U.S. patent application number 13/984806 was filed with the patent office on 2014-03-27 for feedback from cloud or hcp to payer or patient via meter or cell phone.
This patent application is currently assigned to Abbott Diabetes Care Inc.. The applicant listed for this patent is Daniel M. Bernstein, Brittany K. Bradrick, Erwin S. Budiman, Royce Cheng, Eric Davis, Kenneth J. Doniger, Timothy C. Dunn, Gary A. Hayter, Dominic Kyrie, Steve Scott, Todd Winkler, Howard Wolpert. Invention is credited to Daniel M. Bernstein, Brittany K. Bradrick, Erwin S. Budiman, Royce Cheng, Eric Davis, Kenneth J. Doniger, Timothy C. Dunn, Gary A. Hayter, Dominic Kyrie, Steve Scott, Todd Winkler, Howard Wolpert.
Application Number | 20140088392 13/984806 |
Document ID | / |
Family ID | 46638887 |
Filed Date | 2014-03-27 |
United States Patent
Application |
20140088392 |
Kind Code |
A1 |
Bernstein; Daniel M. ; et
al. |
March 27, 2014 |
Feedback from Cloud or HCP to Payer or Patient via Meter or Cell
Phone
Abstract
Presented herein are one or more software applications to help a
user manager their diabetes. Embodiments and descriptions of the
various applications are provided below in conjunction with an
analyte measurement device.
Inventors: |
Bernstein; Daniel M.; (El
Granada, CA) ; Bradrick; Brittany K.; (San Francisco,
CA) ; Budiman; Erwin S.; (Fremont, CA) ;
Cheng; Royce; (San Francisco, CA) ; Davis; Eric;
(Castro Valley, CA) ; Doniger; Kenneth J.; (Menlo
Park, CA) ; Dunn; Timothy C.; (San Francisco, CA)
; Hayter; Gary A.; (Oakland, CA) ; Kyrie;
Dominic; (Cupertino, CA) ; Scott; Steve;
(Pleasanton, CA) ; Winkler; Todd; (Cameron Park,
CA) ; Wolpert; Howard; (Brookline, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Bernstein; Daniel M.
Bradrick; Brittany K.
Budiman; Erwin S.
Cheng; Royce
Davis; Eric
Doniger; Kenneth J.
Dunn; Timothy C.
Hayter; Gary A.
Kyrie; Dominic
Scott; Steve
Winkler; Todd
Wolpert; Howard |
El Granada
San Francisco
Fremont
San Francisco
Castro Valley
Menlo Park
San Francisco
Oakland
Cupertino
Pleasanton
Cameron Park
Brookline |
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
MA |
US
US
US
US
US
US
US
US
US
US
US
US |
|
|
Assignee: |
Abbott Diabetes Care Inc.
Alameda
CA
|
Family ID: |
46638887 |
Appl. No.: |
13/984806 |
Filed: |
December 21, 2011 |
PCT Filed: |
December 21, 2011 |
PCT NO: |
PCT/US11/66610 |
371 Date: |
November 21, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61442085 |
Feb 11, 2011 |
|
|
|
61486117 |
May 13, 2011 |
|
|
|
Current U.S.
Class: |
600/365 |
Current CPC
Class: |
A61B 5/6898 20130101;
A61B 5/7275 20130101; A61B 5/14532 20130101; G16H 20/17 20180101;
A61B 5/7475 20130101; G16H 50/30 20180101; G16H 50/20 20180101;
A61B 5/742 20130101; A61B 5/14546 20130101; A61B 5/150358 20130101;
A61B 5/151 20130101; A61B 5/486 20130101; G16H 40/63 20180101; A61B
5/0022 20130101; A61B 5/743 20130101; G16H 10/20 20180101; A61B
5/4839 20130101; G16H 40/67 20180101 |
Class at
Publication: |
600/365 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/151 20060101 A61B005/151; A61B 5/145 20060101
A61B005/145 |
Claims
1-19. (canceled)
20. A method, comprising: displaying a chart having median glucose
as a first axis and glucose variability as a second axis; and
displaying an identifier on the chart corresponding to a median
glucose value and a glucose variability value for sample data from
a first patient, wherein the identifier represents a state of
glucose control for the first patient.
21. The method of claim 20, wherein glucose variability is defined
by the difference between the median glucose value and a
predetermined percentile value of glucose.
22. The method of claim 21, wherein the predetermined percentage
value is 10 percent.
23. The method of claim 20, comprising: displaying a graphical
overlay representing a chronic clinical risk on the chart, wherein
the graphical overlay derived representing retinopathy risk is
based on eAG and median glucose values for a population of
patients.
24. The method of claim 23, wherein the chronic clinical risk is
retinopathy risk.
25. The method of claim 24, comprising: displaying a graphical
overlay representing an acute risk on the chart, wherein the
graphical overlay representing acute risk of hypoglycemia comprises
one or more lines of hypoglycemia occurrences per a given time
period.
26. The method of claim 25, wherein the acute risk is a risk of
hypoglycemia.
27. The method of claim 26, comprising: directing patient treatment
based on the position of the identifier representing the state of
glucose control for the first patient with respect to the graphical
overlays for retinopathy risk and/or acute risk of
hypoglycemia.
28. The method of claim 20, comprising: displaying a graphical
overlay for one or more clinical risks on the chart, wherein the
graphical overly of the one or more clinical risks is associated
with one or more axis of the chart.
29. The method of claim 20, comprising: determining lines of
constant hypoglycemia rates, wherein the lines of constant
hypoglycemia rates are based on a number of occurrences of
hypoglycemia per a given time period.
30. The method of claim 29, wherein the lines of constant
hypoglycemia rates are determined from a number of occurrences of
hypoglycemia per a given time period for a sample population of
patients.
31. The method of claim 29, wherein the lines of constant
hypoglycemia rates are parallel approximations.
32. The method of claim 29, comprising: determining a threshold
line of constant hypoglycemia rate; and displaying a hypoglycemia
risk zone having a border line on the threshold line of constant
hypoglycemia rate.
33. The method of claim 32, comprising: determining a target A1c
level corresponding to a median glucose value; and displaying a
target zone defined by the target A1c level and the border line of
the hypoglycemia risk zone.
34. The method of claim 33, comprising: displaying a buffer zone
along the border line and adjacent the target zone; and defining
the remaining area of the chart as a fourth zone.
35. The method of claim 34, comprising: directing treatment of the
first patient based on the position of the identifier representing
the state of glucose control for the first patient with respect to
the hypoglycemia risk zone, target zone, buffer zone, and/or fourth
zone.
36. The method of claim 33, comprising: directing treatment of the
first patient based on the position of the identifier representing
the state of glucose control for the first patient with respect to
the hypoglycemia risk zone and/or the target zone.
37. The method of claim 20, wherein the identifier representing the
state of glucose control for the first patient is calculated from
the sample data.
38. The method of claim 20, wherein the identifier representing the
state of glucose control for the first patient is calculated from
fitting the sample data to a probability distribution.
39. The method of claim 38, wherein the probability distribution is
a gamma probability distribution.
40. The method of claim 20, wherein the identifier comprises a
bootstrap point cloud.
41. The method of claim 40, comprising: defining a treatment
recommendation point based on a predetermined threshold percentage
value, wherein the predetermined threshold percentage value
correlates to an acceptable probability threshold of being in a
hypoglycemia risk zone on the chart.
42. The method of claim 20, wherein the identifier comprises a
boundary line of a bootstrap point cloud.
43. The method of claim 20, wherein the identifier comprises
contour plots of a probability distribution function for the state
of glucose control for the first patient, wherein the contour plots
represent uncertainty levels for the estimate.
44. The method of claim 43, wherein the probability distribution
function is based on a first testing schedule over a first time
period.
45. The method of claim 44, comprising: displaying a graphical
overlay representing retinopathy risk on the chart, wherein the
graphical overlay derived representing retinopathy risk is based on
eAG and median glucose values for a population of patients; and
displaying a graphical overlay representing acute risk of
hypoglycemia on the chart, wherein the graphical overlay
representing acute risk of hypoglycemia comprises one or more lines
of hypoglycemia occurrences per a given time period.
46. The method of claim 45, comprising: directing patient treatment
based on a comparison of the contour lines with respect to the
graphical overlays for retinopathy risk and/or acute risk of
hypoglycemia.
47. The method of claim 45, comprising: directing patient treatment
to a second testing schedule based on a comparison of the contour
lines with respect to the graphical overlays for retinopathy risk
and/or acute risk of hypoglycemia.
48. The method of claim 44, wherein the first testing schedule is a
self-monitored blood glucose (SMBG) testing schedule.
49. The method of claim 43, wherein contour plots are associated
with one or more clinical risks.
50. The method of claim 49, wherein the one or more clinical risks
comprises retinopathy risk and/or acute risk of hypoglycemia.
51. The method of claim 49, wherein different SMBG testing
schedules are collected, and wherein for each schedule, the contour
plots are computed a priori from population data.
52. The method of claim 51, wherein the population data is
comprised of CGM values or SMBG data taken every 15 minutes or
faster.
53. The method of claim 51, wherein the population data is obtained
from human studies wearing CGM systems.
54. The method of claim 51, wherein the population data is obtained
from in-silico human models representing a wide range of
demographics and/or state of diabetes.
55. The method of claim 51, wherein the contour plots from the
population data is projected along the gradient of a particular
clinical risk.
56. The method of claim 51, wherein adequate and inadequate areas
in the glucose control chart are identified for each combination of
SMBG testing schedule and clinical risk.
57. The method of claim 56, comprising: identifying the areas of
inadequacy on the chart.
58. The method of claim 49, comprising: recommending a SMBG testing
schedule specific to a latest state of glucose control for the
first patient.
59. The method of claim 58, comprising: identifying adequate and
inadequate areas in the chart; and determining whether initial data
under the recommended SMBG testing schedule falls within the
adequate or the inadequate area.
60. The method of claim 49, comprising: identifying adequate and
inadequate areas in the chart; and recommending a SMBG testing
schedule to position a next predicted state of glucose control in
the adequate area.
61-116. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] Pursuant to 35 U.S.C. .sctn.119(e), this application claims
priority to U.S. Provisional Application No. 61/442,085 filed on
Feb. 11, 2011, and U.S. Provisional Application No. 61/486,117
filed on May 13, 2011, the disclosures of which are herein
incorporated by reference in their entirety.
[0002] This application is related to U.S. Provisional Patent
Application No. 61/442,063 filed on Feb. 11, 2011; U.S. Provisional
Application No. 61/442,092 filed on Feb. 11, 2011; U.S. Provisional
Application No. 61/485,840 filed on May 13, 2011; U.S. Provisional
Application No. 61/442,093 filed on Feb. 11, 2011, and Application
No. 61/442,097 filed on Feb. 11, 2011, the disclosures of which are
all incorporated herein by reference in their entirety and for all
purposes.
BACKGROUND OF THE INVENTION
[0003] 1. The Field of the Invention
[0004] The present invention relates to analyte measurement
systems. More specifically, the present invention relates to a
diabetes management system.
[0005] 2. Background
[0006] One tool used in diabetes management is an analyte meter. An
analyte meter is typically used to measure the blood glucose level
of a user based on a sample of blood. The process of using an
analyte meter is not complicated, and is often performed several
times a day. First, the user inserts an analyte test strip into a
test strip port of the meter. The user then lances her finger to
obtain a small sample of blood. The blood sample is then placed
onto the analyte test strip, and the meter analyzes the blood
sample. The meter then typically displays a blood glucose level
from the analysis.
[0007] Continuous Glucose Monitoring (CGM) systems are also used to
manage diabetes and to collect data. A CGM system consists of a
glucose-detecting sensor embedded in the interstitial layer beneath
the epidermis, and an apparatus to receive and store glucose
values. CGM systems have the advantage of collecting data on a
regular basis, every 10 minutes for example, continuously for the
lifetime of the sensor. This typically ranges from several days to
one week. Much more data is collected, and there is 24-hour
coverage.
[0008] The conventional method for providing analysis tools to help
health care providers (HCPs) and patients absorb and understand
glucose and other diabetes data has been to provide software that
generates different graphs for HCPs and patients to review and
consider. Unfortunately, the providing of graphs is lacking in that
it is time consuming and requires a high level of expertise to be
effective. What is needed is a system that processes data based on
clinical standards and provides expert therapy decisions and
support guidance.
BRIEF DESCRIPTION OF THE FIGURES
[0009] The accompanying drawings, which are incorporated herein,
form part of the specification. Together with this written
description, the drawings further serve to explain the principles
of, and to enable a person skilled in the relevant art(s), to make
and use the present invention.
[0010] FIG. 1 provides a front-side view of handheld analyte
measurement device in accordance with certain embodiments presented
herein.
[0011] FIG. 2 illustrates a glucose control level triage scale.
[0012] FIG. 3 shows a glucose control grid: Hypoglycemia vs. Mean
Glucose Exposure.
[0013] FIG. 4 shows a glucose control grid: Mean Glucose Exposure
vs. Volatility.
[0014] FIG. 5 provides a mean and standard deviation graph of
hourly 10th percentile of glucose values for 80 patients on
multiple daily injections of insulin.
[0015] FIG. 6 provides hourly hypoglycemia risk: hourly mean and
standard deviation of 10th percentile adjusted for 24-hour mean (80
patients on multiple daily injections of insulin), 100% set to mean
hourly hypoglycemic risk.
[0016] FIG. 7 provides 10th percentile of glucose values at 4 AM
for 80 patients on multiple daily injections of insulin.
[0017] FIG. 8 provides 10th percentile of glucose values at 9 AM
for 80 patients on multiple daily injections of insulin.
[0018] FIG. 9 provides hourly volatility risk: mean and standard
deviation of hourly (interquartile range/median) of glucose values
for 80 patients on multiple daily injections of insulin.
[0019] FIG. 10 provides relatively interquartile range (IQR/median)
of glucose values at 9 AM for 80 patients on multiple daily
injections of insulin.
[0020] FIG. 11 provides relative interquartile range (IQR/median)
of glucose values at 4 PM for 80 patients on multiple daily
injections of insulin.
[0021] FIG. 12 provides hourly excess glucose risk exposure: mean
and standard deviation of hourly median of glucose values for 80
patients on multiple daily injections of insulin.
[0022] FIG. 13 provides median of glucose values at 9 AM for 80
patients on multiple daily injections of insulin.
[0023] FIG. 14 provides median of glucose values at 4 PM for 80
patients of multiple daily injections of insulin.
[0024] FIG. 15 provides an example of self-monitoring schedules
according to the dimension of concern.
[0025] FIG. 16 is a flowchart in accordance with certain
embodiments of the present invention.
[0026] FIG. 17 is a flowchart in accordance with certain
embodiments of the present invention.
[0027] FIG. 18 illustrates an example of a Contingency Analysis for
the occurrence of hypoglycemic episodes (dependent variable)
changing with respect to the day of the week (independent variable)
from continuous glucose monitoring.
[0028] FIG. 19 illustrates an example of a Contingency Analysis for
the occurrence of hypoglycemic episodes (dependent variable)
changing with respect to the day of the week (independent variable)
from continuous glucose monitoring.
[0029] FIG. 20 shows the rank-order of days of the week by being
most different in terms of rate of hypoglycemia from what would be
expected (that is, expected if there was no pattern across the days
of the week).
[0030] FIG. 21 illustrates an example of a Contingency
Analysis.
[0031] FIG. 22 illustrates an example of a Contingency
Analysis.
[0032] FIG. 23 is a table illustrating episodes detected for a
patient for four calendar days.
[0033] FIG. 24 is a table illustrating episodes detected and
allocated for a patient for four calendar days.
[0034] FIG. 25 is a chart illustrating glucose levels versus time
of day.
[0035] FIG. 26 is a chart illustrating glucose levels versus time
of day.
[0036] FIG. 27 exemplary data in accordance with one method
presented herein.
[0037] FIG. 28 exemplary data in accordance with one method
presented herein.
[0038] FIG. 29 exemplary data in accordance with one method
presented herein.
[0039] FIG. 30 is a plot of high glucose alarms per day versus high
glucose alarm setting.
[0040] FIG. 31 illustrates a control grid, according to certain
embodiments.
[0041] FIG. 32 illustrates a control grid displaying a centroid,
according to certain embodiments.
[0042] FIG. 33 illustrates a flowchart for a method of recommending
treatment, according to certain embodiments.
[0043] FIG. 34 illustrates a control grid, according to certain
embodiments.
[0044] FIG. 35 illustrates a flowchart for a method of recommending
treatment with multiple control grids, according to certain
embodiments.
[0045] FIG. 36 illustrates an example of a treatment recommendation
lookup table, according to certain embodiments.
[0046] FIG. 37 illustrates a control grid, according to certain
embodiments.
[0047] FIG. 38 illustrates a control grid with lines of constant
hypoglycemia rate shown thereon, according to certain
embodiments.
[0048] FIG. 39 illustrates the control grid shown in FIG. 32 with
parallel approximations of the lines of constant hypoglycemia rate,
according to certain embodiments.
[0049] FIG. 40 illustrates a control grid showing a hypoglycemia
risk zone (Hypo Risk Zone), according to certain embodiments.
[0050] FIG. 41 illustrates possible A1c target values on the
control grid of FIG. 34, according to certain embodiments.
[0051] FIG. 42 illustrates the control grid shown in FIG. 35 with a
defined target zone, according to certain embodiments.
[0052] FIG. 43 illustrates the control grid of FIG. 36 with two
additional zones, according to certain embodiments.
[0053] FIG. 44 illustrates a graph of sample data that has been fit
to a probability distribution, according to certain
embodiments.
[0054] FIG. 45 illustrates a sample point shown on a control grid
along with a cloud of points derived by using a bootstrap,
according to certain embodiments.
[0055] FIG. 46 illustrates the data shown in FIG. 41 with a line
drawn parallel to the border of the Hypo Risk Zone such that only
5% of the bootstrap points are below the line, according to certain
embodiments.
[0056] FIG. 47 illustrates a projection of one bootstrap point
along a line perpendicular to the lines of constant hypoglycemia
rate on a control grid, according to certain embodiments.
[0057] FIG. 48 illustrates a boundary line of a bootstrap cloud
that was calculated using confidence limits, according to certain
embodiments.
[0058] FIG. 49 illustrates a control grid showing a sample point,
TRP, and an anti-TRP value, according to certain embodiments.
[0059] FIG. 50 illustrates the vertical and horizontal distances
from a Hypo Risk Zone border line to a TRP point on a control grid,
according to certain embodiments.
[0060] FIG. 51 illustrates contour plots of a probability
distribution function of a patient's state of glucose control,
according to certain embodiments.
[0061] FIG. 52 shows the same distribution from the same patient
shown in FIG. 47, in the context of two different clinical risks,
namely the risk of retinopathy (horizontal lines) and risk of
severe hypoglycemia (diagonal lines), according to certain
embodiments.
[0062] FIG. 53 illustrates contour lines for 6 additional patients
plotted on the same control grid, according to certain
embodiments.
[0063] FIG. 54 illustrates the contour lines generated from a
relatively stringent testing schedule on the same 7 patients as
shown in FIG. 48, according to certain embodiments.
[0064] FIG. 55 illustrates the determination of areas where a given
testing schedule is adequate vs. inadequate in detecting a
clinically meaningful change in risk of retinopathy, according to
certain embodiments.
[0065] FIG. 56 illustrates the association between HbA1c and risk
of retinopathy and HbA1c and risk of sever hypoglycemia.
[0066] FIG. 57 illustrates a mapping a clinical risk, namely risk
of retinopathy, onto a glucose control chart, according to certain
embodiments.
[0067] FIG. 58 illustrates multiple clinical risks--clinical risk
of retinopathy and acute risk of hypoglycemia--overlaid on glucose
control chart containing data for two separate diabetic
sub-populations, according to certain embodiments.
[0068] FIG. 59 illustrates an example of the progression of a
patient's state of glucose control relative to two clinical risks,
according to certain embodiments.
[0069] FIG. 60 illustrates an example block diagram for the
software application for communicatively coupling the meter with
the remote server via a computer, according to certain
embodiments.
BRIEF SUMMARY
[0070] Presented herein are one or more software applications to
help a user manager their diabetes. Embodiments and descriptions of
the various applications are provided below in conjunction with an
analyte measurement device.
DETAILED DESCRIPTION OF THE INVENTION
[0071] Before the embodiments of the present invention are
described, it is to be understood that this invention is not
limited to particular embodiments described, and as such may, of
course, vary. It is also to be understood that the terminology used
herein is for the purpose of describing particular embodiments
only, and is not intended to be limiting, since the scope of the
embodiments of the invention will be limited only by the appended
claims.
[0072] FIG. 1 provides a front-side view of handheld analyte
measurement device, such as an analyte meter 102, in accordance
with certain embodiments presented herein. In certain embodiments,
analyte meter 102 includes a test strip port 104, a display unit
106, and at least one control button 108. In practice, an analyte
test strip (or sensor) is inserted into test strip port 104 in
order to conduct an analyte test; for example, a blood glucose
reading or a blood ketone reading. Meter 102 includes software to
analyze the sample placed on the test strip, and the results of the
analysis are typically displayed to the user via display unit 106.
The user may also use control button 108 to provide appropriate
instructions to meter 102.
[0073] In certain embodiments, meter 102 includes one or more
diabetes management software applications. The integration of
software applications with meter 102 provides an opportunity to
augment traditional glucose and/or ketone readings to provide more
useful information and feedback to patients and HCPs. As such,
meter 102, with loaded software applications, can be part of a
robust therapy management system. The software applications can be
factory pre-loaded, or installed by the user or health care
provider after first use by the user. In addition to the software
applications discussed below, meter 102 may include one or more of
the software applications described in U.S. Pat. No. 7,766,829; and
U.S. Provisional Patent Application Nos. 61/015,185; 61/262,849;
61/290,841; 61/254,156; and 61/325,155; the disclosures of which
are incorporated herein by reference in their entirety. As such, in
certain embodiments, the analytics described herein may be located
on the patient's personal device.
[0074] In certain embodiments, software applications are licensed,
acquired, or otherwise built by third-parties to be incorporated
into meter 102, or other biometric devices. Examples of the types
of software that may be incorporated include: (a) behavioral
engagement software; (b) therapy recommendation algorithms; (c)
survey applications; (d) report-generating software; etc. A means
may be provided to prevent unauthorized software from being
executed by the device. A number of methods may be used to prevent
such unauthorized execution. For example, the operating system on
the device may require a code imbedded in the software before it
would allow the software to be executed. The code may be provided
by the meter manufacturer to trusted third-party vendors.
Furthermore, the code may be generated from information provided
regarding the application, to prevent the code from being
publicized. In other words, a generation algorithm code may be
provided. The information used to generate the code may be, for
example, the company name or product name or another code. Also,
time based information may be used, such as the earliest date that
the application may be installed. Further, actual loading of the
software on the device may be prevented, using the same techniques.
For example, the installation driver located on the device may
require authorization before it proceeds with installation.
[0075] Analyte meter 102 may further include one or more internal
or external communication modules. The communication module(s) may
be used to receive and/or transmit data and/or program
instructions. The communication module(s) may also download
software applications from one or more servers. In certain
embodiments, the communication module is used to communicate with
one or more external devices; such as, for example, a medication
(drug) deliver device; a cellular phone; a laptop computer; a
mobile device, such as a PDA, iPhone, iPad, tablet computer, etc.;
a desktop computer; an analyte meter; and/or another analyte
measurement system. In certain embodiments, the communication
module can be configured for wired or wireless communication to an
external device. Wireless communication may be provided by, for
example, but not limited to, radio frequency (RF) communication
(e.g., Radio-Frequency Identification (RFID), Zigbee communication
protocols, WiFi, infrared, wireless Universal Serial Bus (USB),
Ultra Wide Band (UWB), Bluetooth.RTM. communication protocols, and
cellular communication, such as code division multiple access
(CDMA) or Global System for Mobile communications (GSM).
[0076] In another embodiment, the patient's analyte meter 102 may
include a communication module to connect to one or more servers
(e.g., networked to a "cloud"). Data obtained from an analyte test
can then be uploaded to the cloud, analyzed (automatically or
semi-automatically) in the cloud, and results of the analysis can
be downloaded to the patient's local device for review by the
patient or an HCP. As such, the patient's local device can have
limited processing capabilities because the bulk of the processing
and analysis will be performed in the cloud. Further, such a system
can allow a diabetes management company to control, manage, and/or
update software in the cloud, without having to control, manage,
and/or update software on the local devices of a large pool of
patients. As such, in certain embodiments, the analytics described
herein may be located on one or more centralized servers (i.e.,
"cloud").
[0077] Further, such system provides an opportunity to employ
systems and methods for analyzing patient analyte data based on
clinical standards, and provide expert therapy decision and support
guidance, even if the patient or her local care provider lacks the
expertise for sophisticated analyte analysis. For example,
presented herein are systems and methods that can be widely
utilized by non-diabetes expert primary caretakers. Achieving this
goal will generally improve the health of people with diabetes and
ultimately reduce health care payor costs.
[0078] In certain embodiments, the systems and methods presented
herein are applicable under the preferred architecture of a
web-based system (e.g., centralized server, cloud, centralized
database, etc.) which includes connectivity to personal devices
used by the patients. Patient devices include glucose meters,
insulin pumps, cellular phones, PDA's, mobile devices, and/or
personal computers. Intermediate connectivity devices may be
included, such as a repeater which acquires the glucose data
periodically from a glucose meter via wired or wireless
communication (e.g., Bluetooth, WiFi, cellular, etc.) and transfers
the data to a server (or intermediate server) via a standard pager
communication network. In addition, a health care provider (HCP),
or health care payor, may use a secondary device (e.g., a personal
computer, web terminal, cellular phone, etc.) to access information
from the server.
[0079] In another embodiment, the analytics described herein may be
located on an HCP's device/system. In addition, the analytics
described herein may be distributed across all the nodes of the
diabetes management system.
[0080] Provided below are exemplary analytic systems and programs,
in the form of software applications or otherwise, for use in the
diabetes management system described herein.
Software Application for Triaging Glucose Control Assessment and
Adapting Self-Monitoring Intervention
[0081] For people with diabetes who are not taking multiple daily
injections of insulin, the utility of self-monitored glucose
measurements has not been well established. The current standard of
care is to use the long-term biomarker of average glucose control
A1C to assess and titrate diabetes therapies, with little or no
regard to self-monitored glucose acquired using strips and sensors
by patients in everyday settings. However, the failure of this
approach is evident in that less than half of patients with
diabetes in the U.S. meet control targets, putting them at higher
risk for diabetes-related complications. The opportunity exists to
create a software application which is capable of identifying
underlying glucose control defects made apparent by self-monitored
glucose that are not identifiable from long-term biomarkers such
A1C.
[0082] Furthermore, for patients who do take insulin,
identification of clinically-important patterns in their
self-monitored glucose is extremely unstructured, and generally
inefficient and time-consuming for care providers. The effect is
such that the vast majority of care providers of insulin-using
patients do not even bother creating computer-generated summary
reports of downloaded self-monitored blood glucose (SMBG)
values.
[0083] Further, A1C, as a long-term biomarker of excess glucose
exposure, falls short of being able to monitor and assess important
dimensions of glucose control. Such dimensions include overall
hypoglycemia and glucose volatility, as well as periods of the day
with excess hypoglycemia, glucose volatility, and glucose exposure.
Therapeutic interventions to achieve target glucose control must
recognize the trade-off between long-term risk of complications and
acute risk of low glucose (hypoglycemia). If therapy is added
improperly, there is the possibility of causing more harm
(hypoglycemia) than good (reduction of long-term complication
risk). To mitigate this situation, self-monitored glucose
measurements may provide the necessary dimensions of control
assessment, such that therapy can be adjusted in a progressively
safe manner. Furthermore, since the physiologic defects in type-2
diabetes progress over time, there is a need to maintain monitoring
of control even once targets have been met.
[0084] In certain embodiments, there is provided a software
application that uses a clinically-rational scale of glucose
control; i.e. a "triage" scale, and associated logic for selecting
self-monitoring schedules (when and how often to measure) that are
optimized to provide assessment of the main control defect
identified on that scale. This logic is envisioned to be
therapy-dependent in that the times of day posing control risk are
expected to vary according to the type of diabetes therapy (e.g.,
single oral agent, combination therapy, background insulin only,
background insulin with other agents, pre-mixed insulin, basal and
bolus insulin, etc.) that is being utilized by the patient.
[0085] The first aspect of this application provides a rational
priority to addressing out-of-target dimensions of glucose control
provided by self-monitored glucose, as shown in FIG. 2, where the
priorities are numbered (beginning with zero) in the order that
they should be addressed. Within the categories it is proposed that
problems identified during the night take priority over those
during the day due to the increased risk of acute problems (low
glucose) being unrecognized by the patient or others while
sleeping. When self-monitored glucose data is analyzed (either on a
meter or external to a meter), this scale helps focus clinicians on
therapy adjustments which address the triaged levels of control in
a safe manner. While the general categories are fixed, the criteria
for satisfying each level may vary according to individual patient
differences or class of patient (e.g., age, therapy used, duration
of disease, diabetes type). In any case, the goal of the software
application is to focus on the optimally addressing a single aspect
of glucose control while keeping other dimensions of glucose
control in proper and safe context.
[0086] Progress along this glucose control scale can be represented
along the dimensions of interest. For example, FIG. 3 shows how the
scale maps to a grid of hypoglycemia versus glucose exposure. This
representation emphasizes that hypoglycemia needs to be addressed
first, even if it results in an increase in glucose exposure to get
control levels 2 or 3. In another example, FIG. 4 shows a grid of
glucose exposure versus glucose volatility. This representation is
more effective at contrasting control levels 2 and 3.
[0087] In another aspect of the present inventions, the software
application proposes how to allocate discrete self-monitored
glucose measurements according to glucose control level. This
aspect is intended to guide how to allocate a relatively small
number of glucose measurements per day (e.g., 1-12) over a number
of days such that the dimension(s) of glucose control of interest
is optimally measured. At minimum, the software application
proposes that there are optimal "schedules" to discrete the
self-monitoring of glucose for each glucose control levels (1-4).
The schedules are likely to be further refined according to
diabetes therapy type being used or other patient characteristics.
Furthermore, algorithms may be developed which automatically shift
between schedules as each dimension of control is assessed and
surpasses thresholds. In some cases, prior knowledge of the initial
condition of the patient, such as non-insulin-using type-2, may be
used to modify the testing schedule to reduce testing for
hypoglycemia.
[0088] To demonstrate this aspect of the invention, it is applied
to a group of patients on multiple daily injections of insulin
(MDI). Each patient had continuous glucose monitoring every 10
minutes for 14 days. Within each hour of the day, the 10th
percentile value was found. Typically this means there were 6
measurements per hour over 14 days for a total of 84 values within
each hour. These values are sorted, highest to lowest, and one
method to define the 10th percentile value is to do a linear
interpolation between the 8th and 9th lowest values (since the
84/100="8.4.sup.th" value doesn't exist). In this way, a glucose
value is found for each hour where 90% of the glucose measurements
in this hour of the day are higher than that value, and 10% are
lower. Combining all the patients, the mean and standard deviation
of their hourly 10th percentile glucose values are shown in FIG. 5.
These values are transformed into hourly hypoglycemia risk by
scaling to the 24-hour mean of the 10th percentile values by:
[1-(24-hr mean-x-hr)/24-hr mean]*100, where x is each hour of the
day, shown in FIG. 6. This shows that for this class of patients
the highest hypoglycemia risk is between 11 PM and 7 AM, shown by
having the error bars exceed the selective threshold of 130%. Two
hours of the day are compared in FIG. 7 and FIG. 8. At 4 AM, 25% of
the patients have 10% of their glucose values below 67.25 mg/dL,
representing more hypoglycemia during that hour of the day then at
9 AM (less than 10% of the patients have 10% of their glucose
values below 67.25 mg/dL). This indicates that self-monitoring
schedule to assess hypoglycemia risk should be more comprehensive
during the night then during the day.
[0089] It is important to manage glucose volatility, since excess
volatility may result in increased hypoglycemia when therapies are
adjusted to reduce overall glycemia. FIG. 9 shows the volatility
per hour of this group of MDI patients, indicating that a
self-monitoring schedule to assess volatility should be more
comprehensive during the hours of 10 PM-7 AM and 2 PM-8 PM. FIG. 10
and FIG. 11 compare the volatility of two different hours of the
day, one with the main time of concern (4 PM) and one outside the
time period of concern (9 AM).
[0090] In order to assess excess glucose exposure, FIG. 12 shows
the exposure per hour for this group of MDI patients, indicating
that a self-monitoring schedule to assess excess glucose exposure
should be more comprehensive during the hours of 7 AM-3 PM and 7
PM-12 AM. FIG. 13 and FIG. 14 compare the excess glucose exposure
of two different hours of the day, one with the main time of
concern (9 AM) and one outside the time period of concern (4
PM).
[0091] In summary, an example of self-monitoring schedules
according to the dimension of concern is shown in FIG. 15.
[0092] In another embodiment, the software application associates
specific therapies with optimal SMBG test schedules, which can be
downloaded to a glucose meter. The software application also
analyzes diabetes data based on median glucose and glucose
variability to prioritize the displayed findings. As such, the
recommended treatment will ultimately reduce median glucose without
causing hypoglycemia.
Software Application for Workflow Automation
[0093] Therapy modifications for diabetes patients have been
traditionally made based on PC software that provides HCP's with
reports that illustrate glucose management. Utilization of these
reports is time consuming and requires a high degree of expertise
by the HCP. In certain embodiments provided herein, there is
provided a method and system to automate analysis of diabetes data,
which generates treatment recommendations and relevant reports that
illustrate problems. In another embodiment, there is provided a
method and system for workflow automation. Specifically, there is
provided an optimal workflow order presented where automatic
treatment recommendations are presented and utilized as part of the
HCP's visit workflow.
[0094] In certain embodiments, there is provided a workflow that
has two phases--one targeted for clinical assistant activities
(e.g., "waiting room" workflow), and a second for the HCP (e.g.,
"exam room" workflow). The first phase involves uploading the
device data and entering data into a PC application based on a
preliminary interview with the patient. This activity can be done
by the clinical assistant. The second phase is where the
application provides a summary of the analysis to the HCP and
provides a means for the HCP to determine and record treatment
modifications, all provided in an interactive display. The HCP can
review these results, which would provide detailed findings,
reports associated with these detailed findings, questions to the
patient associated with the findings, and treatment recommendations
from which to select. Answers to questions, selected treatment
recommendations, and selected reports to illustrate findings and
treatment decisions, or to educate the patient, would be
recorded.
[0095] FIG. 16 is a flowchart in accordance with certain
embodiments of the present invention. In FIG. 16, the boxes noted
1-6 generally correlate with the above-described "Phase 1." Boxes
noted 7-12 (and TBD) generally correlate with the above-described
"Phase 2."
[0096] In certain embodiments, there is provided a workflow method
as follows:
1) Phase 1
[0097] i. Inputs to the automated logic are retrieved [0098] 1.
Glucose data and any other pertinent logged data are uploaded from
the glucose measurement device. [0099] 2. Patient identification
and specific information is retrieved based on the uploaded device
serial number and a prompt is presented to allow confirmation or
correction. [0100] 3. Current patient treatment is retrieved based
on the uploaded device serial number, and a prompt is presented to
allow confirmation or correction to this treatment. [0101] 4. A
prompt is presented for entry of any recent test information, such
as A1C or blood pressure. [0102] 5. A prompt is presented for entry
of any other information that is required as an input to the
analysis logic. [0103] 6. A prompt (optional) is presented for
patients to describe any conditions or problems they wish to
discuss during the exam with the HCP. This may or may not impact
the analysis logic. An example situation, which would impact the
analysis, would be if the patient selects a specific time period(s)
that was difficult to manage and wishes to discuss/review with the
HCP. Such prompts are provided for the patient to enter additional
information that is not typically used as an input to the analysis
logic (e.g., "unguided" or open-ended questions that can be
answered by the patient). [0104] ii. Analysis logic process is
performed using the above inputs
2) Phase 2
[0104] [0105] i An interactive display (e.g., Box 7 in FIG. 16) is
presented that includes: [0106] a. results of the analysis
("Findings" and "Actions to Consider"), structured by the important
periods of the day for diabetes management: "Fasting" and
"Post-Meal", and (optionally) a standardized report; [0107] b.
findings may be color-coded, or otherwise annotated, to indicate
negative and positive findings; [0108] c. means to expand and
collapse Findings, where Findings can be expanded to detail
findings and actions to consider, questions to ask the patient, and
treatment recommendations (e.g., results from the analysis); [0109]
d. means to navigate to a report pertinent to a particular finding;
[0110] e. means to navigate to HCP-oriented reference materials
pertinent to a particular finding and/or action to consider; [0111]
f. means to naviagate to patient-oriented diabetes self-care
education materials pertinent to a particular finding and/or action
to consider [0112] g. means to select or deselect the
patient-oriented diabetes self-care education materials pertinent
to a particular finding and/or action to consider for printing or
electronic delivery to the patient [0113] h. means to add
HCP-selected detailed reports to a summary report for long-term
storage (e.g., "journal" specific reports of value for medical
charting or patient education); [0114] i. means to navigate to
entry screen pertinent to a particular question or treatment
recommendation; [0115] j. a displays of entry screen inputs for
questions answered; [0116] k. a displays of entry screen inputs for
selected treatment recommendations; and/or [0117] l. a displays
entry screen for ad hoc questions answered and treatment
recommendations. [0118] ii A treatment order is prepared based on
the selected treatment recommendations and the results of the
analysis and HCP input (e.g., Box 8 in FIG. 16).
[0119] The selected treatment recommendations may be text fields
and may include modification of treatment fields. The interactive
display may present this information in a dashboard format, a tab
format, an icon based format or some other means. Other interactive
fields may be added to this interactive display, such as means to
compare treatments and treatment results from prior clinical
visits.
[0120] The described workflow may be extended to include two or
more analysis stages. For instance, the first analysis stage may be
as described above, but may generate additional questions to be
asked of the patient that may form inputs to a second analysis
stage (along with inputs to the first analysis stage and outputs
from the first analysis stage).
Software Application for Identifying Episodes
[0121] In certain embodiments, there is provided a process for
identifying features of glucose measurements (i.e., "episodes"),
collecting supporting details about these episodes, using
statistical methods to assess if any particular conditions or
actions appear to influence the likelihood of these episodes
occurring, and describing this analysis in a clinically-meaningful
way to better inform health-care providers about the status of
their patients and potential next steps in their treatment and
disease management. FIG. 17 provides a flowchart in accordance with
said process.
[0122] HCPs who use current systems of glucose data management and
assessment still request further assistance from these systems to
help answer basic questions of clinical relevance. For example,
HCPs want to know if a patient is experiencing hypoglycemia more
often on some days of the week compared to others. Provided herein
is a method to address questions of this nature, with both
statistical and clinical input, to arrive at answers and suggested
clinical actions.
[0123] In certain embodiments, there is provided a method to define
episodes using clinical criteria. The clinical criteria may be
universally applied for many patients of similar disease state and
therapy, or customized for individual patients. Episodes are
generally a categorized period with the possible mutually exclusive
values of "Yes," "No", or "unknown." The "unknown" category would
apply if a period of time has no data to relate whether an episode
may or may not have occurred.
[0124] Episodes are further generally categorized by
clinically-relevant criteria. Examples may include episodes of
hypoglycemia, hyperglycemia, rapid glucose increases, rapid glucose
drops, etc. Examples here will focus on episodes determined from
glucose monitoring, but may be extended to any other frequently
monitored behavior, activity, or biological parameter.
[0125] In certain embodiments, there is provided a method to
collect details of episodes. Details of episodes are collected for
further analysis, such as: 1) when the episode started and ended;
2) the day of the week the episode occurred; 3) the week of the
month the episode occurred; 4) how long before or after the episode
an activity was performed; 5) the glucose levels or statistical
summaries of the glucose levels before, during and after the
episode; 6) the source of the glucose measurements (say "continuous
glucose monitoring" or "self-monitored blood glucose").
[0126] The method further includes finding and allocating episodes
over a monitoring period. Allocation may be assigned according to a
number of possible clinically relevant reasons, such as: week of a
month, day of the week, time period in a day, behavior or
activities (taking medication, eating, sleeping, exercising, etc),
glucose value, glucose rate of change, state of health of the
patient, etc.
[0127] As one example, the allocation may be done over each hour of
the monitoring period being evaluated. For example, if three months
have elapsed since monitoring has been initiated by the patient,
there may be approximately 90*24=2160 hours of monitoring where an
episode can be allocated as "Yes," "No," or "No monitoring
available (unknown)."
[0128] As another example, the allocation may be done according to
the measured glucose value, and whether an episode occurs within
some future time period. Alternatively, when an episode does occur,
descriptive details of that episode can be allocated (such as the
maximum glucose rate of change within 1 hour before the episode)
and statistically and clinically relevant patterns can be
investigated.
[0129] The method further includes identifying patterns using
statistical criteria. Once allocated over the monitoring period of
interest, statistical methods can be used to determine how likely
occurrences of episodes (the "dependent variable") relative to any
possible independent variable (i.e. "clinically relevant reason")
is to be observed simply by mere coincidence. If the likelihood of
coincidental observation, i.e. "by mere chance or luck," is low
(say less than 1 in 20), then independent variables may be
identified as having a strong association with the occurrence of
episodes, and be deemed worthy of further investigation or
discussion between the patient and healthcare provider, and
interpretation by the healthcare provider to suggest therapy or
behavior modifications.
[0130] A statistical method for this technique is the "Contingency
Analysis." Shown FIGS. 18 and 19 are examples of a Contingency
Analysis for the occurrence of hypoglycemic episodes (dependent
variable) changing with respect to the day of the week (independent
variable) from continuous glucose monitoring.
[0131] For the shown example, the statistical methods indicate a
very low likelihood that the increased frequency of hypoglycemia on
Sunday and Saturday is mere chance coincidence. In fact, the
statistics indicate the likelihood of coincidentally seeing this
pattern of hypoglycemia across the days of the week is smaller than
1 in 10,000. Therefore it's reasonable to assume that something
about this patient's behavior or therapy on Saturday and Sunday is
different from the rest of the week in regards to having
hypoglycemic episodes. Therefore, a possible output of the system
may be to summarize these findings: "Statistical analysis indicates
the likelihood of coincidentally seeing this pattern of
hypoglycemia across the days of the week is smaller than 1 in
10,000.
[0132] The rank-order of days of the week by being most different
in terms of rate of hypoglycemia from what would be expected (that
is, expected if there was no pattern across the days of the week),
are shown in FIG. 20. Therefore, a possible output of the system
may be to summarize these findings: "Consider evaluating therapy
and/or behavior differences for those days that are at the extremes
of being different from expected compared to those that are similar
to expected." Or, "Consider ways to address the extremes and reduce
hypoglycemic episode occurrence overall."
[0133] Similar logic can be used for patterns related to hours of
the day and patterns derived from SMBG. However, it is likely that
with infrequent SMBG, the most likely outcome is to have too few
monitoring periods to reach statistical significance, as shown in
FIGS. 21 and 22. Therefore, a possible output of the system may be
to summarize these findings: "The statistical analysis indicates
the likelihood of coincidentally seeing this pattern of
hypoglycemia across the days of the week is bigger than 1 in 4.
Therefore no patterns of hypoglycemia across days of the week
should be concluded, as they are too likely coincidental or `mere
chance` observations."
[0134] In addition, one output of the system may be to indicate
that there are not enough measurements to execute the statistical
analysis accurately. By convention, the rules are that the minimum
expected number in any cell is 1 and that no more than 20% of the
cells have an expected value of less than 5.
[0135] This invention would be incorporated into glucose data
management software, either on a device or external to the glucose
monitoring device. It would primarily be targeted to health care
providers who guide therapy decisions to manage glucose levels. It
would aid in investigating and relating clinically significant
glucose measurements of interest with particular behaviors or
therapy choices that their patient is performing. From there, the
clinician may use his or her judgment to suggest effective actions
for the patient to take in order to mitigate the observations and
improve their disease control.
[0136] In certain embodiments, there is provided a process for
creating summaries of patient-specific diabetes management
information. Such information includes glucose (blood and
interstitial), other analytes (e.g., blood lipids, albuminuria),
markers of kidney (SrCr, eGFR) and liver (ALT, ULN) function, and
daily activity information (meals, medication, exercise, disease
symptoms). In certain embodiments, this information is used in an
analytical tool that includes expert logic to drive automatic
treatment recommendations. The information is used in a manner
designed to make clinical review and therapeutic change decisions
more rapid, consistent, and accurate.
Software Application for Providing Automatic Treatment
Recommendations
[0137] Using glucose measurements to assess and determine changes
to diabetes management and therapy intervention is a complicated
process. HCPs in the field have repeatedly requested methods for
"guidance" and "recommendations" for decisions that may help their
patients better manage their disease. Historically,
state-of-the-art methods for analyzing and summarizing glucose
measurements have lacked clear links with therapy changes, and
therefor automatically producing recommendations based on a priori
logic has not be achieved in any broad sense.
[0138] Provided herein is a process for automatic and/or
clinician-guided interpretation of extreme glycemic episodes for an
individual with diabetes in order to improve selection of therapies
and self-management intervention(s).
[0139] For example, in certain embodiments, there is provided a
method for providing automated treatment recommendations by: (1)
allocating episodes; (2) displaying patterns; and (3) allocating
episode chains. A more specific description is provided below.
[0140] 1. Allocating episodes in "initiators" and "follow-on"
episodes, and linking episodes into "chains," that start with a
single "initiator" and may or may not continue with one or more
"follow-on" episodes. An initiator episode with no follow-on
episodes is considered here to be a "single-link episode chain." An
initiator episode with two follow-on episodes is considered a
"3-link episode chain," and so on. All of these are considered
"episode chains." This allocation of "chains" allows focusing on
self-management problems that do or do not start a series of
negative effects. Review of glucose measurements may reveal an
episode was treated inappropriately, and thus initiated one or more
additional episodes that the individual had to handle. A specific
example would be a high glucose episode that is treated too
aggressively, such that a rapid fall episode and a low glucose
episode occur. This low glucose episode may also be treated
inappropriately, and therefore a rise episode occurs, followed by
another high glucose episode. This example shows the
"roller-coaster" effect that can be the hallmark of
difficult-to-manage diabetes. From a clinical perspective, however,
the best place to intervene in self-management behavior is likely
with the "initiating" episode. If the high glucose is treated
differently, or is avoided altogether, then the train of episodes
following may be reduced or removed effectively.
[0141] 2. Displaying patterns of episodes relative both to
time-of-day and time of initiation of a chain of episodes. Rather
than a conventional 24-hour "modal day" plot, this invention plots
additional "follow-on" episodes beyond the 24-hour time-axis if the
chain extends past midnight of the day the chain initiated.
[0142] 3. Allocating "episode chains" (defined here to minimally
include a single "initiator" episode, and may have one or more
"follow-on" episodes) into clinically relevant categories. This
allocation is done according to the attributes of the chain, and
may be done automatically to help frame the type of therapy
interventions the clinician should likely consider for the
individual under his or her care. Possible attributes of the chain
are detailed more specifically below, but may be considered to
include things such as: a) start time (or "time-of-day") of the
chain; b) glucose value at the start of the chain; c) presence or
absence of a continuous glucose monitoring threshold or projected
glucose alarm; d) order of the types of episodes in the chain,
e.g., a "High-High" chain would be allocated differently than an
"High-Low" chain; and e) user actions (or inactions) before or
during the chain. An "action" may be eating, taking medication,
exercising, or other actions that may affect their diabetes
management and glucose control.
[0143] An example patient may have glucose measurements that are
summarized into a list of Low, High, Rise and Fall episodes, as
shown in FIG. 23. These episodes are then allocated to "initiators"
and "follow-on" episodes. Methods for determining initiator versus
follow-on episodes may be based optimal selection of one or more of
the following criteria: 1) time elapsed since previous episode, for
example, if sufficient time has passed after an episode, the next
episode may automatically be considered an "initiator," which would
be based on the idea that therapeutic or other actions that affect
glucose only have a finite period of influence on the glucose; 2)
time of day of the episode; 3) the order or type of episodes, for
example, all Rise episodes followed by a High episode may be
considered "initiators;" and 4) type of actions the individual has
recently performed, for example, rapid-acting insulin typically has
about 3 to 6 hours of action, while long-acting insulin may be 12
to 24 hours of action. Some evidence suggests exercise may have 1
to 24 hours of action. Eating may have 1 to 8 hours of action,
depending on the composition of the meal. Each of these actions,
and their relevance to diabetes self-management, may suggest
linking or unlinking (and therefore start a new initiating episode)
episodes. Using a combination of these criteria, the example data
is allocated, and shown in FIG. 24. This allocation establishes
three "episode chains," all initiated, in this example, by Rise
episodes (Nov. 4, 2006, 8:56 AM; Nov. 5, 2006, 6:57 AM; Nov. 13,
2006, 9:16 PM).
[0144] For the purposes of reviewing and displaying episode chains,
certain embodiments of the proposed invention is to display them
relative to time of day, as shown in FIG. 25.
[0145] For the purposes of reviewing and displaying episode chains,
certain embodiments of the proposed invention is to display them
relative to time of day and time of episode chain initiation. In
this case, if a follow-on episode occurs after midnight of the
chain initiation, it is shown on subsequent 24-hr periods, expanded
to cover as many 24-hour periods as necessary to show the entire
episode chain. An example is shown in FIG. 26.
[0146] Once episode chains have been identified and displayed, the
chains can also be automatically, or with clinician guidance, be
assigned to clinically relevant categories. This allocation is done
according to the attributes of the chain, and may be done
automatically to help frame the type of therapy interventions the
clinician should likely consider for the individual under his or
her care. Examples of possible attributes of the chain that may be
considered to include: a) start time (or "time-of-day") of the
chain initiator, wherein daytime episodes may have different
interventions than nighttime episodes; b) time of day of the chain
follow-on episodes, wherein episodes occurring during awake versus
sleep periods may be managed differently; c) glucose value at the
start of the chain, wherein, for example, Rises that start less
than 80 mg/dL may be assumed to be a rapid correction of a low
glucose, whereas a Rise that starts above 80 mg/dL may be assumed
to be due only to a typical meal intake; d) presence or absence of
a continuous glucose monitoring threshold or projected glucose
alarm, wherein, for example, a Rise following a glucose alarm may
indicate appropriate or inappropriate management of low glucose
situations; e) order of the types of episodes in the chain,
wherein, for example, a "High-High" chain would be allocated
differently than a "High-Loud" chain; and f) user actions (or
inactions) before or during the chain, wherein, for example, an
action may be eating, taking medication, exercising, or other
actions that may affect their diabetes management and glucose
control.
[0147] This invention would be incorporated into glucose data
management software, either "on device" or external to the glucose
monitoring device. It would primarily be targeted to health care
providers who guide therapy decisions to manage glucose levels. It
would aid in investigating and relating clinically significant
glucose measurements of interest with particular behaviors or
therapy choices that their patient is performing. From there, the
clinician may use his or her judgment to suggest effective actions
for the patient to take to mitigate the observations and improve
their disease control.
Software Application for Estimating Impact of Glucose Monitoring
Alarm Adjustments
[0148] In certain embodiments, there is provided a method for
estimating the impact of glucose monitoring alarm adjustments on
glycemic control.
[0149] People who monitor their glucose with continuous glucose
monitoring systems that have real-time glucose alarms are faced
with the trade-off of optimally setting the high glucose alarm to
achieve glycemic control goals without being burdened by excessive
alarms. Excessive alarms can lead to "alarm fatigue," or "nuisance
alarms," whereby the alarms interfere with the patient's daily
activities and can often lead to disuse of the monitor. Both
patients and HCPs would benefit from a tool that helps establishes
the trade-offs inherent in setting the high glucose alarm, sets
expectations of the frequency of alarm while taking measures to
improve glycemic control, and estimates potential benefits of
improved glucose control.
[0150] Provided herein is a method for retrospectively analyzing
glucose data to estimate the potential improvement in glucose
control if the high glucose alarm were adjusted lower, while
estimating the increased number of alarms that the patient may
expect to have to manage. Given a set of continuous glucose
measurements for a patient (typically 5 days or more), the method
iteratively lowers a theoretical high glucose alarm threshold, for
example from 240 to 160 mg/dL in 20 mg/dL steps. At each iteration,
glucose values that exceed the high glucose alarm setting are
replaced with the value of the high glucose alarm setting. Then,
the overall average glucose is calculated to estimate the
improvement in glucose control that may be expected at that high
glucose alarm setting. Thus, at each iteration of the high glucose
alarm setting, at least two values are output: 1) estimated average
glucose; and 2) estimated number of high glucose alarms. The number
of high glucose alarms may be normalized across time, for example,
the number of high glucose alarms per day. More detailed summaries
of the estimated glucose control may also be calculated. For
example, the hourly average glucose across a "modal 24-hr day" may
be calculated, to see how changes in the high glucose values will
change the typical 24-hr profile of the glucose.
[0151] The patient and clinician can review the analysis and make a
more informed decision of the value of activating the high glucose
alarm at a lower value, both the potential benefits and burdens.
Current glucose data management systems do not facilitate
estimating the impact of changing the high glucose threshold on the
overall glycemic exposure a patient may expect. This method
improves the understanding of the trade-off of more vigilance
towards treatment of high glucose and tolerance of increased high
glucose alarm occurrences with expected long-term reduction in
excessive glucose exposure.
[0152] FIGS. 27-29 show an example of three iterations of the
method. FIG. 27 shows an example of "original" data, that is, no
replacement of glucose values by the high glucose setting has
occurred. This data may or may not have had the high glucose alarm
activated. If it were, then the actual experience of glucose alarm
activation may be summarized. FIG. 28 shows an example of the
hourly glucose distribution after estimating the impact of setting
the high glucose alarm to 200 mg/dL. The estimated overall average
glucose is seen to move closer to the target average glucose. Also,
the hourly average glucose is seen to be impacted during parts of
the day when the glucose was elevated above the high glucose alarm
setting. FIG. 29 extends the example for the setting of the high
glucose alarm to 160 mg/dL.
[0153] More specifically, FIG. 27 is a modal plot of glucose, and
summary statistics table, with original data (no estimation of
alarm change). Plotted are the 90th (gray open squares), 75th (gray
solid squares), 50th (black solid squares), 25th (gray solid
squares) and loth (gray open squares) percentile lines, the hourly
average glucose (red dots), the overall average glucose (black
horizontal dashed line), predicted average glucose (red horizontal
dashed line), and target average glucose (green horizontal line),
over the 24-hour period. FIG. 28 is a modal plot of glucose and
summary statistics table, estimation of high glucose alarm change
to 200 mg/dL. Plotted are the 90th (gray open squares), 75th (gray
solid squares), 50th (black solid squares), 25th (gray solid
squares) and 10th (gray open squares) percentile lines, the hourly
average glucose (red dots), the overall average glucose (black
horizontal dashed line), predicted average glucose (red horizontal
dash line), and target average glucose (green horizontal line),
over the 24-hour period. The predicted average glucose has been
reduced by 8 mg/dL compared to the overall average glucose. FIG. 29
is a modal plot of glucose and summary statistics table, estimation
of high glucose alarm change to 160 mg/dL. Plotted are the 90th
(gray open squares), 75th (gray solid squares), 50th (black solid
squares), 25.sup.th (gray solid squares) and loth (gray open
squares) percentile lines, the hourly average glucose (red dots),
the overall average glucose (black horizontal dashed line),
predicted average glucose (red horizontal dashed line), and target
average glucose (green horizontal line), over the 24-hour period.
The predicted average glucose has been reduced by 8 mg/dL compared
to the overall average glucose.
[0154] FIG. 30 shows a summary of estimated impact of high glucose
alarm setting on high glucose alarms per day and average glucose.
More specifically, FIG. 30 shows an example summary of iterations
of estimating the impact on average glucose and number of high
glucose alarms as the high glucose alarm setting is changed. It may
be assumed that the right-most data point is either "actual"
(experienced by the patient while wearing the device) or
"simulated," for example if the patient has not activated the high
glucose alarm during the monitoring period that is being reviewed.
As the high glucose alarm setting is reduced, the overall average
glucose is reduced, and the number of glucose alarms increases.
However, the invention here proposes estimating these values from
individual monitoring (e.g., CGM) data, and therefore is more
personalized than population-based methods or other methods that
summarize other patients high glucose alarm frequency.
Glucose Metric Mappings to Diabetes Treatment Recommendations
[0155] In some aspects of the present disclosure, there is provided
a method of mapping metrics generated from glucose results in a
manner for determination of an appropriate treatment recommendation
or modification for a patient based on the glucose results. For
example, the metrics are mapped and the appropriate treatment
determined such that the risk of hypoglycemia is taken into account
and minimized. Furthermore, in some aspects of the present
disclosure, there is provided a device or system that implements
such methods and/or displays such mapped metrics generated by such
method. For example, such method may be implemented as software
within a CGM or SMBG device or system that is executed by one or
more processors in the device or system. It should be appreciated
that the following discussion relating to the mapped metrics may
apply to the method of mapping the metrics, as well as the device
or system that implements such methods and/or displays such mapped
metrics generated by such method.
[0156] In certain embodiments, the metrics used are glucose median
and glucose variability, calculated for a specified period of time.
Variability (or volatility) may be estimated using many different
possible metrics. For following example, the lower 40% percentile
is used to represent variability. Median is chosen as it is less
sensitive to outliers than the mean. However, it should be
appreciated that mean or other type of average, or other metric
that represents central tendency of data, may be used in other
embodiments.
The Glucose Control Grid
[0157] The glucose median and variability may be illustrated
graphically where, for instance, the median is represented along
the y-axis and the variability is represented along the x-axis. As
described below, this graph is divided up into zones that represent
possible treatment recommendations. This graph is referred to
herein as the "control grid" or "control chart". These zones may
be, for example, represented mathematically and implemented in
software to provide automated therapy recommendations based on
glucose data. In addition, the control grid itself may be displayed
to the health care provider (HCP) and/or patient by the
software.
[0158] FIG. 31 illustrates a control grid, according to certain
embodiments. The control grid includes glucose median on one axis
and variability on the other axis. A patient's glucose median and
variability can be plotted as a point on the control grid. The
uncertainty in the estimate of the median and variability may be
plotted here, for instance, as represented by a cloud of points, or
a "bubble", or some other representation, as described herein. For
example, in some instances, a boundary line for the cloud of points
may be displayed in place or in addition to the cloud of points.
These metrics are compared to the lines defining the zones--e.g.,
the Hypoglycemia Risk Zone, Target Zone, Buffer Zone, and "Safe to
Titrate" Zone, as described below. It is appreciated that there are
many possible metrics that can be used for comparison, such as the
centroid or "best estimate" of the metric, or the confidence point
(e.g., 95% confidence point) of the metric (referred to as the
Treatment Recommendation Point--TRP), as illustrated in FIG. 32.
Other possible metrics can be readily contemplated.
[0159] On the control grid shown in FIG. 31 there are four zones
defined. In certain embodiments, the Hypo Risk zone is defined as
the region below the hypo risk line where it is determined that, if
the TRP falls below, the patient is at an unacceptable risk of
hypoglycemia. In this case, the displayed treatment recommendations
would be related to reducing the patient's glucose variability
and/or increasing the patient's glucose median. For instance, one
specific recommendation related to reducing glucose variability
would be for the patient to eat more regularly. A specific
recommendation related to increasing glucose median may be to
reduce the dose or dose rate of glucose-lowering medication, for
example.
[0160] The Target zone is the ultimate goal for the patient and HCP
(e.g., doctor or other health care professional). In certain
embodiments, the Target zone is defined as being above the Hypo
Risk line and below a Target line--the Target line can be adjusted
by the HCP to provide an achievable treatment goal appropriate for
a particular patient. For example, in certain embodiments, the
patient is in the Target zone if a) the TRP is not below the Hypo
Risk line and b) the metric centroid falls within the Target
zone.
[0161] In certain embodiments, the Buffer zone is defined as the
region above the Target zone and the Hypo Risk zone, but below a
line defined as an offset above the Hypo Risk zone. This offset is
representative of the possible or expected drop in median due to an
increase in glucose-lowering mediation, for example. For instance,
this zone may represent the region where, if the TRP was contained
within it, it would be unsafe to recommend an increase in
medication, since it may drive the patient into the Hypo Risk zone,
assuming that glucose variability did not change. In such case, for
example, the displayed recommendation may be related to reducing
the patient's glucose variability.
[0162] In certain embodiments, the "Safe to Titrate" zone is
defined as the region where the TRP is above the Buffer zone and
above the Target zone. For example, here the recommendation may be
related to increasing the patient's glucose-lowering medication
dose in order to reduce their median glucose.
[0163] FIG. 33 illustrates a flowchart for a method of recommending
treatment using mapped metrics, according to certain embodiments.
In the embodiment shown, SMBG test data is provided and a metric
(e.g., glucose median and variability) is estimated as well as the
metric uncertainty due to sub-sampling. A confidence bubble (e.g.,
95% confidence bubble) is estimated and compared to the various
zones (e.g., hypo risk zone, target zone, safe-to-treat zone) to
determine an appropriate treatment recommendation. In some
instances, the "in-target" comparison may be based on "expected"
metric rather than 95% confidence. Further, the current medication
dosage may be considered in determining the appropriate treatment
recommended.
[0164] It should be appreciated that the embodiments shown are
exemplary and that the control grid may be fashioned a number of
different ways in other embodiments. For example, the straight
lines in the embodiments shown may be curve--e.g., the Hypo Risk
line.
[0165] As another example, a control grid embodiment is shown in
attached FIG. 34 which illustrates two modifications to the
previous Control Grid embodiment shown in FIG. 31. The first
modification is the removal of the Buffer zone and replacement with
recommendations displayed that specifically indicate the distance
from the Hypo Risk line. For instance, for a TRP that is located 30
mg/dL above the Hypo Risk line in the "Safe-to-Titrate" zone, the
recommendation may read "Increase dose of glucose-lowering
medication, margin to safely reduce median glucose is 30 mg/dL".
The modified recommendations may indicate both the margin above and
below the Hypo Risk line. For instance, for a TRP that is located
10 mg/dL below the Hypo Risk line, the recommendation may read
"Decrease dose of glucose-lowering medication, a 10 mg/dL increase
in median glucose is needed to reduce hypoglycemia risk to a safe
level". Alternatively, the recommendation may indicate the positive
or negative horizontal distance from the Hypo Risk line, in terms
of variability reduction. Combinations of these are also
contemplated. In some instances, one reason to eliminate the fixed
buffer zone is that dose increments may achieve different glucose
median reductions, depending on the medication used or the
patient's physiology. Another embodiment is to provide a mechanism
where the HCP can modify the Buffer zone depending on these factors
that could impact glucose median reductions.
[0166] The second modification to the Control Grid shown in
attached FIG. 34 is the addition of a vertical Variability line
used to drive variability related recommendations. In the control
grid shown, some or all of the zones are further divided into
sub-zones. In the sub-zones where the centroid metric is to the
right of the Variability line, variability related recommendations
are provided. Where the centroid metric is to the left of the
Variability line, variability related recommendations are not
provided. The Variability line may be defined as fixed at a
specific location on the x-axis; that is, at a specific variability
value. In certain embodiments, the x-axis location of the
Variability line depends on the Target line and/or the Hypo Risk
line. For instance, the location may be determined by the
intersection of the Target line and the line defined as 50 mg/dL
above the Hypo Risk line. This provides for variability
recommendations appropriate for the target set for a specific
patient.
[0167] In some embodiments, a control grid includes a buffer zone
at an offset above and/or below the Hypo Risk line. For instance,
if the TRP is within this zone, then the recommendations does not
include a recommendation for medication adjustment. Outside this
zone, the recommendation includes a medication adjustment
recommendation. In some embodiments, a control grid includes a zone
defined by the Hypo Risk zone divided by the Target line. For
example, for a centroid metric above this line, the recommendation
does not include decreasing medication; but below the line, the
recommendation includes decreasing medication. It should be
appreciated that these embodiments are exemplary and demonstrate
how alternative zones may be designed and utilized in various
embodiments.
[0168] In some embodiments, zones may also indicate multiple
recommendations at varying degrees of importance. The degree of
importance may be indicated, for example, by the order in which
they are listed, or by color coding the recommendations, or by any
other appropriate means.
[0169] In some embodiments, recommendations may also include other
factors not directly related to treatment. For example, the
recommendations may pertain to the need to increase SMBG sampling
frequency. Additional sub-zones may be included in the control
grid, for instance, such that when the TRP is below the Hypo Risk
line, but the centroid metric is above the Hypo Risk line, the
recommendation includes reduction in variability and the need to
increase sampling frequency in order to reduce uncertainty in the
metric. The sampling frequency increase recommendation may also be
generated by comparing the size of the "uncertainty bubble" to a
predetermined size and if the bubble crosses one or more of the
lines on the grid, then an increase in sampling frequency is
recommended, for example. Various measures of "uncertainty bubble"
size may be contemplated, including a figure of merit of the
distance between the centroid and the TRP.
Configuration of Control Grid Logic
[0170] In some aspects of the present disclosure, the parameters of
the control grid may be modified by the HCP. For example, the
software that implements the automated therapy recommendation logic
provides an interface, such as a popup screen or window, for the
HCP to modify the control grid--e.g., alter the lines on the
control grid, select certain features of the control grid, etc. For
instance, in certain embodiments, the HCP may select from a list of
possible Target levels and Hypo Risk levels. The Target levels on
the list may be associated with various diabetes complication
statistics such as corresponding A1c. For instance, it may be more
acceptable for a patient with A1c of 10% to have a near-term target
of 9% rather than 7% so as not to be discouraged. The Hypo Risk
levels may be adjusted as necessary to tailor to a patient's
tolerance of hypoglycemia. The Hypo Risk pick list labeling may be
associated with expected frequency of hypoglycemia, a relative
measure of hypoglycemia risk such as High, Medium, Low, or any
other appropriate labeling.
[0171] In the software, the Recommendation algorithm may be
initially run with default parameters (either predefined in the
code or set to the last algorithm run for that patient from a
previous doctor's visit). In some embodiments, a popup window or
other interface may be provided to allow the HCP to alter one or
more of these algorithm input parameters as needed, and the
algorithm is rerun, generating new recommendations.
Control Grid by Time of Day
[0172] In some aspects of the present disclosure, the control grid
based algorithm is used to process data for specific time periods
of the day or relative time periods related to key events. For
example, four key time periods may be defined as overnight/fasting
(12 am-8 am), post breakfast (8 am-12 pm), post lunch (12 pm-6 pm),
and post dinner (6 pm-12 am). Glucose data collected for multiple
days can be grouped into these time periods and the control grid
algorithm run for each group. In this way, recommendations that are
specific to time periods may be generated. For instance,
variability recommendations may be generated specific to meals or
overnight. For patient's whose treatment is multiple daily
injections (MDI) of insulin, the time-period targeted
recommendations may be specific to insulin needs during these times
of day. For instance, the control grid for the over-night/fasting
period may indicate that medication dosage should be increased; the
recommendation may indicate that the patient's long-acting insulin
dose should be increased.
[0173] In some instances, multiple control grids may be used. The
treatment recommendation logic may in some cases be more
complicated when multiple control grids are used. An example of
this logic is shown in the attached FIG. 35. In the embodiment
shown in FIG. 35, the method is repeated for different time-of-day
(TOD) periods--e.g., post breakfast (PB), post lunch (PL), post
dinner/early sleep (PD) and late sleep/walking (W). The results
from each time period analysis may be used to determine an
appropriate treatment recommendation. For example, as shown, the
results for each time-of-day period may be prioritize and/or
weighed to determine the appropriate treatment recommendation.
[0174] In some aspects of the present disclosure, the control grid
algorithm is applied to time periods defined relatively to events.
For example, data grouping may be determined a) 4 hours past
breakfast, b) 4 hours past lunch, c) 4 hours past dinner, and d)
4-10 hours past dinner. It should be appreciated that various
permutations of this example may be implemented in other
embodiments. The data groups may then be processed by the multiple
control grid algorithm described above.
Second-Stage Logic to Drive Recommendations
[0175] In some aspects of the present disclosure, there is provided
an additional logic process to narrow down possible
recommendations. For example, the treatment recommendation
described above using the control grid algorithm may be augmented
to include a second-stage process to further narrow the possible
recommendations that may be made. For instance, there are many
different recommendations for reducing glucose variability, such as
"stop snacking", "don't forget to take your medication", "don't
miss meals", "adjust correction dose of insulin". A glucose control
zone may be associated with a number of these recommendations. A
second-stage process may be implemented to narrow down the list of
recommendations. For example, this second-stage process may utilize
detection of episodic patterns to narrow the list of
recommendations. For instance, if an instance of low fasting
glucose is detected preceded by a post-dinner high glucose, this
may be an indication of occasional correction dosing to mitigate a
high glucose value, and the logic could direct the recommendation
to only include "adjust correction dose of insulin". In some
instances, the process may require a certain frequency of
occurrence of an episodic pattern.
Recommendation Structure and Logic Integrated with Treatment
Stage
[0176] In some aspects of the present disclosure, the mapping of
glucose data to treatment recommendations may be implemented with
the use of a lookup table. For example, the inputs to the lookup
table are the output of the control grid analysis and the current
treatment and treatment stage. The outputs of the lookup table are
recommendations of different types that are displayed. FIG. 36
illustrates an example of a treatment recommendation lookup table,
according to certain embodiments. For the embodiment shown,
multiple recommendations may be associated with a single input
combination. It should be appreciated that while a lookup table has
been used for exemplary purposes, the concept of a lookup table may
easily be extended to more complex glucose metric to recommendation
mappings.
[0177] In some instances, recommendations may comprise text that is
directly displayed, as indicated in the column labeled "Recommended
Text" in FIG. 36. In some embodiments, the recommendations may
comprise links to source documents and specific pages of the source
documents. The content of these source documents may provide more
detailed instructions regarding treatment changes. For instance,
for a recommendation to change dosage of a medication or a change
in treatment, the source document may be published instructions for
medication start and adjustment, and the link may be specified to
present the appropriate page of these instructions. In some
embodiments, recommendations may comprise questions that are
displayed--e.g., questions displayed to guide or otherwise assist
the HCP in interviewing their patient to uncover underlying issues
in self-care management. These questions may be in the form of text
to be directly displayed, or reference material. In some
embodiments, recommendations may also comprise guidance about
testing frequency or how to alter algorithm input parameters.
Again, the information is targeted based on analysis of the
patient's glucose tests. It should be appreciated that one or more
of the various recommendations may be implemented. Further, as
illustrated in FIG. 36, recommendations may be tailored to current
treatment.
[0178] It is appreciated that additional types of recommendations
or outputs associated with the inputs to the lookup table may be
implemented, including for example: links to sources of
definitions, links to appropriate pages of a user guide, links to
graphical displays of the data appropriate to illustrate the
glucose analysis finding and recommendation, etc. In some
instances, for example, the links may be instantiated by the user
via buttons (e.g., buttons associated with the recommendation and
placed when needed), or they could be instantiated similarly with a
hotspot, or could automatically present the linked information in a
popup window or a window dedicated for this information.
[0179] In certain embodiments when recommendations are to be
provided based on multiple time-of-day periods, the structure for
the lookup table may be altered. For example, in some instances,
this may be done using multiple tables or incorporating multiple
algorithm result inputs and multiple associated groups of
recommendations into a single table.
[0180] In certain embodiments, when a second stage process is
employed, the lookup table is adjusted to accommodate the second
stage process. For example, if hypoglycemic risk is detected in 3
of the 4 time-of-day periods, rather than display a separate
recommendation related to reducing hypoglycemic risk for each time
period, the second stage process comprises mapping these into a
general recommendation and indicating that it applies to the three
time periods.
Software Application for Representing Sample Data for Evaluation
and Treatment Recommendation
[0181] In some aspects, there is provided a method for representing
the sample data comprises transforming glucose data from CGM or
from SMBG into a location on a plane that measures (e.g., average)
glucose level and the amount of variability in the glucose values.
In certain embodiments, the method comprises defining the control
grid, transforming the data into average glucose and glucose
variability (e.g., estimating the average glucose and variability
from sample data provided by, for instance, an SMBG device or
system), and plotting the transformed data on the control grid.
[0182] The following paragraphs describe an example method for
representing the sample data, according to certain embodiments.
While some of the information describe may be duplicative to that
described above; the following description and example embodiments
provide further details describing for representing the sample data
with, and mapping the metrics on, a control grid. The following
example utilizes data derived using blinded CGM data from 66
subjects in a study. It should be appreciated that in other
embodiments, a different sample grouping may be used for derivation
purposes.
[0183] In certain embodiments, the method comprises defining a
control grid. For example, the control grid is plane upon which
glucose level and variability is charted. FIG. 37 illustrates a
control grid, according to certain embodiments. In the embodiment
shown, the ordinate of the control grid is median glucose. Median
glucose is a measure of the glucose level. The abscissa of the
control grid is the difference of the median glucose and the 10th
percentile value of the glucose, known as the "south 40." This
difference is a measure of the glucose variability. It should be
appreciated that the ordinate and the abscissa may be different in
other embodiments. For example, the abscissa may be the difference
between the ordinate (e.g., median glucose) and another reference
value for glucose (e.g., a different percentile value other than
10th).
[0184] In some aspects, one or more patient risks is represented.
Example patient risks may include, but are not limited to, chronic
clinical risks such as retinopathy risk, acute risks such as risk
of hypoglycemia. For example, for this example, a patient's risk of
hypoglycemia (also referred to herein as "hypo") is tracked. In
certain embodiments, hypoglycemia is counted in terms of
occurrences, rather than, for example, fraction of time spent below
a threshold value of glucose. For example, one hypoglycemic
occurrence may be defined by a predetermined amount of time below a
threshold glucose value--e.g., at least 40 continuous minutes below
60 mg/dl, or at least 30 continuous minutes below 50 mg/dl, or at
least 20 continuous minutes below 40 mg/dl; etc. In some instances,
requirements may be imposed between two occurrences. For example,
in certain embodiments, two occurrences of hypoglycemia are
separated by a predetermined minimum amount of time of
non-hypoglycemia (e.g., 20 continuous minutes, etc.).
[0185] When occurrences of hypoglycemia are counted and divided by
elapsed time to get the rate of hypo occurrences, lines of constant
rate of hypo occurrence extend from the lower left of the Control
Grid to the upper right. Low rates of occurrence are at the upper
left of the grid, where average levels are high and the variability
is low. High rates of occurrence are at the bottom right of the
grid. Linear classification theory may be used to find straight
lines that mark different rates of occurrence. Other methods, such
as Support Vector Machines, can also be used. FIG. 38 illustrates a
control grid with lines of constant hypoglycemia rate shown
thereon, according to certain embodiments. The black dots shown on
the control grid represent positions on the grid for the 66
subjects used to derive the lines. Constant lines are shown for 1
occurrence per month, 2 occurrences per month, 5 occurrences per
month, and 8 occurrences per month. It should be appreciated that
in some embodiments, the control grid may distinguish between
different types of diabetes--e.g., one symbol used for Type 2
diabetes mellitus and another symbol for Type 1 diabetes
mellitus.
[0186] In some instances, the lines of constant hypoglycemia rate
are adjusted to provide parallel approximations that may be more
useful in certain analysis applications. FIG. 39 illustrates the
control grid shown in FIG. 38 with parallel approximations of the
lines of constant hypoglycemia rate. As shown, the four constant
lines are now parallel to one another. A formula may be derived,
for instance, such that the lines may be defined according to the
rate of hypoglycemia occurrence. For example, the following formula
may be used to represent the values of glucose median at a given
rate of hypo occurrence:
Glucose Median=104.25-5*(hypo rate)+1.25*(South 40)
where glucose median and south 40 are in mg/dL, and hypoglycemia
rate is in occurrences per month.
[0187] In some aspects, a predetermined rate of hypoglycemia
occurrence may be used to represent a danger to the patient. The
predetermined rate may be based on a clinical decision, for
example. Once a predetermined rate is determined, a control grid
can be split into two parts using the predetermined rate. FIG. 40
illustrates a control grid showing a hypoglycemia risk zone (Hypo
Risk Zone), according to certain embodiments. The border between
the two sections is the line of predetermined hypoglycemia rate.
The Hypo Risk Zone may be defined by the border, and patients in
the hypo risk zone at the lower right may be determined to have an
unacceptable risk of hypoglycemia. The first goal of treatment may
be, for example, to get patients out of this area.
[0188] In some aspects, a patient's target A1c level is determined
and represented on the control grid to provide for a target zone.
For example, after setting the Hypo Risk Zone boundary, a clinical
decision may determine the patient's target A1c level. FIG. 41
illustrates possible A1c target values on the control grid of FIG.
40, according to certain embodiments. The A1c target value is
directly related to the value of the glucose level, and not to the
glucose variability, thus the lines are horizontal.
[0189] The target A1c level chosen defines the Target Zone. The
area of the control grid below the A1c target value that is not in
the Hypo Risk Zone becomes the Target Zone. For example, FIG. 42
illustrates the control grid shown in FIG. 41 with a defined target
zone, according to certain embodiments. As shown, an A1c target
value of 7% is used and defines a Target Zone on the control grid
that is below the horizontal line for a 7% A1c target value but not
in the Hypo Risk Zone. In this way, for example, a treatment goal
may comprise keeping patients in the Target Zone or moving patients
into the Target Zone and keeping them there.
[0190] In some aspects, additional zones are added to the control
grid. For example, FIG. 43 illustrates the control grid of FIG. 42
with two additional zones, according to certain embodiments. The
control grid comprises a buffer zone and a "Safe to Titrate" Zone.
The Buffer Zone includes the area along the border of the Hypo Risk
Zone and separates the "Safe to Titrate" Zone and the Hypo Risk
zone.
[0191] The slope of the Hypo Risk Zone border and the Buffer Zone,
and the connection between A1c level and median glucose, may be
determined based on, for example, data from CGM studies and journal
articles. The actual positions of the boundaries may be determined
by, for example, clinical judgment in setting the acceptable risk
of hypoglycemia and the target A1c level. These positions may be
known, or can be calculated.
[0192] In some aspects, the control grid may be used to direct
treatment. For example, a strategy may be devised to direct
treatment such that the patient avoids the Hypo Risk Zone. For
instance, avoiding the Hypo Risk Zone may comprise reducing
variability only (move to the left) when in the Buffer Zone, and
reducing both variability and median when in the "Safe to Titrate"
Zone.
[0193] In some aspects, sample data is incorporated within the
control grid. For example, the sample data may be transformed or
otherwise manipulated for representation on the control grid.
[0194] In certain embodiments, sample data is received for a
patient. The sample data may originate from various methods, such
as SMBG or CGM. The number of data points within the sample may
vary. For example, the sample may originate using SMBG and include
a small number of data points (e.g., 10 data points). Or, as
another example, the sample may originate from CGM and include a
larger number of data points (e.g., several thousand).
[0195] In certain embodiments, the median and south 40 is
calculated from the sample data. The calculated median and south 40
is associated with a single point on the control grid. The
calculated median and south 40 are only estimates, and there may be
uncertainty as to their accuracy. For example, the south 40 is a
difference of two uncertain numbers. Moreover, if the sample size
is small, there may be very few values at the low end, and the 10th
percentile value may be imprecise.
[0196] In some instances, to reduce such imprecision, the sample
data is "fit' to a probability distribution. For example, FIG. 44
illustrates a graph of sample data that has been fit to a
probability distribution, according to certain embodiments. The
jagged plot for the sample data is shown and follows the sample
data points (e.g., CGM data). The plot for the sample data after
being fit to a gamma probability distribution is also shown (the
smoother plot on the graph). Once the fit has been made, the median
and south 40 can be calculated from the distribution. In other
instances, a fit is made to a different probability distribution,
such as a lognormal or Weibull distribution. In yet other
instances, no such fit is made. The median glucose and south 40 are
determined directly from the data.
[0197] In some aspects, uncertainty in the estimate is calculated.
For example, in certain embodiments, uncertainty in the estimate is
calculated by using a bootstrap to generate a "cloud" of points
around the sample point. FIG. 45 illustrates a sample point shown
on a control grid along with a cloud of points derived by using a
bootstrap. The sample point is shown on the control grid as a
triangle and the circular points surrounding the sample point are
bootstrap points. The bootstrap points collectively form a "cloud"
of points.
[0198] In certain embodiments, a parametric bootstrap is used. For
example, the parametric bootstrap may include the following
steps:
[0199] 1. Fit the sample to a gamma distribution, G0.
[0200] 2. Perform a bootstrap replication [0201] 1. Take random
points from G1. Same number of points as sample. [0202] 2. Fit
these points to a gamma distribution, G1. [0203] 3. Calculate
median and south 40 from G1.
[0204] 3. Do many bootstrap replications.
[0205] Additional information regarding parametric bootstrap may be
found in the article entitled, "Performance of the Parametric
Bootstrap Method in Small Sample Interval Estimates", by D. Benton
and K. Krishnamoorthy (Adv. & Appl. in Stat., 2(3) (2002), pp
269-285), the entirety of which is incorporated herein by
reference. Other example ways to generate the point cloud include,
but are not limited to, standard (nonparametric) bootstrap, and
cross validation (CV) methods, including the jackknife.
[0206] In the example shown in FIG. 45, 56 data points make up the
sample. Each bootstrap replication drew 56 random points from G0
(defined in step 1 "Fit the sample to a gamma distribution, G0"
above). 500 replications were performed, resulting in 500 black
dots shown in FIG. 45.
[0207] The sample point and bootstrap values represent probability
density of patient positions on the Control Grid. Although the
triangle is the most likely position, other positions are probable.
For example, if the triangle were located on the border of the Hypo
Risk Zone, there would be approximately a 50% chance that the
patient was actually in the Hypo Risk Zone.
[0208] In some instances, however, a more conservative position may
be desired. For example, it may be determined that the probability
that the patient is in the Hypo Risk Zone should not be more than a
specific percentage (e.g., 5%). Accordingly, a line may be drawn
parallel to the border of the Hypo Risk Zone such that only that
particular percentage (e.g., 5%) of the bootstrap points are below
the line. For example, FIG. 46 illustrates the data shown in FIG.
45 with a line drawn parallel to the border of the Hypo Risk Zone
such that only 5% of the bootstrap points are below the line. The
line is shown extending from the bottom left of the figure to the
top right of the figure. Furthermore, a second line perpendicular
to this line and passing through the sample point may be used to
define a Treatment Recommendation Point (TRP), as shown in FIG. 46.
The intersection of these two lines defines the Treatment
Recommendation Point (TRP) represented by the second triangle
positioned at the intersection of the two lines. The TRP may then
be moved by modifying the patient's treatment to the border of the
Hypo Risk Zone to provide the 5% probability. It should be noted
that 5% is an example, and any percentage may be used in other
embodiments.
[0209] In certain embodiments, the TRP location is defined using
projections of each bootstrap point. For example, defining the TRP
location may comprise finding the projection of each bootstrap
point along the perpendicular line pointing towards the Hypo Risk
Zone. This may done, for example, by taking the dot product of each
bootstrap point with the perpendicular line. FIG. 47 illustrates a
projection of one bootstrap point along a line perpendicular to the
lines of constant hypoglycemia rate on a control grid, according to
certain embodiments. The sample point is shown as a triangle with
the perpendicular line extending through the sample point. The
bootstrap point is shown as the circular dot off to the right of
the sample point, and its projected value on the perpendicular line
is also shown (as the circular dot illustrated on the perpendicular
line).
[0210] Defining the TRP location may also comprise determining a
one-sided confidence limit of the resulting projected values. While
various one-sided confidence limits may be used, a one-side
confidence limit of 95% has been described for the given example.
In certain embodiments, the confidence limit is determined by
counting points. For example, the 95th percentile of the projected
values may be determined Bootstrap-derived confidence limits may be
undependable when sample sizes are small, and thus a more reliable
method may be used. For example, in certain embodiments, the Bias
Corrected and Accelerated method is used. Additional details
regarding this method may be found in the article, "A Practical
Introduction to the Bootstrap Using the SAS System", by N. Barker
(SAS Conference Proceedings, Phuse 2005, paper PK02), the entirety
of which is incorporated herein by reference.
[0211] For example, for the Bias Corrected and Accelerated method,
the bias to be corrected is the difference between the sample point
and the median of the bootstrap projected values. The bias
correction may be represented by:
x 0 = .PHI. - 1 ( number of projected values < sample value
number of projected values ) ##EQU00001##
[0212] where .PHI. is the cumulative density of the normal
probability distribution, and .PHI.-1 is the inverse.
[0213] In some instances, additional calculations are needed for
the acceleration part. For example, a jackknife is first performed
on the sample as follows:
[0214] 1. Leave out one data point from the sample.
[0215] 2. Calculate south 40 and median from the reduced set.
[0216] 3. Calculate the projected value, pi.
[0217] 4. Repeat for every data point.
[0218] The jackknife yields as many estimates as there are sample
values, and the mean value of estimates may be calculated,
<p>. The acceleration factor may be defined as:
a = i ( p - p i ) 3 6 ( i ( p - p i ) 2 ) 3 / 2 ##EQU00002##
[0219] The corrected percentile may be determined using the bias
correction and the acceleration factor. For example, the following
may be used to determine the corrected percentile:
.alpha. 1 = 100 .PHI. ( x 0 + x 0 + .PHI. - 1 ( .alpha. / 100 ) 1 -
a ( x 0 + .PHI. - 1 ( .alpha. / 100 ) ) ) ##EQU00003##
[0220] where .alpha. is the desired percentile (e.g., 95).
[0221] In some instances, confidence limits may be used to
calculate a boundary line or "bubble" that gives an indication of
the size of the bootstrap cloud. For example, FIG. 48 illustrates a
boundary line of a bootstrap cloud that was calculated using
confidence limits, according to certain embodiments. The boundary
line of the bootstrap cloud is shown along with the TRP (shown as a
triangle) and Hypo Risk Zone border line. In this way, the boundary
line of the bootstrap cloud may be used for display instead of the
points themselves.
[0222] Each point on the boundary line of the bootstrap cloud is
found in the same way as the TRP value, for example. The direction
of the line drawn through the sample point varies, and the
resulting polygon is smoothed.
[0223] In some instances, confidence limits may be used to show the
extent of the estimate uncertainty along the direction of hypo
risk. For example, the TRP and the symmetric point on the other
side of the sample point (also referred to herein as an "anti-TRP
point") may be calculated. FIG. 49 illustrates a control grid
showing a sample point, TRP, and an anti-TRP value, according to
certain embodiments. The sample point is the middle triangle shown
in the figure, and the TRP is the triangle at the 95th percentile
along the dashed perpendicular line below and to the right of the
middle triangle. The "anti-TRP" is thus the triangle at the 5th
percentile along the dashed perpendicular line above and to the
left of the middle triangle.
[0224] The distance between the two dashed perpendicular lines is
the 90% two-sided confidence interval of the control grid estimate
in the direction where the hypo rate is varying most quickly. This
may used, for example, to advocate for larger or smaller sample
sizes, because larger samples produce smaller confidence intervals.
For instance, it may be used in-house as a figure of merit to
evaluate various testing schedules.
[0225] In some aspects, treatment may be guided when the
relationship between the TRP and control grid zones is known. For
example, the vertical and horizontal distances of the TRP from the
Hypo Risk Zone boundary may be calculated. FIG. 50, illustrates the
vertical and horizontal distances from a Hypo Risk Zone border line
to a TRP point on a control grid, according to certain
embodiments.
[0226] Positive and negative distance may be defined--e.g.,
distances may be defined as positive if the TRP point is above or
to the left of the border line. In this way, the signs of the
distances determine which zone the TRP is in, and the magnitudes
may be used to guide the treatment changes. For example, if the
vertical distance of the TRP to the Hypo Risk Zone border is -50
mg/dl, then the patient is in the Hypo Risk Zone. The doctor might
decide that the easiest way to leave the zone is to move vertically
upwards by 50 mg/dl, using the patient's insulin sensitivity to
calculate the decrease in basal dose.
[0227] The perpendicular distance from the TRP to the border
(shortest distance) may be derived by:
d .perp. = vh v 2 + h 2 ##EQU00004##
[0228] where v is the vertical distance, and h is the horizontal
distance. This can be seen by calculating the area of the triangle
using the vertical and horizontal distances, and by calculating
again using the perpendicular distance and the hypotenuse
[0229] It should be appreciated that in some instances, one or more
steps of the method described above may vary--e.g., one or more
steps omitted, one or more steps combined, one or more steps
performed in an a different order, one step performed in multiple
steps, etc.
Determination of Appropriate Glucose Testing Schedule Based on
Patient's Glucose Control Level and Clinical Risk
[0230] In some aspects, there is provided a process for determining
an appropriate glucose testing schedule based on a patient's
glucose control level and clinical risk.
[0231] A patient's state of glucose control can be assessed in
terms of 2 simple metrics. The first relates to the ability to
maintain a desirable glucose level on average. The second relates
to the ability to minimize the glucose excursion in the presence of
meals and other factors. As described above, one method to
graphically present these two metrics comprises the median glucose
as the first metric, and the difference between the median and the
10th percentile glucose as the second. This graphical
representation, called glucose control grid or chart. In the
absence of high density data, such as from a continuous glucose
monitoring (CGM) system, a glucose testing schedule may be used to
collect the data.
[0232] In addition to the patient's state of glucose control, other
clinically relevant information can be provided to enhance one's
understanding of the impact of a planned treatment on the patient's
various clinical states.
[0233] In some aspects of the present disclosure, there is provided
a method wherein the appropriate testing schedule is a function of
an a priori (e.g. population-based) uncertainty of various testing
schedules and the relevant clinical risk being reviewed for each
patient, as a reflection of the patient's state of glucose
control.
[0234] As described above, a glucose control grid may be used to
provide a snapshot of a patient's glucose control. The snapshot
represents the multitudes of blood glucose (BG) data from self
monitoring BG (SMBG) fingerstick readings and/or CGM system
recordings over the course of days or weeks in between visits to
the patient's health care provider (HCP), into a single point in
the chart. In the case of SMBG as the sole data source, the patient
needs to follow a pre-determined testing schedule.
[0235] Since the testing schedule may only sample glucose values
and may not capture all the essential nature of a patient's state
of glucose control, the uncertainty surrounding the single point
estimate as presented in the glucose control chart depends on the
testing schedule. FIG. 51 illustrates contour plots of a
probability distribution function of a patient's state of glucose
control, according to certain embodiments. For example, the
distribution may be obtained from a patient performing a prescribed
testing schedule over 14 days, and then using the bootstrap method
to obtain 400 more estimates. This set of 400 glucose control grid
data points is then used to create a probability distribution
function. In the embodiment shown in FIG. 51, the 25%, 50%, and 75%
contour heights are displayed. In this way, the uncertainty level
of the patient's estimated glucose control level is
represented.
[0236] In some aspects, different SMBG testing schedules may be
implemented. For example, depending on the level of glucose control
and the relevant clinical risk being focused on, different SMBG
testing schedules that focus on different times of the day,
different times relative to events such as meals and exercise, and
testing frequency, may be needed. FIG. 52 shows the same
distribution from the same patient shown in FIG. 51, in the context
of two different clinical risks, namely the risk of retinopathy
(horizontal lines) and risk of severe hypoglycemia (diagonal
lines), according to certain embodiments. For example, the control
grid shows the constant lines for 1 occurrence per month, 5
occurrences per month, and 9 occurrences per month. If the
difference between five hypos per month and nine hypos per month is
clinically relevant, then given the patient's current state of
glucose control, the prescribed testing schedule is marginally able
to distinguish a clinically meaningful change in severe
hypoglycemia risk. This is because the extent of the contour lines
is comparable to the distance between very different amounts of
hypoglycemic risk. Assuming there is no large clinical difference
between the 1% and 2% retinopathy lines, the testing schedule is
sufficient to distinguish a clinically meaningful change in risk of
retinopathy. Since the contour line span is significantly smaller
than the gap between the nearest two retinopathy risk lines, the
testing schedule is adequate to measure the patient's level of
retinopathy risk.
[0237] The testing schedule used for the illustration in FIG. 52
may or may not be adequate in assessing different clinical risks
for the same patient. This was already shown by the fact that the
width of the contour lines along the different clinical risk
gradient lines; risk of severe hypoglycemia and risk of
retinopathy; are not the same. Similarly, the testing schedule used
for the illustration in FIG. 52 may or may not be adequate in
assessing the same clinical risks for a different patient, or the
same patient with a significantly altered level of glucose control.
FIG. 53 illustrates contour lines for 6 additional patients plotted
on the same control grid, according to certain embodiments. Each
set of contour lines A, B, C, D, E, F, and G represent the contour
lines for one of the seven patients.
[0238] Note that as the patient's glucose variability increases (as
represented by a larger x-axis value), and as the patient's
propensity for high BG (blood glucose) value increases (as
represented by a larger y-axis value), the same testing schedule
generates different uncertainty levels as indicated by the
different contour line size and shapes. For example, it may not
possible to say anything definite about the patient C's risk of
hypoglycemia, as the uncertainty contours span a range from less
than one occurrence per month to more than nine occurrences per
month. This is important because either extreme can trigger
different sequences of treatment priorities. For example, the
former would put the patient's high mean glucose as the immediate
focus for improvement, while the latter could result in the
temporary reduction of glucose lowering medications to reduce the
acute mortality risk associated with severe hypoglycemia.
[0239] When a more stringent testing schedule is adopted, for
example, the uncertainty of the patient's glucose control generally
decreases regardless of the level of glucose control (e.g.,
regardless of where the point is in the glucose control chart).
FIG. 54 illustrates the contour lines generated from a relatively
stringent testing schedule on the same 7 patients as shown in FIG.
52, according to certain embodiments. Compared to FIG. 53, the
uncertainty levels are markedly decreased, particularly in areas of
poor glucose control. For patients A and E, for example, the more
stringent testing schedule may not offer any significantly better
insight with respect to the two clinical risks discussed as
examples. However, for patient F, who achieves low glucose
variability while maintaining high mean glucose levels, the more
stringent testing schedule may help differentiate the patient's
risk of retinopathy when compared to the next or previous visit
(not shown in the plots). Furthermore, patient D's risk of
hypoglycemia has been clarified.
[0240] In some aspects, there is provided a method to generate
uncertainty levels of a patient's state of glucose control estimate
as a function of specific SMBG testing schedules, a given clinical
risk, and the two axis of the glucose control chart.
[0241] In certain embodiments, different SMBG testing schedules are
collected, wherein for each schedule, uncertainty levels are
computed a priori from population data.
[0242] In certain embodiments, the population data is comprised of
CGM values or SMBG data taken at a relatively frequent interval
that approaches that of a CGM. For example, in some instances, the
frequency interval may range from once every 15 minutes of faster.
In other instances, less frequent frequency intervals may be
used.
[0243] In certain embodiments, the population data is obtained from
human studies wearing CGM systems.
[0244] In certain embodiments, the population data is obtained from
in-silico human models representing a wide range of demographics,
state of diabetes, and various other physiological parameters.
[0245] In certain embodiments, the uncertainty levels from the
population data is projected along the gradient of a particular
clinical risk. This may result, for example, in a map of
uncertainty levels for a given SMBG testing schedule and clinical
risk as a function of the two axis of the glucose control
chart.
[0246] In certain embodiments, adequate and inadequate areas in the
glucose control chart are identified for each combination of SMBG
testing schedule and clinical risk being considered. For example,
an area may be considered adequate if it has a sufficiently small
uncertainty level relative to a clinically relevant gradient
change. Otherwise, it is considered to be inadequate. For example,
FIG. 55 illustrates the determination of areas where a given
testing schedule is adequate vs. inadequate in detecting a
clinically meaningful change in risk of retinopathy, according to
certain embodiments. For FIG. 55, a testing schedule of 4 times a
day at 7 am, 11 am, 5 pm, and 10 pm was used. It should be
appreciated that different testing schedules may be
implemented--e.g., that include any number of additional times in
the day.
[0247] In FIG. 55, The shaded area corresponds to areas where the
particular testing schedule is not adequate in detecting a
clinically meaningful change in the risk of retinopathy, because
the spread of the estimates along the direction of clinical change
is much larger than the distance between local clinical risk lines.
Thus, for example, it may be determined that a patient whose
current state of glucose control puts their estimates in the
inadequate space relative to the clinical risks being considered
will need to follow a different testing schedule. Conversely, a
patient whose glucose state is far from the boundary between
inadequate and adequate region may be determined to be able to use
a potentially less demanding testing schedule. It should be
appreciated that different clinical risks (e.g. severe
hypoglycemia, diabetic ketoacidosis, kidney failure, etc.) may
result in different boundaries between adequate and inadequate
areas for the same testing schedule.
[0248] In some aspects, there is provided a method to utilize the
mapped uncertainty levels in the context of a specific set of
clinical risks to provide a recommended SMBG testing schedule
specific to each patient's latest state of glucose control.
[0249] In certain embodiments, given a patient's initial data under
the particular SMBG testing schedule, the proper map described
above (the map representing uncertainty levels of a patient's state
of glucose control estimate as a function of specific SMBG testing
schedules, a given clinical risk, and the two axis of the glucose
control chart) is referenced to see if the patient's glucose
control estimate falls within the adequate or inadequate
region.
[0250] In certain embodiments, clinical risks being considered, a
recommended SMBG testing schedule is offered that will not render
the patient's testing efforts meaningless for the clinical aspect
being focused on.
[0251] In certain embodiments, given the set of SMBG testing
schedules that a patient can fall into the adequate range, and
given the intended direction of the next treatment leading to the
next follow-up visit, an SMBG testing schedule that puts the next
predicted state of glucose control into an adequate range is
considered, and one that is the least demanding may be offered to
the patient. In this way, the least demanding schedule may be
offered that will still allow for a useful determination of changes
in clinical risk while keeping in mind that while a more demanding
schedule may be ideal, it may practically be the exact opposite
when a patient's concordance is too low.
Graphical Presentation of Clinical Impact on Patient's State of
Glucose Control
[0252] In some aspects, there is provided a method for graphically
representing clinical impact on a patient's state of glucose
control.
[0253] In addition to the patient's state of glucose control, other
clinically relevant information can be provided to enhance one's
understanding of the impact of a planned treatment on the patient's
various clinical state.
[0254] In some aspects, there is provided a method whereby
clinically relevant states (e.g., long term cardiovascular risk,
long term retinopathy risk, short term diabetic ketoacidosis risk,
acute hypoglycemia risk, etc.) are adapted by performing
transformations and mapping from population data. In this way,
given a patient's state of glucose control, as shown in the glucose
control chart, the various clinical risks may be identified, in
order to provide relevancy in selecting a median glucose
target.
[0255] Long-term complications may cause major morbidity and
mortality in patients with insulin-dependent diabetes mellitus.
Studies have established these clinical risks with measurable
markers, where an association between long-term complications and
HbA1c are often made. For example, associations between HbA1c and
risk of progression of retinopathy, and between HbA1c and risk of
severe hypoglycemia have been made--e.g., such as described in the
article, "The Diabetes Control and Complications Trial Research
Group, "The Effect of Intensive Treatment of Diabetes on
Development and Progression of Long-Term Complications in
Insulin-Dependent Diabetes Mellitus" (The New England Journal of
Medicine, v329, n14, 1993), the entirety of which is incorporated
herein by reference. FIG. 56 illustrates the association between
HbA1c and risk of retinopathy and HbA1c and risk of sever
hypoglycemia.
[0256] Focusing on the association between HbA1c and risk of
retinopathy as an example, a connection may be made to the glucose
control chart. For example, one method may use an association
between estimated average glucose (eAG) and HbA1c. Using population
data, a further association can be made between eAG and one or more
axis of the glucose control chart. For example, an association may
be made between eAG and the median glucose axis of the glucose
control chart. Various methods can be used to obtain these
associations. For example, the association between eAG and median
glucose may be made by computing the eAG and median glucose values
of a population of patients, and then using these pairs to fit a
curve by Least Squares Error fit or other curve fitting methods. In
another embodiment, a non parametric approach may be taken, where a
table is generated that maps a certain range of eAG to a range of
median glucose, and vice versa.
[0257] FIG. 57 illustrates a mapping a clinical risk, namely risk
of retinopathy, onto a glucose control chart, according to certain
embodiments. The graphical overlay of retinopathy risk may be added
onto the glucose control chart, as shown in FIG. 57, by, for
example: 1) using a Least Squares Error fit of a line may be used
to define the correlation between median glucose and eAG; 2) using
a correlation between eAG and HbA1c--e.g., using a previously
published correlation between eAG and HbA1c, such as described in
the article by Nathan et. Al, entitled "Translating the A1c Assay
Into Estimated Average Glucose Values" (Diabetes Care, v.31, n8,
2008), the entirety of which is incorporated herein by reference;
and then 3) determining the logarithmic correlation between HbA1c
and risk of retinopathy.
[0258] In FIG. 57, each patient's state of glucose control, as
represented by a single icon (each cross indicates a Type 2
Diabetes Mellitus patient, each circle indicates a Type 1 Diabetes
Mellitus patient), can be viewed directly in terms of their
clinical risk. In particular, the figure shows each patient's
retinopathy risk. In addition, given an expected change in median
glucose and glucose variability, an anticipated change in clinical
risk may be estimated.
[0259] FIG. 58 illustrates multiple clinical risks--clinical risk
of retinopathy and acute risk of hypoglycemia--overlaid on glucose
control chart, according to certain embodiments. The control grid
also shows the constant lines for the number of occurrences of
hypoglycemia per time period (e.g., 1, 5, and 9 occurrences per
month).
[0260] FIG. 59 illustrates an example of a patient's state of
glucose control relative to two clinical risks, according to
certain embodiments. The two risks are risk of retinopathy and
hypoglycemia. If, for example, a patient's latest state of glucose
control is represented by the diamond icon in FIG. 59, the patient
has a 4% chance of suffering from retinopathy, and 5 severe
hypoglycemic events per month, if no change is made on the
patient's state of glucose control. The circle and cross icons
represent, for example, two medical options that are available.
While the second option offers a 50% reduction in retinopathy risk,
the acute risk of hypoglycemia remains practically unchanged. The
first option on the other hand, allows the patient to resolve the
immediate issue of hypoglycemia risk, although at the cost of an
increased risk of retinopathy. Since retinopathy is a longer term
risk, this may be a more rational choice in some instances, as the
subsequent visit to the patient's health care provider may then
focus on further reducing glucose variability (as shown, for
example, by the pentagon icon in FIG. 59) before attempting to
reduce the patient's overall glucose (as represented by the
triangle icon in FIG. 59)
[0261] In some aspects, a method is provided to overly various
clinical risks onto the glucose control chart in order to provide
context of a patient's state of glucose control relative to various
clinical risks. For example, in certain embodiments, risk of
retinopathy and hypoglycemia are used.
[0262] In some aspects, a method is provided to compare the
relative clinical benefits of modifying one ore both aspects of a
patient's glucose control given a patient's current state of
glucose control and given the available treatment options.
[0263] In some aspects, a method is provided to associate clinical
risks to one or more axis of the glucose control chart. For example
risk of retinopathy may be associated to a glucose control chart by
one of the following sequence of maps: median glucose to eAG, eAG
to HbA1c, and HbA1c to risk of retinopathy. It should be
appreciated that different paths may be needed in different
circumstances depending on data availability from clinical
studies.
[0264] In some aspects, a method is provided to allow doctors or
other health care professionals to recommend treatment to patients.
For example, the doctor may select an appropriate median glucose
target from a range of possible targets by associating these
targets with a) long term and short term complications, and b) an
achievable near-term goal based on where the patient metrics
currently fall on the grid. The combination of the target and the
grid provides the clinician with a clinical perspective of how to
modify therapy (e.g., by addressing the median or addressing
variability or both) in relationship to the target. In certain
embodiments, for example, the method may comprise 1) processing
glucose data to determine the median and variability metrics, 2)
plotting the point (or uncertainty bubble) representing these
metrics on the grid, 3) displaying the grid with various targets
related to complications (e.g., this may be done with multiple
tabbed displays where each tab is related to a different
complication), 4) providing a means for the doctor to select a
target (for instance, mouse clicking on the desired target), 5)
displaying the grid with only the selected target, 6) generating
treatment recommendations based on which grid zone the metric falls
into.
Medication Titration Between Clinical Visits Using Glucose Median
and Variability Algorithm
[0265] In some aspects of the present disclosure, the methods and
devices described above that map the glucose median and variability
metric to treatment recommendations may be used to titrate
medication, such as insulin, in a setting outside the standard
clinical visit. For instance, in certain embodiments, algorithms
for such methods may be implemented completely within the glucose
measurement system, such as an SMBG meter, an "on-demand" CG meter
or a CGM, where the outputs of the algorithm are titration
instructions displayed periodically, e.g., once per week, on the
device to the patient. The titration amounts may be preconfigured,
or the algorithm could determine titration amounts based on
preconfigured parameters, such as maximum titration amount, maximum
total dose amount, and parameters common to bolus calculators, such
as insulin sensitivity and carbohydrate ratio.
[0266] In another embodiment, the algorithms for methods described
herein may be implemented partially within the glucose measurement
device and partially within a remote software program accessible to
clinicians (e.g, via a remote computer). The remote software
program may provide, for example, titration recommendations to be
approved by the clinician and then allow the clinician to remotely
configure the glucose measurement device to display updated dosing
instructions. The functionality may be divided between the device
and the remote program a variety of ways. As an example, the device
may upload data periodically to the remote software program, which
contains the mapping algorithm and generates titration
recommendation and other analysis to the clinician. The clinician
may confirm the recommendation or otherwise alter the current
titration and remotely configures the device with the new titration
settings. In this way, the clinician is able to monitor patient
titration. Remote data upload and configuration may be accomplished
using any number of communication mechanisms, wired or unwired,
known in the art.
Software Application for Communicatively Coupling an Analyte
Monitoring Device with a Remote Server Via a Computer
[0267] In some aspects, a software program is provided for
transferring data from an analyte meter to a computer or other data
processing device. For example, in certain embodiments, once loaded
on the patient's computer or data processing device, the software
application will activate whenever their meter is connected to the
computer and will automatically upload the data from the meter to a
remote server. In some instances, no user action may be required by
the user for the automatic upload process. It should be appreciated
that the source of this data from a blood glucose meter or any
other type of analyte monitoring device.
[0268] FIG. 60 illustrates an example block diagram for the
software application for communicatively coupling an analyte
monitoring device (e.g., a glucose meter) with the remote server
via a computer, according to certain embodiments. In certain
embodiments, two separate software programs are used--one program
(#2) runs in the background on the patient's computer ("PC") and
the other comprises scripts (#3a,#3b) running on a remote server
(e.g., PERL scripts or other suitable scripts). The scripts may
include, for example, a script for uploading data (#3a), a script
for viewing data (#3b). The software running on the remote server
may also include a data file (#4) on the remote server that
accumulates uploaded data.
[0269] In certain embodiments, once the PC program is installed, it
runs silently in background mode until the monitoring device is
communicatively coupled to the computer (e.g., plugged into a USB
port or wirelessly connected, for example). If the device is
appropriate, the PC program invokes the remote server script #3a
for uploading some or all of the data ("File #1") by for example
making an HTTP request, which uploaded data is then stored on the
remote server in the data file. For example, the upload script
(#3a) is invoked via HTTP POST request from the program #2, wherein
the data is sent via STDOUT and arrives via STDIN on the server
side. For example, the data file (#4) is written by script #3a to a
server directory protected by HTTP authentication. A browser user
may then visit the URL for Script #3b and logs in via HTTP
authentication to view the uploaded data. Script #3b reads the data
file (#4), formats it into an HTML table, and generates a web page
that is sent to the user's browser.
[0270] In some instances, the communicated data also includes other
related information such as the BGM serial number and/or the MAC
address of the PC which could be used to minimize the possibility
that the BGM data gets associated with the wrong patient. It should
also be appreciated, that in some instances, the software program
may include a script for deleting the data from the monitoring
device. For example, the computer program may invoke a script on
the monitoring device to delete the data from the monitoring device
after the data is uploaded.
Software Application for Configuring a Continuous Glucose Monitor
(CGM)
[0271] In certain embodiments there is provided a CGM alarm
configuration tool. CGM alarms include high and low threshold
crossings and projected threshold crossing based on trend
information. These alarms can be configured to be more or less
sensitive based on the threshold values and the projection time.
For instance, a projected low glucose alarm would occur more often
if the threshold is set high and/or the projected time is long.
This may be annoying for the patient. On the other hand, a high
threshold and/or long projected time may also lead to better
glucose management. So a trade-off can be made between better
glucose management and usability (fewer annoying alarms).
[0272] This trade-off may not be easy for most CGM patients or
their HCPs to understand. Provided herein is a software tool that
can be used to inform patients and HCPs on how to best adjust alarm
settings.
[0273] The software tool incorporates a glucose response simulation
based on a glucose response model defined for that specific patient
or defined for a population of patients. The simulation would be
based on recorded inputs of prior glucose, meals, insulin injection
and other recorded factors that may impact glucose response.
Assuming that the patient would treat a high or low glucose alarm
immediately or soon after occurrence, various alarm setting
scenarios would be simulated and results (glucose control metrics
and alarm occurrences) would be generated for each of these
settings. The "treatments" associated with each scenario, such as
eating and insulin injection, would be included as inputs to the
simulation. The software would provide a "trade-off table" or plot
of glucose control metrics and alarm metrics as a function of alarm
settings. For example, the table may provide average glucose,
hypoglycemia occurrences, and alarms per day versus combinations of
low glucose threshold and projection time. The HCP may then discuss
these results with the patient and decide on appropriate alarm
settings. The software may also have a means to program the CGM
with the selected settings.
[0274] In another embodiment, the software tool uses a
non-parametric model that simulates the statistical characteristics
of a patient's glucose variability. For instance, the spectral
characteristics of the glucose response would be estimated from the
patient's glucose data, using standard data analysis techniques,
and a random simulation based on these characteristics would be
generated for different alarm setting scenarios.
[0275] In another embodiment, the software tool creates a database
of various actual glucose responses based on various alarm settings
and patient glucose control characteristics. For each category of
glucose response characteristic, a trade-off table is generated
using associated data in the database. The patient's glucose
response characteristic would be matched to the closest data set in
the database, and the software would look up the associated
trade-off table.
[0276] These features may be incorporated into any diabetes
analysis software, into a stand-alone application, or into a
stand-alone device (e.g., CGM device).
Software Application for Creating Therapy Recommendation Smart
Reports
[0277] In certain embodiments, there is provided a method for
creating therapy recommendation smart reports.
[0278] A common complaint from HCPs who treat people with diabetes
is the time and involvement required to analyze retrospective data
associated with the diabetic condition. Specifically, HCPs have
favorite reports that they walk through to try to understand how
well the patient is managing their diabetes and to determine
necessary treatment adjustments. Data analysis software that
detects adverse conditions and reports these conditions, and
perhaps makes recommendations, has been disclosed. The condition
would be reported by some means like a textual display listing the
adverse conditions by priority. The data may be summarized on a
standard report with for instance a modal day plot and key
statistics. However, some HCPs like to look through various reports
to get different perspectives of the data and the problem that has
been detected.
[0279] Provided herein is an automated feature associated with data
analysis that detects adverse conditions and makes recommendations.
This feature is to pre-define one or more specific types of reports
for each possible adverse condition and/or recommendation, and then
when the analysis software detects this condition, to display the
specific report(s) that best illustrates the problem. The advantage
here is that HCPs will no longer need to take the time to drill
down to the report they want for a specific condition (some HCPs
may not even know what the best report is to illustrate a
condition/recommendation). The optimum report type is generated
automatically and displayed to the HCP. The report may illustrate
the condition and demonstrate the expected effect from a
recommended treatment adjustment.
[0280] For example, the software would detect that the patient
almost always has low glucose in the morning prior to breakfast.
Along with an indication displayed to the HCP of this condition,
the software may also display a modal day graphic that shows the
aggregate effect of being low in the morning--perhaps only the
first half of the day is displayed. The plot may also highlight the
portion where it is low and show the low target. Finally, the plot
may also show an expected modal plot based on the recommendation
which may be a reduction in the basal insulin dose, demonstrating
how the recommendation would be executed to improve glucose
management. The HCP would see this almost instantly after
initiating the analysis and would not have to search for the
appropriate plot or report.
[0281] Another example would be that the patient is occasionally
very high after the dinner meal. When this is detected by the
analysis routine, an overlay plot where each data series is time
aligned around the dinner meal would be appropriate. The problem
data series may be highlighted and when the cursor is placed over
it, more information about the meal may be presented. Then, the HCP
can have a discussion with the patient about these specific meals
and perhaps find out that the patient is having a problem counting
carbs for a specific type of meal, for instance.
[0282] A final example is where the analysis routine detects that
there is not enough glucose measurements to adequately detect
adverse conditions. Then, the graphic may be a bar chart showing
average BG measurements per each day over the period of analysis.
The HCP can then have a discussion about the importance of taking
frequent measurements.
[0283] The process steps for this invention, in certain
embodiments, would be as follows: 1) retrieve stored data
associated with diabetes management for a specific patient, such as
glucose readings, insulin delivery data, meal data, etc.; 2)
analyze the data based on one or more predefined conditions to
check; 3) for each condition detected, output a specific graphical
report that best demonstrates that condition.
[0284] The process steps for this invention, in another embodiment,
would be as follows: 1) retrieve stored data associated with
diabetes management for a specific patient, such as glucose
readings, insulin delivery data, meal data, etc.; 2) analyze the
data based on one or more predefined conditions to check; 3) for
the pre-defined highest priority condition detected, output a
specific graphical report that best demonstrates that condition; 4)
provide a graphical means for the user to select any lower priority
conditions detected and output the corresponding specific graphical
report that best demonstrates that condition.
[0285] This invention would preferably be implemented on a PC
software program, but may also be incorporated into a handheld
device, that may interact with glucose measurement and/or insulin
delivery.
Software Application for Providing a Single Page Integrated
Multi-Day Report
[0286] In certain embodiments, there is provide a method for
creating a single page integrated multi-day report. For example, in
certain embodiments, there is provided a multi-day report that
integrates insulin, glucose, exercise, meal, and medication details
into one single, readable, page. This integrated multi-day report
concept contains all the contextual glucose, insulin, meal,
exercise, and medication information and presented over a period of
multiple days in an iconic display. The iconic approach to present
different events also enhances port comprehension. The advantage of
a single multi-day report is that more contractual information in a
single page will aid the interpretation and treatment of
diabetes.
Software Application for Providing Recommendations for Glucose
Monitor Type Based on Simulations
[0287] In certain embodiments, there is provided a method for
recommending glucose monitor type based on simulations.
[0288] A therapy calculator determines optimal glucose response
model parameters based on glucose history, meal history, and
insulin delivery history. These parameters can be loaded into an
enhanced bolus calculator that accounts for this model and provides
the patient with more accurate bolus recommendations. Software can
then be used to simulate expected glucose response to treatment
events for different types of glucose measurement devices; e.g.,
SMBG, GoD, CGM, or blind (no glucose measurement device is
used).
[0289] As such, there is provided a process to use these
simulations to recommend to a HCP the appropriate glucose
measurement device to use. The more effective methods typically are
also more costly and inconvenient, so the natural choice of device
is to use the least costly and inconvenient, that can still
effectively treat the patient's condition. The least effective is
generally "blind" or no glucose measurement device, then SMBG, GoD,
and CGM are progressively more effective, in that order.
[0290] The simulations are based on multiple meal and/or correction
events that have been recorded. The therapy calculator uses the
glucose history, meal information and insulin delivery information
around these events to calculate the optimal parameters specific to
the patient for the model. The simulation for the `blind" scenario
then assumes that each meal bolus event is based only on meal
information not glucose. Glucose correction events, for instance
for a CGM simulation where a hyper alarm is indicated, would not
assume a correction occurred for the blind scenario.
[0291] The simulation for the SMBG scenario assumes that for each
meal bolus event, the bolus is now based on the meal information
and the glucose level but not the trend. For correction events, it
should be assumed that like the blind situation, these are not
accounted for.
[0292] The GoD simulation scenario is much like that for SMBG
except that glucose trend can be taken into account for the bolus
calculation. Alternatively, the GoD and SMBG simulation scenarios
may take into account extreme low and high glucose events assuming
that patients may sense these and would initiate a glucose
reading.
[0293] The CGM simulation will assume that whenever the glucose
exceeds a high or low threshold, that a correction bolus occurred,
which is based on glucose level and trend. Alternative, corrections
based on projected high or low thresholds may trigger a correction
event. Finally, correction events may be associated with actual
correction events from the data.
[0294] The process would generate these multiple simulations based
on past glucose, meal and insulin history. Metrics may be generated
from these simulation results to provide an indication of
acceptable glucose control--the least expensive and inconvenient
glucose measurement device that provides an adequate simulated
glucose management metric should be selected for the patient.
Alternatively, or in conjunction with metrics), the simulations may
be presented to the HCP so they can visually determine the
appropriate device for that patient.
[0295] The above invention may also be extended to non-insulin
medication treatments, diet/exercise treatments, etc.
Software Application for Providing Extension of Therapy Calculator
to Treatments Beyond Insulin Delivery
[0296] In certain embodiments, there is provided methods for
providing an extension of the therapy calculator to treatments
beyond insulin delivery.
[0297] A therapy calculator generally records insulin delivery
data, glucose measurements, and food intake (and potentially other
data relevant to diabetes) for a patient for some time, transfers
data to a processor that uses it along with a model of the
patient's physiology, and calculates key model parameters that
would be used to optimize subsequent insulin treatment such that
glucose levels would stay level. Specifically, the treatment
adjustments may include adjustments to the amount of insulin needed
to compensate for meals or glucose level corrections. These
adjustments may include other factors such as when to bolus
relative start of meal, type of bolus and variations related to the
type of meal.
[0298] Provided herein are methods of extending the above-described
design to other treatments for diabetes, and for treatments for
other deceases. Specifically in regards to insulin treatment, these
methods may be used to inform a patient of how much insulin to take
via other mechanisms, such as oral or nasal, for example. A
physiological model is needed for the specific type of insulin
medication/delivery, and one or more parameters are identified
related to insulin amount or timing or any other parameter type in
which the patient may use to determine how to administer the drug.
These parameters may be utilized by the patient directly, such as
to determine how much to inject or ingest, or these parameters may
be loaded (manually or automatically) into a device that calculates
how much insulin should be delivered. The amount may be delivered
manually with an amount indicated by a bolus calculator, or
remotely by a device designed for convenient user interaction, or
automatically by a system that is programmed to delivery this
amount at a various times (for instance the same time(s) every day
or at times relative to other events such as meals, meal
announcements, glucose level/profile indicators, etc.).
[0299] The bolus calculator may take into account one or more CGM
data in order to better compensate for glucose rate-of-change. To
do this, the bolus calculator may incorporate the same or similar
model as the therapy calculator, where some of the model parameters
are identified by the therapy calculator. These parameters would
not need to be such that they are usable directly by the patient
(e.g., insulin-to-carb ratio or insulin-to-glucose ratio) but may
be combinations of these. The bolus calculator may be able to
utilize the identified parameters to provide an optimal dose
information to the patient.
[0300] In certain embodiments, the methods may be extended to drug
therapies other than insulin. For any drug that induces a
physiological response with a means to measure one or more relevant
patient's conditions, a model can be developed to describe this
response. Some model parameters can be considered constant for a
population and others can be identified specifically for that
patient for that point in time. Additionally, some parameters may
be identified for the patient at that time, then subsequently held
constant, while other parameters are identified at a later time.
The parameters may be related to drug amount, or time of day
timing, or timing related to some patient condition indicator. The
parameters may be utilized directly by the patient, or programmed
into a device that provides delivery information, allows the
patient a remote means to initiate delivery, or can be programmed
for automatic delivery.
Integration with Medication Delivery Devices and/or Systems
[0301] In some embodiments, the analyte measurement systems
disclosed herein may be included in and/or integrated with, a
medication delivery device and/or system, e.g., an insulin pump
module, such as an insulin pump or controller module thereof, or
insulin injection pen. In some embodiments the analyte measurement
system is physically integrated into a medication delivery device.
In other embodiments, an analyte measurement system as described
herein may be configured to communicate with a medication delivery
device or another component of a medication delivery system.
Additional information regarding medication delivery devices and/or
systems, such as, for example, integrated systems, is provided in
U.S. Patent Application Publication No. US2006/0224141, published
on Oct. 5, 2006, entitled "Method and System for Providing
Integrated Medication Infusion and Analyte Monitoring System", and
U.S. Patent Application Publication No. US2004/0254434, published
on Dec. 16, 2004, entitled "Glucose Measuring Module and Insulin
Pump Combination," the disclosure of each of which is incorporated
by reference herein in its entirety. Medication delivery devices
which may be provided with analyte measurement system as described
herein include, e.g., a needle, syringe, pump, catheter, inhaler,
transdermal patch, or combination thereof. In some embodiments, the
medication delivery device or system may be in the form of a drug
delivery injection pen such as a pen-type injection device
incorporated within the housing of an analyte measurement system.
Additional information is provided in U.S. Pat. Nos. 5,536,249 and
5,925,021, the disclosures of each of which are incorporated by
reference herein in their entirety.
[0302] The embodiments presented herein provide further advantages
such as: the ability to upgrade strip port modules as new test
strip technologies evolve; the ability to clean or sterilize a
strip port module; and the ability to allow users to replace strip
port modules without returning the entire measurement system to the
manufacture.
[0303] Certain embodiments relate to in vivo (e.g., continuous
monitoring) systems. A continuous monitoring system typically
includes a sensor that is worn or placed below the skin, a
transmitter that collects glucose information from the sensor, and
a receiver that collects the information from the transmitter. The
sensor can collect glucose level information continuously,
periodically, or at other intervals. Advantageously, a user is
relieved from having to repeatedly lance his or her body to collect
a blood sample once the sensor is inserted, although the sensor
(e.g., an electrochemical sensor that is inserted into a body) can
be replaced. U.S. Pat. No. 6,175,752, which is hereby incorporated
by reference in its entirety, discloses additional examples of a
continuous monitoring system.
[0304] Embodiments of the invention relate to components of a
continuous monitoring system that may be replaceable. In certain
embodiments, the interface between the sensor and the transmitter
may become contaminated. The transmitter or sensor control unit,
for example, may have an interface with the sensor that has been
molded to form a barrier between the transmitter's contacts and
circuitry internal to the transmitter. This allows the
transmitter's contacts to be washed without damaging the
transmitter's circuitry. Alternatively, the contacts may be
included in a replaceable port that can be replaced as needed.
Similarly, the interface on the sensor may be molded to form a
barrier to contamination or be replaceable.
[0305] Embodiments of the invention further extend to kits.
Examples of a kit include a measurement device with one or more
strip connectors. In some kits, different strip connectors or ports
for different types of strips may be included. This allows the
measurement device to be used with different strip form factors.
The kits may also include a plurality of test strips. In certain
examples, the measurement device may be configured for use with
disposable test strips as well as with test strips that are
configured for continuous monitoring systems. Thus, the measurement
device may include a receiver to receive information from a
transmitter that collects glucose information from an inserted
sensor. The measurement device may also include a strip connector,
such as those disclosed herein, for use with single use test
strips.
Analyte Test Strips
[0306] Analyte test strips for use with the present devices can be
of any kind, size, or shape known to those skilled in the art; for
example, FREESTYLE.RTM. and FREESTYLE LITE.TM. test strips, as well
as PRECISION.TM. test strips sold by ABBOTT DIABETES CARE Inc. In
addition to the embodiments specifically disclosed herein, the
devices of the present disclosure can be configured to work with a
wide variety of analyte test strips, e.g., those disclosed in U.S.
patent application Ser. No. 11/461,725, filed Aug. 1, 2006; U.S.
Patent Application Publication No. 2007/0095661; U.S. Patent
Application Publication No. 2006/0091006; U.S. Patent Application
Publication No. 2006/0025662; U.S. Patent Application Publication
No. 2008/0267823; U.S. Patent Application Publication No.
2007/0108048; U.S. Patent Application Publication No. 2008/0102441;
U.S. Patent Application Publication No. 2008/0066305; U.S. Patent
Application Publication No. 2007/0199818; U.S. Patent Application
Publication No. 2008/0148873; U.S. Patent Application Publication
No. 2007/0068807; U.S. patent application Ser. No. 12/102,374,
filed Apr. 14, 2008, and U.S. Patent Application Publication No.
2009/0095625; U.S. Pat. No. 6,616,819; U.S. Pat. No. 6,143,164;
U.S. Pat. No. 6,592,745; U.S. Pat. No. 6,071,391 and U.S. Pat. No.
6,893,545; the disclosures of each of which are incorporated by
reference herein in their entirety.
Calculation of Medication Dosage
[0307] In certain embodiments, the analyte measurement system may
be configured to measure the blood glucose concentration of a
patient and include instructions for a long-acting insulin dosage
calculation function. Periodic injection or administration of
long-acting insulin may be used to maintain a baseline blood
glucose concentration in a patient with Type-1 or Type-2 diabetes.
In one aspect, the long-acting medication dosage calculation
function may include an algorithm or routine based on the current
blood glucose concentration of a diabetic patient, to compare the
current measured blood glucose concentration value to a
predetermined threshold or an individually tailored threshold as
determined by a HCP or other treating professional to determine the
appropriate dosage level for maintaining the baseline glucose
level. In certain embodiments, the long-acting insulin dosage
calculation function may be based upon LANTUS.RTM. insulin,
available from Sanofi-Aventis, also known as insulin glargine.
LANTUS.RTM. is a long-acting insulin that has up to a 24 hour
duration of action. Further information on LANTUS.RTM. insulin is
available at the website located by placing "www" immediately in
front of ".lantus.com". Other types of long-acting insulin include
Levemir.RTM. insulin available from NovoNordisk (further
information is available at the website located by placing "www"
immediately in front of ".levemir-us.com". Examples of such
embodiments are described in in US Published Patent Application No.
US2010/01981142, the disclosure of which is incorporated herein by
reference in its entirety.
Strip Port Configured to Receive Test Strips for Different
Analytes
[0308] In another embodiment, there is provided an analyte
measurement system for multichemistry testing. The test strips are
for chemical analysis of a sample, and are adapted for use in
combination with a measuring device having a test port and capable
of performing a multiplicity of testing functionalities. Each type
of test strip corresponds to at least one of the testing
functionalities, and at least some types of test strips have
indicators of the testing functionality on them. The test port is
adapted for use in combination with a multiplicity of different
types of test strips and includes a sensor capable of specifically
interacting with the indicator(s) on the test strips, thereby
selecting at least one of the multiplicity of testing
functionalities corresponding to the type of test strip. Such
system would include a strip port that can be used to read a test
strip for glucose and a test strip for ketone bodies. Examples of
such embodiment are provided in U.S. Pat. No. 6,773,671, which is
incorporated herein by reference in it entirety.
Strip Port Configured to Receive Test Strips Having Different
Dimensions and/or Electrode Configurations
[0309] In some embodiments, an analyte measurement system as
described herein includes a strip port configured to receive test
strips having different dimensions and/or electrode configurations,
e.g., as described in the U.S. patent application Ser. No.
12/695,947 filed on Jan. 28, 2010, and entitled "Universal Test
Strip Port", the disclosure of which is incorporated by reference
herein in its entirety.
Implanted Analyte Sensor
[0310] In some embodiments, an analyte measurement system as
described herein may include an implanted or partially implanted
analyte sensor, e.g., a system including an implanted or partially
implanted glucose sensor (e.g., a continuous glucose sensor). A
system including an implanted or partially implanted glucose sensor
may include an analyte measurement system as described herein,
which is configured to receive analyte data from the implanted or
partially implanted glucose sensor either directly or through an
intermediate device, e.g., an RF-powered measurement circuit
coupled to an implanted or partially implanted analyte sensor. In
some embodiments, where an analyte measurement system according to
the present disclosure is integrated with an implanted sensor, the
analyte measurement system does not include a strip port for
receiving an analyte test strip. In certain embodiments, the
analyte measurement system may be used to calibrate the analyte
monitoring system, e.g., using one point calibration or other
calibration protocol. For additional information, see U.S. Pat. No.
6,175,752, the disclosure of which is incorporated by reference
herein in its entirety. In some embodiments, the analyte
measurement system may be configured to communicate with the
implanted or partially implanted analyte sensor via Radio Frequency
Identification (RFID) and provide for intermittent or periodic
interrogation of the implanted analyte sensor.
[0311] Exemplary analyte monitoring systems that may be utilized in
connection with the disclosed analyte measurement system include
those described in U.S. Pat. No. 7,041,468; U.S. Pat. No.
5,356,786; U.S. Pat. No. 6,175,752; U.S. Pat. No. 6,560,471; U.S.
Pat. No. 5,262,035; U.S. Pat. No. 6,881,551; U.S. Pat. No.
6,121,009; U.S. Pat. No. 7,167,818; U.S. Pat. No. 6,270,455; U.S.
Pat. No. 6,161,095; U.S. Pat. No. 5,918,603; U.S. Pat. No.
6,144,837; U.S. Pat. No. 5,601,435; U.S. Pat. No. 5,822,715; U.S.
Pat. No. 5,899,855; U.S. Pat. No. 6,071,391; U.S. Pat. No.
6,120,676; U.S. Pat. No. 6,143,164; U.S. Pat. No. 6,299,757; U.S.
Pat. No. 6,338,790; U.S. Pat. No. 6,377,894; U.S. Pat. No.
6,600,997; U.S. Pat. No. 6,773,671; U.S. Pat. No. 6,514,460; U.S.
Pat. No. 6,592,745; U.S. Pat. No. 5,628,890; U.S. Pat. No.
5,820,551; U.S. Pat. No. 6,736,957; U.S. Pat. No. 4,545,382; U.S.
Pat. No. 4,711,245; U.S. Pat. No. 5,509,410; U.S. Pat. No.
6,540,891; U.S. Pat. No. 6,730,200; U.S. Pat. No. 6,764,581; U.S.
Pat. No. 6,299,757; U.S. Pat. No. 6,461,496; U.S. Pat. No.
6,503,381; U.S. Pat. No. 6,591,125; U.S. Pat. No. 6,616,819; U.S.
Pat. No. 6,618,934; U.S. Pat. No. 6,676,816; U.S. Pat. No.
6,749,740; U.S. Pat. No. 6,893,545; U.S. Pat. No. 6,942,518; U.S.
Pat. No. 6,514,718; U.S. Pat. No. 5,264,014; U.S. Pat. No.
5,262,305; U.S. Pat. No. 5,320,715; U.S. Pat. No. 5,593,852; U.S.
Pat. No. 6,746,582; U.S. Pat. No. 6,284,478; U.S. Pat. No.
7,299,082; U.S. Patent Application No. 61/149,639, entitled
"Compact On-Body Physiological Monitoring Device and Methods
Thereof", U.S. patent application Ser. No. 11/461,725, filed Aug.
1, 2006, entitled "Analyte Sensors and Methods"; U.S. patent
application Ser. No. 12/495,709, filed Jun. 30, 2009, entitled
"Extruded Electrode Structures and Methods of Using Same"; U.S.
Patent Application Publication No. US2004/0186365; U.S. Patent
Application Publication No. 2007/0095661; U.S. Patent Application
Publication No. 2006/0091006; U.S. Patent Application Publication
No. 2006/0025662; U.S. Patent Application Publication No.
2008/0267823; U.S. Patent Application Publication No. 2007/0108048;
U.S. Patent Application Publication No. 2008/0102441; U.S. Patent
Application Publication No. 2008/0066305; U.S. Patent Application
Publication No. 2007/0199818; U.S. Patent Application Publication
No. 2008/0148873; U.S. Patent Application Publication No.
2007/0068807; US patent Application Publication No. 2010/0198034;
and U.S. provisional application No. 61/149,639 titled "Compact
On-Body Physiological Monitoring Device and Methods Thereof", the
disclosures of each of which are incorporated herein by reference
in their entirety.
Communication Interface
[0312] As discussed previously herein, an analyte measurement
system according to the present disclosure can be configured to
include a communication interface. In some embodiments, the
communication interface includes a receiver and/or transmitter for
communicating with a network and/or another device, e.g., a
medication delivery device and/or a patient monitoring device,
e.g., a continuous glucose monitoring device. In some embodiments,
the communication interface is configured for communication with a
health management system, such as the CoPilot.TM. system available
from Abbott Diabetes Care Inc., Alameda, Calif.
[0313] The communication interface can be configured for wired or
wireless communication, including, but not limited to, radio
frequency (RF) communication (e.g., Radio-Frequency Identification
(RFID), Zigbee communication protocols, WiFi, infrared, wireless
Universal Serial Bus (USB), Ultra Wide Band (UWB), Bluetooth.RTM.
communication protocols, and cellular communication, such as code
division multiple access (CDMA) or Global System for Mobile
communications (GSM).
[0314] In certain embodiments, the communication interface is
configured to include one or more communication ports, e.g.,
physical ports or interfaces such as a USB port, an RS-232 port, or
any other suitable electrical connection port to allow data
communication between the analyte measurement system and other
external devices such as a computer terminal (for example, at a
physician's office or in hospital environment), an external medical
device, such as an infusion device or including an insulin delivery
device, or other devices that are configured for similar
complementary data communication.
[0315] In certain embodiments, the communication interface is
configured for infrared communication, Bluetooth.RTM.
communication, or any other suitable wireless communication
protocol to enable the analyte measurement system to communicate
with other devices such as infusion devices, analyte monitoring
devices, computer terminals and/or networks, communication enabled
mobile telephones, personal digital assistants, or any other
communication devices which the patient or user of the analyte
measurement system may use in conjunction therewith, in managing
the treatment of a health condition, such as diabetes.
[0316] In certain embodiments, the communication interface is
configured to provide a connection for data transfer utilizing
Internet Protocol (IP) through a cell phone network, Short Message
Service (SMS), wireless connection to a personal computer (PC) on a
Local Area Network (LAN) which is connected to the internet, or
WiFi connection to the internet at a WiFi hotspot.
[0317] In certain embodiments, the analyte measurement system is
configured to wirelessly communicate with a server device via the
communication interface, e.g., using a common standard such as
802.11 or Bluetooth.RTM. RF protocol, or an IrDA infrared protocol.
The server device may be another portable device, such as a smart
phone, Personal Digital Assistant (PDA) or notebook computer; or a
larger device such as a desktop computer, appliance, etc. In some
embodiments, the server device has a display, such as a liquid
crystal display (LCD), as well as an input device, such as buttons,
a keyboard, mouse or touch-screen. With such an arrangement, the
user can control the analyte measurement system indirectly by
interacting with the user interface(s) of the server device, which
in turn interacts with the analyte measurement system across a
wireless link.
[0318] In some embodiments, the communication interface is
configured to automatically or semi-automatically communicate data
stored in the analyte measurement system, e.g., in an optional data
storage unit, with a network or server device using one or more of
the communication protocols and/or mechanisms described above.
Input Unit
[0319] As discussed previously herein, an analyte measurement
system according to the present disclosure can be configured to
include an input unit and/or input buttons coupled to the housing
of the analyte measurement system and in communication with a
controller unit and/or processor. In some embodiments, the input
unit includes one or more input buttons and/or keys, wherein each
input button and/or key is designated for a specific task.
Alternatively, or in addition, the input unit may include one or
more input buttons and/or keys that can be `soft buttons` or `soft
keys`. In the case where one or more of the input buttons and/or
keys are `soft buttons` or `soft keys`, these buttons and/or keys
may be used for a variety of functions. The variety of functions
may be determined based on the current mode of the analyte
measurement system, and may be distinguishable to a user by the use
of button instructions shown on an optional display unit of the
analyte measurement system. Yet another input method may be a
touch-sensitive display unit, as described in greater detail
below.
[0320] In addition, in some embodiments, the input unit is
configured such that a user can operate the input unit to adjust
time and/or date information, as well as other features or settings
associated with the operation of an analyte measurement system.
Display Unit
[0321] As discussed previously herein, in some embodiments, an
analyte measurement system according to the present disclosure
includes an optional display unit or a port for coupling an
optional display unit to the analyte measurement system. The
display unit is in communication with a control unit and/or
processor and displays the analyte test strip signals and/or
results determined from the analyte test strip signals including,
for example, analyte concentration, rate of change of analyte
concentration, and/or the exceeding of a threshold analyte
concentration (indicating, for example, hypo- or
hyperglycemia).
[0322] The display unit can be a dot-matrix display, e.g., a
dot-matrix LCD display. In some embodiments, the display unit
includes a liquid-crystal display (LCD), thin film transistor
liquid crystal display (TFT-LCD), plasma display, light-emitting
diode (LED) display, seven-segment display, E-ink (electronic
paper) display or combination of two or more of the above. The
display unit can be configured to provide, an alphanumeric display,
a graphical display, a video display, an audio display, a vibratory
output, or combinations thereof. The display can be a color
display. In some embodiments, the display is a backlit display.
[0323] The display unit can also be configured to provide, for
example, information related to a patient's current analyte
concentration as well as predictive analyte concentrations, such as
trending information.
[0324] In some embodiments an input unit and a display unit are
integrated into a single unit, for example, the display unit can be
configured as a touch sensitive display, e.g., a touch-screen
display, where the user may enter information or commands via the
display area using, for example, the user's finger, a stylus or any
other suitable implement, and where, the touch sensitive display is
configured as the user interface in an icon driven environment, for
example.
[0325] In some embodiments, the display unit does not include a
screen designed to display results visually. Instead, in some
embodiments the optional display unit is configured to communicate
results audibly to a user of the analyte measurement system, e.g.,
via an integrated speaker, or via separate speakers through a
headphone jack or Bluetooth.RTM. headset.
Expanding Menu Item for Improved Readability
[0326] In some embodiments, the display unit includes a graphical
user interface including a plurality of menu items, wherein the
display unit is configured to provide clarification with respect to
the meaning of a menu item based on a user's response speed with
respect to a user input for the menu item. The menu item may take
any of a variety of forms, e.g., text, icon, object or combination
thereof.
[0327] In certain embodiments, the graphical user interface
includes a menu which in turn includes a plurality of selectable
menu items. As a user navigates through the menu, e.g., by
highlighting or scrolling through individual menu items, a menu
item that is either unreadable or incomprehensible to the user may
cause the user to pause over a menu item to be selected. In certain
embodiments, a choice can be presented to the user, e.g., using a
dedicated physical button on an input unit, or a soft key on the
menu, that offers further explanation of the item to be selected
without actually selecting the item. For example, the graphical
user interface can be configured such that after a pre-determined
period of time a soft key offers an explanation of the menu item to
be selected, e.g., by displaying a soft key with the word "MORE",
"ADDITIONAL INFORMATION", "EXPAND", "MAGNIFY", "HELP" or a
variation thereof displayed thereon.
[0328] The pre-determined period of time may be based on a fixed
factory preset value, a value set by the user or a health care
provider, or through an adaptive mechanism based on an analysis of
the user's speed of navigation from past interactions with the
graphical user interface. In certain embodiments, the
pre-determined period of time is from about 5 to about 20 seconds,
e.g., from about 10 to about 15 seconds.
[0329] If the offer for clarification and/or additional information
is selected, e.g., by pressing the softkey, then the menu item to
be selected can be displayed in a "high emphasis" mode, e.g., where
the item is displayed as if a magnifying lens is held on top of the
selected item. In some embodiments, additional emphasis of the menu
item to be selected can be provided, e.g., by making the menu item
change color, blink, or increase in size to a pre-determined
maximum limit.
Support for on-Demand Analyte Determination Using an Analyte
Sensor
[0330] In some embodiments, an analyte measurement system according
to the present disclosure is further configured to receive analyte
concentration data and/or signals indicative of an analyte
concentration from an analyte sensor, e.g., an implanted or
partially implanted analyte sensor or a radio-frequency
(RF)-powered measurement circuit coupled to an implanted or
partially implanted analyte sensor. In some embodiments, the
analyte sensor is a self-powered analyte sensor. An analyte
measurement system according to the present disclosure may include
software configured to analyze signals received from the analyte
sensor. Additional information related to self-powered analyte
sensors and methods of communicating therewith are provided in U.S.
Patent Application Publication No. 2010/0213057, the disclosure of
which is incorporated by reference herein in its entirety.
Analytes
[0331] A variety of analytes can be detected and quantified using
the disclosed analyte measurement system. Analytes that may be
determined include, for example, acetyl choline, amylase,
bilirubin, cholesterol, chorionic gonadotropin, creatine kinase
(e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine,
growth hormones, hormones, ketones (e.g., ketone bodies), lactate,
oxygen, peroxide, prostate-specific antigen, prothrombin, RNA,
thyroid stimulating hormone, and troponin. The concentration of
drugs, such as, for example, antibiotics (e.g., gentamicin,
vancomycin, and the like), digitoxin, digoxin, drugs of abuse,
theophylline, and warfarin, may also be determined. Assays suitable
for determining the concentration of DNA and/or RNA are disclosed
in U.S. Pat. No. 6,281,006 and U.S. Pat. No. 6,638,716, the
disclosures of each of which are incorporated by reference herein
in their entirety.
CONCLUSION
[0332] It should be understood that techniques introduced above can
be implemented by programmable circuitry programmed or configured
by software and/or firmware, or they can be implemented entirely by
special-purpose "hardwired" circuitry, or in a combination of such
forms. Such special-purpose circuitry (if any) can be in the form
of, for example, one or more application-specific integrated
circuits (ASICS), programmable logic devices (PLDs),
field-programmable gate arrays (FPGAs), etc.
[0333] Software or firmware implementing the techniques introduced
herein may be stored on a computer-readable storage medium and may
be executed by one or more general-purpose or special-purpose
programmable microprocessors. A "computer-readable medium", as the
term is used herein, includes any mechanism that can store
information in a form accessible by a computer (a computer may be,
for example, a personal computer, network device, cellular phone,
personal digital assistant (PDA), manufacturing took, any device
with one or more processors, etc.). For example, a
computer-accessible medium includes recordable/non-recordable media
(e.g., read-only memory (ROM); random access memory (RAM); magnetic
disk storage media; optical storage media; flash memory devices;
etc.), etc.
[0334] The foregoing description of the invention has been
presented for purposes of illustration and description. It is not
intended to be exhaustive or to limit the invention to the precise
form disclosed. Other modifications and variations may be possible
in light of the above teachings. The embodiments were chosen and
described in order to best explain the principles of the invention
and its practical application, and to thereby enable others skilled
in the art to best utilize the invention in various embodiments and
various modifications as are suited to the particular use
contemplated. It is intended that the appended claims be construed
to include other alternative embodiments of the invention;
including equivalent structures, components, methods, and
means.
[0335] It is to be appreciated that the Detailed Description
section, and not the Summary and Abstract sections, is intended to
be used to interpret the claims. The Summary and Abstract sections
may set forth one or more, but not all exemplary embodiments of the
present invention as contemplated by the inventor(s), and thus, are
not intended to limit the present invention and the appended claims
in any way.
[0336] Where a range of values is provided, it is understood that
each intervening value, to the tenth of the unit of the lower limit
unless the context clearly dictates otherwise, between the upper
and lower limits of that range is also specifically disclosed. Each
smaller range between any stated value or intervening value in a
stated range and any other stated or intervening value in that
stated range is encompassed within the invention. The upper and
lower limits of these smaller ranges may independently be included
or excluded in the range, and each range where either, neither or
both limits are included in the smaller ranges is also encompassed
within the invention, subject to any specifically excluded limit in
the stated range. Where the stated range includes one or both of
the limits, ranges excluding either or both of those included
limits are also included in the invention.
[0337] In the description of the invention herein, it will be
understood that a word appearing in the singular encompasses its
plural counterpart, and a word appearing in the plural encompasses
its singular counterpart, unless implicitly or explicitly
understood or stated otherwise. Merely by way of example, reference
to "an" or "the" "analyte" encompasses a single analyte, as well as
a combination and/or mixture of two or more different analytes,
reference to "a" or "the" "concentration value" encompasses a
single concentration value, as well as two or more concentration
values, and the like, unless implicitly or explicitly understood or
stated otherwise. Further, it will be understood that for any given
component described herein, any of the possible candidates or
alternatives listed for that component, may generally be used
individually or in combination with one another, unless implicitly
or explicitly understood or stated otherwise. Additionally, it will
be understood that any list of such candidates or alternatives, is
merely illustrative, not limiting, unless implicitly or explicitly
understood or stated otherwise.
[0338] Various terms are described to facilitate an understanding
of the invention. It will be understood that a corresponding
description of these various terms applies to corresponding
linguistic or grammatical variations or forms of these various
terms. It will also be understood that the invention is not limited
to the terminology used herein, or the descriptions thereof, for
the description of particular embodiments. Merely by way of
example, the invention is not limited to particular analytes,
bodily or tissue fluids, blood or capillary blood, or sensor
constructs or usages, unless implicitly or explicitly understood or
stated otherwise, as such may vary.
[0339] The publications discussed herein are provided solely for
their disclosure prior to the filing date of the application.
Nothing herein is to be construed as an admission that the
embodiments of the invention are not entitled to antedate such
publication by virtue of prior invention. Further, the dates of
publication provided may be different from the actual publication
dates which may need to be independently confirmed.
[0340] The detailed description of the figures refers to the
accompanying drawings that illustrate an exemplary embodiment of an
analyte measurement system. Other embodiments are possible.
Modifications may be made to the embodiment described herein
without departing from the spirit and scope of the present
invention. Therefore, the following detailed description is not
meant to be limiting.
[0341] Certain embodiments presented herein relate to electrical
interfaces in measurement devices. Measurement devices often have
electrical interfaces that allow them to electrically connect with
another device or apparatus and perform an analysis of an analyte.
A device that measures blood glucose levels, for example, includes
electrical interfaces that allow the device to measure the blood
glucose level from a small blood sample.
* * * * *