U.S. patent application number 16/504179 was filed with the patent office on 2019-10-31 for system and method for mode switching.
The applicant listed for this patent is DexCom, Inc.. Invention is credited to Naresh C. Bhavaraju, Michael A. Bloom, Leif N. Bowman, Alexandra Lynn Carlton, Hari Hampapuram, Jonathan Hughes, Lauren Hruby Jepson, Apurv Ullas Kamath, Katherine Yerre Koehler, Anna Leigh Rack-Gomer, Peter C. Simpson, Stephen J. Vanslyke.
Application Number | 20190328292 16/504179 |
Document ID | / |
Family ID | 54289086 |
Filed Date | 2019-10-31 |
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United States Patent
Application |
20190328292 |
Kind Code |
A1 |
Bhavaraju; Naresh C. ; et
al. |
October 31, 2019 |
SYSTEM AND METHOD FOR MODE SWITCHING
Abstract
Systems and methods described provide dynamic and intelligent
ways to change the required level of user interaction during use of
a monitoring device. The systems and methods generally relate to
real time switching between a first or initial mode of user
interaction and a second or new mode of user interaction. In some
cases, the switching will be automatic and transparent to the user,
and in other cases user notification may occur. The mode switching
generally affects the user's interaction with the device, and not
just internal processing. The mode switching may relate to
calibration modes, data transmission modes, control modes, or the
like.
Inventors: |
Bhavaraju; Naresh C.; (San
Diego, CA) ; Bloom; Michael A.; (Carlsbad, CA)
; Bowman; Leif N.; (San Diego, CA) ; Carlton;
Alexandra Lynn; (San Marcos, CA) ; Koehler; Katherine
Yerre; (Solana Beach, CA) ; Hampapuram; Hari;
(Carlsbad, CA) ; Hughes; Jonathan; (Encinitas,
CA) ; Jepson; Lauren Hruby; (San Diego, CA) ;
Kamath; Apurv Ullas; (San Diego, CA) ; Rack-Gomer;
Anna Leigh; (Cardiff by the Sea, CA) ; Simpson; Peter
C.; (Cardiff, CA) ; Vanslyke; Stephen J.;
(Carlsbad, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DexCom, Inc. |
San Diego |
CA |
US |
|
|
Family ID: |
54289086 |
Appl. No.: |
16/504179 |
Filed: |
July 5, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15060401 |
Mar 3, 2016 |
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16504179 |
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14862079 |
Sep 22, 2015 |
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15060401 |
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62053733 |
Sep 22, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
Y02A 90/10 20180101;
A61B 5/6898 20130101; A61B 5/7475 20130101; A61M 5/1723 20130101;
A61B 5/746 20130101; A61B 5/14532 20130101; A61B 5/742 20130101;
A61B 5/1486 20130101; A61B 5/7275 20130101; A61B 5/1455 20130101;
A61B 2560/0223 20130101; A61B 2560/0487 20130101; A61B 5/7221
20130101; A61B 5/14546 20130101; A61M 2230/005 20130101; G16H 40/40
20180101; Y02A 90/26 20180101; A61B 5/0002 20130101; A61B 5/14503
20130101; A61B 5/0004 20130101; A61B 5/72 20130101; G16H 40/63
20180101; A61M 2230/201 20130101; A61B 5/1495 20130101; A61B 5/7282
20130101; A61B 5/7435 20130101 |
International
Class: |
A61B 5/145 20060101
A61B005/145; A61B 5/00 20060101 A61B005/00; G16H 40/63 20060101
G16H040/63; A61M 5/172 20060101 A61M005/172; A61B 5/1486 20060101
A61B005/1486; A61B 5/1455 20060101 A61B005/1455; G16H 40/40
20060101 G16H040/40; A61B 5/1495 20060101 A61B005/1495 |
Claims
1-20. (canceled)
21. A method of monitoring glucose using a transcutaneous glucose
monitoring device coupled to a transcutaneous glucose sensor, the
method comprising: obtaining glucose concentration data using the
transcutaneous glucose sensor; transmitting the glucose
concentration data to a transcutaneous glucose monitoring device
according to an initial transmission mode that is a user demand
mode in which glucose concentration data is transmitted upon a user
demand, the user demand mode instigated by a device swipe, wherein
the transcutaneous glucose sensor exchanges data with the
transcutaneous glucose monitoring device, at least temporarily,
using an NFC protocol; and causing the transcutaneous glucose
monitoring device to transition to a new transmission mode
different than the initial transmission mode, the new transmission
mode being a device-initiated mode.
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. 15/060,401, filed Mar. 3, 2016, which is
continuation of U.S. application Ser. No. 14/862,079, filed Sep.
22, 2015, which claims the benefit of U.S. Provisional Application
No. 62/053,733, filed Sep. 22, 2014. 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 present embodiments relate to continuous analyte
monitoring, and, in particular, to control of operation of an
analyte monitor upon changes in available data in a continuous
analyte monitoring system.
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. For example, current
CGMs rely heavily on user interaction, for example, using blood
glucose meter readings to confirm 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.
[0007] This Background is provided to introduce a brief context for
the Summary and Detailed Description that follow. This Background
is not intended to be an aid in determining the scope of the
claimed subject matter nor be viewed as limiting the claimed
subject matter to implementations that solve any or all of the
disadvantages or problems presented above.
SUMMARY
[0008] Systems and methods according to present principles meet the
needs of the above in several ways. In particular, the systems and
methods provide dynamic and intelligent ways to change the required
level of user interaction during use of the monitoring device,
e.g., over the course of the sensor session, as dictated by the
usability of the device as well as in some cases user choice. The
usability of the device is often influenced by the usability of the
data received from the sensor. Such changes may increase or
decrease the level and type of user interaction, depending on
usability of sensor data and also often according to other
available data, but generally are intended to dynamically reduce
the level of user interaction based on the desired or needed
usability of sensor data.
[0009] The systems and methods described here generally relate to
real time switching between a first or initial mode of user
interaction and a second or new mode of user interaction. In some
cases, the switching will be automatic and transparent to the user,
and in other cases user notification (or request for confirmation)
may occur. The mode switching may cause a switch from a first mode
to a second mode, followed by a switch to a third mode or back to
the first or second modes. In any case, the mode switching will
generally affect the user's interaction with the device, and not
just cause internal processing changes within the device, although
such processing changes will generally accompany the mode
switching.
[0010] In many cases the decision or trigger to switch between
modes relates to the usability of a sensor signal, as compared to a
transition criteria, but may also be based on other data, combined
(or not) with usability data, such as the value of the sensor
signal, external data, and the like.
[0011] In one implementation, an analyte monitoring device may
transition or switch types of calibration modes, e.g., from a
user-dependent calibration to a device self-calibration mode, i.e.,
a calibration routine using blood glucose concentration values from
an external meter to a calibration routine performed by the device
itself (i.e., without real time external reference values). In
another implementation, an analyte monitoring device may transition
or switch types of data transmission modes, e.g., from providing
information or data on-demand (e.g., upon user demand) to a mode in
which information or data transmission is initiated by the device,
e.g., as a regular or irregular periodic communication and/or in
response to a trigger such as a large excursion. In some cases, as
described below, such is termed a mode switch from scheduled
transmissions to unscheduled transmissions. In yet another
implementation, an analyte monitoring device may transition or
switch from one type of decision-support mode to another, e.g.,
from a therapeutic use to a non-therapeutic use (e.g., adjunctive
use) or more granularly from one mode or phase of control to
another, including providing educational information versus
providing therapeutic information. More generally, an analyte
monitoring device may be configured to switch between modes or
phases of control, such as described in greater detail below with
respect to FIG. 15A. In all of these implementations, the mode
transition or switches may generally be performed in both
directions, and in the cases of multiple phases of control, between
various phases of control, both sequential and nonsequential.
[0012] A number of triggers will be described, and generally the
triggers are based on one or more criteria, e.g., where a
determined parameter or variable ("determined data") meets,
exceeds, matches, or otherwise bears a predetermined relationship
with a threshold, i.e., a predetermined transition threshold
criterion or criteria, or is determined or predicted to do so in
the future. The determined parameter or variable may be data
associated with the sensor signal, i.e., the sensor signal value or
a scaled representation thereof, data about the sensor signal,
e.g., data from signal analysis indicating its noise level or the
like, data from an external source, e.g., data from a blood glucose
meter, temperature sensor, clock, location sensor, or the like, as
well as other data as will be described below.
[0013] Systems and methods according to present principles may use
one or more of many different triggers, i.e., data and accompanying
transition criteria, on which to base mode switching. In some
cases, for specific types of mode switching, particular determined
parameters or variables will be especially useful. For example,
data usability may be especially pertinent when deciding and
switching calibration modes or decision-support modes. The signal
value itself may be especially pertinent when deciding data or
information transmission modes. However, these are purely exemplary
and it will be understood that in given implementations other
criteria may prove useful.
[0014] In one aspect, the embodiments are directed towards a method
of operating a continuous glucose monitoring device, the continuous
glucose monitoring device coupled to a glucose sensor and operating
in an initial mode of operation, including: measuring a signal
indicative of glucose concentration data; displaying the glucose
concentration data on a user interface of the continuous glucose
monitoring device, the user interface in the initial mode of
operation having an initial mode of user interaction; determining
data indicative of a usability of the continuous glucose monitoring
device; comparing the determined data to one or more transition
criteria; if the comparing indicates the determined data has met or
will meet the transition criteria, causing the continuous glucose
monitoring device to transition to a new mode of operation; and
displaying the glucose concentration data on the user interface of
the continuous glucose monitoring device. The user interface in the
new mode of operation has a different mode of user interaction than
the initial mode, such that the continuous glucose monitoring
device operates in a mode of user interaction according to the
device usability.
[0015] Implementations of the embodiments may include one or more
of the following. The displaying may be based at least in part on
the mode of operation. The determining data may include receiving
data from the sensor. The receiving data from the sensor may
include receiving data from a sensor electronics module coupled to
the sensor. The sensor may be configured for in vivo insertion into
the patient. A first output of the monitoring device in the initial
mode of operation may represent the initial mode of user
interaction and a second output of the monitoring device in the new
mode of operation may represent the new mode of user interaction,
and the first and second outputs may be different. The initial and
new modes of user interaction may be configured such that the new
mode of user interaction requires less user interaction than the
initial mode of user interaction. The initial and new modes of user
interaction may be selected from the group consisting of:
user-dependent calibration and device self-calibration. The analyte
may be glucose and the user-dependent calibration may correspond to
entry of a calibration value from an external blood glucose meter.
The initial and new modes of user interaction may include levels of
confirmation interactions. The analyte may be glucose and the
initial and new modes of user interaction may include different
levels of decision support selected from the group consisting of:
non-therapeutic (adjunctive), therapeutic (non-adjunctive), and
phases of control in an artificial pancreas system. The analyte may
be glucose and the initial and new modes of user interaction may be
data transmission modes selected from the group consisting of
on-demand data transmission and device-initiated data transmission.
The initial and new modes of user interaction may be selected from
the group consisting of: pushed data or pulled data. The determined
data may include an analyte concentration value and/or a time rate
of change thereof.
[0016] The determined data indicative of the usability of the
device and the transition criteria may include one or more
parameters indicative of the usability of a signal from the sensor,
such as one or more parameters selected from the group consisting
of accuracy, reliability, stability, confidence, and/or glycemic
urgency index. The one or more parameters related to the usability
of the signal may correspond to a level of noise or to one or more
faults detected in the signal, and the transition criteria may be a
threshold level of noise or a predetermined type or level of fault,
which may be determined based on a long-term trend of the signal, a
short-term trend of the signal, or on a history of a user's
previous sensor session. The one or more parameters related to the
usability of the signal may correspond to one or more of the group
consisting of: signal value, a range of signal values, or a time
rate of change thereof; analyte concentration value or range of
values; calibration data; a measured error at calibration; data
from self-diagnostics or calibration diagnostics; metadata about
sensor identity; environmental data corresponding to a sensor;
historical pattern data; external data; data about frequency of
calibration; biological data about sensor placement; a time
duration since sensor implantation; an impedance associated with
the signal; a received user response to a prompt displayed on a
user interface; a decision support mode; a data transmission mode;
data about a selected use of the monitoring device; data about
clinical or user goals; or combinations of the above.
[0017] For example, the environmental data may correspond to
altitude or temperature data about a sensor environment. The
calibration data may be selected from the group consisting of:
calibration values, confidence in calibration values, uncertainty
in calibration values, range of calibration values, rate of change
of calibration values, current calibration values compared to
historical calibration values, stability in calibration values,
whether calibration values match expected or predicted values,
confidence in a user's ability to accurately enter calibration
values from a meter, whether entered calibration data corresponds
to a default or pre-entered value, or combinations of the above.
The historical pattern data may include data about rebound
variability.
[0018] The external data may be from an activity monitor, a sleep
monitor, a medicament pump, GPS device, a redundant analyte sensor,
a smart pen, or combinations of the above. The biological data
about sensor placement may correspond to data about: tissue type,
wound response, diffusion distance, or combinations of the above.
The diffusion distance may be proportional to one or more selected
from the group consisting of: impedance, thickness of membrane over
electrode array, oxygen depletion rate, diffusion of specific
species between electrodes, or combinations of the above. The
decision support mode may be selected from the group consisting of:
non-therapeutic (adjunctive), therapeutic (non-adjunctive), and
different levels of control of an artificial pancreas system. The
data about a selected use of the monitoring device may include data
about uses selected from the group consisting of: weight loss
monitoring, monitoring exercise or activity impact on glucose,
post-meal glucose summary, food selection, effect of the monitored
analyte on illness or menstrual cycle or pregnancy, user preference
or convenience, or combinations of the above. The data about
clinical or user goals may include: data about user knowledge of
device, desired accuracy of device, desired convenience of device,
use of device for hypoglycemic avoidance, use of device for
nighttime control, use of device for postprandial control,
qualitative or quantitative desired duration of sensor session, or
combinations of the above. The desired convenience of the device
may correspond to a number of required external meter calibration
values.
[0019] The initial mode may be user-dependent calibration, and
before the causing step, the method may further include causing the
device to periodically and temporarily enter a self-calibration
mode, to interrogate the sensor and to examine a transient
response, followed by a re-entering of the user-dependent
calibration initial mode. The method may further include displaying
output data based on the new mode. The method may further include
displaying an indication of an expected duration of the new mode.
The method may further include displaying an indication of sensor
performance.
[0020] Certain implementations may particularly apply to
calibration mode. For example, the initial mode may be
user-dependent calibration and the new mode may be device
self-calibration; or the initial mode may be device
self-calibration and the new mode may be user-dependent
calibration. The determined data may be sensor signal or data
usability and the transition criteria may be a threshold level of
sensor signal or data usability. The transition criteria may be
further dependent on a decision support mode, the decision support
mode may be selected from the group consisting of adjunctive
(non-therapeutic), therapeutic (non-adjunctive), or a phase or mode
of control in an artificial pancreas system. The transition
criteria may be further dependent on data entered or received about
a user or clinician use of information displayed by the monitoring
device.
[0021] A decision support mode associated with the initial mode may
be therapeutic and a decision support mode associated with the new
mode may be adjunctive, and the determined data may be such that
the sensor signal usability decreased below the threshold level of
sensor signal usability associated with the transition criterion.
The method may further include: prompting a user on a periodic
basis to enter a calibration value from an external meter for blood
glucose; and receiving the calibration value for blood glucose. The
periodicity may be less in the new mode than in the initial
mode.
[0022] A decision support mode associated with the initial mode may
be adjunctive and a decision support mode associated with the new
mode may be therapeutic, and the determined data may be such that
the sensor signal usability increased above the threshold level of
sensor signal usability associated with the transition criterion.
The method may further include: prompting a user on a periodic
basis to enter a calibration value for blood glucose; and receiving
the calibration value for blood glucose. The periodicity may be
greater in the new mode than in the initial mode.
[0023] The method may further include determining an intended mode
of the monitoring device. The determining may include detecting
whether a medicament delivery device is coupled to the monitoring
device, and if so, configuring the monitoring device to a mode that
is therapeutic. The determining may include: prompting a user to
indicate an intended use of the monitoring device; receiving the
indication; and configuring the monitoring device to a mode
associated with the received indication. A number of blood glucose
calibration readings required of the user may be based on the
configured mode. Where the intended use is therapeutic, the method
may further include configuring the monitoring device to a
user-dependent calibration mode. Where the intended use is
adjunctive, the method may further include configuring the
monitoring device to a device self-calibration mode.
[0024] Where the initial mode is device self-calibration and the
new mode is user-dependent calibration, the method may further
include: prompting a user to enter a calibration value for blood
glucose; receiving the calibration value for blood glucose; and
using the received calibration value to inform the device
self-calibration. The received calibration value may inform the
device self-calibration by modifying the device self-calibration.
The initial mode may be device self-calibration and the new mode
may be user-dependent calibration, and the determined data and the
transition criteria may include one or more parameters related to
the usability of a signal from the sensor, where the one or more
parameters are selected from the group consisting of: data from
diagnostic routines indicating a shift in sensitivity; data entered
by a user about a perceived error; data from a connected device;
data from historic analyte values; time of day; a day of the week;
whether a glucose value is high or low as compared to respective
thresholds; a glucose urgency index; data about glucose
concentration value variability; data about a level of user
responsiveness; sensor signal value trajectory pre- and
post-insertion of a new sensor; redundant or overlapping sensor
data; user feedback on alerts and alarms; meal or exercise data as
compared to predicted signal responses to meal or exercise data;
data about a decision support mode configured for the monitoring
device; or combinations of the above.
[0025] In the above, the data from diagnostic routines may include
impedance data detecting shifts in sensitivity. The diagnostic
routines may be performed on a periodic basis or upon detection of
an error. The data entered by a user about a perceived error may
include a blood glucose calibration value entered by a user in the
absence of a prompt from the monitoring device, or a detection of a
greater-than-average number of blood glucose calibration values
entered by a user. The data from a connected device may include
data from an external blood glucose meter. The initial mode may be
device self-calibration and the new mode may be user-dependent
calibration, and the method may further include: if the comparing
indicates the determined data has met or will meet the transition
criteria, then before the causing step, prompting a user to enter a
reason for the determined data; receiving the reason for the
determined data; and based on the received reason, causing the
monitoring device to maintain the initial mode of operation. The
reason may be a user-perceived outlier, a user-perceived false
alarm, or meal or exercise data.
[0026] The method may further include comparing the entered meal or
exercise data to prior user-entered meal or exercise data,
comparing a current signal to a signal associated with the prior
user-entered meal or exercise data, and determining if the current
signal and entered meal or exercise data are consistent with the
prior signal and prior meal or exercise data. The initial mode may
be device self-calibration and the new mode may be user-dependent
calibration, and the method may further include: determining if a
number of blood glucose measurements taken and entered into the
monitoring device as calibration values exceed a predetermined
threshold over a predetermined period of time, and if so, causing
the monitoring device to transition to a user-dependent calibration
mode.
[0027] The initial mode may be user-dependent and the new mode may
be device self-calibration, the transition criteria may correspond
to a level of confidence in the device self-calibration, and the
method may further include: prompting a user to enter a calibration
value for blood glucose, and using the entered value as the
determined data; and if the comparing indicates the determined data
meets the transition criteria, then performing the causing (a mode
transition) step. The initial mode may be user-dependent
calibration and the new mode may be device self-calibration, and
the determined data and the transition criteria may correspond to
the usability of entered blood glucose data, where the usability of
entered blood glucose data corresponds to an accuracy, reliability,
stability, or confidence in the blood glucose data. The method may
further include confirming that entered blood glucose data is
within a particular confidence interval or stability criterion, and
if it is not, then performing the causing step. The method may
further include confirming that entered blood glucose data is
within an expected range based on an a priori or internal
calibration, and if it is not, then performing the causing
step.
[0028] The transition criteria may be based at least in part on a
decision support mode in which the device is configured. The
determined data and the transition criteria may indicate that the
device continues to require external reference data for
calibration, and the method may further include maintaining the
initial mode. The determined data and the transition criteria may
indicate that the device no longer requires external reference data
for calibration, and the method may further include performing the
causing step. The method may further include a package of sensors
manufactured from the same lot, and the sensor may be a first of a
plurality of sensors in the pack. In this case, the determined data
and the transition criteria may indicate that the device no longer
requires external reference data for calibration, and the method
may further include: performing the causing step of causing the
monitoring device to transition to a new mode of operation; and for
subsequent sensors in the pack, initializing the device in device
self-calibration mode, using one or more calibration settings
associated with the first sensor.
[0029] The method may further include: initializing the monitoring
device in two modes simultaneously, a first mode being
user-dependent calibration and a second mode being device
self-calibration; receiving and comparing two glucose concentration
values, one glucose concentration value from the first mode and
another glucose concentration value from the second mode;
determining and displaying a glucose concentration value based on
the two glucose concentration values; determining a level of
confidence in the glucose concentration value from the second mode,
using at least the two glucose concentration values; and once the
determined level of confidence in the glucose concentration value
from the second mode reaches a predetermined threshold, then only
displaying the glucose concentration value from the second mode.
The determining a level of confidence in the glucose concentration
value from the second mode may include comparing at least the
glucose concentration value from the second mode to a calibration
value from an external meter.
[0030] The method may further include detecting a fault, and upon
detection of the fault, displaying the glucose concentration value
according to the first mode. The comparing may include comparing
results of diagnostic tests or internal calibration information.
The internal calibration information may be based on an impedance
measurement. The predetermined threshold may be based at least in
part on a decision support mode in which the device is configured.
The comparing may include comparing slope and baseline information
for the two modes. The comparing may further include: comparing
errors in slope and baseline data for each of the two modes; and
once the error in the slope or baseline for the second mode is
equivalent to that in the first mode, then only displaying the
glucose concentration value from the second mode. The comparing may
further include determining slope and baseline information for each
of the two modes with respective slope and baseline information for
each of the two modes from a prior session. The method may further
include displaying an indication of when a calibration value from
an external meter is required.
[0031] The method may further include: initializing the monitoring
device in two parallel modes, a first mode being user-dependent
calibration and a second mode being device self-calibration;
receiving and comparing two glucose concentration values, one
glucose concentration value from the first mode and another glucose
concentration value from the second mode; providing a weighting of
the two glucose concentration values; and displaying a glucose
concentration value according to the weighted glucose concentration
values. The weighting may be proportional to the usability of the
data determined by each of the modes. Once the weighting for a
given mode reaches a predetermined threshold, the glucose
concentration value displayed may be determined based on only the
given mode.
[0032] In some implementations, the modes correspond to a mode of
decision support. For example, the determined data may correspond
to a sensor signal, and the transition criteria may correspond at
least to a usability of the sensor signal. The transition criteria
may be at least in part based on the initial mode of operation. The
initial mode may be a therapeutic mode, and the new mode may be an
adjunctive mode. The displaying in the new mode of operation may
further include, while in the adjunctive mode, displaying data to a
user in such a way as to indicate its usability adjunctively. The
displaying in the new mode of operation may further include
indicating the usability of the data by displaying a zone or range
of glycemic data instead of a single value. The displaying in the
new mode of operation may further include requiring the user to
clear a prompt before displaying a subsequent glucose concentration
value or a range of glucose concentration values. The usability may
be indicated by colors and/or flashing numerals and/or a dot size
on a trend graph. The displaying in the new mode of operation may
further include restricting displayed data to only a rate of change
arrow and not a glucose concentration value. The usability may be
indicated by a displayed change in a prediction horizon. The
displaying in the new mode of operation may further include, while
in the therapeutic mode, displaying data to a user in such a way as
to indicate its usability therapeutically. The displaying in the
new mode of operation may further include indicating the usability
of the data by displaying a determined single value of glucose
concentration. The usability may be indicated by a displayed change
in a prediction horizon. The usability may be indicated by colors
and/or flashing numerals and/or a dot size on a trend graph. The
transition criteria may be further at least partially based on time
of day or day of week. The usability of the sensor signal may be
based on one or more parameters selected from the group consisting
of: a user response to a query about a perceived accuracy or
perceived user glucose range; data about likelihood of a potential
fault or failure mode; data about glucose context; a user response
to a query about a glycemic event; a user response to a query about
a potential false alarm; a confirmatory meter reading requested of
a user via a displayed prompt; a calibration mode; a data
transmission mode; a user indication of desired monitoring device
responsiveness; or combinations of the above.
[0033] The method may further include changing a calibration mode
along with the change from the initial to the new mode of
operation. The method may further include transmitting a signal to
a medicament delivery pump. The new mode may be therapeutic, and
the signal may instruct the pump to receive and follow signals from
the monitoring device. The new mode may be adjunctive, and the
signal may instruct the pump to disregard received signals from the
monitoring device. The new mode may be therapeutic, and the signal
may instruct the pump to receive and follow signals from the
monitoring device to control the user glucose concentration value
to a target value. The new mode may be therapeutic, and the signal
may instruct the pump to receive and follow signals from the
monitoring device to control the user glucose concentration value
to a target range of values. The new mode may be therapeutic, and
the signal may instruct the pump to receive and follow signals from
the monitoring device to control the user glucose concentration
value only when the glucose concentration value is below a
predetermined value, above a predetermined value, or within a
predetermined range of values.
[0034] The initial mode may be adjunctive and the new mode may be
therapeutic, and in the new mode the monitoring device may be
configured to calculate a recommended insulin bolus and the
displaying on the user interface may further include displaying the
calculated recommended insulin bolus without a calibration meter
reading, and in the initial mode the monitoring device may be
configured to not calculate and display a recommended insulin bolus
without a calibration meter reading. Upon a step of sensor start
up, the initial mode may be adjunctive, and the displaying
indicating the new mode of operation may further include displaying
low-resolution data.
[0035] The method may further include determining a level of
confidence in the sensor over a period of time, and once the
measured level of confidence has reached a predetermined threshold,
the method may further include the step of displaying
high-resolution data and causing a transition to a therapeutic
mode. The determining a level of confidence may include receiving
an external blood glucose meter reading. The external blood glucose
meter reading may correlate to what the monitoring device estimates
the glucose concentration value to be or may be used to calibrate
the monitoring device. The method may further include configuring
the monitoring device to enter a user-dependent calibration mode of
operation. The method may further include receiving an external
blood glucose meter reading, developing a level of confidence in
the sensor over a period of time, and once the level of confidence
has reached a predetermined threshold, causing the monitoring
device to enter a user-dependent calibration mode of operation.
[0036] The monitoring device may operate in two modes of operation
concurrently, one adjunctive and one therapeutic, and the
displaying may further include displaying an initial splash screen
with data displayed in the adjunctive mode of operation. Upon
receiving a selection from a user interface for data requiring a
new mode of operation, the method may further include causing a
transition to the new mode of operation, receiving one or more data
values required by the new mode of operation, and displaying the
data using the new mode of operation. The selected data may include
a hypoglycemic safety alarm, and the new mode of operation may be
user-dependent calibration.
[0037] Certain implementations of the embodiments may pertain to
transmission modes. For example, the determined data may include
data based on a glucose concentration value, and the transition
criteria may be selected from the group consisting of: a glycemic
state threshold, a GUI threshold, a glucose threshold, a glucose
rate of change threshold, a glucose acceleration threshold, a
predicted value of glucose or any of its rates of change, an
excursion beyond a predetermined threshold, an alert criteria, a
criteria for a glycemic danger zone, or a combination of the
above.
[0038] The transition criteria may be selected from the group
consisting of: a duration of time since a user last requested a
glucose concentration value, a decision support mode, a user
response to a query, a calibration mode of the monitoring device,
or a combination of the above. The determining data may include
transmitting a signal to cause a sensor to send a glucose
concentration value. The determining data may include receiving a
signal from a sensor corresponding to a glucose concentration
value. The initial mode may be on-demand transmission, the new mode
may be device-initiated transmission, the determined data may be a
glucose concentration value, and the transition criteria may be the
glucose concentration value being in a dangerous range for a period
exceeding a first predetermined duration of time.
[0039] The method may further include displaying an alert to the
user on a user interface of the monitoring device until the user
performs an action of responding to the alert. The initial mode may
be device-initiated transmission, the new mode may be on-demand
transmission, the determined data may be a glucose concentration
value, and the transition criteria may be the glucose concentration
value being in a dangerous range for a period exceeding a second
predetermined duration of time.
[0040] In another aspect, the embodiments are directed towards a
system for performing any of the above methods. In another aspect,
the embodiments are directed towards a device or system or method
substantially as shown and/or described in the specification and/or
drawings.
[0041] In another aspect, the embodiments are directed towards an
electronic device for monitoring data associated with a
physiological condition, including: a continuous analyte sensor,
where the continuous analyte sensor is configured to substantially
continuously measure the concentration of analyte (such as glucose)
in the host, and to provide continuous sensor data indicative of
the analyte concentration in the host; and a processor module
configured to perform any one of the described methods.
[0042] In another aspect, the embodiments are directed towards an
electronic device for delivering a medicament to a host, the device
including: a medicament delivery device configured to deliver
medicament to the host, where the medicament delivery device is
operably connected to a continuous analyte sensor, where the
continuous analyte sensor is configured to substantially
continuously measure the concentration of analyte (such as glucose)
in the host, and to provide continuous sensor data indicative of
the analyte concentration in the host; and a processor module
configured to perform any one of the described methods.
[0043] To ease the understanding of the described features,
continuous glucose monitoring is used as part of the explanations
that follow. It will be appreciated that the systems and methods
described are applicable to other continuous monitoring systems,
e.g., for analysis and monitoring of other analytes, as will be
noted in greater detail below.
[0044] Any of the features of embodiments of the various aspects
disclosed is applicable to all aspects and embodiments identified.
Moreover, any of the features of an embodiment is independently
combinable, partly or wholly with other embodiments described
herein, in any way, e.g., one, two, or three or more embodiments
may be combinable in whole or in part. Further, any of the features
of an embodiment of the various aspects may be made optional to
other aspects or embodiments. Any aspect or embodiment of a method
can be performed by a system or apparatus of another aspect or
embodiment, and any aspect or embodiment of the system can be
configured to perform a method of another aspect or embodiment.
[0045] Advantages may include, in certain embodiments, one or more
of the following. Continuous analyte monitoring may be made more
adaptable to a given situation, requiring less user interaction or
input when such is not required, enhancing usability and user
friendliness of a monitoring device. Other advantages will be
understood from the description that follows, including the figures
and claims.
[0046] Any of the features of embodiments of the various aspects
disclosed is applicable to all aspects and embodiments identified.
Moreover, any of the features of an embodiment is independently
combinable, partly or wholly with other embodiments described
herein, in any way, e.g., one, two, or three or more embodiments
may be combinable in whole or in part. Further, any of the features
of an embodiment of the various aspects may be made optional to
other aspects or embodiments. Any aspect or embodiment of a method
can be performed by a system or apparatus of another aspect or
embodiment, and any aspect or embodiment of the system can be
configured to perform a method of another aspect or embodiment.
[0047] This Summary is provided to introduce a selection of
concepts in a simplified form. The concepts are further described
in the Detailed Description section. Elements or steps other than
those described in this Summary are possible, and no element or
step is necessarily required. This Summary is not intended to
identify key features or essential features of the claimed subject
matter, nor is it intended for use as an aid in determining the
scope of the claimed subject matter. The claimed subject matter is
not limited to implementations that solve any or all disadvantages
noted in any part of this disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0048] The present embodiments now will be discussed in detail with
an emphasis on highlighting the advantageous features. These
embodiments depict the novel and non-obvious mode switching systems
and methods according to present principles, for use in analyte
monitoring and other purposes, shown in the accompanying drawings,
which are for illustrative purposes only. These drawings include
the following figures, in which like numerals indicate like
parts:
[0049] FIG. 1 is a flowchart according to present principles
showing one implementation of a general method of mode
switching.
[0050] FIG. 2 is a diagram according to present principles showing
types of user interactions.
[0051] FIG. 3A-3C are flowcharts according to present principles
showing other implementations of general methods of mode
switching.
[0052] FIG. 4 is a flowchart according to present principles
showing another implementation of a method of mode switching, based
on comparing signal or data usability to one or more criteria such
as a threshold.
[0053] FIG. 5 is a diagram according to present principles showing
aspects of data usability.
[0054] FIG. 6 is a diagram according to present principles showing
aspects of data from signal analysis.
[0055] FIG. 7 is a diagram according to present principles showing
aspects of data values per se, or data based on such data
values.
[0056] FIG. 8 is a diagram according to present principles showing
aspects of other data which may be employed in the determination of
data usability, including data from other, e.g., external,
devices.
[0057] FIG. 9 is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting decision-support aspects such as an intended
use of CGM data.
[0058] FIG. 10 is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting system detection of external devices.
[0059] FIG. 11 is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting transitions between calibration modes.
[0060] FIG. 12 is a flowchart according to present principles
showing another implementation of a method of mode switching, again
depicting transitions between calibration modes.
[0061] FIG. 13A is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting multiple modes operating concurrently. FIG.
13B is a chart illustrating matching of calibration parameters to
allow switching.
[0062] FIG. 14 is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting transitions between data transmission
modes.
[0063] FIG. 15A illustrates a progressive sequence of modes,
phases, or stages, detailing levels or phases of control within an
artificial pancreas system.
[0064] FIG. 15B illustrates a schematic diagram of an artificial
pancreas system.
[0065] FIG. 15C is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting transitions between control modes, e.g.,
various therapeutic and adjunctive (non-therapeutic) modes, and
which may be applicable to the modes shown in FIG. 15A.
[0066] FIG. 16A is a flowchart showing mode switching between
phases described in an artificial pancreas system.
[0067] FIG. 16B is a diagram showing mode switching between phases
described in an artificial pancreas system.
[0068] FIG. 16C is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting exertion of varying levels of pump control
within therapeutic modes or phases.
[0069] FIG. 17 is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting responsive processing and display of data
according to mode switching between therapeutic and adjunctive
(non-therapeutic) modes.
[0070] FIG. 18A-18D illustrate exemplary user interfaces which may
be employed according to present principles.
[0071] FIG. 19 is a chart showing ways to depict data of various
confidence levels.
[0072] FIG. 20 is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular depicting multimodal transitions.
[0073] FIG. 21A is a diagram depicting ways to display adjunctive
(non-therapeutic) mode data and FIG. 21B is a flowchart according
to present principles showing another implementation of a method of
mode switching, in particular depicting transitions to
higher-calibration-required modes caused by user requests for
additional or different data.
[0074] FIG. 22 is a flowchart according to present principles
showing another implementation of a method of mode switching, in
particular identifying and treating hypoglycemia unawareness.
[0075] FIG. 23 is a block diagram that illustrates sensor
electronics in one embodiment.
[0076] FIG. 24 is a schematic view of a receiver in one
implementation.
[0077] FIG. 25 is a block diagram of receiver electronics in one
embodiment.
[0078] Like reference numerals refer to like elements throughout.
Elements are not to scale unless otherwise noted.
DETAILED DESCRIPTION
Definitions
[0079] In order to facilitate an understanding of the preferred
embodiments, a number of terms are defined below.
[0080] The term "analyte" as used herein generally relates 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 can 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 is analyte.
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-B 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, hemoglobin A, hemoglobin S,
hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, 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 B-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, B); 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 can
also constitute analytes in certain embodiments. The analyte can be
naturally present in the biological fluid, for example, a metabolic
product, a hormone, an antigen, an antibody, and the like.
Alternatively, the analyte can 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 can
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).
[0081] The term "calibration" as used herein generally relates to
the process of determining the relationship between the sensor data
and the corresponding reference data, which can be used to convert
sensor data into meaningful values substantially equivalent to the
reference data, with or without utilizing reference data in real
time. In some embodiments, namely, in continuous analyte sensors,
calibration can be updated or recalibrated (at the factory, in real
time and/or retrospectively) over time as changes in the
relationship between the sensor data and reference data occur, for
example, due to changes in sensitivity, baseline, transport,
metabolism, and the like.
[0082] The terms "calibrated data" and "calibrated data stream" as
used herein generally relate to data that has been transformed from
its raw state (e.g., digital or analog) to another state using a
function, for example a conversion function, to provide a
meaningful value to a user.
[0083] The term "algorithm" as used herein generally relates to a
computational process (for example, programs) involved in
transforming information from one state to another, for example, by
using computer processing.
[0084] The term "counts" as used herein generally relates 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 (e.g.,
converted by an A/D converter), which is directly related to
current from the working electrode. In another example, counter
electrode voltage measured in counts is directly related to a
voltage.
[0085] The term "sensor" as used herein generally relates to the
component or region of a device by which an analyte can be
quantified.
[0086] The terms "glucose sensor" and "member for determining the
amount of glucose in a biological sample" as used herein generally
relate to any mechanism (e.g., enzymatic or non-enzymatic) by which
glucose can be quantified. For example, some embodiments utilize a
membrane that contains glucose oxidase that catalyzes the
conversion of oxygen and glucose to hydrogen peroxide and
gluconate, as illustrated by the following chemical reaction:
Glucose+O.sub.2.fwdarw.Gluconate+H.sub.2O.sub.2
[0087] Because for each glucose molecule metabolized, there is a
proportional change in the co-reactant O.sub.2 and the product
H.sub.2O.sub.2, one can use an electrode to monitor the current
change in either the co-reactant or the product to determine
glucose concentration.
[0088] The terms "operably connected" and "operably linked" as used
herein generally relate 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, e.g., an electrical or
electromagnetic signal; the signal can then be transmitted to an
electronic circuit. In this case, the electrode is "operably
linked" to the electronic circuitry. These terms are broad enough
to include wireless connectivity.
[0089] The terms "in vivo portion" as used herein generally relates
to the portion of the device (for example, a sensor) adapted for
insertion into and/or existence within a living body of a host.
[0090] The terms "reference analyte monitor," "reference analyte
meter," and "reference analyte sensor" as used herein generally
relate to a device that measures a concentration of an analyte and
can be used as a reference for the continuous analyte sensor, for
example a self-monitoring blood glucose meter (SMBG) can be used as
a reference for a continuous glucose sensor for comparison,
calibration, and the like.
[0091] The term "system noise" as used herein generally relates to
unwanted electronic or diffusion-related noise which can include
Gaussian, motion-related, flicker, kinetic, or other white noise,
for example.
[0092] The terms "noise," "noise event(s)," "noise episode(s),"
"signal artifact(s)," "signal artifact event(s)," and "signal
artifact episode(s)" as used herein generally relate to signal
noise that is caused by substantially non-glucose related, such as
interfering species, macro- or micro-motion, ischemia, pH changes,
temperature changes, pressure, stress, or even unknown sources of
mechanical, electrical and/or biochemical noise for example. In
some embodiments, signal artifacts are transient and characterized
by a higher amplitude than system noise, and described as
"transient non-glucose related signal artifact(s) that have a
higher amplitude than system noise." In some embodiments, noise is
caused by rate-limiting (or rate-increasing) phenomena. In some
circumstances, the source of the noise is unknown.
[0093] The terms "constant noise" and "constant background" as used
herein generally relate to the component of the noise signal that
remains relatively constant over time. In some embodiments,
constant noise may be referred to as "background" or "baseline."
For example, certain electroactive compounds found in the human
body are relatively constant factors (e.g., baseline of the host's
physiology). In some circumstances, constant background noise can
slowly drift over time (e.g., increase or decrease), however this
drift need not adversely affect the accuracy of a sensor, for
example, because a sensor can be calibrated and re-calibrated
and/or the drift measured and compensated for.
[0094] The terms "non-constant noise," "non-constant background,"
"noise event(s)," "noise episode(s)," "signal artifact(s)," "signal
artifact event(s)," and "signal artifact episode(s)" as used herein
generally relate to a component of the background signal that is
relatively non-constant, for example, transient and/or
intermittent. For example, certain electroactive compounds, are
relatively non-constant due to the host's ingestion, metabolism,
wound healing, and other mechanical, chemical and/or biochemical
factors), which create intermittent (e.g., non-constant) "noise" on
the sensor signal that can be difficult to "calibrate out" using a
standard calibration equations (e.g., because the background of the
signal does not remain constant).
[0095] The terms "low noise" as used herein generally relates to
noise that substantially decreases signal amplitude.
[0096] The terms "high noise" and "high spikes" as used herein
generally relate to noise that substantially increases signal
amplitude.
[0097] The term "variation" as used herein generally relates to a
divergence or amount of change from a point, line, or set of data.
In one embodiment, estimated analyte values can have a variation
including a range of values outside of the estimated analyte values
that represent a range of possibilities based on known
physiological patterns, for example.
[0098] The terms "physiological parameters" and "physiological
boundaries" as used herein generally relate to the parameters
obtained from continuous studies of physiological data in humans
and/or animals. For example, a maximal sustained rate of change of
glucose in humans of about 4 to 5 mg/dL/min and a maximum
acceleration of the rate of change of about 0.1 to 0.2
mg/dL/min.sup.2 are deemed physiologically feasible limits; values
outside of these limits would be considered non-physiological. As
another example, the rate of change of glucose is lowest at the
maxima and minima of the daily glucose range, which are the areas
of greatest risk in patient treatment, thus a physiologically
feasible rate of change can be set at the maxima and minima based
on continuous studies of glucose data. As a further example, it has
been observed that the best solution for the shape of the curve at
any point along glucose signal data stream over a certain time
period (for example, about 20 to 30 minutes) is a straight line,
which can be used to set physiological limits. These terms are
broad enough to include physiological parameters for any
analyte.
[0099] The term "measured analyte values" as used herein generally
relates to an analyte value or set of analyte values for a time
period for which analyte data has been measured by an analyte
sensor. The term is broad enough to include data from the analyte
sensor before or after data processing in the sensor and/or
receiver (for example, data smoothing, calibration, and the
like).
[0100] The term "estimated analyte values" as used herein generally
relates to an analyte value or set of analyte values, which have
been algorithmically extrapolated from measured analyte values.
[0101] As employed herein, the following abbreviations apply: Eq
and Eqs (equivalents); mEq (milliequivalents); M (molar); mM
(millimolar) .mu.M (micromolar); N (Normal); mol (moles); mmol
(millimoles); .mu.mol (micromoles); nmol (nanomoles); g (grams); mg
(milligrams); .mu.g (micrograms); Kg (kilograms); L (liters); mL
(milliliters); dL (deciliters); .mu.L (microliters); cm
(centimeters); mm (millimeters); .mu.m (micrometers); nm
(nanometers); h and hr (hours); min. (minutes); s and sec.
(seconds); .degree. C. (degrees Centigrade).
[0102] The phrase "continuous glucose sensor" as used herein
generally relates to a device that continuously or continually
measures the glucose concentration of a bodily fluid (e.g., blood,
plasma, interstitial fluid and the like), for example, at time
intervals ranging from fractions of a second up to, for example, 1,
2, or 5 minutes, or longer.
[0103] The phrases "continuous glucose sensing" or "continuous
glucose monitoring" as used herein generally relate to the period
in which monitoring of the glucose concentration of a host's bodily
fluid (e.g., blood, serum, plasma, extracellular fluid, tears etc.)
is continuously or continually performed, for example, at time
intervals ranging from fractions of a second up to, for example, 1,
2, or 5 minutes, or longer. In one exemplary embodiment, the
glucose concentration of a host's extracellular fluid is measured
every 1, 2, 5, 10, 20, 30, 40, 50 or 60 seconds.
[0104] The term "substantially" as used herein generally relates to
being largely but not necessarily wholly that which is specified,
which may include an amount greater than 50 percent, an amount
greater than 60 percent, an amount greater than 70 percent, an
amount greater than 80 percent, an amount greater than 90 percent,
or more.
[0105] The terms "processor" and "processor module," as used herein
generally relate to a computer system, state machine, processor, or
the like, designed to perform arithmetic or logic operations using
logic circuitry that responds to and processes the basic
instructions that drive a computer. In some embodiments, the terms
can include ROM and/or RAM associated therewith.
[0106] The terms "usability", "data usability", and "signal
usability", as used herein generally relate to accuracy,
reliability, and/or confidence in sensor data. In some cases such
terms may also relate to stability of the sensor data.
[0107] Exemplary embodiments disclosed herein relate to the use of
a glucose sensor that measures a concentration of glucose or a
substance indicative of the concentration or presence of another
analyte. In some embodiments, the glucose sensor is a continuous
device, for example a subcutaneous, transdermal, transcutaneous,
non-invasive, intraocular and/or intravascular (e.g., intravenous)
device. In some embodiments, the device can analyze a plurality of
intermittent blood samples. The glucose sensor can use any method
of glucose measurement, including enzymatic, chemical, physical,
electrochemical, optical, optochemical, fluorescence-based,
spectrophotometric, spectroscopic (e.g., optical absorption
spectroscopy, Raman spectroscopy, etc.), polarimetric,
calorimetric, iontophoretic, radiometric, and the like.
[0108] The glucose sensor can use any known detection method,
including invasive, minimally invasive, and non-invasive sensing
techniques, to provide a data stream indicative of the
concentration of the analyte in a host. The data stream is
typically a raw data signal that is used to provide a useful value
of the analyte to a user, such as a patient or health care
professional (e.g., doctor), who may be using the sensor.
[0109] Although much of the description and examples are drawn to a
glucose sensor capable of measuring the concentration of glucose in
a host, the systems and methods of embodiments can be applied to
any measurable analyte. Some exemplary embodiments described below
utilize an implantable glucose sensor. However, it should be
understood that the devices and methods described herein can be
applied to any device capable of detecting a concentration of
analyte and providing an output signal that represents the
concentration of the analyte.
[0110] In some embodiments, the analyte sensor is an implantable
glucose sensor, such as described with reference to U.S. Pat. Nos.
6,001,067 and 8,828,201. In some embodiments, the analyte sensor is
a transcutaneous glucose sensor, such as described with reference
to U.S. Pat. No. 7,497,827. In yet other embodiments, the analyte
sensor is a dual electrode analyte sensor, such as described with
reference to U.S. Pat. No. 8,478,377. In still other embodiments,
the sensor is configured to be implanted in a host vessel or
extracorporeally, such as is described in U.S. Pat. No. 7,460,898.
These patents and publications are incorporated herein by reference
in their entirety.
[0111] The following description and examples describe the present
embodiments with reference to the drawings. In the drawings,
reference numbers label elements of the present embodiments. These
reference numbers are reproduced below in connection with the
discussion of the corresponding drawing features.
[0112] In general, systems and methods according to present
principles relate to real time switching between a first or initial
mode of user interaction and a second or new mode of user
interaction. In some cases, users may confirm such switching before
it occurs, or may be notified about the same. In many cases,
however, the switching will be automatic and transparent to the
user. The mode switching may cause a switch from an initial mode to
a new mode, followed by a switch to a subsequent mode or back to
the initial mode. In any case, the mode switching will generally
affect the user's interaction with the device, and not just cause
internal processing changes within the device, although such
processing changes will generally accompany the mode switching.
Such user interaction with the device may generally affect how the
user calibrates the monitoring device, views and uses the data, or
the like. The mode switching may generally have a significant
effect on the user interaction as embodied in the user interface,
including aspects from both the input and the output. B
[0113] In many cases the decision or trigger to switch between
modes relates to a usability of the monitoring device which is in
many cases (but not always) related to the usability of a sensor
signal, as compared to a transition criteria, but may also be based
on other data.
[0114] For example, referring to the flowchart 100 of FIG. 1, a
monitoring device may start in a first, original, or initial mode
of operation or user interaction 102. Upon the occurrence of a
trigger 106, the monitoring device may switch to a second or new
mode of operation or interaction 104. The trigger 106 generally
comes about when one or more trigger criteria are met, e.g., when
one or more values of determined data meets, or is predicted to
meet, one or more respective predetermined threshold transition
criteria. One type of criterion used in some implementations is
data or signal usability, but other data and accompanying criteria
are also often used.
[0115] As one example, an analyte monitoring device may transition
or switch from a user-dependent calibration mode to a device
self-calibration mode, i.e., from a calibration routine using blood
glucose concentration values from an external meter to a
calibration routine performed by the device it without requiring a
user to provide a glucose value from, e.g., a self-monitoring blood
glucose meter. Device self-calibration generally requires one or
more of stringent manufacturing controls, internal measurements,
entry of calibration codes from the manufacturer (which themselves
constitute a priori information) and/or associated algorithms that
enable calibration of the device without external measurement
obtained during sensor wear.
[0116] Additional details of systems and methods for device
self-calibration are disclosed in U.S. patent application Ser. No.
13/827,119; and US Patent Publication No. 2012/0265035-A1, both of
which are owned by the assignee of the present application and
herein incorporated by reference in their entireties.
[0117] The converse transition or switch may also be performed. As
another example, an analyte monitoring device may transition or
switch from providing information or data on-demand to a user to a
mode in which information or data is provided as initiated by the
device, e.g., as a regular or irregular periodic communication. The
converse transition or switch may also be performed. As yet another
example, an analyte monitoring device may transition or switch from
providing information or data in one decision-support mode to
another, e.g., from a therapeutic (or non-adjunctive) use to an
adjunctive (or non-therapeutic) use or from one phase of control to
another. Again, the converse transition or switch may also be
performed, as well as transitions or switches to other modes.
[0118] A number of triggers will be described, and generally the
triggers are such that one or more criteria are met or satisfied.
In one common type of criteria, a determined parameter or variable
meets or exceeds a predetermined threshold, or is determined to do
so in the future. The determined parameter or variable may be data
associated with the sensor signal, i.e., the sensor signal value or
a scaled representative thereof, data about the sensor signal,
e.g., data indicating its noise level or the like, data from an
external source, e.g., data from a blood glucose meter, temperature
sensor, clock, location sensor, or the like, as well as other data
as will be described below.
[0119] Systems and methods according to present principles may use
one or many triggers, i.e., transition criteria, on which to base
mode switching for a given implementation. In some cases, for
specific types of mode switching, particular determined parameters
or variables will be especially useful. For example, signal
usability may be especially pertinent when deciding and switching
calibration modes or decision-support modes. The signal value
itself may be especially pertinent when deciding data transmission
modes, e.g., whether a large excursion has occurred. However, these
are purely exemplary and it will be understood that in given
implementations other criteria will prove useful. It will further
be understood that in any given implementation any number or type
of transition criteria may be employed, including in some cases a
single criterion.
[0120] FIG. 2 shows a diagram 125 illustrating a number of types of
user interactions 108, which may be identified with modes of user
interaction. For example, and as noted above, one variety of user
interactions 108 which may be subject to mode switching involves a
calibration mode 110. Calibration modes may include user-dependent
calibration, device self-calibration, and so on. The calibration
mode may be in part dependent on data usability, as well as on
other data.
[0121] Another type of user interaction 108 involves the use to
which the user will put the data, such as the use of the data in a
decision-support mode 114. Exemplary uses include therapeutic,
where the monitoring device is used in the calculation and/or to
direct delivery of insulin, or adjunctive, where the monitoring
device is used for information but where insulin dosing is based on
external user calculations and/or a confirmatory external meter
value, e.g., from a blood glucose meter. If the use is directly
related to patient health, e.g., therapeutic, then the requirements
of the signal will generally be higher than if not. Thus, lower
signal usabilities (less reliable data) may result in transitions
from therapeutic to adjunctive modes. Some decision-support modes
include modes used only for information or augmentation of
education, e.g., tracking and trending, as well as (on the other
extreme) "closed-loop" modes in which data from the monitoring
device is directly used to drive a medicament delivery pump.
[0122] In the context of Type I diabetes, the terms
"non-therapeutic" and "adjunctive" may be used interchangeably or
as synonyms, similarly the terms "therapeutic" and "non-adjunctive"
may also be used interchangeably or as synonyms. The terms
therapeutic (non-adjunctive) and adjunctive (non-therapeutic) are
used in a relative sense, and it should be noted that the terms may
have different connotations for other health indication that may be
dependent upon populations and/or technology. For example, for a
type I user, a therapeutic or non-adjunctive mode may mean one in
which the user receives dosing recommendations, while an adjunctive
or non-therapeutic mode may refer to one in which recommendations
are given but the same do not relate to insulin dosing. The
descriptions herein may be primarily directed towards a usage of
the terms "therapeutic" and "adjunctive" as pertaining to delivery
of therapy such as insulin dosing for type I diabetics. However,
the same terms or similar may be employed for other populations.
For example, for type II users or for those interested in health or
fitness optimization, a therapeutic use may be one in which a meal
or exercise is suggested, while an adjunctive use is one in which
information is delivered without a specific recommendation
associated therewith.
[0123] In health indications other than Type I diabetes, the terms
"non-therapeutic" and "adjunctive" have meanings that are
overlapping, but not necessarily identical; similarly, the terms
"therapeutic" and "non-adjunctive" have meanings that are
overlapping, but not necessarily identical. In the example of a
type II user, or a user with a general interest in food or exercise
optimization, a therapeutic mode may mean one in which the user
receives food and exercise recommendations, while an
non-therapeutic mode may refer to one in which information is
provided to the user, without any specific recommendations. For
example, such a user may include those with interests in optimizing
sports or fitness routines or eating habits, users interested in
losing weight or otherwise increasing their health, or indeed any
other user interested in bettering their health or learning more
about how their habits and actions affect their health. For these
users, therapeutic recommendations may include suggested meals,
foods, or recipes, based on data known by the system including
glucose value, glucose rate of change, activity level, sleeping
patterns, and the like. For users particularly interested in
fitness optimization, data may be employed such as activity data,
such as may be received from an accelerometer or GPS device. Such
optimization routines may also include data gained from CGM
including glucose, glucose rate of change, and the like.
Informational or adjunctive data may be provided, e.g., total
calories burned that day, and so on.
[0124] Additional details about type II user recommendations may be
found in co-pending U.S. Provisional Patent Application Ser. No.
62/158,463, owned by the assignee of the present application and
herein incorporated by reference in its entirety.
[0125] More generally, an analyte monitoring device may be
configured to switch between different modes or phases of an
artificial pancreas system as illustrated in FIG. 15A.
[0126] Yet another type of user interaction involves the mode 110
in which transmission of the data from the sensor electronics to
the monitoring device occurs. If data is pulled on-demand by the
monitoring device from the sensor or accompanying sensor
electronics, then such is termed an on-demand mode, while if the
data is pushed periodically or upon occurrence of an event, such is
termed a device self-initiated mode.
[0127] Yet another type of user interaction mode is a confirmation
mode 112. This mode may be considered separately from the modes
above, but often plays a role in the implementation of one of the
other modes. In this mode, a user is prompted by a confirmatory
prompt or query prior to a mode switch or in some cases prior to a
change within a particular mode. For example, prior to switching
from device self-calibration mode to user-dependent calibration
mode, the user may be prompted to confirm that they understand that
finger sticks will now be required.
[0128] The flowchart 150 of FIG. 3A indicates a general method for
mode switching. In a first step, the monitoring device is operating
in an initial mode (step 117). The monitoring device may have
started following power up in the initial mode, or may have
switched to the mode using systems and methods similar to those
disclosed here. A next step is that data is received or otherwise
determined (step 118). The data that is received or otherwise
determined as discussed in greater detail below, but generally
relates to a data signal from the sensor, a data quality parameter
associated with the data signal from the sensor using signal
analysis, external data, or the like. A next step is to check if
the data received or determined is such that that a criterion for
mode switching is met (step 120). In other words, the received or
determined data is checked to see if the same should cause a switch
of modes, e.g., whether it meets, matches, or satisfies certain
criteria, e.g., meets or exceeds a threshold for a transition
criteria.
[0129] Of course, it will be understood that different transition
criteria may be satisfied or met by different determined data in
any given situation, and the satisfaction of a criteria for one
type of transition may be accompanied by other concurrent or
overlapping mode switching steps.
[0130] This situation is illustrated in FIG. 3B, in which an
initial mode 137 may be caused to transition to a first new mode
141 if a first transition criterion 139 is met by a determined
parameter or variable, and in the same way the initial mode 137 may
be caused to transition to a second new mode 145 if a second
transition criterion 143 is met, and so on for transitions to modes
149 and 153 caused by the meeting of transition criteria 147 and
151, respectively. For example, a device may switch to a user
dependent calibration mode and may simultaneously (or not)
transition to a therapeutic mode. Exemplary such multimodal
transitions are described in greater detail below.
[0131] The meeting of multiple transition criteria may result in a
different mode being transitioned to as compared to the case where
just one transition criterion is met. This situation is illustrated
in the flowchart 155 of FIG. 3C. An initial mode 157 can transition
to a new mode 161 upon the meeting of a first transition criterion
159 (solid lines). However, an initial mode 157 can also transition
to a second new mode 165 upon the meeting of both a first
transition criterion 159 and a second transition criterion 163
(dotted lines).
[0132] As noted above, in many cases a "determined data" that is
compared to a transition criterion will relate to data or signal
usability. In a specific method using such, as illustrated by the
flowchart 175 of FIG. 4, in a first step, a first level of signal
usability is determined (step 128). It is assumed that the first
level of signal usability does not result in a switch or transition
of modes. A subsequent determination is made of a level of signal
usability (step 130), and if the subsequent level of signal
usability is sufficiently different from the first level (step
132), i.e., satisfies a transition criteria, e.g., is greater than
or equal to a threshold level of difference, then the mode may be
changed from the initial mode to a subsequent mode (step 134). That
is, the mode may switch depending on whether the evaluation
determined that the criteria (or criterion) for mode switching was
met.
[0133] If the subsequent level of signal usability is not
sufficiently different, then the cycle may begin again. In a
related implementation, the signal usability need not be measured
against a prior measured level, but may simply be compared to a
criterion based on an absolute standard. If the signal usability
varies from the absolute standard by greater than a predetermined
threshold amount, the transition to the new mode may again
occur.
[0134] In any case, data is output on a user interface based on the
mode determined, e.g., either the initial mode or the new mode. In
some implementations it may be useful to indicate to the user how
long a new mode is expected to last, e.g., whether the same is
transient or long-term. As an example, if a decision-support mode
switches from therapeutic to adjunctive, due to a sensor fault that
is transient, e.g., a dip and recover fault, the user may be
informed that the adjunctive use is temporary and should last for
less than about one day. As another example, in calibration modes,
if there is full confidence in the device self-calibration, then
the device may indicate that no additional blood glucose meter
values may be required for the remainder of the sensor session.
Similarly, in addition to selectively outputting based on the mode,
and providing an indication of the mode, the UI may display a
performance indicator for how well the sensor is performing.
[0135] Specific triggering or transition criteria are now
described, and it is noted that such may be applied to certain or
all embodiments noted herein. The criteria may be used
individually, as part of a single-mode or single-variable
transition, or multiple criteria and data may be monitored and
compared simultaneously or at nearly the same time, enabling
multimodal transitions, e.g., causing a change in calibration type
and a change in decision support mode. The embodiments described
herein in which the below criteria may be employed include, e.g.,
the general transitions noted in FIGS. 1, 3A, 3B, 3C, and 10, the
transitions noted in FIGS. 2, 9, 11-17, 20 and 21A, the specific
use of criteria related to data usability in FIG. 4, including the
use of thresholds, and the criteria may be applied to the specific
types of data described in FIGS. 5-8, with results as displayed in
FIGS. 18A-18D and 24.
[0136] Referring to the diagram 200 of FIG. 5, a parameter or
variable 136 relating to the data usability may be considered to
occupy one or more of a number of categories. The parameter or
variable 136 may also be based on a combination of contributions
from these categories, e.g., a linear or nonlinear combination, and
in some implementations with weightings applied.
[0137] One category of data on which the data usability 136 may be
based is on the data values per se 138. This category generally
relates to sensor signal values as well as scaled or normalized
representations of these values, e.g., where the first is measured
in an analog or digital value (e.g., counts) and the second is
often measured by a concentration such as mg/dL. It may also relate
to data based on such values, e.g., time rates of change,
determined patterns, historical values, excursions, and the like.
In some cases fault data may be used in the calculation of data
usability as the same may be based on data values per se.
[0138] Another category of data on which the data usability 136 may
be based is on data signal analysis 140. The data signal analysis
140 pertains to data about the received signal, e.g., noise levels,
variability, in some cases discriminated faults, frequency
analysis, and other inputs such as those related to data
quality.
[0139] Another category of data on which the data usability 136 may
be based is on a decision-support mode 144. As noted above, such
modes relate to uses to which users and/or clinicians put the data,
and may include modes such as therapeutic, adjunctive, or different
phases of control of an artificial pancreas system as illustrated
in FIG. 15A., and the like.
[0140] A further category of data on which the data usability 136
may be based is on other external data 142. This category includes
data from sources other than the sensor or the decision-support
mode. Such data may include user-entered data, e.g., about how the
user is feeling, meals ingested, exercise performed, and the like,
as well as data from other external devices, e.g., blood glucose
monitors, temperature sensors, and the like. Certain of these
external devices may be signally coupled or connected to the
monitoring device, and others may be employed by having the user
enter data from the external device into the monitoring device.
[0141] It will be understood that certain factors may be considered
to occupy more than one of these categories. It will be further
understood that other categories of data may also be employed.
Finally, it is noted that a particular mode transition may be, and
often is, based on multiple sources of data from multiple
respective categories. Each of these factors and categories of data
is described further below with respect to subsequent diagrams.
Data Signal Analysis
[0142] FIG. 6 is a diagram 225 illustrating exemplary types of data
140 from signal analysis. In most of these types, sensor signal
data is analyzed resulting in data 140 that indicates in some way
the usability of the sensor signal data, e.g., its reliability,
accuracy, stability, confidence, or other aspects as described or
understood. In certain other cases, data 140 does not relate to
usability, but may still be employed in the determination of mode
switching.
[0143] In one specific example, the signal analysis data 140 may
include data 164 about noise in the signal. That is, if the signal
has a noise level which satisfies a criteria, e.g., is above a
predetermined threshold, the usability of the signal or resulting
data may be deemed, calculated, or determined to be lower than if
the noise level were below the predetermined threshold.
Accordingly, in high noise situations, certain modes, such as a
therapeutic decision-support mode, may be caused to transition to
an adjunctive mode, as the therapeutic reliability of the signal is
in question. Alternatively, where the therapeutic level of control
is enumerated by phases, the same may be caused to transition to a
lower phase. Noise level determination may also be made over the
short term or the long term, and in some cases can be determined by
analysis of the signal value itself over the long-term or
short-term. For example, a long-term drift in a signal baseline
value may be indicative of certain types of faults as described in
the applications incorporated by reference below.
[0144] The signal analysis data 140 may also be based on data 166
about faults detected or discriminated in the signal. Such faults
include, e.g., end of life faults, compression faults, dip and
recover faults, water spike faults, and the like. In many cases the
analysis of such faults includes comparing signal patterns or
certain criteria known to be associated with such faults. In this
sense the data 140 may be considered to depend to a certain extent
on signal values per se. In some cases, where a significant such
fault is discriminated, a transition may be caused from, e.g., a
device self-calibration mode to a user-dependent calibration
mode.
[0145] Additional details of systems and methods for noise and
fault detection and discrimination are disclosed in U.S. Pat. No.
8,010,174; US Patent Publication No. 2009/0192366-A1, or U.S. Pat.
No. 8,290,559; and U.S. Patent Application No. 62/009,065, which
are owned by the assignee of the present application and herein
incorporated by reference in their entireties.
[0146] The signal analysis data 140 may also be based on data 158
about a patient's previous sensor session. For example, in some
cases a particular patient may consistently experience a long
startup time for their sensor sessions. In these cases, it may be
expected that modes relying on external data may be determined to
be more reliable during such times. Accordingly, those modes may be
switched to from other modes. As a specific example, user-dependent
calibration may be relied on more than device self-calibration
during these times.
[0147] The signal analysis data 140 may also be based on data 160
about errors determined at calibration. Such errors at calibration
are related to a systematic bias between a sensor signal value and
a reference external meter value. For example, if a significant
error at calibration is determined, a mode may be caused to switch
from a therapeutic one to an adjunctive one, because of a lessened
reliability or usability of the signal. Additional details of
systems and methods for determination of such errors at calibration
are disclosed in US Patent Publication No. 2014/0182350-A1, which
is owned by the assignee of the present application and herein
incorporated by reference in its entirety.
[0148] The signal analysis data 140 may also be based on data 148
about or from various diagnostic routines, including
self-diagnostics routines 152 and calibration-diagnostics routines
154. The former may include periodic diagnostic routines run by the
monitoring device and/or sensor electronics to detect errors
introduced during routine operation. The latter relate to routines
involved with device self-calibration. The results of calibration
diagnostics may include the determination and usage, e.g., in
transition criteria, of a confidence interval in displayed readings
based on the determined calibration and, in particular, its
quality. For example, if calibration-diagnostics routines indicate
that the device is not well-calibrated by a device self-calibration
routine (e.g., low confidence level), the mode may be caused to
switch to user-dependent calibration. In some cases, the monitoring
device may periodically enter a self-diagnostics mode to, e.g.,
cause an interrogation of the sensor, as well as to cause an
examination of the resulting transient response. If the response is
expected or consistent, mode switching may be contraindicated. If
the response is unusual or indicates a problem with the sensor,
then mode switching may be indicated, e.g., a switch to a more
conservative, safer, or user-protective mode, even if the same
results in lessened user convenience.
[0149] Additional details of systems and methods for determination
of data from diagnostic routines are disclosed in US Patent
Publication No. 2012/0265035-A1, which is owned by the assignee of
the present application and herein incorporated by reference in its
entirety.
[0150] The signal analysis data 140 may also be based on data 162
from other algorithms, e.g., which estimate errors in determined
slopes and/or baselines corresponding to rate of change data. In
some cases, where these are statistically equivalent between modes,
such may be an indicator that a mode switch is possible. Additional
details of such aspects are described below with respect to FIG. 13
and elsewhere
[0151] Other types and aspects of signal analysis data are
disclosed in U.S. Pat. No. 8,010,174; US Patent Publication No.
2009/0192366-A1; or U.S. Pat. No. 8,290,559; US Patent Publication
No. 2012/0265035-A1; U.S. patent application Ser. No. 13/827,119;
US Patent Publication No. 2014/0182350-A1; U.S. Patent Application
No. 61/978,151; and U.S. patent application No. 009,065, which are
owned by the assignee of the present application and herein
incorporated by reference in their entireties.
Data Values Per Se
[0152] FIG. 7 is a diagram 250 illustrating exemplary types of
analysis 138 pertaining to data values per se.
[0153] The analysis of data value 138 may be based on data 182
directly related to the glucose concentration value itself, or
related to values uniquely determined by the glucose concentration
value, e.g., a glucose rate of change, including slope and/or
acceleration, or glycemic state, e.g., hypoglycemia, hyperglycemia,
or euglycemia. A particular example of the use of such data is
given by analysis of large excursions 170, in which if a large
excursions from a desired value or range is encountered, the mode
is caused to switch so as to provide additional safety for a user.
For example, if a large excursion is seen (e.g., glucose value drop
from 150 to 50 within an hour), and the monitoring device is in a
device self-calibration mode, the same may be switched to a
user-dependent calibration in order to obtain additional and
external data, outside of the context of the monitoring device
itself. Such data 178 may further be based on a glucose or glycemic
zone, e.g., a range of glucose concentration values. For example, a
mode may be caused to switch in a hypoglycemic zone but not in a
hyperglycemic one.
[0154] In a similar way, the data 138 may also be based on data 172
of duration within a range or zone of glucose concentration values,
or even within a range or zone of a particular rate of change or
acceleration. For example, mode switching, which in a given
implementation is partially based on a glycemic range, may be
limited to situations in which the glycemic range is maintained for
greater than a predetermined threshold period of time. In this way,
mode switching is avoided where a glycemic range is simply passed
through or only briefly encountered.
[0155] The data 138 may also be based on data 178 of a glycemic
urgency index, which is a quantity determined by analysis of
various parameters or variables, and which relates to not just a
glycemic value but more generally to an indication of a patient or
user's risk state. For example, a glycemic urgency index may be
employed to determine not just if a patient is hyperglycemic, but
to further quantify a measure of risk due to a patient's
hyperglycemia. As applied to mode switching, if a patient has a
high glycemic urgency index, the mode may switch to a more
conservative or protective one, to ensure patient safety during the
time of the high value.
[0156] Additional details of systems and methods for determining in
using a glycemic urgency index are disclosed in U.S. Patent
Application No. 61/978,151, which is owned by the assignee of the
present application and herein incorporated by reference in its
entirety.
[0157] The data 138 may include data 186 based on comparisons to
historical glucose signals and patterns which may be determined or
identified therefrom. In these comparisons, current sensor values
and patterns may be compared to prior or historic values and
patterns, and similarities and deviations determined. As a specific
example, if glucose patterns diverge from a normal glucose patterns
for a particular host by a particular amount, then adjunctive mode
may be triggered.
[0158] The data 138 may also be based on data 188 about data
trends, which may include short-term data trends 190 and long-term
data trends 192. Certain aspects of trend data are discussed above
in context with data signal analysis, but here it is noted that
overall trends in data may be noted and taken account of in
calculations or determinations for mode switching. For example, if
an overall trend is seen for a signal reduction in amplitude, e.g.,
due to the sensor saturation or sensor lack of sensitivity, then
such may be noted and in some cases used as the basis for causing
mode switching. Of course, in some cases, the sensitivity may be
reduced so much that the usability of the signal is no longer
appropriate for a particular mode, and thus the mode must be
switched to one in which the signal usability is appropriate and
within a specified confidence level.
[0159] The data 138 may also be based on data 174 based on rebound
variability. Rebounding is a phenomenon that occurs when a high
blood glucose level occurs directly after and in response to a low
blood glucose level. Rebounding may occur when a user ingests a
significant amount of carbohydrates to address a hypoglycemia
situation. As rebounding is a known phenomenon, its occurrence,
when consistent, may not necessarily result in a mode switch or
transition. However, when rebounding occurs variably and not
consistently, then the usability of the data may be placed into
question. In such cases, mode switching may be an appropriate
response. For example, a therapeutic mode may transition from one
phase to another of control or to an adjunctive mode, a device
self-calibration mode may transition to a user-dependent
calibration mode, and the like.
[0160] The data 138 may also be based on data 194 about glucose
context, e.g., whether the data concerns nighttime hypoglycemia.
Additional details of systems and methods for determination of
glucose context are disclosed in U.S. Patent Application No.
61/978,151, incorporated by reference above, and applications
incorporated by reference therein.
[0161] The data 138 may also be based on data 196 entered by a
user, e.g., where a user is prompted to enter such or where the
user enters such on their own. For example, a user may be prompted
to enter whether they believe data to be unusually high or low,
whether an inaccuracy is perceived, potential causes, or the like.
Such user-entered data is further described below with respect to
FIGS. 8, 10, 11, 12, and 15.
[0162] The data 138 may also be based on data 176 received from
other sensors, particularly where there are concurrent or
overlapping sessions, i.e., with two sensors simultaneously
installed in a patient. In such systems, the data may be compared
or shared. A related type of data is comparison data or difference
data, which is a difference value or other comparison indicator
between the data 176 and that from a subject or main sensor
signal.
[0163] The data 138 may also be based on data 184 which represents
a historical data value from a previously-installed sensor. For
example, a last set of data values from and immediately--prior
sensor may be compared with the first set of data values from a
newly--installed sensor to determine if, e.g., the sensor signal
trajectory is consistent. If not, and if the historical data values
had a high confidence level, then the usability of the signal from
the new sensor may be lowered.
Other Data Including External Data
[0164] FIG. 8 is a diagram 275 illustrating exemplary types of
external data 142 corresponding to non-sensor signal data,
including data such as from connected (or not connected)
devices.
[0165] As a first example, the external data 142 may be metadata
224. For example, metadata may include information about a
particular lot from which a group of sensors is drawn. Such
information is employed on occasion for purposes of identifying
products for various reasons. Metadata analysis may be employed in
the determination of transition criteria using such data because
metadata may be used to identify like sensors which behave and
operate identically or similarly to other sensors. For example, if
given certain conditions a given sensor transitions from one mode
to another, it may be expected that under similar conditions a
sensor from the same lot (as identified by metadata) may act
similarly. In other words, historical data based on prior sensors
may be extrapolated for use in later sensors, either for an
individual, for a group, or for a manufactured lot. Where data is
compared across a group of users or a lot, cloud or other network
technology may be employed to transmit such metadata or other
information.
[0166] The external data 142 may also include sensor environment
data 168. It is noted that sensor environment data may also be to
some extent identified and quantified by data 140 from signal
analysis or data 138 pertaining to data values per se. The sensor
environment data 168 may include such information as temperature or
altitude, as sensors may act differently in different environments.
For example, a mode which may be switched to on the basis of
comparing a determined data value to a criterion may not cause a
transition in another situation, e.g., because the change in value
of the data may be determined to be based on altitude rather than a
change in a user physiological parameter. The reception of such
external data may be via a user input on a user interface of the
monitoring device, by automatic detection via a connected device,
or by other means. For example, a temperature sensor may be
disposed along with the analyte sensor to determine temperature
(data 254) and to transmit the same to the monitoring device. As
another example, in the determination and use of altitude in a
calculation, an altimeter or GPS device may be employed, the latter
being available in many mobile devices. Data transmission may occur
via wired or wireless, e.g., Bluetooth.RTM., NFC, or other like
techniques.
[0167] More generally, the external data 142 may be based on data
222 from other connected devices. Other connected devices may
include, e.g., activity monitors, sleep monitors, insulin or other
medicament delivery pumps, insulin pens, e.g., smart pens, or the
like. Other connected devices may also include devices not specific
to medical purposes, e.g., GPS devices or mobile devices running
GPS location applications, and so on. Other connected devices may
further include data from other types of sensors disposed locally
with a main analyte sensor. For example, impedance readings may be
determined from the sensor data or along with the sensor data, and
such may bear on the usability of a signal as such may be employed
in the determination of sensor sensitivity. Auxiliary or redundant
sensors may also be so used.
[0168] For example, an activity monitor or a sleep monitor may be
employed to determine a status of a user. In a specific
implementation and situation, a CGM device may detect hypoglycemia
in a user. If CGM device is operating in an on-demand data
transmission mode, and if data from an activity monitor or sleep
monitor indicates that the patient is sleeping, then the mode may
switch to a "pushed" mode of data transmission to pro-actively
alert the user of a potential "overnight low", which is generally a
very dangerous condition.
[0169] The external data 142 may be as noted based on data 220
about location from, e.g., GPS or other location data sources. For
example, a user may desire that a CGM is in a first mode at home
and a second mode at work or school. In a specific implementation,
a user may desire that data transmission may be on-demand at work
or school and in a periodic or device-initiated transmission mode
at home. Other variations will also be understood.
[0170] The external data 142 may further be based on data from
other (e.g., glucose) sensors, which can serve as a check on the
reliability of a subject monitoring device. For example, if other
devices show a glucose concentration value that greatly differs
from the subject monitoring device, such may call into question the
main sensor or the usability of the data from the subject
monitoring device. Conversely, if other devices show an analyte
concentration value consistent with the subject monitoring device,
such may increase the confidence level of the glucose concentration
value from the subject monitoring device.
[0171] The external data 142 may also be based on data 214 about a
frequency of calibration. Such frequency of calibration data may be
determined by the monitoring device, based on the number of entries
provided by the user as calibration measurements. In an example,
the entering of more calibration entries than usual may indicate
that the user fails to trust the CGM device, for one reason or
another. This increase in frequency of calibration may be
particularly evident of a low signal usability when the difference
between the CGM value and the blood glucose concentration value
differ significantly, e.g., more than about 20 mg/dL or 20%. On the
other hand, in some cases, an increase in frequency is associated
with greater usability as additional data values are received.
[0172] The external data 142 may also include data 221 about the
tissue in which the sensor is located, and other tissue/sensor
interface data. Such data may include characterization of tissue
type, wound response, diffusion distance, temperature as determined
independently or by temperature data 254, oxygen depletion rate, as
well as related sensor data such as thickness of membrane over
electrode array, a type of membrane, diffusion of specific species
between the electrodes, and the like. In some cases certain of
these parameters may be determined by impedance data 258, which may
be determined by data signal analysis as well. The above data may
be entered by the user or identified by metadata analysis, or
obtained via other means.
[0173] The external data 142 may also be based on data 248 of the
time since implant. In particular, data about the time since
implantation of the sensor may bear strongly on the determination
and discrimination of certain faults such as those associated with
"dip and recover" or end-of-life phenomena. By analyzing the time
since implant, systems and methods according to present principles
may discriminate such faults, or determine such faults to have not
occurred, with greater confidence. In use, within a system and
method according to present principles, data based on analysis 248
may, e.g., determine that a "dip and recover" fault is likely, and
as a consequence cause a transition from a therapeutic mode to an
adjunctive mode. Confidence in the discrimination of such a fault
may be even further enhanced by data signal analysis and/or data
value analysis.
[0174] Additional details of systems and methods for determining
and discriminating faults and failure modes are disclosed in U.S.
Patent Application No. 62/009,065, which is owned by the assignee
of the present application and herein incorporated by reference in
its entirety.
[0175] The external data 142 may also be based on data 226 of
user-supplied information, e.g., user responses to queries or
prompts on the user interface of the monitoring device. For
example, a user may be questioned as to whether an adhesive on the
sensor electronics/transmitter appears to be peeling. If the user
responds in the affirmative, the result may be less confidence in
the sensor reading and thus reduced sensor signal usability. The
data 226 can also relate to user selection of responsiveness 240,
e.g., whether a user prefers smooth data with a time lag or noisy
data that is more responsive to actual glucose changes. Such a
selection may be entered on a user interface that, e.g., appears as
a slider switch on the user interface where the user slides their
"responsiveness" preference.
[0176] The data 226 may further include data 246 where a user flags
outlier data, e.g., where the patient flags data that is unusually
high or low, or that represents false alarms, e.g., where the
patient identifies where the CGM is showing highs or lows
incorrectly, as well as user feedback 244 about alerts or alarms.
Data 242 about responsive actions may also be included in data 226.
For example, if the user takes no actions or responses to alerts or
alarms, then it may be inferred that the user is not a reliable
source of data. These aspects are described in greater detail below
in the context of specific mode transitions.
[0177] The data 226 of user-entered or user-supplied information
may further include information in the form of user-entered data
230. The data 230 can include data 232 about user perceptions,
e.g., whether CGM data appears accurate, or whether it appears high
or low. Such data can also include user perceptions about glycemic
ranges, e.g., whether the user feels like they have low or high
blood sugar. The data 230 can also include data based on user
input, but which itself is different from the actual data input by
the user. For example, a user-perceived error in the monitoring
device may be deduced by the user providing external meter values
with or without prompts, or if the user provides external meter
values at a greater frequency or number than usual for that
patient, or at a greater frequency or number than requested by the
monitoring device itself.
[0178] The data 230 can also include information about meals
ingested, insulin taken, exercise performed, or the like, and in
this case when data 230 is entered, an optional step may be to
determine data 234 which represents whether the entered data, e.g.,
a meal response or a distribution of the same, is consistent with
prior signal values seen when similar meals were ingested or
exercise performed. In particular, the comparison may be against
prior entered meal/insulin/exercise information 236 known, or the
same may be compared with predictive models 238. If meal or insulin
responses change for a patient for the same entered meal or
carbohydrate count, as compared to previously declared meal or
carbohydrate counts of the same amount, then an error may be
suspected and the usability of the data for therapeutic decision
making may be in question (e.g., resulting in lower confidence
level). The comparison may be with respect to the signal response
to carbohydrates, a rate of change, a duration of the meal
response, or the like. Prompts or queries may also be employed
where the same are not necessarily compared against prior meals,
insulin, or exercise, but where the prompts attempt to confirm
notable readings. For example, if the glucose concentration value
sharply rises, the query could prompt the user to enter if they
just ate a large meal. The responses to such queries or prompts can
determine if the signal is acting in an expected or consistent way,
or if the signal is acting in erroneous fashion affecting the
usability of the data and potentially causing a mode switch.
[0179] Another type of external or other data 142 includes a time
duration 252 since the last data push or pull. In other words, if a
significant time period has elapsed since the last time data was
received by the monitoring device, such may be employed as a
criterion in a mode transition. As a specific example, if a long
period of time has elapsed since the device last received data, a
pushed or automatic data transmission mode may be entered, so as to
alert the user to their current glycemic state.
[0180] Another type of external or other data 142 includes data
from an external meter 198, e.g., an external blood glucose meter.
Such data may be received on the basis of a prompt, for
confirmation or calibration purposes, or based on other initiators.
Such data 198 is not limited to only meter reading values, but may
also be based on data 212 about an external meter type or model.
The data 198 may further include calibration data 204 about an
expected range of signal values (given the current measurement) or
calibration data 202 related to reliability, e.g., noise values or
other signal characteristics values. The external meter data 198
may further include data 206 about an uncertainty in measured
values, including data 208 about a number of data points, their
range, their rate of change, or the like, as well as data 210 about
outlier values.
[0181] Another type of external or other data 142 which may be
employed, particularly to modify triggers or alter criteria, e.g.,
thresholds for transitions, is data 216 about other parallel or
concurrent modes in which the monitoring device is operating, which
may include calibration modes, decision-support modes, transmission
modes, or any other such operating mode. In this implementation it
is data about a different parallel mode that is being employed as
an input in the determination of whether to switch a subject mode.
The other different parallel mode has already been presumably
determined, e.g., whether by user or device selection or by another
of the mode switching examples.
[0182] One such parallel mode is the decision-support mode 144 (see
FIG. 5). In particular, any of the "usability" criteria described
in FIGS. 6-8 could be modified by the system in real time based on
a decision support mode of the device. Put another way, the
criteria determinative for mode switching, above, may depend on how
the user would like to use the data from the device. FIG. 9 is a
flowchart 300 illustrating how data about other operating modes may
be employed as criteria for mode switching of a subject mode.
[0183] In more detail, CGM may have many different uses or
indications. Some users may prefer CGM for general educational and
behavioral modification, e.g., Type II diabetics on oral
medications, while others may use CGM to dose insulin throughout
the day (e.g., Type I or insulin-dependent Type II patients), or
even as a data feed into a closed-loop artificial pancreas system.
The criteria for usability/reliability of data for insulin dosing
should be more stringent than the criteria for
usability/reliability for education purposes. While multiple
different CGM sensor designs could be deployed for each specific
indication or use, it would be simpler to provide one solution for
CGM and allow that system to adapt to that solution. Accordingly,
in some implementations the monitoring device may request or
require the user to provide information regarding that patient
and/or how the patient intends to use the data.
[0184] Accordingly, in the first step of flowchart 300, the
monitoring device is operating in an original or initial mode 260.
The decision or determination by the monitoring device to switch to
a new mode 262 may still be based on a factor related to data
usability 264 (as determined by a criterion), but the same may also
be based on other data 266, e.g., about the intended use of the
monitoring device (such data is indicated in FIG. 8 as data 230).
Such data 266 may include data 268 about a decision-support type,
e.g., data 270 about whether the use is adjunctive or therapeutic,
data 272 about a phase in which the device or devices are
operating, the phase representing a level of control, or the like.
It will be understood that data 270 and data 272 may overlap, and
that combinations of such data may be employed.
[0185] Data 276 about other uses of the device which could affect
decision-support may also be indicated by the user and included as
data 266. These may include data 276 about user intentions, or
desired use information relating to weight loss, exercise impact,
post-meal glucose summary, a desire to select more nutritious foods
versus junk food, or the like. The data 266 may also be drawn from
patient current health data 278 including data about whether the
user is ill, whether the user is experiencing pregnancy or their
menstrual cycle, level of activity, type of activity, and the like.
The data 266 may further be drawn from specific patient goals such
as hypoglycemic avoidance, nighttime control, postprandial control,
longer duration of the sensor session, and the like.
[0186] The data 266 may further include data 274 about user
preference for convenience versus accuracy. In other words, the
user can indicate a preference for accuracy over convenience, and
the criteria can adjust for that. For example, the system can call
for additional blood glucose external meter calibration values, and
such would increase accuracy at the expense of user convenience.
Alternatively, the user can indicate that they prefer convenience
over accuracy, and the criteria can adjust for that. In this case,
the system may perform more device self-calibration and less
user-dependent calibration. The system can in some cases tighten
other controls to attempt to make up for the loss in accuracy. For
example, thresholds for hypoglycemic and hyperglycemic ranges may
be loosened, so that while accuracy is lessened, a user may be
alerted earlier to the entry into such glycemic states. User
preference may also be employed to determine which mode to start
on. Specific user goals may be employed in this mode determination,
e.g., a new user may have more simple or less complex goals, and
such can be reflected in the determined mode. Whatever the user
goal, the user interface may be provided with an indicator to
clearly indicate the transition, as well as (in many cases) an
indicator of the new mode of operation and accompanying new mode of
user interaction in which the device is operating. The indicator
may in some cases expressly indicate the transition, e.g.,
textually or verbally, or may indicate the same in other ways,
e.g., by the user of color, shapes, pictures, or the like. In some
cases the indication is not to a person but to a system, e.g., a
pump, and in this case the transition will also be clearly
indicated by, e.g., a flag or other data indicator. The pump or
other downstream device may then treat received data according to
the new mode, which may in some cases be different from how data
was treated under the old mode, although such is not necessarily
required.
[0187] In another implementation, as shown in the flowchart 325 of
FIG. 10, the system may be employed to detect usage of the device.
In a first step, a session begins and/or the monitoring device
starts up (step 280). In some cases the monitoring device starts up
in a fully configured mode as enumerated above, e.g., by simply
starting up in a last-identified mode. In other implementations the
monitoring device powers up and as part of its initial
configuration determines what mode to start in. As part of this
determination, the monitoring device may detect the usage of the
device (step 282). For example, the monitoring device may detect
that it is signally coupled to a medicament delivery device such as
an insulin pump. In this case, the monitoring device may start up
in a therapeutic mode, subject to, e.g., optional patient
confirmation. The user interface of the monitoring device may also
request input as to the usage of the device (step 284). The system
detected usage and the user-entered information may be employed in
a final determination of what mode the monitoring device is set to
(step 286). In other words, the system-detected usage and the
user-entered usage data may not by themselves uniquely determine
the most appropriate mode, but the two may act as data inputs and
which together (and optionally with other data) determine the
device mode. Moreover, in some cases it may not be desired to have
a user enter a particular mode per se, but rather to have the user
enter more user-friendly information that is in turn converted to a
determined mode. For example, it may be more understandable to a
user to indicate that their CGM is driving their pump then to
necessarily have the user indicate the more technical distinction
that the device is in a therapeutic mode rather than an adjunctive
one.
[0188] In any case, once the mode is set (step 286), the mode may
be switched according to methods above if determined data meets
transition criteria (step 288). Specific examples of mode switching
are described below.
User-Dependent Calibration Versus Device Self-Calibration
[0189] One exemplary type of mode switching is between
user-dependent calibration and device self-calibration, e.g.,
switching between using no blood glucose meter readings (e.g.,
using a priori, internal (e.g., using impedance), prior derived
values, or device self-calibration only) and using blood glucose
meter calibration, or vice versa. User-dependent calibration
generally requires ongoing input of external reference data, e.g.,
glucose concentration meter values, to maintain calibration,
although a priori information may be included as a part of the
algorithm.
[0190] Additional details of systems and methods for user-dependent
calibration are disclosed in U.S. Pat. Nos. 7,778,680; and
7,920,906, which are owned by the assignee of the present
application and herein incorporated by reference in their
entireties.
[0191] Device self-calibration mode does not require ongoing input
of external reference data from the patient, although previously
entered external reference data may be used to influence the
calibration. While it is desirable to ensure the reliability or
stability of device self-calibration, one problem that has been
encountered is that the usability of device self-calibration may be
influenced by a number of variables, including: stability of the
calibration factors over time in vivo; in vitro to in vivo
predictability of calibration factors (which may be influenced by
shelf-life); and patient-to-patient variability of the calibration
factors, as examples.
[0192] Additional details of systems and methods for device
self-calibration are disclosed in U.S. patent application Ser. No.
13/827,119, which is owned by the assignee of the present
application and herein incorporated by reference in its
entirety.
[0193] Similarly, the usability of user-dependent calibration has
challenges as well, as the same may be influenced by a number of
variables, including the frequency and reliability of the user's
blood glucose calibration entries. Accordingly, one mode or another
may be preferred at a given time. In certain systems and methods
according to present principles, the mode switching between these
two modes occurs to preferentially select the mode with the most
preferred data usability, with the mode switching occurring
adaptively and dynamically, in real time, based on the evaluation
of transition criteria.
[0194] FIG. 11 shows a flowchart 350 for accomplishing calibration
mode switching. Several transition criteria that are particularly
useful in calibration mode switching are described in the figure,
but it will be understood that any of the transition criteria
described in FIGS. 5-9 may also be employed. Moreover, while FIG.
11 details transitions between calibration modes, it will be
understood that other types of modes may be running in parallel or
concurrently, e.g., decision support modes, data transmission
modes, and the like.
[0195] In a first step, a monitoring device is presumed to be in a
prior, original, first, or initial mode (step 290). For purposes of
discussion this mode is shown as a self-calibration mode. In this
mode, sensor data is transformed using calibration factors
determined by the device itself, without recourse to external
values.
[0196] An optional next step is to determine a usage type (step
292). The usage type may pertain to a decision-support mode, a user
intention, a user goal, or other aspects as described above. Such
data may also pertain to other patient characteristics which
require increased safety requirements versus convenience, and may
thus change the evaluated transition criteria. Such patient
characteristics may include data about gastroparesis, hypoglycemia
awareness, etc.
[0197] The usage type determined may be assisted by the detection
294 of other coupled devices, as well as by user input 296. In this
regard it is noted that the safety requirements for a therapeutic
mode (e.g., of whatever phase) are generally higher than the safety
requirements for an adjunctive mode, while the convenience
requirements may be higher for an adjunctive mode as compared to a
therapeutic mode. Accordingly, the criteria for switching modes may
be weighted more for safety if in a therapeutic mode and may be
weighted more for convenience in the adjunctive mode. Accordingly,
the optional step 292 of requesting usage type information is
included in the flowchart 350, either as the same is automatically
detected (step 294), e.g., when the sensor is connected to an
insulin pump, or prompted as input by the user (step 296). For
example, one or a series of questions could be asked of the user at
the start of a session or when setting up the device that
determines how the user intends to use the sensor data. For
example, a user could select a use case for the data, e.g.,
therapeutic versus adjunctive, or, e.g., educational e.g., just
watching trends, versus decision-support based. Such data may even
be used to determine or modify aspects within a mode. For example,
within a user-dependent calibration mode, a monitoring device
algorithm may adjust to more or less external blood glucose meter
prompts, as some users would rather calibrate more often and
achieve greater accuracy, e.g., traditionally-treated type I
diabetes patients, while other patients would rather not calibrate
and do not require a high-resolution of accuracy, e.g., type II
diabetics on oral medications. This determination of accuracy can
further be used in the startup mode to drive which mode the
monitoring device is initially configured to, e.g., user-dependent
calibration versus device self-calibration.
[0198] As another example of where the usage type is determined in
part by detection of other devices (step 294), the monitoring
device may detect that it is connected to a medicament delivery
pump and may further detect a setting that indicates its use in a
closed-loop--decision support configuration for the pump. The
monitoring device may then adjust criteria for calibration modes
based thereon, e.g., increasing the calibration requirements based
thereon.
[0199] In any case, a next step is to determine the usability of
the current calibration mode by comparison to one or more criteria
(step 298). In particular, a data signal, e.g., from the sensor, or
other determined data, is received (step 304), and one or more
criteria are received or retrieved (step 302). For example, the
criteria may be received from memory or other local or online
storage.
[0200] The criteria used may be one or more from the group
described with respect to FIGS. 5-9, but certain exemplary criteria
will be described and/or reiterated here.
[0201] One criterion which may be employed includes data that is
internally detected by the monitoring device and which indicates
sensitivity. For example, as shown in FIG. 6, self-diagnostics
which measure impedance can be employed to detect shifts in
sensitivity. Such self-diagnostics may be performed on a regular
basis or whenever errors are perceived. Where a shift in
sensitivity is detected, the same may be employed to inform the
device self-calibration or the same may be employed to cause a
transition to a new mode, e.g., user-dependent calibration, if the
shift in sensitivity is greater than a predetermined criterion.
[0202] Another criterion which may be employed is based on data
from other connected devices (see FIG. 8). This
"machine-to-machine" checking can be employed to, e.g., check CGM
values against a connected blood glucose meter. The connection
between such devices may be peer-to-peer or over a network. In this
example, if a blood glucose meter does not match the CGM value, a
query may be initiated to either perform diagnostics without user
input or to prompt the user to perform a step to assist in the
reconciliation of such values.
[0203] Referring in addition to FIG. 7, the evaluation of
determined data against a criteria may include evaluation of
current data against historic values of, e.g., glucose ranges, mean
values, patient specific profiles, patterns, or the like. If the
determined data does not follow a patient's individual normal
glucose profile within certain criteria, e.g., within 25%, 10%, or
the like, a patient may be prompted to input whether the patient
believes the data to be unusually high or low, or whether the
patient perceives an inaccuracy. The patient may further be
prompted as to potential reasons for abnormal glucose readings. The
comparison may be made more granular by comparing based on time of
day, day of week, the context of the glucose, e.g., whether the
same pertains to nighttime hypoglycemia, a post-meal high, and so
on. The comparison may further employ statistical factors such as
variance, mean, rate of change, the variability of glucose
concentration values, or the like.
[0204] Other criteria which may be employed include any of those
described above in connection with FIGS. 5-9, including without
limitation, e.g., user responsive actions to alerts and alarms
(e.g., amount of time between alert and user acknowledgement), user
feedback on alerts (e.g., levels of perceived accuracy), the entry
by the user of outlier data and false alarms (e.g., as determined
by outlier detection and/or measures of alarm fatigue), the glucose
values resulting from confirmatory external meter readings, which
may be prompted for by the monitoring device or otherwise provided
by the user, meal and insulin data entered and glucose value
responses, as well as comparisons to prior meal and insulin data,
whether sensor trajectory matches that of previously installed
sensors (e.g., profiles associated with break-in, changes in
sensitivity, etc.), other concurrent operating modes, and so on.
Other criteria may include, if multiple sensors are present,
whether one or a "main" sensor reading is consistent with others of
the multiple sensors, including situations in which the main sensor
tracks the same hormone or biological analyte as the other sensors
as well as situations in which the main sensor tracks other
hormones or other biological analytes, particularly where such
hormones and other biological analytes are known to bear a
relationship with the hormone or other biological analyte tracked
by the one or main sensor.
[0205] In any case, if the usability of the data matches the one or
more transition criteria, a transition may occur to the new mode,
e.g., a user-dependent calibration mode (step 310). In this mode,
data is calibrated based on an external meter, e.g., using finger
sticks for calibration (step 316).
[0206] In some cases, where a transition to a user-dependent
calibration mode is not immediately performed, but where it can be
determined that such a transition will likely be necessary, the
monitoring device may render a display indicating that an external
meter reading is likely to be necessary. In this way, a user can be
prepared to carry the necessary meter on their person.
[0207] In some implementations, a transition to a new mode may be
postponed by the performance of one or more steps. For example, in
the above example where a transition to a user-dependent mode is
contemplated, an external meter reading may be requested of the
user to provide confirmation of the device self-calibration (see,
e.g., user confirmatory blood glucose data 228 in FIG. 8).
Depending on the results of the external meter reading, the device
self-calibration may be confirmed, the device self-calibration may
be modified or adjusted, or the monitoring device may determine
that a mode switch is necessary.
[0208] Data may then be output accordingly, pursuant to the new
mode. For example, the monitoring device may show different types
of displays based on the mode or based on a setting within the
mode. In a particular implementation, for data with high usability,
numerical values of blood glucose measurements may be displayed.
For data with lower usability, ranges of data values may be
displayed, or the data may appear in a selected color based on the
range. Additional details of systems and methods for displaying
data in different formats are disclosed in U.S. Provisional Patent
Application Ser. No. 61/978,151, which is owned by the assignee of
the present application and herein incorporated by reference in its
entirety. Further details are described below with respect to FIGS.
18A-18D, which are in the context of displays for different
decision-support modes, but which can be extended to different
calibration modes, data transmission modes, as well as other
modes.
[0209] Similar consideration pertain to the reverse transition,
e.g., from user-dependent calibration to device self-calibration. A
usage type may be determined (step 318) as in step 292,
particularly if the usage type has changed since the occurrence of
step 292. Usability of data is determined by comparing determined
data against one or more criteria (step 320), and if the usability
of the data matches one or more criteria, a transition may be
caused (step 322), e.g., back to the device self-calibration mode
(or to another mode).
[0210] In general, if calibration parameters evaluated meet certain
criteria indicative of necessary external reference data for
calibration, the user-dependent calibration mode may be entered. On
the other hand, if calibration parameters meet other criteria
indicative of sufficiency of device self-calibration going forward,
the device self-calibration mode may be entered. In some cases a
user may be prompted before switching to device self-calibration,
and the prompt could be informational or require confirmation by
the user prior to the mode switching.
[0211] A more detailed flowchart 375 is depicted in FIG. 12 for
this aspect. In addition, specific switching criteria from
user-dependent calibration to device self-calibration are also
shown and described.
[0212] In this particular scenario of the reverse transition from
an initial user-dependent calibration mode 324 to a new device
self-calibration mode 326, one criterion which may be employed is
whether or not the user has entered external meter values, or a
sufficient number thereof. If the user has not, then the monitoring
device may transition to a self-calibration mode until an external
meter value, or a sufficient number of the same, are entered, which
may then trigger user-dependent calibration.
[0213] Other criteria which may be employed include any described
above in connection with FIGS. 5-9, as well as other variables and
parameters, particularly those related to a determination (step
328) of the usability of the device self-calibration over time,
e.g., error at calibration, an individual user profile, and the
like. One way to determine the usability of the device
self-calibration is by the evaluation (step 330) of device
self-calibration parameters, which are subject to ongoing
calibration input (step 332). A general goal in certain
implementations is to ensure the reliability, accuracy, and/or
stability of the device self-calibration for an individual patient,
especially the first time a patient uses the device, so that the
same may be safely transitioned into from a user-dependent
calibration mode. Once sensor usability properties have been
determined, e.g., stability, accuracy, reliability, confidence, or
the like, the same may be seeded into internal calibration
parameters for the remainder of the session and even in some cases
for future sessions.
[0214] Accordingly, one transition criteria which may be employed
includes data about whether calibration parameters from external
meter readings are within a particular confidence interval or
otherwise reliable or stable (step 334), e.g., no more than a
certain percentage change, e.g., 5, 4, 3, 2, or 1% over one day,
two days, or the like, and whether the same match those determined
by the device self-calibration. A related transition criteria
include whether calibration parameters are within an expected range
(step 336) based on a priori or predetermined internal measures,
e.g., within the same range as a set of previous patient-specific
calibration parameters, within expected factory determined ranges,
and/or within expected internal testing ranges, e.g., as determined
by impedance measurements. Other transition criteria include
confidence in the user's external blood glucose meter used for
calibration, as well as the user ability to enter a correct reading
from the meter. Such can be determined by analyzing meter data
(step 338) based on analysis of a number of outliers detected
during a calibration routine (step 340), analysis of data about the
meter brand and type (step 342), or the like. In this example, the
criteria for mode switching may be at least in part dependent upon
a number or type of outliers detected (e.g., number of BG reference
values that appear in accurate based on outlier detection
criteria). Yet additional related meter related data include the
uncertainty of the blood glucose meter values (step 344) entered
for calibration based on analysis of a number of factors (step 346)
including the number of values available, e.g., as more values
means a more accurate regression; glucose range, as more data
points spread across a full glucose range again allows a more
accurate regression and subsequent linearity across the range;
and/or rate of change, because time lag between interstitial
glucose and capillary glucose are more noticeable when glucose is
rising or falling. In these examples, the criteria for mode
switching may be at least in part dependent upon a certainty level
threshold of such data.
[0215] In some cases, the determination of whether glucose values
and calibration parameters are within expected ranges may be
determined probabilistically, and details about such methods are
provided in WO 2014/158327A2, owned by the assignee of the present
application and herein incorporated by reference in its
entirety.
[0216] Yet another related transition criteria may relate to the
calibration or confirmation external meter reading value itself,
particularly where the value is the same as a default reading. For
example, in some implementations, a monitoring device may default
to 120 mg/dL for manual entry of blood glucose concentration
values. The monitoring device may be configured to question or to
give a lower confidence in default values that are regularly
accepted, particularly where such are found to be outliers.
[0217] Other transition criteria may include decision-support mode,
e.g., whether the use is adjunctive or therapeutic, or the like.
Yet other transition criteria may be as described above, including
those employing user input (step 348).
[0218] In a particular example of mode switching between
user-dependent calibration and device self-calibration, a user may
start with user-dependent calibration for a first sensor in a four
pack of sensors, where the four pack of sensors are all from the
same manufacturing lot. Once calibration is established for the
first sensor using user-dependent calibration, the next three
sensors in the pack/kit could switch to device self-calibration
based on the calibration of the first sensor in the pack. This
ability is predicated on the likelihood that different sensors
within the same lot have similar or the same specific
characteristics that when used by the same host, will perform in a
similar manner (similar correlations could be made for different
hosts using the sensors from the same lot). As noted, such lot data
may be determined using analysis of metadata.
[0219] As noted above, while mode switching may occur to
user-dependent calibration or to device self-calibration, generally
the latter is preferred if possible due to increased user
convenience. FIG. 13 illustrates a logical diagram and accompanying
chart which show how present principles may be employed to gain
confidence in device self-calibration. In the solution of FIG. 13,
both user-dependent calibration (mode 354) and device
self-calibration (mode 356) are run in parallel. Upon receipt of
inputs or information, when confidence in the device
self-calibration satisfies certain criteria, e.g., exceeds a
predetermined threshold, then no additional external meter readings
may be required for calibration. In this case, the output is
entirely based on the device self-calibration. In other
implementations, the outputs of both algorithms may be combined and
weighted accordingly to arrive at a displayed output.
[0220] In such a parallel system, the output of each calibration
mode algorithm is evaluated against criteria 360, and the accuracy,
reliability, stability, and/or confidence in the different
calibration parameters resulting from each of the two modes running
in parallel is compared, or alternatively, the usability of the
signal from each of the two calibration mode algorithms is
compared, particularly over an optional transition region 358.
[0221] For example, one or both outputs may be compared to incoming
blood glucose concentration values. To ensure accuracy over a
range, the compared blood glucose (external meter) values may be
provided and compared at extreme high and low values 352.
[0222] Other determined signals and transition criteria include any
of the transition criteria described above, but certain types will
be described below as particularly useful in certain
implementations. Such may include internal inputs, such as the
effectiveness of self-diagnostics or internal calibration
information, e.g., impedance. Yet other signal analysis criteria
include comparisons of slope and baseline from user-dependent
calibrations versus slope and baseline from device self-calibration
algorithms. Such may be particularly employed to examine
correlations to a prior session. Such exemplary implementations are
particularly useful in that some patients have certain
physiological differences, such as a typically high baseline, as
compared to patients with, e.g., a low baseline, and such
comparisons may thus be useful in cases where baseline estimates
are made.
[0223] Yet other sorts of signals and transition criteria include
those related to fault discrimination, which may be employed to
determine a type of artifact or the like. During such times of
artifact presence, the user-dependent calibration may not be relied
upon, or its influence may be lowered in the weighting. For
example, if compression artifacts are discriminated, and a blood
glucose meter value is entered into the system at about the same
time, and the device self-calibration mode should be relied upon
more heavily.
[0224] Yet another sort of signal and transition criteria relates
to values and estimated errors in values of calibration parameters,
including calibrated slopes, baselines, and/or drift values for
each algorithm individually (as opposed to the values themselves
noted above). In this implementation, wherein the device
self-calibrated values and user-dependent calibrated values are
roughly equivalent statistically, e.g., such agree within +/-10%,
then it may be considered safe to switch modes, e.g., to drop
user-dependent calibration and only operate the device in a
self-calibration mode. An example of such equivalency is shown in
FIG. 13B, in which axis 365 represents sensor readings and axis 367
represents glucose values. Error bars 369a, 369b, 369c, and
generally 369i represent a range of readings for different glucose
values for which, if a reading falls within the error bars, the
device self-calibration may be said to be valid. The points shown
represent matched data pairs from external measurements, e.g., for
a user-dependent calibration, these points falling along a line
371. As the user-dependent calibration values are within the error
bars of the device self-calibration, the accuracy of each may be
considered equivalent by the system, and consequently the
user-dependent calibration may be dropped in favor of only device
self-calibration for future calibrations during the session (until
and unless a subsequent determination of device or data usability
indicates that user-dependent calibration should once again be
initiated).
[0225] Additional details of such aspects may be found in the WO
2014/158327A2 publication incorporated by reference above.
[0226] As noted above, the signal and transition criteria may be
dependent on other modes, which may dictate that one or another
mode is switched to, or may also indicate that the two modes may
and should continue to run in parallel.
[0227] A weighting may be applied to one or both of the values from
the respective mode algorithms, and weighting of one output may be
greater than that of the other due to confidence in data from the
respective mode. For example, if parameters in the device
self-calibration suggests the same has an 80% confidence level, the
output from the device self-calibration mode may be assigned an 80%
weighting, leading to the value from the user-dependent calibration
being assigned a 20% weighting. This type of weighting scheme 362
could occur iteratively and dynamically over time until one of the
algorithms outweighs the other by so much, e.g., 95% to 5%, that
the weighting itself is a criterion that allows for the triggering
of the new mode. This is seen in FIG. 13A where a user-dependent
calibration mode I is indicated on the left side of the diagram, a
device self-calibration mode II is indicated on the right side of
the diagram, and a transition region III is indicated which is an
average or a weighted average of the outputs of the two algorithms.
The portion of the curve 359 indicates a weighted average, while
the portion of the curve 361 indicates a section in which the
weighting has become heavily towards the user-dependent calibration
mode, and upon meeting certain criteria, e.g., passing a threshold
transition level 364, the monitoring device is caused to completely
enter the user-dependent calibration mode. Similarly, the portion
of the curve 363 indicates a section in which the weighting has
become heavily towards the device self-calibration mode, and upon
passing a threshold transition level 366, the monitoring device is
caused to completely enter the device self-calibration mode.
[0228] Whether a single mode or a combination of modes is employed,
calibration may be performed and sensor data may be output and
displayed based on the calibration mode determined.
[0229] In a variation that combine concepts according to present
principles, a hybrid mode may be entered in which the device
operates under device self-calibration until such time as a user
indicates via an appropriate entry on a user interface that the
user believes the shown value, according to device
self-calibration, is incorrect or off. In this hybrid mode, the
user may enter a fingerstick SMBG value as a check on the operation
of the device under device self-calibration. The value measured and
entered in the CGM may be used as part of the calibration of the
device, and the weighting of the value may decrease as time passes.
Such a value may be used to adjust the factory calibration. In some
implementations, no additional SMBG values need be entered. In
other implementations, subsequent SMBG values may be entered, e.g.,
every 12 hours, every 24 hours, and so on. Such calibrations may
serve as an iterative update calibration, increasing the accuracy
of the CGM reading.
Scheduled and Unscheduled Modes of Data Transmission and
Display
[0230] Referring to the flowchart 425 of FIG. 14, another exemplary
type of mode switch involves switching between different data
transmission modes. In one implementation according to present
principles, one data transmission mode is termed "unscheduled" and
another is termed "scheduled". Scheduled transmission of a sensor
transmitter to a receiver or smart phone (or, e.g., a wearable
device such as a watch) generally correspond to regular periodic
transmissions of data for display and analysis, though the same
encompass non-periodic transmissions as well. Unscheduled
transmissions may be broken down into event-driven unscheduled
transmissions and user-driven unscheduled transmissions.
Event-driven unscheduled transmissions may occur when the device
notices a trend for which the user should be made aware, e.g., an
impending hypoglycemic or hyperglycemic event. User-driven
unscheduled transmissions may correspond to "on demand" types of
transmissions, e.g., where the user pulls data from the sensor and
transmitter (or receiver/smart phone) because they wish to view
their current status. In some cases pulled data is desirable
because it minimizes battery drain. However, such modes do not give
the user the most information, unless even driven unscheduled
transmissions are also enabled.
[0231] While automatic transmission of periodic data may provide
more timely data to a user without required action by the user, it
can significantly drain the battery power of on-skin sensor
electronics connected to the on-skin device (as well as the
receiver), which may create difficulty in miniaturizing the
electronics sufficiently for comfortable wear and inexpensive
manufacture. On the other hand, on-demand transmission glucose
sensor data may also be employed, e.g., responsive to user
requests, e.g., using near field communication. Unfortunately,
on-demand transmission does not allow for timely alerts and/or
alarms to be presented to the user if they do not request data at
the appropriate time.
[0232] One solution is disclosed below is to provide dynamic
switching between on-demand and automatic transmission modes of
operation. In this way, both of the requirements noted above can be
satisfied. In particular, switching occurs between a data
transmission mode that stores or buffers data for on-demand
transmission triggered by user request and a mode of automatic
transmission of data triggered by a condition, e.g., a large
excursion, the meeting of alert criteria, a glucose concentration
value within a glycemic danger zone, or data delivered at a
predetermined or user set frequency, e.g., once every 5 minutes,
once every 15 minutes, once every half-hour, once every hour, or
the like.
[0233] The mode switching may occur either at the monitoring device
or at the sensor electronics. In the first case, the monitoring
device can send a signal to the sensor electronics to indicate,
prompt, or cause the mode switch. The data generally requires
presence at the monitoring device for such a determination to be
made. However, the determination to switch modes may also occur at
the sensor electronics, i.e., where the signal is initially
received from the in vivo sensor.
[0234] In a first step of the method, the monitoring device starts
in a first or initial mode of data transmission, which may be the
mode the device initializes in, a mode the device has switched to
in a prior step, or the like. In FIG. 14, an exemplary initial mode
of on-demand data transmission is shown (step 368). Data is
transmitted according to this mode (step 372) from the sensor
electronics operably connected to the on-skin sensor device, and
the same is rendered on the monitoring device display. In on-demand
data transmission, sensor data is generated responsive to a request
by the monitoring device, e.g., a user-prompted request.
[0235] In a next step, data is evaluated against one or more
criteria (step 376), and if the criteria, or, e.g., threshold
associated with the same, is met, a mode switch occurs (step 370),
in this example to a periodic or automatic mode of user
interaction. Data may then be transmitted according to the new mode
(step 374).
[0236] The reverse mode transition or switch can also occur, where
in the periodic or automatic transmission mode, data is evaluated
against one or more criteria (step 386), and if the requisite
criteria is met or matched, a transition may be made to the
on-demand mode of user interaction. Hybrid modes are also
envisioned.
[0237] The criteria applied may be any of those noted above with
respect to FIGS. 5-9, and certain exemplary such criteria are noted
below.
[0238] One transition criteria of particular use in data
transmission mode switching includes analysis of large excursions
(step 378), which may be point-to-point changes in glucose
concentration values above a certain change threshold, or similarly
large excursions in rate of change or glucose acceleration as
compared to thresholds. Predictions about such values may also be
used in these calculations. Determining that a glucose
concentration value is in a glycemic danger zone (step 382) may
also be employed, where upon a user's glucose concentration value
entering such a danger zone (where the edge of the danger zone
represents a criterion such as a threshold), a mode switch may be
caused to happen. Similar criteria may be defined for GUI
thresholds or other glucose-related values. Notably, the device
initiated transmission may be to a dedicated receiver/phone and/or
to the "Internet of Things" depending on user preferences.
[0239] Another transition criteria of particular use in data
transmission mode switching includes the amount of time since a
user last requested a value. For example, a determined criterion
may be the amount of time since a last user request for data, and a
transition criteria may be a threshold duration such as eight
hours. If the determined data is greater than the threshold
criterion, e.g., if it has been more than eight hours since the
last user request for data, a mode switch may occur, e.g., to an
automatic data transmission mode.
[0240] Another transition criteria which may be applicable to
transmission mode switching involves an analysis of alert criteria
(step 380), e.g., user's responses to alerts or prompts provided on
the user interface of the monitoring device. For example, where a
mode switch may be potentially indicated, a user could be asked to
confirm data on which the mode switch was predicated. A lack of
such confirmation, or a contrary indication, may lead to the
potential mode switch being suppressed or otherwise not
performed.
[0241] Other transition criteria 384, as noted herein, may also be
employed. In particular, data about other concurrent or parallel
modes may be employed to inform data transmission mode switching.
For example, whether the user is in a therapeutic mode versus an
adjunctive mode, or whether a monitoring device is calibrated on
the basis of device self-calibration versus user-dependent
calibration. The decision-support mode may be particularly useful
criteria to evaluate for mode switching in data transmission. For
example, the system may detect that it is being relied upon for a
portion of a closed-loop algorithm. In such a mode, automatic
transmission may be a default setting, so that the monitoring
device receives information needed to drive medicament delivery
pumps. On the other hand, in an adjunctive mode, different
transmission criteria may apply. For example, where the monitoring
device is only being employed for educational purposes, an
on-demand transmission mode may be sufficient. Transmission
criteria may also be based on phase or mode where such are provided
as different phases of control of an artificial pancreas system as
illustrated in FIG. 15A.
[0242] Yet another transition criteria which may be employed,
particularly in a transition or mode switch from an automatic data
transmission mode to an on-demand mode, relates to analysis of data
(step 388) about remaining battery power. For example, if sensor
electronics are running low on battery power, so long as the system
can determine the user is protected and otherwise within a safe
glycemic state (and appears likely to remain so), an on-demand mode
of user interaction may be entered in order to conserve remaining
battery power.
[0243] Specific examples are now provided.
[0244] In one example, the device may start in an on-demand mode,
but if the user's glucose is in a dangerous zone for a
predetermined period of time, then the device may switch to an
automatic transmission mode. Such a mode may be maintained until,
e.g., a user acknowledges an alert or actively requests to switch
back to on-demand mode. In another example, the device may start in
an automatic mode, but if the user's glucose stays within a desired
range for at least, e.g., four hours, then the device may switch to
on-demand mode. The on-demand mode may be maintained until the
user's glucose concentration value travels out of range, is
predicted to go out of range, or otherwise enters an undesirable
state.
[0245] In another example, mode switching may occur based on the
use to which the user desires to put the data. In this regard it is
noted that certain analyte monitors employ `beacon` signals which
emanate from the transmitter and which a receiver device, e.g., a
dedicated device, smart phone, or the like, may then engage. Such
beacon signals are disclosed in, e.g., US PGP 2013/0078912 and US
PGP 2015/0123810, owned by the assignee of the present application
and herein incorporated by reference in their entireties.
[0246] For simplicity the case of a smart phone will be discussed,
but the situation for a dedicated receiver is similar. The smart
phone may choose to engage the transmitter upon receipt or
detection of a beacon signal, and following a handshaking step data
may be transferred. In some cases, a mode may be entered in which
communications do not occur with every beacon signal. For example,
in the case of a user employing the CGM monitoring for purposes of
health or fitness optimization, or weight loss, it may not be
desired to receive data with every beacon signal. A benefit to not
receiving transferred data with every beacon signal includes
battery life, but other benefits will also be understood.
[0247] To enable the above, the system may be configured such that
the smart phone does not even poll for beacon signals for a certain
period of time, e.g., over the period of several beacon signals.
Alternatively, the transmitter may enter a mode where it does not
emit a beacon signal for a period of time. For example, instead of
emitting a beacon signal every 5 minutes, it may emit one only
every 15, 30, or 60 minutes. Such data will generally have less
resolution and less actionable, but depending on the use to which
the data is put, such resolution may be sufficient.
[0248] In another example, in an on-demand mode, if the user
desired to base therapy on the data, and if the user was employing
a communications scheme such as NFC, the user may perform a device
`swipe` which would be itself begin to enable the user to receive
higher resolution data. First, the swipe would result in an initial
higher resolution of data. Second, the swipe may in some
implementations be used to indicate to the system that higher
resolution data should be obtained.
Decision-Support Modes, e.g., Therapeutic (Non-Adjunctive) Versus
Adjunctive (Non-Therapeutic) Mode Switching and Mode Switching
Between Phases Therein
[0249] Yet another exemplary type of mode switching involves
switching between different decision-support modes, e.g.,
therapeutic, adjunctive and/or phases of control of an artificial
pancreas system as described with reference to FIG. 15A. In more
detail, currently all commercial CGM devices in the US are used
adjunctively, which means that the device does not replace the
information obtained from a standard home blood glucose meter but
rather is used to complement the information obtained from the
blood glucose meter. Patients are instructed to make therapy
decisions, e.g., insulin dosing, based on the meter value, rather
than the CGM value. For patients, it would be preferable for a
device to be used therapeutically, which means the information
obtained from the CGM can be used to determine therapy decisions,
e.g., insulin dosing, for the patient with diabetes.
[0250] One issue with providing varying levels of control based on
CGM usage is that the performance of the device, and the use of the
information from the device, may differ from patient to patient.
Even in a device specifically intended for therapeutic use, it
would be advantageous to detect and trigger an adjunctive mode if
the usability of the data for a particular patient does not meet
certain standards, e.g., where the usability relates to factors
noted above, e.g., accuracy, stability, reliability, or confidence
in the data. A solution is to evaluate the usability of the sensor
data and to switch between therapeutic and adjunctive modes in real
time accordingly. As described in greater detail below, the system
may be further extended to include levels of therapeutic control,
e.g., as described by phases in an artificial pancreas system as
described in connection with FIG. 15A.
[0251] More generally, the therapeutic/adjunctive paradigm may be
broadened to a spectrum of levels of control of pump function, from
an adjunctive mode where the CGM does not control the pump in any
way, up to a point where analyte monitors control all medicament
delivery, e.g., insulin as well as others, and no patient
involvement is needed. In one exemplary implementation, the
spectrum of levels of pump control may be the phases shown in
diagram 445 of FIG. 15A. In this diagram, the phases progress from
the system exerting no control to the system exerting the most
control.
[0252] FIG. 15B illustrates schematically an exemplary such
artificial pancreas system 411. In this system, control is exerted
to various extents in respective various phases (in the
implementation of FIG. 15A, for phases above and including phase
1). Generally, a control system 421 receives a signal from an
analyte sensor 415 in a biological system 413 and exerts control
over the analyte concentration in the biological system by
controlling intake of one or more substances into the biological
system using, e.g., a medicament administration device 417. The
medicament administration device 417 may include, e.g., a pump, an
IV, and/or one or more other devices which can controllably
administer a substance into a body. In an artificial pancreas
system, the analyte sensor may be that associated with a continuous
glucose monitor, and the one or more substances may include insulin
administered by pumps or injections. In a more advanced system,
other analytes may be monitored including insulin, and the one or
more substances may include, e.g., glucagon. Extending beyond the
artificial pancreas, control may be extended in this way to other
hormones besides insulin. While the control system 421 is
illustrated in FIG. 15B as being situated within a display system
419, the same may also be situated as part of the sensor and more
particularly as part of sensor electronics (see control system 421'
within analyte sensor block 415) or as part of the medicament
administration device 417 (see control system 421'').
Alternatively, control system modules within two or more of these
blocks may work together to accomplish both the control required of
the phase and the potential mode switching according to
implementations described here.
[0253] The use of an artificial pancreas system according to FIGS.
15A and 15B in the context of the described mode switching
implementations can include the use of the mode switching
implementations to drive, base, or inform switching between the
various described phases of FIG. 15A (or other phases which may be
developed). The use can also include use of the operating mode or
phase to drive, base, or inform display on a user interface of the
display system 419. In particular, the display on the user
interface may include an indication of the monitored analyte, as
well as in some implementations an indication of the mode or phase
in which the system is operating, i.e., the mode of user
interaction. For example, the user interface may display the
glucose concentration as well as an indication that the artificial
pancreas system is controlling for hypoglycemia but not
hyperglycemia. In another example, the user interface may provide
an indication that the user is, e.g., in "Phase 6" and that all
control is currently being provided by the system. Numerous
variations will be understood given this teaching and the examples
provided below.
[0254] The display system 419 is described in greater detail below
with respect to FIG. 18 and FIG. 24, but here it is noted the same
may constitute a dedicated receiver or a general purpose device
such as a mobile device, e.g., a smart phone.
[0255] Referring back to FIG. 15A, in an initial phase, i.e., Phase
0, shown by reference numeral 449, the use may be adjunctive only.
In other words, the CGM is not used for any sort of pump control.
Significant levels of information may still be provided in this
phase, as well as in the other phases. However, such information
may be indicated on a user interface so as to be only for tracking
purposes, determining trends, or for educational purposes. For a
user who is pre-diabetic, phase 0 may be the only phase needed.
[0256] In a next phase, i.e., phase 1, shown by reference numeral
451, an insulin pump may be controlled so as to turn off at times
when the user is encountering low glucose levels. In phase 1 and
subsequent phases, it will be understood that while pump actions
are disclosed, the same may be accomplished by an indication on a
user interface of the monitoring device directly employable by a
user for dosing, e.g., by injection, ingestion, or the like.
[0257] In a subsequent phase, i.e., phase 2, shown by reference
numeral 453, phase 1 may be enhanced, e.g., by allowing
hypoglycemia predictions to occur, and causing alarms when such
conditions are present or likely to occur. If such alarms go
unheeded, a phase 2 system may cause a reduction or cessation of
insulin if the user's glucose level is below a threshold.
[0258] In a next phase, i.e., phase 3, shown by reference numeral
455, phase 2 may be enhanced, e.g., by including a step of insulin
dosing when the user's glucose level passes above another
threshold, i.e., a high threshold. In a subsequent phase, i.e.,
phase 4, shown by reference numeral 457, the system may be
essentially closed loop, except for mealtime manual assist
bolusing. In particular, the system may cause insulin reduction or
cessation at low glucose levels, and insulin dosing at high glucose
levels. However, due to glucose variability at meal times, bolusing
at such times may be performed manually.
[0259] In a next phase, i.e., phase 5, shown by reference numeral
459, such mealtime manual assist bolusing may be removed, and the
system may be closed loop for insulin. In a final phase, i.e.,
phase 6, shown by reference numeral 461, the closed loop system may
be extended from just insulin to contemplate and control other
hormones as well, for a closed loop multi-hormone system.
[0260] In one implementation, therefore, the above mode phases may
be employed in a control scheme, and mode switching may occur
between the various phases. While the modes or phases or have been
described in the order of least control to most control, there is
no requirement that the system proceed in such an order (in either
direction) in the control of a therapy. Each mode or phase is
stand-alone, and may be entered or exited independently according
to the determined data and mode switching criteria. For example,
the system may transition from phase six to phase zero, and more
generally from phase i to phase j, where i and j are any of the
phases zero to six.
[0261] Mode switching between phases is illustrated in greater
detail by the flowchart 450 of FIG. 15C. An exemplary initial mode
or phase is posited (step 392). Data is transmitted according to
this mode (step 394), and such may indicate (step 428) the use of
the data. If the phase has a therapeutic component, the display may
so indicate. For example, the UI may indicate that the data
determined may be used to treat diabetes without confirmatory
external meter values for, e.g., insulin dosing. In so doing, the
data may be employed by the user to determine insulin dosing in the
context of a bolus calculator or the data may be directly
transmitted to an integrated pump controller. In the language of
artificial pancreas phases, the UI may indicate the current phase,
e.g., "Phase 1--System Will Stop Insulin at Low Glucose".
[0262] In a next step, data is evaluated against one or more
criteria (step 402), and if the criteria, or a threshold associated
with the same, is met, a mode switch occurs (step 396), in this
example to a second mode, e.g., a different phase. For example, the
mode may switch to Phase 0, and the UI may so indicate by a
displayed notation such as "Phase 0--Readings are for Adjunctive
Use Only". Data may then be transmitted according to the new mode
(step 398). Such may include displaying the data to a user in such
a way to indicate its usability according to this second mode or
phase (step 412), e.g., in the above example where Phase 0 was
switched to, the display may indicate that other information such
as external meter values should be used to make actual treatment
decisions for the user's diabetes. In this specific example, the
data displayed may also provide an indication of user interaction,
e.g., that CGM data may not be relied on fully for calculating
insulin dosing, whether used in a bolus calculator or in an
integrated pump controller.
[0263] The reverse mode transition or switch can also occur, where
in the second mode or phase, data is evaluated against one or more
criteria (step 414), and if the requisite criteria is met or
matched, a transition may be made back to the first mode or phase
or to a third mode or phase.
[0264] The criteria applied may be any of those noted above with
respect to FIGS. 5-9 (step 410), including data usability, and
certain such criteria are described in greater detail below.
[0265] For example, therapeutic use for a pre-diabetic user or
patient may only require relative confidence that the display is
providing an accurate zone of glycemia and/or an accurate post-meal
rate of change. So long as these are believed to be accurate to a
predetermined criterion, e.g., a threshold level, then the device
may be essentially therapeutic in nature. In contrast, for an
intensively-managed insulin-dependent type I (or II) diabetic, the
therapeutic mode may have criteria that requires a certain level of
accuracy or confidence in the full range of glycemia (40-400 mg/dl)
and at all physiological rates of change.
[0266] As a particular example, in a therapeutic mode, a user may
be instructed to examine the display device, e.g., receiver or
smart phone, and verify that at least three acceptable data points
in a row have been received immediately prior to the time period in
which the therapeutic decision is to be made, e.g., dosing. Instead
of user examination, the system may automatically only make a
decision recommendation if the three immediately prior consecutive
values exist, have been reported, or are "good" or "satisfactory"
according to a predetermined criteria. For example, the previous
values may be prior "five-minute" values, where values are
transmitted from the sensor and received in the receiver every 5
minutes. Additionally, based on prior sensor data values, a trend
of the glucose value may be determined and displayed, e.g., a trend
arrow may be displayed indicative of whether the glucose value is
rising or falling (and may also indicate a rate of change). Such
aspects are important in the determination of a therapeutic
recommendation, and thus by requiring the same an additional level
of safety is achieved. In one specific implementation, three
immediately prior trend points are required, along with a trend
arrow and a sensor glucose reading, before therapeutic decision
support is allowed based on the CGM data. In some cases, in the
absence of such, the adjunctive or non-therapeutic mode may be
entered. In other cases, again in the absence of such data, the
therapeutic mode may be maintained, but with an appropriate warning
to the user, or alternatively an indication as to how clinically
actionable the data is, e.g., whether dosing may be based on the
same or not.
[0267] While three immediately prior consecutive values have been
disclosed above, it will be understood that the number of values,
their timing, and whether they are consecutive may vary, and in
general the prior values may be such that a trend can be
determined.
[0268] The trend or data points discussed above are generally
displayed on a graph, and user examination can reveal if the
immediately prior three data points appear and are consistent with
other trend data or user expectations. Such examination and
determination are easily performed on a receiver or smart phone or
other such device with a similar form factor. However, if the
device is smaller, such as the user interface on a smart watch,
screen real estate may be insufficient for display of a trend
graph. In this case, trend arrows may be displayed, along with the
current glucose value, and the same employed by the user for
therapeutic decision support. The slope of the arrow may indicate
the velocity of the analyte concentration value trajectory, or the
same may be indicated with multiple arrows, e.g., one up arrow for
a slow rise, two up arrows for a moderate rise, and three arrows
for a rapid rise (with corresponding down arrows for decreasing
analyte concentration values). In another variation, the user
interface of the wearable device, e.g., smart watch, may directly
indicate the clinical or therapeutic actionable nature of the data.
For example, the user interface itself may indicate whether it has
received immediately prior data values, particularly of sufficient
quantity to indicate a trend, e.g., by comparing the number of
values it has received with the number of values it should have
received given the known transmission rate of the data from the
sensor.
[0269] In light of the above, one transition criteria of particular
use in mode switching between therapeutic and adjunctive modes, or
between phases of control as in FIG. 15A, includes a step of
determining decision-support use (step 403), which may in turn
control the criteria for determining usability of data (step
402).
[0270] Another type of data and accompanying transition criteria
which may be advantageously employed in certain implementations of
such mode switching include those related to detected faults, e.g.,
the likelihood of an "end-of-life" or other failure mode. Such
faults may be detected by comparing current data to known failure
modes as identified by signatures or patterns in trace data.
Additional aspects are described elsewhere and in U.S. Provisional
Patent Application Ser. No. 62/009,065, incorporated by reference
above.
[0271] Another aspect which may bear on criteria employed includes
use (step 424) of preset default modes. That is, modes may default
to predefined ones based on factors such as, e.g., time of day,
glucose level, or the like. As one example, a user may desire that
their device be used adjunctively during the day and
therapeutically at night, e.g., with varying levels of control and
more granularly as the phases shown in FIG. 15A, e.g., to warn and
control against low glucose at night (phase 2), but to give the
user more control during the day (only phase 1).
[0272] Yet another type of data and accompanying transition
criteria include those relating to glucose context, e.g., whether
the glucose context is nighttime hypoglycemia, adjusting insulin
dosing, or other such contexts. For example, for the same patient,
a therapeutic mode, e.g., phase 1 control, may be used during
nighttime hypoglycemia whereas an adjunctive mode may be used when
determining insulin dosing so as to treat hyperglycemia.
[0273] The evaluation against transition criteria could further
employ internally detected parameters to detect shifts in
sensitivity including those from self-diagnostic routines, e.g.,
using impedance to detect shifts in sensitivity as discussed above
and further in the applications incorporated by reference above.
The same may be performed on a regular basis or when an error is
perceived.
[0274] Another type of data and accompanying transition criteria
which may be employed involves the use of user-perceived error
(step 408). As described above in the context of other types of
mode switching, if a user specifically indicates an error or
provides a greater than average number of external meter values, or
provides external meter values without prompting, a user-perceived
error may be inferred. Accordingly data usability may be decreased
and/or the user may be prompted to enter one or more reasons as to
the perceived errors.
[0275] The evaluation may include a comparison of current or recent
data to historic glucose concentration values, e.g., ranges, means,
patient specific profiles, or the like. If the data does not follow
the patient's individual normal glucose profile according to the
criteria, e.g., within a threshold, the patient may be prompted as
to whether they believe the data to be unusually high or low,
whether the patient otherwise perceives an inaccuracy, e.g., feels
that an alert or alarm constitutes a false alarm, or whether the
patient has a reason explaining an unusually high or low or
abnormal reading. Similarly, an option may be provided on the user
interface for the user to flag outlier data.
[0276] In the same way, the user interface could provide an option
for the patient to state whether they feel they have high blood
sugar or low blood sugar, and the monitoring device may perform a
system check to determine whether the determined glucose
concentration value is consistent with the feeling. Such an option
may also be employed to identify hypoglycemia unawareness in a
patient, e.g., by asking whether the patient feels low when CGM
data shows that the patient has entered a hypoglycemic range.
[0277] In a similar way, the monitoring device could identify an
event, e.g., such as high or low glucose, and ask the user to
identify feelings associated with the current event, or potential
causes for the event. In both cases, the user may select from a
list of potential feelings or causes.
[0278] In some cases, the monitoring device could prompt for a
confirmatory blood glucose meter reading. Such a meter reading
would not necessarily be used for calibration, but as confirmation
of a reading from the CGM. The value may be used to adjust
calibration parameters or just for information.
[0279] The evaluation may depend on other parallel or concurrently
running modes (step 412). In particular, transition criteria or
triggers may be modified depending on such other modes. In one
case, a data transmission mode may be used in the determination of
decision-support mode switching. In another case, a calibration
mode may be used in the same determination.
[0280] User-selected responsiveness may also be employed as
described above. In particular, users may select whether they
prefer smooth data with a time lag or noisy data that is more
responsive to actual glucose changes.
[0281] Whatever the criterion or criteria used, if determined data
satisfies the criteria or criterion, e.g., meets, matches, or
exceeds a threshold level, or in another way satisfies the criteria
or criterion, mode switching may occur. In some cases, the subject
mode switching, e.g., therapeutic versus adjunctive, or from one
phase to another, may be accompanied by other mode switching, e.g.,
device self-calibration versus user-dependent calibration.
[0282] Whether to switch modes, or what mode to switch to, may also
be based on a glucose range the patient is currently in. Put
another way, range-dependent triggers may be employed (step 406) to
inform mode switching. For example, a first mode may be triggered
only for hypoglycemia, while a second mode is triggered if the
patient is hyperglycemic.
[0283] FIG. 16A is a flowchart 700 illustrating a particular
implementation of mode switching between different phases of an
artificial pancreas system as described in connection with FIG.
15A. In this figure, an initial phase (I) 712 is illustrated which
upon occurrence of a trigger 716 transitions the operating mode to
phase (J) 714, generally with a new mode of operation and a new
mode of user interaction, particularly as displayed on a user
interface of the monitoring device.
[0284] FIG. 16B illustrates a particular set of transitions between
phases. For example, a system in phase 6 (802) may be caused to
transition to phase 5 (804) upon the occurrence of a particular
hormone's sensor failing. A system in phase 5 (804) may be caused
to transition to phase 4 (806) upon the occurrence of an event such
as user detection of perceived errors, especially ones that relate
to the accuracy of mealtime highs. A system in phase 4 (806) may be
caused to transition to phase 0 (808) upon the occurrence of a
lowering of signal usability, because the lowered signal usability
may cause an accurate prediction to no longer be possible. As
another example, a system in phase 0 (808) may be caused to
transition to phase 1 (810) upon the obtaining of a signal of
greater data usability, allowing the system to obtain accurate
readings again.
[0285] Numerous other types of transitions will similarly be
understood given these teachings, and a non-exhaustive set of such
transitions are shown below in Table I.
TABLE-US-00001 TABLE I TRIGGER(S) (exemplary and ORIGINAL MODE
non-exhaustive) NEW MODE First level of user Change in usability of
data Second level of user interaction interaction User-dependent
Increasing usability of self- Device self-calibration calibration
calibration (factory) Device self-calibration Fault detected in
calibration User-dependent calibration (factory) On-demand
information Gui increased, entry into Regular or periodic pushed
glycemic danger zone, entry information into hypo/hyper states
Regular or periodic Gui decreased, exit from On-demand information
pushed information glycemic danger zone, exit from hypo/hyper
states Adjunctive Increasing Usability Of Data, Therapeutic
Increasing Confidence In Data, User Desire For Therapeutic Support
Therapeutic Decreased Signal Usability, Adjunctive Fault Detected,
User Choice Adjunctive & user- Increased signal usability &
Therapeutic mode, device dependent calibration increased confidence
in self-calibration mode, or both modes calibration On-demand
information Fault, decrease in signal Pushed information, user-
& device self-calibration usability, entry into glycemic
dependent calibration, or both danger zone, increased gui, entry
into hypo/hyper states Phase 6 Analyte sensor (not insulin) fail
Phase 5 Phase 6 All analyte sensors decrease in Phase 0 signal or
data usability Phase 5, 4, 3, 2, 1 Glucose sensor decreases in
Phase 0 signal or data usability Phase 5 User detection of
perceived Phase 4 errors Phase 4 Decrease in signal usability Phase
3, 2, 1, or 0 (depending on signal usability) Phase 0 Increase in
signal usability to Phase 1 point where analyte concentration value
is trusted but not predictive value
[0286] This process responsiveness (of mode or phase to criteria
such as signal usability) may be implemented in some cases by
causing different commands to be sent to an insulin pump
controller. For example, and referring to the flowchart 475 of FIG.
16C, in a mode or phase that is at least partially therapeutic
(step 430), pump control may be exerted at least partially under
control of the monitoring device (step 436). And as noted if data
usability becomes very low, the monitoring device may be caused to
transition to an adjunctive mode (step 432) where the monitoring
device does not control the pump.
[0287] According to, e.g., the sequence of FIG. 15A, various levels
of pump control may be exerted short of full pump control.
Alternatively, even if data usability is high, a user or clinician
may choose to exert, or the monitoring device may be configured to
switch to a mode exerting, a varying or reduced level of pump
control.
[0288] The levels of pump control exerted in step 436 may be
considered to select an aggressiveness of the pump control.
Specific examples of such aggressiveness levels are now
described.
[0289] A number of actions are described within FIG. 16C, and many
of these are described as "do or do not" perform a given step,
e.g., treat hypoglycemia, control to target, calculate bolus, etc.
As part of a procedure within phase 0, the "do not" portions of
these steps may be generally employed, e.g., do not allow
hypo/hyper minimizer (step 438), do not suspend for low glucose
(step 440), do not calculate bolus (step 442), do not treat
hyperglycemia/hypoglycemia (step 444), do not control to range
(step 447), and do not control to target (step 448).
[0290] The "do" portions of the steps may then be enabled to
perform one or more functions within a system for phased
therapeutic control, e.g., as noted with regard to FIG. 15A. For
example, to accomplish phase 1, the system may be configured to
suspend pump actions for low glucose concentration values (step
440). To accomplish phase 2, a level of prediction may be enabled,
and alarms may be caused if hypoglycemia is predicted. The same may
be accompanied by a reduction or cessation of insulin below a
predetermined threshold.
[0291] To accomplish phase 3, the system may be configured to allow
a "hypo-hyper minimizer" (step 438), where the same is a
closed-loop system that only controls insulin using sensor data at
low and high glucose levels, rather than using the same also within
target ranges. To accomplish phase 4, the system may be configured
to calculate a bolus (step 442) during mealtime. In this way the
system acts in many ways closed loop but allows additional user
control during particularly uncontrolled times, e.g., meal times
area
[0292] To accomplish phase 5, the system made more generally be
configured within to treat hyperglycemia and hypoglycemia, e.g., by
dosing insulin and, e.g., glucagon, as appropriate. Finally, to
accomplish phase 6, other hormones may be employed to provide even
greater balance and stability to the patient's biochemistry. In so
doing, the system may control to a target value of the hormone
(step 448), or alternatively to a target range (step 447).
[0293] Within these phases, it will be understood that the exerted
level of pump control may vary. For example, closed-loop control
such as available in phases 4-6 may in certain implementations be
used only within certain glucose ranges, e.g., 20-70 mg/dL. Ranges
may be individually-controlled based on certain criteria and
whether the mode is adjunctive or therapeutic or based on the
phase. For example, depending on the evaluation step, there could
be different criteria in adjunctive mode for treating hyperglycemia
versus hypoglycemia.
[0294] FIG. 17 is a flowchart 500 illustrating processing and
display changes which may be caused or performed when mode
switching (step 456) occurs between modes. For simplicity, FIG. 17
contemplates a therapeutic mode 452 and an adjunctive mode 454,
although similar UI aspects can be extended to other types of mode
switching as well as switching between phases as described above.
Generally, the way in which data is displayed on the user interface
may be changed to indicate at least in part the usability of the
data (step 462).
[0295] In one example, an actual glucose concentration value may be
selected to be shown versus a zone of glucose concentration values
(step 464). This situation is also illustrated in FIGS. 18A and
18B, where the figures show a monitoring device 560 having a
glycemic range 562 illustrated by a color indication, as well as an
indicator 564 of a trend, in FIG. 18, a downward trend. In FIG.
18A, a numerical value 566 is illustrated, showing a glucose
concentration value of 75 mg/dL, while in FIG. 18B, no such value
is displayed. In the former, the confidence level or usability of
the data satisfies a criterion, e.g., is greater than a threshold
level, allowing the data to be illustrated. In the latter, the
confidence level or usability has not met the criterion, e.g., is
below the threshold, and thus display of the numerical value is
suppressed. In both cases, the confidence level or usability of the
data about the glycemic range, as well as the rate of change,
satisfies the criterion, e.g., is higher than the threshold, and
thus this data is present on both exemplary user interfaces.
[0296] Referring back to FIG. 17, in another implementation,
different levels of advice or help, e.g., such as displayed help
from an avatar or other on-screen display, may be provided to a
user (step 466). For example, in a therapeutic mode in which a
bolus may be enabled to be calculated, an avatar may inform the
user about an amount of insulin directed to be pumped. In a mode
employed for educational purposes, an avatar may provide not only
glucose data, but a significant level of suggestion and information
about a current glycemic range, or other data which may be useful
to a user in developing a knowledge of CGM operation and disease
management.
[0297] Additional details of systems and methods for use of avatars
are disclosed in US Patent Publication No. 2010/0261987-A1; and US
Patent Publication No. 2014/0184422-A1, both of which are owned by
the assignee of the present application and herein incorporated by
reference in their entireties.
[0298] In yet another implementation, an avatar may be employed to
appear and inform the user about how they should be using their
device (step 478). In an implementation, a user may be required to
acknowledge receipt of such data before viewing glucose data again.
Such an implementation may be particularly advantageous where it
can be deduced that a user is not using their device correctly or
is not consulting the same frequently enough.
[0299] In another implementation, colors or other visual effects
may be employed to indicate mode and/or usability of determined
data (step 468). For example, and referring to FIGS. 18C and 18D,
FIG. 18C shows a monitoring device 560 in which one mode is
indicated by a widely spaced vertical line pattern 568. FIG. 18D
shows the same monitoring device 560 in which a different mode has
been entered, and the same indicated by a different (more narrowly
spaced) vertical line pattern 572. In both cases, a common glucose
reading 570 is displayed on the background pattern. Using this
implementation, a user can quickly and visually identify the mode
their device is in. Patterns may be implemented which represent
multiple modes as well. For example, a pattern may represent a
therapeutic mode in which user-dependent calibration is performed
while another pattern may represent that an adjunctive mode is
operating in which device self-calibration is employed. Other
combinations and implementations will also be understood given this
teaching, as is described above and in applications incorporated by
reference.
[0300] The level of data usability may be indicated in other ways
as well, e.g., by the altering of a data indicator on a user
interface to indicate a level of data usability. In one example,
the displayed glucose concentration values themselves may be caused
to flash or be steady to indicate usability, e.g., flashing data
may be associated with a lower usability, e.g., may have less
reliability or a lower confidence level, than steadily-rendered
data. Another way to indicate usability by altering a data
indicator includes altering another aspect of a chart display (step
472), as shown in the inset chart 475 in FIG. 17, in which points
477i represent glucose concentration values on an axis 471 and
which are plotted against time on axis 473. The size of a dot
representing glucose on the trend graph may be made larger when its
usability is less, e.g., where its corresponding data is of a lower
confidence. In this way the dot size indicates range information,
i.e., the size of the dot can represent the range. Such may then
show a difference between more usable (in this case reliable) data
having a value of, e.g., 78 mg/dL, versus less usable data, having
a value within a range of 60-90 mg/dL. Other variations will also
be understood, including the use of large dots to indicate greater
data usability.
[0301] Another method to indicate data usability on a user
interface is to show ranges versus numbers in a selective fashion
as shown by the chart 525 of FIG. 19. In this chart, certain data
492 are illustrated by dots while other data 490 are illustrated by
ranges of values in a way similar to error bars. The ranges and
dots are indicated on the x-axis as being of high data usability,
e.g., in a high confidence range 484 and 488 (the dots 492), or as
being of low data usability, e.g., in a low confidence range 486
(the range bars 490).
[0302] In another implementation, a subset of data may be displayed
(step 470) in order to indicate, e.g., a lack of confidence in
certain un-displayed information. In other words, a restricted
information display may be used to show, e.g., a rate of change
arrow, but not an actual glucose concentration value. In this way,
data is not displayed unless it satisfies a criterion for
usability. Such an implementation may be similar to the information
displayed on the monitoring devices of FIGS. 18A and 18B.
[0303] Referring back to FIG. 17, in yet another implementation, a
change in the prediction horizon may be displayed where the same
depends on the usability of the signal (step 476). The change in
prediction horizon may include whether the device employs a
prediction horizon, whether the device displays a prediction, as
well as whether alerts or alarms are employed based on such
predictions. In particular, the prediction horizon may be varied
from 10, 20, or 30 minutes in the future, depending on the
confidence of the prediction, which may be affected by
usability-related parameters and variables such as signal quality,
confidence in a calibration, confidence in resulting data,
acceleration, or the like.
[0304] In the particular case of the adjunctive mode, the display
may continue to show glucose concentration values, but an alert or
alarm screen could indicate a warning that external meter values
should be used to confirm CGM values prior to dosing (step
482).
[0305] In one specific example, an insulin bolus calculator on the
monitoring device may run in an adjunctive mode or therapeutic mode
based on the relative confidence in the CGM data, which in turn can
be part of a transition criterion or criteria. For example, if the
confidence is above a threshold, then the bolus calculator can
determine a recommended insulin bolus without input from an
external meter (step 460). However, if the confidence is below the
threshold, then the bolus calculator may request external meter
reference values for use in bolus estimation.
[0306] In another specific example, an insulin pump may be
programmed with basal estimates by the user, while bolus estimates
may be entered by the user. In an adjunctive mode, CGM data may be
used to make suggestions (step 480), such as "bolus may be too
high", or "may need a different type of bolus". In therapeutic
mode, which may also depend on phase, CGM data may be employed to
modify the basal or bolus estimates, with or without user
confirmation (step 458).
[0307] In another specific example, as shown by the flowchart 550
of FIG. 20, startup of the monitoring device may occur immediately
after sensor insertion (step 494), e.g., within 15 minutes or the
like, and low-resolution data may be immediately displayed (step
496). This is in contrast to many present systems in which sensors
do not start up until, e.g., two hours, after insertion.
Low-resolution data may be displayed that includes, e.g., a
glycemic range the patient is in, glucose range information, or
other data which may be considered adjunctive-mode-type data. Over
time, a confidence level or other indicator of the usability of the
data may rise (step 498). This rise may occur in a number of ways.
For example, an external meter value may be provided (step 508)
that either correlates to what the CGM estimates the glucose
concentration value to be and/or the external meter value entered
may trigger a mode switch to a user-dependent calibration mode
(step 510), and if the usability is high enough, a therapeutic mode
may be entered (step 506). The usability may also dictate the phase
in which the device is operating, if such phases are enabled (see
FIG. 15A). In another alternative, an increase may occur in a level
of certainty in the device self-calibration mode (step 502)
sufficiently so as to display glucose concentration values at a
higher resolution (step 504), and thus justify a transition if
desired by the user into a therapeutic mode or phase (step
506).
[0308] In other words, an adjunctive mode is maintained until
external meter data indicates a mode switch to user-dependent
calibration, at which point sufficient resolution may be achieved
to perform a further mode switch of the adjunctive mode to the
therapeutic mode. Correspondingly, a criteria for determining the
calibration mode, e.g., whether or not to use user-dependent
calibration, may be based on whether or not the therapeutic mode
versus the adjunctive mode has been triggered. For example, if the
therapeutic mode has been triggered, it may be desirable to switch
to a user-dependent calibration mode. Similarly, if the adjunctive
mode has been triggered, it may be desirable to switch to a device
self-calibration mode if calibration parameters allow. In these
fashions, FIG. 20 illustrates an interplay between the calibration
mode and the therapeutic/adjunctive use modes, as well as their
potential effects on each other.
[0309] As one example, signal usability of a monitoring device may
cause the same to be operating in a user-dependent calibration
mode. For example, the monitoring device may be registering a fault
in the sensor due to a shower spike. However, if the fault is
resolved, and confidence rises in the device self-calibration, at
some point the device self-calibration may be allowed to "take
over" and reduce the need for user-dependent calibration. At the
same time, if confidence also rises in the signal usability, the
decision support mode may be caused to transition from an
adjunctive one to a therapeutic one, or in the case where phases of
control are implemented, to a higher control phase.
[0310] In another specific example, as shown by the diagram 575 of
FIG. 21A and the flowchart 600 of FIG. 21B, adjunctive and
therapeutic modes may be operated in parallel, simultaneously, or
concurrently, where a basic screen 512 is provided that shows
low-resolution data, e.g., trends, arrows, and/or ranges. Such data
may be sufficient for adjunctive use and require little or no
calibration. The user can then select other screens via a menu, and
in some cases such other screens such as a complex screen 514 will
provide an alert or alarm to the user that additional and higher
levels of data usability, e.g., calibration or accuracy, are
required, in which case the more complex or therapeutic modes
(running in parallel, simultaneously, or concurrently) may be
switched to if the usability of the data allows.
[0311] There could be different levels of information provided by
the screens, which, when selected, would trigger different
requirements/criteria in different submodes, based on what the user
wants to see or do. For example, a basic information screen 516 may
provide a simple arrow indicating rate of change as well as a
glycemic range. A hypoglycemia safety screen 518 may be provided
that alarms on hypoglycemia only. Such a screen may require some
degree of calibration, including potentially user-dependent
calibration. A full management information screen 520 may also be
provided, which alerts and alarms for high and low values as well
as high or low rates of change. This type of screen display may
require even more signal usability than the previously described
screens (which may require more or greater calibration in some
circumstances). If the required levels of data resolution become
high enough, the mode may switch to another mode, as noted above,
e.g., the mode may switch to the therapeutic mode if the user has
indicated a desire for such a switch. This system allows
flexibility in mode switching--the user is enabled to view a high
resolution mode but is not required to do so and in fact can employ
a simple UI even with data that is innately of high resolution. The
parallel modes, which as noted may run concurrently or
simultaneously, need not run concurrently or simultaneously for an
entire duration of a session. But while they are running in
parallel, modes may be easily switched from one to another. Of
course, when they are not running in parallel, the modes may be
switched using any of the other methods described herein, e.g.,
with respect to FIG. 1, 3A, 3B, 4, 9-15A, 15C, 17, or 20.
[0312] As seen in the flowchart 600, in a first step a basic screen
may be provided on the user interface of the monitoring device for
adjunctive use (step 522). Options may then be provided for
additional uses (step 524), where such options are provided on the
user interface of the monitoring device. If the user selects an
information screen which requires additional information (step
526), the monitoring device may either automatically switch to a
mode in which sufficient calibration or other data are received to
enable the additional information, or the user may be prompted
before such modes are entered. Once such data is received by the
monitoring device, the additional uses may be enabled (step 528),
and the screen with additional information displayed.
[0313] In another specific example, as shown by the flowchart 625
of FIG. 22, the monitoring device may identify hypoglycemia
unawareness in a user (step 530), such as by a lack of correlation
between a user feeling low and CGM data actually being low. In this
case, a mode switch may occur to a mode that is more sensitive
(step 532). In an optional step, accuracy criteria may also be
modified (step 534). As a result of the mode switch, the monitoring
device may output and display more information for the user (step
536), especially where the device is in therapeutic mode. The
additional information may further educate the user as to how to
identify hypoglycemia, how to treat the same, and the ramifications
of the hypoglycemic state.
Overview/General Description of System
[0314] The glucose sensor can use any system or method to provide a
data stream indicative of the concentration of glucose in a host.
The data stream is typically a raw data signal that is transformed
to provide a useful value of glucose to a user, such as a patient
or doctor, who may be using the sensor. Faults may occur, however,
which may be detectable by analysis of the signal, analysis of the
clinical context, or both. Such faults require discrimination to
distinguish the same from actual measured signal behavior, as well
as for responsive signal processing, which can vary according to
the fault. Accordingly, appropriate fault discrimination and
responsive processing techniques are employed.
Glucose Sensor
[0315] The glucose sensor can be any device capable of measuring
the concentration of glucose. One exemplary embodiment is described
below, which utilizes an implantable glucose sensor. However, it
should be understood that the devices and methods described herein
can be applied to any device capable of detecting a concentration
of glucose and providing an output signal that represents the
concentration of glucose.
[0316] Exemplary embodiments disclosed herein relate to the use of
a glucose sensor that measures a concentration of glucose or a
substance indicative of the concentration or presence of another
analyte. In some embodiments, the glucose sensor is a continuous
device, for example a subcutaneous, transdermal, transcutaneous,
non-invasive, intraocular and/or intravascular (e.g., intravenous)
device. In some embodiments, the device can analyze a plurality of
intermittent blood samples. The glucose sensor can use any method
of glucose measurement, including enzymatic, chemical, physical,
electrochemical, optical, optochemical, fluorescence-based,
spectrophotometric, spectroscopic (e.g., optical absorption
spectroscopy, Raman spectroscopy, etc.), polarimetric,
calorimetric, iontophoretic, radiometric, and the like.
[0317] The glucose sensor can use any known detection method,
including invasive, minimally invasive, and non-invasive sensing
techniques, to provide a data stream indicative of the
concentration of the analyte in a host. The data stream is
typically a raw data signal that is used to provide a useful value
of the analyte to a user, such as a patient or health care
professional (e.g., doctor), who may be using the sensor.
[0318] Although much of the description and examples are drawn to a
glucose sensor capable of measuring the concentration of glucose in
a host, the systems and methods of embodiments can be applied to
any measurable analyte, a non-exhaustive list of appropriate
analytes noted above. Some exemplary embodiments described below
utilize an implantable glucose sensor. However, it should be
understood that the devices and methods described herein can be
applied to any device capable of detecting a concentration of
analyte and providing an output signal that represents the
concentration of the analyte.
[0319] In one preferred embodiment, the analyte sensor is an
implantable glucose sensor, such as described with reference to
U.S. Pat. No. 6,001,067 and U.S. Patent Publication No.
US-2005-0027463-A1. In another preferred embodiment, the analyte
sensor is a transcutaneous glucose sensor, such as described with
reference to U.S. Patent Publication No. US-2006-0020187-A1. In
still other embodiments, the sensor is configured to be implanted
in a host vessel or extracorporeally, such as is described in U.S.
Patent Publication No. US-2007-0027385-A1, co-pending U.S. patent
application Ser. No. 11/543,396 filed Oct. 4, 2006, co-pending U.S.
patent application Ser. No. 11/691,426 filed on Mar. 26, 2007, and
co-pending U.S. patent application Ser. No. 11/675,063 filed on
Feb. 14, 2007. In one alternative embodiment, the continuous
glucose sensor comprises a transcutaneous sensor such as described
in U.S. Pat. No. 6,565,509 to Say et al., for example. In another
alternative embodiment, the continuous glucose sensor comprises a
subcutaneous sensor such as described with reference to U.S. Pat.
No. 6,579,690 to Bonnecaze et al. or U.S. Pat. No. 6,484,046 to Say
et al., for example. In another alternative embodiment, the
continuous glucose sensor comprises a refillable subcutaneous
sensor such as described with reference to U.S. Pat. No. 6,512,939
to Colvin et al., for example. In another alternative embodiment,
the continuous glucose sensor comprises an intravascular sensor
such as described with reference to U.S. Pat. No. 6,477,395 to
Schulman et al., for example. In another alternative embodiment,
the continuous glucose sensor comprises an intravascular sensor
such as described with reference to U.S. Pat. No. 6,424,847 to
Mastrototaro et al.
[0320] Various sensors may be employed. In the case of continuous
glucose sensing, it is contemplated that the sensing region may
include any of a variety of electrode configurations. For example,
in some embodiments, in addition to one or more glucose-measuring
working electrodes, the sensing region may also include a reference
electrode or other electrodes associated with the working
electrode. In these particular embodiments, the sensing region may
also include a separate reference or counter electrode associated
with one or more optional auxiliary working electrodes. In other
embodiments, the sensing region may include a glucose-measuring
working electrode, an auxiliary working electrode, two counter
electrodes (one for each working electrode), and one shared
reference electrode. In yet other embodiments, the sensing region
may include a glucose-measuring working electrode, an auxiliary
working electrode, two reference electrodes, and one shared counter
electrode.
[0321] U. S. Patent Publication No. US-2008-0119703-A1 and U.S.
Patent Publication No. US-2005-0245799-A1 describe additional
configurations for using the continuous sensor in different body
locations. In some embodiments, the sensor is configured for
transcutaneous implantation in the host. In alternative
embodiments, the sensor is configured for insertion into the
circulatory system, such as a peripheral vein or artery. However,
in other embodiments, the sensor is configured for insertion into
the central circulatory system, such as but not limited to the vena
cava. In still other embodiments, the sensor can be placed in an
extracorporeal circulation system, such as but not limited to an
intravascular access device providing extracorporeal access to a
blood vessel, an intravenous fluid infusion system, an
extracorporeal blood chemistry analysis device, a dialysis machine,
a heart-lung machine (i.e., a device used to provide blood
circulation and oxygenation while the heart is stopped during heart
surgery), etc. In still other embodiments, the sensor can be
configured to be wholly implantable, as described in U.S. Pat. No.
6,001,067.
[0322] FIG. 23 is a block diagram that illustrates one possible
configuration of the sensor electronics in one embodiment. In this
embodiment, a potentiostat 720 is shown, which is operatively
connected to an electrode system and provides a voltage to the
electrodes, which biases the sensor to enable measurement of a
current value indicative of the analyte concentration in the host
(also referred to as the analog portion). In some embodiments, the
potentiostat includes a resistor (not shown) that translates the
current into voltage. In some alternative embodiments, a current to
frequency converter is provided that is configured to continuously
integrate the measured current, for example, using a charge
counting device. In the illustrated embodiment, an A/D converter
721 digitizes the analog signal into "counts" for processing.
Accordingly, the resulting raw data stream in counts is directly
related to the current measured by the potentiostat 720.
[0323] A processor module 722 is the central control unit that
controls the processing of the sensor electronics. In some
embodiments, the processor module includes a microprocessor,
however a computer system other than a microprocessor can be used
to process data as described herein, for example an ASIC can be
used for some or all of the sensor's central processing. The
processor typically provides semi-permanent storage of data, for
example, storing data such as sensor identifier (ID) and
programming to process data streams (for example, programming for
data smoothing and/or replacement of signal artifacts such as is
described in more detail elsewhere herein). The processor
additionally can be used for the system's cache memory, for example
for temporarily storing recent sensor data. In some embodiments,
the processor module comprises memory storage components such as
various types of ROM, RAM, flash memory, and the like. In one
exemplary embodiment, ROM 723 provides semi-permanent storage of
data, for example, storing data such as sensor identifier (ID) and
programming to process data streams (e.g., programming for signal
artifacts detection and/or replacement such as described elsewhere
herein). In one exemplary embodiment, RAM 724 can be used for the
system's cache memory, for example for temporarily storing recent
sensor data.
[0324] In some embodiments, the processor module comprises a
digital filter, for example, an IIR or FIR filter, configured to
smooth the raw data stream from the A/D converter. Generally,
digital filters are programmed to filter data sampled at a
predetermined time interval (also referred to as a sample rate). In
some embodiments, wherein the potentiostat is configured to measure
the analyte at discrete time intervals, these time intervals
determine the sample rate of the digital filter. In some
alternative embodiments, wherein the potentiostat is configured to
continuously measure the analyte, for example, using a
current-to-frequency converter, the processor module can be
programmed to request a digital value from the A/D converter at a
predetermined time interval, also referred to as the acquisition
time. In these alternative embodiments, the values obtained by the
processor are advantageously averaged over the acquisition time due
the continuity of the current measurement. Accordingly, the
acquisition time determines the sample rate of the digital filter.
In preferred embodiments, the processor module is configured with a
programmable acquisition time, namely, the predetermined time
interval for requesting the digital value from the A/D converter is
programmable by a user within the digital circuitry of the
processor module. An acquisition time of from about 2 seconds to
about 512 seconds is preferred; however any acquisition time can be
programmed into the processor module. A programmable acquisition
time is advantageous in optimizing noise filtration, time lag, and
processing/battery power.
[0325] Preferably, the processor module is configured to build the
data packet for transmission to an outside source, for example, an
RF transmission to a receiver as described in more detail below.
Generally, the data packet comprises a plurality of bits that can
include a sensor ID code, raw data, filtered data, and/or error
detection or correction. The processor module can be configured to
transmit any combination of raw and/or filtered data.
[0326] A battery 725 is operatively connected to the processor 722
and provides the necessary power for the sensor (e.g., 800). In one
embodiment, the battery is a Lithium Manganese Dioxide battery,
however any appropriately sized and powered battery can be used
(e.g., AAA, Nickel-cadmium, Zinc-carbon, Alkaline, Lithium,
Nickel-metal hydride, Lithium-ion, Zinc-air, Zinc-mercury oxide,
Silver-zinc, or hermetically-sealed). In some embodiments the
battery is rechargeable. In some embodiments, a plurality of
batteries can be used to power the system. In yet other
embodiments, the receiver can be transcutaneously powered via an
inductive coupling, for example. A Quartz Crystal 726 is
operatively connected to the processor 22 and maintains system time
for the computer system as a whole.
[0327] An RF module, (e.g., an RF Transceiver) 727 is operably
connected to the processor 722 and transmits the sensor data from
the sensor (e.g., 800) to a receiver (see FIGS. 27 and 28).
Although an RF transceiver is shown here, some other embodiments
can include a wired rather than wireless connection to the
receiver. A second quartz crystal 728 provides the system time for
synchronizing the data transmissions from the RF transceiver. It is
noted that the transceiver 727 can be substituted with a
transmitter in other embodiments. In some alternative embodiments,
however, other mechanisms, such as optical, infrared radiation
(IR), ultrasonic, and the like, can be used to transmit and/or
receive data.
[0328] In some embodiments, a Signal Artifacts Detector 729 is
provided that includes one or more of the following: an oxygen
detector 729a, a pH detector 729b, a temperature detector 729c, and
a pressure/stress detector 729d, which is described in more detail
with reference to signal artifacts and faults/errors detection and
discrimination. It is noted that in some embodiments the signal
artifacts detector 729 is a separate entity (e.g., temperature
detector) operatively connected to the processor, while in other
embodiments, the signal artifacts detector is a part of the
processor and utilizes readings from the electrodes, for example,
to detect signal faults and artifacts. Although the above
description includes some embodiments in which all discrimination
occurs within the sensor, other embodiments provide for systems and
methods for detecting signal faults in the sensor and/or receiver
electronics (e.g., processor module) as described in more detail
elsewhere herein.
Receiver
[0329] FIG. 24 is a schematic view of a receiver 730 including a
representation of an estimated glucose value on its user interface.
The receiver 730 includes systems to receive, process, and display
sensor data from the glucose sensor (e.g., 800), such as described
herein. Particularly, the receiver 730 can be a mobile phone type
device, for example, and comprise a user interface that has a
physical button 732 and a display screen 734, as well as one or
more input/output (UO) devices, such as a touch screen, one or more
buttons 755 and/or switches 757, which when activated or clicked
perform one or more functions. In FIG. 24, the UI also shows
historical trend data, as well as a compass-type icon element
surrounding the glucose concentration value indicating a
rate-of-change trend. Various other features are shown, including
UI icons which may be employed to enter medicament information,
e.g., insulin boluses, exercise data, and to provide social
networking functionality.
[0330] In the illustrated embodiment, the electronic device is a
smartphone, and the display 734 comprises a touchscreen, which also
functions as an I/O device. In some embodiments, the user interface
can also include a keyboard, a speaker, and a vibrator. The
functions of the receiver or smart phone can also be implemented as
functions within an application running on a tablet computer, or
like device. In other embodiments, the receiver may comprise a
device or devices other than a smartphone, such as a smartwatch, a
tablet computer, a mini-tablet computer, a handheld personal
digital assistant (PDA), a game console, a multimedia player, a
wearable device, such as those described above, a screen in an
automobile or other vehicle, a dedicated receiver device, etc. The
receiver may also be a medicament administration device such as an
insulin pump.
[0331] In some embodiments, the user will be able to interactively
select the type of output displayed on their user interface. In
other embodiments, the sensor output can have alternative
configurations. In yet other embodiments, the type of output
displayed on the user interface will display on the mode switched
to, e.g., indicating an operating mode, a type of user interaction,
data appropriate thereto, and/or the like.
[0332] FIG. 25 is a block diagram that illustrates one possible
configuration of the receiver, e.g., a smart phone, electronics. It
is noted that the receiver can comprise a configuration such as
described with reference to FIG. 24, above. Alternatively, the
receiver can comprise other configurations, including a desktop
computer, laptop computer, a personal digital assistant (PDA), a
server (local or remote to the receiver), and the like. In some
embodiments, the receiver can be adapted to connect (via wired or
wireless connection) to a desktop computer, laptop computer, PDA,
server (local or remote to the receiver), and the like, in order to
download data from the receiver. In some alternative embodiments,
the receiver and/or receiver electronics can be housed within or
directly connected to the sensor (e.g., 800) in a manner that
allows sensor and receiver electronics to work directly together
and/or share data processing resources. Accordingly, the receiver's
electronics (or any combination of sensor and/or receiver
electronics) can be generally referred to as a "computer
system."
[0333] A quartz crystal 740 is operatively connected to an RF
transceiver 741 that together function to receive and synchronize
data streams (e.g., raw data streams transmitted from the RF
transceiver). Once received, a processor 742 processes the signals,
such as described below.
[0334] The processor 742, also referred to as the processor module,
is the central control unit that performs the processing, such as
comparing determined data against criteria to determine if mode
switching should occur, storing data, analyzing data streams,
calibrating analyte sensor data, predicting analyte values,
comparing predicted analyte values with corresponding measured
analyte values, analyzing a variation of predicted analyte values,
downloading data, and controlling the user interface by providing
analyte values, prompts, messages, warnings, alarms, and the like.
The processor includes hardware and software that performs the
processing described herein, for example flash memory provides
permanent or semi-permanent storage of data, storing data such as
sensor ID, receiver ID, and programming to process data streams
(for example, programming for performing prediction and other
algorithms described elsewhere herein) and random access memory
(RAM) stores the system's cache memory and is helpful in data
processing.
[0335] In one exemplary embodiment, the processor is a
microprocessor that provides the processing, such as calibration
algorithms stored within a ROM 743. The ROM 743 is operatively
connected to the processor 742 and provides semi-permanent storage
of data, storing data such as receiver ID and programming to
process data streams (e.g., programming for performing calibration
and other algorithms described elsewhere herein). In this exemplary
embodiment, a RAM 744 is used for the system's cache memory and is
helpful in data processing.
[0336] A battery 745 is operatively connected to the processor 742
and provides power for the receiver. In one embodiment, the battery
is a standard AAA alkaline battery, however any appropriately sized
and powered battery can be used. In some embodiments, a plurality
of batteries can be used to power the system. A quartz crystal 746
is operatively connected to the processor 742 and maintains system
time for the computer system as a whole.
[0337] A user interface 747 comprises a keyboard 2, speaker 3,
vibrator 4, backlight 5, liquid crystal display (LCD 6), and one or
more buttons 7, which may be implemented as physical buttons or
buttons on a touchscreen interface. The components that comprise
the user interface 47 provide controls to interact with the user.
The keyboard 2 can allow, for example, input of user information
about himself/herself, such as mealtime, exercise, insulin
administration, and reference glucose values. The speaker 3 can
provide, for example, audible signals or alerts for conditions such
as present and/or predicted hyper- and hypoglycemic conditions. The
vibrator 4 can provide, for example, tactile signals or alerts for
reasons such as described with reference to the speaker, above. The
backlight 5 can be provided, for example, to aid the user in
reading the LCD in low light conditions. The LCD 6 can be provided,
for example, to provide the user with visual data output such as is
illustrated in FIG. 24. The buttons 7 can provide for toggle, menu
selection, option selection, mode selection, and reset, for
example.
[0338] In some embodiments, prompts or messages can be displayed on
the user interface to convey information to the user, such as
requests for reference analyte values, therapy recommendations,
deviation of the measured analyte values from the predicted analyte
values, and the like. Additionally, prompts can be displayed to
guide the user through calibration or trouble-shooting of the
calibration.
[0339] In some implementations, the continuous analyte sensor
system includes a Dexcom G4.RTM. Platinum glucose sensor and
transmitter commercially available from Dexcom, Inc., for
continuously monitoring a host's glucose levels.
[0340] In some embodiments, the system may execute various
applications, for example, a CGM application, which may be
downloaded to the receiver or other electronic device over the
Internet and/or a cellular network, and the like. Data for various
applications may be shared between the device and one or more other
devices/systems, and stored by cloud or network storage and/or on
one or more other devices/systems.
[0341] What has been disclosed are systems and methods for
dynamically and iteratively changing or switching modes and/or
submodes of a monitoring device based on determined data, generally
compared against a trigger such as one or more transition criteria.
A variety of methods have been disclosed for determining when and
how to switch modes, as well as a number of potential types of data
and criteria.
[0342] Variations will be understood to one of ordinary skill in
the art given this teaching. For example, while multimodal
transitions have been described, it will be understood that such
may include multiple transition criteria from one category or
multiple transition criteria drawn from multiple categories, e.g.,
from multiple of flowcharts from FIGS. 5-9.
[0343] The connections between the elements shown in the figures
illustrate exemplary communication paths. Additional communication
paths, either direct or via an intermediary, may be included to
further facilitate the exchange of information between the
elements. The communication paths may be bi-directional
communication paths allowing the elements to exchange
information.
[0344] As used herein, the term "determining" encompasses a wide
variety of actions. For example, "determining" may include
calculating, computing, processing, deriving, investigating,
looking up (e.g., looking up in a table, a database or another data
structure), ascertaining and the like. Also, "determining" may
include receiving (e.g., receiving information), accessing (e.g.,
accessing data in a memory) and the like. Also, "determining" may
include resolving, selecting, choosing, establishing and the
like.
[0345] The various operations of methods described above may be
performed by any suitable means capable of performing the
operations, such as various hardware and/or software component(s),
circuits, and/or module(s). Generally, any operations illustrated
in the figures may be performed by corresponding functional means
capable of performing the operations.
[0346] The various illustrative logical blocks, modules and
circuits described in connection with the present disclosure may be
implemented or performed with a general purpose processor, a
digital signal processor (DSP), an application specific integrated
circuit (ASIC), a field programmable gate array signal (FPGA) or
other programmable logic device (PLD), discrete gate or transistor
logic, discrete hardware components or any combination thereof
designed to perform the functions described herein. A general
purpose processor may be a microprocessor, but in the alternative,
the processor may be any commercially available processor,
controller, microcontroller or state machine. A processor may also
be implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0347] In one or more aspects, the functions described may be
implemented in hardware, software, firmware, or any combination
thereof. If implemented in software, the functions may be stored on
or transmitted over as one or more instructions or code on a
computer-readable medium. Computer-readable media includes both
computer storage media and communication media including any medium
that facilitates transfer of a computer program from one place to
another. A storage media may be any available media that can be
accessed by a computer. By way of example, and not limitation, such
computer-readable media can comprise RAM, ROM, CD-ROM or other
optical disk storage, magnetic disk storage or other magnetic
storage devices, or any other medium that can be used to carry or
store desired program code in the form of instructions or data
structures and that can be accessed by a computer. Also, any
connection is properly termed a computer-readable medium. For
example, if the software is transmitted from a website, server, or
other remote source using a coaxial cable, fiber optic cable,
twisted pair, digital subscriber line (DSL), or wireless
technologies such as infrared, radio, and microwave, then the
coaxial cable, fiber optic cable, twisted pair, DSL, or wireless
technologies such as infrared, radio, and microwave are included in
the definition of medium. Disk and disc, as used herein, includes
compact disc (CD), laser disc, optical disc, digital versatile disc
(DVD), floppy disk and Blu-ray.RTM. disc where disks usually
reproduce data magnetically, while discs reproduce data optically
with lasers. Thus, in some aspects a computer-readable medium may
comprise non-transitory computer-readable medium (e.g., tangible
media). In addition, in some aspects a computer-readable medium may
comprise transitory computer-readable medium (e.g., a signal).
Combinations of the above should also be included within the scope
of computer-readable media.
[0348] The methods disclosed herein comprise one or more steps or
actions for achieving the described methods. The method steps
and/or actions may be interchanged with one another without
departing from the scope of the claims. In other words, unless a
specific order of steps or actions is specified, the order and/or
use of specific steps and/or actions may be modified without
departing from the scope of the claims.
[0349] Certain aspects may comprise a computer program product for
performing the operations presented herein. For example, such a
computer program product may comprise a computer-readable medium
having instructions stored (and/or encoded) thereon, the
instructions being executable by one or more processors to perform
the operations described herein. For certain aspects, the computer
program product may include packaging material.
[0350] Software or instructions may also be transmitted over a
transmission medium. For example, if the software is transmitted
from a website, server, or other remote source using a coaxial
cable, fiber optic cable, twisted pair, digital subscriber line
(DSL), or wireless technologies such as infrared, radio, and
microwave, then the coaxial cable, fiber optic cable, twisted pair,
DSL, or wireless technologies such as infrared, radio, and
microwave are included in the definition of transmission
medium.
[0351] Further, it should be appreciated that modules and/or other
appropriate means for performing the methods and techniques
described herein can be downloaded and/or otherwise obtained. For
example, a device can be coupled to a server to facilitate the
transfer of means for performing the methods described herein.
Alternatively, various methods described herein can be provided via
storage means (e.g., RAM, ROM, a physical storage medium such as a
compact disc (CD) or floppy disk, etc.), such that a user terminal
and/or base station can obtain the various methods upon coupling or
providing the storage means to the device. Moreover, any other
suitable technique for providing the methods and techniques
described herein to a device can be utilized.
[0352] It is to be understood that the claims are not limited to
the precise configuration and components illustrated above. Various
modifications, changes and variations may be made in the
arrangement, operation and details of the methods and apparatus
described above without departing from the scope of the claims.
[0353] 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.
[0354] 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.
[0355] 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.
[0356] 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., as
including any combination of the listed items, including single
members (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."
[0357] 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.
[0358] 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.
[0359] Headings are included herein for reference and to aid in
locating various sections. These headings are not intended to limit
the scope of the concepts described with respect thereto. Such
concepts may have applicability throughout the entire
specification.
[0360] 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.
[0361] The system and method may be fully implemented in any number
of computing devices. Typically, instructions are laid out on
computer-readable media, generally non-transitory, and these
instructions are sufficient to allow a processor in the computing
device to implement the method of the invention. The
computer-readable medium may be a hard drive or solid state storage
having instructions that, when run, are loaded into random access
memory. Inputs to the application, e.g., from the plurality of
users or from any one user, may be by any number of appropriate
computer input devices. For example, users may employ a keyboard,
mouse, touchscreen, joystick, trackpad, other pointing device, or
any other such computer input device to input data relevant to the
calculations. Data may also be input by way of an inserted memory
chip, hard drive, flash drives, flash memory, optical media,
magnetic media, or any other type of file--storing medium. The
outputs may be delivered to a user by way of a video graphics card
or integrated graphics chipset coupled to a display that maybe seen
by a user. Alternatively, a printer may be employed to output hard
copies of the results. Given this teaching, any number of other
tangible outputs will also be understood to be contemplated by the
invention. For example, outputs may be stored on a memory chip,
hard drive, flash drives, flash memory, optical media, magnetic
media, or any other type of output. It should also be noted that
the invention may be implemented on any number of different types
of computing devices, e.g., personal computers, laptop computers,
notebook computers, net book computers, handheld computers,
personal digital assistants, mobile phones, smart phones, tablet
computers, and also on devices specifically designed for these
purpose. In one implementation, a user of a smart phone or
wi-fi--connected device downloads a copy of the application to
their device from a server using a wireless Internet connection. An
appropriate authentication procedure and secure transaction process
may provide for payment to be made to the seller. The application
may download over the mobile connection, or over the WiFi or other
wireless network connection. The application may then be run by the
user. Such a networked system may provide a suitable computing
environment for an implementation in which a plurality of users
provide separate inputs to the system and method. In the below
system where mode switching is contemplated, the plural inputs may
allow plural users or devices to input relevant data and criteria
at the same time.
[0362] Various methods and devices are provided.
[0363] In Method 1, provided is a method of operating a continuous
glucose monitoring device, the continuous glucose monitoring device
coupled to a glucose sensor and operating in an initial mode of
operation, comprising: measuring a signal indicative of glucose
concentration data; displaying the glucose concentration data on a
user interface of the continuous glucose monitoring device, the
user interface in the initial mode of operation having an initial
mode of user interaction; determining data indicative of a
usability of the continuous glucose monitoring device; comparing
the determined data to one or more transition criteria; if the
comparing indicates the determined data has met or will meet the
transition criteria, causing the continuous glucose monitoring
device to transition to a new mode of operation; and displaying the
glucose concentration data on the user interface of the continuous
glucose monitoring device, the user interface in the new mode of
operation having a different mode of user interaction than the
initial mode, such that the continuous glucose monitoring device
operates in a mode of user interaction according to the device
usability.
[0364] In Method 2, which is a variant of Method 1, the displaying
is based at least in part on the mode of operation.
[0365] In Method 3, which is a variant of Method 1 or 2, the
determining data includes receiving data from the sensor.
[0366] In Method 4, which is a variant of Method 3, the receiving
data from the sensor includes receiving data from a sensor
electronics module coupled to the sensor.
[0367] In Method 5, which is a variant of any one of the above
Methods, the sensor is configured for in vivo insertion into the
patient.
[0368] In Method 6, which is a variant of any one of the above
Methods, a first output of the monitoring device in the initial
mode of operation represents the initial mode of user interaction
and a second output of the monitoring device in the new mode of
operation represents the new mode of user interaction, and wherein
the first and second outputs are different.
[0369] In Method 7, which is a variant of Method 6, the initial and
new modes of user interaction are configured such that the new mode
of user interaction requires less user interaction than the initial
mode of user interaction.
[0370] In Method 8, which is a variant of Method 8, the initial and
new modes of user interaction are selected from the group
consisting of: user-dependent calibration and device
self-calibration.
[0371] In Method 9, which is a variant of Method 8, the analyte is
glucose and wherein the user-dependent calibration corresponds to
entry of a calibration value from an external blood glucose
meter.
[0372] In Method 10, which is a variant of Method 6, the initial
and new modes of user interaction include levels of confirmation
interactions.
[0373] In Method 11, which is a variant of Method 6, the analyte is
glucose and wherein the initial and new modes of user interaction
include different levels of control in an artificial pancreas
system.
[0374] In Method 12, which is a variant of Method 6, the analyte is
glucose and wherein the initial and new modes of user interaction
are data transmission modes selected from the group consisting of:
on-demand data transmission and device-initiated data
transmission.
[0375] In Method 13, which is a variant of Method 6, the initial
and new modes of user interaction are selected from the group
consisting of: pushed data or pulled data.
[0376] In Method 14, which is a variant of Method 4, which is a
variant of any one of the above Methods, the determined data
includes an analyte concentration value and/or a time rate of
change thereof.
[0377] In Method 15, which is a variant of any one of the above
Methods, the determined data indicative of the usability of the
device and the transition criteria include one or more parameters
indicative of the usability of a signal from the sensor.
[0378] In Method 16, which is a variant of Method 15, the one or
more parameters related to the usability of the signal corresponds
to one or more parameters selected from the group consisting of
accuracy, reliability, stability, confidence, and/or glycemic
urgency index.
[0379] In Method 17, which is a variant of Method 15 or 16, the one
or more parameters related to the usability of the signal
correspond to a level of noise or to one or more faults detected in
the signal, and wherein the transition criteria is a threshold
level of noise or a predetermined type or level of fault.
[0380] In Method 18, which is a variant of Method 17, the level of
noise or the one or more faults detected in the signal are
determined based on a long-term trend of the signal, a short-term
trend of the signal, or on a history of a user's previous sensor
session.
[0381] In Method 19, which is a variant of Method 15 or 16, the one
or more parameters related to the usability of the signal
correspond to one or more of the group consisting of: signal value,
a range of signal values, or a time rate of change thereof; analyte
concentration value or range of values; calibration data; a
measured error at calibration; data from self-diagnostics or
calibration diagnostics; metadata about sensor identity;
environmental data corresponding to a sensor; historical pattern
data; external data; data about frequency of calibration;
biological data about sensor placement; a time duration since
sensor implantation; an impedance associated with the signal; a
received user response to a prompt displayed on a user interface; a
decision support mode; a data transmission mode; data about a
selected use of the monitoring device; data about clinical or user
goals; or combinations of the above.
[0382] In Method 20, which is a variant of Method 19, the
environmental data corresponds to altitude or temperature data
about a sensor environment.
[0383] In Method 21, which is a variant of Method 19, the
calibration data is selected from the group consisting of:
calibration values, confidence in calibration values, uncertainty
in calibration values, range of calibration values, rate of change
of calibration values, current calibration values compared to
historical calibration values, stability in calibration values,
whether calibration values match expected or predicted values,
confidence in a user's ability to accurately enter calibration
values from a meter, whether entered calibration data corresponds
to a default or pre-entered value, or combinations of the
above.
[0384] In Method 22, which is a variant of Method 19, the
historical pattern data includes data about rebound
variability.
[0385] In Method 23, which is a variant of Method 19, the external
data is from an activity monitor, a sleep monitor, a medicament
pump, GPS device, a redundant analyte sensor, or a smart pen, or
combinations of the above.
[0386] In Method 24, which is a variant of Method 19, the
biological data about sensor placement corresponds to data about:
tissue type, wound response, diffusion distance, or combinations of
the above.
[0387] In Method 25, which is a variant of Method 24, the diffusion
distance is proportional to one or more selected from the group
consisting of: impedance, thickness of membrane over electrode
array, oxygen depletion rate, or diffusion of specific species
between electrodes, or combinations of the above.
[0388] In Method 26, which is a variant of Method 19, the decision
support mode is selected from different levels of control of an
artificial pancreas system.
[0389] In Method 27, which is a variant of Method 19, the data
about a selected use of the monitoring device includes data about
uses selected from the group consisting of: weight loss monitoring,
monitoring exercise or activity impact on glucose, post-meal
glucose summary, food selection, effect of the monitored analyte on
illness or menstrual cycle or pregnancy, user preference or
convenience, or combinations of the above.
[0390] In Method 28, which is a variant of Method 19, the data
about clinical or user goals includes: data about user knowledge of
device, desired accuracy of device, desired convenience of device,
use of device for hypoglycemic avoidance, use of device for
nighttime control, use of device for postprandial control,
qualitative or quantitative desired duration of sensor session, or
combinations of the above.
[0391] In Method 29, which is a variant of Method 28, the desired
convenience of the device corresponds to a number of required
external meter calibration values.
[0392] In Method 30, which is a variant of Method 4, which is a
variant of any one of the above Methods, the initial mode is
user-dependent calibration, and before the causing step, causing
the device to periodically and temporarily enter a self-calibration
mode, to interrogate the sensor and to examine a transient
response, followed by a re-entering of the user-dependent
calibration initial mode.
[0393] The method of any one of the above claims, the Method
further comprises displaying output data based on the new mode.
[0394] In Method 32, which is a variant of Method 31, the Method
further comprises displaying an indication of an expected duration
of the new mode.
[0395] In Method 33, which is a variant of Method 31, the Method
further comprises displaying an indication of sensor
performance.
[0396] In Method 34, which is a variant of Method 1-9, the initial
mode is user-dependent calibration and the new mode is device
self-calibration; or wherein the initial mode is device
self-calibration and the new mode is user-dependent
calibration.
[0397] In Method 35, which is a variant of Method 34, the
determined data is sensor signal or data usability and the
transition criteria is a threshold level of sensor signal or data
usability.
[0398] In Method 36, which is a variant of Method 35, the
transition criteria is further dependent on a decision support
mode, wherein the decision support mode is selected from the group
consisting of adjunctive, therapeutic, or a phase or mode of
control in an artificial pancreas system.
[0399] In Method 37, which is a variant of Method 35, the
transition criteria is further dependent on data entered or
received about a user or clinician use of information displayed by
the monitoring device.
[0400] In Method 38, which is a variant of Method 36, a decision
support mode associated with the initial mode is therapeutic and a
decision support mode associated with the new mode is adjunctive,
and wherein the determined data is such that the sensor signal
usability decreased below the threshold level of sensor signal
usability associated with the transition criterion.
[0401] In Method 39, which is a variant of Method 38, comprising:
prompting a user on a periodic basis to enter a calibration value
from an external meter for blood glucose; and receiving the
calibration value for blood glucose.
[0402] In Method 40, which is a variant of Method 39, the
periodicity is less in the new mode than in the initial mode.
[0403] In Method 41, which is a variant of Method 36, a decision
support mode associated with the initial mode is adjunctive and a
decision support mode associated with the new mode is therapeutic,
and wherein the determined data is such that the sensor signal
usability increased above the threshold level of sensor signal
usability associated with the transition criterion.
[0404] In Method 42, which is a variant of Method 41, the Method
further comprises prompting a user on a periodic basis to enter a
calibration value for blood glucose; and receiving the calibration
value for blood glucose.
[0405] In Method 43, which is a variant of Method 42, the
periodicity is greater in the new mode than in the initial
mode.
[0406] In Method 44, which is a variant of Method 1-9, the Method
further comprises determining an intended mode of the monitoring
device.
[0407] In Method 45, which is a variant of Method 44, the
determining includes detecting whether a medicament delivery device
is coupled to the monitoring device, and if so, configuring the
monitoring device to a mode that is therapeutic.
[0408] In Method 46, which is a variant of Method 44, the
determining includes: prompting a user to indicate an intended use
of the monitoring device; receiving the indication; and configuring
the monitoring device to a mode associated with the received
indication.
[0409] In Method 47, which is a variant of Method 46, a number of
blood glucose calibration readings required of the user is based on
the configured mode.
[0410] In Method 48, which is a variant of Method 46, the intended
use is therapeutic, and configuring the monitoring device to a
user-dependent calibration mode.
[0411] In Method 49, which is a variant of Method 46, the intended
use is adjunctive, and configuring the monitoring device to a
device self-calibration mode.
[0412] In Method 50, which is a variant of Method 1-9, the initial
mode is device self-calibration and the new mode is user-dependent
calibration, and further comprising: prompting a user to enter a
calibration value for blood glucose; receiving the calibration
value for blood glucose; and using the received calibration value
to inform the device self-calibration.
[0413] In Method 51, which is a variant of Method 50, the received
calibration value informs the device self-calibration by modifying
the device self-calibration.
[0414] In Method 52, which is a variant of Method 1-9, the initial
mode is device self-calibration and the new mode is user-dependent
calibration, and wherein the determined data and the transition
criteria include one or more parameters related to the usability of
a signal from the sensor, and wherein the one or more parameters
are selected from the group consisting of: data from diagnostic
routines indicating a shift in sensitivity; data entered by a user
about a perceived error; data from a connected device; data from
historic analyte values; time of day; day of week; whether a
glucose value is high or low as compared to respective thresholds;
glucose urgency index; data about glucose concentration value
variability; data about a level of user responsiveness; sensor
signal value trajectory pre- and post-insertion of a new sensor;
redundant or overlapping sensor data; user feedback on alerts and
alarms; meal or exercise data as compared to predicted signal
responses to meal or exercise data; data about a decision support
mode configured for the monitoring device; or combinations of the
above.
[0415] In Method 53, which is a variant of Method 52, the data from
diagnostic routines includes impedance data detecting shifts in
sensitivity.
[0416] In Method 54, which is a variant of Method 52, the
diagnostic routines are performed on a periodic basis or upon
detection of an error.
[0417] In Method 55, which is a variant of Method 52, the data
entered by a user about a perceived error includes a blood glucose
calibration value entered by a user in the absence of a prompt from
the monitoring device, or a detection of a greater than average
number of blood glucose calibration values entered by a user.
[0418] In Method 56, which is a variant of Method 52, the data from
a connected device includes data from an external blood glucose
meter.
[0419] In Method 57, which is a variant of Method 1-9, the initial
mode is device self-calibration and the new mode is user-dependent
calibration, and further comprising: if the comparing indicates the
determined data has met or will meet the transition criteria, then
before the causing step, prompting a user to enter a reason for the
determined data; receiving the reason for the determined data; and
based on the received reason, causing the monitoring device to
maintain the initial mode of operation.
[0420] In Method 58, which is a variant of Method 57, the reason is
a user-perceived outlier, a user-perceived false alarm, or meal or
exercise data.
[0421] In Method 59, which is a variant of Method 58, the Method
further comprises comparing the entered meal or exercise data to
prior user-entered meal or exercise data, and comparing a current
signal to a signal associated with the prior user-entered meal or
exercise data, and determining if the current signal and entered
meal or exercise data are consistent with the prior signal and
prior meal or exercise data.
[0422] In Method 60, which is a variant of Method 1-9, the initial
mode is device self-calibration and the new mode is user-dependent
calibration, and further comprising: determining if a number of
blood glucose measurements taken and entered into the monitoring
device as calibration values exceed a predetermined threshold over
a predetermined period of time; and if so, causing the monitoring
device to transition to a user-dependent calibration mode.
[0423] In Method 61, which is a variant of Method 1-9, the initial
mode is user-dependent and the new mode is device self-calibration,
and wherein the transition criteria corresponds to a level of
confidence in the device self-calibration, and further comprising:
prompting a user to enter a calibration value for blood glucose,
and using the entered value as the determined data; and if the
comparing indicates the determined data meets the transition
criteria, then performing the causing step.
[0424] In Method 62, which is a variant of Method 1-9, the initial
mode is user-dependent calibration and the new mode is device
self-calibration, and wherein the determined data and the
transition criteria correspond to usability of entered blood
glucose data.
[0425] In Method 63, which is a variant of Method 62, the usability
of entered blood glucose data corresponds to an accuracy,
reliability, stability, or confidence in the blood glucose
data.
[0426] In Method 64, which is a variant of Method 63, the Method
further comprises confirming that entered blood glucose data is
within a particular confidence interval or stability criterion, and
if it is not, then performing the causing step.
[0427] In Method 65, which is a variant of Method 63, the Method
further comprises confirming that entered blood glucose data is
within an expected range based on an a priori or internal
calibration, and if it is not, then performing the causing
step.
[0428] In Method 66, which is a variant of Method 62, the
transition criteria is based at least in part on a decision support
mode in which the device is configured.
[0429] In Method 67, which is a variant of Method 62, the
determined data and the transition criteria indicate that the
device continues to require external reference data for
calibration, and further comprising maintaining the initial
mode.
[0430] In Method 68, which is a variant of Method 62, the
determined data and the transition criteria indicate that the
device no longer requires external reference data for calibration,
and further comprising performing the causing step.
[0431] In Method 69, which is a variant of Method 62, the Method
further comprises a package of sensors manufactured from the same
lot, and wherein the sensor is a first of a plurality of sensors in
the pack, and wherein the determined data and the transition
criteria indicate that the device no longer requires external
reference data for calibration, and further comprising: performing
the causing step of causing the monitoring device to transition to
a new mode of operation; and for subsequent sensors in the pack,
initializing the device in device self-calibration mode, using one
or more calibration settings associated with the first sensor.
[0432] In Method 70, which is a variant of Method 1-9, the Method
further comprises initializing the monitoring device in two modes
simultaneously, a first mode being user-dependent calibration and a
second mode being device self-calibration; receiving and comparing
two glucose concentration values, one glucose concentration value
from the first mode and another glucose concentration value from
the second mode; determining and displaying a glucose concentration
value based on the two glucose concentration values; determining a
level of confidence in the glucose concentration value from the
second mode, using at least the two glucose concentration values;
and once the determined level of confidence in the glucose
concentration value from the second mode reaches a predetermined
threshold, then only displaying the glucose concentration value
from the second mode.
[0433] In Method 71, which is a variant of Method 70, the
determining a level of confidence in the glucose concentration
value from the second mode includes comparing at least the glucose
concentration value from the second mode to a calibration value
from an external meter.
[0434] In Method 72, which is a variant of Method 70, the Method
further comprises detecting a fault, and upon detection of the
fault, displaying the glucose concentration value according to the
first mode.
[0435] In Method 73, which is a variant of Method 70, the comparing
includes comparing results of diagnostic tests or internal
calibration information.
[0436] In Method 74, which is a variant of Method 73, the internal
calibration information is based on an impedance measurement.
[0437] In Method 75, which is a variant of Method 70, the
predetermined threshold is based at least in part on a decision
support mode in which the device is configured.
[0438] In Method 76, which is a variant of Method 70, the comparing
includes comparing slope and baseline information for the two
modes.
[0439] In Method 77, which is a variant of Method 76, the comparing
further comprises: comparing errors in slope and baseline data for
each of the two modes; and once the error in the slope or baseline
for the second mode is equivalent to that in the first mode, then
only displaying the glucose concentration value from the second
mode.
[0440] In Method 78, which is a variant of Method 70, the comparing
further comprises determining slope and baseline information for
each of the two modes with respective slope and baseline
information for each of the two modes from a prior session.
[0441] In Method 79, which is a variant of Method 70, the Method
further comprises displaying an indication of when a calibration
value from an external meter is required.
[0442] In Method 80, which is a variant of Method 1-7 or 9, the
Method further comprises initializing the monitoring device in two
parallel modes, a first mode being user-dependent calibration and a
second mode being device self-calibration; receiving and comparing
two glucose concentration values, one glucose concentration value
from the first mode and another glucose concentration value from
the second mode; providing a weighting of the two glucose
concentration values; and displaying a glucose concentration value
according to the weighted glucose concentration values.
[0443] In Method 81, which is a variant of Method 80, the weighting
is proportional to the usability of the data determined by each of
the modes.
[0444] In Method 82, which is a variant of Method 81, once the
weighting for a given mode reaches a predetermined threshold, the
glucose concentration value displayed is determined based on only
the given mode.
[0445] In Method 83, which is a variant of Method 1-7 or 11, the
determined data corresponds to a sensor signal, and wherein the
transition criteria corresponds at least to a usability of the
sensor signal.
[0446] In Method 84, which is a variant of Method 83, the
transition criteria is at least in part based on the initial mode
of operation.
[0447] In Method 85, which is a variant of Method 1-7 or 11, the
initial mode is a therapeutic mode, and the new mode is an
adjunctive mode.
[0448] In Method 86, which is a variant of Method 85, the
displaying in the new mode of operation further comprises, while in
the adjunctive mode, displaying data to a user in such a way as to
indicate its usability adjunctively.
[0449] In Method 87, which is a variant of Method 86, the
displaying in the new mode of operation further comprises
indicating the usability of the data by displaying a zone or range
of glycemic data instead of a single value.
[0450] In Method 88, which is a variant of Method 86, the
displaying in the new mode of operation further comprises requiring
the user to clear a prompt before displaying a subsequent glucose
concentration value or range of glucose concentration values.
[0451] In Method 89, which is a variant of Method 86, the usability
is indicated by colors and/or flashing numerals and/or a dot size
on a trend graph.
[0452] In Method 90, which is a variant of Method 86, the
displaying in the new mode of operation further comprises
restricting displayed data to only a rate of change arrow and not a
glucose concentration value.
[0453] In Method 91, which is a variant of Method 86, the usability
is indicated by a displayed change in a prediction horizon.
[0454] In Method 92, which is a variant of Method 85, the
displaying in the new mode of operation further comprises, while in
the therapeutic mode, displaying data to a user in such a way as to
indicate its usability therapeutically.
[0455] In Method 93, which is a variant of Method 92, the
displaying in the new mode of operation further comprises
indicating the usability of the data by displaying a determined
single value of glucose concentration.
[0456] In Method 94, which is a variant of Method 92, the usability
is indicated by a displayed change in a prediction horizon.
[0457] In Method 95, which is a variant of Method 92, the usability
is indicated by colors and/or flashing numerals and/or a dot size
on a trend graph.
[0458] In Method 96, which is a variant of Method 1-7 or 11, the
transition criteria is further at least partially based on time of
day or day of week.
[0459] In Method 97, which is a variant of Method 83, the usability
of the sensor signal is based on one or more parameters according
to one or more of Methods 16, 17, 19, 21, 28, or 52.
[0460] In Method 98, which is a variant of Method 83, the usability
of the sensor signal is based on one or more parameters selected
from the group consisting of: a user response to a query about a
perceived accuracy or perceived user glucose range; data about
likelihood of a potential fault or failure mode; data about glucose
context; a user response to a query about a glycemic event; a user
response to a query about a potential false alarm; a confirmatory
meter reading requested of a user via a displayed prompt; a
calibration mode; a data transmission mode; a user indication of
desired monitoring device responsiveness; or combinations of the
above.
[0461] In Method 99, which is a variant of Method 83, the Method
further comprises changing a calibration mode along with the change
from the initial to the new mode of operation.
[0462] In Method 100, which is a variant of Method 83, the Method
further comprises transmitting a signal to a medicament delivery
pump.
[0463] In Method 101, which is a variant of Method 100, the new
mode is therapeutic, and the signal instructs the pump to receive
and follow signals from the monitoring device.
[0464] In Method 102, which is a variant of Method 100, the new
mode is adjunctive, and the signal instructs the pump to disregard
received signals from the monitoring device.
[0465] In Method 103, which is a variant of Method 100, the new
mode is therapeutic, and the signal instructs the pump to receive
and follow signals from the monitoring device to control the user
glucose concentration value to a target value.
[0466] In Method 104, which is a variant of Method 100, the new
mode is therapeutic, and the signal instructs the pump to receive
and follow signals from the monitoring device to control the user
glucose concentration value to a target range of values.
[0467] In Method 105, which is a variant of Method 100, the new
mode is therapeutic, and the signal instructs the pump to receive
and follow signals from the monitoring device to control the user
glucose concentration value only when the glucose concentration
value is below a predetermined value, above a predetermined value,
or within a predetermined range of values.
[0468] In Method 106, which is a variant of Method 1-7 or 11, the
initial mode is adjunctive and the new mode is therapeutic, and
wherein in the new mode the monitoring device is configured to
calculate a recommended insulin bolus and wherein the displaying on
the user interface further comprises displaying the calculated
recommended insulin bolus without a calibration meter reading, and
wherein in the initial mode the monitoring device is configured to
not calculate and display a recommended insulin bolus without a
calibration meter reading.
[0469] In Method 107, which is a variant of Method 1-7 or 11, upon
a step of sensor start up, the initial mode is adjunctive, and
wherein the displaying indicating the new mode of operation further
comprises displaying low-resolution data.
[0470] In Method 108, which is a variant of Method 107, the Method
further comprises determining a level of confidence in the sensor
over a period of time, and once the measured level of confidence
has reached a predetermined threshold, the step of displaying
further comprises displaying high-resolution data and causing a
transition to a therapeutic mode.
[0471] In Method 109, which is a variant of Method 108, the
determining a level of confidence includes receiving an external
blood glucose meter reading.
[0472] In Method 110, which is a variant of Method 109, the
external blood glucose meter reading correlates to what the
monitoring device estimates the glucose concentration value to be
or is used to calibrate the monitoring device.
[0473] In Method 111, which is a variant of Method 109, the Method
further comprises configuring the monitoring device to enter a
user-dependent calibration mode of operation.
[0474] In Method 112, which is a variant of Method 107, the Method
further comprises receiving an external blood glucose meter
reading, developing a level of confidence in the sensor over a
period of time, and once the level of confidence has reached a
predetermined threshold, causing the monitoring device to enter a
user-dependent calibration mode of operation.
[0475] In Method 113, which is a variant of Method 1-7 or 11, the
monitoring device operates in two modes of operation concurrently,
one adjunctive and one therapeutic, and wherein the displaying
further comprises displaying an initial splash screen with data
displayed in the adjunctive mode of operation.
[0476] In Method 114, which is a variant of Method 113, upon
receiving a selection from a user interface for data requiring a
new mode of operation, causing a transition to the new mode of
operation, receiving one or more data values required by the new
mode of operation, and displaying the data using the new mode of
operation.
[0477] In Method 115, which is a variant of Method 114, the
selected data includes a hypoglycemic safety alarm, and wherein the
new mode of operation is user-dependent calibration.
[0478] In Method 116, which is a variant of Method 1-7 or 12, the
determined data includes data based on a glucose concentration
value, and wherein the transition criteria is selected from the
group consisting of: a glycemic state threshold, a GUI threshold, a
glucose threshold, a glucose rate of change threshold, a glucose
acceleration threshold, a predicted value of glucose or any of its
rates of change, an excursion beyond a predetermined threshold, an
alert criteria, a criteria for a glycemic danger zone, or a
combination of the above.
[0479] In Method 117, which is a variant of Method 1-7 or 12, the
transition criteria is selected from the group consisting of: a
duration of time since a user last requested a glucose
concentration value, a decision support mode, a user response to a
query, a calibration mode of the monitoring device, or a
combination of the above.
[0480] In Method 118, which is a variant of Method 1-7 or 12, the
determining data includes transmitting a signal to cause a sensor
to send a glucose concentration value.
[0481] In Method 119, which is a variant of Method 1-7 or 12, the
determining data includes receiving a signal from a sensor
corresponding to a glucose concentration value.
[0482] In Method 120, which is a variant of Method 1-7 or 12, the
initial mode is on-demand transmission, the new mode is
device-initiated transmission, the determined data is a glucose
concentration value, and the transition criteria is the glucose
concentration value being in a dangerous range for a period
exceeding a first predetermined duration of time.
[0483] In Method 121, which is a variant of Method 120, the Method
further comprises displaying an alert to the user on a user
interface of the monitoring device until the user performs an
action of responding to the alert.
[0484] In Method 122, which is a variant of Method 1-7 or 12, the
initial mode is device-initiated transmission, the new mode is
on-demand transmission, the determined data is a glucose
concentration value, and the transition criteria is the glucose
concentration value being in a dangerous range for a period
exceeding a second predetermined duration of time.
[0485] System 123 is a system for performing any one of Methods
1-122.
[0486] Device 124 is substantially as shown and/or described in the
specification and/or drawings.
[0487] System 125 is substantially as shown and/or described in the
specification and/or drawings.
[0488] Method 126 is substantially as shown and/or described in the
specification and/or drawings.
[0489] Electronic Device 127 is provided, for monitoring data
associated with a physiological condition, comprising: a continuous
analyte sensor, wherein the continuous analyte sensor is configured
to substantially continuously measure the concentration of analyte
in the host, and to provide continuous sensor data indicative of
the analyte concentration in the host; and a processor module
configured to perform any one of Methods 1-122.
[0490] In Electronic Device 128, which is a variant of Electronic
Device 127, the analyte is glucose.
[0491] Electronic Device 129 is provided, for delivering a
medicament to a host, the device comprising: a medicament delivery
device configured to deliver medicament to the host, wherein the
medicament delivery device is operably connected to a continuous
analyte sensor, wherein the continuous analyte sensor is configured
to substantially continuously measure the concentration of analyte
in the host, and to provide continuous sensor data indicative of
the analyte concentration in the host; and a processor module
configured to perform any one of Methods 1-122.
[0492] In Electronic Device 130, which is a variant of Electronic
Device 129, the analyte is glucose and the medicament is
insulin.
* * * * *