U.S. patent application number 16/402116 was filed with the patent office on 2019-11-07 for automatic analyte sensor calibration and error detection.
The applicant listed for this patent is DexCom, Inc.. Invention is credited to Naresh C. Bhavaraju, Becky L. Clark, Vincent P. Crabtree, Anna Leigh Davis, Chris W. Dring, Arturo Garcia, Jason Halac, Hari Hampapuram, Jonathan Hughes, Jeff Jackson, Lauren Hruby Jepson, David I-Chun Lee, Ted Tang Lee, Rui Ma, Aditya Sagar Mandapaka, Zebediah L. McDaniel, Jason Mitchell, Andrew Attila Pal, Daiting Rong, Disha B. Sheth, Peter C. Simpson, Alexander Leroy Teeter, Stephen J. Vanslyke, Liang Wang, Matthew D. Wightlin.
Application Number | 20190339223 16/402116 |
Document ID | / |
Family ID | 68385037 |
Filed Date | 2019-11-07 |
View All Diagrams
United States Patent
Application |
20190339223 |
Kind Code |
A1 |
Bhavaraju; Naresh C. ; et
al. |
November 7, 2019 |
AUTOMATIC ANALYTE SENSOR CALIBRATION AND ERROR DETECTION
Abstract
Systems and methods are provided that address the need to
frequently calibrate analyte sensors, according to implementation.
In more detail, systems and methods provide a preconnected analyte
sensor system that physically combines an analyte sensor to
measurement electronics during the manufacturing phase of the
sensor and in some cases in subsequent life phases of the sensor,
so as to allow an improved recognition of sensor environment over
time to improve subsequent calibration of the sensor.
Inventors: |
Bhavaraju; Naresh C.; (San
Diego, CA) ; Clark; Becky L.; (San Diego, CA)
; Crabtree; Vincent P.; (San Diego, CA) ; Dring;
Chris W.; (San Diego, CA) ; Garcia; Arturo;
(Chula Vista, CA) ; Halac; Jason; (San Diego,
CA) ; Hughes; Jonathan; (Encinitas, CA) ;
Jackson; Jeff; (Poway, CA) ; Jepson; Lauren
Hruby; (San Diego, CA) ; Lee; David I-Chun;
(San Diego, CA) ; Lee; Ted Tang; (San Diego,
CA) ; Ma; Rui; (San Diego, CA) ; McDaniel;
Zebediah L.; (San Diego, CA) ; Mitchell; Jason;
(Poway, CA) ; Pal; Andrew Attila; (San Diego,
CA) ; Rong; Daiting; (San Diego, CA) ; Sheth;
Disha B.; (Oceanside, CA) ; Simpson; Peter C.;
(Cardiff, CA) ; Vanslyke; Stephen J.; (Carlsbad,
CA) ; Wightlin; Matthew D.; (San Diego, CA) ;
Davis; Anna Leigh; (Cardiff by the Sea, CA) ;
Hampapuram; Hari; (San Diego, CA) ; Mandapaka; Aditya
Sagar; (San Diego, CA) ; Teeter; Alexander Leroy;
(Poway, CA) ; Wang; Liang; (La Jolla, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DexCom, Inc. |
San Diego |
CA |
US |
|
|
Family ID: |
68385037 |
Appl. No.: |
16/402116 |
Filed: |
May 2, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16402013 |
May 2, 2019 |
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16402116 |
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62666606 |
May 3, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 27/3274
20130101 |
International
Class: |
G01N 27/327 20060101
G01N027/327 |
Claims
1. An analyte sensor system comprising: a continuous analyte sensor
operable to obtain measurements indicative of analyte levels in a
body of a host; and a sensor electronics unit in communications
with the continuous analyte sensor and configured for use in
retrospectively calibrating the continuous analyte sensor by: (i)
generating an actual analyte sensor signal from the obtained
measurements indicative of analyte levels, the actual analyte
sensor signal extending over a first period of time; (ii)
identifying a start point and an end point of an artifact in the
actual analyte sensor signal; (iii) replacing the artifact with a
predicted analyte sensor signal; (iv) evaluating a difference
between the predicted analyte sensor signal and the actual analyte
sensor signal at the start point and the end point; and (v) using
the differences as a feedback signal to correct the predicted
analyte sensor signal and repeat (iii) using the corrected
predicted analyte sensor signal.
2. The analyte sensor system of claim 1, further comprising
correcting the predicted analyte sensor signal by distributing the
differences over a duration of the predicted analyte sensor signal
to eliminate discontinuities in the corrected analyte sensor
signal.
3. The analyte sensor system of claim 2, further comprising
distributing the differences evenly over the predicted analyte
sensor signal.
4. The analyte sensor system of claim 2, further comprising
distributing the differences over the predicted analyte sensor
signal using a weighted average.
5. The analyte sensor system of claim 1, wherein a duration of the
artifact is less than 40 minutes.
6. A system for monitoring data relating to analyte levels,
comprising: a continuous analyte sensor unit configured to prepare
data relating to analyte levels; a wireless transmitter operatively
associated with the sensor and being configured to wirelessly
transmit the data relating to the analyte levels; and at least one
display device configured to receive the transmitted data relating
to the analyte levels, wherein the wireless transmitter is further
configured to transmit a calibration request to the display device
at a prescribed frequency that is determined based at least in part
on an availability of pre-existing calibration information.
7. The system of claim 6, wherein the prescribed frequency is a
default frequency if no pre-existing calibration information is
available.
8. The system of claim 6, further comprising transferring
calibration coefficients from a memory in the sensor unit to the
transmitter.
9. The system of claim 8, further comprising transferring
additional information for the memory to the transmitter, the
additional information including at least one item selected from
the group including a sensor lot number, a sensor expiration date,
and sensor authentication information.
10. The system of claim 8, wherein the transmitter stores a default
set of calibration coefficients and wherein transferring
calibration coefficients from the memory to the transmitter
includes transferring difference values between the calibration
coefficients and the default set of calibration coefficients.
11. The system of claim 10, wherein the default set of calibration
coefficients includes a first ordered list of paired values each
specifying a coefficient identifier and a calibration coefficient
value, wherein the difference values are provided as a second
ordered list of paired values each specifying a coefficient
identifier and a difference between a value of a calibration
coefficient and a value of the default calibration coefficient.
12. The system of claim 6, wherein the prescribed frequency is
further determined based at least in part on a type of application
that receives the calibration request on the display device.
13. A system for monitoring data relating to analyte levels,
comprising: a continuous analyte sensor unit configured to prepare
data relating to analyte levels, the analyte sensor unit including
a sensor having a working electrode and a reference electrode and a
memory element electrically coupled to the working electrode for
receiving power and data signals; a wireless transmitter
operatively associated with the sensor and being configured to
wirelessly transmit the data relating to the analyte levels; and at
least one display device configured to receive the transmitted data
relating to the analyte levels, wherein the wireless transmitter is
further configured to: periodically interrogate the sensor for a
response indicating that the sensor is a new sensor; and if the
response indicates that the sensor is a new sensor, request
calibration coefficients stored in the memory element by causing
signal pulses to be applied to the memory element, the signal
pulses having a predetermined voltage greater than a nominal
operating bias voltage of the sensor.
14. The system of claim 13, wherein the memory element includes a
short-term charge storage device to power the memory element while
communicating the calibration coefficients to the transmitter.
15. The system of claim 13, further comprising communicating an
end-of-life indicator to the memory element indicating that the
sensor has expired, the indicator being stored in the memory
element and preventing re-use of the sensor.
16. The system of claim 13, wherein the wireless transmitter is
further configured to periodically interrogate the sensor by
periodically waking up from a sleep state and if the response
indicates that the sensor is not new sensor, returning to the sleep
state.
17. A system for monitoring data relating to analyte levels,
comprising: a continuous analyte sensor unit configured to prepare
data relating to analyte levels; a wireless transmitter operatively
associated with the sensor and being configured to wirelessly
transmit the data relating to the analyte levels; and at least one
display device configured to receive the transmitted data relating
to the analyte levels and to provide the transmitter with
calibration data provided to the display device by a user, the
display device being further configured to: receive biometric data
of the user; evaluate the biometric data and the calibration data
provided to the display device by the user to determine if an
incompatibility exists between the biometric data and the
calibration data; if an incompatibility exists, generating an error
message; and if no incompatibility exists, communicating the
calibration data to the transmitter.
18. The system of claim 17, wherein evaluating the biometric data
and the calibration data includes evaluating the biometric data,
the calibration data and analyte sensor data received from the
analyte sensor unit to determine if an incompatibility exists
between the biometric data and the calibration data.
19. The system of claim 17, wherein the biometric data includes a
heart rate of the user.
20. The system of claim 17, wherein the calibration data is
manually entered into the display device by the user.
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. 16/402,013, filed May 2, 2019, which
claims the benefit of U.S. Provisional Application No. 62/666,606,
filed May 3, 2018. 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 disorder in which the pancreas cannot
create sufficient insulin (Type I or insulin-dependent) and/or in
which insulin is not effective (Type II or non-insulin-dependent).
In the diabetic state, the patient or user suffers from high blood
sugar, which can cause an array of physiological derangements
associated with the deterioration of small blood vessels, for
example, kidney failure, skin ulcers, or bleeding into the vitreous
of the eye. A hypoglycemic reaction (low blood sugar) can be
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] Conventionally, a person with diabetes carries a
self-monitoring blood glucose (SMBG) monitor, which typically
requires uncomfortable finger pricking methods. Due to the lack of
comfort and convenience, a person with diabetes normally only
measures his or her glucose levels two to four times per day.
Unfortunately, such time intervals are so far spread apart that the
person with diabetes likely finds out too late of a hyperglycemic
or hypoglycemic condition, sometimes incurring dangerous side
effects. It is not only unlikely that a person with diabetes will
become aware of a dangerous condition in time to counteract it, but
it is also likely that he or she will not know whether his or her
blood glucose concentration value is going up (higher) or down
(lower) based on conventional methods. Diabetics thus may be
inhibited from making educated insulin therapy decisions.
[0005] Another device that some diabetics used to monitor their
blood glucose is a continuous analyte sensor, e.g., a continuous
glucose monitor (CGM). A CGM typically includes a sensor that is
placed invasively, minimally invasively or non-invasively. The
sensor measures the concentration of a given analyte within the
body, e.g., glucose, and generates a raw signal using electronics
associated with the sensor. The raw signal is converted into an
output value that is rendered on a display. The output value that
results from the conversion of the raw signal is typically
expressed in a form that provides the user with meaningful
information, and in which form users have become familiar with
analyzing, such as blood glucose expressed in mg/dL.
[0006] The above discussion assumes the output value is reliable
and true, and the same generally requires a significant degree of
user interaction to ensure proper calibration. Typically, a
calibration check is performed before the analyte sensor leaves the
factory; during the calibration check, sensitivity values are
derived in vitro. However, the calibration check only provides a
snapshot of the sensitivity at a given point in time and does not
take into account that sensor sensitivity changes over time.
Moreover, two sensors that have the same result from the
calibration check procedure can still act differently in use in a
patient, as the values of sensitivity can diverge over time
depending on conditions before and after use.
[0007] One way of accounting for this is by use of reference value
checks during use, e.g., by self monitoring blood glucose meters.
Many current CGMs rely heavily on such user interactions,
confirming glucose concentration values before dosing insulin.
However, additional user action adds a significant source of error
in the monitoring and reduces convenience by requiring more action
of the user than desired.
SUMMARY OF THE INVENTION
[0008] Systems and methods according to present principles address
many of the issues above concerning the need to frequently
calibrate analyte sensors, according to implementation. In more
detail, systems and methods provide a preconnected analyte sensor
system that physically combines an analyte sensor to measurement
electronics during the manufacturing phase of the sensor and in
some cases in subsequent life phases of the sensor.
[0009] In one embodiment, at a minimum the system includes an
analyte sensor capable of measuring an analyte level in a host and
measurement electronics containing a potentiostat circuit capable
of placing a controlled voltage bias between two or more electrodes
and measuring the amount of current that flows. The analyte sensor
is preconnected to the measurement electronics.
[0010] There are also several optional features: a sensor
interconnection module capable of securing an analyte sensor in
position and/or robust electrical coupling, and a measurement
electronics module which may include one or more of the following:
a temperature measurement circuit capable of taking temperature
readings from one or more temperature sensors, an impedance
measurement circuit capable of detecting impedance values from the
analyte sensor or other electrical components, a capacitive
measurement circuit capable of detecting capacitance values from
the analyte sensor or other electrical components, a motion
detecting circuit using one or more sensors such as an
accelerometer or gyroscope to detecting and quantifying physical
motion and/or orientation, a humidity measurement circuit with one
or more sensors able to measure humidity, a clock capable of
keeping a measure of time, and/or a pressure measurement circuit
with one or more pressure sensors capable of measuring pressure of
a gas (e.g., barometric pressure) or changes in pressure applied to
the device (e.g., force applied to a surface of a housing), and one
or more processors capable of processing data. Other features may
include one or more radios capable of wirelessly transmitting data,
one or more display/status indicators capable of communicating data
to a user, one or more data storage units capable of storing
relevant information for future access, and one or more power
sources (e.g., a battery) capable of delivering reliable power for
use by the measurement electronics.
[0011] A preconnected analyte sensor can address various sources of
error that may otherwise arise. These sources of error may involve
both errors in accuracy and precision, which are key factors in
determining the true value of a measurement performed by a
measurement system. Accuracy can be described as the closeness of a
measured value to a standard or known value. For example, when
taking a width measurement of a known 1 cm cube and a value
obtained is 1.1 cm, the measurement is accurate to 0.1 cm.
Precision is the degree to which repeated measurements under
unchanged conditions show the same results. In the same cube
example if three measurements are taken and the values obtained are
1.1 cm, 1.2 cm, and 1.0 cm the measurement is precise to within 0.1
cm. However, precision and accuracy error are compounded in the
determination of the trueness of a measurement.
[0012] Precision and accuracy are not static factors that can
impact errors in a measurement system. Rather, they are dynamic
factors in which precision and accuracy can vary over time.
Typically a model is used (e.g. linear, non-linear, etc.) to
quantify a sensor response to signal, and so deviations to the
precision and accuracy of the model used to quantify a sensor
response add additional error in the conversion of a sensor signal
to a reported value.
[0013] Therefore, preconnecting an analyte sensor system to
measurement electronics in the manufacturing phase and then using
the same configuration during the sensor use phase has several
advantages.
[0014] The preconnected analyte sensor system can compensate for
errors introduced by the accuracy and precision of manufacturing
equipment. Variations in the manufacturing process may give rise to
different values for various parameters that are measured (e.g.,
analyte sensitivity, baseline, impedance, capacitance, interferent
sensitivity, etc.), and the errors resulting from these different
parameter values are compounded into the error of the overall
system. The more variations there are in the manufacturing setup,
the more significant the consequences to the error introduced in
the system. These variations may include: changes to equipment over
time, frequency of equipment calibration, number of different
measurement stations, multiple manufacturing lines, multiple
manufacturing locations, equipment precision, calibration trueness,
equipment cleanliness, etc.
[0015] The preconnected analyte sensor system limits error caused
by the physical connection of the analyte sensor to the electronics
portion of the sensor, and where the electronics portion includes
measurement electronics, allows measurements to be taken during and
after the manufacturing process. Several of the possible
measurement types that can be taken by measurement electronics are
sensitive to factors such as: contact resistance, leakage current,
length of electrical pathways, component volume, manufacturing
tolerances, material properties, etc.
[0016] The preconnected analyte sensor system limits error
introduced by the measurement electronics. Measurement electronics
are limited by their own manufacturing tolerances and their design
limitations. Typically, calibration equipment is used to
characterize a measurement electronic system. The accuracy and
precision are measured and correction factors (e.g., gain, offset,
linearity, temperature, resolution, etc.) are used by the circuit
to compensate for absolute error. This adds cost and complexity to
the manufacturing phase as testing time and programming time must
be added to the process. Also, depending on the time period and the
equipment used to calibrate the system, changes in various
properties may arise from the time of calibration. It is therefore
advantageous to calibrate the system as late in the manufacturing
process as possible.
[0017] In manufacturing, having fewer steps in the process has
advantages for efficiency and reducing opportunity for error. By
performing a sensor calibration using the measurement electronics
that will be used in the final product, calibration can be
accomplished as a system. For example, to calibrate the electronics
and sensor as a single step in a known calibration solution, only
the value of the calibration solution must be controlled. The
measurement electronics at minimum are placing a voltage bias on
the sensor, measuring an analog value of current, and converting
that analog value to a digital value. This digital value can be
correlated to the actual value of the calibration solution. For
this particular set of measurement electronics in combination with
this particular sensor, the relationship between an analyte
concentration in a calibration solution is now linked to a digital
value that is corrected for individual measurement component
variation (e.g. potentiostat variability, analog to digital
converter error, leakage error, connection resistance variability,
etc.). This system also eliminates manufacturing measurement
electronic error from calibration equipment.
[0018] This direct-to-calibration solution type of system
calibration can be performed over a broad range of analyte values,
interferent materials, and other factors that affect sensor
performance (e.g., low oxygen). This correlation of digital values
to analyte concentration in a solution over a range can be used to
build an accurate compensation model for in-vivo sensor
performance.
[0019] In an alternative embodiment this process of calibration can
be extended to other types of possible measurements performed by
measurement electronics (e.g., impedance, capacitance, temperature,
time, current, voltage, humidity, motion, etc.).
[0020] The value of a system that connects measurement electronics
to an analyte sensor during manufacture can be extended beyond the
calibration portion of manufacturing. This enables the system to
capture data during the following system phases: manufacturing,
packaging, sterilization, shipping, storage, insertion, and in
vivo. Useful measurements can be taken before, during, or after one
or more of the following steps: sensor connection, membrane
application, curing, environmental excursions, sterilization,
shipping, storage, insertion, in vivo, etc.
[0021] In transcutaneous analyte measurement systems that are
currently available on the market, the sensor and measurement
electronics are coupled immediately prior or during sensor
insertion. This configuration prevents measurements of a coupled
system during any system phase prior to the measurement electronic
and analyte sensor coupling. The additional measurements that are
only capable of being captured with a preconnected system can be
provided to an analyte processing algorithm. These measurements can
be correlated to in vivo performance, fault detection, sensor life,
sensitivity shift, calibration shift, sensor performance indicator,
accuracy, etc. The measurement correlations can be used to identify
or compensate for system experience over an extended time period
that is useful during the in vivo system phase.
[0022] For multiple measurements at different time points and
system phases a multi variate model can be created. This frequency
and breadth of data gathering can more accurately model system
characteristics. Some of this analysis can be accomplished using
measurements taken by manufacturing or calibration equipment. These
input measurements may optionally be incorporated in addition to
measurements taken by measurement electronics. In other embodiments
the model may only include input from manufacturing and/or
calibration equipment. The output measurements may be taken by
manufacturing and/or calibration equipment or during the in vivo
phase by reference measurements of blood analyte levels (e.g. YSI,
finger stick blood glucose meters, laboratory analysis, etc.).
[0023] For example, measurements such as impedance, temperature,
current measurements, time, etc. may be taken by preconnected
measurement electronics during various phases of manufacture such
as pre-sensor attachment, post-sensor attachment, membrane
application, curing, and calibration. The preconnected system may
collect spatial information such as location in a fixture, location
in equipment, or an equipment identifier. This data set may be
combined with an additional data set from sensors placed in
manufacturing equipment that gather variables such as humidity,
temperature, material viscosity, time, equipment identifier, etc.
An additional data set can also be gathered that track external
variables such as time, date, room temperature, room humidity,
manufacturing equipment, calibration equipment, operator,
manufacturing line, manufacturing location, etc.
[0024] The collated measurements can be interpreted immediately or
stored for further processing at a later time. The information can
be used to adjust manufacturing parameters or to build a correction
factor, determine lot classification, reject sensors, or used by an
analyte processing algorithm. This large amount of data can be
input into tools such as machine learning algorithms to identify
correlations.
[0025] The multi variable model can be used to identify and correct
for relationships between input parameters and output parameters.
Some of these relationships are well known (e.g. the relationship
of temperature on analyte sensitivity measurements) and others have
yet to be identified. Tools used to identify and model these
relationships may be: linear regression additive models,
generalized linear modeling optionally incorporating one or more
nonlinear functions, non-parametric data fitting to empirical
modeling, nonlinear regression modeling, neural network models, or
other suitable models. This list is only exemplary and any suitable
statistical or analysis tools can be used to model system
relationships. Other suitable methods of data analysis are
described in "Handbook of Chemometrics and Qualimetrics, Volume
20A" and "Handbook of Chemometrics and Qualimetrics, Volume 20B"
published by Elsevier Science 1998 and incorporated by
reference.
[0026] Many system measurements that can be taken have known
correlations to additional system parameters. In this way it is
possible to draw correlations to parameters that are not directly
measured but which may be useful to input or process with an
analyte algorithm processing unit. This has several advantages such
as requiring less physical sensor components that add cost and
complexity, gathering information that is not easily measured due
to location or sensor size, providing redundancy or improved
accuracy to additional sensors (e.g. compensating for temperature
in a current measuring circuit).
[0027] Example applications utilizing inferred measurements may be
some of the following: using temperature and sensor impedance
measurements to infer humidity levels ex vivo; using one or more
temperature sensors to calculate a temperature gradient; using the
temperature gradient data to estimate temperature of a non-measured
point such as the tip of an analyte sensor in vivo; using
temperature and accelerometer data to estimate physical exertion.
This is not a complete list and any of the sensed measurements can
be combined with one or more other sensed measurements to estimate
one or more non-sensed measurements.
[0028] By pre-connecting the sensor to some or all of the sensor
electronics, the sensor can be monitored throughout all or part of
its life, and most especially during the part of the sensor's life
after it leaves the factory. Sensor monitoring may be advantageous
for a number of reasons. In particular, it can address issues
concerning variability (the divergence over time from a sensor's
calibration value assigned in the factory), accuracy (the error
added to the overall analyte sensor system arising from variability
in the individual components that make up the system) and
manufacturing processes that reduce consistency from sensor to
sensor and sensor lot to sensor lot. Additionally, a preconnected
sensor can facilitate data transfer from the sensor to external
devices and provide improvements to sensor safety by detecting when
a sensor deployed in the field is potentially unsafe.
[0029] In one aspect, variability issues are addressed by
performing various active measurements that are taken
post-manufacturing. For instance, in one embodiment, environmental
conditions (e.g., temperature, humidity) under which the sensor and
preconnected electronics are maintained while sealed in packaging
during storage and prior to use may be monitored. In the case of
temperature, an on-board electronics temperature sensor such as a
thermistor or thermocouple may be used to measure and store
temperature data. Likewise, an on-board electronics humidity sensor
may be provided to monitor humidity. Alternatively, an external
temperature and/or humidity sensor physically coupled to the
electronics (e.g., in the base, in the package) may be used to
measure and store temperature and/or humidity data. In other cases
an independent temperature and/or humidity sensor that is in
wireless communication with the electronics may be used. In some
cases there may be an individual temperature and/or humidity sensor
assigned to each analyte sensor. Alternatively, there may be a
single temperature and/or or humidity sensor assigned to each
box/shipper/pallet of analyte sensors. In another implementation
the analyte sensor wire itself may be used to determine temperature
and/or humidity by inference via impedance or current measurements,
which measurements may be stored in the preconnected
electronics.
[0030] In some embodiments another environmental condition that may
be monitored is the radiation dose that is imparted to the sensor
for sterilization purposes after the sensor and any preconnected
electronics have been sealed in packaging. In one example a
sterilization detector may be provided on the electronics so that
the detector is able to quantify the dose amount using the active
electronics. In some cases material may be added to the packaging
that is sensitive to the sterilization dose and which can be
electronically interrogated by the electronics post-sterilization
to determine sensor characteristics such as impedance, resistance
and/or capacitance. From this it may be possible to infer the
orientation of the device in the packaging during sterilization.
Bulk detection of the sterilization dose may also be obtained for
each box/shipper/pallet of analyte sensors. The dosage that is
measured may be used to assign a value to the analyte sensor via
wireless communication with the preconnected electronics, the value
for later use in deriving subsequent calibration parameters.
[0031] In an additional aspect, another environmental condition
that may be monitored is movement of the analyte sensor using an
accelerometer, a triggering break fuse or other motion sensor. In
this way vibrations or impact due to dropping or the like may be
detected, which can cause damage to the sensor membrane or
applicator mechanism.
[0032] Yet other environmental conditions that may be monitored
include ambient gas exposure and the duration of time that has
elapsed since sensor manufacture.
[0033] In addition to or instead of the active monitoring
techniques discussed above to address variability issues, passive
techniques may also be used. For instance, in one implementation,
described in U.S. Application No. 62/521,969, filed Jun. 19, 2017,
entitled "Applicators for Applying Transcutaneous Analyte Sensors
and Associated Methods of Manufacture, the packaging material that
is used may provide a humidity barrier that can maintain the
moisture vapor transmission rate below some threshold level, e.g.,
less than 10 grams/100 in.sup.2/day or less than 1 grams/100
in.sup.2/day. Examples of packaging material that may be used
include metallic foil (e.g. aluminum, titanium), a metallic
substrate, aluminum oxide coated polymer, silicon dioxide coated
polymer, a polymer substrate coated with a metal applied via vapor
metallization, or low MVTR polymers (e.g. PET, HDPE, PVC, PP,
PLA).
[0034] Yet another passive technique that may be used to monitor
environmental conditions, includes the provision of a visual
indicator material in the packaging which changes color or
visibility with exposure to temperature and/or humidity over time.
Alternatively, instead of a visual indicator, the indicator may
undergo a dimensional change in length or position in response to
temperature or humidity changes.
[0035] In some embodiments that employ humidity and/or temperature
monitoring in the packaging, if either or both such monitors
determine that the environmental conditions have, at some point,
for some duration, exceeded acceptable limits, the packaging may be
provided with a mechanism to physically prevent the sensor in that
packaging from being used. For instance, a material that changes in
dimensions with temperature and/or humidity such as a bimetal
(similar to those used in thermostats), metal, or polymer may be
used in combination with an interlocking feature in the applicator
to physically (either permanently or temporarily) prevent the
applicator from deploying, preventing the packaging from being
opened, and/or preventing a button or the like from being
activated. The physical change in the material dimensions will
automatically enable this feature when the predetermined
environmental conditions are exceeded.
[0036] In another aspect, system level compensation may be achieved
which allows for greater parameter variability among individual
system components while reducing overall error. This may be
accomplished using the data stored in the preconnected electronics
concerning the monitored environmental conditions as input to an
algorithm that is used to adjust the sensor calibration model. The
adjustments may be made to the initial and/or final sensor
sensitivity, the background signal and/or the equilibration rate.
In some cases, the data that is gathered and stored for an
individual sensor or a sensor lot may be tailored to an individual
patient. Moreover, the adjustments that are needed may also use as
an additional input information that has been previously obtained
over time for large numbers of sensors and patients to calculate
calibration compensation values based on the performance of sensors
that had experienced similar conditions.
[0037] The algorithm that is used to adjust the sensor calibration
model may also include a time component that uses data obtained by
examining the sensitivity profile and background signal profile of
the sensor over the time from insertion (when the factory
calibrated initial sensitivity and background signal is used) to
the transition to a stable final sensitivity and background signal.
The sensor calibration model may be compensated based on the
difference between the factory calibration value and the rate of
change during the sensitivity transition period. Typical break-in
curves can be obtained for sensors from this data as well as
changes to the curves arising from changes induced by
sterilization, temperature, humidity and/or storage time. These
break-in curves may be used to compensate the sensor calibration
model for deviations from the factory calibration.
[0038] In another aspect, the sensor calibration model that is
updated based on the data stored in the preconnected electronics
concerning the monitored environmental conditions may be used to
make adjustments to the sensor calibration value prior to insertion
of the analyte sensor in the patient. For example, the voltage bias
applied to the analyte sensor may be adjusted based on the stored
data. In some cases the voltage bias may be applied while the
analyte sensor is in its packaging to change the sensor properties
in order to, for example, have the sensor undergo break-in while in
the packaging. In addition, the packaging may contain a calibration
solution that may be embedded in a foam, gel, etc., to prevent
spillage. The calibration solution can be released shortly before
the package is opened or while the package is being opened to
facilitate calibration of the sensor in the package. In yet another
aspect, the estimated break-in time that the sensor needs prior to
start up may be adjusted based on the stored data, including the
age of sensor and its measured impedance. The break-in time
estimated in this manner may be displayed on the display of the
system.
[0039] In another aspect the stored data may be used in conjunction
with measurements obtained in vivo to adjust for sensitivity shifts
that arise in vivo. For instance, the impedance may be measured in
vivo in response to a stimulus signal, which may be a pulse, single
frequency, multiple frequency, or spectroscopy (EIS) signal. The
measured impedance shift can be correlated to changes in
sensitivity, but the correlation may be made more complex by
changes in temperature and ionic concentration (such as sodium) in
the surrounding fluid. To address this issue, impedance
measurements can be taken at one or more temperatures in the
factory and changes in temperature can be mapped to shifts in the
impedance measurement. This information can then be used in vivo by
taking a temperature measurement in vivo and making any adjustments
to the relationship between the measured impedance shift and
changes in sensitivity. Likewise, impedance measurements can be
taken at one or more ionic concentrations in the factory and
changes in concentration can be mapped to shifts in the impedance
measurement. This information can then be used in vivo by taking an
ionic concentration measurement in vivo and making any adjustments
to the relationship between the measured impedance shift and
changes in sensitivity. The ionic concentration may be measured
using a secondary electrode circuit that may be located on the same
body as the analyte measurement circuit or on another subcutaneous
sensor body. In some cases the ionic concentration may be obtained
by optical measurements via changes to the refractive index of the
fluid. The light source for such optical measurements may be
ambient light or a dedicated light source that exposes the fluid to
light of a known wavelength.
[0040] The accuracy of a preconnected sensor depends in part on the
error added to the system in the factory by combining components
with individual variability. Such errors that can impact the system
level calibration may arise from the sensor sensitivity (e.g., the
slope, baseline and O.sub.2), membrane defects (e.g., impedance
detection), electronics (e.g., voltage bias accuracy, current
measurement linearity, leakage current), the calibration process
(e.g., solution accuracy, measurement equipment accuracy) and the
interconnect coupling the analyte sensor and the electronics (e.g.,
the resistance value and variations between the analyte sensor and
measurement electronics, and between the analyte sensor and the
calibration electronics).
[0041] In another aspect, pre-connecting the analyte sensor and the
various components of the electronics may give rise to
manufacturing improvements. For instance, such a pre-connection can
allow for improved sensor tracking and serialization by providing a
component attached to the sensor that has a surface on which a code
(e.g. barcode, label, etc.) can be located for use in
identification. The code, which may serve as a unique identifier,
may be applied during or before manufacturing. The code may also
include sensor data such as a calibration code, sensitivity value,
etc., which are obtained during manufacturing. In some cases
wireless communication may be established with the preconnected
sensor during the manufacturing process. For example, the sensors
can be identified and tracked via wireless interrogation using
short-range wireless communication protocols such as RFID, NFC and
Bluetooth. Likewise, the analyte sensor can actively broadcast data
or its identifier using a short range wireless communication
protocol. In this way the handling efficiency of the analyte sensor
during manufacturing can be improved as the sensors are moved,
connected and disconnected multiple times. The body of the
preconnected electronics can also serve as an anchoring body for
connection and alignment that may improve the manufacturing flow.
Further improvements can arise from replacing physical electrical
connections with non-contact wireless methods.
[0042] In another aspect, the calibration code affixed to the
sensor, transmitter, packaging or other component may be a dynamic
calibration code that changes with changes in environmental
conditions. For example, portions of a printed code (e.g., a
barcode) may be obscured by environmentally reactive pigments such
as a thermochromatic dye, which cause the value of the code to
change. In the shipping industry, reactive pigments are employed
which turn black (or some other color), or which turn from
transparent to black based on exposure to heat, cold, humidity or
shock (by being dropped, for example). Thus, if a calibration code
were printed on the packaging, for instance, it could contain a
base calibration code which adjusts the calibration curve for a
sensor. Additional digits may be printed such that they either
disappear or appear when exposed to an environmental factor that
impacts calibration.
[0043] For instance, in an example of a dynamic calibration code in
the form of a barcode, a predetermined digit, say "3," may indicate
heat exposure. If in this example the package is exposed to heat
over a threshold value the digit 3 disappears, as does its
corresponding portion of the barcode. Another digit, say "7," may
indicate that humidity exposure is at a threshold. If the humidity
surpasses the threshold the digit 7 appears, as does its
corresponding portion of the barcode. When scanned, or otherwise
entered into the software within a patient's mobile device or other
receiver, a calibration curve offset or adjustment can be
generated. Additionally, this information may be transmitted back
to the manufacturer to determine lot variability as well as
variability during shipping, thereby identifying poorly stored
sensors. This information may also flow back to accounting for
inventory write down as well. Additional reactive pigments may
include a "cut off" threshold which are located on the periphery of
the code and which would appear or disappear if the sensor was
exposed to something which renders it unusable. This same
information may be used to accrue an end user credit and reshipment
as well as the aforementioned accounting write down.
[0044] In another aspect, pre-connecting the analyte sensor and the
various components of the electronics may allow manufacturing
improvements by using closed loop manufacturing feedback, which can
allow manufacturing variables to be monitored in real time to
modify the manufacturing process to improve the resulting sensors.
The sensors can be in the form of a brick, fixture, or individual
sensors. Variables that can be monitored include, by way of
illustration, temperature, humidity, the content (e.g., PVP,
ethanol, etc.) of the particular coating solution in which the
sensor is dipped (which may be determined from the refractive index
of the solution), the duration of the dip, the number of times the
sensors are dipped in the solution, and the duration, temperature
and humidity of the curing process. The data gathered during this
monitoring process may allow large sensor data sets concerning the
manufacturing process to be obtained, which can be used to create
outcome-based predictors. For instance, if as a result of this
process it is determined that at some point during the
manufacturing process the temperature was higher than its mean
value, the humidity was lower than its mean value and the sensor
sensitivity was higher than its mean value, an update to the
manufacturing process may be implemented based on this insight to
reduce deviations in the sensor sensitivity from the mean value.
Moreover, since the processes can be continuously monitored, it can
be determined if the updates to the manufacturing process actually
improve the outcome.
[0045] In addition to using data gathered about individual sensors
as feedback during the manufacturing process, sensor lot
information may be obtained and stored. In this way additional
information may be obtained that can be used as feedback during the
manufacturing process. For instance, long term testing for shifts
in e.g., the sensitivity, of sensor lots may be stored in the cloud
for use in a suitable algorithm. Likewise, information concerning
the sensor shipping process (geographic information, means of
transportation used, duration of shipping process, etc.) may be
obtained and stored so that it can be subsequently correlated with
sensor data to determine the effects of environmental exposure.
[0046] In another aspect, in addition to using data gathered during
manufacturing as part of a closed loop feedback process, data
concerning the sensor and the patient while the sensor is in vivo
may also be used. For instance, analytics from individual sensor
performance in a patient may be used as input data into any number
of algorithms used during the manufacturing process. Such data may
be obtained from devices such as a mobile phone or other receiver
that are in communication with the sensor while in use. The data
that is obtained may be any available information such as
temperature, humidity, sensor motion (which may indicate, for
instance, that the patient is sleeping, exercising, etc.),
compressional forces that can be determined from an accelerometer
and which may be exerted on the sensor while the user is in
different positions (e.g., sitting, standing, laying down) and
patient proximity to known locations (e.g., Wi-Fi beacons, cell
towers, internet-of-things (TOT) devices).
[0047] In another aspect, the stored data obtained from the sensor
during and after manufacturing can be used to reduce the risk of
potentially unsafe sensors being deployed in the field. Such data
may be used to examine the efficacy of various storage conditions
(e.g., packaging barriers and packaging indictors) and
sterilization conditions (by, e.g., sampling sensor lots that
undergo sterilization) and to better determine when a sensor is
expected to expire based on its age and the available data
concerning the manufacturing, storage and other environmental
conditions experienced by the sensor. In this way the patient can
be automatically notified (by e.g., an app pop-up, email, automated
phone call) when a sensor is expected to expire.
[0048] In a first aspect, a method is provided for self-calibration
of an analyte sensor system that includes an analyte sensor
operatively coupled to sensor electronics, comprising: applying a
bias voltage with the sensor electronics to the analyte sensor to
generate sensor data, the analyte sensor system having an initial
characteristic metric determined at a first time; using the sensor
electronics at a second time subsequent to the first time to
determine a change to the initial characteristic sensitivity metric
of the analyte sensor system based at least in part on one or more
manufacturing and/or environmental parameters; and using the sensor
electronics to automatically calibrate, without user intervention,
the analyte sensor system based at least in part on the determined
change to the initial characteristic metric.
[0049] In an embodiment of the first aspect or any other embodiment
thereof, one or more environmental parameters are monitored between
the first time and second time.
[0050] In an embodiment of the first aspect or any other embodiment
monitoring the one or more environmental parameters includes
measuring an impedance of the analyte sensor by: applying a
stimulus signal to the analyte sensor; measuring a signal response
to the stimulus signal; calculating the impedance based on the
signal response; and determining a value for the environmental
parameter based on an established relationship between the
impedance and the environmental parameter.
[0051] In an embodiment of the first aspect or any other embodiment
thereof, the first time is subsequent to sensor fabrication and the
second time is prior to sensor use in vivo.
[0052] In an embodiment of the first aspect or any other embodiment
thereof, the first time is subsequent to sensor fabrication and the
second time is subsequent to initiation of sensor use in vivo.
[0053] In an embodiment of the first aspect or any other embodiment
thereof, the initial characteristic metric is determined by
initially calibrating the analyte sensor while the analyte sensor
is operatively coupled to a sensor interface that is configured to
provide an electrical communication interface between the analyte
sensor and each of a manufacturing station and the sensor
electronics.
[0054] In an embodiment of the first aspect or any other embodiment
thereof, the initial characteristic metric is further determined by
measuring an in vitro sensitivity characteristics of the analyte
sensor.
[0055] In an embodiment of the first aspect or any other embodiment
thereof, the initial characteristic metric is determined by
initially calibrating the analyte sensor while the analyte sensor
is operatively coupled to one or more components of the sensor
electronics.
[0056] In an embodiment of the first aspect or any other embodiment
thereof, the one or more components includes a potentiostat.
[0057] In an embodiment of the first aspect or any other embodiment
thereof, the analyte sensor is continuously operatively coupled to
the one or more components of the sensor electronics between the
first and second times without interruption.
[0058] In an embodiment of the first aspect or any other embodiment
thereof, the first time is during a first portion of a
manufacturing life phase of the analyte sensor and the second time
is during a second portion of the manufacturing life phase that is
subsequent to packaging the analyte sensor and the one or more
components of the sensor electronics in the sterile package.
[0059] In an embodiment of the first aspect or any other embodiment
thereof, the first time is during a manufacturing life phase of the
analyte sensor and the second time is during sensor use in
vivo.
[0060] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the one or more environmental parameters
includes monitoring a temperature of the analyte sensor while in a
sterile package.
[0061] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the temperature includes measuring an impedance
of the analyte sensor by: applying a stimulus signal to the analyte
sensor; measuring a signal response to the stimulus signal;
calculating the impedance based on the signal response; determining
a value for the temperature based on an established relationship
between the impedance and the temperature.
[0062] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the temperature includes measuring the
temperature using a temperature sensor included in the sterile
package, the temperature sensor being operatively couplable to the
sensor electronics.
[0063] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the one or more environmental parameters
includes monitoring a humidity of the analyte sensor environment
while in a sterile package.
[0064] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the humidity includes measuring an impedance of
the analyte sensor by: applying a stimulus signal to the analyte
sensor; measuring a signal response to the stimulus signal;
calculating the impedance based on the signal response; determining
a value for the humidity based on an established relationship
between the impedance and the humidity.
[0065] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the humidity includes measuring the humidity
using a humidity sensor included in the sterile package, the
humidity sensor being operatively couplable to the sensor
electronics.
[0066] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the one or more environmental parameters
includes monitoring a sterilization dosage used to sterilize the
analyte sensor.
[0067] In an embodiment of the first aspect or any other embodiment
thereof, determining the change to the initial characteristic
metric includes determining the change through use of a
mathematical function.
[0068] In an embodiment of the first aspect or any other embodiment
thereof, the manufacturing parameters are obtained from an
identifier of the analyte sensor.
[0069] In an embodiment of the first aspect or any other embodiment
thereof, the identifier is affixed to the analyte sensor.
[0070] In an embodiment of the first aspect or any other embodiment
thereof, the identifier is obtained by wirelessly interrogating the
analyte sensor.
[0071] In an embodiment of the first aspect or any other embodiment
thereof, the identifier is associated with a manufacturing lot from
which the analyte sensor was obtained.
[0072] In an embodiment of the first aspect or any other embodiment
thereof, a user is selected to receive the analyte sensor system
based at least in part on one or more analyte sensor
characteristics.
[0073] In an embodiment of the first aspect or any other embodiment
thereof the one or more sensor characteristics includes an updated
characteristic metric that is derived from the determined change to
the initial characteristic metric.
[0074] In an embodiment of the first aspect or any other embodiment
thereof, values for the monitored environmental parameters are
stored for subsequent use when automatically calibrating the
analyte sensor system.
[0075] In an embodiment of the first aspect or any other embodiment
thereof, monitoring the temperature of the analyte sensor while in
the sterile package includes determining if the temperature exceeds
or falls below pre-established threshold values.
[0076] In an embodiment of the first aspect or any other embodiment
thereof monitoring the temperature of the analyte sensor while in
the sterile package includes determining if the humidity exceeds or
falls below pre-established threshold values.
[0077] In an embodiment of the first aspect or any other embodiment
thereof, the initial characteristic metric is reflective of an
initial sensor sensitivity.
[0078] In an embodiment of the first aspect or any other embodiment
thereof, the initial characteristic metric is reflective of an
initial sensor sensitivity and baseline value.
[0079] In an embodiment of the first aspect or any other embodiment
thereof, the initial characteristic metric is reflective of an
initial sensor sensitivity profile.
[0080] In an embodiment of the first aspect or any other embodiment
thereof, an initial calibration factor is derived from the sensor
characteristic metric.
[0081] In an embodiment of the first aspect or any other embodiment
thereof, the change to the initial sensor characteristic is
indicative of sensor failure.
[0082] In an embodiment of the first aspect or any other embodiment
thereof, the one or more manufacturing parameters are measured
prior to the second time.
[0083] In an embodiment of the first aspect or any other embodiment
thereof, the one or more manufacturing parameters are measured
prior to the first time.
[0084] In a second aspect, a method is provided for
self-calibration of an analyte sensor system that includes an
analyte sensor operatively coupled to sensor electronics,
comprising: applying a bias voltage with the sensor electronics to
the analyte sensor to generate sensor data, the analyte sensor
system having an initial characteristic metric determined at a
first time when the analyte sensor is operatively connected to one
or more components of the sensor electronics; using the sensor
electronics at a second time subsequent to the first time to
determine a change to the initial characteristic metric of the
analyte sensor system based at least in part on one or more
manufacturing and/or environmental parameters, wherein the second
time is before or during sensor use in vivo; and using the sensor
electronics to automatically calibrate, without user intervention,
the analyte sensor system based at least in part on the determined
change to the initial characteristic metric.
[0085] In a third aspect, a method is provided for self-calibrating
an analyte sensor system that includes an analyte sensor
operatively coupled to sensor electronics, comprising: applying a
bias voltage with the sensor electronics to the analyte sensor to
generate sensor data, the analyte sensor system having an initial
calibration factor that is used to convert sensor data to analyte
concentration values; using the sensor electronics to update the
calibration factor of the analyte sensor system at a plurality of
times during one or more life phases of the analyte sensor based at
least in part on one or more manufacturing and/or environmental
parameters that are monitored during one or more life phases; and
using the sensor electronics to automatically calibrate, without
user intervention, the analyte sensor system based at least in part
on the updated calibration factor.
[0086] In an embodiment of the third aspect or any other embodiment
thereof, the one or more life phases include a plurality of life
phases.
[0087] In an embodiment of the third aspect or any other embodiment
thereof, the plurality of life phases includes manufacturing,
shipping, storage, insertion and use phases.
[0088] In an embodiment of the third aspect or any other embodiment
thereof, using the sensor electronics to update the calibration
factor of the analyte sensor system includes determining a complex
adaptive calibration value that is based at least in part on
manufacturing conditions and environmental conditions experienced
by the analyte sensor during a plurality of the life phases of the
analyte sensor.
[0089] In an embodiment of the third aspect or any other embodiment
thereof, the manufacturing parameters include process and/or design
parameters.
[0090] In an embodiment of the third aspect or any other embodiment
thereof, the manufacturing parameters include process parameters,
the process parameters including temperature, humidity, curing,
time and dip time.
[0091] In an embodiment of the third aspect or any other embodiment
thereof, the manufacturing parameters include design parameters,
the design parameters including analyte sensor membrane thickness
and raw material characteristics.
[0092] In an embodiment of the third aspect or any other embodiment
thereof, the sensor electronics is used to receive remotely stored
sensor performance data to update the calibration factor.
[0093] In an embodiment of the third aspect or any other embodiment
thereof, the remotely stored sensor performance data that is
received concerns analyte sensors that have experienced or been
exposed to manufacturing and/or environmental parameters that are
most similar to one or more of the monitored manufacturing and/or
environmental parameters.
[0094] In a fourth aspect, a method is provided in which the sensor
experiences a plurality of life phases including manufacture,
shipping, storage and insertion and use in a user as part of a
sensor session, comprising: disposing measurement electronics in
operable connection to the sensor; during the manufacture life
phase in a factory, the manufacture life phase manufacturing a
sensor using a plurality of manufacturing parameters, determining a
first calibration factor; during the shipping or storage phases,
determining a second calibration factor; and upon insertion in a
user, using a combination calibration factor in a user monitoring
device to calibrate signals from the sensor, wherein the
combination calibration factor is based on both the first
calibration factor and the second calibration factor.
[0095] In an embodiment of the fourth aspect or any other
embodiment thereof, the first calibration factor is stored in the
sensor electronics or in measurement electronics associated with
the sensor assembly.
[0096] In an embodiment of the fourth aspect or any other
embodiment thereof, the measurement electronics form a part of the
sensor electronics.
[0097] In an embodiment of the fourth aspect or any other
embodiment thereof, the measurement electronics are separate from
the sensor electronics.
[0098] In an embodiment of the fourth aspect or any other
embodiment thereof, the measurement electronics is disposed in the
same package as the sensor electronics.
[0099] In an embodiment of the fourth aspect or any other
embodiment thereof, the measurement electronics is disposed in a
different package than the sensor electronics.
[0100] In an embodiment of the fourth aspect or any other
embodiment thereof, the sensor assembly and the measurement
electronics are disposed in a package for shipping.
[0101] In an embodiment of the fourth aspect or any other
embodiment thereof, the user monitoring device is a dedicated
receiver or a smart phone.
[0102] In an embodiment of the fourth aspect or any other
embodiment thereof, the transmitting is from the sensor electronics
or the measurement electronics to the dedicated receiver or the
smart phone.
[0103] In an embodiment of the fourth aspect or any other
embodiment thereof, the second calibration factor is stored within
the measurement electronics or the sensor electronics.
[0104] In an embodiment of the fourth aspect or any other
embodiment thereof, the combination calibration factor is
transmitted to a cloud server.
[0105] In an embodiment of the fourth aspect or any other
embodiment thereof, the combination calibration factor, or the
second calibration factor, or both, are transmitted to the factory,
to cause a change in one of the plurality of manufacturing
parameters.
[0106] In an embodiment of the fourth aspect or any other
embodiment thereof, measuring a second calibration factor is
performed by the measurement electronics.
[0107] In an embodiment of the fourth aspect or any other
embodiment thereof, the first calibration factor is a system level
calibration factor pertaining to the calibration of all of the
components in the sensor assembly.
[0108] In an embodiment of the fourth aspect or any other
embodiment thereof, the transmitting further comprises transmitting
a sensor tracking number or serial number to a cloud server or to
the factory along with the combination calibration factor, whereby
a lot associated with the sensor can be identified.
[0109] In an embodiment of the fourth aspect or any other
embodiment thereof, the measurement electronics are configured to
detect faults in the sensor electronics or sensor.
[0110] In an embodiment of the fourth aspect or any other
embodiment thereof, the transmitting further comprises transmitting
data about detected faults in the sensor electronics or sensor.
[0111] In an embodiment of the fourth aspect or any other
embodiment thereof, a calibration factor stored in the user
monitoring device is modified to compensate for the detected
fault.
[0112] In an embodiment of the fourth aspect or any other
embodiment thereof, the measurement electronics are configured to
detect electrical signals from the sensor wire, the sensor
electronics, the housing, or a combination.
[0113] In an embodiment of the fourth aspect or any other
embodiment thereof, the combination calibration factor is
configured to correct for individual process and shipping/storage
variations of an in vivo sensor.
[0114] In an embodiment of the fourth aspect or any other
embodiment thereof, the first calibration factor or the second
calibration factor, or both, are indicative of a measured
impedance.
[0115] In an embodiment of the fourth aspect or any other
embodiment thereof, the impedance measurement is performed by
measuring a step response at a single frequency or at multiple
frequencies.
[0116] In an embodiment of the fourth aspect or any other
embodiment thereof, a third calibration factor is measured prior to
shipping, and wherein the third calibration factor is indicative of
impedance.
[0117] In an embodiment of the fourth aspect or any other
embodiment thereof, the first calibration factor or the second
calibration factor, or both, are indicative of a measured
temperature.
[0118] In an embodiment of the fourth aspect or any other
embodiment thereof, the first calibration factor or the second
calibration factor, or both, are indicative of a measured
humidity.
[0119] In an embodiment of the fourth aspect or any other
embodiment thereof, the combination calibration factor is used to
calculate a modified calibration value, detect physical damage to
the sensor, or detect exposure of the sensor assembly to
temperature and/or humidity.
[0120] In an embodiment of the fourth aspect or any other
embodiment thereof, the combination calibration factor is a complex
adaptive value that combines calibration values collected during
sensor manufacturer and conditions experienced during the time from
sensor manufacturer to sensor insertion.
[0121] In an embodiment of the fourth aspect or any other
embodiment thereof, a user is selected to receive the sensor based
on the first calibration factor, whereby population data or
individual user data determines that the sensor with the first
calibration factor is optimized for the user.
[0122] In an embodiment of the fourth aspect or any other
embodiment thereof, the user is known to have a high average
glucose level, and wherein the first calibration factor is a
relatively low sensitivity.
[0123] In an embodiment of the fourth aspect or any other
embodiment thereof, the manufacturing life phase includes a
packaging phase in which the preconnected sensor assembly is
packaged in a sterile package, the first calibration factor being
determined after the preconnected sensor assembly is packaged in
the sterile package.
[0124] In a fifth aspect, an improved method is provided of
calibrating a sensor associated with a preconnected sensor assembly
in which the sensor experiences a plurality of life phases
including manufacture, shipping, storage and insertion and use in a
user as part of a sensor session, comprising: disposing measurement
electronics in operable connection to the sensor electronics;
during the manufacture life phase in a factory, the manufacture
life phase manufacturing a sensor using a plurality of
manufacturing parameters, determining a first calibration factor;
during the shipping or storage phases, measuring a second
calibration factor; and upon insertion in a user, calculating a
combination calibration factor and storing the same within the
sensor electronics, wherein the combination calibration factor is
based on both the first calibration factor and the second
calibration factor, wherein the combination calibration factor is
configured to provide a conversion between a detected signal from
the sensor wire and an analyte concentration in the user.
[0125] In an embodiment of the fifth aspect or any other embodiment
thereof, an indication is displayed of the analyte
concentration.
[0126] In an embodiment of the fifth aspect or any other embodiment
thereof, the displaying occurs on a user monitoring device in
signal communication with the sensor electronics.
[0127] In an embodiment of the fifth aspect or any other embodiment
thereof, the user monitoring device is a dedicated receiver or a
smart phone.
[0128] In a sixth aspect, an improved method is provided of
manufacturing a sensor assembly including a sensor wire, a housing,
and sensor electronics, comprising: pre-connecting at least a
sensor wire to at least a portion of the sensor electronics
sufficient to monitor manufacturing parameters; monitoring the
manufacturing parameters while completing manufacturing of the
sensor assembly; modifying one or more of the manufacturing
parameters during subsequent manufacturing processes used to
manufacture additional sensor assemblies, the modifying being based
at least in part on the monitored manufacturing parameters.
[0129] In an embodiment of the sixth aspect or any other embodiment
thereof, the sensor electronics is preconnected to the sensor wire
but where the battery and radio remain disconnected.
[0130] In an embodiment of the sixth aspect or any other embodiment
thereof, the battery is pre-connected to the sensor wire and the
portion of the sensor electronics sufficient to monitor
manufacturing parameters.
[0131] In an embodiment of the sixth aspect or any other embodiment
thereof, the radio is preconnected to the battery and the sensor
wire and the portion of the sensor electronics sufficient to
monitor manufacturing parameters.
[0132] In an embodiment of the sixth aspect or any other embodiment
thereof, the combined error of the preconnected sensor wire and the
portion of the sensor electronics sufficient to monitor
manufacturing parameters in is less than a propagated or summed
error of the sensor wire and the portion of the sensor electronics
sufficient to monitor manufacturing parameters considered
individually.
[0133] In seventh aspect, an improved preconnected sensor assembly
is provided that includes a sensor wire, a housing, and sensor
electronics, where the sensor is preconnected to a housing and/or
to sensor electronics, and wherein the sensor wire is at least
preconnected to an interposer which is configured for allowing
measurements of sensor physical properties without requiring a
direct connection to the sensor wire.
[0134] In an embodiment of the seventh or any other embodiment
thereof, the battery is preconnected to the sensor wire and the
housing and/or sensor electronics.
[0135] In an embodiment of the seventh aspect or any other
embodiment thereof, the radio is preconnected to the battery and
the sensor wire and the housing and/or sensor electronics.
[0136] In an eighth aspect, a method is provided for
self-calibration of an analyte sensor system that includes an
analyte sensor operatively couplable to sensor electronics,
comprising: operatively coupling at a first time the analyte sensor
to one or more components of the sensor electronics to define a
packagable analyte sensor arrangement, the packagable sensor
arrangement having an initial sensitivity metric determined
subsequent to the first time; applying an analyte interrogation
signal with the one or more components of the sensor electronics to
the analyte sensor at a second time subsequent to the first time;
measuring a signal response to the stimulus signal; based at least
in part on the measured signal response, determining a second
sensitivity metric; automatically calibrating, without user
intervention, the packagable sensor arrangement based at least in
part on the initial sensitivity metric and the second sensitivity
metric.
[0137] In an embodiment of the eighth aspect or any other
embodiment thereof, the analyte sensor is continuously operatively
coupled to the one or more components of the sensor electronics
between the first and second times without interruption.
[0138] In an embodiment of the eighth aspect or any other
embodiment thereof, applying an analyte interrogation signal
includes applying a stimulus signal to the analyte sensor and
measuring the signal response includes measuring an impedance of
the packagable analyte sensor arrangement.
[0139] In an embodiment of the eighth aspect or any other
embodiment thereof, automatically calibrating the packagable sensor
arrangement is based on an established relationship between the
impedance and analyte sensor sensitivity, wherein automatically
calibrating the packagable sensor arrangement includes
automatically calibrating the packagable sensor arrangement in
vivo.
[0140] In a ninth aspect, a method is provided for performing an
action with an analyte sensor system that includes an analyte
sensor operatively coupled to sensor electronics, comprising:
applying a bias voltage with the sensor electronics to the analyte
sensor to generate sensor data, the analyte sensor system having an
initial characteristic metric determined at a first time when the
analyte sensor is operatively connected to one or more components
of the sensor electronics; using the sensor electronics at a second
time subsequent to the first time to determine a change to the
initial characteristic metric of the analyte sensor system based at
least in part on one or more manufacturing and/or environmental
parameters, wherein the second time is before or during sensor use
in vivo; and based at least in part on the determined change to the
initial characteristic metric, performing an action selected from
the group comprising: generating a message, initiating a
re-calibration process, using a default calibration value and using
a temperature and/or humidity compensated calibration value.
[0141] In an embodiment of the ninth aspect or any other embodiment
thereof, generating the message includes generating an error
message.
[0142] In an embodiment of the ninth aspect or any other embodiment
thereof, generating the message includes generating a message
requesting a manual recalibration.
BRIEF DESCRIPTION OF THE DRAWINGS
[0143] 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:
[0144] FIG. 1 is a schematic view of an analyte sensor system
attached to a host and communicating with a plurality of example
devices, according to some embodiments.
[0145] FIG. 2 is a block diagram that illustrates electronics
associated with the sensor system of FIG. 1, according to some
embodiments.
[0146] FIG. 3 illustrates a perspective view of a wearable device
having an analyte sensor, according to some embodiments.
[0147] FIG. 4 illustrates a schematic of a preconnected analyte
sensor, according to some embodiments.
[0148] FIG. 5 illustrates a block diagram of a system having a
manufacturing system and a wearable device for an analyte sensor,
according to some embodiments.
[0149] FIG. 6 illustrates a schematic diagram of sensor sensitivity
as a function of time during a sensor session, in accordance with
one embodiment;
[0150] FIG. 7 illustrates schematic diagrams of conversion
functions at different time periods of a sensor session, in
accordance with the embodiment of FIG. 6.
[0151] FIGS. 8A-8B show examples of various phases in an analyte
sensor system lifecycle.
[0152] FIG. 9 shows a schematic block diagram of one particular
example of a preconnected analyte sensor system
[0153] FIG. 10 is a Monte Carlo simulation of 5000 samples using a
randomly selected number within the statistical distribution of the
input variables that compares a non-preconnected system and a
preconnected system.
[0154] FIG. 11 shows an example of an automatic calibration process
that may be performed by the sensor electronics in the analyte
monitoring system without user intervention.
[0155] FIG. 12 includes timelines showing the monitored temperature
(a), humidity (b) and sensitivity (c), respectively, over various
phases over the lifetime of an analyte sensor.
[0156] FIG. 13 shows a sensor output signal obtained from an
analyte sensor during various steps during the manufacturing
process.
[0157] FIG. 14 shows the NMR spectrum of PVP in DMSO-d6.
[0158] FIG. 15 shows the HNMR spectrum of Carbosil in DMSO.
[0159] FIG. 16 shows the HNMR spectrum of an RL film (Carbosil/PVP
blend with removal of solvent).
[0160] FIG. 17 shows a composition of an RL solution that was
prepared with different Carbosil/PVP ratios.
[0161] FIG. 18 shows an HNMR calibration curve.
[0162] FIG. 19 is a graph showing initial sensor drift when
ethylene oxide (ETO) sterilization is employed.
[0163] FIG. 20 shows an example of various life phases that an
analyte sensor may undergo.
DETAILED DESCRIPTION
Definitions
[0164] In order to facilitate an understanding of the embodiments
described herein, a number of terms are defined below.
[0165] 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 (barbiturates,
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).
[0166] 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.
[0167] 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.
[0168] 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.
[0169] 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.
[0170] 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.
[0171] 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.
[0172] 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.
[0173] 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.
[0174] 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.
[0175] 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.
[0176] 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.
[0177] 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.
[0178] 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).
[0179] 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.
[0180] 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.
[0181] 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.
[0182] 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.
[0183] 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.
[0184] 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.
[0185] 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.
[0186] 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.
[0187] 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.
[0188] 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.
[0189] 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.
[0190] 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.
[0191] 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.
[0192] 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.
[0193] 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.
[0194] 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.
[0195] The term "process set point," as used herein, is 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 desired
or target value for a variable or process value of a system.
[0196] The term "process variability" 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
measure of the deviation from a set point and is usually expressed
as a standard deviation.
[0197] The term "Monte Carlo Simulation," 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 defining a domain of possible inputs, generating inputs randomly
from a probability distribution over the domain, performing a
deterministic computation on the inputs and aggregating the
results. Monte Carlo simulations sample from a probability
distribution for each variable to produce hundreds or thousands of
possible outcomes. The results are analyzed to get probabilities of
different outcomes occurring.
[0198] The term "accuracy," 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 closeness
of a measured value to a standard or known value.
[0199] The term "precision," 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 degree to
which repeated measurements under unchanged conditions show the
same results.
Overview
[0200] Commercially available transcutaneous analyte measurement
systems consist of discrete modules that are physically
interconnected immediately prior or just following final sensor
placement. Generally, the analyte sensor module is characterized by
a variety of measurement factors (e.g. analyte sensitivity,
baseline, impedance, capacitance, temperature, time, humidity,
interferent sensitivity, etc.) These characteristics have
historically been quantified once at the completion of the analyte
sensor manufacturing process using manufacturing test equipment.
These measurements are taken on the sensor subsystem using test
configurations such as placing the analyte sensor in one or more
solutions of known analyte concentration.
[0201] The measurements derived from the manufacturing process on
the analyte sensor are sometimes used to create one or more metrics
of the sensor performance. These metrics can be transferred using
various methods (e.g. calibration code, wireless transfer, lot
matching) to an analyte algorithm processing unit. In other
embodiments the measurements are used to determine if an individual
sensor or lot of sensors meets acceptable quality criteria. Analyte
sensor metrics, in-vivo calibrations, environmental condition
sensors, and a priori information are typical inputs to an analyte
algorithm processing unit.
[0202] 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.
[0203] 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.
[0204] 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. As noted, such can be very burdensome to the patient
no matter the setting.
[0205] Described herein are continuous analyte sensors that are
factory calibrated or 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). Factory calibration refers generally to an initial
calibration that is typically performed before the sensor leaves
the factory and which is not changed over time. Automatic
self-calibration, on the other hand, refers to a process in which
the calibration is updated without user intervention at one or more
intervals of time subsequent to factory calibration, where the
updating is based on information obtained during manufacturing
and/or during later life phases of the analyte sensor. The updating
of the calibration is generally accomplished by sending a signal
from e.g., the cloud, to the sensor or the sensor electronics.
[0206] FIG. 20 shows an example of various life phases that an
analyte sensor may undergo, which illustratively include a sensor
manufacturing phase, a sensor packaging phase, a sensor storage
phase, a pre in vivo phase, and a sensor session phase. As will be
discussed in more detail below, in some cases the sensor may
undergo additional, or fewer, life phases as well. At various times
t during these phases a complex adaptive calibration factor C(t,
p.sub.i) may be generated that is a function of the time t since
the sensor was manufactured and various parameters p.sub.i, where
i.gtoreq.1. The parameters p.sub.i represent, for instance,
environmental conditions experienced by the analyte sensor (and any
preconnected electronics, if present) from sensor manufacture to
sensor use during the sensor session phase, possibly combined with
additional information such as patient-specific data. The complex
adaptive calibration factor may reflect changes to the analyte
sensor (and any preconnected electronics, if present) that have
arisen since one or more initial calibration factors C.sub.m were
obtained during sensor manufacture. For instance, in FIG. 20 an
initial calibration factor CM is obtained by a "cal check"
procedure in the factory during which the sensor undergoes in vitro
calibration. At subsequent times, such as t1 (during the shipping
phase) and t2 (during the sensor session phase), for example,
complex adaptive calibration factors C.sub.t1 and C.sub.t2 may be
respectively obtained using C.sub.M1 and the measured values of the
parameters. Additional complex adaptive calibration factors may be
obtained during the shipping phase (e.g., C.sub.S1, C.sub.S2 . . .
C.sub.Sn), the storage phase (C.sub.ST1 and C.sub.ST2), the pre in
vivo phase (e.g., C.sub.P) and the sensor session phase (e.g.,
C.sub.SS1, C.sub.SS2 . . . C.sub.SSn). In this way the experience
of the analyte sensor during its lifetime is encoded in a form that
allows it to be used by a suitable calibration algorithm to
determine, for instance, the sensitivity of the sensor and/or its
baseline value.
[0207] 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.
[0208] 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.
[0209] 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-A1, 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 interferents, 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.
[0210] In the prior art, calibrating sensor data from continuous
analyte sensors generally involved 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 have been obtained in
many forms. For example, in certain cases, the reference
measurement may be determined from in vivo analyte concentration
measurements.
[0211] With factory calibration or automatic 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.
[0212] Turning to a basic description of glucose sensor
functionality, 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.
[0213] 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.
[0214] 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 of known values (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.
[0215] Some of the continuous analyte sensors described herein may
be configured to measure a signal associated with a non-analyte
constant signal 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
signal 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.
[0216] 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.
[0217] 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.
Sensor System
[0218] FIG. 1 depicts an example system 100, in accordance with
some example implementations. The system 100 includes an analyte
sensor system 101 including sensor electronics 112 and an analyte
sensor 138. The system 100 may include other devices and/or
sensors, such as medicament delivery pump 102 and glucose meter
104. The analyte sensor 138 may be physically connected to sensor
electronics 112 and may be integral with (e.g., non-releasably
attached to) or releasably attachable to the sensor electronics.
For example, continuous analyte sensor 138 may be connected to
sensor electronics 112 via a sensor interposer that mechanically
and electrically interfaces the analyte sensor 138 with the sensor
electronics. The sensor electronics 112, medicament delivery pump
102, and/or glucose meter 104 may couple with one or more devices,
such as display devices 114, 116, 118, and/or 120.
[0219] In some example implementations, the system 100 may include
a cloud-based analyte processor 490 configured to analyze analyte
data (and/or other patient-related data) provided via network 409
(e.g., via wired, wireless, or a combination thereof) from sensor
system 101 and other devices, such as display devices 114, 116,
118, and/or 120 and the like, associated with the host (also
referred to as a patient) and generate reports providing high-level
information, such as statistics, regarding the measured analyte
over a certain time frame. A full discussion of using a cloud-based
analyte processing system may be found in U.S. patent application
Ser. No. 13/788,375, filed Mar. 7, 2013 and published as
US-2013-0325352-A1, entitled "Cloud-Based Processing of Analyte
Data", herein incorporated by reference in its entirety. In some
implementations, one or more steps of the factory calibration or
automatic self-calibration algorithm can be performed in the
cloud.
[0220] In some example implementations, the sensor electronics 112
may include electronic circuitry associated with measuring and
processing data generated by the analyte sensor 138. This generated
analyte sensor data may also include algorithms, which can be used
to process and calibrate the analyte sensor data, although these
algorithms may be provided in other ways as well. The sensor
electronics 112 may include hardware, firmware, software, or a
combination thereof, to provide measurement of levels of the
analyte via an analyte sensor, such as a glucose sensor. An example
implementation of the sensor electronics 112 is described further
below with respect to FIG. 2.
[0221] In one implementation, the factory or self calibration
algorithms described herein may be performed by the sensor
electronics.
[0222] The sensor electronics 112 may, as noted, couple (e.g.,
wirelessly and the like) with one or more devices, such as display
devices 114, 116, 118, and/or 120. The display devices 114, 116,
118, and/or 120 may be configured for presenting information
(and/or alarming), such as sensor information transmitted by the
sensor electronics 112 for display at the display devices 114,
116,118, and/or 120.
[0223] In one implementation, the factory or self calibration
algorithms described herein may be performed at least in part by
the display devices.
[0224] In some example implementations, the relatively small, key
fob-like display device 114 may comprise a wrist watch, a belt, a
necklace, a pendent, a piece of jewelry, an adhesive patch, a
pager, a key fob, a plastic card (e.g., credit card), an
identification (ID) card, and/or the like. This small display
device 114 may include a relatively small display (e.g., smaller
than the large display device 116) and may be configured to display
certain types of displayable sensor information, such as a
numerical value, and an arrow, or a color code.
[0225] In some example implementations, the relatively large,
hand-held display device 116 may comprise a smart phone, hand-held
receiver device, a palm-top computer, and/or the like. This large
display device may include a relatively larger display (e.g.,
larger than the small display device 114) and may be configured to
display information, such as a graphical representation of the
sensor data including current and historic sensor data output by
sensor system 100.
[0226] In some example implementations, the analyte sensor 138 may
comprise a glucose sensor configured to measure glucose in the
blood or interstitial fluid using one or more measurement
techniques, such as enzymatic, chemical, physical, electrochemical,
spectrophotometric, polarimetric, calorimetric, iontophoretic,
radiometric, immunochemical, and the like. In implementations in
which the analyte sensor 138 includes a glucose sensor, the glucose
sensor may comprise any device capable of measuring the
concentration of glucose and may use a variety of techniques to
measure glucose including invasive, minimally invasive, and
non-invasive sensing techniques (e.g., fluorescence monitoring), to
provide data, such as a data stream, indicative of the
concentration of glucose in a host. The data stream may be sensor
data (raw and/or filtered), which may be converted into a
calibrated data stream used to provide a value of glucose to a
host, such as a user, a patient, or a caretaker (e.g., a parent, a
relative, a guardian, a teacher, a doctor, a nurse, or any other
individual that has an interest in the wellbeing of the host).
Moreover, the analyte sensor 138 may be implanted as at least one
of the following types of analyte sensors: an implantable glucose
sensor, a transcutaneous glucose sensor, implanted in a host vessel
or extra corporeally, a subcutaneous sensor, a refillable
subcutaneous sensor, an intravascular sensor.
[0227] Although the disclosure herein refers to some
implementations that include an analyte sensor 138 comprising a
glucose sensor, the analyte sensor 138 may comprise other types of
analyte sensors as well. Moreover, although some implementations
refer to the glucose sensor as an implantable glucose sensor, other
types of devices capable of detecting a concentration of glucose
and providing an output signal representative of glucose
concentration may be used as well. These may include, for example,
fully implantable, subcutaneous, transcutaneous sensors.
Furthermore, although the description herein refers to glucose as
the analyte being measured, processed, and the like, other analytes
may be used as well including, for example, ketone bodies (e.g.,
acetone, acetoacetic acid and beta hydroxybutyric acid, lactate,
etc.), glucagon, acetyl-CoA, triglycerides, fatty acids,
intermediaries in the citric acid cycle, choline, insulin,
cortisol, testosterone, and the like.
[0228] FIG. 2 depicts an example of electronics 12 that may be used
in sensor electronics 112 or may be implemented in a manufacturing
station such as a testing station, a calibration station, a smart
carrier, or other equipment used during manufacturing of device
101, in accordance with some example implementations. The sensor
electronics 112 may include electronics components that are
configured to process sensor information, such as sensor data, and
generate transformed sensor data and displayable sensor
information, e.g., via a processor module. For example, the
processor module may transform sensor data into one or more of the
following: filtered sensor data (e.g., one or more filtered analyte
concentration values), raw sensor data, calibrated sensor data
(e.g., one or more calibrated analyte concentration values), rate
of change information, trend information, rate of
acceleration/deceleration information, sensor diagnostic
information, location information, alarm/alert information,
calibration information such as may be determined by factory or
self-calibration algorithms as disclosed herein, smoothing and/or
filtering algorithms of sensor data, and/or the like.
[0229] In some embodiments, a processor module 214 is configured to
achieve a substantial portion, if not all, of the data processing,
including data processing pertaining to factory or
self-calibration. Processor module 214 may be integral to sensor
electronics 12 and/or may be located remotely, such as in one or
more of devices 114, 116, 118, and/or 120 and/or cloud 490. For
example, in some embodiments, processor module 214 may be located
at least partially within a cloud-based analyte processor 490 or
elsewhere in network 406.
[0230] In some example implementations, the processor module 214
may be configured to calibrate the sensor data, and the data
storage memory 220 may store the calibrated sensor data points as
transformed sensor data. Moreover, the processor module 214 may be
configured, in some example implementations, to wirelessly receive
calibration information from a display device, such as devices 114,
116, 118, and/or 120, to enable calibration of the sensor data from
sensor 138. Furthermore, the processor module 214 may be configured
to perform additional algorithmic processing on the sensor data
(e.g., calibrated and/or filtered data and/or other sensor
information), and the data storage memory 220 may be configured to
store the transformed sensor data and/or sensor diagnostic
information associated with the algorithms. The processor module
214 may further be configured to store and use calibration
information determined from a factory or self-calibration, as
described below.
[0231] In some example implementations, the sensor electronics 112
may comprise an application-specific integrated circuit (ASIC) 205
coupled to a user interface 222. The ASIC 205 may further include a
potentiostat 210, a telemetry module 232 for transmitting data from
the sensor electronics 112 to one or more devices, such as devices
114, 116, 118, and/or 120, and/or other components for signal
processing and data storage (e.g., processor module 214 and data
storage memory 220). Although FIG. 1I depicts ASIC 205, other types
of circuitry may be used as well, including field programmable gate
arrays (FPGA), one or more microprocessors configured to provide
some (if not all of) the processing performed by the sensor
electronics 12, analog circuitry, digital circuitry, or a
combination thereof.
[0232] In the example depicted in FIG. 1I, through a first input
port 211 for sensor data the potentiostat 210 is coupled to an
analyte sensor 138, such as a glucose sensor to generate sensor
data from the analyte. The potentiostat 210 may also provide via
data line 212 a voltage to the analyte sensor 138 to bias the
sensor for measurement of a value (e.g., a current and the like)
indicative of the analyte concentration in a host (also referred to
as the analog portion of the sensor). The potentiostat 210 may have
one or more channels depending on the number of working electrodes
at the analyte sensor 138.
[0233] In some example implementations, the potentiostat 210 may
include a resistor that translates a current value from the sensor
138 into a voltage value, while in some example implementations, a
current-to-frequency converter (not shown) may also be configured
to integrate continuously a measured current value from the sensor
138 using, for example, a charge-counting device. In some example
implementations, an analog-to-digital converter (not shown) may
digitize the analog signal from the sensor 138 into so-called
"counts" to allow processing by the processor module 214. The
resulting counts may be directly related to the current measured by
the potentiostat 210, which may be directly related to an analyte
level, such as a glucose level, in the host.
[0234] The telemetry module 232 may be operably connected to
processor module 214 and may provide the hardware, firmware, and/or
software that enable wireless communication between the sensor
electronics 112 and one or more other devices, such as display
devices, processors, network access devices, and the like. A
variety of wireless radio technologies that can be implemented in
the telemetry module 232 include Bluetooth, Bluetooth Low-Energy,
ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular radio access
technologies, radio frequency (RF), infrared (IR), paging network
communication, magnetic induction, satellite data communication,
spread spectrum communication, frequency hopping communication,
near field communications, and/or the like. In some example
implementations, the telemetry module 232 comprises a Bluetooth
chip, although Bluetooth technology may also be implemented in a
combination of the telemetry module 232 and the processor module
214.
[0235] The processor module 214 may control the processing
performed by the sensor electronics 112. For example, the processor
module 214 may be configured to process data (e.g., counts), from
the sensor, filter the data, calibrate the data, perform fail-safe
checking, and/or the like.
[0236] Potentiostat 210 may measure the analyte (e.g., glucose
and/or the like) at discrete time intervals or continuously.
[0237] The processor module 214 may further include a data
generator (not shown) configured to generate data packages for
transmission to devices, such as the display devices 114, 116, 118,
and/or 120. Furthermore, the processor module 214 may generate data
packets for transmission to these outside sources via telemetry
module 232. In some example implementations, the data packages may
include an identifier code for the sensor and/or sensor electronics
112, raw data, filtered data, calibrated data, rate of change
information, trend information, error detection or correction,
and/or the like.
[0238] The processor module 214 may also include a program memory
216 and other memory 218. The processor module 214 may be coupled
to a communications interface, such as a communication port 238,
and a source of power, such as a battery 234. Moreover, the battery
234 may be further coupled to a battery charger and/or regulator
236 to provide power to sensor electronics 12 and/or charge the
battery 234.
[0239] The program memory 216 may be implemented as a semi-static
memory for storing data, such as an identifier for a coupled sensor
138 (e.g., a sensor identifier (ID)) and for storing code (also
referred to as program code) to configure the ASIC 205 to perform
one or more of the operations/functions described herein. For
example, the program code may configure processor module 214 to
process data streams or counts, filter, perform the calibration
methods described below, perform fail-safe checking, and the
like.
[0240] The memory 218 may also be used to store information. For
example, the processor module 214 including memory 218 may be used
as the system's cache memory, where temporary storage is provided
for recent sensor data received from the sensor. In some example
implementations, the memory may comprise memory storage components,
such as read-only memory (ROM), random-access memory (RAM),
dynamic-RAM, static-RAM, non-static RAM, electrically erasable
programmable read only memory (EEPROM), rewritable ROMs, flash
memory, and the like.
[0241] The data storage memory 220 may be coupled to the processor
module 214 and may be configured to store a variety of sensor
information. In some example implementations, the data storage
memory 220 stores one or more days of analyte sensor data. The
stored sensor information may include one or more of the following:
a time stamp, raw sensor data (one or more raw analyte
concentration values), calibrated data, filtered data, transformed
sensor data, and/or any other displayable sensor information,
calibration information (e.g., reference BG values and/or prior
calibration information such as from factory calibration), sensor
diagnostic information, and the like.
[0242] The user interface 222 may include a variety of interfaces,
such as one or more buttons 224, a liquid crystal display (LCD)
226, a vibrator 228, an audio transducer (e.g., speaker) 230, a
backlight (not shown), and/or the like. The components that
comprise the user interface 222 may provide controls to interact
with the user (e.g., the host).
[0243] The battery 234 may be operatively connected to the
processor module 214 (and possibly other components of the sensor
electronics 12) and provide the necessary power for the sensor
electronics 112. In other implementations, the receiver can be
transcutaneously powered via an inductive coupling, for
example.
[0244] A battery charger and/or regulator 236 may be configured to
receive energy from an internal and/or external charger. In some
example implementations, the battery 234 (or batteries) is
configured to be charged via an inductive and/or wireless charging
pad, although any other charging and/or power mechanism may be used
as well.
[0245] One or more communication ports 238, also referred to as
external connector(s), may be provided to allow communication with
other devices, for example a PC communication (com) port can be
provided to enable communication with systems that are separate
from, or integral with, the sensor electronics 112. The
communication port, for example, may comprise a serial (e.g.,
universal serial bus or "USB") communication port, and allow for
communicating with another computer system (e.g., PC, personal
digital assistant or "PDA," server, or the like). In some example
implementations, factory information or other data may be sent to
or received from the sensor, the algorithm or a cloud data
source.
[0246] The one or more communication ports 238 may further include
a second input port 237 in which calibration data may be received,
and an output port 239 which may be employed to transmit calibrated
data, or data to be calibrated, to a receiver or mobile device.
FIG. 2 illustrates these aspects schematically. It will be
understood that the ports may be separated physically, but in
alternative implementations a single communication port may provide
the functions of both the second input port and the output
port.
[0247] In some analyte sensor systems, an on-skin portion of the
sensor electronics may be simplified to minimize complexity and/or
size of on-skin electronics, for example, providing only raw,
calibrated, and/or filtered data to a display device configured to
run calibration and other algorithms required for displaying the
sensor data. However, the sensor electronics 112 (e.g., via
processor module 214) may be implemented to execute prospective
algorithms used to generate transformed sensor data and/or
displayable sensor information, including, for example, algorithms
that: evaluate a clinical acceptability of optional reference
and/or sensor data, evaluate calibration data for best calibration
based on inclusion criteria, evaluate a quality of the calibration,
compare estimated analyte values with time corresponding measured
analyte values, analyze a variation of estimated analyte values,
evaluate a stability of the sensor and/or sensor data, detect
signal artifacts (noise), replace signal artifacts, determine a
rate of change and/or trend of the sensor data, perform dynamic and
intelligent analyte value estimation, perform diagnostics on the
sensor and/or sensor data, set modes of operation, evaluate the
data for aberrancies, and/or the like. A connected receiver or
smart device or wearable may perform one or more of such
calculations.
[0248] FIG. 3 illustrates a perspective view of an exemplary
implementation of analyte sensor system 101 implemented as a
wearable device such as an on-skin sensor assembly 600. As shown in
FIG. 3, on-skin sensor assembly includes a base 128. An adhesive
126 can couple base 128 to the skin of the host. The adhesive 126
can be an adhesive suitable for skin adhesion but not generally,
e.g., foam-based adhesives.
[0249] In some embodiments, electronics unit 500 (e.g., a
transmitter) may be coupled to base 128 (e.g., via mechanical
interlocks such as snap fits and/or interference features). The
electronics unit 500 can include sensor electronics 112 operable to
measure and/or analyze glucose indicators sensed by glucose sensor
138. Sensor electronics 112 within electronics unit 500 can
transmit information (e.g., measurements, analyte data, and glucose
data) to a remotely located device (e.g., 114-120 shown in FIG.
1).
[0250] Sensor 138 may be provided as a part of a preconnected
sensor that includes a sensor interposer. The sensor interposer
(not visible in FIG. 3) may be secured between base 128 and
electronics unit 500 and electrically coupled to electronics unit
500 to couple sensor 138 to the sensor electronics (e.g., sensor
electronics 112 of FIG. 1).
[0251] FIG. 4 shows a schematic illustration of a preconnected
sensor 400. As shown in FIG. 4, preconnected sensor 400 includes
sensor interposer 402 permanently attached to sensor 138. In the
example of FIG. 4, sensor interposer 402 includes substrate 404,
first contact 406, and second contact 408. Contact 406 is
electrically coupled to a first contact on a proximal end of sensor
138 and contact 408 is electrically coupled to a second contact on
the proximal end of sensor 138. The distal end of sensor 138 is a
free end configured for insertion into the skin of the host.
[0252] As shown in FIG. 4, contact 406 is coupled to an external
contact 410 and contact 408 is coupled to an external contact 412.
As described in further detail hereinafter, external contacts 410
and 412 are sized, shaped, and positioned to electrically interface
with sensor electronics 112 in electronics unit 500 in addition to
electrically interfacing with processing circuitry of manufacturing
equipment such one or more testing stations and/or one or more
calibration stations. Although various examples are described
herein in which two contacts 410 and 412 on the interposer are
coupled to two corresponding contacts 406 and 408 on sensor 138,
this is merely illustrative. In other implementations, interposer
402 and sensor 138 may each be provided with a single contact or
may each be provided with more than two contacts. In some
implementations, interposer 402 and sensor 138 may have a same
number of contacts. In some implementations, interposer 402 and
sensor 138 may have a different number of contacts. For example, in
some implementations, interposer 402 may have additional contacts
for coupling to or between various components of a manufacturing
station.
[0253] Substrate 404 may be sized and shaped to mechanically
interface with base 128 and/or electronics unit 500 in addition to
mechanically interfacing with manufacturing equipment such one or
more assembly equipment, testing stations and/or one or more
calibration stations. Interposer 402 may be attached and/or
electrically coupled to sensor 138. Interposer 402 may be attached
to sensor 138 using, as examples, adhesive, spring contacts,
wrapped flexible circuitry, a conductive elastomer, a barrel
connector, a molded interconnect device structure, magnets,
anisotropic conductive films, or other suitable structures or
materials for mechanically and electrically attaching interposer
402 to sensor 138 before or during assembly, manufacturing, testing
and/or calibration operations. Interposer 402 may be attached to
sensor 138 using, as examples, spot welding, swaging, crimping,
clipping, soldering or brazing, plastic welding, overmolding, or
other suitable methods for mechanically and electrically attaching
interposer 402 to sensor 138 before or during assembly,
manufacturing, testing and/or calibration operations. Substrate 404
may include datum features (sometimes referred to as datum
structures) such as a recess, an opening, a surface or a protrusion
for aligning, positioning, and orienting sensor 138 relative to
interposer 402. Substrate 404 may also include, or may itself form,
one or more anchoring features for securing and aligning the
analyte sensor during manufacturing (e.g., relative to a
manufacturing station).
[0254] FIG. 5 shows a block diagram of an exemplary system 5000
having manufacturing equipment such as one or more manufacturing
stations 5091, one or more positioning or testing stations 5002
and/or one more calibration stations 5004, and having an on-skin
sensor assembly 600, each configured to receive sensor interposer
402 and to communicatively couple to sensor 138 via sensor
interposer 402.
[0255] System 5000 may include one or more positioning or testing
stations 5002 having processing circuitry 5012 configured to
perform testing operations with sensor 138 to determine parameters
and/or to verify the operational integrity of sensor 138. Testing
operations may include verifying electrical properties of a sensor
138, verifying communication between a working electrode and
contact 408, verifying communication between a reference electrode
or additional electrodes and contact 406, and/or other electronic
verification operations for sensor 138. Processing circuitry 5012
may be communicatively coupled with sensor 138 for testing
operations by inserting substrate 404 into a receptacle 5006 (e.g.,
a recess in a housing of testing station 5002) until contact 410 is
coupled to contact 5010 of testing station 5002 and contact 412 is
coupled to contact 5008 of testing station 5002.
[0256] System 5000 may include one or more calibration stations
5004 having processing circuitry 5020 configured to perform
calibration operations with sensor 138 to obtain calibration data
for in vivo operation of sensor 138. Calibration data obtained by
calibration equipment 5004 may be provided to on-skin sensor
assembly 600 to be used during operation of sensor 138 in vivo.
Processing circuitry 5020 may be communicatively coupled with
sensor 138 for calibration operations by inserting substrate 404
into a receptacle 5014 (e.g., a recess in a housing of calibration
station 5004) until contact 410 is coupled to contact 5018 of
testing station 5002 and contact 412 is coupled to contact 5016 of
testing station 5002.
[0257] System 5000 may include one or more manufacturing stations
5091. Manufacturing station 5091 may also serve in providing the
functions of a testing station as described herein, a calibration
station as described herein, or another manufacturing station.
Manufacturing station 5091 may include processing circuitry 5092
and/or mechanical components 5094 operable to perform testing
operations, calibration operations, and/or other manufacturing
operations such as sensor straightening operations, membrane
application operations, baking operations, calibration-check
operations, glucose sensitivity operations (e.g., sensitivity
slope, baseline, and/or noise calibration operations), and/or
visual inspection operations. Manufacturing parameters that may be
measured during these various operations may include, by way of
illustration, temperature, humidity, the content (e.g., PVP,
ethanol, etc.) of the particular coating solution in which the
sensor is dipped (which may be determined from the refractive index
of the solution), the duration of the dip, the number of times the
sensors are dipped in the solution, and the duration, temperature
and humidity of the curing process.
[0258] In the example of FIG. 5, testing station 5002 and
calibration station 5004 include receptacles 5006 and 5014.
However, this is merely illustrative and interposer 402 may be
mounted to testing station 5002 and calibration station 5004 and/or
manufacturing station 5091 using other mounting features such as
grasping, clipping, or clamping figures. For example, manufacturing
station 5091 includes grasping structures 5093 and 5095, at least
one of which is movable to grasp interposer 402 (or a carrier
having multiple interposers and sensors). Structure 5093 may be a
stationary feature having one or more electrical contacts such as
contact 5008. Structure 5095 may be a movable feature that moves
(e.g., slides in a direction 5097) to grasp and secure interposer
402 in an electrically coupled position for manufacturing station
5091. In other implementations, both features 5093 and 5095 are
movable.
[0259] Sensor interposer 402 may also include an identifier 450
(see, e.g., FIG. 4). Identifier 450 may be formed on or embedded
within substrate 404. Identifier 450 may be implemented as a visual
or optical identifier (e.g., a barcode pre-printed or printed
on-the-fly on substrate 404 or etched in to substrate 404), a radio
frequency (RF) identifier, or an electrical identifier (e.g., a
laser-trimmed resistor, a capacitive identifier, an inductive
identifier, or a micro storage circuit (e.g., an integrated circuit
or other circuitry in which the identifier is encoded in memory of
the identifier) programmable with an identifier and/or other data
before, during, or after testing and calibration). Identifier 450
may be used for tracking each sensor through the manufacturing
process for that sensor (e.g., by storing a history of testing
and/or calibration data for each sensor). For example, identifier
450 may be used for binning of testing and calibration performance
data. Identifier 450 may be a discrete raw value or may encode
information in addition to an identification number. Identifier 450
may be used for digitally storing data in non-volatile memory on
substrate 404 or as a reference number for storing data external to
interposer 402.
[0260] Testing station 5002 may include a reader 5011 (e.g., an
optical sensor, an RF sensor, or an electrical interface such as an
integrated circuit interface) that reads identifier 450 to obtain a
unique identifier of sensor 138. Testing data obtained by testing
station 5002 may be stored and/or transmitted along with the
identifier of sensor 138.
[0261] Calibration station 5004 may include a reader 5011 (e.g., an
optical sensor, an RF sensor, or an electrical interface) that
reads identifier 450 to obtain a unique identifier of sensor 138.
Calibration data obtained by calibration station 5004 may be stored
and/or transmitted along with the identifier of sensor 138. In some
implementations, calibration data obtained by calibration station
5004 may be added to identifier 450 by calibration station 5004
(e.g., by programming the calibration data into the identifier). In
some implementations, calibration data obtained by calibration
station 5004 may be transmitted to a remote system or device along
with identifier 450 by calibration station.
[0262] As shown in FIG. 5, on-skin sensor assembly 600 may include
one or more contacts such as contact 5022 configured to couple
electronics unit 500 to contacts 410 and 412 of interposer 402 and
thus to sensor 138. Interposer 402 may be sized and shaped to be
secured within a cavity 5024 between base 128 and electronics unit
500 such that sensor 138 is coupled to electronics unit 500 via
interposer 402, identifier 450 is accessible by reader 5013, and
sensor 138 is positionally secured to extend through opening 180
for insertion for in vivo operations.
[0263] Although one calibration station and one testing station are
shown in FIG. 5, it should be appreciated that more than one
testing station and/or more than one calibration station may be
included in system 5000. Although calibration station 5004 and
testing station 5002 are shown as separate stations in FIG. 5, it
should be appreciated that, in some implementations calibration
stations and testing stations may be combined into one or more
calibration/testing stations (e.g., stations in which processing
circuitry for performing testing and calibration operations is
provided within a common housing and coupled to a single interface
5006). In addition, data from one or more manufacturing stations
may be compiled and stored in and/or stored and associated with the
sensor and interposer.
[0264] On-skin sensor assembly 600 may also include a reader 5013
(e.g., an optical sensor, an RF sensor, or an electrical interface)
that reads identifier 450 to obtain a unique identifier of sensor
138. Sensor electronics in electronics unit 500 may obtain
calibration data for in vivo operation of sensor 138 based on the
read identifier 450. The calibration data may be stored in, and
obtained, from identifier 450 itself, or identifier 450 may be used
to obtain the calibration data for the installed sensor 138 from a
remote system such as a cloud-based system.
[0265] Additional details concerning the example of the sensor
system shown FIGS. 1-5 may be found in U.S. Pat. Appl. Ser. No.
62/576,560, filed Oct. 24, 2017, entitled "Preconnected Analyte
Sensors," which is hereby incorporated by reference in its
entirety.
Determination of Sensor Sensitivity
[0266] 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.
[0267] 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. 6 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. 7
provides three plots of conversion functions at three different
time periods of a sensor session. As shown in FIG. 7, 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.
[0268] Referring back to the study associated with FIG. 6, 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.
[0269] 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.
[0270] 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. 6, 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.
[0271] 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.
[0272] 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.
[0273] 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.
[0274] 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.
[0275] 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.
[0276] 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.
[0277] 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. Again, 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 in
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 factory
calibrated or automatically 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
[0278] The process of manufacturing continuous analyte sensors may
sometimes be subjected to a degree of variability between sensor
lots, as will be described in greater detail below. 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).
[0279] 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 a parameter that affects sensor sensitivity or provides
information about sensor sensitivity. The other calibration codes
may be associated with any a priori information or parameter
described elsewhere herein and/or any parameter that helps define a
mathematical relationship between the measured signal and analyte
concentration. The calibration code may be developed from these
measurements or may be developed based on manufacturing parameters
known, determined, or measured during fabrication of, e.g., a lot,
or by a combination of these.
[0280] 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.
[0281] 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.
[0282] 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).
Advanced and/or Multivariate Calibration
[0283] As previously mentioned, the sensitivity of an analyte
sensor may change over time as a result of a variety of different
manufacturing and environmental parameters. In some embodiments
some of these parameters involve information that can be obtained
during distinct phases of the analyte sensor lifecycle. As shown in
FIGS. 8A and 8B, in certain embodiments these different phases may
include one or more of the following: a sensor manufacturing phase
5702, a sensor packaging phase 5704, a sensor package sterilization
phase 5706 in which the sensor is sterilized while in the package
(using, e.g., any suitable sterilization gas, which may include
conventional sterilization gases or, alternatively, nitrogen
dioxide, chlorine dioxide or ethylene oxide, or alternatively,
using an e-beam, at least to sterilize the transmitter, whose
interior may be shielded to deflect the e-beam), a sensor shipping
phase 5708, a sensor storage phase 5710 (e.g., in a warehouse,
retail environment, user premises), a sensor startup/insertion
phase 5712 during which the sensor is placed in vivo, and a sensor
in vivo phase 5714 in which the sensor is operational while in
vivo. Of course, in other embodiments, the analyte sensor lifecycle
may be divided into different lifecycles. FIGS. 8A-8B further show
examples of environmental and other factors that may be monitored
during each lifecycle phase and which may be taken into account
when calibrating the sensor.
[0284] Moreover, the various lifecycle phases enumerated above in
some cases may be further divided into identifiable sub-stages. For
instance, in some embodiments the sensor manufacturing phase may
include a sub-phase in which the analyte sensor may be preconnected
to one or more components of the sensor electronics or even all of
the sensor electronics. If the analyte sensor system employs a
sensor interface such as the sensor interposer 402 shown in the
embodiment of FIG. 4, then the sensor may be preconnected to the
sensor interface, and possibly one or more components of the sensor
electronics as well. Alternatively, instead of treating the
pre-connection process as part of the sensor manufacturing phase,
it may be identified as a separate phase that occurs before or
after the sensor manufacturing phase.
[0285] The sensor manufacturing phase may be further divided into a
wire cutting phase, a wire coating phase, a wire baking phase, a
wire skiving phase, and so on.
[0286] In addition to, or instead of the sensor interposer, the
components of the sensor electronics that may be preconnected to
the analyte sensor may include a processor (e.g., processor module
214 in the embodiment of FIG. 2), a memory (e.g., data storage
memory 220 in the embodiment of FIG. 2), a potentiostat (e.g.,
potentiostat 210 in the embodiment of FIG. 2), an analog
measurement circuit, a digital measurement circuit, and/or a
transmitter (e.g., telemetry module 232 in the embodiment of FIG.
2).
[0287] FIG. 9 shows a schematic block diagram of one particular
example of a preconnected analyte sensor system that includes an
analyte sensor 5602, a sensor interconnection module 5604 (e.g.,
the sensor interposer) and measurement electronics 5608. The
measure electronics 5608 include a potentiostat 5610 and a number
of optional components. The optional components may include any one
or more of the following: a temperature measurement circuit 5612,
an impedance measurement circuit 5614, a processor 5616, a radio
5618, a humidity measurement circuit 5620, a pressure measurement
circuit 5622, a motion detector circuit 5624, a capacitive
measurement circuit 5626, a display/status indicator 5628, a data
storage 5630, a power source 5632 and a clock 5634.
[0288] FIG. 9 also shows potential sources of error 5640 and 5650
that may be reduced or eliminated by using a preconnected analyte
sensor system. These sources of error may include, for example,
errors that may arise when a user is required to connect the sensor
to the transmitter or other electronics such as the contact
resistance, connection stability, electronic noise and
environmental factors. In addition, FIG. 9 shows various errors in
the measurement electronics that may be reduced or eliminated by
use of a preconnected analyte sensor system. These errors may
include, for example, the tolerance of the electronic components,
leakage current, measurement error, resolution error, electronic
noise and environmental factors that impact the electronics.
[0289] FIG. 10 is a Monte Carlo simulation of 5000 samples using a
randomly selected number within the statistical distribution of the
input variables that compares a non-preconnected system and a
preconnected system. This shows the number of samples falling
within the 10 mg/dL or 10% glucose concentration error target. It
shows a reduction of the statistical distribution in error that can
be achieved with a preconnected system versus a non-preconnected
system in which a variety of individual components having a
distribution in their characteristics (e.g., gain, offset, contact,
etc.) are combined into a system.
[0290] The comparison uses a unitless measurement of current
(counts) and calibrates to a known glucose calibration solution.
This is an exemplary model and not all variables that affect the
system are taken into account. The variation induced by component
and measurement variations are eliminated. In particular the values
of gain and offset are not measured and calibrated to a unit value
so their induced error is eliminated. The system is calibrated with
the exact components that influence the values of contact
resistance, leakage current and bias voltage. Therefore, their
variability is eliminated and they can be modeled as fixed
values.
[0291] In some embodiments some of the electronics may be
incorporated in the interposer or other interconnect component that
is connected to the sensor. In this way by pre-connecting the
interposer to the sensor, some or all of the electronics will also
be preconnected to the sensor. This would allow calibration and
other data to be conveniently stored during the manufacturing
process. In some embodiments the interposer (or other component
connected to the sensor) may be used for other purposes as well.
For instance, it may be used to store a code that can be used to
track the sensor during manufacturing and/or other life phases of
the sensor. The code may be embodied, for instance, in a series of
resistors that are printed on an interposer or the like. The code
may be programmed by laser cutting selected traces to impart a
final resistance to or on the printed resistor. The resistance may
be read out by the transmitter when the transmitter is installed
via spring contacts or the like on the interposer.
[0292] In some embodiments any of these or other components that
may be preconnected to the analyte sensor may be configured so that
the connection is maintained through multiple periods during a
sensor lifecycle. In this way the pre-connection may be maintained
throughout the entirety or multiple sequences of the analyte
sensor's lifecycle. Hence, the system-level calibrations (i.e., the
analyte sensor and the preconnected components of the sensor
electronics) that are performed over the analyte sensor's and/or
sensor electronics lifecycle should correlate to changes in the
system during one or more phases.
[0293] The components to which analyte sensor is preconnected may
be packaged along with the analyte sensor in the sterile package
that is used to ship and store the analyte sensor. Accordingly, in
these embodiments it may be advantageous if the preconnected
components are single-use, disposable components.
[0294] Parameters that may uniquely impact the analyte sensor
sensitivity during the manufacturing phase may include, without
limitation, parameters such as the 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 exposed to a particular coating solution, the
enzyme activity level, amount of coating applied, etc. Parameters
that may uniquely impact the analyte sensor sensitivity during the
packaging phase may include, without limitation, the sterilization
dosage, sterilization method, enzyme activity, packaging material,
etc. Additional parameters that may impact the analyte sensor
sensitivity during any and all phases may include various
environmental parameters such as temperature and humidity and the
duration of time at which the sensor was exposed to the measured
values of temperature and humidity, for example.
[0295] In some embodiments the analyte sensor may be calibrated
based on measurements of one or more of the various parameters that
impact the analyte sensor sensitivity during two or more phases of
the analyte sensor lifecycle. An illustrative calibration process
2400 in accordance with some embodiments will now be discussed with
reference to FIG. 11. The calibration process may be performed by
the sensor electronics in the analyte monitoring system, without
user intervention, thereby avoiding the need for external user
calibration when the device is in use. The process begins at block
2402 when the analyte sensor is preconnected to one or more
components of the sensor electronics during, e.g., a manufacturing
phase. Next, at block 2404, the analyte sensor, along with the
preconnected sensor electronic component(s), undergoes an initial
calibration process. The initial calibration process may use any
available a priori information comprising sensor sensitivity
information in order to obtain a calibration factor that can be
used to convert sensor data (e.g., in units of current or counts)
into estimated analyte values (e.g., in unit of analyte
concentration).
[0296] A number of advantages may arise from performing the initial
calibration process after the analyte sensor has been preconnected
to the one or more components of the sensor electronics.
Measurements may be taken during the manufacturing process phase to
establish reference values for comparison at a later time period.
These reference values may be used by a processing algorithm to
quantify scale and offset values from a known state. In some cases
the reference measurement value is dependent on a sensor
characteristic that is influenced by a connection property. This
may enable a measurement to be taken that would not be possible in
a separable system. For instance, errors that may separately arise
in the analyte sensor and the sensor electronics may be reduced or
eliminated by calibrating them as a single unit. In addition,
errors that may arise from the act of connecting (and
disconnecting) the analyte sensor to the sensor electronics can
also be reduced or eliminated. For example, the impedance
measurement of the sensor may be more stable if the sensor remains
continuously connected to the sensor electronics.
[0297] After preconnecting the analyte sensor to one or more
components of the sensor electronics, the calibration process 2400
proceeds to block 2406 where one or more environmental parameters
affecting sensor sensitivity are monitored during one or more
phases of the analyte sensor lifecycle subsequent to the
manufacturing phase. For instance, environmental parameters may be
monitored during the sensor packaging stage, sensor package
sterilization phase, sensor shipping stage, sensor storage stage,
sensor insertion stage and/or the sensor use stage.
[0298] The monitoring of the environmental parameters may be
accomplished in any number of different ways. For instance, if the
analyte sensor is preconnected to at least the components of the
sensor electronics that are used to apply a stimulus signal to an
analyte sensor and measure a signal response to the stimulus
signal, the signal response can be used to determine an impedance
value of the analyte sensor. Various techniques for calculating
analyte sensor impedance values based on the signal response are
described elsewhere herein, such as one or more of the techniques
discussed in U.S. patent application Ser. No. 14/144,343, published
as US-2014-0114156-A1 and entitled "Advanced Analyte Sensor
Calibration and Error Detection," which is hereby incorporated by
reference in its entirety. The determined impedance may then be
compared to a pre-established impedance-to-environmental parameter
relationship such as a pre-established impedance-to-temperature
relationship, a pre-established impedance-to-humidity relationship,
or a pre-established impedance-to-membrane damage relationship, as
also discussed in the aforementioned patent document. In this way,
the environmental parameter(s) may be monitored.
[0299] In an alternative embodiment, the environmental parameters
may be monitored using an environmental sensor such as a
temperature monitor or a humidity monitor. For instance, such
monitors may be incorporated into the sterile package in which the
analyte sensor is stored when it leaves the factory. Alternatively,
the monitors, such as the temperature monitor, may be directly
incorporated into the sensor electronics themselves. In some cases
the monitors need not provide a numerical value for the
environmental parameters, but may simply indicate if the
environmental parameter has fallen outside of specified ranges
within which the sensitivity of the analyte sensor is known to
remain relatively stable. In this way a relatively simple
environmental monitor may be employed.
[0300] Once the manufacturing and environmental parameter(s) has
been obtained, the calibration process 2400 proceeds to block 2408
where an updated calibration factor is determined based on a
pre-established environmental and/or manufacturing
parameters-to-analyte sensor sensitivity relationship. In
determining the updated calibrated factor, information in addition
to the measured parameter(s) may be taken into account. For
instance, the initial calibration factor may be used as well. The
updated calibration factor may be used to properly calibrate the
analyte sensor so that sensor data (e.g., in units of current or
counts) can be converted into analyte values (in units of analyte
concentration).
[0301] The updated calibration factor may be determined at any
suitable time after the environmental parameters have been
obtained. In part, this will depend on the particular components of
the sensor electronics that have been preconnected to the analyte
sensor. For example, if the preconnected components include a
suitable processor and associated memory and a power source (e.g.,
a battery), then the updated calibration factor may be determined
as soon as the environmental parameters are obtained e.g., while in
the sterile package or while in storage. Alternatively, if such a
processor is not available, the environmental parameters may be
stored in one of the preconnected components and communicated when
the remainder of the sensor electronics are connected, such as when
the sensor insertion phase is initiated. Alternatively, if the
preconnected components also include a transmitter, then the
environmental parameters may be transmitted to the remainder of the
sensor electronics or to another connected device.
[0302] In some embodiments the updated calibration parameter may be
determined by a processor and associated algorithms that are not
incorporated in the sensor electronics. Rather, the updated
calibration parameter may be stored in the preconnected electronic
components and uploaded at a suitable time to a device which with
the sensor electronics communicates (e.g., display devices 114,
116, 118 and/or 120 in FIG. 1) or to a cloud-based processor (e.g.
cloud-based analyte processor 490 in FIG. 1). The cloud-based
processor or other device that calculates the updated calibration
parameter then downloads it to the sensor electronics for use in
calibrating the analyte sensor.
[0303] As previously mentioned, environmental parameters may be
obtained at multiple times during the various phases of the analyte
sensor lifecycle and even at multiple times during a single phase
(e.g., storage). The environmental parameters obtained at each of
these different times may then be used, potentially in combination
with other factors (e.g. lot factors, in vivo measured values,
cloud data, time since sensor manufacture, individual patient
factors) to determine a final updated calibration factor.
[0304] In some embodiments instead of, or in addition to,
periodically updating the calibration factor at multiple time
during the various lifecycle phases, a single complex adaptive
calibration factor may be generated during the sensor use phase.
The complex adaptive calibration factor may combine an initial
calibration factor obtained during sensor manufacture with
environmental conditions experienced by the analyte sensor (and any
preconnected electronics, if present) from sensor manufacture to
sensor insertion. In this way the experience of the analyte sensor
during its lifetime is encoded in a form that allows it to be used
by the calibration algorithm. Thus, instead of separately
accounting for each individual environmental parameter such as
temperature and humidity and sensor characteristics such as
impedance, a single encoded value or profile may be provided to the
calibration algorithm that encapsulates all the manufacturing
and/or environmental parameters and sensor characteristics.
[0305] FIGS. 12(a)-12(c) are timelines showing various phases over
the lifetime of an analyte sensor. This example shows a
manufacturing phase, a sterilization phase, a shipping/storage
phase and an in vivo stage. The monitored temperature and humidity
experience by the sensor over these phases is shown in FIGS. 12(a)
and 12(b), respectively, and FIG. 12(c) shows the changes in the
analyte sensor sensitivity that are determined based on these
environmental parameters. The spikes in temperature that are shown
during the manufacturing stage arise during the curing of the
analyte sensor. It should be noted that the sensitivity may not
appreciably change when the spikes are relatively small and/or
short in duration, and thus not all such spikes will require
re-calibration of the analyte sensor. Other spikes that are larger
in magnitude and/or duration do lead to sensitivity changes, thus
indicating that re-calibration may be required when the spikes
exceed these thresholds. Accordingly, environmental parameters such
as temperature and humidity may only need to be monitored to
determine if they exceed or fall below certain thresholds which
have been shown to significantly affect the sensitivity.
[0306] It should be noted that all of the parameters mentioned
above which impact the sensitivity of an analyte sensor and which
are monitored at various points in time may also impact the
baseline signal of the sensor. Accordingly, in addition to
monitoring these parameters to calibrate or otherwise adjust the
sensitivity of the analyte sensor, these parameters may also be
monitored to calibrate or otherwise adjust the baseline of the
analyte sensor. More generally, the monitored parameters may be
used to adjust any characteristic metric of the analyte sensor and
not just the sensitivity and/or the baseline. Examples of such
characteristic metrics include, without limitation, long term
drift, analyte sensor current, rate of exponential drift, ratio
between fast and slow components in a dual-exponential sensitivity
model, non-glucose baseline, compartmental bias between glucose
concentration in local tissue surrounding the sensor and the blood
glucose, constant baseline, asymptotic magnitude of baseline rise
due to membrane degradation, asymptotic magnitude of baseline rise
due to membrane degradation, onset/transition time of baseline rise
due to membrane degradation, drift rate of baseline rise due to
membrane degradation, initial magnitude of fast electrochemical
break-in, drift rate of fast electrochemical break-in, initial
magnitude of slow electrochemical break-in, drift rate of slow
electrochemical break-in. initial magnitude of compartmental bias,
final magnitude of compartmental bias and drift rate of the
disappearing compartmental bias.
[0307] The previously mentioned complex adaptive calibration factor
may be determined in part using predetermined statistical
correlations that have been identified between sensor behavior
while in use and sensor behavior that was measured during the
various phases of the analyte sensor lifecycle for a large number
of previously deployed sensors. That is, instead of simply using
pre-established environmental and/or manufacturing
parameters-to-analyte sensor sensitivity relationships to calibrate
a particular sensor, a relationship between one or more of the
characteristic metrics of a large sampling of sensors that are
measured at various points in time and the resulting sensitivity or
other characteristic metrics of the sensor samples during the use
phase may be used to develop the complex adaptive calibration
factor.
[0308] For example, FIG. 13 shows a sensor output signal obtained
from a sensor during steps of the manufacturing process, including
at least one cure step, a membrane application step during which
the sensor is coated in a particular membrane, and a manufacture
calibration measurement step to determine an initial or in vitro
value of analyte sensitivity, baseline, interferent sensitivity,
impedance value, etc. As shown, the sensor output signal varies
during each step. The shape of this signal over all or some of
these steps defines a sensor signature that may be obtained for a
large number of sensors during the manufacturing phase. By
examining the behavior of these sensors during subsequent phases,
particularly during the use phase, statistically useful
correlations may be found between the sensor signature and sensor
behavior. In this way by measuring the sensor signature of a
particular sensor during the various steps of the manufacturing
phase it may be possible to predict its behavior (e.g., one or more
of the characteristic metrics) at a later time. For instance, it
may be possible to obtain a predicted sensitivity profile of
sensitivity change over time for a particular sensor.
[0309] While the preceding discussion has focused on the use of the
manufacturing and/or environmental parameters monitored during
various lifecycle phases of an analyte sensor to facilitate
calibration of the sensor, these monitored parameters may also be
used for other purposes as well. For instance, based on the
monitored parameters various actions may be taken by analyte
monitoring system. For instance, if one or more of the
environmental parameters exceeds a specified threshold for a
certain period of time, a message may be generated on a receiver
(e.g., a user's mobile communication device) indicating to the
user, for instance, that the expected end of life of the sensor has
been reduced, or that the quality of the calibration is below some
recommended value or that that the confidence level in the sensor
reliability is below some recommended value, or that the sensor is
only suitable for certain operating modes (e.g., sensor life,
accuracy, insights, trends, analyte value, alarms, monitoring type
1 diabetic patients but not type 2 diabetic patients, or vice
versa, or that the sensor is only suitable for implantation at
certain sites such as the abdomen or arm). Alternatively, or
addition thereto, other actions that may be performed as a result
of monitoring the environmental parameters may include adjusting
various startup parameters of the analyte monitoring system (e.g.,
requiring a longer than normal break-in period for the sensor),
switching to an operating mode in which glucose levels are only
reported as being in a range (e.g. low, medium or high) instead of
an operating mode in which glucose concentrations are reported,
initiating an in vivo calibration process, using a default
calibration value, and using a temperature, humidity and/or complex
compensated calibration value.
[0310] The reduction in errors that can be achieved by calibrating
a preconnected system in comparison to a conventional
(non-preconnected) system have been modeled by taking into account
a subset of the variables that affect the system. The model only
uses a unitless measurement of current (counts) and calibrates to a
known glucose calibration solution. The variation induced by
component and measurement variation are eliminated. In particular,
the values of gain and offset are not measured and are calibrated
to a unit value so that their induced error is eliminated. The
system has been calibrated with the exact components that influence
the values of the contact resistance, leakage current and vias
voltage. Therefore their variability is eliminated and they can be
modeled as fixed values.
Hierarchical Models for Sensor Manufacturing Process, Sensor Bench
Characterization, and In Vivo Performance
[0311] In one variant of the subject matter described herein,
statistical processes may be employed as part of the closed-loop
feedback manufacturing process.
[0312] It has been found that the sensor manufacturing process,
sensor bench characterization, and in-vivo sensor properties are
all loosely tied with each other. That is, while sensor process
parameters may be monitored to ensure they are within limits and
each sensor is then evaluated to determine if it meets the
predefined criteria, process parameters and in vitro properties are
not highly predictive of sensor in vivo properties (e.g.,
sensitivity). Typically, in vivo properties are estimated less from
manufacturing process variables and more from calibration, since in
a conventional system each sensor is generally calibrated every 12
hours or so with a blood glucose meter.
[0313] When automatic calibration techniques are employed it may
become necessary to rely more and more on sensor characteristics
and less on calibration through meters and the like. Thus, the
mathematical relationship between manufacturing process variables,
sensor in-vitro (or bench) characteristics and in-vivo properties
becomes important. In addition, as the number of sensors being
manufactured increases, the necessary resources and time may not be
available to exhaustively test each sensor for pass/fail criteria
or to estimate their in vitro properties. A
mathematical/statistical framework could thus provide an
alternative way for relating the sensor manufacturing process with
sensor in vitro sensitivity and expected in vivo sensitivity.
Ideally, process variables can be set to produce sensors with
specific sensitivities.
[0314] There are a number of sensor process and design parameters
that may be adjusted to build sensors with specific properties.
These include relative humidity, temperature, cure time, dip time,
layer thicknesses, and raw material properties/proportions. The
behavior of sensors in vitro and in vivo depends on these process
variables and may be modeled using mathematical and statistical
models. Hierarchical models are a type of multi-level statistical
model where different random effects that impact processes and
measurements are quantified in multiple levels as conditional
probabilities. For example, the variability of the process at
specific set points may be modeled in level 1 (highest level), the
variability of sensor behavior in vitro at level 2, and variability
of sensors in vivo at level 3. The model eventually may relate
these levels so that variables from one level (e.g., second or
third) can be used to estimate variables in a different level (e.g.
level 1).
[0315] One example of a framework for a hierarchical model for
sensor manufacturing and in vivo properties is described below,
where:
[0316] Xp represents process and design parameters, such as
relative humidity, temperature, curing time, dip time, layer
thickness, raw material characteristics, etc.
[0317] Mp is a vector of target sensor properties defined by the
process parameters (Xp)
Mp=N(f(Xp),.SIGMA..sup.2)
[0318] That is, sensor properties are a function of all of the
sensor design and process parameters. The overall distribution of
sensor properties has a mean of process set-point or target with a
variance of .SIGMA.2. Note that non-normal distributions are also
possible.
[0319] The vector of sensor properties that are verified on the
bench is:
Mb=N(Mp,.GAMMA..sup.2)
[0320] For example, if a lot of 10,000 sensors is manufactured, 100
of them may be sampled to estimate process properties. So the
distribution of bench verified properties is normal with a mean
target lot Mp and a variance of .GAMMA..sup.2.
Mi:N(g(Mb),V.sup.2);
[0321] These are the actual in-vivo properties of the sensor. The
distribution of the sensors is described by a function `g` that
translates the in-vitro properties into in-vivo. This is also
referred to as in-vivo to in-vitro correlation. A simple example of
the function `g` is a proportionality constant from in vitro to in
vivo. In a general case the function `g` is a transformation from
in vitro to in vivo with multiple factors. In a matrix form this
may be written as:
Mi=G*Mb,
[0322] where G may have factors for sensitivity, drift, and
baseline and interdependencies.
G = [ s s_d s_b s_d d 0 s_b 0 d ] ##EQU00001##
[0323] where the diagonal terms s, d, and b are sensitivity, drift,
and baseline related in vitro to in vivo factors, while the
off-diagonal terms are the cross-correlations between sensitivity
and drift s_d, and sensitivity and baseline s_b. The elements of
the matrix may be time-varying.
[0324] Once this model is developed there are various multiple
applications in which it may be employed. In one application,
process information may be incorporated into a continuous glucose
monitor (CGM) algorithm (i.e., a joint probability algorithm), thus
enabling reduced and factory calibration, as described in
US-2014-0278189-A1, entitled "Advanced Calibration for Analyte
Sensors", incorporated by reference in its entirety. In another
application, given that large scale sampling of the manufacturing
process is cumbersome and expensive, this hierarchical model may be
used to estimate the process parameters and target sensor
properties through sampling of multiple lots from in vitro and in
vivo. A third application involves the tracking of field
performance and directly correlating it with manufacturing. The
model may help proactively track process parameters based on field
data, allowing corrective actions to be taken more rapidly.
Estimating Sensor Properties for Longitudinal Field Data
[0325] In another variant of the subject matter described herein,
sensor properties such as sensitivity may be estimated from field
data. For example, predictive models may be created by mapping
manufacturing parameters to in vivo sensor behavior from very large
datasets (assuming the sensors have unique sensor IDs to trace the
field data to the manufacturing data).
[0326] Sensor sensitivity is typically estimated by comparing
sensor current to reference glucose measurements. However, this
becomes difficult or impossible when field data either has no
reference glucose measurements for comparison (i.e. for a factory
calibrated product) or when reference glucose measurements are
unreliable (e.g. if the meter is of poor or unknown quality and
reference measurements are not trustworthy). This problem can be
addressed as follows.
[0327] Individual users may have stable glucose dynamics across
several weeks or months, assuming they are consistent with their
therapy approach and their underlying physiology is not changing
dramatically. As a result, observed differences in raw sensor
signal statistics (e.g. mean, standard deviation, median,
percentiles, skewness, etc.) from sensor to sensor may reveal
differences in sensor properties such as sensitivity. Although
sensitivity estimated in this way may not be as reliable as
sensitivity measured through comparison with accurate reference
glucose measurements, with sufficiently large datasets the
information may be useful for detecting patterns in sensor behavior
and be used to construct predictive models of field sensor behavior
or detect unexpected shifts in field sensor behavior.
[0328] For example, when a wire is obtained from a new wire vendor
is introduced to production, it generally is not anticipated to
have any impact on sensor sensitivity. However, field data shows
that across thousands of users, standard deviations of raw sensor
readings are, e.g., about 2% higher, in sensors from lots using the
new wire vendor than the historical standard deviations for each
user. This pattern could trigger further investigation into the
impact of the wire vendor, or the data could be incorporated into
factory calibration models. In this way the predicted sensitivity
in the factory calibration algorithm can be adjusted to account for
the impact of the wire vendor, leading to improved accuracy.
NMR Method to Characterize Carbosil/PVP Ratio in Diffusion
Resistance Layer Solution
[0329] In yet another variant of the subject matter described
herein, which may be used to improve the accuracy of sensors during
manufacturing, a method may be employed to characterize the
Carbosil/PVP ratio in the diffusion resistance layer of the
sensor.
[0330] The diffusion resistance layer is one the most important
layers in the CGM membrane of the sensor, which provides stable,
predicable glucose and oxygen permeation and blocks some
interference agents. Currently, certain sensors use a Carbosil
2090A and PVP (K90) blend system. Carbosil dissolves in THF but is
not able to be dissolved in ethanol. However, PVP can be dissolved
in ethanol but not in THF. So the current diffusion resistance
layer solutions are prepared by using a THF/Ethanol mixed solvent
to dissolve both Carbosil and PVP.
[0331] Sensor performance is related to the Carbosil/PVP ratio
(e.g., a high PVP will yield high sensitivity). In particular, the
uniformity of the dip coating will be affected by Carbosil/PVP
ratio. Also, the diffusion resistance layer dipping solution
viscosity will be affected by Carbosil/PVP ratio change. Overall,
sensor stability will be affected by Carbosil/PVP ratio.
[0332] In order to make reproducible sensors, a consistent,
accurate Carbosil/PVP ratio in the RL dipping solution is an
important parameter to control. However, up to now, no method has
been developed to evaluate the Carbosil/PVP ratio in an RL
solution. Thus, in order to improve sensor accuracy and thus
enhance the ability of automatic calibration, it is important to
track the quality of each RL dipping solution before the sensors
are dipped.
[0333] In one aspect, nuclear magnetic resonance (NMR) spectroscopy
is used to determine the Carbosil/PVP ratio in the diffusion
resistance layer solution. In particular, proton NMR technology may
be employed.
[0334] One particular example of a process that may be employed to
determine the Carbosil/PVP ratio is described by the following
steps:
[0335] 1. Prepare sample [0336] 1.1 C2090A/PVP THF/EtOH solution
with 22 wt. % EtOH, 13.6 wt. % of PVP. [0337] 1.2 Cast a film using
RL solution and dry at 50 C overnight till a consistent weight
achieved. Remove solvent. [0338] 1.3 Cut a piece of thin film and
dissolve in DMSO-d6 with concentration of 10 mg/mL. (20 mg/mL, 50
mg/mL)
[0339] 2. Run proton NMR and obtain FID signal followed by a
baseline correction, tune phase and obtain spectra.
[0340] 3. Integration of MDI peaks in Carbosil; calculate
integration number of each proton.
[0341] 4. Integration of H2 peaks in PVP; calculate integration
number of each proton.
[0342] 5. Calculate mole ratio Carbosil and PVP
[0343] 6. Obtain calibration curve of Carbosil/PVP blend.
[0344] 7. Calculate Carbosil/PVP wt. %/wt. % ratio based upon
calibration curve.
[0345] FIG. 14 shows the NMR spectrum of PVP in DMSO-d6. FIG. 15
shows the HNMR spectrum of Carbosil in DMSO. FIG. 16 shows the HNMR
spectrum of an RL film (Carbosil/PVP blend with removal of
solvent). The MDI peak in Carbosil and the H2 peak in PVP were
selected to calculate the Carbosil/PVP ratio.
[0346] An HNMR Calibration process was conducted to validate the
method. First, an RL solution with different Carbosil/PVP ratio
with a predetermined Carbosil/PVP ratio was prepared as shown in
the Table shown in FIG. 17. Then, H NMR was run using DMSO-d6 as
the solvent. FIG. 18 shows the resulting HNMR calibration
curve.
Temperature and Humidity Sensing During Storage
[0347] As discussed above, impedance measurements of the analyte
sensor may be obtained during the shipping and storage phases to
monitor humidity for a sensor preconnected to electronics. In
addition, the temperature sensor in the transmitter could record
the temperature and thus the temperature and humidity sensors could
indicate if the analyte sensor was outside its recommended humidity
and temperature during shipping and storage. In addition, an
algorithm could be created to compensate the initial factory
calibration parameters based on the temperature and humidity
conditions and the duration of exposure. (It should be noted that
the initial factory calibration may be performed on a single sensor
using a single bath or on a lot or brick of sensors e.g., 30
sensors, which can be simultaneously calibrated using a single
large bath).
[0348] In one variant, measuring current alone may be sufficient to
indicate humidity or extreme humidity. Some embodiments of the
sensor system may wake up periodically and perform a measurement to
identify when the sensor has a signal to indicate a system start up
(due to hydration after deployment). A fully preconnected sensor
would also measure current when only humidity is present.
Accordingly, it would be a useful indicator indicating that the
analyte sensor was exposed to humidity conditions during shipping
and storage. If the system is not fully integrated with the
electronics, a removable adhesive tab (e.g., a "sticky tab") could
be placed on the transmitter's electrodes, which would conduct
current when humidity is present. This would allow the transmitter
to measure humidity. The tab would be removed before transmitter
use.
[0349] In another variant the sensor storage conditions may be
determined using resistors or other materials that have a known
response to temperature, humidity, or a combination of both, and
which generate an electrical signature (e.g., resistance, current).
In addition to the circuit that causes a transmitter to be
activated when it detects a sensor, the same circuit or a separate
circuit could be arranged to be triggered whenever the temperature
and/or humidity exceeds a threshold. Based on the duration of the
trigger and the magnitude of the measurement (reflective of the
temperature and/or humidity), the system would be able to adjust
the calibration factor to better predict in-vivo performance by
inferring changes due to environmental conditions. In one
particular implementation, a strip of the environmentally-sensitive
material may be placed across the transmitter electrodes so that it
only allows current to pass under specific environmental
conditions. In some cases this serves as an irreversible circuit or
material change that is only triggered above a threshold, creating
an on/off indicator to shift the predicted sensor response to a new
performance bin or to prevent use of the product if extreme
conditions were reached.
[0350] In yet another variant, the packaging in which the sensor is
stored may include a temperature and/or humidity sensitive material
that changes color based on the temperature and/or humidity so that
a color change would indicate the storage conditions experienced by
the sensor. For instance, in one example the material may be
located on the package interior in the form of a small region
(e.g., a dot). The color of the material may be detected by a
camera or other detector in the mobile device in which the system
app is located, which can determine the degree of color change.
Alternatively, the color change could be detected directly by the
transmitter or other sensor electronics, which as noted above, can
be used to better predict in-vivo performance by inferring changes
due to environmental conditions.
[0351] In yet another variant, the calibration parameters that are
used by the calibration algorithm may differ from sensor to sensor
based on the sensor manufacturing details and other factors. From
the transmitter point of view, the user inserts a sensor and enters
the "sensor id" into the display and based on this, the display
will either send the actual set of parameters that needs to be used
or sends a code that causes one of a predefined set of parameters
to be used. To achieve this, the transmitter may store multiple
sets of parameters. If the set of parameters is large, storing
multiple copies of the parameters may occupy too much storage
space.
[0352] To address this problem, in some cases only one default set
of calibration parameters may be stored on the transmitter and, to
obtain an updated set, only the differences between the default set
and the updated set need to be sent. Since usually the differences
are going to be small, this may be more efficient. This approach
also provides the flexibility to change any individual parameter.
That is, the set of parameters does not have to be fixed and they
can change during the factory calibration process. If the set of
parameters is an ordered list, then the changes can be specified as
a list of paired values such as (parameter number, new value).
Calibration Code Encoding
[0353] In yet another aspect, a sensor calibration code or some
other code assigned to the sensor in the factory may be linked to
the customer's account in the following manner. In this example the
transmitter that is shipped with the sensor is assumed to be
re-useable and ships with enough sensors to cover the duration of
the transmitter's life (generally determined by its battery). For
instance, a transmitter that is usable for three months would need
6 sensors that last 14 days each. In such a system a factory
calibration code associated with the sensor may be communicated to
the user's mobile device using the following method.
[0354] First, at step 1 the customer orders a package of sensors,
possibly using a dedicated app on their mobile device. At step 2,
while in the factory the package and the sensors to be included
therein are scanned to establish a link between the packaging and
the sensors. At shipping (step 3), a shipping label with the
customer's account information is scanned along with the packaging
to thereby create a link between the customer's credentials and the
packaging. This link is stored by the manufacturer in a cloud
server or the like for future reference.
[0355] At step 4 the package with the sensor is shipped to the
customer. At step 5 the customer inserts a new sensor and installs
the new transmitter and the package initiates a session. After the
user initiates the session, at step 6, the sensor code information
stored in the cloud server or the like can be retrieved since the
package of sensors and the transmitter has been previously linked
to the customer's account.
Enhancements to Closed Loop Manufacturing Feedback Process
[0356] In another variant, additional information may be used to
supplement the information that is available concerning the
manufacturing process, which is stored by a sensor that is
preconnected to sensor electronics. For example, the manufacturing
process typically involves a sequence of steps that are performed
at different stations in the factory. In principle the amount of
time needed by the operator to perform any given step should be
about the same for each part or component that is being assembled
or process being performed at that station. Any significantly high
variability in these times may indicate an immature station or a
process where the operator has to excessively make adjustments to
parts and fixtures during assembly, which could highlight areas for
process improvements. In some cases a small device may be placed at
each workstation to perform a study of the time needed to perform
the activity required at that station. The device may include an
actuator (e.g., button, motion sensor, light sensor) that provides
a simple, unintrusive means by which the operator can quickly
interact to allow a microcontroller in the device to record the
time for each device interaction. The operator would be instructed
to interact with the device every time they complete the task at
their station. The device then stores the times, which can be
subsequently output for analysis.
Initial Calibration
[0357] In another variant, when ethylene oxide (ETO) sterilization
is employed (instead of e-beam sterilization) the initial drift
profiles for some of the conditions are found to be very flat (see
the graph of FIG. 19, where group 4 (left) is an ETO condition and
group 6 (right) is the unsterile condition using the same timescale
with about 12 sensor drift profiles for each group). ETO processing
may thus be used to stabilize the sensor against high humidity
storage or shipping excursions.
[0358] In another variant, sensors may undergo ETO sterilization
with a rechargeable desiccant present in the packaging during the
ETO process. The desiccant may then be "baked out" after ETO to
recharge its desiccating capability. After sterilization in ETO, an
additional desiccant may be added to the final sensor packaging
and/or the final packaging may employ a moisture barrier to
minimize humidity. Several sensors may be sterilized in this manner
using bulk packaging that contains the sensors and the
desiccant.
Communication of Sensor Parameters Via NFC
[0359] In yet another variant, sensor information (e.g., sensor
parameters, calibration factors or codes, environmental
characteristics) of the type described herein that is to be
communicated may be sent from the sensor to the transmitter via an
NFC protocol. In one embodiment, this may be accomplished by
providing the sensor base or interposer with an NFC tag and
providing an NFC reader on the receiver (e.g., a user mobile
device). The sensor information received by the receiver from the
sensor base or interposer can be subsequently communicated to the
transmitter at e.g., system startup.
Electronic Hardware Correction
[0360] Factory calibration correction techniques for continuous
analyte monitoring systems have typically employed digital
techniques for storing and adjusting for sensor lot variability. In
some embodiments it is useful to use analog electronic circuitry to
modify the sensor signal. Using a resistor with a known value can
serve to modify an analog signal and change the amount of current
or the measured voltage. In one example the resistor may be
combined as part of a gain circuit with an operational amplifier to
tune the gain on the output signal. The resistor can be selected
from a variety of known resistance values or configured through a
process (e.g. laser trimmed resistor).
Factors Influencing Sensitivity and Impedance
[0361] A nonlimiting set of factors that have been found to
influence the impedance and/or sensitivity of a preconnected
analyte sensor is shown in the Table of FIG. [IFD1675]. These
factors, which are directed to select manufacturing and storage
conditions, can be measured for individual sensors and/or sensor
lots and correlated with sensitivity and impedance measurements at
different times during the sensor lifetime. In this way the values
of these factors may be used individually and/or in combination
with one another to determine the relationship between the measured
impedance and sensitivity at any time during the sensor lifetime,
thereby allowing adjustments to the calibration factor that is used
to calibrate the sensor at any time during its operational
life.
Updating of Slope Parameters on a Regular Basis
[0362] Currently, a "cal check" procedure is performed in the
factory in which a sensor undergoes in vitro calibration to obtain
a slope value. This value is used to seed a joint probability
algorithm with initial and final sensitivity values using linear
transformations. That is:
Mean Initial Slope=calcheck*mstart+bstart
Mean Final Slope=calcheck*mfinal+bfinal
[0363] Deviations from this linear relationship can be taken into
account by updating the mean of the initial and final slope using a
linear combination of parameters measured at the factory (e.g.
during cal check) and parameters measured in real time. For
instance, the equation for the final slope can be revised as:
Mean Final
Slope=a*calcheck+b*meanSensorCurrent+c*sigmaSensorCurrent+d*sensorCv+e*ca-
lcheck+ . . . +Offset
where the real time parameters include the mean sensor current
(meanSensorCurrent), the standard deviation of the sensor current
(signaSensorCurrent) and the coefficient of variation of the sensor
current (sensorCv). Other real time parameters that may be included
in the mean final slope include mean sensor current, the root mean
square of the sensor current, and the sensor current taken at a
specified percentile within the distribution of sensor current
values. A similar approach can be taken to adjust the mean of the
starting sensitivity. By using a combination of factory and
real-time measurements in this way, the performance of the system
can be improved because the linear combinations allows factory
information to be linked with in-vivo sensor measurements. The
parameters that describe the equation below may be such that the
final slope estimate may be updated periodically (e.g., every day)
during sensor wear.
Retrospective Calibration of CGM Signal
[0364] Retrospectively calibrating the CGM signal with or without
the use of SMBG is important for the professional CGM market and
other use cases such as technical support and for benchmarking the
performance of factory calibrated CGM. With retrospective
calibration, there is an opportunity to remove certain artifacts
that corrupt the real-time CGM signal, such as time-lag, transient
faults, compression, noise, and data gaps. As described below, in
some embodiments data gaps, noise and artifacts in the CGM signal
can be removed using prediction algorithms. This approach generally
works best after the removal of time lag from the signal and
smoothing.
[0365] It is commonly believed that glucose levels can be predicted
reasonably well about 30 minutes into future. The accuracy of the
predicted signal drops as the prediction horizon goes beyond 30 to
40 minutes. Thus, any signal artifacts or data gaps that are
shorter than 30-40 minutes can be replaced with a predicted signal
without losing key information need for clinical use. Further,
given the retrospective use, any errors in the predicted glucose
level may be removed by the analysis of data. Some ways that can be
accomplished are as follows: [0366] 1. Identify the area(s) of
artifacts in the signal. [0367] 2. Replace the artifact signal with
a predicted glucose level. [0368] 3. Evaluate the difference
between the predicted glucose level at a final point in time and an
initial point in time of the post artifact signal. [0369] 4.
Correct the predicted signal by feeding back this error into the
prediction. For example, if there is an error of 30 mg/dL between
the final predicted CGM and the initial time point of post-artifact
CGM, this error can be distributed evenly (or using a weighted
average) over the duration of the predicted signal. This way, the
predicted signal is corrected to result in a smooth correction of
the artifact, without discontinuities.
[0370] In another embodiment, prediction can be used
bidirectionally, to increase the duration of the artifacts that can
be corrected. The following describes how longer duration artifacts
may be corrected: [0371] 1. Identify the beginning and end of
artifacts that need removal/replacement. [0372] 2. Create two CGM
time series signals, the first time series being the normal signal
(time moving forward from the beginning to the end of session) and
the second time series being in reverse time (from the end of the
session to the beginning). [0373] 3. Use the prediction to replace
artifacts on both the forward and reverse time series. i.e., each
artifact will have two possible replacements, one based on the
forward time series signal and one based on the reverse time series
signal. [0374] 4. Pick the midway point between the two replaced
artifacts. These should correspond to the same time point in the
CGM signal. Depending on how variable the glucose signal is during
this period, the two signals may be meet at the midway point or be
different at the midway point. [0375] 5. Given that the prediction
is reasonably accurate for short durations, the best estimate of
the glucose level at the midway point is the mean of the values
from the two time series. [0376] 6. Now, the error between the mean
and the actual midpoint values from the time series can be fed back
into the predicted artifact replacements to correct them. [0377] 7.
The corrections can be weighted depending on the quality of the
signal before or after the artifact.
[0378] This approach for the correction of artifacts makes the
signal more reliable and increases the duration of artifacts that
can be correct/removed.
Replacement Sensors
[0379] Sensors sometimes fail before their marketed duration (e.g.
7 days). In some cases the sensor electronics (e.g., a transmitter)
can be packaged with a 3 month supply of sensors in a single box
(e.g. a 6 pack). In one variant, the transmitter can then be coded
with a single common sensor code. If one of the sensors in that
sensor box fails and a replacement sensor needs to be sent to the
customer, the transmitter can send the sensor code to the dedicated
app on the customer's mobile device. The customer can then ask for
a replacement sensor through the app. The app can then relay the
sensor code to the manufacturer or the like, who can send the
customer the appropriate sensor with the correct code that matches
the transmitter that was included in the original sensor box.
Configurable Calibration Frequency
[0380] In one variant, the frequency at which a transmitter issues
a calibration request to the dedicated app on the customer's mobile
device can be configurable. In one example, the transmitter can
have a default calibration frequency (e.g., one calibration per
day, two calibrations on day one followed by one calibration per
day thereafter, etc.) if it has not been supplied with pre-existing
calibration information. In another example the transmitter may or
may not issue calibration requests to the dedicated app based on
the availability of the pre-existing calibration information.
Moreover, the calibration frequency may be based on the type of app
being run on the mobile device. The transmitter may also store
different default calibration frequencies based on the type of app
being used.
Transfer of Calibration Data to the Transmitter
[0381] In another variant, a method for transferring calibration
coefficients to the transmitter or other sensor electronics from a
disposable sensor without user intervention may operate as follows.
This method employs a memory embedded in the sensor which transfers
the calibration coefficients and/or other information to the
transmitter. The information that may be transferred could include,
for example, a lot number, expiration dates, and the authentication
information that could allow the manufacturer to assure that
genuine sensors are being used. Such authentication information may
operate in accordance with cryptographic and other algorithms such
as hashes (e.g., SHA-256) and/or may operate in accordance with
standards such as the Federal Information Processing Standards.
[0382] While the information may be transmitted from the sensor to
the transmitter using any suitable connector or wirelessly using,
for instance, RFID, these are not always appropriate for low cost,
environmentally robust systems and may require significant
development or tooling changes. Instead, the following technique
may be employed to transfer calibration codes and/or other
information.
[0383] Without loss of generality, this technique will be described
as being applicable to a sensor that uses a low bias voltage (e.g.,
less than 1 volt) and has at least 2 electrical connectors (e.g., a
reference and working electrode). The sensor is assumed to be wired
or otherwise configured with a memory element in which the
information is stored. The memory element uses a single wire for
power and signal and is connected to the sensor's working
electrode. A ground connection is connected to the sensor's
reference electrode.
[0384] To initiate a session, the transmitter periodically checks
for the presence of a new sensor by waking up from sleep mode,
enabling the bias voltage and looking for a predetermined response
from the sensor. If the response indicates the presence of a new
sensor, the transmitter will transition into an operating mode as
described below. If the response is not as expected, indicating no
sensor present, the transmitter will go back to sleep for a
predetermined period.
[0385] If a predetermined signal indicates the presence of a new
sensor, the transmitter will attempt to recover calibration
coefficients and other information from the memory device. The
memory device is configured to only respond to signal pulses if
they are above a predetermined voltage level, above the nominal
operating bias voltage of the sensor. The memory device is both
powered and communicates using the same pin. The memory device may
operate in an active mode, where it incorporates a short term
charge storage device (such as a capacitor) to power the memory
chip while it signals back to the transmitter while the transmitter
places its pin connected to the memory element in high impedance.
Alternatively, the memory device may operate in a passive mode and
present a high or low load to the transmitter in order to signal
the appropriate information back to the transmitter, using the
transmitter as the master clock.
[0386] The time needed to communicate the relevant calibration
coefficients and any other information (such as expiration date,
serial number) is generally short relative to the lifetime of the
sensor, and the higher voltages used during communication for such
a short time will not damage any enzymes used in the sensor. Hence
the short term overpotential does not affect the long term
operation of the sensor, and may even help with sensor
electrochemical break in. Once the memory device has passed the
required information and the transmitter ramps down to the nominal
sensor bias voltage, it either may enter a very high impedance
state so as not to falsely elevate the observed signal current from
the sensor, or it may be designed to draw a known current which can
be subtracted from the sensor signal, or it may do a combination of
both.
[0387] In some embodiments, the transmitter may signal to the
memory element the end of life of the sensor, which will place an
indicator or the like in its internal memory indicating that the
sensor is expired. This can prevent accidental reuse of the sensor
since the memory element will communicate to the transmitter that
it has already been used, even if it is disconnected and
reconnected. At this point the duration of the communication
session and the value of the applied voltage is less crucial since
it does not matter if the enzyme is damaged, since the sensor has
already reached its end of life.
[0388] In an alternative embodiment, when initiating a session, the
transmitter, upon waking up, may simply interrogate the connections
for the presence of a memory device, and later check that the
sensor is operating normally once the calibration data has been
successfully transferred.
Calibration of EGV in a Closed-Loop System
[0389] In closed-loop systems (e.g., artificial pancreas systems),
updating estimated glucose values (EGVs) as a result of a
calibration can lead to incorrect dosing because when a calibration
happens, the EGVs are likely to change more than natural glucose
levels change. When such changes in EGVs are input to artificial
pancreas algorithms, they may lead them to incorrectly predict
EGVs. Current artificial pancreas algorithms accept calibration
updates and update the EGVs after the calibration is completed.
[0390] This problem can be addressed in some embodiments by
updating a few EGV data points prior to calibration as well as
after calibration for use by the artificial pancreas algorithms. In
this way the algorithms can capture the correct EGV changes.
Use of Biometric Data in Preventing Incorrect Entry of Calibration
Data
[0391] Manual entry of calibration data or other reference
information can be prone to error. One way to detect and prevent
the use of incorrectly entered data may use biometric data of the
user. Such information may be available to the dedicated app on the
user's mobile device, either from sensors incorporated in the
mobile device or from third party devices that are able to provide
the biometric data to the mobile device. If the calibration or
other data that is entered is found to be incompatible or
inconsistent with the biometric data, the app can present an error
message or take other appropriate action. As a simple example, if a
35 year old male is found from a biometric sensor to have a heart
rate of 170 bpm and the CGM shows a glucose reading of 40 mg/dl,
this is indicative that the glucose reading is in error.
Efficient Storage of Calibration Coefficients and Other
Parameters
[0392] The transmitter needs to store relevant calibration
coefficients and/or other parameters for different sensors. When a
sensor is inserted, the user typically enters the sensor ID into
the dedicated app and the app in turn sends either the parameters
to the transmitter or an identifier that corresponds to a
predefined set of parameters already stored in or otherwise
available to the transmitter. In any case, the transmitter may need
to store multiple sets of parameters. However, if the set or sets
of parameters are large, there may not be sufficient memory
available to the transmitter to store all the necessary
parameters.
[0393] In one variant, the transmitter may store a limited number
(e.g., one) of sets of parameters that can serve as default sets of
parameters. Then, when a newer set of parameters are to be used,
only the differences between the values in the default set of
parameters and the new set of parameters need to be stored in the
transmitter. Since the differences are usually going to be small,
this can be a more efficient way to store the data. This also
provides the flexibility to change any parameters since the
parameters established during factory calibration do not have to
remain fixed. In one embodiment, the default set of parameters can
be provided as an ordered list and the changes can be provided as a
list of paired values that specify the parameter number and the
value of the difference from the default value.
Exemplary Sensor System Configurations
[0394] 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.
[0395] 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 that may include raw sensor data, algorithm processed,
transformed sensor data, and/or any other sensor data, for example.
The sensor electronics module can include a processor and
computer-readable program instructions to implement the processes
discussed herein, including the functions specified in the
flowchart block or blocks presented herein.
[0396] 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 portable device, or a
general purpose device, such as a P.C., smart phone, tablet
computer, smart watch, wearable display, haptic device 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 processed data, and include a processing module for
processing and/or display the received data. The receiver can also
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 an input method (e.g.
keyboard or touch-sensitive display screen), and can also be
configured to receive information from external devices, such as
insulin pumps, insulin pens, wearable sensors, connected devices,
accelerometers, 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.
[0397] 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.
[0398] 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.
[0399] 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.
[0400] 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.
[0401] 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.
[0402] 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."
[0403] 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.
[0404] 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.
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