an embodiment of the twenty-seventh aspect or any other
embodiment thereof, the determining the sensitivity or sensitivity
change further comprises correlating the impedance to a
predetermined impedance to sensitivity relationship.
[0169] In an embodiment of the twenty-seventh aspect or any other
embodiment thereof, the applying comprises determining or modifying
a length of time the over potential is applied to the sensor.
[0170] In an embodiment of the twenty-seventh aspect or any other
embodiment thereof, the applying comprises determining or modifying
a magnitude of the over potential applied to the sensor.
[0171] In a twenty-eighth aspect, a sensor system is provided
configured to implement the methods of the twenty-seventh aspect or
any embodiments thereof.
[0172] In an embodiment of the twenty-eighth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the twenty-seventh aspect or any embodiment
thereof.
[0173] In a twenty-ninth aspect, a method is provided for
determining a property of a continuous analyte sensor, the method
comprising: applying a stimulus signal to a first analyte sensor
having a first working electrode and a first reference electrode;
measuring a signal response of the stimulus signal using a second
analyte sensor having a second working electrode and a second
reference electrode; and determining a property of the first sensor
by correlating the response to a predetermined relationship.
[0174] In an embodiment of the twenty-ninth aspect or any other
embodiment thereof, the method further comprises generating sensor
data by applying a bias voltage to the first working electrode and
measuring a response to the bias voltage.
[0175] In an embodiment of the twenty-ninth aspect or any other
embodiment thereof, the method further comprises calibrating the
sensor data using the determined property.
[0176] In an embodiment of the twenty-ninth aspect or any other
embodiment thereof, the determined property is one of an impedance
and a temperature.
[0177] In an embodiment of the twenty-ninth aspect or any other
embodiment thereof, the method further comprises determining sensor
membrane damage using the determined property.
[0178] In an embodiment of the twenty-ninth aspect or any other
embodiment thereof, the method further comprises determining
moisture ingress in a sensor system encompassing the first and
second analyte sensors using the determined property.
[0179] In a thirtieth aspect, a sensor system is provided
configured to implement the methods of one of the twenty-ninth
aspect or any embodiments thereof.
[0180] In an embodiment of the thirtieth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the twenty-ninth aspect or any embodiments
thereof.
[0181] In a thirty-first aspect, a method is provided for
determining a scaling used in a continuous analyte sensor system,
the method comprising: applying a first stimulus signal to a first
working electrode of an analyte sensor; measuring a response to the
first stimulus signal; applying a second stimulus signal to a
second working electrode of the analyte sensor; measuring a
response to the first stimulus signal; determining, using sensor
electronics, a scaling factor based on the measured responses to
the first and second stimulus signals; and using the scaling factor
to generate estimated analyte values based on sensor data generated
by the analyte sensor.
[0182] In an embodiment of the thirty-first aspect or any other
embodiment thereof, the method further comprises the method is
performed periodically.
[0183] In an embodiment of the thirty-first aspect or any other
embodiment thereof, the determining comprises calculating a first
impedance using the response to the first stimulus signal and
calculating a second impedance using the response to the second
stimulus signal, and wherein the scaling factor is a ratio of the
first impedance and the second impedance.
[0184] In an embodiment of the thirty-first aspect or any other
embodiment thereof, the first working electrode has a membrane
comprising an enzyme configured to react with the analyte and the
second working electrode has a membrane does not have the
enzyme.
[0185] In an embodiment of the thirty-first aspect or any other
embodiment thereof, determining the scaling factor comprises
updating a previous scaling factor based on the measured responses
to the first and second stimulus signals.
[0186] In an embodiment of the thirty-first aspect or any other
embodiment thereof, scaling factor is an acetaminophen scaling
factor, wherein the method further comprises updating a further
scaling factor based on the acetaminophen scaling factor, and
wherein the further scaling factor applied to the sensor data to
generate the estimated analyte values.
[0187] In a thirty-second aspect, a sensor system is provided
configured to implement the methods of the thirty-first aspect or
any embodiments thereof.
[0188] In an embodiment of the thirty-second aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the thirty-first aspect or any embodiment
thereof.
[0189] In a thirty-third aspect, a method is provided for
calibrating an analyte sensor, the method comprising: applying a
predetermined signal to an analyte sensor; measuring a response to
the applied signal; determining, using sensor electronics, a change
in impedance associated with a membrane of the analyte sensor based
on the measured response; calculating a sensitivity change of the
analyte sensor based on the determined impedance; calculating a
corrected sensitivity based on the calculated sensitivity change
and a previously used sensitivity of the analyte sensor; and
generating estimated analyte values using the corrected
sensitivity.
[0190] In an embodiment of the thirty-third aspect or any other
embodiment thereof, calculating the sensitivity change comprises
applying a non-linear compensation function.
[0191] In an embodiment of the thirty-third aspect or any other
embodiment thereof, the non-linear compensation function is
expressed as the equation .DELTA.S=(a*log(t)+b)*.DELTA.I, where
.DELTA.S is the change in sensitivity, t is a time since
calibration of the analyte sensor, .DELTA.I is the determined
change in impedance, and a and b are a predetermined
coefficients.
[0192] In an embodiment of the thirty-third aspect or any other
embodiment thereof, a and b are determined by prior testing of
similar analyte sensors.
[0193] In a thirty-fourth aspect, a sensor system is provided
configured to implement the methods of the thirty-third aspect or
any embodiments thereof.
[0194] In an embodiment of the thirty-fourth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the thirty-third aspect or any embodiments
thereof.
[0195] In a thirty-fifth aspect, a method is provided for
calibrating an analyte sensor, comprising: generating sensor data
using a subcutaneous analyte sensor; forming or modifying a
conversion function, wherein pre-implant information, internal
diagnostic information, and/or external reference information are
used as inputs to form or modify the conversion function; and
calibrating the sensor data using the conversion function.
[0196] In an embodiment of the thirty-fifth aspect or any other
embodiment thereof, the pre-implant information comprises
information selected from the group consisting of: a predetermined
sensitivity profile associated with the analyte sensor, a
previously determined relationship between a measured sensor
property and sensor sensitivity, one or more previously determined
relationships between a measured sensor property and sensor
temperature; sensor data obtained from previously used analyte
sensors, a calibration code associated with the analyte sensor,
patient specific relationships between the analyte sensor and one
or more of sensitivity, baseline, drift and impedance, information
indicative of a site of sensor implantation; time since manufacture
of the analyte sensor, and information indicative of the analyte
being exposed to temperature or humidity.
[0197] In an embodiment of the thirty-fifth aspect or any other
embodiment thereof, the internal diagnostic information comprises
information selected from the group consisting of: stimulus signal
output; sensor data indicative of an analyte concentration measured
by the sensor; temperature measurements using the sensor or a
separate sensor; sensor data generated by a redundant sensor, where
the redundant sensor is designed to be substantially the same as
analyte sensor; sensor data generated by an auxiliary sensors,
where the auxiliary sensor is having a different modality as the
analyte sensor; a time since the sensor was implanted or connected
to sensor electronics coupled to the sensor; data representative of
a pressure on the sensor or sensor system generated by a pressure
sensor; data generated by an accelerometer; a measure of moisture
ingress; and a measure of noise in an analyte concentration
signal.
[0198] In an embodiment of the thirty-fifth aspect or any other
embodiment thereof, the reference information comprises information
selected from the group consisting of: real-time and/or prior
analyte concentration information obtained from a reference
monitor, information relating to a type/brand of reference monitor
used to provide reference data; information relating to an amount
of carbohydrate consumed by a user; information received from a
medicament delivery device, glucagon sensitivity information, and
information gathered from population based data.
[0199] In a thirty-sixth aspect, a sensor system is provided
configured to implement the methods of the thirty-fifth aspect or
any embodiments thereof.
[0200] In an embodiment of the thirty-sixth aspect or any other
embodiment thereof, the sensor system comprises instructions stored
in computer memory, wherein the instructions, when executed by one
or more processors of the sensor system, cause the sensor system to
implement the method of the thirty-fifth aspect or any embodiment
thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
[0201] These and other features and advantages will be appreciated,
as they become better understood by reference to the following
detailed description when considered in connection with the
accompanying drawings, wherein:
[0202] FIG. 1A illustrates a schematic diagram of sensor
sensitivity as a function of time during a sensor session, in
accordance with one embodiment; FIG. 1B illustrates schematic
diagrams of conversion functions at different time periods of a
sensor session, in accordance with the embodiment of FIG. 1A.
[0203] FIGS. 2A-2B and FIGS. 2C-2D collectively illustrate
different embodiments of processes for generating a sensor
sensitivity profile.
[0204] FIG. 3A is a Bland-Altman plot illustrating differences
between YSI reference measurements and certain in vivo continuous
analyte sensors that were factory calibrated, in accordance with
one embodiment; FIG. 3B is a Clarke error grid associated with data
from the continuous analyte sensors associated with FIG. 3A.
[0205] FIG. 4 illustrates data from one study that examined the
accuracy level of continuous analyte sensors that accepted one
reference measurement about one hour after insertion into patients,
in accordance with one embodiment.
[0206] FIG. 5 illustrates a diagram showing different types of
information that can be input into the sensor system to define the
sensor sensitivity profile over time
[0207] FIG. 6 illustrates a schematic diagram of sensor sensitivity
as a function of time between completion of sensor fabrication and
the start of the sensor session, in accordance with one
embodiment.
[0208] FIG. 7A is a schematic diagram depicting distribution curves
of sensor sensitivity corresponding to the Bayesian learning
process, in accordance with one embodiment; FIG. 7B is a schematic
diagram depicting confidence levels, associated with the sensor
sensitivity profile, that correspond with the distribution curves
shown in FIG. 7A.
[0209] FIG. 8 illustrates a graph that provides a comparison
between an estimated glucose equivalent baseline and detected
glucose equivalent baseline, in accordance with one study.
[0210] FIG. 9 is a schematic of a model sensor circuit in
accordance with one embodiment.
[0211] FIG. 10 is a Bode plot of an analyte sensor in accordance
with one embodiment.
[0212] FIG. 11 is a flowchart describing a process for determining
an impedance of a sensor in accordance with one embodiment.
[0213] FIG. 12 is a flowchart describing a process for determining
an impedance of a sensor based on a derivative response in
accordance with one embodiment.
[0214] FIG. 13 is a flowchart describing a process for determining
an impedance of a sensor based on a peak current response in
accordance with one embodiment.
[0215] FIG. 14A illustrates a step voltage applied to a sensor and
FIG. 14B illustrates a response to the step voltage in accordance
with one embodiment.
[0216] FIG. 15 is a flowchart describing a process for estimating
analyte concentration values using a corrected signal based on an
impedance measurement in accordance with one embodiment.
[0217] FIG. 16 is a flowchart describing a process for estimating
analyte concentration values using a predetermined sensitivity
profile selected based on an impedance measurement in accordance
with one embodiment.
[0218] FIG. 17 is a flowchart describing a process for determining
an error based whether a sensitivity determined using an impedance
measurement sufficiently corresponds to a predetermined sensitivity
in accordance with one embodiment.
[0219] FIG. 18 is a flowchart describing a process for determining
a temperature associated with a sensor by correlating an impedance
measurement to a predetermined temperature-to-impedance
relationship in accordance with one embodiment.
[0220] FIG. 19 is a flowchart describing a process for determining
moisture egress in sensor electronics associates with an analyte
sensor accordance with one embodiment.
[0221] FIG. 20 is a flowchart describing a process for determining
membrane damage associated with an analyte sensor in accordance
with one embodiment.
[0222] FIG. 21 is a flowchart describing a first process for
determining sensor reuse associated in accordance with one
embodiment.
[0223] FIG. 22 is a flowchart describing a second process for
determining sensor reuse associated in accordance with one
embodiment.
[0224] FIG. 23 is a schematic of a dual-electrode configuration
used to determine sensor properties in accordance with one
embodiment.
[0225] FIG. 24 is a diagram of a calibration process that uses
various inputs to form a conversion function in accordance with one
embodiment.
[0226] FIGS. 25-49 collectively illustrate results of studies using
stimulus signals to determine sensor properties.
[0227] FIG. 50 is a flowchart describing a process for generating
estimated analyte values using a compensation algorithm based on a
measured change in impedance in accordance with one embodiment.
[0228] FIGS. 51-53 are graphs of studies comparing use of
compensation algorithms based on measured changes in impedance.
DETAILED DESCRIPTION
Definitions
[0229] In order to facilitate an understanding of the embodiments
described herein, a number of terms are defined below.
[0230] The term "analyte," as used herein, is a broad term, and is
to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is are not to be limited to a
special or customized meaning), and refers without limitation to a
substance or chemical constituent in a biological fluid (for
example, blood, interstitial fluid, cerebral spinal fluid, lymph
fluid or urine) that can be analyzed. Analytes may include
naturally occurring substances, artificial substances, metabolites,
and/or reaction products. In some embodiments, the analyte for
measurement by the sensor heads, devices, and methods disclosed
herein is glucose. However, other analytes are contemplated as
well, including but not limited to acarboxyprothrombin;
acylcarnitine; adenine phosphoribosyl transferase; adenosine
deaminase; albumin; alpha-fetoprotein; amino acid profiles
(arginine (Krebs cycle), histidine/urocanic acid, homocysteine,
phenylalanine/tyrosine, tryptophan); andrenostenedione; antipyrine;
arabinitol enantiomers; arginase; benzoylecgonine (cocaine);
biotinidase; biopterin; c-reactive protein; carnitine; carnosinase;
CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine;
cholesterol; cholinesterase; conjugated 1-.beta. hydroxy-cholic
acid; cortisol; creatine kinase; creatine kinase MM isoenzyme;
cyclosporin A; d-penicillamine; de-ethylchloroquine;
dehydroepiandrosterone sulfate; DNA (acetylator polymorphism,
alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis,
Duchenne/Becker muscular dystrophy, analyte-6-phosphate
dehydrogenase, hemoglobinopathies, A,S,C,E, D-Punjab,
beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber
hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,
sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;
dihydropteridine reductase; diptheria/tetanus antitoxin;
erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty
acids/acylglycines; free .beta.-human chorionic gonadotropin; free
erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine
(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;
galactose-1-phosphate uridyltransferase; gentamicin;
analyte-6-phosphate dehydrogenase; glutathione; glutathione
perioxidase; glycocholic acid; glycosylated hemoglobin;
halofantrine; hemoglobin variants; hexosaminidase A; human
erythrocyte carbonic anhydrase I; 17 alpha-hydroxyprogesterone;
hypoxanthine phosphoribosyl transferase; immunoreactive trypsin;
lactate; lead; lipoproteins ((a), B/A-1, .beta.); lysozyme;
mefloquine; netilmicin; phenobarbitone; phenytoin;
phytanic/pristanic acid; progesterone; prolactin; prolidase; purine
nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);
selenium; serum pancreatic lipase; sissomicin; somatomedin C;
specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta
antibody, arbovirus, Aujeszky's disease virus, dengue virus,
Dracunculus medinensis, Echinococcus granulosus, Entamoeba
histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori,
hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease),
influenza virus, Leishmania donovani, leptospira,
measles/mumps/rubella, Mycobacterium leprae, Mycoplasma pneumoniae,
Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium
falciparum, poliovirus, Pseudomonas aeruginosa, respiratory
syncytial virus, Rickettsia (scrub typhus), Schistosoma mansoni,
Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi/rangeli,
vesicular stomatis virus, Wuchereria bancrofti, yellow fever
virus); specific antigens (hepatitis B virus, HIV-1);
succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH);
thyroxine (T4); thyroxine-binding globulin; trace elements;
transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I
synthase; vitamin A; white blood cells; and zinc protoporphyrin.
Salts, sugar, protein, fat, vitamins and hormones naturally
occurring in blood or interstitial fluids may also constitute
analytes in certain embodiments. The analyte may be naturally
present in the biological fluid, for example, a metabolic product,
a hormone, an antigen, an antibody, and the like. Alternatively,
the analyte may be introduced into the body, for example, a
contrast agent for imaging, a radioisotope, a chemical agent, a
fluorocarbon-based synthetic blood, or a drug or pharmaceutical
composition, including but not limited to insulin; ethanol;
cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants
(nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons,
hydrocarbons); cocaine (crack cocaine); stimulants (amphetamines,
methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState,
Voranil, Sandrex, Plegine); depressants (barbituates, methaqualone,
tranquilizers such as Valium, Librium, Miltown, Serax, Equanil,
Tranxene); hallucinogens (phencyclidine, lysergic acid, mescaline,
peyote, psilocybin); narcotics (heroin, codeine, morphine, opium,
meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon,
Talwin, Lomotil); designer drugs (analogs of fentanyl, meperidine,
amphetamines, methamphetamines, and phencyclidine, for example,
Ecstasy); anabolic steroids; and nicotine. The metabolic products
of drugs and pharmaceutical compositions are also contemplated
analytes. Analytes such as neurochemicals and other chemicals
generated within the body may also be analyzed, such as, for
example, ascorbic acid, uric acid, dopamine, noradrenaline,
3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),
Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and
5-Hydroxyindoleacetic acid (FHIAA).
[0231] The terms "continuous analyte sensor," and "continuous
glucose sensor," as used herein, are broad terms, and are to be
given their ordinary and customary meaning to a person of ordinary
skill in the art (and are not to be limited to a special or
customized meaning), and refer without limitation to a device that
continuously or continually measures a concentration of an
analyte/glucose and/or calibrates the device (e.g., by continuously
or continually adjusting or determining the sensor's sensitivity
and background), for example, at time intervals ranging from
fractions of a second up to, for example, 1, 2, or 5 minutes, or
longer.
[0232] The term "biological sample," as used herein, is a broad
term, and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and refers without limitation to
sample derived from the body or tissue of a host, such as, for
example, blood, interstitial fluid, spinal fluid, saliva, urine,
tears, sweat, or other like fluids.
[0233] The term "host," as used herein, is a broad term, and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to animals,
including humans.
[0234] The term "membrane system," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to a
permeable or semi-permeable membrane that can be comprised of two
or more domains and is typically constructed of materials of a few
microns thickness or more, which may be permeable to oxygen and are
optionally permeable to glucose. In one example, the membrane
system comprises an immobilized glucose oxidase enzyme, which
enables an electrochemical reaction to occur to measure a
concentration of glucose.
[0235] The term "domain," as used herein, is a broad term, and is
to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to regions of a
membrane that can be layers, uniform or non-uniform gradients (for
example, anisotropic), functional aspects of a material, or
provided as portions of the membrane.
[0236] The term "sensing region," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to the region
of a monitoring device responsible for the detection of a
particular analyte. In one embodiment, the sensing region generally
comprises a non-conductive body, at least one electrode, a
reference electrode and a optionally a counter electrode passing
through and secured within the body forming an electroactive
surface at one location on the body and an electronic connection at
another location on the body, and a membrane system affixed to the
body and covering the electroactive surface.
[0237] The term "electroactive surface," as used herein, is a broad
term, and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and refers without limitation to
the surface of an electrode where an electrochemical reaction takes
place. In one embodiment, a working electrode measures hydrogen
peroxide (H.sub.2O.sub.2) creating a measurable electronic
current.
[0238] The term "baseline," as used herein is a broad term, and is
to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to the component
of an analyte sensor signal that is not related to the analyte
concentration. In one example of a glucose sensor, the baseline is
composed substantially of signal contribution due to factors other
than glucose (for example, interfering species,
non-reaction-related hydrogen peroxide, or other electroactive
species with an oxidation potential that overlaps with hydrogen
peroxide). In some embodiments wherein a calibration is defined by
solving for the equation y=mx+b, the value of b represents the
baseline of the signal. In certain embodiments, the value of b
(i.e., the baseline) can be zero or about zero. This can be the
result of a baseline-subtracting electrode or low bias potential
settings, for example. As a result, for these embodiments,
calibration can be defined by solving for the equation y=mx.
[0239] The term "inactive enzyme," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to an enzyme
(e.g., glucose oxidase, GOx) that has been rendered inactive (e.g.,
by denaturing of the enzyme) and has substantially no enzymatic
activity. Enzymes can be inactivated using a variety of techniques
known in the art, such as but not limited to heating, freeze-thaw,
denaturing in organic solvent, acids or bases, cross-linking,
genetically changing enzymatically critical amino acids, and the
like. In some embodiments, a solution containing active enzyme can
be applied to the sensor, and the applied enzyme subsequently
inactivated by heating or treatment with an inactivating
solvent.
[0240] The term "non-enzymatic," as used herein is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to a lack of
enzyme activity. In some embodiments, a "non-enzymatic" membrane
portion contains no enzyme; while in other embodiments, the
"non-enzymatic" membrane portion contains inactive enzyme. In some
embodiments, an enzyme solution containing inactive enzyme or no
enzyme is applied.
[0241] The term "substantially," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to being
largely but not necessarily wholly that which is specified.
[0242] The term "about," as used herein, is a broad term, and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and when associated with any numerical values
or ranges, refers without limitation to the understanding that the
amount or condition the terms modify can vary some beyond the
stated amount so long as the function of the disclosure is
realized.
[0243] The term "ROM," as used herein, is a broad term, and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to read-only
memory, which is a type of data storage device manufactured with
fixed contents. ROM is broad enough to include EEPROM, for example,
which is electrically erasable programmable read-only memory
(ROM).
[0244] The term "RAM," as used herein, is a broad term, and is to
be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to a data
storage device for which the order of access to different locations
does not affect the speed of access. RAM is broad enough to include
SRAM, for example, which is static random access memory that
retains data bits in its memory as long as power is being
supplied.
[0245] The term "A/D Converter," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to hardware
and/or software that converts analog electrical signals into
corresponding digital signals.
[0246] The terms "raw data stream" and "data stream," as used
herein, are broad terms, and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and are
not to be limited to a special or customized meaning), and refer
without limitation to an analog or digital signal directly related
to the analyte concentration measured by the analyte sensor. In one
example, the raw data stream is digital data in counts converted by
an A/D converter from an analog signal (for example, voltage or
amps) representative of an analyte concentration. The terms broadly
encompass a plurality of time spaced data points from a
substantially continuous analyte sensor, which comprises individual
measurements taken at time intervals ranging from fractions of a
second up to, for example, 1, 2, or 5 minutes or longer.
[0247] The term "counts," as used herein, is a broad term, and is
to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to a unit of
measurement of a digital signal. In one example, a raw data stream
measured in counts is directly related to a voltage (for example,
converted by an A/D converter), which is directly related to
current from a working electrode.
[0248] The term "sensor electronics," as used herein, is a broad
term, and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and refers without limitation to
the components (for example, hardware and/or software) of a device
configured to process data. In the case of an analyte sensor, the
data includes biological information obtained by a sensor regarding
the concentration of the analyte in a biological fluid. U.S. Pat.
Nos. 4,757,022, 5,497,772 and 4,787,398 describe suitable
electronic circuits that can be utilized with devices of certain
embodiments.
[0249] The term "potentiostat," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to an
electrical system that applies a potential between the working and
reference electrodes of a two- or three-electrode cell at a preset
value and measures the current flow through the working electrode.
The potentiostat forces whatever current is necessary to flow
between the working and counter electrodes to keep the desired
potential, as long as the needed cell voltage and current do not
exceed the compliance limits of the potentiostat.
[0250] The term "operably connected," as used herein, is a broad
term, and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and refers without limitation to
one or more components being linked to another component(s) in a
manner that allows transmission of signals between the components.
For example, one or more electrodes can be used to detect the
amount of glucose in a sample and convert that information into a
signal; the signal can then be transmitted to an electronic
circuit. In this case, the electrode is "operably linked" to the
electronic circuit. These terms are broad enough to include wired
and wireless connectivity.
[0251] The term "filtering," as used herein, is a broad term, and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to modification
of a set of data to make it smoother and more continuous and remove
or diminish outlying points, for example, by performing a moving
average of the raw data stream.
[0252] The term "algorithm," as used herein, is a broad term, and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to the
computational processes (for example, programs) involved in
transforming information from one state to another, for example
using computer processing.
[0253] The term "calibration," as used herein, is a broad term, and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to the process
of determining the graduation of a sensor giving quantitative
measurements (e.g., analyte concentration). As an example,
calibration may be updated or recalibrated over time to account for
changes associated with the sensor, such as changes in sensor
sensitivity and sensor background. In addition, calibration of the
sensor can involve, automatic, self-calibration, e.g., without
using reference analyte values after point of use.
[0254] The terms "sensor data," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to data
received from a continuous analyte sensor, including one or more
time-spaced sensor data points.
[0255] The terms "reference analyte values" and "reference data,"
as used herein, are broad terms, and are to be given their ordinary
and customary meaning to a person of ordinary skill in the art (and
are not to be limited to a special or customized meaning), and
refer without limitation to reference data from a reference analyte
monitor, such as a blood glucose meter, or the like, including one
or more reference data points. In some embodiments, the reference
glucose values are obtained from a self-monitored blood glucose
(SMBG) test (for example, from a finger or forearm blood test) or a
YSI (Yellow Springs Instruments) test, for example.
[0256] The terms "interferents" and "interfering species," as used
herein, are broad terms, and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and are
not to be limited to a special or customized meaning), and refer
without limitation to effects and/or species that interfere with
the measurement of an analyte of interest in a sensor to produce a
signal that does not accurately represent the analyte measurement.
In one example of an electrochemical sensor, interfering species
are compounds with an oxidation potential that overlaps with the
analyte to be measured, producing a false positive signal.
[0257] The term "sensor session," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and is not to be limited to a special
or customized meaning), and refers without limitation to the period
of time the sensor is applied to (e.g. implanted in) the host or is
being used to obtain sensor values. For example, in some
embodiments, a sensor session extends from the time of sensor
implantation (e.g. including insertion of the sensor into
subcutaneous tissue and placing the sensor into fluid communication
with a host's circulatory system) to the time when the sensor is
removed.
[0258] The terms "sensitivity" or "sensor sensitivity," as used
herein, are broad terms, and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and is
not to be limited to a special or customized meaning), and refer
without limitation to an amount of signal produced by a certain
concentration of a measured analyte, or a measured species (e.g.,
H.sub.2O.sub.2) associated with the measured analyte (e.g.,
glucose). For example, in one embodiment, a sensor has a
sensitivity of from about 1 to about 300 picoAmps of current for
every 1 mg/dL of glucose analyte.
[0259] The term "sensitivity profile" or "sensitivity curve," as
used herein, are broad terms, and are to be given their ordinary
and customary meaning to a person of ordinary skill in the art (and
is not to be limited to a special or customized meaning), and refer
without limitation to a representation of a change in sensitivity
over time
Overview
[0260] Conventional in vivo continuous analyte sensing technology
has typically relied on reference measurements performed during a
sensor session for calibration of the continuous analyte sensor.
The reference measurements are matched with substantially time
corresponding sensor data to create matched data pairs. Regression
is then performed on the matched data pairs (e.g., by using least
squares regression) to generate a conversion function that defines
a relationship between a sensor signal and an estimated glucose
concentration.
[0261] In critical care settings, calibration of continuous analyte
sensors is often performed by using, as reference, a calibration
solution with a known concentration of the analyte. This
calibration procedure can be cumbersome, as a calibration bag,
separate from (and an addition to) an IV (intravenous) bag, is
typically used. In the ambulatory setting, calibration of
continuous analyte sensors has traditionally been performed by
capillary blood glucose measurements (e.g., a finger stick glucose
test), through which reference data is obtained and input into the
continuous analyte sensor system. This calibration procedure
typically involves frequent finger stick measurements, which can be
inconvenient and painful.
[0262] Heretofore, systems and methods for in vitro calibration of
a continuous analyte sensor by the manufacturer (e.g., factory
calibration), without reliance on periodic recalibration, have for
the most part been inadequate with respect to high levels of sensor
accuracy. Part of this can be attributed to changes in sensor
properties (e.g., sensor sensitivity) that can occur during sensor
use. Thus, calibration of continuous analyte sensors has typically
involved periodic inputs of reference data, whether they are
associated with a calibration solution or with a finger stick
measurement. This can be very burdensome to the patient in the
ambulatory setting or the hospital staff in the critical care
setting.
[0263] Described herein are continuous analyte sensors that are
capable of continuous, automatic self-calibration during a sensor
session and capable of achieving high levels of accuracy, without
(or with reduced) reliance on reference data from a reference
analyte monitor (e.g., from a blood glucose meter). In some
embodiments, the continuous analyte sensor is an invasive,
minimally invasive, or non-invasive device. The continuous analyte
sensor can be a subcutaneous, transdermal, or intravascular device.
In certain embodiments, one or more of these devices may form a
continuous analyte sensor system. For instance, the continuous
analyte sensor system may be comprised of a combination of a
subcutaneous device and a transdermal device, a combination of a
subcutaneous device and an intravascular device, a combination of a
transdermal device and an intravascular device, or a combination of
a subcutaneous device, a transdermal device, and an intravascular
device. In some embodiments, the continuous analyte sensor can
analyze a plurality of intermittent biological samples (e.g., blood
samples). The continuous analyte sensor can use any
glucose-measurement method, including methods involving enzymatic,
chemical, physical, electrochemical, spectrophotometric,
polarimetric, calorimetric, iontophoretic, and radiometric
mechanisms, and the like.
[0264] In certain embodiments, the continuous analyte sensor
includes one or more working electrodes and one or more reference
electrode, which operate together to measure a signal associated
with a concentration of the analyte in the host. The output signal
from the working electrode is typically a raw data stream that is
calibrated, processed, and used to generate an estimated analyte
(e.g., glucose) concentration. In certain embodiments, the
continuous analyte sensor may measure an additional signal
associated with the baseline and/or sensitivity of the sensor,
thereby enabling monitoring of baseline and/or additional
monitoring of sensitivity changes or drift that may occur in a
continuous analyte sensor over time.
[0265] In some embodiments, the sensor extends through a housing,
which maintains the sensor on the skin and provides for electrical
connection of the sensor to sensor electronics. In one embodiment,
the sensor is formed from a wire. For example, the sensor can
include an elongated conductive body, such as a bare elongated
conductive core (e.g., a metal wire) or an elongated conductive
core coated with one, two, three, four, five, or more layers of
material, each of which may or may not be conductive. The elongated
sensor may be long and thin, yet flexible and strong. For example,
in some embodiments the smallest dimension of the elongated
conductive body is less than about 0.1 inches, 0.075 inches, 0.05
inches, 0.025 inches, 0.01 inches, 0.004 inches, or 0.002 inches.
Other embodiments of the elongated conductive body are disclosed in
U.S. Patent Application Publication No. 2011/0027127, which is
incorporated herein by reference in its entirety. Preferably, a
membrane system is deposited over at least a portion of
electroactive surfaces of the sensor 102 (including a working
electrode and optionally a reference electrode) and provides
protection of the exposed electrode surface from the biological
environment, diffusion resistance (limitation) of the analyte if
needed, a catalyst for enabling an enzymatic reaction, limitation
or blocking of interferants, and/or hydrophilicity at the
electrochemically reactive surfaces of the sensor interface.
Disclosures regarding the different membrane systems that may be
used with the embodiments described herein are described in U.S.
Patent Publication No. US-2009-0247856-A1, which is incorporated
herein by reference in its entirety.
[0266] Calibrating sensor data from continuous analyte sensors
generally involves defining a relationship between sensor-generated
measurements (e.g., in units of nA or digital counts after A/D
conversion) and one or more reference measurement (e.g., in units
of mg/dL or mmol/L). In certain embodiments, one or more reference
measurements obtained shortly after the analyte sensor is
manufactured, and before sensor use, are used for calibration. The
reference measurement may be obtained in many forms. For example,
in certain embodiments, the reference measurement may be determined
from a ratio or correlation between the sensitivity of a sensor
(e.g., from a certain sensor lot) with respect to in vivo analyte
concentration measurements and the sensitivity of another sensor
(e.g., from the same lot made in substantially the same way under
substantially same conditions) with respect to in vitro analyte
concentration measurements at a certain time period. By providing a
continuous analyte sensor with a predetermined in vivo to in vitro
ratio and a predetermined sensitivity profile (as described in more
detail elsewhere herein), self-calibration of the sensor can be
achieved in conjunction with high levels of sensor accuracy.
[0267] With self-calibration, the need for recalibration, by using
reference data during a sensor session, may be eliminated, or else
lessened, such that recalibration may be called for only in certain
limited circumstances, such as when sensor failure is detected.
Additionally or alternatively, in some embodiments, the continuous
analyte sensor may be configured to request and accept one or more
reference measurements (e.g., from a finger stick glucose
measurement or a calibration solution) at the start of the sensor
session. In some embodiments, use of a reference measurement at the
start of the sensor session in conjunction with a predetermined
sensor sensitivity profile can eliminate or substantially reduce
the need for further reference measurements.
[0268] With certain implantable enzyme-based electrochemical
glucose sensors, the sensing mechanism depends on certain phenomena
that have a generally linear relationship with glucose
concentration, for example: (1) diffusion of an analyte through a
membrane system situated between an implantation site (e.g.,
subcutaneous space) and an electroactive surface, (2) rate of an
enzyme-catalyzed reaction of the analyte to produce a measured
species within the membrane system (e.g., the rate of a glucose
oxidase-catalyzed reaction of glucose with O.sub.2 which produces
gluconic acid and H.sub.2O.sub.2), and (3) diffusion of the
measured species (e.g., H.sub.2O.sub.2) to the electroactive
surface. Because of this generally linear relationship, calibration
of the sensor is obtained by solving the equation:
y=mx+b
wherein y represents the sensor signal (counts), x represents the
estimated glucose concentration (mg/dL), m represents the sensor
sensitivity to analyte concentration (counts/mg/dL), and b
represents the baseline signal (counts). As described elsewhere
herein, in certain embodiments, the value b (i.e., the baseline)
can be zero or about zero. As a result, for these embodiments,
calibration can be defined by solving for the equation y=mx.
[0269] In some embodiments, the continuous analyte sensor system is
configured to estimate changes or drift in sensitivity of the
sensor for an entire sensor session as a function of time (e.g.,
elapsed time since start of the sensor session). As described
elsewhere herein, this sensitivity function plotted against time
may resemble a curve. Additionally or alternatively, the system can
also be configured to determine sensor sensitivity changes or drift
as a function of time and one or more other parameters that can
also affect sensor sensitivity or provide additional information
about sensor sensitivity. These parameters can affect sensor
sensitivity or provide additional information about sensor
sensitivity prior to the sensor session, such as parameters
associated with the sensor fabrication (e.g., materials used to
fabricate sensor membrane, the thickness of the sensor membrane,
the temperature at which the sensor membrane was cured, the length
of time the sensor was dipped in a particular coating solution,
etc.). In certain embodiments, some of the parameters involve
information, obtained prior to the sensor session, which can be
accounted for in a calibration code that is associated with a
particular sensor lot. Other parameters can be associated with
conditions surrounding the sensor after its manufacture, but before
the sensor session, such as, for example, the level of exposure of
the sensor to certain levels of humidity or temperature while the
sensor is in a package in transit from the manufacturing facility
to the patient. Yet other parameters (e.g., sensor membrane
permeability, temperature at the sample site, pH at the sample
site, oxygen level at the sample site, etc.) can affect sensor
sensitivity or provide additional information about sensor
sensitivity during the sensor session.
[0270] Determination of sensor sensitivity at different times of a
sensor session based on the predetermined sensor sensitivity
profile can be performed prior to the sensor session or at the
start of the sensor session. Additionally, in certain embodiments,
determination of sensor sensitivity, based on the sensor
sensitivity profile, can be continuously adjusted to account for
parameters that affect sensor sensitivity or provide additional
information about sensor sensitivity during the sensor session.
These determinations of sensor sensitivity change or drift can be
used to provide self-calibration, update calibration, supplement
calibration based on measurements (e.g., from a reference analyte
monitor), and/or validate or reject reference analyte measurements
from a reference analyte monitor. In some embodiments, validation
or rejection of reference analyte measurements can be based on
whether the reference analyte measurements are within a range of
values associated with the predetermined sensor sensitivity
profile.
[0271] Some of the continuous analyte sensors described herein may
be configured to measure a signal associated with a non-analyte
constant in the host. Preferably, the non-analyte constant signal
is measured beneath the membrane system on the sensor. In one
example of a continuous glucose sensor, a non-glucose constant that
can be measured is oxygen. In some embodiments, a change in oxygen
transport, which can be indicative of a change or drift in the
sensitivity of the glucose signal, can be measured by switching the
bias potential of the working electrode, an auxiliary
oxygen-measuring electrode, an oxygen sensor, or the like.
[0272] Additionally, some of the continuous analyte sensors
described herein may be configured to measure changes in the amount
of background noise in the signal. Detection of changes which
exceed a certain threshold can provide the basis for triggering
calibration, updating calibration, and/or validating or rejecting
inaccurate reference analyte values from a reference analyte
monitor. In one example of a continuous glucose sensor, the
background noise is composed substantially of signal contribution
from factors other than glucose (for example, interfering species,
non-reaction-related hydrogen peroxide, or other electroactive
species with an oxidation potential that overlaps with hydrogen
peroxide). Namely, the continuous glucose sensor is configured to
measure a signal associated with the baseline (which includes
substantially all non-glucose related current generated), as
measured by the sensor in the host. In some embodiments, an
auxiliary electrode located beneath a non-enzymatic portion of the
membrane system is used to measure the baseline signal. The
baseline signal can be subtracted from the glucose+baseline signal
to obtain a signal associated entirely or substantially entirely
with glucose concentration. Subtraction may be accomplished
electronically in the sensor using a differential amplifier,
digitally in the receiver, and/or otherwise in the hardware or
software of the sensor or receiver as described in more detail
elsewhere herein.
[0273] Together, by determining sensor sensitivity based on a
sensitivity profile and by measuring a baseline signal, the
continuous analyte sensor can be continuously self-calibrated
during a sensor session without (or with reduced) reliance on
reference measurements from a reference analyte monitor or
calibration solution.
Determination of Sensor Sensitivity
[0274] As described elsewhere herein, in certain embodiments,
self-calibration of the analyte sensor system can be performed by
determining sensor sensitivity based on a sensitivity profile (and
a measured or estimated baseline), so that the following equation
can be solved:
y=mx+b
wherein y represents the sensor signal (counts), x represents the
estimated glucose concentration (mg/dL), m represents the sensor
sensitivity to the analyte (counts/mg/dL), and b represents the
baseline signal (counts). From this equation, a conversion function
can be formed, whereby a sensor signal is converted into an
estimated glucose concentration.
[0275] It has been found that a sensor's sensitivity to analyte
concentration during a sensor session will often change or drift as
a function of time. FIG. 1A illustrates this phenomenon and
provides a plot of sensor sensitivities 110 of a group of
continuous glucose sensors as a function of time during a sensor
session. FIG. 1B provides three plots of conversion functions at
three different time periods of a sensor session. As shown in FIG.
1B, the three conversion functions have different slopes, each of
which correspond to a different sensor sensitivity. Accordingly,
the differences in slopes over time illustrate that changes or
drift in sensor sensitivity occur over a sensor session.
[0276] Referring back to the study associated with FIG. 1A, the
sensors were made in substantially the same way under substantially
the same conditions. The sensor sensitivities associated with the
y-axis of the plot are expressed as a percentage of a substantially
steady state sensitivity that was reached about three days after
start of the sensor session. In addition, these sensor
sensitivities correspond to measurements obtained from YSI tests.
As shown in the plot, the sensitivities (expressed as a percentage
of a steady state sensitivity) of each sensor, as measured, are
very close to sensitivities of other sensors in the group at any
given time of the sensor session. While not wishing to be bound by
theory, it is believed that the observed upward trend in
sensitivity (over time), which is particularly pronounced in the
early part of the sensor session, can be attributed to conditioning
and hydration of sensing regions of the working electrode. It is
also believed that the glucose concentration of the fluid
surrounding the continuous glucose sensor during startup of the
sensor can also affect the sensitivity drift.
[0277] With the sensors tested in this study, the change in sensor
sensitivity (expressed as a percentage of a substantially steady
state sensitivity), over a time defined by a sensor session,
resembled a logarithmic growth curve. It should be understood that
other continuous analyte sensors fabricated with different
techniques, with different specifications (e.g., different membrane
thickness or composition), or under different manufacturing
conditions, may exhibit a different sensor sensitivity profile
(e.g., one associated with a linear function). Nonetheless, with
improved control over operating conditions of the sensor
fabrication process, high levels of reproducibility have been
achieved, such that sensitivity profiles exhibited by individual
sensors of a sensor population (e.g., a sensor lot) are
substantially similar and sometimes nearly identical.
[0278] It has been discovered that the change or drift in
sensitivity over a sensor session is not only substantially
consistent among sensors manufactured in substantially the same way
under substantially same conditions, but also that modeling can be
performed through mathematical functions that can accurately
estimate this change or drift. As illustrated in FIG. 1A, an
estimative algorithm function 120 can be used to define the
relationship between time during the sensor session and sensor
sensitivity. The estimative algorithm function may be generated by
testing a sample set (comprising one or more sensors) from a sensor
lot under in vivo and/or in vitro conditions. Alternatively, the
estimative algorithm function may be generated by testing each
sensor under in vivo and/or in vitro conditions.
[0279] In some embodiments, a sensor may undergo an in vitro sensor
sensitivity drift test, in which the sensor is exposed to changing
conditions (e.g., step changes of glucose concentrations in a
solution), and an in vitro sensitivity profile of the sensor is
generated over a certain time period. The time period of the test
may substantially match an entire sensor session of a corresponding
in vivo sensor, or it may encompass a portion of the sensor session
(e.g., the first day, the first two days, or the first three days
of the sensor session, etc.). It is contemplated that the
above-described test may be performed on each individual sensor, or
alternatively on one or more sample sensors of a sensor lot. From
this test, an in vitro sensitivity profile may be created, from
which an in vivo sensitivity profile may be modeled and/or
formed.
[0280] From the in vivo or in vitro testing, one or more data sets,
each comprising data points associating sensitivity with time, may
be generated and plotted. A sensitivity profile or curve can then
be fitted to the data points. If the curve fit is determined to be
satisfactory (e.g., if the standard deviation of the generated data
points is less a certain threshold), then the sensor sensitivity
profile or curve may be judged to have passed a quality control and
suitable for release. From there, the sensor sensitivity profile
can be transformed into an estimative algorithm function or
alternatively into a look-up table. The algorithm function or
look-up table can be stored in a computer-readable memory, for
example, and accessed by a computer processor.
[0281] The estimative algorithm function may be formed by applying
curve fitting techniques that regressively fit a curve to data
points by adjusting the function (e.g., by adjusting constants of
the function) until an optimal fit to the available data points is
obtained. Simply put, a "curve" (i.e., a function sometimes
referred to as a "model") is fitted and generated that relates one
data value to one or more other data values and selecting
parameters of the curve such that the curve estimates the
relationship between the data values. By way of example, selection
of the parameters of the curve may involve selection of
coefficients of a polynomial function. In some embodiments, the
curve fitting process may involve evaluating how closely the curve
determined in the curve fitting process estimates the relationship
between the data values, to determine the optimal fit. The term
"curve," as used herein, is a broad term, and is to be given its
ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning),
and refers to a function or a graph of a function, which can
involve a rounded curve or a straight curve, i.e., a line.
[0282] The curve may be formed by any of a variety of curve fitting
techniques, such as, for example, the linear least squares fitting
method, the non-linear least squares fitting method, the
Nelder-Mead Simplex method, the Levenberg-Marquardt method, and
variations thereof. In addition, the curve may be fitted using any
of a variety of functions, including, but not limited to, a linear
function (including a constant function), logarithmic function,
quadratic function, cubic function, square root function, power
function, polynomial function, rational function, exponential
function, sinusoidal function, and variations and combinations
thereof. For example, in some embodiments, the estimative algorithm
comprises a linear function component which is accorded a first
weight w1, a logarithmic function component which is accorded a
second weight w2, and an exponential function component which is
accorded a third weight w3. In further embodiments, the weights
associated with each component can vary as a function of time
and/or other parameters, but in alternative embodiment, one or more
of these weights are constant as a function of time.
[0283] In certain embodiments, the estimative algorithm function's
correlation (e.g., R2 value), which is a measure of the quality of
the fit of the curve to the data points, with respect to data
obtained from the sample sensors, may be one metric used to
determine whether a function is optimal. In certain embodiments,
the estimative algorithm function formed from the curve fitting
analysis may be adjusted to account for other parameters, e.g.,
other parameters that may affect sensor sensitivity or provide
additional information about sensor sensitivity. For example, the
estimative algorithm function may be adjusted to account for the
sensitivity of the sensor to hydrogen peroxide or other chemical
species.
[0284] Estimative algorithms formed and used to accurately estimate
an individual sensor's sensitivity, at any time during a sensor
session, can be based on factory calibration and/or based on a
single early reference measurement (e.g., using a single point
blood glucose monitor). In some embodiments, sensors across a
population of continuous analyte sensors manufactured in
substantially the same way under substantially same conditions
exhibit a substantially fixed in vivo to in vitro sensitivity
relationship. For example, in one embodiment, the in vivo
sensitivity of a sensor at a certain time after start of sensor use
(e.g., at t=about 5, 10, 15, 30, 60, 120, or 180 minutes after
sensor use) is consistently equal to a measured in vitro
sensitivity of the sensor or of an equivalent sensor. From this
relationship, an initial value of in vivo sensitivity can be
generated, from which an algorithmic function corresponding to the
sensor sensitivity profile can be formed. Put another way, from
this initial value (which represents one point in the sensor
sensitivity profile), the rest of the entire sensor sensitivity
profile can be determined and plotted. The initial value of in vivo
sensitivity can be associated with any portion of the sensor
sensitivity profile. In certain embodiments, multiple initial
values of in vivo sensitivities, which are time-spaced apart, and
which correspond to multiple in vitro sensitivities, can be
calculated and combined together to generate the sensor sensitivity
profile.
[0285] In some embodiments, as illustrated in FIG. 2A, the initial
value 210 of in vivo sensitivity is associated with a time
corresponding to the start (near the start) of the sensor session.
As illustrated in FIG. 2B, based on this initial value 210, the
rest of the sensor sensitivity profile 220 is plotted (i.e.,
plotted forward and backward across the x-axis corresponding to
time). However, as illustrated in FIG. 2C, in some embodiments, the
initial value 210' may be associated with any other time of the
sensor session. For example, as illustrated in FIG. 2C, in one
embodiment, the in initial value 210' of in vivo sensitivity is
associated with a time (e.g., at about day 3) when the sensitivity
has substantially reached steady state. From the initial value
210', the rest of the sensor sensitivity profile 220' is plotted,
as illustrated in FIG. 2D.
[0286] With other embodiments, although the in vivo to in vitro
sensitivity relationship was not equal, the relationship
nonetheless involved a consistently fixed ratio. By having a
substantially fixed in vivo to in vitro sensitivity relationship,
some of the sensors described herein can be factory calibrated by
evaluating the in vitro sensitivity characteristic (e.g., one or
more sensitivity values measured at certain time periods) of a
sensor from a particular sensor lot at a manufacturing facility,
defining the in vivo sensitivity characteristic of other sensors in
the same sensor lot based on its relationship with the measured in
vitro sensitivity characteristic, and storing this calculated in
vivo sensitivity characteristic onto electronics associated with
the sensors (e.g., in computer memory of a sensor electronics,
discussed more elsewhere herein, configured to be operably coupled
to the sensor during sensor use).
[0287] Accordingly, with information obtained prior to the sensor
session relating to an in vivo to in vitro sensor sensitivity
relationship and a predetermined sensor sensitivity profile,
factory calibration is achieved in conjunction with high levels of
sensor accuracy. For example, in some embodiments, the sensor was
capable of achieving an accuracy corresponding to a mean absolute
relative difference of no more than about 10% over a sensor session
of at least about 3 days, and sometimes at least about 4, 5, 6, 7,
or 10 days. In some embodiments, the sensor was capable of
achieving an accuracy, over a over a sensor session of at least
about 3 days, corresponding to a mean absolute relative difference
of no more than about 7%, 5%, or 3%. With factory calibration, the
need for recalibration may be eliminated, or else required only in
certain circumstances, such as in response to detection of sensor
failure.
[0288] With reference back to the study associated with FIG. 1A,
the sensors were built with a working electrode configured to
measure a glucose+baseline signal and a corresponding auxiliary
electrode configured to measure only the baseline signal. Sensor
electronics in the sensor system subtracted the baseline signal
from the glucose+baseline signal to obtain a signal associated
entirely or substantially entirely to glucose concentration. In
addition, an algorithmic function was generated and stored in
sensor electronics associated with the sensors to estimate the
sensitivity of these sensors during their lives. This algorithmic
function is plotted in FIG. 1A and shown closely overlying the
measured sensor sensitivities of the sensors. With the
determination of baseline and sensitivity at any given time during
the life of a sensor, a conversion function is formed, whereby a
sensor signal is converted into an estimated glucose
concentration.
[0289] As illustrated in FIG. 3A, which is a Bland-Altman plot
showing differences between YSI reference measurements and certain
in vivo continuous analyte sensors that were factory calibrated,
the measurements from these sensors exhibited very high accuracy.
The lines in FIG. 3A represent accuracy standards corresponding to
a deviation from actual measured values (using YSI tests) of less
than .+-.10 mg/dL at glucose concentrations between about 40 and 75
mg/dL, and less than .+-.15% at glucose concentrations between
about 75 mg/dL and 400 mg/dL. Indeed, the difference between
estimated glucose concentration values, which are calculated using
a predetermined sensor sensitivity profile, and actual measured
values (using YSI tests) over a sensor life, differed by no more
than about 10 mg/dL at glucose concentrations between about 40 and
75 mg/dL, and no more than 15% at glucose concentrations between
about 75 mg/dL and 400 mg/dL. Furthermore, at glucose
concentrations between about 40 mg/dL and 75 mg/dL, about 97% of
the estimated glucose concentrations values were within .+-.5 mg/dL
of corresponding YSI measured values, and at glucose concentrations
between about 70 mg/dL and 400 mg/dL, about 99% of the estimated
glucose concentrations were within .+-.10% of corresponding YSI
measured values.
[0290] FIG. 3B illustrates a Clarke error grid associated with the
factory calibration study associated with FIG. 3A. The Clarke error
grid of FIG. 3B is a Clarke error grid is based on a correlation
plot of the performance of the above-described factory calibration
method with respect to a reference method in the form of YSI
measurements. If the correlation was perfect, all points would fall
on a 45.degree. line. The area surrounding this line is divided
into zones that predict the clinical consequences in terms of
action taken by the patient, depending on where the measurements by
the factory calibration method fall off the line. Zone A
corresponds to a clinically accurate decision (e.g., take insulin,
take glucose, or do nothing), zone B a clinically acceptable
decision, and zone D corresponds to a clinically erroneous
decision. As shown in FIG. 3B, all of data points from the factory
calibration study fell within either zone A or zone B. In fact,
almost all of the data points fell within Zone A, thus establishing
that the above-described factory calibration study provided very
accurate glucose concentration measurements.
[0291] While individual sensors of a sensor group manufactured
under substantially identical conditions have been found to
generally exhibit a substantially similar or a nearly identical
sensor sensitivity profile and have a substantially similar or a
nearly identical in vivo to in vitro sensor sensitivity
relationship, it has been found that at times the actual sensor
sensitivity (i.e., sensitivity expressed as an actual sensitivity
value, and not as a percentage of a substantially steady state
sensitivity) can vary between sensors. For example, even though
individual sensors may have been manufactured under substantially
identical conditions, they can have different sensitivity
characteristics during use if they are exposed to different
environment conditions (e.g., exposure to radiation, extreme
temperature, abnormal dehydration conditions, or any environment
that can damage the enzyme in the sensor membrane or other parts of
the sensor, etc.) during the time period between sensor fabrication
and sensor use.
[0292] Accordingly, to compensate for potential effects resulting
from these conditions, in certain embodiments, the continuous
analyte sensors are configured to request and accept one or more
reference measurements (e.g., from a finger stick glucose
measurement or from a calibration solution) at the start of the
sensor session. For example, the request for one or more reference
measurements can be made at about 15 minutes, 30 minutes, 45
minutes, 1 hour, 2 hours, 3 hours, etc., after activation of the
sensor. In some embodiments, sensor electronics are configured to
process and use the reference data to generate (or adjust) a sensor
sensitivity profile in response to the input of one or more
reference measurements into the sensor. For example, if a reference
measurement of glucose concentration is taken and input into the
sensor at time=x, an algorithmic function of sensor sensitivity can
be generated by matching the sensor sensitivity profile at time=x
with the reference measurement. Use of the one of the one or more
reference measurements at the start of the sensor in conjunction
with a predetermined sensor sensitivity profile permits
self-calibration of the sensor without or with a reduced need for
further reference measurements.
[0293] FIG. 4 illustrates a Bland-Altman plot showing differences
between YSI reference measurements and in vivo continuous analyte
sensors that accepted one reference measurement about one hour
after insertion into patients. The lines in FIG. 4 represent
accuracy standards corresponding to a deviation from actual
measured values (using YSI tests) of less than about .+-.20 mg/dL
at glucose concentrations between about 40 mg/dL and 75 mg/dL, and
less than about .+-.20% at glucose concentrations between about 75
mg/dL and 400 mg/dL. From this reference measurement, an initial
value of in vivo sensor sensitivity was generated, which in turned
allow for the formation of an algorithmic function corresponding to
the sensitivity profile for the rest of the sensor session. The
sensors were built with a working electrode and an auxiliary
electrode used to measure a baseline signal, which was subtracted
from the glucose+baseline signal obtained by the working electrode.
As shown, about 85% of the estimated glucose concentrations were
within the range 410, defined as .+-.20 mg/dL from corresponding
YSI measured values for glucose concentrations between about 40
mg/dL and 75 mg/dL and .+-.20% from corresponding YSI measured
values for glucose concentrations between about 75 mg/dL and 400
mg/dL. Additionally, at glucose concentrations between about 40
mg/dL and 75 mg/dL, about 95% of the estimated glucose
concentrations values were within .+-.20 mg/dL of corresponding YSI
measured values. The sensors in this study obtained an overall
accuracy level corresponding to a Mean Absolute Relative Difference
of about 12% over a sensor session of at least seven days, and a
first-day accuracy level corresponding to a Mean Absolute Relative
Difference of about 11%. The Median Absolute Relative Difference
obtained were about 10% for both overall and first-day accuracy
levels.
[0294] FIG. 5 is a diagram illustrating different types of
information that can be input into the sensor system to define the
sensor sensitivity profile over time, in one embodiment. Input
information can include information obtained prior to the sensor
session 510 and information obtained during the sensor session 520.
In the embodiment depicted in FIG. 5, both information obtained
prior to the sensor session 510 and information obtained during the
sensor session 520 are used to generate, adjust, or update a
function 530 associated with the sensor sensitivity profile, but in
another embodiment, the sensor system may be configured to use only
information obtained prior to the sensor session. In certain
embodiments, formation of an initial sensor sensitivity profile can
occur prior to the sensor session, at the start of the sensor
session, or shortly after the start of the sensor session.
Additionally, in certain embodiments, the sensor sensitivity
profile can be continuously adjusted, regenerated, or updated to
account for parameters that may affect sensor sensitivity or
provide additional information about sensor sensitivity during the
sensor session. Information obtained prior to the sensor session
can include, for example, the sensor sensitivity profile that is
generated before or at the start of the sensor session, as
previously described. It can also include a sensitivity value
associated with a substantially fixed in vivo to in vitro sensor
sensitivity relationship, as previously described.
[0295] Alternatively, instead of a fixed sensitivity value, the in
vivo to in vitro sensor sensitivity relationship may be defined as
a function of time between completion of sensor fabrication (or the
time calibration check was performed on sensors from the same lot)
and the start of the sensor session. As shown in FIG. 6, it has
been discovered that a sensor's sensitivity to analyte
concentration can change as a function of time between completion
of sensor fabrication and the start of the sensor session. FIG. 6
illustrates this phenomenon through a plot, which resembles a
downward trend in sensitivity over time between completion of
sensor fabrication and the start of the sensor session. Similar to
the discovered change or drift in sensitivity over time of a sensor
session, this change or drift in sensitivity over time between
completion of sensor fabrication and the start of the sensor
session is generally consistent among sensors that have not only
been manufactured in substantially the same way under substantially
same conditions, but that also have avoided exposure to certain
conditions (e.g., exposure to radiation, extreme temperature,
abnormal dehydration conditions, or any environment that can damage
the enzyme in the sensor membrane or other parts of the sensor,
etc.) Accordingly, the change or drift in sensitivity over time
between completion of sensor fabrication and the start of the
sensor session can also be modeled through a mathematical function
620 that accurately estimates this change or drift. The estimative
algorithm function 620 may be any of a variety of functions, such
as, for example, a linear function (including a constant function),
logarithmic function, quadratic function, cubic function, square
root function, power function, polynomial function, rational
function, exponential function, sinusoidal function, and
combinations thereof.
[0296] Information obtained prior to the sensor session can also
include information relating to certain sensor characteristics or
properties. By way of example and not to be limiting, information
obtained prior to the sensor session may include the particular
materials used to fabricate the sensor (e.g., materials used to
form the sensor membrane), the thickness of the sensor membrane,
the membrane's permeability to glucose or other chemical species,
the in vivo or in vitro sensor sensitivity profile of another
sensor made in substantially the same way under substantially same
conditions, etc. In certain embodiments, information obtained prior
to the sensor session can include information relating to the
process conditions under which the sensor is fabricated. This
information can include, for example, the temperature at which the
sensor membrane was cured, the length of time the sensor was dipped
in a particular coating solution, etc. In other embodiments,
information obtained prior to the sensor session can relate to
patient physiological information. For example, the patient's age,
body mass index, gender, and/or historic patient sensitivity
profiles, can be used as parameters to form the sensor sensitivity
profile. Other information obtained prior to the sensor session
that may also be used includes information relating to sensor
insertion, such as, for example, location (e.g., abdomen vs. back)
or depth of sensor insertion.
[0297] In general, the sensor sensitivity functions can be created
by theoretical or empirical methods, or both, and stored as
functions or as look-up-tables, thereby allowing for sensor
self-calibration that eliminates (or substantially reduces) the
need for reference measurements. The sensor sensitivity functions
can be generated at the manufacturing facility and shipped with the
system or generated by the system shortly prior to (or during) use.
The term "self-calibration," as used herein, is a broad term, and
is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to calibration
of a sensor or device which is performed by a control system
manufacturer or installer, the sensor manufacturer, or anyone other
than the user of the sensor. The sensor calibration can be
performed on the individual sensor on which the control system is
installed, or it can be performed on a reference sensor, for
example one from the same sensor lot, and the calibration functions
can be transferred from one control system to another. In some
embodiments, systems may be shipped with some self-calibration
functions and then have others added by the sensor user. Also,
sensor systems may be shipped with self-calibration functions and
only need adjustments or touch-up calibrations during use.
[0298] In certain embodiments, the sensor sensitivity profile may
be adjusted during sensor use to account for, in real-time, certain
parameters that may affect sensor sensitivity or provide additional
information about sensor sensitivity. These parameters may include,
but are not limited to, parameters associated with sensor
properties, such as, for example, sensor membrane permeability or
the level of sensor hydration, or parameters associated with
patient physiological information, such as, for example, patient
temperature (e.g., temperature at the sample site or skin
temperature), pH at the sample site, hematocrit level, or oxygen
level at the sample site. In some embodiments, the continuous
analyte sensor may be fitted with a thermistor on an ex vivo
portion of the sensor and a thermal conductive line that extends
from the thermistor to the sample site.
[0299] In some embodiments, calibration methods can be improved by
algorithmically quantifying each unique sensor/user environment.
Quantification can be achieved by generation or adjustment of the
sensitivity profile, which may involve an inference engine
involving a causal probabilistic network such as a Bayesian
network. A Bayesian network includes a conditional
probability-based network that relies on the Bayes theorem to
characterize the likelihood of different outcomes based on known
prior probabilities (e.g., prevalence of observed in vivo
sensitivities under certain parameter conditions) and newly
acquired information (e.g., occurrence or nonoccurrence of the
aforementioned conditions).
[0300] In certain embodiments, quantification is achieved through
the application of an analytical Bayesian framework to data already
existing within the system algorithm. Quantification can involve:
(1) wedge (e.g., maximum or minimum) values for parameters related
to sensitivity and baseline; (2) sensitivity values calculated
during a sensor session; and (3) baselines calculated during the
sensor session. In some embodiments, the first set of wedge values
are based on parameter distributions of "prior" data (e.g., data
collected from previous clinical studies). By using a Bayesian
network, a system can learn from data collected during the sensor
session, and adapt the wedge values (or other algorithm parameters
and/or constraints) to a particular sensor/user environment. In
turn, the system's calibration method can be improved to have
better accuracy, reliability, and overall performance.
[0301] In one embodiment, the Bayesian framework used involves
using known prior information to establish a framework and then
accounting for gathered new information to make inferences from
their combination (i.e., combination of prior and new information),
to generate posterior information. When the prior information and
the new information are mathematically related and combined to form
posterior information that can be represented functionally, the
relationship is defined as conjugate. The Bayesian framework of one
embodiment employs conjugate distributional relationships that
result in the posterior distributional parameters (e.g., mean,
variance) being directly available. This can be advantageous under
a computationally restrictive space where both time and resources
are constrained.
[0302] In some embodiments, under the Bayesian framework, in which
the wedge parameters follow a prior substantially normal
distribution for each parameter, and in which the algorithmically
calculated sensitivity and baseline values follow their own
substantially normal distribution representing the new information,
an independent posterior distribution for sensitivity and/or
baseline can be generated. These two posterior distributions are
then used in parallel to construct glucose threshold values used
for accepting, rejecting, or modifying a user's manual calibration
entry (e.g., entry from a fingerstick measurement). In certain
embodiments, the posterior distributions of sensitivity and
baseline can also be used for semi-self-calibration, thereby
reducing the number of fingerstick measurements required for manual
calibration.
[0303] In one exemplary embodiment, from prior information, the
wedge min/max values for a sensor's sensitivity to glucose are
known to be A and B at the 95% confidence interval in a
substantially normal distribution. Using algorithmically calculated
sensitivity values as new information, the previous three, four,
five, six, seven, or more sensitivity values calculated through
manual calibration can be used as new information to generate a
posterior distribution, which will typically have reduced
variability than the distribution based on prior information, and
which is a direct quantification of a unique sensor/user
environment. Because of reduced variability, the posterior
distribution generated will typically have wedge values, which
should be closer together than the difference between A and B,
i.e., the wedge values of the distribution generated from prior
information. This tightening of the difference between the wedge
values between the prior distribution and the posterior
distribution allow the system to more reliably reject manual
calibration entries that are clearly erroneous, such as, for
example, a calibration entry of 60 mg/dL that was intended by the
user to be an entry of 160 mg/dL
[0304] Bayesian networks use causal knowledge and model
probabilistic dependence and independence relationships between
different events. FIG. 7A depicts distribution curves of sensor
sensitivity corresponding to the Bayesian learning process, in
accordance with one embodiment. FIG. 7B depicts confidence levels,
associated with the sensor sensitivity profile, that correspond
with the distribution curves shown in FIG. 7A. Distribution curve
720 and confidence level 730 (e.g., 25%, 33%, 50%, 75%, 95%, or 99%
confidence level) are associated with a lack of initial knowledge
about certain parameters that affect sensor sensitivity or provide
additional information about sensor sensitivity. For example,
distribution curve 720 can be associated with factory information.
As information regarding a certain parameter is acquired, the
distribution curve 720' becomes steeper and the confidence interval
730' becomes narrower, as certainty of sensor sensitivity profile
710 is increased. Examples of information that may be used to
change the distribution curves can include a reference analyte
value, a cal-check of the sensor at the factory, patient history
information, and any other information described elsewhere herein
that can affect sensor sensitivity or provide information about
sensor sensitivity. As information regarding still another
parameter is acquired, the distribution curve 720'' becomes even
steeper and the confidence interval 730'' becomes even narrower, as
certainty of sensor sensitivity profile 710 is further
increased.
[0305] During sensor use, the confidence interval curves 730, 730',
and/or 730'' may be used to form the sensitivity profile 710, which
provides an estimated sensitivity value at a given time. In turn
the estimated sensitivity value may be used to calibrate the
sensor, which allows for processing of sensor data to generate a
glucose concentration value that is displayed to the user. In some
embodiments, a first estimated sensitivity profile 710, formed from
the confidence interval curves 730, 730', and/or 730'', may be used
to monitor and display glucose concentrations. In addition, one or
more of the confidence interval curves 730, 730', 730'', or
combinations thereof, can be used to form a second estimated
sensitivity profile that, while possibly not as accurate as the
first estimated sensitivity profile, is nonetheless more likely to
result in the detection of a hypoglycemic or hyperglycemic range
than the first estimated sensitivity profile 710.
Determination of Baseline
[0306] A variety of types of noise can occur when a sensor is
implanted in a host. Some implantable sensors measure a signal
(e.g., counts) that comprises two components, the baseline signal
and the analyte signal. The baseline signal is substantially
comprised of a signal contribution from factors other than the
measure analyte (e.g., interfering species, non-reaction-related
hydrogen peroxide, or other electroactive species with an oxidation
potential that overlaps with the analyte or co-analyte). The
analyte signal (e.g., glucose signal) is substantially comprised of
a signal contribution from the analyte. Consequently, because the
signal includes these two components, calibration can be performed
to determine the analyte (e.g., glucose) concentration by solving
for the equation y=mx+b, wherein the value of b represents the
baseline of the signal. In some circumstances, the baseline is
comprised of constant and non-constant non-analyte factors.
Generally, it is desirable to reduce or remove the background
signal, to provide a more accurate analyte concentration to the
patient or health care professional.
[0307] In certain embodiments, an analyte sensor (e.g., glucose
sensor) is configured for insertion into a host for measuring an
analyte in the host. The sensor includes a working electrode
disposed beneath an active enzymatic portion of a membrane on the
sensor, an auxiliary electrode disposed beneath an inactive- or
non-enzymatic portion of the membrane on the sensor, and sensor
electronics operably connected to the working and auxiliary
electrodes. The sensor electronics are configured to process
signals from the electrodes to generate an analyte (e.g., glucose)
concentration estimate that substantially excludes signal
contribution from non-glucose related noise artifacts.
[0308] In some embodiments, the working electrode is configured to
generate via sensor electronics a first signal associated with both
the analyte and non-analyte related electroactive compounds that
have a oxidation potential less than or similar to a first
oxidation potential. The auxiliary electrode is configured to
generate a second signal associated with the non-analyte related
electroactive compounds. Non-analyte related electroactive
compounds can be any compound, present in the sensor's local
environment, which has an oxidation potential less than or similar
to the oxidation potential of the measured species (e.g., H2O2).
While not wishing to be bound by theory, it is believed that with a
glucose-measuring electrode, both the signal directly related to
the enzyme-catalyzed reaction of glucose (which produces H2O2 that
is oxidized at the first working electrode) and signals from
unknown compounds that are in the extracellular milieu surrounding
the sensor can be measured. These unknown compounds can be constant
or non-constant (e.g., intermittent or transient) in concentration
and/or effect. In some circumstances, it is believed that some of
these unknown compounds can be related to the host's disease state.
For example, it is known that blood chemistry can change
dramatically during/after a heart attack (e.g., pH changes, changes
in the concentration of various blood components/protein, and the
like). As another example, the transcutaneous insertion of a
needle-type sensor can initiate a cascade of events that includes
the release of various reactive molecules by macrophages. Other
compounds that can contribute to the non-glucose related signal are
compounds associated with the wound healing process, which can be
initiated by implantation/insertion of the sensor into the host, as
described in more detail with reference to U.S. Patent Publication
No. US-2007-0027370-A1.
[0309] As described above, the auxiliary electrode is configured to
generate a second signal associated with the non-analyte related
electroactive compounds that have an oxidation potential less than
or similar to the first oxidation potential. Non-analyte related
electroactive species can include interfering species,
non-reaction-related species (e.g., H2O2) that correspond to the
measured species, and other electroactive species. Interfering
species includes any compound that is not directly related to the
electrochemical signal generated by the enzyme-catalyzed reaction
of the analyte, such as, electroactive species in the local
environment produced by other bodily processes (e.g., cellular
metabolism, wound healing, a disease process, and the like).
Non-reaction-related species includes any compound from sources
other than the enzyme-catalyzed reaction, such as, H2O2 released by
nearby cells during the course of the cells' metabolism, H2O2
produced by other enzymatic reactions (e.g., extracellular enzymes
around the sensor or such as can be released during the death of
nearby cells or such as can be released by activated macrophages),
and the like. Other electroactive species includes any compound
that has an oxidation potential less than or similar to the
oxidation potential of H2O2.
[0310] The non-analyte signal produced by compounds other than the
analyte (e.g., glucose) is considered as background noise and can
obscure the signal related to the analyte, thereby contributing to
sensor inaccuracy. As described in greater detail elsewhere herein,
background noise can include both constant and non-constant
components and can be removed to accurately calculate the analyte
concentration.
[0311] In certain embodiments, the analyte sensor system is
configured in a way (e.g., with a certain symmetry, coaxial design,
and/or integral formation) such that the working and auxiliary
electrodes are influenced by substantially the same
external/environmental factors, thereby enabling substantially
equivalent measurement of both the constant and non-constant
species/noise. This allows for the substantial elimination of noise
on the sensor signal by using sensor electronics described
elsewhere herein. In turn, the substantial reduction or elimination
of signal effects associated with noise, including non-constant
noise (e.g., transient, unpredictable biologically related noise)
increases accuracy of continuous sensor signals.
[0312] In some embodiments, sensor electronics are operably
connected to the working and auxiliary electrodes. The sensor
electronics may be configured to measure the current (or voltage)
to generate the first and second signals. Collectively, the first
and second signals can be used to produce glucose concentration
data without substantial signal contribution from
non-glucose-related noise. This can be performed, for example, by
subtraction of the second signal from the first signal to produce a
signal associated with analyte concentration and without
substantial noise contribution, or by alternative data analysis
techniques.
[0313] In other embodiments, the sensor electronics are operably
connected to one or more working electrodes only, as an auxiliary
electrode is not needed. For example, the sensor membrane in some
embodiments may comprise polymers that contain mediators and
enzymes that chemically attach to the polymers. The mediator used
may oxidize at lower potentials than hydrogen peroxide, and thus
fewer oxidizable interferents are oxidized at these low potentials.
Accordingly, in some embodiments, a very low baseline (i.e., a
baseline that approaches a zero baseline and that does not receive
substantial signal contribution from non-glucose-related noise) may
be achieved, thereby potentially eliminating (or reducing) the need
for an auxiliary electrode that measures signal contribution from
non-glucose-related noise.
[0314] The sensor electronics can be comprised of a potentiostat,
A/D converter, RAM, ROM, transceiver, processor, and/or the like.
The potentiostat may be used to provide a bias to the electrodes
and to convert the raw data (e.g., raw counts) collected from the
sensor to an analyte concentration value (e.g., a glucose
concentration value expressed in units of mg/dL). The transmitter
may be used to transmit the first and second signals to a receiver,
where additional data analysis and/or calibration of analyte
concentration can be processed. In certain embodiments, the sensor
electronics may perform additional operations, such as, for
example, data filtering and noise analysis.
[0315] In certain embodiments, the sensor electronics may be
configured to analyze an analyte equivalent baseline or normalized
baseline, instead of the baseline. The normalized baseline is
calculated as the b/m or y-intercept divided by slope (from the
equation y=mx+b). The unit for analyte equivalent baseline may be
expressed as the analyte concentration unit (mg/dL), which
correlates with the output of the continuous analyte sensor. By
using the analyte equivalent baseline (normalized baseline), the
influence of glucose sensitivity on the baseline may be eliminated,
thereby making it possible to evaluate the baselines of different
sensors (e.g., from the same sensor lot or from different sensor
lots) with different glucose sensitivities.
[0316] Although some embodiments have been described herein which
employ an auxiliary electrode to allow for the subtraction of a
baseline signal from a glucose+baseline signal, it should be
understood that the use of this electrode is optional and may not
be used in other embodiments. For example, in certain embodiments,
the membrane system covering the working electrode is capable of
substantially blocking interferents, and substantially reducing the
baseline to a level that is negligible, such that the baseline can
be estimated. Estimation of the baseline can be based on an
assumption of the sensor's baseline at physiological conditions
associated with a typical patient. For example, baseline estimation
can be modeled after in vivo or in vitro measurements of baseline
in accordance with certain physiological levels of interferents
that are inherent in the body. FIG. 8 is a graph that provides a
comparison between an estimated glucose equivalent baseline and
detected glucose equivalent baseline, in accordance with one study.
The estimated glucose equivalent baseline was formed by conducting
in vitro measurements of the baseline of glucose sensors in a
solution that mimicked physiological levels of interferents in a
human. The interferents included uric acid with a concentration of
about 4 mg/dL and ascorbic acid, with a concentration of about 1
mg/dL of ascorbic acid. As shown in FIG. 8, it was found in this
study that the estimated baseline closely resembled the detected
baseline. Accordingly, with the possibility of accurate estimation
of baseline and/or with a baseline that is negligible, a single
working electrode alone (i.e., without use of an ancillary
electrode), together with a predetermined sensor sensitivity
profile, may be sufficient to provide a sensor system with
self-calibration.
[0317] With some embodiments, it has been found that not only does
the sensor's sensitivity tend to drift over time, but that the
sensor's baseline also drifts over time. Accordingly, in certain
embodiments, the concepts behind the methods and systems used to
predict sensitivity drift can also be applied to create a model
that predicts baseline drift over time. Although not wishing to be
bound by theory, it is believed that the total signal received by
the sensor electrode is comprised of a glucose signal component, an
interference signal component, and a electrode-related baseline
signal component that is a function of the electrode and that is
substantially independent of the environment (e.g., extracellular
matrix) surrounding the electrode. As noted above, the term
"baseline," as used herein, refers without limitation to the
component of an analyte sensor signal that is not related to the
analyte concentration. Accordingly, the baseline, as the term is
defined herein, is comprised of the interference signal component
and the electrode-related baseline signal component. While not
wishing to be bound by theory, it is believed that increased
membrane permeability typically not only results in an increased
rate of glucose diffusion across the sensor membrane, but also to
an increased rate of diffusion of interferents across the sensor
membrane. Accordingly, changes in sensor membrane permeability over
time which causes sensor sensitivity drift, will similarly also
likely cause the interference signal component of the baseline to
drift. Simply put, the interference signal component of the
baseline is not static, and is typically changing as a function of
time, which, in turn, causes the baseline to also drift over time.
By analyzing how each of the aforementioned components of the
baseline reacts to changing conditions and to time (e.g., as a
function of time, temperature), a predictive model can be developed
to predict how the baseline of a sensor will drift during a sensor
session. By being able to prospectively predict both sensitivity
and baseline of the sensor, it is believed that a self-calibrating
continuous analyte sensor can be achieved, i.e., a sensor that does
not require use of reference measurements (e.g., a fingerstick
measurement) for calibration.
Calibration Code
[0318] The process of manufacturing continuous analyte sensors may
sometimes be subjected to a degree of variability between sensor
lots. To compensate for this variability, one or more calibration
codes may be assigned to each sensor or sensor set to define
parameters that can affect sensor sensitivity or provide additional
information about the sensitivity profile. The calibration codes
can reduce variability in the different sensors, ensuring that the
results obtained from using sensors from different sensors lots
will be generally equal and consistent by applying an algorithm
that adjusts for the differences. In one embodiment, the analyte
sensor system may be configured such that one or more calibration
codes are to be manually entered into the system by a user. In
other embodiments, the calibration codes may be part of a
calibration encoded label that is adhered to (or inserted into) a
package of multiple sensors. The calibration encoded label itself
may be read or interrogated by any of a variety of techniques,
including, but not limited to, optical techniques, RFID
(Radio-frequency identification), or the like, and combinations
thereof. These techniques for transferring the code to the sensor
system may be more automatic, accurate, and convenient for the
patient, and less prone to error, as compared to manual entry.
Manual entry, for instance, possesses the inherent risk of an error
caused by a patient or hospital staff entering the wrong code,
which can lead to an incorrect calibration, and thus inaccurate
glucose concentration readings. In turn, this may result in a
patient or hospital staff taking an inappropriate action (e.g.,
injecting insulin while in a hypoglycemic state).
[0319] In some embodiments, calibration codes assigned to a sensor
may include a first calibration code associated with a
predetermined logarithmic function corresponding to a sensitivity
profile, a second calibration code associated with an initial in
vivo sensitivity value, and other calibration codes, with each code
defining parameter that affects sensor sensitivity or provides
information about sensor sensitivity. The other calibration codes
may be associated with any priori information or parameter
described elsewhere herein and/or any parameter that helps define a
mathematical relationship between the measured signal and analyte
concentration.
[0320] In some embodiments, the package used to store and transport
a continuous analyte sensor (or sensor set) may include detectors
configured to measure certain parameters that may affect sensor
sensitivity or provide additional information about sensor
sensitivity or other sensor characteristics. For example, in one
embodiment, the sensor package may include a temperature detector
configured to provide calibration information relating to whether
the sensor has been exposed to a temperature state greater than
(and or less than) one or more predetermined temperature values. In
some embodiments, the one or more predetermined temperature value
may be greater than about 75.degree. F., greater than about
80.degree. F., greater than about 85.degree. F., greater than about
90.degree. F., greater than about 95.degree. F., greater than about
100.degree. F., greater than about 105.degree. F., and/or greater
than about 110.degree. F. Additionally or alternatively, the one or
more predetermined temperature value may be less than about
75.degree. F., less than about 70.degree. F., less than about
60.degree. F., less than about 55.degree. F., less than about
40.degree. F., less than about 32.degree. F., less than about
10.degree. F., and/or less than about 0.degree. F. In certain
embodiments, the sensor package may include a humidity exposure
indicator configured to provide calibration information relating to
whether the sensor has been exposed to humidity levels greater than
or less than one or more predetermined humidity values. In some
embodiments, the one or more predetermined humidity value may be
greater than about 60% relative humidity, greater than about 70%
relative humidity, greater than about 80% relative humidity, and/or
greater than about 90% relative humidity. Alternatively or
additionally, the one or more predetermined humidity value may be
less than about 30% relative humidity, less than about 20% relative
humidity, and/or less than about 10% relative humidity.
[0321] Upon detection of exposure of the sensor to certain levels
of temperature and/or humidity, a corresponding calibration code
may be changed to account for possible effects of this exposure on
sensor sensitivity or other sensor characteristics. This
calibration code change may be automatically performed by a control
system associated with the sensor package. Alternatively, in other
embodiments, an indicator (e.g., a color indicator) that is adapted
to undergo a change (e.g., a change in color) upon exposure to
certain environments may be used. By way of example and not to be
limiting, the sensor package may include an indicator that
irreversibly changes color from a blue color to a red color, upon
exposure of the package to a temperature greater than about
85.degree. F., and also include instructions to the user to enter a
certain calibration code when the indicator has a red color.
Although exposure to temperature and humidity are described herein
as examples of conditions that may be detected by the sensor
package, and used to activate a change in calibration code
information, it should be understood that other conditions may also
be detected and used to activate a change in calibration code
information.
[0322] In certain embodiments, the continuous analyte system may
comprise a library of stored sensor sensitivity functions or
calibration functions associated with one or more calibration
codes. Each sensitivity function or calibration function results in
calibrating the system for a different set of conditions. Different
conditions during sensor use may be associated with temperature,
body mass index, and any of a variety of conditions or parameters
that may affect sensor sensitivity or provide additional
information about sensor sensitivity. The library can also include
sensitivity profiles or calibrations for different types of sensors
or different sensor lots. For example, a single sensitivity profile
library can include sub-libraries of sensitivity profiles for
different sensors made from different sensor lots and/or made with
different design configurations (e.g., different design
configurations customized for patients with different body mass
index).
Determining Sensor Properties and Calibrating Sensor Data Using One
or More Stimulus Signals
[0323] Some embodiments apply one or more stimulus signals to a
sensor to determine properties of a sensor and/or calibrate sensor
data. The term "stimulus signal," as used herein, is a broad term,
and is to be given its ordinary and customary meaning to a person
of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and refers without limitation to a
signal (e.g., any time-varying or spatial-varying quantity, such as
an electric voltage, current or field strength) applied to a system
being used (e.g., an analyte sensor) to cause or elicit a
response.
[0324] Non-limiting examples of stimulus signals that can be used
in the embodiments described herein can be a waveform including one
or more of: a step increase in voltage of a first magnitude, a step
decrease in voltage of a second magnitude (where the first and
second magnitudes can be the same or different), an increase in
voltage over time at first rate, a gradual decrease in voltage over
time having a second rate (where the first rate and the second rate
can be different or the same), one or more sine waves overlayed on
the input signal having the same or different frequencies and/or
amplitudes and the like. A response to the stimulus signal can then
be measured and analyzed (the response is also referred to herein
as the "signal response"). The analysis can include one or more of
calculating impedance values, capacitance values, and correlating
the signal response to one or more predetermined relationships. As
used herein, the term "impendence value" can mean a value for
expressing an electrical impedance, including but not limited to, a
value that only represents a magnitude of impedance or a value that
express both magnitude and phase of impendence, should impendence
be represented in a polar form, or expresses a real impendence only
or both a real and complex impendence, should impedance be
represented in a Cartesian form. Based on the calculated impedance
values, capacitance values and/or predetermined relationships,
various sensor properties can be determined and/or characterized,
such as one or more of the sensor properties discussed herein.
[0325] The sensor information can then be used to determine if the
analyte sensor is functioning properly or not, and/or to calibrate
the sensor. For example, the techniques described herein can be
used to generate calibration information (e.g., one or more of
baseline, sensor sensitivity, and temperature information) that can
in turn be used to form or modify a conversion function, or
calibration factor, used to convert sensor data (e.g., in units of
electrical current) into blood glucose data (e.g., glucose
concentration values in units of mg/dL or mmol/L), as described in
more detail elsewhere herein. The sensor information can
alternatively or additionally be used to first correct uncalibrated
sensor data (e.g., raw sensor data) and then apply a conversion
function to convert the corrected, uncalibrated data to calibrated
sensor data (e.g., glucose concentration values in units of glucose
concentration).
[0326] For example, one technique that can be used to determine
properties of a system being used (e.g., an analyte sensor) is
Electrochemical Impedance Spectroscopy (EIS). EIS is an
electrochemical technique based on the measurement of electrical
impedance of the system being used over a range of different
frequencies. Changes in the system being used can reflect changes
in the frequency spectrum. As an example, a reduction in impedance
may be observed at a particular frequency over a time period if the
system being used has a sensitivity change over that period of
time. Other techniques can also be used to determine properties of
a system being used as discussed further below.
[0327] As one illustrative example of how a stimulus signal can be
used to determine sensor properties, reference will now be made to
a schematic diagram of an equivalent sensor circuit model 900
illustrated in FIG. 9. Sensor circuit model 900 can represent
electrical properties of an analyte sensor, such as an embodiment
of a continuous glucose sensor. Circuit 900 includes working
electrode 904 and reference electrode 902. Operatively connected in
serial to reference electrode is Rsolution, representative of a
resistance of bulk between the working and reference electrodes.
The bulk can be a liquid or other medium in which the sensor is
placed, such as a buffer solution in the example of a bench
laboratory study, or, in the example of the use as a subcutaneously
placed sensor, the bulk can be representative of the resistance of
the subcutaneous tissue between the working and reference
electrodes. Operatively connected to Rsolution is Cmembrane,
representative of a capacitance of the sensor membrane, and
Rmembrane, representative of a resistance of the sensor membrane. A
parallel network of Cdouble layer and Rpolarization are operatively
connected to Rmembrane. The parallel network of Cdouble layer and
Rpolarization is representative of the reactions occurring at the
surface of a platinum interface of the working electrode. In
particular, Cdouble layer is representative of the charge that is
built up when a platinum electrode is in the bulk and Rpolarization
is the polarization resistance of the electrochemical reactions
that occur at the platinum interface.
[0328] FIG. 10 is a Bode plot (i.e. |Z.sub.real| vs. log w, wherein
Z.sub.real is real impedance, .omega.=2.pi.f and f is frequency) of
an analyte sensor in accordance with one embodiment. The analyte
sensor can have properties of sensor circuit model 900 of FIG. 9.
Referring back to the Bode plot of FIG. 10, the x-axis is the
frequency of a stimulus signal applied to the analyte sensor and
the y-axis is the impedance derived from a response signal of the
analyte sensor. Although not wishing to be bound by theory, it is
believed that different frequencies can be used to measure or
determine different material properties of the sensor. For example,
in the plot of FIG. 10, the impedance value derived from the
measured response to an input signal having a frequency of about 7
Hz can be indicative of Cdouble layer, a frequency of about 1 kHz
can be indicative of Rmembrane, and a frequency in the range of
about 10-20 kHz can indicative of Cmembrane.
[0329] Based on this information, one can determine a state of
particular properties of the sensor by applying a stimulus signal
of a particular frequency or comprising a plurality of frequencies
to the sensor and determining a sensor impedance based on the
signal response. For instance, a capacitance of a sensor having the
characteristics of the Bode plot of FIG. 10 can be determined using
a stimulus signal having a frequency in the range of about 1 Hz to
100 Hz, for example 10 Hz or greater than 10 kHz. In addition, a
resistance of a sensor can be determined using a stimulus signal
having a frequency in the range of about 100 Hz to 10 kHz, for
example 1 kHz.
[0330] FIG. 11 is a flowchart illustrating a process 1100 for
determining an impedance of a sensor in accordance with one
embodiment. At step 1102, a stimulus signal in the form of an
active current (ac) voltage at a given frequency is applied to a
working electrode of the sensor being studied. The ac voltage can
be overlayed on a bias potential and can be relatively small as
compared to the bias potential, such as a voltage that is in the
range of about 1% to 10% of the bias voltage. In one embodiment,
the ac voltage is a sine wave having an amplitude in the range of
10-50 mV and a frequency in the range of between about 100-1 kHz.
The sine wave can be overlayed on a 600 mV bias voltage. The
response signal (e.g., in units of current) can then be measured in
step 1104 and analyzed in step 1106 to determine an impedance at
the given frequency. Should the impedance of the sensor at a range
of frequencies be of interest, process 1100 can be repeated by
applying an ac voltage at each frequency of interest and analyzing
a corresponding output response.
[0331] Reference will now be made to FIG. 12, which describes a
process for determining an impedance or plurality of impedances of
a sensor being studied by applying one or more stimulus signals and
converting the response signal or signals to a frequency domain in
accordance with one embodiment. The data can be converted to the
frequency domain using a Fourier transform technique, such as a
fast Fourier transform (FFT), discrete time Fourier transform
(DTFT) or the like. At step 1202, a stimulus signal in the form of
a voltage step can be applied to a bias voltage of the sensor. The
voltage step can be in the range of 10-50 mV, for example 10 mV,
and the bias voltage can be 600 mV. The signal response can then be
measured and recorded (e.g., an output current) at step 1204, and a
derivative of the response can be taken at step 1206. At step 1208,
a Fourier transform of the derivative of the response can then be
calculated to yield ac currents in the frequency domain. One or
more impedances of the sensor over a wide spectrum of frequencies
can then be calculated based on the ac currents at step 1210.
[0332] FIG. 13 is a flowchart of process 1300 for determining an
impedance of a sensor being studied, such as the impedance of the
sensor's membrane, in accordance with one embodiment. At step 1302,
a stimulus signal in the form of a voltage step above a bias
voltage is applied to the sensor. The signal response is measured
at step 1304, and, at step 1306, a peak current of the response is
determined. Next, at step 1308, one or more impedance
characteristics (e.g., resistance) of the sensor membrane (e.g.,
Rmembrane) is calculated based on the peak current. The one or more
impedance characteristics can then be correlated to a property of
the sensor.
[0333] In an alternative embodiment, instead of calculating a
sensor impedance based on the peak current, the peak current can be
correlated to one or more predetermined sensor relationships to
determine a property of the sensor, such as the sensor's
sensitivity. That is, in the alternative embodiment, the step of
calculating the one or more impendence characteristics is
omitted.
[0334] The relationship between a signal response resulting from a
stimulus signal in the form of a voltage step and a sensor membrane
resistance of embodiments of analyte sensors will now be discussed
further with reference to FIGS. 14A, 14B and FIG. 9.
[0335] FIG. 14A is a graph of an input voltage 1400 applied to an
analyte sensor over time in accordance with one embodiment. The
input voltage 1400 applied to the analyte sensor initially
corresponds to the bias voltage, which in one embodiment is about
600 mV. A stimulus signal in the form of a voltage step is then
applied to the input voltage at time t1. The magnitude of the
voltage step, Av, can be in the range of 10-50 mV, for example 10
mV.
[0336] FIG. 14B is a graph of a current response 1402 of the
analyte sensor to the input voltage 1400 of FIG. 14A. As
illustrated in FIG. 14B, the current response 1402 can include a
sharp spike in current starting at time t2, which corresponds to
the time in which the voltage step begins to impact the response.
The current response 1402 includes a peak current at point 1404 and
then the current response 1402 gradually decreases and levels off
to a slightly higher level due to the increase in input voltage
1400 as compared to before the voltage step.
[0337] In one embodiment, a change in current, .DELTA.i, measured
as the difference between the magnitude of the current response
1402 prior to the voltage step and the peak current 1404 resulting
from the voltage step, can then be used to estimate the sensor
membrane resistance, such as Rmembrane in FIG. 9. In one
embodiment, an estimated sensor membrane resistance can be
calculated in accordance with Ohms Law, where
Rmembrane=.DELTA.v/.DELTA.i
[0338] As discussed above, .DELTA.v is the step voltage increase
and .DELTA.i is the change in current response due to the step
voltage increase.
[0339] Although not wishing to be bound by theory, it is believed
that certain embodiments of sensors provide a direct relationship
between a change in current in response to a voltage step to the
sensor's membrane impendence characteristics (e.g.,
resistance).
[0340] As a non-limiting example of such a relationship, the
following description refers back to sensor circuit model 900 of
FIG. 9. In some embodiments, the capacitance, Cmembrane, is much
smaller than the capacitance, Cdouble layer. For example, Cmembrane
can have a value that is about 1/1000 smaller than Cdouble layer.
The bulk resistance, Rsolution, of sensor circuit 900 is typically
much smaller than the resistance Rmembrane, and the resistance,
Rpolarization, can be quite large, such as around 3 MOhms. Due to
such sensor properties, a voltage step, Av, applied to circuit 900
can cause the current to flow substantially through circuit 900
along a path from lead 902, through Rsolution, Rmembrane, Cdouble
layer and finally to lead 904. Specifically, because capacitive
resistance is inversely proportional to the capacitance and the
frequency, the capacitive resistance of Cmembrane is initially very
large due to the voltage step, as the voltage step is,
theoretically, an extremely high frequency. Substantially all of
the current flows through Rmembrane, rather than through Cmembrane
because of the high capacitive resistance of Cmembrane. Further,
the current substantially flows through Cdouble layer instead of
Rpolarization because the capacitive resistance of Cdouble is
initially small due to the voltage step (high capacitive value of
Cdouble layer results in low capacitive resistance at high
frequencies, e.g., at the time of a voltage step) and the
relatively large resistance of Rpolarization. Consequently, the
initial total resistance through which substantially all of the
current flows through circuit 900 when the step voltage is applied
to circuit 900 can be summed up as the series resistance of
Rsolution plus Rmembrane. However, because Rsolution is much
smaller than Rmembrane in this example, the total resistance can be
estimated as the membrane resistance, Rmembrane.
[0341] Thus, because the membrane resistance, Rmembrane, is
essentially the resistance of the circuit 900 at the time of the
voltage step, it has been found that the value of Rmembrane can be
estimated using Ohms Law using the known value of the step
increase, Av, and the measured change in current response,
.DELTA.i, due to the voltage step.
[0342] a. Sensitivity
[0343] As discussed herein, a sensor's sensitivity to analyte
concentration during a sensor session can often change as a
function of time. This change in sensitivity can manifest itself as
an increase in current for a particular level of sensitivity. In
some embodiments, the sensitivity increases during the first 24-48
hours with a relative change in tens of percents. In order to
provide an accurate analyte concentration reading to a user, system
calibrations using reference meters (e.g., strip-based blood
glucose measurements) may be needed. Typically, the rate of
calibrations can be 1, 2 or more calibrations a day.
[0344] As discussed further below, a relationship between
sensitivity and impedance has been observed in embodiments of
analyte sensors. Although not wishing to be bound by theory,
embodiments of analyte sensors are believed to have a relationship
between an impedance of a sensor's membrane and the diffusivity of
the membrane. For example, a change in impedance of an analyte
sensor can indicate a proportional change in diffusivity of the
analyte sensor's membrane. Further, an increase in diffusivity can
yield an increased transport of the analyte being measured (e.g.,
glucose) through the membrane, resulting in an increased sensor
output current. That is, a change in diffusivity can result in a
proportional change in sensor sensitivity. It is noted that other
factors may also contribute to changes in sensitivity apart from
just changes in diffusivity of the sensor membrane, depending upon
the characteristics of sensor and the environment in which the
sensor is used.
[0345] A relationship between sensitivity and impedance can be used
to estimate a sensor sensitivity value and/or correct for
sensitivity changes of the sensor over time, resulting in increased
accuracy, a reduction in required calibrations or both. In addition
to detection of sensitivity, some embodiments can detect other
characteristics of an analyte sensor system based on measurements
of electrical impedance over one or more frequencies. These
characteristics include, but are not limited to, temperature,
moisture ingress into sensor electronics components and sensor
membrane damage.
[0346] In some exemplary embodiments, a relationship between a
sensor's impedance and the sensor's sensitivity can be used to
calculate and compensate for sensitivity changes of an analyte
sensor. For example, a change in impedance of an analyte sensor can
correspond to a proportional change in sensitivity of the sensor.
In addition, an absolute value of an impedance of an analyte sensor
can correspond to an absolute value of the analyte sensor's
sensitivity and the corresponding sensitivity value can be
determined based on a predetermined relationship determined from
prior studies of similar sensors. Sensor data can then be
compensated for changes in sensitivity based on an
impedance-to-sensitivity relationship.
[0347] FIG. 15 is a flowchart of an exemplary process 1500 for
compensating sensor data for changes in sensitivity in accordance
with one embodiment. At step 1502, a stimulus signal can be applied
to the sensor that can be used to determine an impedance of the
sensor's membrane, such as a signal having a given frequency, as
discussed with respect to FIG. 11, or a voltage step, as discussed
with respect to FIGS. 12-14. A response to the applied signal is
then measured at step 1504 and an impedance of the sensor's
membrane is determined based on the response at step 1506. Next, at
step 1508, the determined impedance is compared to an established
impedance-to-sensor sensitivity relationship. The established
relationship can be determined from prior studies of analyte
sensors that exhibit similar sensitivity-to-impedance relationships
as the analyte sensor currently being used; for example, sensors
that were made in substantially the same way under substantially
the same conditions as the sensor currently being used. At step
1510, a sensor signal (e.g., in units of electrical current or
counts) of the sensor currently being used is corrected using the
impedance to sensitivity relationship. An estimated analyte
concentration value or values is then calculated based on the
corrected sensor signal at step 1512 using, for example, a
conversion function. The estimated analyte concentration values can
then be used for further processing and/or outputting, such as
triggering alerts, displaying information representative of the
estimated values on a user device and/or outputting the information
to an external device.
[0348] It should be understood that process 1500 is but one example
of using an impedance of a sensor to compensate for changes in
sensor sensitivity, and that various modifications can be made to
process 1500 that fall within the scope of the embodiments For
example, an established impedance-to-sensitivity relationship can
be used to determine a sensitivity value of the sensor being used,
and the sensitivity value can then be used to modify or form a
conversion function used to convert a sensor signal of the sensor
being used into one or more estimated glucose concentration values.
In addition, instead of calculating an impedance based on the
stimulus signal response, one or more properties of the stimulus
signal response (e.g., peak current value, counts, etc.) can be
directly correlated to a sensitivity based on a predetermined
relationship between the stimulus signal property and the
sensitivity.
[0349] Some embodiments use one or more impedance values of the
sensor to form, modify or select a sensitivity profile of an
analyte sensor. As discussed above, a sensor can have a sensitivity
profile that indicates the sensor's change in sensitivity over
time. Although sensors made in substantially the same way under
substantially the same conditions can exhibit similar sensitivity
profiles, the profiles can still vary. For example, the environment
in which a particular sensor is used can cause the sensor's
sensitivity profile to differ from other, similar sensors.
Accordingly, some embodiments can, for example, select a
sensitivity profile out of a plurality of predetermined sensitivity
profiles based on a correlation of the calculated one or more
impedance values to the selected sensitivity profile. Further, some
embodiments modify a sensor sensitivity profile already associated
with the analyte sensor being used to more closely predict the
sensor's sensitivity profile, where the modification is based on
the one or more impedance values.
[0350] FIG. 16 is a flowchart of an exemplary process 1600 for
determining a predicted sensitivity profile using one or more
sensor membrane impedance measurements in accordance with one
embodiment. At step 1602, a stimulus signal is applied to an
analyte sensor being used and a response is measured at step 1604.
Next, one or more sensor membrane impedance values are calculated
based on the response at step 1606. Various techniques for
calculating sensor membrane impedance values based on the response
that can be used in process 1600 are described elsewhere herein,
such as one or more of the techniques discussed with reference to
FIGS. 11-14. A sensitivity profile is then determined based on the
one or more calculated impedance values in step 1608. Process 1600
then calculates (which can include retrospectively correcting
and/or prospectively calculating) estimated analyte concentration
values using the determined sensitivity profile. The estimated
analyte concentration values can then be used for further
processing and outputting, such as displaying information
representative of the estimated values on a user device and/or
outputting the information to an external computing device.
[0351] Further to step 1608, various techniques can be used to
determine the sensitivity profile. One exemplary technique can
compare the one or more calculated impedance values to a plurality
of different predicted sensitivity profiles and select a predicted
sensitivity profile that best fits the one or more calculated
impedance values. The plurality of different predicted sensitivity
profiles can be predetermined and stored in computer memory of
sensor electronics, for example. Another technique that can be used
includes using an estimative algorithm to predict or determine a
sensitivity profile based on the one or more calculated impedance
values. A further technique includes determining a sensitivity
profile by modifying a sensitivity profile associated with the
sensor being used (e.g., a sensor profile previously used to
generate estimated glucose values using the sensor). Modifying the
sensitivity profile can include using an estimative algorithm to
modify the sensitivity profile to more closely track the
sensitivity profile of the sensor being used based on the one or
more calculated impedance values.
[0352] Some embodiments compare one or more impedance values of an
analyte sensor being used to a predetermined or predicted
sensitivity profile associated with the sensor to determine if the
sensor is functioning properly. As discussed above, a sensor can be
predicted to have a particular sensitivity profile based on, for
example, a study of sensitivity changes over time of sensors made
in substantially the same way and used under substantially the same
conditions. However, it can be determined that a sensor is
functioning improperly--due to, for example, improper sensor
insertion, damage to the sensor during shipping, manufacturing
defects and the like--if the sensor is found not to be sufficiently
tracking its predicted sensitivity profile based on sensitivities
derived from impedance measurements of the sensor. Put another way,
it can be determined that a sensor is not functioning properly if
one or more impedance values of a sensor's membrane do not
sufficiently correspond to a predicted sensitivity profile (e.g.,
because the impedance of a sensor membrane can indicate a
sensitivity of the sensor) of the sensor.
[0353] FIG. 17 is a flowchart of an exemplary process 1700 for
determining whether an analyte sensor being used is functioning
properly based on a predicted sensitivity profile and one or more
impedance measurements. At step 1702, a stimulus signal is applied
to an analyte sensor being used and a response is measured at step
1704. Next, one or more sensor membrane impedance values are
calculated based on the signal response at step 1706. Various
stimulus signals and techniques for calculating sensor membrane
impedance values based on the signal response that can be used in
process 1700 are described elsewhere herein, such as with any one
of the techniques discussed with reference to FIGS. 11-14. Process
1700 then determines a correspondence of the one or more calculated
impedance values to a sensitivity profile in step 1708. Next, in
decision step 1710, process 1700 queries whether the one or more
calculated impedance values sufficiently correspond to the
predicted sensitivity profile. If it is determined that the one or
more calculated impedance values sufficiently correspond to the
predicted sensitivity profile, then process 1700 confirms proper
operation of the analyte sensor being used. If confirmed to be
proper in step 1710, process 1700 may then be repeated after a
predetermined time delay ranging from about 1 minute to 1 day, for
example about 10 minutes, 1 hour, 12 hours, or 1 day. However,
process 1700 initiates an error routine 1712 if it is determined
that the one or more calculated impedance values do not
sufficiently correspond to the predicted sensitivity profile. Error
routine 1712 can include one or more of triggering and audible
alarm, displaying an error message on a user display, discontinuing
display of sensor data on a user display, sending a message to a
remote communication device over a communication network, such as a
mobile phone over a cellular network or remote computer over the
internet, and the like. The error routine can also include
modifying the predicted sensitivity profile--based on the one or
more impedance measurements, for example--or selecting a new
predicted sensitivity profile based on the one or more impedance
measurements. The modified predicted sensitivity profile or new
predicted sensitivity profile can be a sensitivity profile that
more closely corresponds to changes in sensitivity of the sensor
being used based on the one or more impedance measurements as
compared to the unmodified or previously used predicted sensitivity
profile.
[0354] Further to step 1708 of process 1700, various statistical
analysis techniques can be used to determine a correspondence of
the one or more impedance values to the predicted sensitivity
profile. As one example, correspondence can be determined based on
whether a sensitivity value derived from the calculated impedance
value (e.g., derived from a predetermined relationship of impedance
and sensitivity) differs by no less than a predetermined threshold
amount from a predicted sensitivity value as determined from the
predicted sensitivity profile. The predetermined threshold amount
can be in terms of an absolute value or a percentage. As another
example, correspondence can be determined based on a data
association function. The term "data association function," as used
herein, is a broad term and is used in its ordinary sense,
including, without limitation, a statistical analysis of data and
particularly its correlation to, or deviation from, from a
particular curve. A data association function can be used to show
data association. For example, sensor sensitivity data derived from
impedance measurements described herein may be analyzed
mathematically to determine its correlation to, or deviation from,
a curve (e.g., line or set of lines) that defines a sensor
sensitivity profile; this correlation or deviation is the data
association. Examples of a data association function that can be
used includes, but is not limited to, linear regression, non-linear
mapping/regression, rank (e.g., non-parametric) correlation, least
mean square fit, mean absolute deviation (MAD), and mean absolute
relative difference. In one such example, the correlation
coefficient of linear regression is indicative of the amount of
data association of sensitivity data derived from impedance
measurements from a sensitivity profile, and thus the quality of
the data and/or sensitivity profile. Of course, other statistical
analysis methods that determine a correlation of one or more points
to a curve can be used in process 1700 in addition to those
described herein.
[0355] As discussed above, processes 1600 and 1700 can use one or
more impedance values. When more than one impedance value is used,
each impedance value can be time-spaced from the other impedance
value(s). In other words, one impedance value can be taken at a
first point in time t1 (indicative of a sensor impedance at time
t1), a second impedance value can be taken at a second, later point
in time t2 (indicative of a sensor impedance at time t2), and third
impedance value taken at a third, even later point in time t3
(indicative of a sensor impedance at time t3), and so on. Further,
the time between t1 and t2 can be a first amount of time and the
time between t2 and t3 can be a second amount of time that is
either the same or different than the first amount of time. The
time-spaced impedance values can then be used separately or
combined using a statistical algorithm (e.g., calculating an
average or median value of the time-spaced values). The separate
values or combined value can then be used to determine a
sensitivity value and/or sensitivity profile in step 1608 of
process 1600 or determine a correspondence with a sensitivity
profile in step 1708 of process 1700, for example. Additionally or
alternatively, more than one of the impedance values can be taken
at substantially the same time, but each derived using a different
measurement technique, such as using two of the measurement
techniques described herein. For example, a first impedance can be
calculated using a step voltage technique as described in the
process of FIG. 13, and a second impedance can be calculated using
a sine wave overlay technique as described in the process of FIG.
11. The impedance values derived from different measurement
techniques can then be applied to a statistical algorithm (e.g.,
calculating an average or median value) to determine a processed
impedance value. The processed impedance value can then be used to
determine a sensitivity value and/or sensitivity profile in step
1608 of process 1600 or determine a correspondence with a
sensitivity profile in step 1708 of process 1700, for example.
[0356] b. Temperature
[0357] Some embodiments can use signal processing techniques to
determine a temperature of the sensor. For example, a stimulus
signal can be applied to a sensor and a signal response measured
and, based on the signal response, a temperature of the sensor can
be derived.
[0358] An impedance of a sensor membrane, as determined using one
of the techniques described with reference to FIGS. 11-14, for
example, can be used to estimate a temperature of the sensor in
accordance with one embodiment. Although not wishing to be bound by
theory, it is believed that sensitivity of a sensor is affected by
temperature, where a higher temperature can result in a higher
sensitivity and a lower temperature can result in a lower
sensitivity. Similarly, because an impedance of a sensor membrane
can have a direct relationship to the sensor's sensitivity, it is
believed that a higher temperature can result in lower impedance
and a lower temperature can result in higher impedance. That is,
sensitivity and impedance can have a direct relationship to the
sensor's temperature. Accordingly, using a known relationship
between impedance and temperature--based on previously conducted
studies of substantially similar sensors, for example--one can
estimate a sensor's temperature based on a sensor impedance
measurement.
[0359] FIG. 18 is a flowchart of an exemplary process 1800 for
determining a sensor temperature in accordance with one embodiment.
At step 1802, a stimulus signal is applied to an analyte sensor
being used, and a response is measured and recorded at step 1804.
Impedance is calculated based on the signal response at step 1806.
The impedance can be calculated using, for example, any of the
techniques described herein such as those described with reference
to FIGS. 11-14. A temperature of the sensor is then estimated based
on a predetermined relationship between impedance and temperature
at step 1808. The temperature can then be used to estimate analyte
concentration values (e.g., glucose concentration) using sensor
data or otherwise used for further processing and/or outputting.
For example, the temperature can be used to compensate for
temperature effects on sensor sensitivity, more accurate analyte
concentration values can be estimated based on the sensitivity
compensation, and the more accurate analyte concentrations can be
outputted to a display or used to trigger an alert using a glucose
monitoring system.
[0360] A relationship between sensor sensitivity and different
temperatures can be mathematically modeled (e.g., by fitting a
mathematical curve to data using one of the modeling techniques
described herein), and the mathematical model can then be used to
compensate for temperature effects on the sensor sensitivity. That
is, a sensitivity of a sensor (which is affected by the sensor's
temperature) can be determined based on associating a measured
impedance of the sensor to the mathematical curve. The
predetermined relationship between impedance and temperature can be
determined by studying impedances of similar sensors over a range
of temperatures. Sensor data can then be converted to estimated
analyte concentration values based on the determined sensor
sensitivity.
[0361] As a non-limiting example, some embodiments of analyte
sensors can have an essentially linear relationship of impedance to
temperature after a sensor run-in period (e.g., a period of time
after sensor implantation in which the sensor stabilizes, which can
last one to five hours in some embodiments). The slope of the
linear relationship can be established by studying sensors made in
substantially the same way as the sensor being used over a range of
temperatures. Thus, a sensor temperature can be estimated by
measuring an impedance value of the sensor's membrane and applying
the impedance value to the established linear relationship. Other
embodiments can have a non-linear relationship of impedance to
temperature and with these other embodiments the relationship can
be characterized using an established non-linear relationship.
[0362] Some embodiments can compare a first sensor temperature,
where the first temperature is derived from an impedance
measurement of an analyte sensor, with a second sensor temperature,
where the second sensor temperature is derived independent from the
impedance measurement. The second estimated temperature can be
measured using a thermistor, for example. In the example of using a
thermistor, the thermistor can be configured to measure an in vivo
or ex vivo temperature, and can be located on the analyte sensor or
separate from the analyte sensor. As non-limiting examples, the
thermistor can be integral with the analyte sensor, positioned on
the surface of the skin of a host adjacent to an insertion site in
which the analyte sensor is implanted, positioned on the skin of
the host at a location away from the insertion site or spaced apart
from the host entirely, such as on a handheld device carried by the
host. Factors contributing to a change in sensor sensitivity or a
change in other sensor properties can then be determined or
confirmed based, at least in part, on the comparison of the first
and second temperatures.
[0363] c. Moisture Ingress
[0364] In some embodiments, moisture ingress into sensor
electronics can be determined based on measuring an impedance of
the sensor at a particular frequency or range of frequencies.
Should the measured impedance not correspond sufficiently with
predetermined impedance value(s), then a sensor system operatively
connected to the sensor can initiate a moisture ingress error
routine. Correspondence can be determined using one or more
threshold, a data association function, and the like. Further, it
should be noted that it has been found that impedance phase
information can provide beneficial information in determining
moisture egress. Accordingly, in some embodiments, the impedance
measurement can be broken into separate impendence magnitude and
phase components, and one or both impendence components can be
compared to predetermined values to determine the
correspondence.
[0365] FIG. 19 is a flowchart of an exemplary process 1900 for
determining moisture ingress. At step 1902, a stimulus signal
having a particular frequency or a signal comprising a spectrum of
frequencies (e.g., a voltage step) is applied to an analyte sensor
being used, and a signal response is measured and recorded at step
1904. Impedance magnitude and phase is calculated based on the
signal response at step 1906. Process 1900 then determines whether
the impedance magnitude and phase values fall within respective
predefined levels at decision step 1908. If the impedance magnitude
and phase values exceed one or both of the respective predefined
levels, then process 1900 initiates an error routine at step 1910.
The error routine can include one or more of triggering an audible
alarm and/or visual alarm on a display screen to alert a user that
the sensor system may not be functioning properly. The alarm can
notify a user that the current sensor system is defective, for
example. If, on the other hand, one or both of the impedance and
phase values fall within the respective predefined levels, then
process 1900 ends.
[0366] Although the above description describes calculating
separate impedance and phase values, it is understood that the
above process 1900 can determine a complex impedance value and
determine a correspondence of the determined complex impedance
value to one or more predetermined complex impedance value(s), such
as by using a data association function or comparing the determined
complex impedance to a threshold or range of predetermined complex
impendence values. The error routine can then be initiated
responsive to the correspondence.
[0367] d. Membrane Damage
[0368] In some embodiments, membrane damage can be detected based
on measuring an impedance at a particular frequency or range of
frequencies. Should the measured impedance not correspond
sufficiently with predetermined one or more impedance values, then
a sensor system operatively connected to the sensor can initiate a
membrane damage error routine. Correspondence can be determined
using a data association function.
[0369] FIG. 20 is a flowchart of an exemplary process 2000 for
determining membrane damage. At step 2002, a stimulus signal having
a particular frequency, multiple signals having different
frequencies and/or a signal comprising a spectrum of frequencies is
applied to an analyte sensor being used, and the signal response(s)
is/are measured and recorded at step 2004. Both impedance magnitude
and phase is calculated based on the signal response(s) at step
2006. Process 2000 then determines whether the impedance magnitude
and phase value(s) fall within respective predefined levels at
decision step 2008. If the impedance magnitude and phase values
exceed one or both of the respective predefined levels, then
process 2000 initiates an error routine at step 2010. The error
routine can include one or more of triggering an audible alarm
and/or visual alarm on a display screen to alert a user that the
sensor system is not functioning properly. The alarm can notify a
user that the currently used sensor is damaged and needs to be
replaced, for example. If, on the other hand, one or both of the
impedance magnitude and phase values fall within the respective
predefined levels, then process 2000 ends.
[0370] Although the above description describes using separate
impedance magnitude and phase values, it is understood that the
above process 2000 can use a complex impedance value and determine
a correspondence of the complex impedance value to a predetermined
values or levels. The error routine can be initiated responsive to
the determined correspondence.
[0371] e. Sensor Reuse
[0372] In some embodiments, sensor reuse can be detected.
Embodiments of glucose sensors described herein may have a defined
life in which a sensor can provide reliable sensor data. After the
defined life, the sensor may no longer be reliable, providing
inaccurate sensor data. To prevent use beyond the predefined life,
some embodiments notify a user to change the sensor after it has
been determined that the sensor should no longer be used. Various
methods can be used to determine whether a sensor should no longer
be used, such as a predetermined amount of time transpiring since
the sensor was first used (e.g., when first implanted into a user
or when first electrically connected to a sensor electronics
module) or a determination that the sensor is defective (e.g., due
to membrane rupture, unstable sensitivity and the like). Once it is
determined that the sensor should no longer be used, the sensor
system can notify a user that a new sensor should be used by
audibly and/or visually prompting a user to use a new sensor and/or
shutting down a display or ceasing to display sensor data on the
display, for example. However, a user may try to reuse the same
sensor instead of using a new sensor. This can be dangerous to the
user because the sensor may provide inaccurate data upon which the
user may rely.
[0373] Accordingly, some embodiments can be configured determine
sensor reuse based at least in part on one or more measurements of
the impedance of the sensor. As discussed in more detail elsewhere
herein, the impedance that relates to the membrane resistance of a
sensor is typically initially high and then gradually decreases as
the sensor is run-in. Thus, in one embodiment, sensor re-use can be
detected if an impedance measured soon after sensor implantation is
greater than what a sensor should typically have when a sensor is
initially implanted, as this can indicate that the sensor had
already been used.
[0374] FIG. 21 is a flowchart of an exemplary process 2100 for
determining sensor reuse in accordance with one embodiment. At step
2102, a sensor insertion event is triggered. An insertion event can
be one of any number of possible events that indicate a new sensor
has been implanted, such as a user providing input to a sensor
system that a new sensor has been implanted, the sensor system
detecting electrical connection to a sensor, a predetermined amount
of time transpiring since the system prompted a user to use a new
sensor, and the like. Next, at step 2104, a stimulus signal is
applied to an analyte sensor being used, and a response is measured
and recorded at step 2106. Impedance is calculated based on the
signal response at step 2108. The stimulus signal and technique for
calculating impedance in steps 2106 and 2108 can be any of the
signals and techniques described herein such as those described
with reference to FIGS. 11-14. Then, at decision step 2110, the
calculated impedance is compared to a predetermined threshold.
Should it be determined that the impedance exceeds the threshold,
then a sensor reuse routine is initiated at step 2112. If it is
determined in decision step 2110 that the impedance does not exceed
the threshold, then the process 2100 ends at step 2114.
[0375] The sensor reuse routine of step 2112 can include triggering
an audible and/or visual alarm notifying the user of improper
sensor reuse. The alarm can also inform the user why sensor reuse
may be undesirable, such as potentially providing inaccurate and
unreliable sensor readings. The sensor reuse routine 2112 can
alternatively or additionally cause the sensor system to fully or
partially shut down and/or cease display of sensor data on a user
interface of the sensor system.
[0376] In an embodiment, recent impedance measurement information
(e.g., one or more recent impedance measurement values) of a
previously-used sensor (such as the sensor used immediately prior
to the newly implanted sensor) or a predetermined sensor profile
can be stored in computer memory and compared to impedance
measurement information of a newly implanted sensor (e.g., what is
supposed to be a newly used sensor). It can then be determined that
the sensor is being reused should the comparison indicate that the
impedances of the previously used sensor at a time close to when
the use of the prior sensor was discontinued (e.g., removed) and
the newly inserted sensor are too similar using, for example, a
data association function, as a new sensor should have a
significantly different impedance soon after initial implantation
than the impedance of the previously used sensor substantially
prior to its discontinuation of use. If it is determined that the
sensor is being reused, then the sensor system can trigger an error
routine, which can include notifying the user via audible and/or
visual alarms using a user interface of the sensor system of
improper sensor reuse and prompting the user to use a new sensor,
as discussed above with respect to step 2112.
[0377] FIG. 22 is a flowchart of another exemplary process 2200 for
determining sensor reuse in accordance with one embodiment. At step
2202, a sensor insertion event is triggered. A an insertion event
can be one of any number of possible events that indicate a new
sensor has been implanted, such as a user providing input to a
sensor system that a new sensor has been implanted, the sensor
system detecting electrical connection to a sensor, a predetermined
amount of time transpiring since the system prompted a user to use
a new sensor, and the like. Next, at step 2204, a stimulus signal
is applied to an analyte sensor being used, and a response is
measured and recorded at step 2206. Impedance is calculated based
on the signal response at step 2208. The stimulus signal and
technique for calculating impedance in steps 2206 and 2208 can be
any of the signals and techniques described herein such as those
described with reference to FIGS. 11-14. Then, at decision step
2210, the calculated impedance is compared to one or more
previously measured impedance values measured using one or more
previously implanted sensors (at what at least should have been a
previously implanted sensor according to the sensor system). Should
it be determined that the calculated impedance correlates within a
predetermined amount with the one or more previously measured
impedance measurements, then a sensor reuse routine is initiated at
step 2112. Correlation can be determined using a data association
function, such as one of the data association functions described
herein. If it is determined in decision step 2110 that the
impedance does not correlate within the predetermined amount to
previously measured impedance values, then the process 2100 ends at
step 2114, wherein the system continues to use the sensor to
measure glucose concentrations of the host.
[0378] The sensor reuse routine of step 2212 can include triggering
an audible and/or visual alarm notifying the user of improper
sensor reuse. The alarm can also inform the user why sensor reuse
may be undesirable, such as potentially providing inaccurate and
unreliable sensor readings. The sensor reuse routine 2212 can
alternatively or additionally cause the sensor system to fully or
partially shut down and/or cease display of sensor data on a
display of the sensor system.
[0379] f. Sensor Overpotential
[0380] Some embodiments apply an overpotential routine based on one
or more measured impedances of the sensor membrane. It has been
found that applying an overpotential (e.g., a voltage potential
greater than the bias potential applied to the sensor when used for
continuous analyte sensing) to some embodiments of analyte sensors
can aid in stabilizing the analyte sensor, thereby reducing a
run-in period of the sensor. The overpotential may need to be
discontinued once the sensor has sufficiently stabilized, otherwise
damage to the sensor can occur. Accordingly, one or more impedance
measurements of the sensor can be used to determine a sensitivity
or change in sensitivity of the sensor. Any of the techniques
described herein, such as those described with reference to FIGS.
11-14, can be used to measure an impendence of the sensor. The
determined sensitivity or sensitivity change can be, in turn, used
to indicate whether or not the sensor has stabilized or will
stabilize within a determined amount of time by, for example,
determining a correspondence of the measured impedance to
predetermined sensitivity-to-impedance relationships. Upon
determining that the sensor has stabilized or will stabilize within
a determined amount of time, application of the overpotential can
be discontinued or reduced. In addition, a magnitude of an
overpotential and/or length of time in which the overpotential is
to be applied to the sensor can be determined or modified based on
one or more impedance measurements taken prior to application of
the overpotential or during application of the overpotential. That
is, an overpotential routine can be modified or discontinued
according to one or more impedance measurements in accordance with
some embodiments.
[0381] g. Multi-Electrode or Multi-Sensor Configuration
[0382] Some embodiments of sensor systems comprise a plurality of
sensor electrodes. For example, as discussed above, in addition to
an analyte sensing electrode, some embodiments can include an
auxiliary electrode to allow for the subtraction of a baseline
signal from an analyte+baseline signal. Some embodiments can also
include one or more redundant analyte sensors.
[0383] In accordance with one embodiment, a first stimulus signal
can be applied to a first sensor and a second stimulus signal can
be applied to a second sensor. The first sensor can be configured
to sense an analyte concentration (e.g., glucose) and, in this
regard, can generate an analyte+baseline signal. The second sensor
can be an auxiliary sensor configured to measure a baseline signal
that can be subtracted from the signal of the first sensor. In
addition, the first stimulus signal can have the same waveform or
different waveform than the second stimulus signal. A response to
the first stimulus signal and a response to the second stimulus
signal can each be measured and recorded. The first response and
the second response can be processed, and sensor characteristics
can be determined based on the processing, such as impedance values
associated with each sensor. The processing can include a
comparison (e.g., using a data association function described
herein) of the first and second response signals to one another
and/or compare each of the first and second response signals to
established relationships. The sensor characteristics can include
any of the sensor characteristics described herein, including a
sensitivity value or sensitivity change of the first and/or second
electrodes, a temperature of the first and/or second electrodes,
magnitude of detected membrane damage of the first electrode, and
the like. Further, sensor data can be compensated for changes in
sensor characteristics, such as changes in sensor sensitivity,
based on the processing of the first and second response
signals.
[0384] FIG. 23 is a schematic diagram of a dual-sensor
configuration that can be used in a sensor system in accordance
with some embodiments. The dual-sensor configuration includes a
first sensor 2302, having a working electrode 2304, reference
electrode 2306 and membrane 2308, and a second sensor 2310, also
having a working electrode 2312, reference electrode 2314 and
membrane 2316. Each sensor can be essentially the same, including
having the same type of membrane, or each sensor can be different,
such as having different membranes or even one of the sensors not
having a membrane. In one embodiment, each sensor 2302 and 2310 is
configured to measure an analyte concentration in a host. In an
alternative embodiment, the second sensor 2310 does not have a
discrete reference electrode. In this alternative embodiment, the
reference electrode of the first sensor can also function as the
reference electrode of the second sensor.
[0385] As illustrated in FIG. 23, a stimulus signal can be applied
to the first sensor 2302 using signal generator/detector 2318 (the
stimulus signal can be any stimulus signal described herein, such
as a voltage step). The stimulus signal can elicit electric field
lines 2320 to emanate from the first sensor 2302 and evoke an
electrical response in the second sensor 2310. The electrical
response can be measured using signal generator/detector 2322
electrically connected to the second sensor 2314. The signal
generator/detectors 2318 and 2322 can include any known electrical
circuitry capable of generating a desired stimulus signal discussed
herein and capable of measuring a response to the stimulus
signal.
[0386] Further to FIG. 23, the response measured in the second
sensor 2310 can then be used to determine sensor properties, such
as any sensor properties discussed herein. For example, the
response can be used to calculate an impedance of the first sensor
2302 and thereafter used to determine a sensitivity of the first
sensor 2302 and/or correct sensor data generated by the first
sensor, using one of the processes described herein.
[0387] Alternatively or in addition, a first stimulus signal can be
applied to the first sensor 2302 and measured using the second
sensor 2310 and a second stimulus signal can be applied to the
second sensor 2310 and measured using the first sensor 2302. The
first and second stimulus signals can be essentially the same or
can be different. The responses to each of the first and second
stimulus signals can then be used to determine a sensor property,
such as any of the senor properties discussed herein.
[0388] The sensor to which the stimulus signal is applied can also
be used to measure a response to the stimulus signal in addition to
the other sensor measuring a response to the stimulus signal. For
example, a first stimulus signal can be applied to the first sensor
2302 and first and second responses to the first stimulus signal
can be measured by the first sensor 2302 and the second sensor
2310, respectively. The first and second responses can then be used
to determine sensor properties discussed herein of either or both
of the first sensor 2302 and second sensor 2310.
[0389] h. Scaling Factor
[0390] In some embodiments, a scaling factor can be used to correct
differences in responses of a dual-electrode analyte sensor. In
some embodiments, a dual-electrode analyte sensor that can be used
is a reference sensor/system, whereby reference data can be
provided for calibration (e.g., internal to the system), without
the use of an external (e.g., separate from the system)
analyte-measuring device. In some embodiments, the dual-electrode
analyte sensor includes a first electrode that includes an enzyme
reactive with a particular analyte (which can be referred to as the
plus-enzyme electrode) and a second electrode that does not include
the enzyme (which can be referred to as the minus-enzyme
electrode).
[0391] In some embodiments, the sensor system (such as a sensor
electronics module of the sensor system) is configured to determine
a scaling factor (k). Briefly, a scaling factor defines a
relationship between the electrodes of the dual-electrode analyte
sensor. Accordingly, in some embodiments, the sensor system is
configured to calibrate the analyte sensor data using the scaling
factor, such that the calibrated sensor data does not include
inaccuracies that can arise due to differences between the first
and second working electrodes, respectively. That is, the scaling
factor can be used to calculate estimated analyte values based on
data generated by the sensor system
[0392] U.S. Patent Publication No. US-2009-0242399-A1, the contents
of which are hereby incorporated by reference in its entirety,
describes in more detail dual-electrode analyte sensor systems,
scaling factors and methods for using scaling factors that can be
used in some embodiments.
[0393] In accordance with some embodiments, the membrane impedance
of each electrode of a dual-electrode system can be used to
determine or update a scaling factor. The scaling factor can then
be used to calculate estimated analyte concentration values based
on data generated by the sensor system.
[0394] The following illustrates an exemplary process for
determining a scaling factor using impendence in accordance with
one embodiment. First, membrane impedance is measured for both
electrodes of a dual-electrode sensor system. Techniques described
herein, such as those described with reference to FIGS. 11-14 and
under the heading "Multi-electrode or multi-sensor configuration"
can be used to measure the membrane impendence of each of the
electrodes of the dual-electrode analyte sensor. The impendence can
be measured periodically during sensor use. A scaling factor can be
generated using a ratio of the measured membrane impendence of the
two electrodes (e.g., a ratio of the membrane impendence of a
plus-enzyme electrode and the membrane impedance of a minus-enzyme
electrode). A scaling factor to generate estimated analyte values
can then be determined or updated based on the generated scaling
factor. The determined or updated scaling factor can then be used
to generate estimated analyte values.
[0395] It has been found that acetaminophen can interfere with
glucose measurements using some sensor embodiments. It has also
been found that acetaminophen response can be proportional to its
diffusion through a sensor membrane and sensor impedance can be
indicated of membrane impedance. Thus, the above illustrative
process for determining a scaling factor can be used to
periodically determine an acetaminophen scaling factor by
determining a ratio of the plus-enzyme and minus-enzyme electrode
impedance. The acetaminophen scaling factor can be used to update a
scale factor, such as one of those described above, used to
calculate estimated glucose concentrations.
[0396] i. Calibration
[0397] An exemplary calibration process in accordance with some
embodiments will now be discussed with reference to FIG. 24.
Calibration process 2400 can use one or more of pre-implant
information 2402, internal diagnostic information 2404 and external
reference information 2406 as inputs to form or modify a conversion
function 2408. Conversion function 2408 can be used to convert
sensor data (e.g., in units of current or counts) into estimated
analyte values 2410 (e.g., in units of analyte concentration).
Information representative of the estimated analyte values can then
outputted 2412, such as displayed on a user display, transmitted to
an external device (e.g., an insulin pump, PC computer, mobile
computing device, etc.) and/or otherwise processed further. The
analyte can be, glucose, for example.
[0398] In process 2400, pre-implant information 2402 can mean
information that was generated prior to implantation of the
sensor(s) presently being calibrated. Pre-implant information 2402
can include any of the following types of information: [0399]
predetermined sensitivity profile(s) associated with the currently
used (e.g., implanted) sensor, such a predicted profile of
sensitivity change over time of a sensor; [0400] previously
determined relationships between particular stimulus signal output
(e.g., output indicative of an impedance, capacitance or other
electrical or chemical property of the sensor) to sensor
sensitivity (e.g., determined from prior in vivo and/or ex vivo
studies); [0401] previously determined relationships between
particular stimulus signal output (e.g., output indicative of an
impedance, capacitance or other electrical or chemical property of
the sensor) to sensor temperature (e.g., determined from prior in
vivo and/or ex vivo studies); [0402] sensor data obtained from
previously implanted analyte concentration sensors; [0403]
calibration code(s) associated with a sensor being calibrated, as
discussed herein; [0404] patient specific relationships between
sensor and sensitivity, baseline, drift, impedance,
impedance/temperature relationship (e.g., determined from prior
studies of the patient or other patients having common
characteristics with the patient); [0405] site of sensor
implantation (abdomen, arm, etc.) specific relationships (different
sites may have different vascular density); [0406] time since
sensor manufacture (e.g., time sensor on shelf, date when sensor
was manufactured and or shipped, time between when the sensor was
manufactured an/or shipped and when the sensor is implanted); and
[0407] exposure of sensor to temperature, humidity, external
factors, on shelf.
[0408] In process 2400, internal diagnostic information 2402 can
mean information generated by the sensor system in which the
implanted analyte sensor (the data of which is being calibrated) is
being used. Internal diagnostic information 2402 can include any of
the following types of information: [0409] stimulus signal output
(e.g., the output of which can be indicative of the sensor's
impedance) of sensor using any of the stimulus signal techniques
described herein (the stimulus signal output can be obtained and
processed in real time); [0410] sensor data measured by the
implanted sensor indicative of an analyte concentration (real-time
data and/or previously generated sensor data using the currently
implanted sensor); [0411] temperature measurements using the
implanted sensor or an auxiliary sensor (such as a thermistor)
co-located with the implanted analyte sensor or separately from the
implanted analyte sensor; [0412] sensor data from multi-electrode
sensors; for example, where one electrode of the sensor is designed
to determine a baseline signal as described herein; [0413] sensor
data generated by redundant sensors, where one or more of the
redundant sensors is designed to be substantially the same as at
least some (e.g., have the same sensor membrane type), if not all,
of the other redundant sensors; [0414] sensor data generated by one
or more auxiliary sensors, where the auxiliary sensor is having a
different modality such (as optical, thermal, capacitive, etc.)
co-located with analyte sensor or located apart from the analyte
sensor; [0415] time since sensor was implanted and/or connected
(e.g., physically or electronically) to a sensor electronics of a
sensor system; [0416] data representative of a pressure on
sensor/sensor system generated by, for example, a pressure sensor
(e.g., to detect compression artifact); [0417] data generated by an
accelerometer (e.g., indicative of exercise/movement/activity of a
host); [0418] measure of moisture ingress (e.g., indicative of an
integrity of a moisture seal of the sensor system); and [0419] a
measure of noise in an analyte concentration signal (which can be
referred to as a residual between raw and filtered signals in some
embodiments).
[0420] In process 2400, external reference information 2402 can
mean information generated from sources while the implanted analyte
sensor (the data of which is being calibrated) is being used.
External reference information 2402 can include any of the
following types of information: [0421] real-time and/or prior
analyte concentration information obtained from a reference monitor
(e.g., an analyte concentration value obtained from separate
sensor, such as a finger stick glucose meter); [0422] type/brand of
reference meter (different meters can have different
bias/precision); [0423] information indicative of carbohydrates
consumed by patient; [0424] information from a medicament pen/pump,
such as insulin on board, insulin sensitivity, glucagon on board;
[0425] glucagon sensitivity information; and [0426] information
gathered from population based data (e.g., based on data collected
from sensors having similar characteristics, such as sensors from
the same lot).
Exemplary Sensor System Configurations
[0427] Embodiments of the present invention are described above and
below with reference to flowchart illustrations of methods,
apparatus, and computer program products. It will be understood
that each block of the flowchart illustrations, and combinations of
blocks in the flowchart illustrations, can be implemented by
execution of computer program instructions. These computer program
instructions may be loaded onto a computer or other programmable
data processing apparatus (such as a controller, microcontroller,
microprocessor or the like) in a sensor electronics system to
produce a machine, such that the instructions which execute on the
computer or other programmable data processing apparatus create
instructions for implementing the functions specified in the
flowchart block or blocks. These computer program instructions may
also be stored in a computer-readable memory that can direct a
computer or other programmable data processing apparatus to
function in a particular manner, such that the instructions stored
in the computer-readable memory produce an article of manufacture
including instructions which implement the function specified in
the flowchart block or blocks. The computer program instructions
may also be loaded onto a computer or other programmable data
processing apparatus to cause a series of operational steps to be
performed on the computer or other programmable apparatus to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide steps for implementing the functions specified in the
flowchart block or blocks presented herein.
[0428] In some embodiments, a sensor system is provided for
continuous measurement of an analyte (e.g., glucose) in a host that
includes: a continuous analyte sensor configured to continuously
measure a concentration of the analyte in the host and a sensor
electronics module physically connected to the continuous analyte
sensor during sensor use. In one embodiment, the sensor electronics
module includes electronics configured to process a data stream
associated with an analyte concentration measured by the continuous
analyte sensor in order to process the sensor data and generate
displayable sensor information that includes raw sensor data,
transformed sensor data, and/or any other sensor data, for example.
The sensor electronics module can include electronics configured to
process a data stream associated with an analyte concentration
measured by the continuous analyte sensor in order to process the
sensor data and generate displayable sensor information that
includes raw sensor data, transformed sensor data, and/or any other
sensor data, for example. The sensor electronics module can include
a processor and computer program instructions to implement the
processes discussed herein, including the functions specified in
the flowchart block or blocks presented herein.
[0429] In some embodiments, a receiver, which can also be referred
to as a display device, is in communication with the sensor
electronics module (e.g., via wired or wireless communication). The
receiver can be an application-specific hand-held device, or a
general purpose device, such as a P.C., smart phone, tablet
computer, and the like. In one embodiment, a receiver can be in
data communication with the sensor electronics module for receiving
sensor data, such as raw and/or displayable data, and include a
processing module for processing and/or display the received sensor
data. The receiver can also and include an input module configured
to receive input, such as calibration codes, reference analyte
values, and any other information discussed herein, from a user via
a keyboard or touch-sensitive display screen, for example, and can
also be configured to receive information from external devices,
such as insulin pumps and reference meters, via wired or wireless
data communication. The input can be processed alone or in
combination with information received from the sensor electronics
module. The receiver's processing module can include a processor
and computer program instructions to implement any of the processes
discussed herein, including the functions specified in the
flowchart block or blocks presented herein.
EXAMPLES
[0430] Embodiments are further detailed in the following Examples,
which are offered by way of illustration and are not intended to
limit the invention in any manner. As disclosed herein, studies
have been conducted using a Gamry potentiostat system to analyze
complex impedance of glucose sensors placed in a buffer. The Gamry
potentiostat system is commercially available from Gamry under the
model name Ref600. The buffer is a modified isolyte having a known
concentration of glucose. In many of the examples, the glucose
concentration is about 100 mg/dL. The impedance measurement error
in the examples was found to be about 1 to 5% by testing the system
with a known impedance.
[0431] Although some of the following examples were performed on a
lab bench, the sensors under test are configured to be used in vivo
to continuously or substantially continuously measure a glucose
concentration of a host.
[0432] Analyte sensors used in the following examples were selected
from different types of sensors. The sensors under test include
sensors taken from different sensor lots, where sensors from a
first lot may have been made in a different way and under different
conditions, which can result in sensors for different lots
exhibiting different sensitivity profiles. Further, some of the
sensors studied in these examples are configured for placement in
transcutaneous tissue of a host to measure the host's glucose
concentration, while other sensors are configured to measure an
intravenous blood glucose concentration of a host. In the following
experiments, sensors intended to be used to measure an intravenous
blood glucose concentration can be referred to as an "IVBG sensor
type" and sensors intended to measure a blood glucose concentration
in transcutaneous tissue of a host can be referred to as a
"transcutaneous sensor type."
Example 1
Sensitivity and Impedance Relationship
[0433] Example 1 illustrates a relationship between sensitivity of
a sensor and an impedance of the sensor. In this example, an IVBG
sensor was connected to a Gamry potentiostat system and placed in a
in a buffer solution of a modified isolyte having a glucose
concentration of 100 mg/dL. The temperature during the experiment
was 37 C. An impedance spectrum was captured at fixed intervals of
time. The impedance spectrum analyzed in this experiment ranged
from 1 Hz to 100 kHz and measurements of the sensor impedance and
sensor sensitivity were taken at 15 minute intervals over a period
of about 1200 minutes.
[0434] Reference is now made to FIG. 25, which is a graph showing
absolute values of sensitivity and impedance of the sensor based on
an input signal having a frequency of 1 kHz. Data points 2502
represent measured values of sensor sensitivity over a time period
of 1200 minutes (20 hours), where t=0 corresponds to the time when
the sensor is initially placed in the buffer. Data points 1104
represent measured values of impedance over the same time
period.
[0435] The sensitivity and impedance values of FIG. 25 appear to
have an inverse correlation. That is, the sensitivity initially
increases quickly and then the rate of the increase gradually slows
down and levels off, and the impedance initially deceases quickly
and then the rate of the decrease gradually slows down and levels
off. Although not wishing to be bound by theory, the initial
increase in sensitivity and decrease in impedance is believed to be
due to sensor run-in.
[0436] FIG. 26 is a plot of the data in FIG. 25, but the
sensitivity and impedance are in terms of percent change versus one
hour instead of absolute values. As can be seen, the sensitivity
2602 and impedance 2604 relative changes versus one hour also
appear to have an inverse correlation.
Example 2
Retrospectively Compensating for Sensitivity Drift Using
Impedance
[0437] FIG. 27 is a plot of sensitivity and impedance points
measured at various intervals over time using seven different
sensors, Sensors A-G. Sensors A-G were transcutaneous-type sensors,
but selected from several different sensor lots. Thus, even though
Sensors A-G were all transcutaneous sensors, sensors from different
lots may have been made in a slightly different way or under
slightly different conditions, which can result in sensors from
different lots exhibiting different sensitivity profiles. In this
Example, Sensors A and D were selected from a first lot, Sensor B
was selected from a second lot, and Sensors C, E, F and G were
selected from a third lot.
[0438] Further to FIG. 27, the plotted data points are sensitivity
and impedance values for each Sensor A-G. Because the sensitivity
of each Sensor A-G gradually increases over time, the right most
points of each Sensor's plotted data points tend to correspond to
values measured around t=0 and the left most points tend to
correspond to values at around t=24 hr.
[0439] As can be seen in FIG. 27, the impedance and sensitivity
values of each Sensor A-G have an essentially linear relationship,
where the sensitivity gradually increases and the impedance
decreases correspondingly over time. The data taken from all seven
Sensors A-G generally follow this linear relationship, although the
data points of each sensor may be shifted as compared to the data
points of the other sensors. Put another way, while the initial
impedance and sensitivity values for each sensor may be different,
the change in sensitivity and impedance for each sensor changes at
about the same linear rate.
[0440] FIG. 28 is a graph further illustrating the linear
relationship of impedance and sensitivity of Sensors A-G. The graph
of FIG. 28 is based on the same data used in FIG. 27, but the data
is graphed in terms of percent change rather than in absolute
values. As can be seen in FIG. 28, Sensors A-G appear to exhibit a
very similar correspondence between the change in impedance and
change in sensitivity.
[0441] A computer-implemented estimative algorithm function can be
used to model a relationship between the change in sensitivity and
change in impedance values of the sensor data generated by Sensors
A-G. The estimative algorithm function may be formed by applying
curve fitting techniques that regressively fit a curve to data
points by adjusting the function (e.g., by adjusting constants of
the function) until an optimal fit to the available data points is
obtained, as discussed above with respect to forming an estimative
curve for a sensitivity profile. Additionally or alternatively, the
relationship can be modeled into a look-up table stored in computer
memory.
[0442] Further to FIG. 28, an estimative curve 2802 of the combined
sensor data of Sensors A-G is also plotted. The curve in FIG. 28 is
a straight line, but can be other types of curves depending upon
the relationship between impedance and sensitivity of the sensor.
As discussed herein, the curve 2802 can be used to compensate for
sensitivity drift of a sensor.
[0443] FIG. 29 and FIG. 30 illustrate compensating sensor data
taken by the same sensors used to derive the estimative curve 2802.
The measurements of FIGS. 29 and 30 were taken at 37 C. (Note that
estimative curve 2802 was derived based on sensor measurements
taken at 37 C, as discussed above with reference to FIGS. 27 and
28.) FIG. 29 is a plot of uncompensated measurements using Sensors
A-G. FIG. 30 is a plot of percent estimated sensitivity error of
measurements compensated using the impedance to sensitivity
relationship based on the estimative curve 2802. The Mean Absolute
Relative Difference (MARD) of the uncompensated sensor data is
21.8%. The MARD of the compensated data is 1.8%, which is a
noticeable improvement over the uncompensated data.
[0444] FIGS. 31 and 32 are also plots of uncompensated data and
percent sensitivity error of compensated data, respectively. The
data in FIGS. 31 and 32 are based on measurements using Sensors
H-L. Sensors H, J, K and L were selected from the same lot of
sensors as Sensors C, F and G described with reference to FIG. 27
and Sensor I was selected from the same lot as Sensors A and D
described with reference to FIG. 27. Further, the measurements
plotted in FIGS. 31 and 32 were taken at 25 C instead of 37 C. The
estimative curve 2802 derived from Sensors A-G, however, was used
to compensate the data measured by Sensors H-L. (Note the
estimative curve 2802 was also based on measurements taken at 37
C.) The MARD of the uncompensated data is 21.9%, nearly the same as
the MARD of 21.8% calculated with respect to the sensor data in
FIG. 29. The MARD of the compensated data of FIG. 18 is 4.4%, which
is close to, although slightly higher than the MARD of the
compensated data of FIG. 30, but still much smaller than the
uncompensated MARD.
[0445] FIGS. 33 and 34 are graphs of uncompensated data and percent
sensitivity error of compensated data, respectively. The data in
FIGS. 33 and 34 are based on data obtained using Sensors M-Q.
Sensors M, 0, P and Q were selected from the same lot as Sensors C,
F and G described with reference to FIG. 27 and Sensor N was
selected from the same lot as Sensors A and D described with
reference to FIG. 27. The measurements of Sensors M-Q were taken at
42 C. The estimative curve 2802 derived from Sensors A-G was also
used to compensate the data in FIG. 34. Here, the MARD of the
uncompensated data is 13.1% and the MARD of the compensated data is
4.6%.
[0446] Accordingly, Example 2 illustrates that a change in
sensitivity to change in impedance relationship determined at a
first temperature can be used to compensate for sensitivity drift
at temperatures different from the first temperature.
Example 3
Prospective Calibration of Sensor Data Using Impedance
Measurements
[0447] Example 5 pertains to prospective calibration. Further, in
this experiment, calibration of sensor data is based on a change of
sensitivity to change in impedance relationship previously derived
from sensors from a different sensor lot. That is, in Example 3,
the estimative curve 2802 is used to compensate data obtained using
Sensors R-U, each of which was selected from a fourth sensor lot,
the fourth sensor lot was not included in the group of sensors used
to derive the estimative curve 2802. Example 5 shows that data can
be calibrated using a change in sensitivity to change in impedance
relationship derived from different sensor types than the type of
sensor being calibrated. This can indicate that a sensor factory
calibration code need not be used to compensate for sensitivity
drift.
[0448] FIGS. 35 and 36 illustrate prospectively calibrating sensor
data obtained from Sensors R-U. FIG. 35 is a plot of the percent
sensitivity change versus 30 minutes of each of the sensors over
approximately 1400 minutes. FIG. 36 illustrates the percent
estimated sensitivity error of the compensated data. The MARD of
the uncompensated sensor data is 24.8% (the MARD of uncompensated
data in the example of FIG. 15 was 21.8%) and the MARD of the
compensated sensor data is 6.6% (the MARD of compensated data in
the example of FIG. 16 was 1.8%).
[0449] Prospective calibration of sensors will now be discussed
with reference to FIGS. 37-39. Here, sensitivity and impedance data
is collected from Sensors V-X, Z and AA having a membrane formed by
a dipping process and Sensor Y having a membrane formed by a spray
process. Referring to FIG. 37, an estimative curve 3702 is
calculated based on the sensitivity and impedance data from all six
Sensors V-Z and AA (i.e. using sensor data obtained from both
sensors having dipped and sprayed membrane). FIG. 39 is a graph of
the percent sensitivity change of versus 60 minutes of the
uncompensated data of each of the six sensors. FIG. 39 is a graph
of the percent estimated sensitivity error of the data of all six
sensors after being compensated using estimative curve 3702
discussed above with respect to FIG. 37. The MARD of the
uncompensated data is 25.3% and the MARD of the compensated data is
5.2%.
[0450] Thus, Example 5 indicates that using a change in sensitivity
to change in impedance relationship derived from sensors selected
from different lots than the sensor being calibrated can
never-the-less significantly compensate for sensor sensitivity
drift. It should be noted that curve 3702 is a straight line. It is
contemplated that non-linear fits or correlations can be used
instead, which may yield better results.
Example 4
Effect of Temperature
[0451] FIG. 40 illustrates a relationship of temperature on
impedance and sensitivity of a sensor. Points 4002 are sensitivity
values of a sensor measured over a three day time period and points
4004 are impedance values of the sensor measured over the same time
period. In Example 4, the sensor is a transcutaneous-type of
sensor. The temperature was initially set and maintained at 37 C,
then raised to 45 C, and finally lowered to 25 C, as indicated in
FIG. 40.
[0452] As illustrated in FIG. 40, both sensitivity and impedance of
the sensor appear to have an inversely proportional relationship
with changes in temperature.
[0453] FIG. 41 is a plot of the sensitivity measurement values
versus the impedance measurement values of FIG. 40. FIG. 41
illustrates points measured during sensor run-in as diamonds and
points measured after run-in as squares.
Example 5
Temperature Compensation
[0454] FIG. 42 illustrates compensating analyte concentration data
measured by the sensor of Example 4 for effects of temperature
after sensor run-in. Here, a relationship between impedance and
temperature was used to compensate the sensor data. In this
example, the relationship was based on an estimative curve derived
from the data of FIG. 41.
[0455] The relationship between sensor sensitivity and different
temperatures can then be mathematically modeled (e.g., by fitting a
mathematical curve to data using one of the modeling techniques
used herein), and the mathematical model can then be used to
compensate for temperature effects on the sensor sensitivity. That
is, a sensitivity of a sensor (which is affected by the sensor's
temperature) can be determined based on a measured impedance of the
sensor applied to the mathematical curve. Sensor data can then be
converted to estimated glucose values based on the determined
sensor sensitivity.
[0456] Further to FIG. 42, the MARD of the uncompensated data was
calculated as 9.3% and the MARD of the compensated data was
calculated as 2.8%.
Example 6
Moisture Ingress Detection
[0457] Example 6 involves detection of moisture ingress in
components of sensor electronics used to drive a sensor. In this
example, an input signal is applied to the sensor having a
frequency ranging from 100 Hz to 1 kHz. A response of the signal is
then measured over the range of frequencies and both impedance and
phase change derived therefrom. Next, contacts typically positioned
inside a sealed section of a sensor electronics module are wetted.
The frequency of an input signal is varied between 100 and 1000 Hz.
A response is once again measured and both impedance and phase
change is derived.
[0458] In a first experiment, the contacts were wetted to the point
of causing a gross failure of the sensor. The current of the sensor
increased from 2.3 nA when dry to 36 nA when wetted. In a second
experiment, the contacts were only slightly wetted, where the
current of the sensor increased from 2.3 nA when dry to 6 nA when
wetted.
[0459] FIG. 43 is a graph of the impedance and phase changes of the
first experiment, where the contacts were wetted to a point of
causing a gross failure of the sensor system. Curves 4302 and 4304
are impedance and phase values, respectively, of the sensor prior
to the wetting of the contacts. Curves 4306 and 4308 are impedance
and phase values, respectively, of the sensor after wetting the
contacts. As illustrated in FIG. 43, the impedance of the dry curve
4302 and the wet curve 4306 are noticeably different at around 1000
Hz and the phase of the dry curve 4304 and wet curve 4308 are
noticeably different at about 100 Hz.
[0460] FIG. 44 is a graph of the impedance and phase changes of the
second experiment, where the contacts are only slightly wetted.
Curves 4402 and 4404 are impedance and phase, respectively, of the
sensor prior to the wetting of the contacts. Curves 4406 and 4408
are impedance and phase, respectively of the sensor after wetting
the contacts. As illustrated in FIG. 44, the impedance of the dry
curve 4402 and the wet curve 44006 are noticeably different at
around 100 Hz and the phase of the dry curve 4404 and wet curve
4408 are noticeably different around 1 kHz.
Example 7
Membrane Damage Detection
[0461] Example 7 involves detection of sensor membrane damage using
impedance measurements. In this example, the impedance of an
analyte sensor is measured over a range of frequencies ranging from
100 to 1 kHz. A portion of the sensor's membrane is then cut using
a razor blade to cause membrane damage. The impedance of the sensor
is once again measured over the same range of frequencies. The
impedance measurements of the sensor before the membrane is cut are
then compared to the measurements of the sensor after the membrane
is cut to determine whether the impedance can be used to detect
membrane damage.
[0462] FIG. 45 is a graph of the sensor measurements before and
after the membrane damage. Curves 4502 and 4504 are impedance and
phase, respectively, of the sensor prior to membrane damage and
curves 4506 and 4508 are impedance and phase, respectively, of the
sensor after membrane damage. Both the impedance and phase
relationships appear to change around the 1 kHz frequency. This
relationship can then be used to detect membrane damage.
Example 8
Study Using FFT to Calculate Impedance
[0463] FIG. 46 is graph plotting impedance calculated using Fast
Fourier Transform (FFT) and impedance measured using the Gamry for
comparison. FIG. 46 illustrates FFT and corresponding Gamry
measurements for 30 min., 1.5 hrs., 3 hrs. and 13 hrs. of input
data. The FFT data substantially tracked the Gamry data until about
the 1 kHz frequency. Note, the discrepancy after the 1 kHz
frequency in FIG. 33 is believed to be due to known limitations on
the system used to calculate the FFT. Thus, it is believed that
using FFT can provide accurate impedance data even past the 1 kHz
spectrum, thereby providing good correspondence to impedance
measured using the Gamry.
Example 9
In Vivo Sensitivity Change Compensation
[0464] FIGS. 47-49 relate to compensating for sensitivity changes
in a human using an in vivo glucose sensor.
[0465] FIG. 47 is a graph illustrating in vivo data of sensor
impedance and sensitivity of a subcutaneous continuous glucose
sensor (also referred to herein as a CGM sensor) over about a 25
hour time period. FIG. 47 is similar to FIG. 25, but uses in vivo
data instead of in vitro data. Sensitivity data was generated using
data obtained from a blood glucose finger stick monitor and raw
sensor current measurements using the CGM sensor (i.e. dividing raw
CGM sensor current in units of counts by time-corresponding meter
glucose value in units of mg/dL to generate the sensitivity value).
The impedance data was generated by applying a step voltage to the
CGM sensor and calculating an impedance based on the peak current
of the response, as discussed herein with reference to FIG. 13. As
illustrated in FIG. 47, the impedance and sensitivity data appear
to generally track a similar profile as the in vitro data of FIG.
25.
[0466] FIG. 48 is a graph of estimated glucose values using the
data generated by the CGM sensor before and after sensitivity
compensation using the impedance measurements of FIG. 47. The
compensated and uncompensated data was calibrated once using a
blood glucose reference meter measurement at the one hour mark. The
compensation was performed in a manner like that described above
with respect to the in vitro study of FIG. 30. FIG. 48 also
illustrates blood glucose reference measurements of the host. Based
on the data of FIG. 48, it appears that the compensated CGM sensor
data more closely corresponds to the reference measurement data
than the uncompensated CGM sensor data.
[0467] FIG. 49 is an accuracy plot showing differences between the
uncompensated and compensated CGM sensor data and the finger-stick
meter reference data, where the reference data corresponds to the
zero line of the plot. The MARD of the uncompensated CGM sensor
data is 28.5% and the MARD of the compensated CGM sensor data is
7.8%. Thus, the compensation appears to improve accuracy of the CGM
sensor data.
Example 9
Comparison of a Linear Impendence-to-Sensitivity Correlation to a
Non-Linear Impendence-to-Sensitivity Correlation
[0468] Laboratory experiments showed that there is an inverse
relationship between the changes in sensor sensitivity (drift) and
the changes in impedance. Based on the measured impedance, the
sensor sensitivity drift could be compensated using either linear
or non-linear sensor sensitivity-impedance correlation.
[0469] The linear correlation can be expressed as the equation
.DELTA.S=a*.DELTA.I+b, where a and b can be pre-determined
coefficients determined from prior testing of similar sensors. The
non-linear correlation can be expressed as
.DELTA.S=(a*log(t)+b)*.DELTA.I, where a and b are pre-determined
constants determined from prior testing of similar sensors. The
prior testing of similar sensors was performed using in vitro bench
testing and the constants were derived using conventional computer
plotting techniques in this experiment.
[0470] The linear and non-linear equations are derived from the
relationships of change in sensitivity and change of impedance at
time t since the last calibration. The percent the change of
sensitivity at time t is expressed as:
.DELTA.S=% change in sensitivity=(Sensitivity at t-Sc)/Sc*100%
where Sc is a sensitivity determined at calibration, and t is the
time since the last calibration.
[0471] The sensor impendence change from time t is expressed
as:
.DELTA.I=% impedance change=(impedance at t-Ic)/Ic*100%
where Ic is an impedance at the time of calibration.
[0472] To calculate the linear correlation, conventional computer
plotting software was used to plot change in sensitivity (y-axis)
versus change in impendence (x-axis) based on in vitro test data
and determine the linear best fit of the data. In this experiment,
the linear best fit was .DELTA.S=-4.807*.DELTA.I-0.742. The linear
best fit can is used as the linear sensitivity-to-impedance
correlation.
[0473] To calculate the non-linear correlation, conventional
computer plotting software was used to plot change in sensitivity
over change in impedance .DELTA.S/.quadrature..DELTA.I (y-axis)
versus log(t). The linear best fit of the plotted data is then
determined using the plotting software to yield
.DELTA.S/.quadrature..DELTA.I=a*log(t)+b, from which the change is
sensitivity is derived: .DELTA.S=(a*log(t)+b)*.DELTA.I. In this
experiment, the plotting software yielded
.DELTA.S/.DELTA.I=-1.175*log(t)-1.233. From this equation, .DELTA.S
is derived: .DELTA.S=(-1.175*log(t)-1.233)*.DELTA.I.
[0474] The linear or non-linear correlation can then be applied to
correct for changes in sensitivity of the sensor using impedance
measurements of the sensor.
[0475] FIG. 50 illustrates process 5000 for correcting estimated
glucose values used in this experiment. Process 5000 begins at
block 5002 with sensor insertion at time t=0. Process 5000 then
divides into two branches.
[0476] The first branch beings at block 5004, where sensor data is
generated using the continuous glucose sensor in the form of
current or counts. The first branch then proceeds to decision block
5006, where process 5000 determines whether a calibration is
needed. In this experiment, a calibration is determined to be
needed one hour and 24 hours after sensor insertion. If it is
determined that calibration is not needed, then the first branch
essentially ends. On the other hand, if it is determined that
calibration is required, then process 5000 acquires a reference
measurement using a finger stick meter at blocks 5008 and 5010.
Sensor data corresponding in time to the reference measurement (at
time Tc) is determined at block 5012 and used to calculate a sensor
sensitivity, Sc, along with the reference data at block 5014.
[0477] The second branch of process 5000 begins with measuring an
impendence of the sensor at block 5016. The impedance is measured
using the peak current technique discussed in this application with
reference to FIG. 13. Next it is determined if calibration is
needed in decision block 5018. In this experiment, this is the same
decision made in decision block 5006. If calibration is determined
to be needed, then the impedance measured in block 5016 is flagged
and stored as impedance Ic (impedance calibration) at block 5020.
If calibration is not needed, then process 5000 skips block 5020
and proceeds to determine a change in impedance at block 5022 from
the impedance flagged as impedance Ic, which in this experiment is
the difference between the impendence measured in block 5016 and
the impedance Ic. A compensation algorithm (using the linear or the
non-linear compensation algorithm) is then used in block 5024 to
calculate a change in sensitivity at block 5026 based on the change
in impedance determined at block 5022.
[0478] The first and second branches of process 5000 then merge at
block 5028 where a corrected sensitivity is determined. The
corrected sensitivity is then used to convert sensor data in units
of current or counts to an estimated glucose value in units of
glucose concentration.
[0479] Process 5000 is repeated for each sensor data point that is
converted into an estimated glucose value.
[0480] In this experiment, sensor performance improvement by
impedance compensation is demonstrated below. The sensors were
tested using continuous glucose sensors placed in sheep serum for 2
days. The sensor sensitivity was calculated at 1 hour and 24 hours.
In FIG. 51, the sensor sensitivity obtained at calibrations (1 hour
or 24 hours) was used throughout the day without any correction. In
FIG. 52, sensitivity was corrected using linear impedance
correction in accordance with the process illustrated in FIG. 50.
In FIG. 53, sensitivity was adjusted with non-linear
sensitivity-impedance correlation in accordance with the process
illustrated in FIG. 50. While both linear and non-linear
corrections demonstrated improved sensor performance, the
non-linear correction produced better results.
[0481] Some embodiments disclosed herein continuously or
iteratively apply a stimulus signal during a continuous sensor
session or use and extrapolate information from the output
associated with the stimulus, such as using a peak current
measurement, EIS, etc. Although certain electrochemical analysis
techniques were described herein, it is understood that many other
techniques can be used instead to detect characteristics of sensors
described herein, such as voltammetry, chronopotentiometry, current
or potential step techniques, differential chrono-potentiometry,
oxygen absorption rate measurements, potential/current sweep or
pulse methods, etc.
[0482] While the disclosure has been illustrated and described in
detail in the drawings and foregoing description, such illustration
and description are to be considered illustrative or exemplary and
not restrictive. The disclosure is not limited to the disclosed
embodiments. Variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practicing
the claimed disclosure, from a study of the drawings, the
disclosure and the appended claims.
[0483] All references cited herein are incorporated herein by
reference in their entirety. To the extent publications and patents
or patent applications incorporated by reference contradict the
disclosure contained in the specification, the specification is
intended to supersede and/or take precedence over any such
contradictory material.
[0484] Unless otherwise defined, all terms (including technical and
scientific terms) are to be given their ordinary and customary
meaning to a person of ordinary skill in the art, and are not to be
limited to a special or customized meaning unless expressly so
defined herein. It should be noted that the use of particular
terminology when describing certain features or aspects of the
disclosure should not be taken to imply that the terminology is
being re-defined herein to be restricted to include any specific
characteristics of the features or aspects of the disclosure with
which that terminology is associated. Terms and phrases used in
this application, and variations thereof, especially in the
appended claims, unless otherwise expressly stated, should be
construed as open ended as opposed to limiting. As examples of the
foregoing, the term `including` should be read to mean `including,
without limitation,` `including but not limited to,` or the like;
the term `comprising` as used herein is synonymous with
`including,` `containing,` or `characterized by,` and is inclusive
or open-ended and does not exclude additional, unrecited elements
or method steps; the term `having` should be interpreted as `having
at least;` the term `includes` should be interpreted as `includes
but is not limited to;` the term `example` is used to provide
exemplary instances of the item in discussion, not an exhaustive or
limiting list thereof; adjectives such as `known`, `normal`,
`standard`, and terms of similar meaning should not be construed as
limiting the item described to a given time period or to an item
available as of a given time, but instead should be read to
encompass known, normal, or standard technologies that may be
available or known now or at any time in the future; and use of
terms like `preferably,` `preferred,` `desired,` or `desirable,`
and words of similar meaning should not be understood as implying
that certain features are critical, essential, or even important to
the structure or function of the invention, but instead as merely
intended to highlight alternative or additional features that may
or may not be utilized in a particular embodiment of the invention.
Likewise, a group of items linked with the conjunction `and` should
not be read as requiring that each and every one of those items be
present in the grouping, but rather should be read as `and/or`
unless expressly stated otherwise. Similarly, a group of items
linked with the conjunction `or` should not be read as requiring
mutual exclusivity among that group, but rather should be read as
`and/or` unless expressly stated otherwise.
[0485] Where a range of values is provided, it is understood that
the upper and lower limit, and each intervening value between the
upper and lower limit of the range is encompassed within the
embodiments.
[0486] With respect to the use of substantially any plural and/or
singular terms herein, those having skill in the art can translate
from the plural to the singular and/or from the singular to the
plural as is appropriate to the context and/or application. The
various singular/plural permutations may be expressly set forth
herein for sake of clarity. The indefinite article "a" or "an" does
not exclude a plurality. A single processor or other unit may
fulfill the functions of several items recited in the claims. The
mere fact that certain measures are recited in mutually different
dependent claims does not indicate that a combination of these
measures cannot be used to advantage. Any reference signs in the
claims should not be construed as limiting the scope.
[0487] It will be further understood by those within the art that
if a specific number of an introduced claim recitation is intended,
such an intent will be explicitly recited in the claim, and in the
absence of such recitation no such intent is present. For example,
as an aid to understanding, the following appended claims may
contain usage of the introductory phrases "at least one" and "one
or more" to introduce claim recitations. However, the use of such
phrases should not be construed to imply that the introduction of a
claim recitation by the indefinite articles "a" or "an" limits any
particular claim containing such introduced claim recitation to
embodiments containing only one such recitation, even when the same
claim includes the introductory phrases "one or more" or "at least
one" and indefinite articles such as "a" or "an" (e.g., "a" and/or
"an" should typically be interpreted to mean "at least one" or "one
or more"); the same holds true for the use of definite articles
used to introduce claim recitations. In addition, even if a
specific number of an introduced claim recitation is explicitly
recited, those skilled in the art will recognize that such
recitation should typically be interpreted to mean at least the
recited number (e.g., the bare recitation of "two recitations,"
without other modifiers, typically means at least two recitations,
or two or more recitations). Furthermore, in those instances where
a convention analogous to "at least one of A, B, and C, etc." is
used, in general such a construction is intended in the sense one
having skill in the art would understand the convention (e.g., "a
system having at least one of A, B, and C" would include but not be
limited to systems that have A alone, B alone, C alone, A and B
together, A and C together, B and C together, and/or A, B, and C
together, etc.). In those instances where a convention analogous to
"at least one of A, B, or C, etc." is used, in general such a
construction is intended in the sense one having skill in the art
would understand the convention (e.g., "a system having at least
one of A, B, or C" would include but not be limited to systems that
have A alone, B alone, C alone, A and B together, A and C together,
B and C together, and/or A, B, and C together, etc.). It will be
further understood by those within the art that virtually any
disjunctive word and/or phrase presenting two or more alternative
terms, whether in the description, claims, or drawings, should be
understood to contemplate the possibilities of including one of the
terms, either of the terms, or both terms. For example, the phrase
"A or B" will be understood to include the possibilities of "A" or
"B" or "A and B."
[0488] All numbers expressing quantities of ingredients, reaction
conditions, and so forth used in the specification are to be
understood as being modified in all instances by the term `about.`
Accordingly, unless indicated to the contrary, the numerical
parameters set forth herein are approximations that may vary
depending upon the desired properties sought to be obtained. At the
very least, and not as an attempt to limit the application of the
doctrine of equivalents to the scope of any claims in any
application claiming priority to the present application, each
numerical parameter should be construed in light of the number of
significant digits and ordinary rounding approaches.
[0489] Furthermore, although the foregoing has been described in
some detail by way of illustrations and examples for purposes of
clarity and understanding, it is apparent to those skilled in the
art that certain changes and modifications may be practiced.
Therefore, the description and examples should not be construed as
limiting the scope of the invention to the specific embodiments and
examples described herein, but rather to also cover all
modification and alternatives coming with the true scope and spirit
of the invention.
* * * * *