U.S. patent application number 11/759923 was filed with the patent office on 2008-03-13 for analyte monitoring system and method.
This patent application is currently assigned to Abbott Diabetes Care, Inc.. Invention is credited to Benjamin J. Feldman, Timothy T. Goodnow, John C. Mazza, Geoffrey V. McGarraugh, Thomas A. Peyser, Kerstin Rebrin.
Application Number | 20080064937 11/759923 |
Document ID | / |
Family ID | 38802144 |
Filed Date | 2008-03-13 |
United States Patent
Application |
20080064937 |
Kind Code |
A1 |
McGarraugh; Geoffrey V. ; et
al. |
March 13, 2008 |
ANALYTE MONITORING SYSTEM AND METHOD
Abstract
Devices and methods for monitoring an analyte are provided.
Embodiments include continuous analyte sensors having a high degree
of accuracy.
Inventors: |
McGarraugh; Geoffrey V.;
(Oakland, CA) ; Feldman; Benjamin J.; (Oakland,
CA) ; Peyser; Thomas A.; (Menlo Park, CA) ;
Mazza; John C.; (Pleasanton, CA) ; Goodnow; Timothy
T.; (Pleasanton, CA) ; Rebrin; Kerstin;
(Alameda, CA) |
Correspondence
Address: |
JACKSON & CO., LLP
6114 LA SALLE AVENUE
#507
OAKLAND
CA
94611-2802
US
|
Assignee: |
Abbott Diabetes Care, Inc.
Alameda
CA
|
Family ID: |
38802144 |
Appl. No.: |
11/759923 |
Filed: |
June 7, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60804170 |
Jun 7, 2006 |
|
|
|
60804169 |
Jun 7, 2006 |
|
|
|
Current U.S.
Class: |
600/309 |
Current CPC
Class: |
A61B 5/076 20130101;
A61B 5/7282 20130101; A61B 5/1495 20130101; A61B 5/14532 20130101;
A61M 5/1723 20130101; A61B 5/14546 20130101; A61B 5/1486 20130101;
A61B 5/746 20130101; A61B 5/14865 20130101; G01N 33/48792 20130101;
A61M 2005/1726 20130101; C12Q 1/006 20130101; A61B 2562/125
20130101 |
Class at
Publication: |
600/309 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A continuous analyte monitoring system, comprising: an analyte
sensor having at least about 80% of its paired data points within
zone A and at least about 95% of its paired data points within zone
A and zone B of the Clarke Error Grid; a transmitter capable of
receiving information from the sensor; and a receiver capable of
receiving information from the transmitter.
2. The system of claim 1, wherein the analyte sensor has at least
about 85% of its paired data points within zone A of the Clarke
Error Grid.
3. The system of claim 1, wherein the analyte sensor has at least
about 90% of its paired data points within zone A of the Clarke
Error Grid.
4. The system of claim 1 wherein the analyte sensor has more than
approximately 90% of its paired data points within zone A of the
Clarke Error Grid.
5. The system of claim 1, wherein the analyte sensor has at least
about 85% of its paired data points within zone A of the Consensus
Error Grid.
6. The system of claim 5, wherein the analyte sensor has at least
about 90% of its paired data points within zone A of the Continuous
Glucose Error Grid Analysis.
7. The system of claim 1, wherein the analyte sensor is a glucose
sensor.
8. The system of claim 1, wherein the system does not require
confirmation of analyte data obtained by the system.
9. The system of claim 1, further comprising a drug delivery
device.
10. The system of claim 9, wherein one or more of the transmitter
and the receiver is adapted to transmit analyte information to the
drug delivery device.
11. The system of claim 1 wherein the analyte sensor is calibrated
using single point calibration.
12. A continuous analyte monitoring system, comprising: an analyte
sensor having at least about 85% of its paired data points within
zone A and at least about 95% of its paired data points within zone
A and zone B of the Consensus Error Grid; a transmitter capable of
receiving information from the sensor; and a receiver capable of
receiving information from the transmitter.
13. The system of claim 12, wherein the analyte sensor has at least
about 85% of its paired data points within zone A of the Consensus
Error Grid.
14. The system of claim 12, wherein the analyte sensor has at least
about 90% of its paired data points within zone A of the Consensus
Error Grid.
15. The system of claim 12, wherein the analyte sensor has more
than approximately 90% of its paired data points within zone A of
the Consensus Error Grid.
16. The system of claim 12, wherein the analyte sensor is a glucose
sensor.
17. The system of claim 12, wherein the system does not require
confirmation of analyte data obtained by the system.
18. The system of claim 12, further comprising a drug delivery
device.
19. The system of claim 18, wherein one or more of the transmitter
and the receiver is adapted to transmit analyte information to the
drug delivery device.
20. The system of claim 12 wherein the analyte sensor is calibrated
using single point calibration.
Description
RELATED APPLICATIONS
[0001] This application claims priority under 35 USC .sctn. 119 to
Provisional Application No. 60/804,170 filed Jun. 7, 2006 entitled
"Analyte Monitoring", and to Provisional Application No. 60/804,169
filed Jun. 7, 2006 entitled "Analyte Monitoring System" the
disclosure of each of which are incorporated in their entirety by
reference for all purposes
BACKGROUND OF THE INVENTION
[0002] The association of chronic hyperglycemia and the devastating
long-term complications of diabetes was clearly established by the
Diabetes Control and Complication Trial (DCCT) (The Diabetes
Control and Complications Trial Research Group. "The effect of
intensive treatment of diabetes on the development and progression
of long-term complications of insulin-dependent diabetes mellitus"
N Engl J Med 329: 978-986, 1993; Santiago J V "Lessons from the
Diabetes Control and Complications Trial" Diabetes 1993, 42:
1549-1554).
[0003] The DCCT found that in patients receiving intensive insulin
therapy, there was a reduced risk of 76% for diabetic retinopathy,
50% for diabetic nephropathy and 60% for diabetic neuropathy. The
long-term benefits of tight glycemic control have been further
substantiated by the Epidemiology of Diabetes Interventions and
Complications study which found over a 50% reduced risk of
macrovascular disease as a result of intensive insulin therapy (The
Diabetes Control and Complications Trial/Epidemiology of Diabetes
Intervention and Complication (DCCT/EDIC) Study Group, "Intensive
diabetes treatment and cardiovascular disease in patients with type
1 diabetes", 353, 2643-2653, 2005).
[0004] However, the DCCT found that patients receiving intensive
insulin therapy were at a threefold increased risk of severe
hypoglycemia. Patients adhering to intensive insulin therapy
regimens were found to have lowered thresholds for activation of
neurogenic warning systems and consequently were at increased risk
for more severe hypoglycemic events. (Amiel S A, Tamborlane W V,
Simonson D C, Sherwin R S., "Defective glucose counterregulation
after strict glycemic control of insulin-dependent diabetes
mellitus." N Engl J. Med. 1987 28; 316(22):1376-83).
[0005] The increased risk of hypoglycemia and the fear associated
with patients' perception of that risk has been cited as the
leading obstacle for patients to achieve the targeted glycemic
levels (Cryer P E. "Hypoglycaemia: The limiting factor in the
glycemic management of type I and type II diabetes" Diabetologia,
2002, 45: 937-948). In addition to the problem of chronic
hyperglycemia contributing to long-term complications and the
problem of acute iatrogenic hypoglycemia contributing to short-term
complications, recent research suggests that transient episodes of
hyperglycemia can lead to a wide range of serious medical problems
besides previously identified microvascular complications as well
as macrovascular complications such as increased risk for heart
disease. (Haffner S "The importance of postprandial hyperglycemia
in development of cardiovascular disease in people with diabetes"
International Journal of Clinical Practice, 2001, Supplement 123:
24-26; Hanefeld M: "Postprandial hyperglycemia: noxious effects on
the vessel wall" International Journal of Clinical Practice, 2002,
Supplement 129: 45-50).
[0006] Additional research has found that glycemic variation and
the associated oxidative stress may be implicated in the
pathogenesis of diabetic complications (Hirsh I B, Brownlee M
"Should minimal blood glucose variability become the gold standard
of glycemic control?" J of Diabetes and Its Complications, 2005,
19: 178-181; Monnier, L., Mas, E., Ginet, C., Michel, F., Villon L,
Cristol J-P, and Collette C, "Activation of oxidative stress by
acute glucose fluctuations compared with sustained chronic
hyperglycemia in patients with type 2 diabetes". JAMA 2006, 295,
1681-1687). Glycemic variation has also been identified as a
possible explanation for the increased prevalence of depression in
both type 1 and type 2 diabetes (Van der Does F E. De Neeling J N,
Snoek F J, Kostense P J, Grootenhuis P A, Bouter L M, and R J
Heine: Symptoms and well-being in relation to glycemic control in
type II diabetes Diabetes Care, 1996, 19: 204-210; De Sonnaville J
J. Snoek F J. Colly L P. Deville W. Wijkel D. Heine R J:
"Well-being and symptoms in relation to insulin therapy in type 2
diabetes" Diabetes Care, 1998, 21: 919-24; Cox D J,
Gonder-Frederick L A, McCall A, et al. "The effects of glucose
fluctuation on cognitive function and QOL: the functional costs of
hypoglycaemia and hyperglycaemia among adults with type 1 or type 2
diabetes" International Journal of Clinical Practice, 2002,
Supplement 129: 20-26).
[0007] The potential benefits of continuous glucose monitoring have
been recognized by numerous researchers in the field (Skyler J S
"The economic burden of diabetes and the benefits of improved
glycemic control: the potential role of a continuous glucose
monitoring system" Diabetes Technol Ther 2 (Suppl 1): S7-S12, 2000;
Tansey M J, Beck R W, Buckingham B A, Mauras N, Fiallo-Scharer R,
Xing D, Kollman C, Tamborlane W V, Ruedy K J, "Accuracy of the
modified Continuous Glucose Monitoring System (CGMS) sensor in an
outpatient setting: results from a diabetes research in children
network (DirecNet) study." Diab. Tech. Ther. 7(1):109-14, 2005;
Klonoff, D C: "Continuous glucose monitoring: Roadmap for 21st
century diabetes therapy" Diabetes Care, 2005, 28: 1231:1239).
Accurate and reliable real-time continuous glucose monitoring
devices have the ability to alert patients of high or low blood
sugars that might otherwise be undetected by episodic capillary
blood glucose measurements.
[0008] Continuous glucose monitors have the potential to permit
more successful adherence to intensive insulin therapy regimens and
also to enable patients to reduce the frequency and extent of
glycemic fluctuations. However, the development of this technology
has proceeded more slowly than anticipated. For example, two recent
comprehensive reviews of decades of research in the field cited the
lack of accuracy and reliability as the major factor limiting the
acceptance of this new technology as well as the development of an
artificial pancreas (Chia, C. W. and Saudek, C. D., "Glucose
sensors: toward closed loop insulin delivery" Endocrinol. Metab.
Clin. N. Am., 33, 174-195, 2004; Hovorka, R. "Continuous glucose
monitoring and closed-loop systems" Diabet. Med. 23, 1-12,
2006).
[0009] As continuous analyte monitoring becomes more prevalent, of
use are continuous analyte sensors and systems that are accurate to
such a high degree that confirmatory analyte measurement are not
needed to verify the continuous sensing measurements, e.g., prior
to a user relying on the continuous measurements. Also of interest
are such sensors that work in concert with a drug delivery
device.
SUMMARY OF THE INVENTION
[0010] Generally, the present disclosure relates to methods and
devices for monitoring of the level of an analyte using a
continuous and/or automatic in vivo monitoring analyte sensor.
Embodiments include sensors in which at least a portion of the
sensor is adapted to be positioned beneath the skin of a user and
which are adapted for providing clinically accurate analyte data,
i.e., data with accuracy sufficient so that a user may confidently
rely on the sensor results, e.g., to manage a disease condition
and/or make a healthcare decision based thereon. Accordingly,
sensors capable of providing clinically accurate (i.e., clinically
relevant) analyte information to a user are provided.
[0011] Embodiments include continuous analyte monitoring systems
that do not require additional analyte information obtained by a
second system and/or sensor to confirm the results reported by the
continuous sensing system.
[0012] Embodiments also include high accuracy continuous analyte
sensors and systems with drug delivery systems e.g., insulin pumps,
or the like. A communication link (e.g., by cable or wirelessly
such as by infrared (IR) or RF link or the like) may be provided
for transfer of data from the sensor to the drug delivery device.
The drug delivery device may include a processor to determine the
amount of drug to be delivered using sensor data, and may deliver
such drug automatically or after user direction to do so.
[0013] Also provided are methods of analyte monitoring using highly
accurate continuous analyte sensors.
[0014] These and other objects, features and advantages of the
present disclosure will become more fully apparent from the
following detailed description of the embodiments, the appended
claims and the accompanying drawings.
BRIEF DESCRIPTIONS OF THE DRAWINGS
[0015] The figures shown herein are not necessarily drawn to scale,
with some components and features being exaggerated for clarity.
Each of the figures diagrammatically illustrates aspects of the
present disclosure. Of these:
[0016] FIG. 1 is a block diagram of one embodiment of a highly
accurate continuous glucose monitoring system such as Freestyle
Navigator.RTM. system using a subcutaneously implantable analyte
sensor, according to one embodiment of the present disclosure;
[0017] FIG. 2 shows five day accuracy data for the monitoring
system of FIG. 1 (arm and abdomen) and 50 hours of YSI venous
sampling in one embodiment;
[0018] FIG. 3 shows a Clarke error grid for the continuous
monitoring system of FIG. 1 in one embodiment;
[0019] FIG. 4A shows a view (four hour duration) of profile plot
centered glucose challenge, and FIG. 4B shows a view (four hour
duration) of profile plot centered insulin challenge;
[0020] FIG. 5 shows rate of change histogram showing underlying
rate of change at high resolution (in units of 0.25 mg/dL/min) and
in units of the continuous monitoring system of FIG. 1 receiver
trend arrows (1.0 mg/dL/min);
[0021] FIG. 6 shows a Clarke error grid for YSI rates of change
between -1 to 1 mg/dL/min;
[0022] FIG. 7 shows the Clarke error grid from a high accurate
continuous glucose monitoring system user study; and
[0023] FIG. 8 illustrates the time spent in hypoglycemic,
euglycemic, and hyperglycemic ranges for type 1 and 2 subjects in
the blinded and unblinded phases of the study described in
conjunction with FIG. 7.
DETAILED DESCRIPTION
[0024] Before the various embodiments of the present disclosure is
described, it is to be understood that this disclosure is not
limited to particular embodiments described, 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
present disclosure will be limited only by the appended claims.
[0025] 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 limit of that range and any other stated or intervening
value in that stated range, is encompassed within the present
disclosure. The upper and lower limits of these smaller ranges may
independently be included in the smaller ranges is also encompassed
within the present disclosure, 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 present disclosure.
[0026] Unless defined otherwise, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this present disclosure belongs.
Although any methods and materials similar or equivalent to those
described herein can also be used in the practice or testing of
various embodiments of the present disclosure, exemplary methods
and materials are now described. All publications mentioned herein
are incorporated herein by reference to disclose and describe the
methods and/or materials in connection with which the publications
are cited.
[0027] It must be noted that as used herein and in the appended
claims, the singular forms "a", "an", and "the" include plural
referents unless the context clearly dictates otherwise.
[0028] The publications discussed herein are provided solely for
their disclosure prior to the filing date of the present
application. Nothing herein is to be construed as an admission that
the present disclosure is 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.
[0029] As will be apparent to those of skill in the art upon
reading this disclosure, each of the individual embodiments
described and illustrated herein has discrete components and
features which may be readily separated from or combined with the
features of any of the other several embodiments without departing
from the scope or spirit of the present disclosure.
[0030] The present disclosure is applicable to analyte monitoring
systems using a sensor--at least a portion of which is positioned
beneath the skin of the user, for the in vivo determination of a
concentration of an analyte, such as glucose, lactate, and the
like, in a body fluid. The sensor may be, for example,
subcutaneously positioned in a patient for the continuous or
periodic monitoring an analyte in a patient's interstitial fluid.
This may be used to infer the glucose level in the patient's
bloodstream. The sensors of the subject disclosure also include in
vivo analyte sensors for insertion into a vein, artery, or other
portion of the body containing fluid. A sensor of the subject
disclosure may be configured for monitoring the level of the
analyte over a time period which may range from hours, days, weeks,
or longer, as described in greater detail below.
[0031] More specifically, FIG. 1 illustrates a data monitoring and
management system such as, for example, analyte (e.g., glucose)
monitoring system 100, in accordance with one embodiment of the
present disclosure. The subject disclosure is further described
primarily with respect to a glucose monitoring system for
convenience and such description is in no way intended to limit the
scope of the present disclosure. It is to be understood that the
analyte monitoring system may be configured to monitor a variety of
analytes. Analytes that may be monitored 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, lactate, peroxide, prostate-specific antigen, prothrombin,
RNA, thyroid stimulating hormone, and troponin, and the like. The
concentration of drugs, such as, for example, antibiotics (e.g.,
gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of
abuse, theophylline, and warfarin, and the like, may also be
monitored.
[0032] The analyte monitoring system 100 includes a highly accurate
sensor 101, a transmitter unit 102 coupled to the sensor 101, and a
receiver unit 104 which is configured to communicate with the
transmitter unit 102 via a communication link 103. The receiver
unit 104 may be further configured to transmit data to a data
processing terminal 105 for evaluating the data received by the
receiver unit 104. Moreover, the data processing terminal in one
embodiment may be configured to receive data directly from the
transmitter unit 102 via a communication link 106 which may
optionally be configured for bi-directional communication. Some or
all of the various components may be separate components, or some
or all may be integrated into a single unit.
[0033] Only one sensor 101, transmitter unit 102, receiver unit
104, communication link 103, and data processing terminal 105 are
shown in the embodiment of the analyte monitoring system 100
illustrated in FIG. 1. However, it will be appreciated by one of
ordinary skill in the art that the analyte monitoring system 100
may include one or more sensor 101, transmitter unit 102, receiver
unit 104, communication link 103, and data processing terminal 105.
Moreover, within the scope of the present disclosure, the analyte
monitoring system 100 may be a continuous monitoring system, or
semi-continuous, or a discrete monitoring system. In a
multi-component environment, each device is configured to be
uniquely identified by each of the other devices in the system so
that communication conflict is readily resolved between the various
components within the analyte monitoring system 100.
[0034] In one embodiment of the present disclosure, the sensor 101
is physically positioned in or on the body of a user whose analyte
level is being monitored. The sensor 101 may be configured to
continuously sample the analyte level of the user and convert the
sampled analyte level into a corresponding data signal for
transmission by the transmitter unit 102. In one embodiment, the
transmitter unit 102 is coupled to, e.g., mounted on, the sensor
101 so that both devices are positioned on the user's body. The
transmitter unit 102 performs data processing such as filtering and
encoding on data signals, each of which corresponds to a sampled
analyte level of the user, for transmission to the receiver unit
104 via the communication link 103.
[0035] In one embodiment, the analyte monitoring system 100 is
configured as a one-way RF communication path from the transmitter
unit 102 to the receiver unit 104. In such embodiment, the
transmitter unit 102 transmits the sampled data signals received
from the sensor 101 without acknowledgement from the receiver unit
104 that the transmitted sampled data signals have been received.
For example, the transmitter unit 102 may be configured to transmit
the encoded sampled data signals at a fixed rate (e.g., at one
minute intervals) after the completion of the initial power on
procedure. Likewise, the receiver unit 104 may be configured to
detect such transmitted encoded sampled data signals at
predetermined time intervals. Alternatively, the analyte monitoring
system 100 may be configured with a bi-directional RF (or
otherwise) communication between the transmitter unit 102 and the
receiver unit 104.
[0036] Additionally, in one aspect, the receiver unit 104 may
include two sections. The first section is an analog interface
section that is configured to communicate with the transmitter unit
102 via the communication link 103. In one embodiment, the analog
interface section may include an RF receiver and an antenna for
receiving and amplifying the data signals from the transmitter unit
102, which are thereafter, demodulated with a local oscillator and
filtered through a band-pass filter. The second section of the
receiver unit 104 is a data processing section which is configured
to process the data signals received from the transmitter unit 102
such as by performing data decoding, error detection and
correction, data clock generation, and data bit recovery.
[0037] In certain embodiments, in operation, the receiver unit 104
is configured to detect the presence of the transmitter unit 102
within its range based on, for example, the strength of the
detected data signals received from the transmitter unit 102 or a
predetermined transmitter identification information. Upon
successful synchronization with the corresponding transmitter unit
102, the receiver unit 104 is configured to begin receiving from
the transmitter unit 102 data signals corresponding to the user's
detected analyte level. More specifically, the receiver unit 104 in
one embodiment is configured to perform synchronized time hopping
with the corresponding synchronized transmitter unit 102 via the
communication link 103 to obtain the user's detected analyte
level.
[0038] Referring again to FIG. 1, the data processing terminal 105
may include a personal computer, a portable computer such as a
laptop or a handheld device (e.g., personal digital assistants
(PDAs)), and the like, each of which may be configured for data
communication with the receiver via a wired or a wireless
connection. Additionally, the data processing terminal 105 may
further be connected to a data network (not shown) for storing,
retrieving and updating data corresponding to the detected analyte
level of the user.
[0039] Within the scope of the present disclosure, the data
processing terminal 105 may include an infusion device such as an
insulin infusion pump or the like, which may be configured to
administer insulin to patients, and which may be configured to
communicate with the receiver unit 104 for receiving, among others,
the measured analyte level. Alternatively, the receiver unit 104
may be configured to integrate an infusion device therein so that
the receiver unit 104 is configured to administer insulin therapy
to patients, for example, for administering and modifying basal
profiles, as well as for determining appropriate boluses for
administration based on, among others, the detected analyte levels
received from the transmitter unit 102.
[0040] Additionally, the transmitter unit 102, the receiver unit
104 and the data processing terminal 105 may each be configured for
bi-directional wireless communication such that each of the
transmitter unit 102, the receiver unit 104 and the data processing
terminal 105 may be configured to communicate (that is, transmit
data to and receive data from) with each other via the wireless
communication link 103. More specifically, the data processing
terminal 105 may in one embodiment be configured to receive data
directly from the transmitter unit 102 via the communication link
106, where the communication link 106, as described above, may be
configured for bi-directional communication.
[0041] In this embodiment, the data processing terminal 105 which
may include an insulin pump, may be configured to receive the
analyte signals from the transmitter unit 102, and thus,
incorporate the functions of the receiver 103 including data
processing for managing the patient's insulin therapy and analyte
monitoring. In one embodiment, the communication link 103 may
include one or more of an RF communication protocol, an infrared
communication protocol, a Bluetooth enabled communication protocol,
an 802.11x wireless communication protocol, or an equivalent
wireless communication protocol which would allow secure, wireless
communication of several units (for example, per HIPPA
requirements) while avoiding potential data collision and
interference.
[0042] Continuous Glucose Monitoring Sensors and Systems
[0043] As described above, the various embodiments of the present
disclosure relate to continuous analyte sensors and systems having
a high degree of accuracy, e.g., as demonstrated by a Clark Error
Grid, Parks Error Grid, Continuous Glucose Error Grid, MARD
analysis, and the like. The high degree of accuracy permits a user
to rely on the results of the sensor without the need to confirm
sensor results. In certain embodiments, the sensors have at least
about 80% of its paired data points within zone A of one or more of
the Clark Error Grid, the Consensus Error Grid, or the Continuous
Glucose Error Grid Analysis, e.g., at least about 85% of its paired
data points within zone A of one or more of the Clark Error Grid,
the Consensus Error Grid, or the Continuous Glucose Error Grid
Analysis, e.g., at least about 90% of its paired data points within
zone A of one or more of the Clark Error Grid, the Consensus Error
Grid, or the Continuous Glucose Error Grid Analysis, e.g., at least
about 95% of its paired data points within zone A of one or more of
the Clark Error Grid, the Consensus Error Grid, or the Continuous
Glucose Error Grid Analysis.
[0044] In certain embodiments, a sensor may have about 80% or
greater, e.g., 85% or greater, e.g., 90% or greater of its paired
data points within zone A of the Clark Error Grid, and 80% or
greater, e.g., 85% or greater, e.g., 90% or greater, of its paired
data points within zone A of the Consensus Error Grid.
[0045] The sensors are continuous analyte monitoring sensors. The
sensors are adapted to continuously or periodically monitor analyte
levels for a period of time, e.g., usually at least about 24 hours,
e.g., about 1 day to about 30 days, e.g., about 3 days to about 7
days, e.g., a 5 day sensor or 7 day sensor.
[0046] Embodiments of the clinically accurate continuous glucose
monitoring systems of the present disclosure include four
components: a small, miniaturized analyte sensor element (which may
be an electrochemical or optical sensor) for placement in the
subcutaneous adipose tissue in the arm or abdomen (or elsewhere); a
disposable sensor delivery unit containing a spring-loaded sharp
for mechanical insertion of the sensor into the tissue and a sensor
support mount; a transmitter (e.g., wireless transmitter) which
connects to the sensor support mount on the skin surface and to the
inserted electrochemical sensor; and a hand-held receiver device
for communication (e.g., wireless) with the transmitter and for the
communication (e.g., audio and/or visual display) of the continuous
glucose values to the user. The system may also include a data
management system in which information from the receiver (and/or
transmitter) is forwarded (e.g., wirelessly or otherwise) to a data
management system such as a personal computer ("PC"), personal
digital assistant ("PDA"), telephone, facsimile machine, drug
delivery device (e.g., internal or external insulin pump) or the
like.
[0047] Embodiments of the sensors of the present disclosure vary,
but in all embodiments have a high degree of accuracy. In other
words, the sensors' accuracy enables a user of the system to solely
and confidently rely on the sensors' results that are reportable to
the user, e.g., to manage a disease state such as diabetes or the
like, make healthcare decisions (e.g., insulin delivery, meals,
exercise, etc.). In this manner, adjunctive measurements are not
required to confirm the readings of the highly accurate sensors of
the present disclosure, thereby eliminating burdensome and painful
fingersticks required for testing analyte using conventional blood
analyte monitoring systems such as blood glucose test strips and
the like, used for such confirmations.
[0048] In certain embodiments a sensor is adapted to be wholly or
partially positioned beneath the skin surface of a user. A sensor
may be a transcutaneous sensor in which a portion of the sensor is
configured to be positioned beneath a skin surface and portion is
configured to be positioned above the skin surface. In many
embodiments at least a portion of the sensor is configured to be
inserted into the subcutaneous adipose tissue. Sensors may vary in
size, where in certain embodiments a sensor may be about 5.5 mm
long, about 600 microns wide and about 250 microns thick. Sensors
having different lengths and/or widths and/or thicknesses are also
encompassed by the present disclosure. The sensors are configured
to accurately measure an analyte, e.g., glucose concentration in
the interstitial fluid, which has correlates with blood glucose.
The sensor is typically provided to a user as a sterile, single-use
disposable element.
[0049] The sensors may be configured to continuously monitor
analyte levels of a user for a period of time. In certain
embodiments, the period of time ranges from about 1 day to about 30
days, e.g., from about 3 days to about 7 days, where in certain
embodiments a sensor may configured for up to about five days of
continuous use. A system may include two or more sensors, which may
be temporally overlapped for a certain period of usage time,
thereby extending the amount of time of continuous sensing and/or
doing away with any time gaps that may result from removing a first
sensor and inserting a second. Furthermore, a sensor may be
calibrated from a previous sensor in certain embodiments.
[0050] The glucose measurement is made using sensing chemistry.
Sensing chemistry may include an enzyme and may include a mediator.
In certain embodiments, the sensing chemistry is a modified glucose
oxidase polymeric matrix with an osmium dopant in the supporting
polymer matrix. The sensing chemistry (also referred to as the
"transduction chemistry") used in the sensors of the present
disclosure permits detection of signal, e.g., a nanoampere
electrical current from the reaction with an applied potential,
such as of only about 40 mV.
[0051] More specifically, in one embodiment, the sensor includes at
least one working electrode formed on a substrate. The sensor may
also include at least one counter electrode (or counter/reference
electrode) and/or at least one reference electrode. The counter
electrode and/or reference electrode may be formed on the substrate
or may be separate units. For example, the counter electrode and/or
reference electrode may be formed on a second substrate which is
also implanted in the patient or, for some embodiments of the
implantable sensors, the counter electrode and/or reference
electrode may be placed on the skin of the patient with the working
electrode or electrodes being implanted into the patient.
[0052] The working electrode or electrodes are formed using
conductive traces disposed on the substrate. The counter electrode
and/or reference electrode, as well as other optional portions of
the sensor, such as a temperature probe, may also be formed using
conductive traces disposed on the substrate. These conductive
traces may be formed over a smooth surface of the substrate or
within channels formed by, for example, embossing, indenting or
otherwise creating a depression in the substrate.
[0053] A sensing layer is often formed proximate to or on at least
one of the working electrodes to facilitate the electrochemical
detection of the analyte and the determination of its level in the
sample fluid, particularly if the analyte can not be electrolyzed
at a desired rate and/or with a desired specificity on a bare
electrode. The sensing layer may include an electron transfer agent
to transfer electrons directly or indirectly between the analyte
and the working electrode. The sensing layer may also contain a
catalyst to catalyze a reaction of the analyte. The components of
the sensing layer may be in a fluid or gel that is proximate to or
in contact with the working electrode. Alternatively, the
components of the sensing layer may be disposed in a polymeric or
sol-gel matrix that is proximate to or on the working electrode. In
one aspect, the components of the sensing layer are non-leachably
disposed within the sensor. Further, the components of the sensor
are immobilized within the sensor.
[0054] In addition to the electrodes and the sensing layer, the
sensor may also include a temperature probe, a mass transport
limiting layer, a biocompatible layer, and/or other optional
components, as described below. Each of these items enhances the
functioning of and/or results from the sensor, as discussed
below.
The Substrate
[0055] The substrate may be formed using a variety of
non-conducting materials, including, for example, polymeric or
plastic materials and ceramic materials. Suitable materials for a
particular sensor may be determined, at least in part, based on the
desired use of the sensor and properties of the materials.
[0056] In some embodiments, the substrate is flexible. In other
embodiments, the sensors are made using a relatively rigid
substrate to, for example, provide structural support against
bending or breaking.
Conductive Traces
[0057] At least one conductive trace is formed on the substrate for
use in constructing a working electrode. In addition, other
conductive traces may be formed on the substrate for use as
electrodes (e.g., additional working electrodes, as well as
counter, counter/reference, and/or reference electrodes) and other
components, such as a temperature probe. The conductive traces may
be formed on the substrate by a variety of techniques, including,
for example, photolithography, screen printing, or other impact or
non-impact printing techniques. The conductive traces may also be
formed by carbonizing conductive traces in an organic (e.g.,
polymeric or plastic) substrate using a laser.
[0058] The conductive traces are typically formed using a
conductive material 56 such as carbon (e.g., graphite), a
conductive polymer, a metal or alloy (e.g., gold or gold alloy), or
a metallic compound (e.g., ruthenium dioxide or titanium dioxide).
The formation of films of carbon, conductive polymer, metal, alloy,
or metallic compound are well-known and include, for example,
chemical vapor deposition (CVD), physical vapor deposition,
sputtering, reactive sputtering, printing, coating, and
painting.
[0059] In addition to the particles of carbon, metal, alloy, or
metallic compound, the conductive ink may also contain a binder.
The binder may optionally be cured to further bind the conductive
material within the channel and/or on the substrate.
[0060] Suitable redox couples for binding to the conductive
material of the reference electrode include, for example, redox
polymers (e.g., polymers having multiple redox centers.). In one
aspect, the reference electrode surface may be non-corroding so
that an erroneous potential is not measured. Examples of conductive
materials include less corrosive metals, such as gold and
palladium, and may include non-corrosive materials including
non-metallic conductors, such as carbon and conducting polymers. A
redox polymer can be adsorbed on or covalently bound to the
conductive material of the reference electrode, such as a carbon
surface of a conductive trace. Non-polymeric redox couples can be
similarly bound to carbon or gold surfaces.
[0061] A variety of methods may be used to immobilize a redox
polymer on an electrode surface. One method is adsorptive
immobilization. This method is particularly useful for redox
polymers with relatively high molecular weights. The molecular
weight of a polymer may be increased, for example, by
cross-linking.
[0062] Another method for immobilizing the redox polymer includes
the functionalization of the electrode surface and then the
chemical bonding, often covalently, of the redox polymer to the
functional groups on the electrode surface.
Sensing Layer
[0063] Some analytes, such as oxygen, can be directly
electrooxidized or electroreduced on the working electrode. Other
analytes, such as glucose and lactate, require the presence of at
least one electron transfer agent and/or at least one catalyst to
facilitate the electrooxidation or electroreduction of the analyte.
Catalysts may also be used for those analyte, such as oxygen, that
can be directly electrooxidized or electroreduced on the working
electrode. For these analytes, each working electrode has a sensing
layer formed proximate to or on a working surface of the working
electrode. Typically, the sensing layer is formed near or on only a
small portion of the working electrode, often near a tip of the
sensor. This limits the amount of material needed to form the
sensor and places the sensing layer 64 in the best position for
contact with the analyte-containing fluid (e.g., a body fluid,
sample fluid, or carrier fluid).
Electron Transfer Agent
[0064] In many embodiments, the sensing layer contains one or more
electron transfer agents in contact with the conductive material of
the working electrode. In some embodiments of the present
disclosure, there is little or no leaching of the electron transfer
agent away from the working electrode during the period in which
the sensor is implanted in the patient. A diffusing or leachable
(i.e., releasable) electron transfer agent often diffuses into the
analyte-containing fluid, thereby reducing the effectiveness of the
electrode by reducing the sensitivity of the sensor over time.
[0065] In some embodiments of the present disclosure, to prevent
leaching, the electron transfer agents are bound or otherwise
immobilized on the working electrode or between or within one or
more membranes or films disposed over the working electrode. The
electron transfer agent may be immobilized on the working electrode
using, for example, a polymeric or sol-gel immobilization
technique. Alternatively, the electron transfer agent may be
chemically (e.g., ionically, covalently, or coordinatively) bound
to the working electrode, either directly or indirectly through
another molecule, such as a polymer, that is in turn bound to the
working electrode.
[0066] In general, electron transfer agents may be electroreducible
and electrooxidizable ions or molecules having redox potentials
that are a few hundred millivolts above or below the redox
potential of the standard calomel electrode (SCE). Further, the
electron transfer agents are not more reducing than about -150 mV
and not more oxidizing than about +400 mV versus SCE.
Catalyst
[0067] The sensing layer may also include a catalyst which is
capable of catalyzing a reaction of the analyte. The catalyst may
also, in some embodiments, act as an electron transfer agent. One
example of a suitable catalyst is an enzyme which catalyzes a
reaction of the analyte. In one aspect, the catalyst is
non-leachably disposed on the sensor, whether the catalyst is part
of a solid sensing layer in the sensor or solvated in a fluid
within the sensing layer. In a further aspect, the catalyst is
immobilized within the sensor (e.g., on the electrode and/or within
or between a membrane or film) to prevent unwanted leaching of the
catalyst away from the working electrode and into the patient. This
may be accomplished, for example, by attaching the catalyst to a
polymer, cross linking the catalyst with another electron transfer
agent (which can be polymeric), and/or providing one or more
barrier membranes or films with pore sizes smaller than the
catalyst.
Biocompatible Layer
[0068] An optional film layer is formed over at least that portion
of the sensor which is subcutaneously inserted into the patient.
This optional film layer may serve one or more functions. The film
layer prevents the penetration of large biomolecules into the
electrodes. This is accomplished by using a film layer having a
pore size that is smaller than the biomolecules that are to be
excluded. Such biomolecules may foul the electrodes and/or the
sensing layer thereby reducing the effectiveness of the sensor and
altering the expected signal amplitude for a given analyte
concentration. The fouling of the working electrodes may also
decrease the effective life of the sensor. The biocompatible layer
may also prevent protein adhesion to the sensor, formation of blood
clots, and other undesirable interactions between the sensor and
body.
Interferent-Eliminating Layer
[0069] An interferent-eliminating layer may be included in the
sensor. The interferent-eliminating layer may be incorporated in
the biocompatible layer or in the mass transport limiting layer
(described below) or may be a separate layer. Interferents are
molecules or other species that are electroreduced or
electrooxidized at the electrode, either directly or via an
electron transfer agent, to produce a false signal. In one
embodiment, a film or membrane prevents the penetration of one or
more interferents into the region around the working electrodes. In
one aspect, this type of interferent-eliminating layer is much less
permeable to one or more of the interferents than to the
analyte.
Mass Transport Limiting Layer
[0070] A mass transport limiting layer may be included with the
sensor to act as a diffusion-limiting barrier to reduce the rate of
mass transport of the analyte, for example, glucose or lactate,
into the region around the working electrodes. By limiting the
diffusion of the analyte, the steady state concentration of the
analyte in the proximity of the working electrode (which is
proportional to the concentration of the analyte in the body or
sample fluid) can be reduced. This extends the upper range of
analyte concentrations that can still be accurately measured and
may also expand the range in which the current increases
approximately linearly with the level of the analyte. Particularly
useful materials for the film layer are membranes that do not swell
in the analyte-containing fluid that the sensor tests.
[0071] Suitable membranes include 3 to 20,000 nm diameter pores.
Membranes having 5 to 500 nm diameter pores with well-defined,
uniform pore sizes and high aspect ratios may be used. In one
embodiment, the aspect ratio of the pores may be two or greater, or
in one aspect five or greater.
[0072] Embodiments of the system include a receiver that includes
both the signal processing algorithms and the user interface system
for operation of the system and display of the results--although
one or both may be incorporated wholly or partially into the
transmitter of the system. In operation, the glucose display on the
main screen of the receiver is updated during a predetermined time
period, e.g., about once a minute or the like, and gives the
instantaneous continuous glucose value. Also provided may be the
direction and/or rate of change averaged over a predetermined
period of time, e.g., the preceding fifteen minutes, or the like.
The direction may be communicated using any suitable audio and/or
visual indicator(s). For example, direction may be displayed with
trend arrows that give quantitative ranges of the rate of change in
units of about 1 mg/dL/min from about -2 mg/dL/min to about +2
mg/dL/min. The receiver may also include threshold and/or projected
warnings--audible and/or visual warnings. These may be settable at
the factory and/or by the user to different glucose levels to
provide warnings of actual and impending hypo- or hyperglycemia.
Other warnings may also be included, e.g., battery level, and the
like. Time-to-calibrate indicators may also be included.
[0073] The system may also include a blood glucose ("BG") meter for
use with glucose test strips which may be used for calibration of
the continuous glucose sensor, but as noted above, is not needed to
confirm the continuous sensor results. The BG meter may be a
separate, though connectable component, or may be integrated into
the receiver as a single unitary device. For example, the receiver
may include a test strip port and a processor to process a reading
from the test strip. The built-in blood glucose meter eliminates
the possibility of transcription errors during sensor calibration
and also provides the user with a backup glucose meter system.
[0074] The continuous glucose systems of the present disclosure may
be calibrated according to a predetermined calibration schedule. In
certain embodiments, this schedule may be limited to factory-only
calibration. However in certain embodiments, the calibration
schedule may include calibrations by the user. For example, over
the period of use of the system, it may be calibrated from about 0
to about 10 times, e.g., from about 1 to about 5 times, e.g., about
4 times. An exemplary calibration schedule may include calibration
4 times over a 5 day period, e.g., at 10, 12, 24 and 72 hours after
sensor insertion. In certain embodiments, the system may be
configured for single point calibration, e.g., as described in U.S.
Pat. No. 6,121,009 and elsewhere. In other embodiments, exemplary
calibration schedule may include calibration 1-2 times over a 5-7
day period. The system may be configured to accept calibration
values that fall within a certain range or are at least meet a
threshold value. For example, calibration values may be accepted
for blood glucose input between about 60 and about 300 mg/dL and
when the absolute rate of change of glucose is estimated to be less
than about 2 mg/dL/min. These constraints on the acceptance of
calibration input values are designed to limit the potential
adverse effects of the intrinsic physiological lag between
interstitial fluid glucose and blood glucose.
[0075] In the embodiments in which at least one calibration by the
user is required, the system may be configured so that it does not
display (i.e., does not report to the user) real-time glucose
values from the continuous monitor until the first calibration,
e.g., at about ten hours after sensor insertion in certain
instances. This delay after insertion is designed so that the
initial system calibration is performed after the sensor has
reached a stable equilibrium with the surrounding tissue.
[0076] Moreover, in one embodiment, the use of fingerstick
calibration in response to the Freestyle Navigator.RTM. system
hypoglycemic alarm may increase the overall system accuracy.
[0077] An exemplary, analyte sensor and sensing system having the
high accuracy described herein is the Freestyle Navigator.RTM.
continuous glucose monitoring system from Abbott Diabetes Care,
Inc., of Alameda, Calif.
[0078] Kits
[0079] Finally, kits are also provided. Embodiments of the subject
kits may include one or more highly accurate sensors as described
herein. Embodiments may also include a sensor insertion device
and/or transmitter and/or receiver. Embodiments may also include a
drug delivery device such as an insulin pump.
[0080] In certain embodiments, a kit may include a blood glucose
meter to be used with the continuous sensing system, e.g., for
calibration. The meter may be a separate component from continuous
sensing components (in which case a communication link for
transferring data from the meter to the sensing system (such as to
the receiver) may be included) or may be integrated therein, e.g.,
the receiver may include a blood glucose meter.
[0081] The subject kits may also include written instructions for
using a sensor. The instructions may be printed on a substrate,
such as paper or plastic, etc. As such, the instructions may be
present in the kits as a package insert, in the labeling of the
container of the kit or components thereof (i.e., associated with
the packaging or sub-packaging) etc. In other embodiments, the
instructions are present as an electronic storage data file present
on a suitable computer readable storage medium, e.g., CD-ROM,
diskette, etc. In yet other embodiments, the actual instructions
are not present in the kit, but means for obtaining the
instructions from a remote source, e.g. via the Internet, are
provided. An example of this embodiment is a kit that includes a
web address where the instructions can be viewed and/or from which
the instructions can be downloaded. As with the instructions, this
means for obtaining the instructions is recorded on a suitable
substrate.
[0082] In many embodiments of the subject kits, the components of
the kit are packaged in a kit containment element to make a single,
easily handled unit, where the kit containment element, e.g., box
or analogous structure, may or may not be an airtight container,
e.g., to further preserve the one or more sensors and additional
reagents (e.g., control solutions), if present, until use.
EXPERIMENTAL
[0083] The accuracy of a highly accurate continuous monitoring
system such as the Freestyle Navigator.RTM. continuous glucose
monitoring system measuring glucose in the interstitial fluid is
studied, in comparison with a laboratory reference method over five
days of sensor wear.
Study Design and Methods
[0084] Fifty-eight subjects with Type 1 diabetes ranging in age
from 18-64 were enrolled in a multi-center, prospective, single-arm
study. Each subject wore two sensors simultaneously--one on the arm
and the other on the abdomen. All the FreeStyle Navigator.RTM.
devices were calibrated with separate capillary fingerstick
measurements at 10, 12, 24 and 72 hours after sensor insertion.
Data from the continuous glucose monitor was collected at
one-minute intervals for the entire study. Measurements from the
FreeStyle Navigator.RTM. system were compared with reference venous
sample measurements taken in an in-patient clinical research center
once every fifteen minutes over a fifty hour time period covering a
distribution over the entire 120 hour wear period for the Freestyle
Navigator.RTM. sensor.
[0085] The subjects were admitted to a healthcare facility either
in the evening or in the morning for sensor insertion. The sensors
were inserted by a health care professional on both the lateral or
posterior upper arm and the right or left lower abdominal quadrant
using the disposable sensor delivery unit. The subjects returned to
the clinic approximately nine hours later for the placement of the
venous access line and for the calibration of the sensor using the
built-in FreeStyle.RTM. blood glucose meter. Calibration of the
FreeStyle Navigator.RTM. device in this study was deliberately
scheduled to occur at different times of day as well as both pre-
and post-prandially. During two separate periods in which the
subjects were in the clinic and venous samples were being taken,
each subject was administered intravenous insulin or a 75 gram
fast-acting glucose drink, such as Glucola, in order to obtain data
for evaluation of the sensor performance during
deliberately-induced periods of rapidly-falling and rapidly-rising
glucose. Data from the sensor and transmitter were stored in the
receiver with a one minute frequency, but were not displayed to the
subjects or the clinic staff. Throughout the study, all the
subjects continued with their previously established diabetes
management regimen. The high frequency and volume of the venous
blood samples, 2.5 mL once every fifteen minutes, required a
limitation of 50 hours of intensive testing in order to maintain
the total volume of blood drawn from each subject within accepted
safety limits. Subjects were assigned to different study schedules
so as to provide an optimal distribution of the fifty hours of
laboratory reference data over the total five day duration of the
sensor life.
[0086] FIG. 2 illustrates five-day data from the Freestyle
Navigator.RTM. continuous glucose monitor (arm and abdomen) and 50
hours of YSI venous sampling taken two separate in-patient
admissions from one subject. The timing of the glucose and insulin
challenges is also shown. The shaded blocks are night time. The
black solid line is the Freestyle Navigator.RTM. sensor in the arm,
the dashed line is the sensor in the abdomen. YSI measurements are
shown in white triangles. The plus and cross symbols are the
Freestyle Navigator.RTM. system blood glucose calibrations for the
arm and abdominal sensors, respectively.
[0087] Referring to FIG. 2, a typical profile plot for the
five-days of the study with one-minute data from the arm and
abdominal sensors as well as the fifteen minute venous samples
taken over three separate periods during the five days. The glucose
concentration from the venous sample was measured using a YSI 2300
STAT Plus.TM. Glucose & Lactate Analyzer YSI analyzer (YSI Life
Sciences, Yellow Springs, Ohio). All YSI measurements were made in
duplicate from a single blood sample. YSI measurements were
multiplied by 1.12 to obtain plasma equivalent value.
Results
[0088] A number of separate metrics were used to evaluate the
accuracy and performance of the FreeStyle Navigator.RTM. system
compared with the venous blood samples measured with the laboratory
reference method. These metrics included the Clarke error grid, the
Consensus error grid, the mean and median absolute relative
difference as well as cross-correlation statistics for comparison
of abdominal and upper arm sensors. The sensor performance was
evaluated for the entire five days, for each day individually as
well as diurnally and nocturnally. Characteristic physiological lag
times were derived from analysis of the data. The data was also
analyzed using the Continuous Glucose Error Grid Analysis (CG-EGA)
(Kovatchev, 2004). Finally, the accuracy of the FreeStyle
Navigator.RTM. system compared to the venous reference samples was
analyzed as a function of the measured rates of change in the
underlying blood glucose.
[0089] Comparison of the FreeStyle Navigator.RTM. continuous
glucose measurements with the laboratory reference method
(n=20,362) gave a mean absolute relative difference of 12.8% and a
median absolute relative difference of 9.3%. The percentage in the
clinically-accurate Clarke Error Grid zone A was 81.7% and 16.7% in
the clinically-acceptable B zone. This included periods of high
rates of change of blood glucose during intravenous glucose and
insulin challenges. The precision of the matched Freestyle
Navigator.RTM. sensors worn on the arm and abdomen had a
coefficient of variation of 10% (n=312,953). The accuracy remained
unchanged over the five days with the percent of data in the Clarke
Error Grid Zone A equal to 82.5% on the first day and 80.9% on the
fifth day.
[0090] Clinical Accuracy Overall
[0091] FIG. 3 shows the Clarke error grid for the study reported
herein. More specifically, FIG. 3 illustrates an overall Clarke
error grid showing 81.7% in the clinically-accurate A zone, 16.7%
of the paired points in the clinically-acceptable or benign error
zone B and only 1.7% outside of the A and B zones
[0092] The Clarke error grid was developed to assess the clinical
implications of new glucose monitoring technology relative to
accepted reference methods (Cox D J, Clarke W L, Gonder-Frederick L
A, Pohl S, Hoover C, Snyder A, "Accuracy of perceiving blood
glucose in IDDM", Diabetes Care, 8(6):529-36, 1985; Clarke W L, Cox
D, Gonder-Frederick L A, Carter W and Pohl S L. "Evaluating
clinical accuracy of systems for self-monitoring of blood glucose"
Diabetes Care, 10, 622-628, 1987). There were a total of 20,362
paired points for all 58 subjects with YSI venous measurements and
Freestyle Navigator.RTM. system interstitial fluid glucose
measurements. 81.7% of the paired points fell in the Clarke error
grid zone A indicating a high level of clinical accuracy. There
were 16.7% of the paired points in the clinically-acceptable
(benign error) zone "B", 0.1% in the overtreatment error zone "C",
1.9% in the failure to detect error zone "D" and 0.01% in the
clinically inaccurate and dangerous error zone "E".
[0093] The Consensus error grid has been proposed as an alternative
to the original error grid zone demarcations, specifically to
eliminate the physical proximity of the clinically-unacceptable D
zone with the clinically-accurate A zone in the lower left portion
of the grid. The results of the Clarke error grid and the Consensus
error grid are summarized in the Table (1) below. The Consensus
error grid was also defined with five distinct risk levels, but the
definitions were specified in terms of effect on clinical action by
the patient. Zone A has no effect. Zone B has little or no effect.
Zone C has altered clinical action. Zone D has altered clinical
action with significant medical risk. Zone E has altered clinical
action with potentially dangerous consequences.
[0094] On the Clarke error grid, there were 316 individual points
in the D zone. Ninety-five percent of these points were in the
lower left quadrant of the error grid. TABLE-US-00001 TABLE (1)
Summary statistics of Clarke and Consensus Error Grid Clarke
Consensus Error Error Grid Grid Zone % N = 20362 % N = 20362 A 81.7
16627 85.5 17419 B 16.7 3398 13.6 2776 C 0.1 19 0.8 161 D 1.6 316
0.03 6 E 0.0 2 0.0 0
[0095] On the Consensus error grid, by contrast, the number of
points in the significant medical risk D zone is reduced to 6. In
addition to the reduction in D zone points, the Consensus error
grid shows a higher percentage in the clinically-accurate A zone, a
slightly lower percentage in the clinically-acceptable B zone, a
slightly higher percentage in the altered clinical action C zone
and no points in the dangerous consequence E zone.
[0096] The performance of the Freestyle Navigator.RTM. system was
also assessed using the mean and median absolute relative
difference between the sensor interstitial glucose measurements and
the YSI venous sample measurements. The mean absolute relative
difference was 12.8% and the median absolute relative difference
was 9.3%. A comparison of accuracy and performance by day shows
that the system's performance on the fifth day is equivalent to the
performance of the first or second day. Table (2) contains data
with the error grid statistics as well as the mean and median
absolute relative difference from the study separated by day.
TABLE-US-00002 TABLE (2) Clarke Error Grid, mean and median
absolute relative difference by day Day 1 Day 2 Day 3 Day 4 Day 5
Zone N % N % N % N % N % A 4354 82.5 3215 82.4 2903 79.4 1688 84.0
4467 80.9 B 865 16.4 646 16.6 668 18.3 285 14.2 934 16.9 C 12 0.2 4
0.1 1 0.0 0 0.0 2 0.0 D 47 0.9 34 0.9 82 2.2 37 1.8 116 2.1 E 0 0.0
2 0.1 0 0.0 0 0.0 0 0.0 Mean ARD 12.6 12.3 14.1 11.9 13.0 Median
ARD 9.4 9.3 9.9 7.8 9.5 Total 5278 100.0 3901 100.0 3654 100.0 2010
100.0 5519 100.0
[0097] Additional analysis was done comparing the accuracy and
performance of the Freestyle Navigator.RTM. system nocturnally and
diurnally. The percentage of points in the Clarke error grid A zone
was 87.1% at night and 80.6% during the day. The difference in
accuracy during the day may be associated with the higher rates of
change during the daytime, when all of the glucose and insulin
challenges were conducted.
[0098] The data from the present study has also been analyzed using
the Continuous Glucose Error Grid Analysis (CG-EGA), designed to
incorporate the extra temporal dimension of data provided by
continuous glucose monitoring systems (Kovatchev et al.). The rate
analysis using the CG-EGA gave a 81.1% in the rate error grid A
zone, 14.4% in the rate error grid B zone, 1.5% in the rate error
grid C zone, 2.3% in the rate error grid D zone, and 0.7% in the
rate error grid E zone. The point analysis using the CG-EGA gave a
83.6% in point error grid A zone, 15.0% in point error grid B zone,
0.1% in point error grid C zone, 1.3% in point error grid D zone,
and 0% in point error grid E zone. The CG-EGA analysis combining
rate and point information revealed that accuracy, measured as a
percentage of accurate readings plus benign errors, was 97.5%
(94.2% accurate, 3.4% benign). The CG-EGA accuracy stratified by
glycemic state gave 60.4% in hypoglycemia (53.1% accurate, 7.3%
benign), 99.3% in euglycemia (95.7% accurate, 3.6% benign) and
98.2% in hyperglycemia (95.4% accurate, 2.8% benign). The
difference in accuracy between the hypoglycemic, euglycemic, and
hyperglycemic ranges may be related to the high rate of change
often associated with the descent into hypoglycemia. Standard
egression analysis and Deming regression analysis both gave small,
but significant offsets 24.9 and 14.3 mg/dL) that could contribute
to the slight decrease in accuracy in hypoglycemia.
[0099] FIGS. 4A and 4B give an expanded view of the data from FIG.
2 on a four-hour time axis and centered about the glucose challenge
and the insulin challenge, respectively. More specifically, FIG. 4A
illustrates a zoomed in view (four hour duration) of Freestyle
Navigator.RTM..TM. sensor data and YSI measurements during the
glucose challenge. Referring to FIG. 4A, the continuous glucose
sensor data in one minute intervals are shown in the two solid
curves (solid from the arm, dashed from the abdomen). The 15 minute
YSI venous sample data are shown in the triangles. The time between
the nadir of the YSI data and the Freestyle Navigator.RTM. system
is approximately 24 minutes. The time between the peak of the YSI
data and the Freestyle Navigator.RTM. system data is approximately
19 minutes.
[0100] Additionally, FIG. 4B shows data from two Freestyle
Navigator.RTM. sensors, compared with fifteen minutes venous
samples measured with the YSI from the insulin challenge in one
patient in the study. Referring to FIG. 4B, the Freestyle
Navigator.RTM. projected alarm, would have alerted the subject to
an impending hypoglycemic event 26 minutes before the blood sugar
crossed the 70 mg/dL hypoglycemic threshold. At the time of the
alarm, the Freestyle Navigator.RTM. system glucose was
approximately 175 mg/dL and the YSI reading was approximately 90
mg/dL and the rate of change was -3.5 mg/dL/min.
[0101] Both FIGS. 4A and 4B show the temporal tracking of the
FreeStyle Navigator.RTM. system compared against the venous
reference samples. The expanded temporal axis used in FIGS. 4A and
4B also permits more direct visualization of the time lag between
the Freestyle Navigator.RTM. system interstitial fluid glucose
measurement and the venous reference sample measurements. The
temporal offset between the FreeStyle.RTM. Navigator system and the
venous reference measurements was also analyzed by applying a time
shift in order to minimize the mean absolute relative
difference.
[0102] After correction for the calibration bias, this resulted in
an average 12.8 minute lag between the glucose values measured in
the interstitial fluid and in the venous samples. This is
consistent with previously published studies on the physiological
lag between interstitial fluid glucose and blood glucose (see for
example: Rebrin K, Steil G M, van Antwerp W P, Mastrotoraro J J,
"Subcutaneous glucose predicts plasma glucose independent of
insulin: implications for continuous monitoring", Am J Physiol.,
277 (3 Pt 1):E561-71, 1999; Steil G M, Rebrin K, Mastrototaro J,
Bernaba B, Saad M F, "Determination of plasma glucose during rapid
glucose excursions with a subcutaneous glucose sensor", Diab. Tech.
Ther, 5:27-31, 2003; Thennadil S N, Rennert J L, Wenzel B J, Hazen
K H, Ruchti T L, Block MB, "Comparison of glucose concentration in
interstitial fluid, and capillary and venous blood during rapid
changes in blood glucose levels", Diab. Tech. Ther., 3(3):357-65,
2001).
[0103] The performance of the arm and abdominal sensors was
comparable with equivalent Clarke error grid statistics and mean
absolute relative difference. The precision of the matched
Freestyle Navigator.RTM. sensors worn on the arm and abdomen had a
coefficient of variation of 10% (n=312, 953). There was no
difference in performance of the sensor as a function of age,
gender or ethnicity. However, there were small but measurable
differences in the accuracy of the sensor depending on the
subject's BMI and also on the years since diagnosis. Subjects with
BMI less than 25.0 had 78.8% in the Clarke error grid A zone
(N=4844), whereas subjects with BMI between 25.0 and 30.0 had 82.2%
in the Clarke error grid A zone (N=7855) and subjects with BMI
greater than 30.0 had 84.4% in the Clarke error grid A zone
(N=3928). Similarly, there were small but measurable differences in
accuracy depending on the years since diagnosis of type 1 diabetes.
The highest accuracy, 88.5% in the Clarke error grid A zone, was
found in subjects who had been diagnosed with diabetes for five
years or less (N=2066) and 81.3% for subjects diagnosed between 5
and 25 years (N=9133). Subjects diagnosed with type 1 diabetes for
over 25 years had 79.9% in the Clarke error grid A zone
(N=5448).
Clinical Accuracy Under Special Circumstances
[0104] The evaluation of the overall accuracy and performance of
the FreeStyle Navigator.RTM. continuous glucose monitor included
periods of deliberately-induced rapidly rising and rapidly falling
blood glucose, i.e. in response to the glucose and insulin
challenges. There were significant differences in the accuracy
compared with the laboratory reference measurements depending on
the different rates of change of the underlying blood glucose.
Table (3) gives the Clarke error grid statistics and the median
absolute relative difference percentage as a function of the rate
of change of blood glucose as determined by the YSI measurements.
The effect of the physiological lag on the accuracy of the sensor
values compared to venous reference samples is more pronounced at
the high rates of change, particularly during when the absolute
rate of change exceeds 2 mg/dL/min. TABLE-US-00003 TABLE (3) Rate
of change and Clarke error grid statistics and median ARD Rate of
Change Clarke Error Grid Region Median (mg/dL/min) N A B C D E ARD
% <-2 601 54.6 42.3 1.3 1.8 0.0 17.4 -2 to -1 1728 71.7 26.2 0.3
1.8 0.0 11.8 -1 to 1 14653 84.9 13.5 0.0 1.5 0.0 8.5 1 to 2 1954
79.8 18.9 0.0 1.3 0.0 11.0 >2 691 63.5 34.7 0.0 1.7 0.0 16.9
[0105] FIG. 5 illustrates the rate of change histogram showing
underlying rate of change at high resolution (in units of 0.25
mg/dL/min) and in units of the Navigator receiver trend arrows (1.0
mg/dL/min). The rate of change of glucose as measured by the sensor
was between -1 and +1 mg/dL/min 74.6% of the time. Referring to
FIG. 5, there is a slight difference in the measured occurrence of
absolute rates of change less than 1 mg/dL/min due to the different
sampling frequency and temporal extent of the Freestyle
Navigator.RTM. system and YSI measurements.
[0106] The Freestyle Navigator.RTM. trend arrows would have been in
the horizontal position indicating an absolute rate of change less
than 1 mg/dL/min 74.1% of the time for which the YSI data revealed
71.9% of all readings in this range. Both values are consistent
with previously reported results (see for example: Dunn T C,
Eastman R C, Tamada J A, "Rates of glucose change measured by blood
glucose meter and the GlucoWatch Biographer during day, night, and
around mealtimes", Diabetes Care 27: 2161-2165, 2004; Kovatchev, B.
P., Clarke, W. L., Breton, M., Brayman, K. and McCall, A.
"Quantifying Temporal Glucose Variability in Diabetes via
Continuous Glucose Monitoring: Mathematical Methods and Clinical
Application" Diab. Technol. Thera., 7, 849-862, 2005).
[0107] FIG. 6 illustrates Clarke error grid for YSI rates of change
between -1 to 1 mg/dL/min showing increase in accuracy during
modest rates of change. Referring to FIG. 6, whereas the overall
percentage of paired points in the Clarke error grid A zone was
81.7%, the percentage in the A zone for rates of change between -1
mg/dL/min and +1 mg/dL/min was significantly higher at 84.9%.
Similarly, the mean and median absolute relative differences at
these times were 11.4% and 8.5% respectively.
[0108] The accuracy of the Freestyle Navigator.RTM. continuous
glucose monitor was evaluated in comparison to a standard
laboratory reference method using venous blood samples. The overall
mean and median absolute relative difference of the sensor in the
current study of 12.8% and 9.3% represent a significantly higher
level of accuracy than previously published results from other
continuous glucose monitoring systems (see for example, Diabetes
Research in Children Network (DirecNet) Study Group: "The Accuracy
of the CGMS in Children with Type 1 Diabetes: Results of the
Diabetes Research in Children Network (DirecNet) Accuracy Study".
Diabetes Technol Ther 5(5):781-789, 2003; Diabetes Research in
Children Network (DirecNet) Study Group: "The Accuracy of the
GlucoWatch G2 Biographer in Children with Type 1 Diabetes: Results
of the Diabetes Research in Children Network (DirecNet) Accuracy
Study". Diabetes Technol Ther 5(5):791-800, 2003; Tansey M J, Beck
R W, Buckingham B A, Mauras N, Fiallo-Scharer R, Xing D, Kollman C,
Tamborlane W V, Ruedy K J, "Accuracy of the modified Continuous
Glucose Monitoring System (CGMS) sensor in an outpatient setting:
results from a diabetes research in children network (DirecNet)
study." Diab. Tech. Ther. 7(1):109-14, 2005; Garg S., Zisser H.,
Schwartz S., Bailey T., Kaplan R., Ellis S. and Jovanovic L,
"Improvement in glycemic excursions with a transcutaneous,
real-time continuous glucose sensor", Diabetes Care, 29, 44-50,
2006).
[0109] The high accuracy of the system as measured by the
percentage in the Clarke error grid A zone and the mean and median
absolute relative differences remained high over the entire five
days. There was a small, but measurable improvement in the Clarke
error grid statistics and the absolute relative difference values
on the fourth day. This is due principally to the fact that there
were no glucose challenges administered on the fourth day of the
study resulting in fewer rates of change on that day less than 2
mg/dL/min than on other days. In addition, there may be a small
increase in accuracy on the fourth day associated with the final
system calibration at 72 hours after sensor insertion. Similarly,
the slight decrease in accuracy observed on the third and fifth
days of the sensor wear may be associated with the fact that these
days had a greater number of glucose and insulin challenges than
other days in the study, resulting in more absolute rates of change
on those days in excess of 2 mg/dL/min.
[0110] A significant portion of the apparent discrepant points
between the Freestyle Navigator.RTM. and the venous reference
samples are likely due to the physiological lag alone. An example
of the effect of physiological lag on accuracy is the point at the
nadir of the curves in FIG. 4B, which is categorized in the Clarke
error grid analysis as a clinically unacceptable D zone point. In
this case, although the point-wise comparison of the Freestyle
Navigator.RTM. sensor value and the venous reference sample value
suggests a failure to detect a hypoglycemic event, it is clear from
the data that the Freestyle Navigator.RTM. system is correctly
tracking the fall of the subject's glucose level.
[0111] In the case shown in FIG. 4B, with the projected alarm
capability enabled and the detection threshold set at 70 mg/dL, the
device would have alerted the user to a predicted change in
clinical state from euglycemia to hypoglycemia when the Freestyle
Navigator.RTM. glucose value was approximately 175 mg/dL and the
measured rate of glucose decrease was in excess of -3.5 mg/dL/min.
At that moment, the trend arrow was in the downward vertical
direction, indicating a rate of glucose decrease of greater than 2
mg/dL/min, and the device's alarm would have used predictive
algorithm to identify that the subject would be hypoglycemic in
thirty minutes.
[0112] At the time when the projected alarm would have alerted the
subject to an impending hypoglycemic event, the YSI reading was
approximately 90 mg/dL. An interpolation of the YSI data indicates
that the subject's blood sugar crossed the 70 mg/dL threshold for
hypoglycemia approximately twenty-six minutes later. Although the
paired YSI and Freestyle Navigator.RTM. system points at the nadir
of the curve result in a D zone point on the Clarke error grid, it
is clear from a detailed analysis that the projected alarm would
have alerted the subject to an impending hypoglycemic event in a
timely manner.
[0113] Another important measure of the clinical accuracy, and
ultimately the clinical utility, of the Freestyle Navigator.RTM.
system is the percentage of points in the clinically-accurate
Clarke error grid A zone. A recent numerical simulation study
evaluated the effect of sensor inaccuracy on the statistics
associated with glucose monitoring error grid analysis using data
from a clinical trial of a continuous glucose monitoring system in
type 1 children and adolescents (Kollman et al., 2005). In the
numerical study, paired points from the actual continuous glucose
monitoring system and a laboratory reference method were randomly
"shuffled" to simulate a high degree of sensor inaccuracy. The
study found that 78% of the randomly shuffled paired points were
still in the combined A and B zones of the Clarke error grid. A
more useful measure of the clinical accuracy and utility of new
glucose monitoring technology may be the percentage of points in
the clinically-accurate Clarke error grid A zone alone. (Kollman C,
Wilson D M, Wysocki T, Tamborlane W V, Beck R W, "Limitations of
the Statistical measures of Error in Assessing the Accuracy of
continuous Glucose Sensors", Diab. Tech. Ther., 7(5):665-672,
2005). An alternative to the more commonly-used metric of combined
A and B zone percentage is to rely instead on the total percentage
in the A zone alone. The results of the present study showing the
Freestyle Navigator.RTM. system achieving 81.7% in the A zone alone
represent a new level of performance for continuous glucose
monitoring systems.
[0114] The high accuracy and performance of the Freestyle
Navigator.RTM. system at night is also in contrast with previous
reports of continuous glucose monitoring systems that exhibited
sustained periods of anomalous nocturnal hypoglycemia (see for
example: MTcGowan K. Thomas W, Moraii A. "Spurious reporting of
nocturnal J hypoglycemia by CGMS in patients with tightly
controlled type I diabetes" Diabetes Care 2002; 25: 1499-1503;
Metzger M, Leibowitz G, Wainstein J, Glaser B, Raz I.
"Reproducibility of glucose measurements using the glucose sensor"
Diabetes Care 2002; 25: 1185-1191; Mauras N, Beck R W, Ruedy K J,
Koliman C, Tamborlane W V, Chase H P "Lack of accuracy of
continuous glucose sensors in healthy nondiabetic children: results
of the Diabetes Research in (Children Network (DirecNet) accuracy
study" J Pediatr 2004; 144:770-775).
[0115] The difference in accuracy as a function of BMI may be
related to the length of the Freestyle Navigator.RTM. sensor and
the thickness of the subcutaneous adipose tissue layer in subjects
with BMI less than 25. Anthropometric data strongly suggests that
the insertion of the Freestyle Navigator.RTM. sensor in the upper
arm or abdomen will result in the sensor being placed as intended
in the subcutaneous adipose tissue layer in most individuals
(Horejsi, R., Moller, R., Pieber, T R, Wallner, S., Sudi, K,
Reibnegger, G. and Tafeit "Differences of subcutaneous adipose
tissue topography between type 2 diabetic men and healthy controls"
Exp. Biol. Med., 227, 794-798, 2002). However, in some individuals
with low BMI, the data indicate that the subcutaneous adipose
tissue layer thickness on the posterior arm upper arm or even the
lower abdominal quadrant may be only slightly greater than the
required 6 mm thickness needed to properly accommodate the sensor.
Although the overall sensor performance in subjects with BMI less
than 25 is still excellent (78.8% in the clinically-accurate Clarke
error grid A zone), there is a small but measurable difference when
compared with subjects with BMI greater than 30 (84.4% in the
clinically-accurate Clarke error grid A zone). In the low BMI
subjects with reduced subcutaneous adipose tissue layer thickness,
the proximity of skeletal muscle tissue to the sensor in the
adipose tissue could increase the effect reported by Moberg et al.
in which tissue glucose nadirs were not only delayed relative to
plasma, but also reduced especially during insulin-induced
hypoglycemia (Moberg E, Hagstrom-Toft E, Amer P. and Bolinder J.
"Protracted glucose fall in subcutaneous adipose tissue and
skeletal muscle compared with blood during insulin-induced
hypoglycaemia" Diabetologia 40, 1320-1326, 1997).
[0116] In the present study, the apparent difference in accuracy as
a function of years since diagnosis is most likely also a result of
the weak dependence of accuracy on BMI. The 6 subjects with a
diagnosis of diabetes less than five years, for whom there was the
highest percentage in the Clarke error grid A zone and the lowest
median absolute relative difference, also by chance had the highest
mean BMI (29.8). Similarly, the 18 subjects with lowest BMI
(<24.9) in the study happened to also have the highest mean
years since diagnosis of diabetes (30.1 years).
[0117] Insulin adjustment Procedure--Clinical Decision Analyses
Insulin Adjustment Analysis
[0118] The Insulin Adjustment Analysis evaluates the difference
between insulin dosing based on Freestyle Navigator.RTM. Continuous
Glucose Monitoring System (CM) readings and that based on reference
readings. The interpretation of the analysis is best understood
considering a hypothetical patient with a glucose target level of
90-120 mg/dL and an insulin sensitivity of 30 mg/dL/unit. The
glucose target level represents aggressive therapy where the
therapeutic goal is to keep glucose squarely in the normal range.
The analysis is targeted to meet the requirements of intensive
insulin therapy. The choice of insulin sensitivity was made to
simplify interpretation--the treatment differences between
Navigator CM and YSI are calculated in whole number differences in
the units of insulin. This seemingly arbitrary choice of the
hypothetical patient has no influence on the results of the Insulin
Adjustment Analysis--the choice was based on the goals of intensive
insulin therapy and the ease of interpretation of the results.
[0119] The Insulin Adjustment Analysis data is reported as
differences in units of insulin. (see Table 4). This is an
intermediate result that allows a more detailed characterization of
the data than the final summary (see Table 5). Decisions with
Navigator CM were rated Correct 89.3% (1180/1322) of the time and
Acceptable 7.6% (100/1322) of the time. Since the Acceptable rating
translates to a glucose adjustment to within the normal glucose
range, accurate adjustments are the sum of Correct and Acceptable
categories, 96.8% (1280/1322). TABLE-US-00004 TABLE 4 Treatment
Difference for the Hypothetical Patient with Insulin Sensitivity =
30 mg/dL/unit and Glucose Target = 90-120 mg/dL Navigator CM - YSI
Treatment Difference Glucose < 200 mg/dL Glucose .gtoreq. 200
mg/dL (Units of insulin) N % Category N % Category -4 0 0
Hyperglycemia 2 4 0.6 Hyperglycemia 2 -3 1 0.1 Hyperglycemia 1 13
2.0 Hyperglycemia 1 -2 11 1.6 Acceptable 78 12.1 Acceptable -1 120
17.7 Correct 215 33.4 Correct 0 353 52.0 Correct 240 37.3 Correct 1
173 25.5 Correct 79 12.3 Correct 2 18 2.7 Possible Error 11 1.7
Acceptable 3 2 0.3 Error 3 0.5 Possible Error 4 1 0.1 Error 0 0
Error Total 679 100 643 100
[0120] TABLE-US-00005 TABLE 5 Insulin Adjustment Analysis Summary
Category Effect on Blood Glucose N % Correct Within .+-.30 mg/dL of
target 1180 89.3 glucose Acceptable Within normal glucose range 100
7.6 Possible Error (hypo) 60 mg/dL below target glucose 21 1.6
Error (hypo) .gtoreq.90 mg/dl below target glucose 3 0.2
Hyperglycemia 1 90 mg/dL above target glucose 14 1.1 Hyperglycemia
2 .gtoreq.120 mg/dl above target glucose 4 0.3 Total 1322 100
[0121] In summary, this analysis describes 3 occurrences of "Error
(hypo)" and 4 occurrences of "hyperglycemia 2" being potentially
indicated from 1322 decision points
Glucose Peak
[0122] Continuous glucose monitoring provides the ability to
identify and quantify the maximum glucose excursions after meals
and during the night. The quantification of glucose peaks was
clinically accurate 88.1% of the time and clinically useful 97.6%
of the time (see Table 6). TABLE-US-00006 TABLE 6 Glucose Peak
Analysis Difference Clinical Assessment N % .+-.15 mg/dL Accurate
263 41.5 .+-.45 mg/dL Accurate 295 46.6 .+-.75 mg/dL Useful 60 9.5
.+-.105 mg/dL Misclassification 14 2.2 .+-.135 mg/dL
Misclassification 1 0.2 Total 633 100.0
Insulin Adjustment Analysis
[0123] The Insulin Adjustment Analysis evaluates the hypothetical
difference between insulin dosing based on Navigator CM readings to
that based on a blood glucose meter such as Freestyle Blood Glucose
(BG) readings. The interpretation of the analysis is best
understood considering a hypothetical patient with a glucose target
level of 90-120 mg/dL and an insulin sensitivity of 30 mg/dL/unit.
The glucose target level represents aggressive therapy where the
therapeutic goal is to keep glucose squarely in the normal range.
The analysis is targeted to meet the requirements of intensive
insulin therapy. The choice of insulin sensitivity was made to
simplify interpretation--the treatment differences between
Navigator CM and Freestyle BG YSI (see Table 7) are calculated in
whole number differences in the units of insulin. This seemingly
arbitrary choice of the hypothetical patient has no influence on
the results of the Insulin Adjustment Analysis--the choice was
based the goals of intensive insulin therapy and the ease of
interpretation of the results.
[0124] The Insulin Adjustment Analysis data is reported as
differences in units of insulin (see Table 7). There were 6,040
paired (Navigator CM-Freestyle BG) glucose readings available at
times of subject-reported insulin dosing or bedtime in the Home Use
Study. The analysis is summarized in Table 8 with 86.5% (5226/6040)
of the readings correct and 94.3% (5696/6040) accurate or
acceptable. These results provide approximately 89.3% (1180/1322)
correct and 96.8% (1280/1322) accurate or acceptable.
TABLE-US-00007 TABLE 7 Treatment Difference for the Hypothetical
Patient with Insulin Sensitivity = 30 mg/dL/unit and Glucose Target
= 90-120 mg/dL Glucose < 200 Glucose .gtoreq. 200 Difference in
Insulin Dose mg/dL mg/dL (Units) N (%) N (%) 4 0 0 1 0.0 3 11 0.3 2
0.1 2 84 2.1 14 0.7 1 810 20.1 162 8.1 0 2362 58.5 530 26.5 -1 675
16.7 687 34.3 -2 89 2.2 367 18.3 -3 8 0.2 163 8.1 -4 0 0 75 3.7
Total 4039 -- 2001 --
[0125] TABLE-US-00008 TABLE 8 Insulin Adjustment Analysis Summary
Category Effect on Blood Glucose N % Correct Within .+-.30 mg/dL of
target 5226 86.5 glucose Acceptable Within normal glucose range 470
7.8 Possible Error 60 mg/dL below target glucose 86 1.4 (hypo)
Error (hypo) .gtoreq.90 mg/dl below target 12 0.2 glucose
Hyperglycemia 1 90 mg/dL above target glucose 171 2.8 Hyperglycemia
2 .gtoreq.120 mg/dl above target 75 1.2 glucose Total 6040 100
[0126] Insulin dosing or bedtime was not indicated for 5,447 of the
11,487 Freestyle BG duplicate points. The Insulin Adjustment
Analysis was also conducted using the 5,447 Freestyle BG duplicate
points for which there was no indication of insulin injection to
determine if there was a substantive difference between the two
populations. The Insulin Adjustment Analysis data is reported as
differences in units of insulin (See Table 9). The results are
slightly better for the points where insulin injections were not
indicated (See Table 10) with 89.4% (4868/5447) correct and 95.5
(5203/5447) correct or acceptable. TABLE-US-00009 TABLE 9 Treatment
Difference for the Hypothetical Patient with Insulin Sensitivity =
30 mg/dL/unit and Glucose Target = 90-120 mg/dL - Non-insulin
Injection Points Glucose < 200 Glucose .gtoreq. 200 Difference
in Insulin Dose mg/dL mg/dL (Units) N (%) N (%) 4 2 0.0 0 0 3 11
0.3 1 0.1 2 95 2.3 26 2.0 1 876 21.2 132 10.0 0 2473 59.9 388 29.5
-1 588 14.2 411 31.2 -2 81 2.0 228 17.3 -3 5 0.1 88 6.7 -4 0 0 42
3.2 Total 4131 -- 1316 --
[0127] TABLE-US-00010 TABLE 10 Insulin Adjustment Analysis Summary
Non-insulin Injection Points Category Effect on Blood Glucose N %
Correct Within .+-.30 mg/dL of target 4868 89.4 glucose Acceptable
Within normal glucose range 335 6.2 Possible Error 60 mg/dL below
target glucose 96 1.8 (hypo) Error (hypo) .gtoreq.90 mg/dl below
target glucose 13 0.2 Hyperglycemia 1 90 mg/dL above target glucose
93 1.7 Hyperglycemia 2 .gtoreq.120 mg/dl above target glucose 42
0.8 Total 5447 100
[0128] When a patient adjusts an insulin dose using a blood glucose
meter such as Freestyle Blood Glucose monitor, there is no
indication if glucose is changing. If glucose is rising at the time
of glucose dosing, there is insufficient insulin to stabilize blood
glucose and the predicted insulin dose will be too small. Likewise,
if glucose is descending, there is already insulin in the blood,
and the predicted insulin dose will be too large. The rate of
glucose change indicated by Navigator CM at the time of insulin
dosing (see Table 11) indicates glucose changes >.+-.2
mg/dL/minute 4.0% of the time, and >.+-.1 mg/dL/minute 18.3% of
the time. The agreement of static the blood glucose meter readings
with static reference readings is excellent, but the interpretation
of this agreement to suggest accurate insulin dosing with the blood
glucose meter is not correct. When insulin is dosed with no
knowledge of changing glucose levels, the dosing will be incorrect
a significant fraction of the time. The determination of 94.3%
Navigator CM dosing accuracy in this study and 96.8% Navigator CM
dosing accuracy in a previous study provide realistic estimations
when the rate of glucose change is also known. TABLE-US-00011 TABLE
11 Navigator CM Rate Indication at the Time of Insulin Dosing
Navigator CM Rate of Change (mg/dL/minute) N (%) >2.0 330 3.3
1.0 to 2.0 897 9.0 -1.0 to 1.0 8140 81.7 -2.0 to -1.0 526 5.3
<-2.0 72 0.7
[0129] The description below details a further user study results
from a highly accurate continuous glucose monitoring system such
as, for example, Freestyle Navigator.RTM. system. Of the 137
subjects enrolled in the investigation, 123 completed the 40-day
monitoring period. The other 14 subjects withdrew from the study
due to non-compliance with protocol demands (n=8) or difficulties
handling the device (n=6). None of the discontinued subjects
participated in the unblinded portion of the study. The glucose
data available for the discontinued subjects was included in the
paired point analysis.
[0130] The performance of the Freestyle Navigator.RTM. was assessed
using the absolute relative difference between the sensor
interstitial glucose measurements and the blood glucose
measurements. Data from 961 sensors with 11,487 paired FreeStyle BG
reference values were evaluated. The mean absolute relative
difference was 14.4% and the median absolute relative difference
was 111%. The mean absolute relative difference indicates that, on
average, the CM reading was 14.4% higher or lower than the
corresponding BG reading. The median absolute relative difference
indicates that the CM reading was equally as likely to be within
11.1% of the BG reading, either higher or lower, as it was to be
outside of that range.
[0131] The equation for the Deming regression had a slope of 0.83,
an intercept of 21.8 mg/dL and correlation coefficient of 0.92.
These results demonstrate a strong correlation between CM and BG
readings.
[0132] FIG. 7 shows the Clarke error grid for the study. There were
a total of 11,487 paired points with averaged duplicate BG
reference values and interpolated CM values, from 131 subjects. No
paired points were available from six subjects. Of the 11,487
paired points, 77.2% fell in the Clarke error grid zone A,
indicating a high level of correspondence between the reference
blood glucose measurements and the CM results. There were 19.6% of
the paired points in zone B and only 3.2% outside the A and B
zones. Results for all the Clarke error grid zones are shown in
Table 12 below. The results of the Consensus error grid are also
included in Table 12. TABLE-US-00012 TABLE 12 Summary statistics of
Clarke and Consensus Error Grid Clarke Error Consensus Grid Error
Grid Zone N (%) N (%) A 8863 77.2 9180 79.9 B 2255 19.6 2194 19.1 C
1 0.0 109 0.9 D 365 3.2 4 0.0 E 3 0.0 0 0.0 N paired points 11487
11487
[0133] On the Clarke error grid, there were 365 individual points
in the D zone. On the Consensus error grid, by contrast, the number
of points in the D zone is reduced to four. In addition, the
Consensus error grid shows 79.9% in the A zone, 99.0% in the A and
B zones, less than 1% in the C and D zones and no points in the E
zone.
[0134] A comparison of accuracy and performance by day of sensor
wear shows that the system's performance on the fifth day is nearly
equivalent to the performance on the first or second day. Table 13
contains data with the error grid statistics as well as the mean
absolute relative difference from the study separated by day.
TABLE-US-00013 TABLE 13 Clarke Error Grid, absolute relative
difference by day Day 1 Day 2 Day 3 Day 4 Day 5 Zone N/(%) N/(%)
N/(%) N/(%) N/(%) Clarke A 1061 (77.8) 2182 (77.4) 2110 (77.7) 1884
(79.3) 1626 (73.5) Clarke B 266 (19.5) 551 (19.6) 516 (19.0) 427
(18.0) 495 (22.4) Clarke C 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.0)
Clarke D 36 (2.6) 84 (3.0) 91 (3.3) 63 (2.7) 91 (4.1) Clarke E 1
(0.1) 1 (0.0) 0 (0.0) 1 (0.0) 0 (0.0) N paired 1364 2818 2717 2375
2213 points Consensus A Consensus B Consensus C Consensus D
Consensus E N paired 1364 2818 2717 2375 2213 points Mean 14.8 14.3
14.0 13.9 15.3 ARD Median ARD
[0135] Table 14 shows that the CM readings are optimal when blood
glucose is relatively stable (i.e., when the rate is within +/-1
mg/dL/min). As expected the bias increases somewhat as the
magnitude of the rate of glucose change increases. However, the
displayed rate arrow provides the necessary information to properly
interpret the glucose result in these situations. The mean bias for
glucose <100 mg/dL and the mean percent bias for glucose
.gtoreq.100 mg/dL become increasingly positive as the rate
decreases from +2 mg/dL/minute to -2 mg/dL/minute. Lag in the
interstitial readings versus capillary blood glucose readings is
the explanation for this result. When glucose levels were rising,
the CM values were low, on average, versus BG with the difference
versus BG lower for rising glucose (>1 mg/dL/minute) than for
stable glucose (.+-.1 mg/dL/minute). When glucose levels were
falling CM was high, on average, versus BG with the difference
versus BG higher for falling glucose (<1 mg/dL/minute) than for
stable glucose (.+-.1 mg/dL/minute). TABLE-US-00014 TABLE 14
Difference measures vs. glucose rate of change Navigator CM Rate of
Change (mg/dL per minute) Mean Median N Difference (mg/dL) for
glucose < 100 mg/dL >2.0 3.7 -1.2 3 1.0 to 2.0 4.7 5.5 33
-1.0 to 1.0 7.6 7.1 2028 -2.0 to -1.0 17.9 18.7 261 <-2.0 26.5
24.4 50 Absolute difference (mg/dL) for glucose < 100 mg/dL
>2.0 11.0 9.8 3 1.0 to 2.0 12.5 9.9 33 -1.0 to 1.0 13.3 10.8
2028 -2.0 to -1.0 21.5 19.4 261 <-2.0 32.4 27.0 50 Percent
difference % for glucose .gtoreq.= 100 mg/dL >2.0 -13.7 -14.3
152 1.0 to 2.0 -10.9 -10.7 581 -1.0 to 1.0 -3.5 -3.7 7245 -2.0 to
-1.0 6.9 6.8 432 <-2.0 7.5 9.1 69 Absolute % difference % for
glucose .gtoreq.= 100 mg/dL >2.0 17.0 16.1 152 1.0 to 2.0 14.8
12.6 581 -1.0 to 1.0 12.2 9.8 7274 -2.0 to -1.0 15.9 12.6 432
<-2.0 18.3 14.5 69
[0136] The Clarke EGA as a function of Navigator rate (Table 15)
exhibits the expected behavior. When glucose is descending by at
least -2 mg/dL/min, there is a higher likelihood that a reading
would fall into the left Zone D than when the glucose is stable or
rising. When glucose is rising, there is a higher likelihood that a
reading would fall into the right Zone D. The rate arrow provides
the valuable information to properly interpret the glucose result
(i.e. when glucose is rapidly descending Navigator CM tends to be
higher than Navigator BG and when glucose is rapidly ascending
Navigator CM tends to be lower than Navigator BG). TABLE-US-00015
TABLE 15 Clarke EGA vs. glucose rate of change Zone <-2.0 % -2.0
to -1.0 % -1.0 to 1.0 % 1.0 to 2.0 % >2.0 % A 61 51.3 425 61.3
7372 79.3 455 74.1 101 65.2 B 45 37.8 194 28.0 1688 18.1 149 24.3
48 31.0 C 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 D 12 10.1 73 10.5 240 2.6
10 1.6 6 3.9 E 1 0.8 0 0.0 2 0.0 0 0.0 0 0.0 Total 119 692 9302 614
155
Sensor Success Measures
[0137] The rate of successful sensor insertions was evaluated from
reported results of each sensor insertion attempt, as well as the
electronic records stored by the Receiver. The electronic records
were used to determine whether each sensor was detected by the
Receiver, and whether the user followed the steps in the labeling.
The percentage of insertions that were successful, when used as
directed, was similar for the blinded (96.0%) and unblinded (96.8%)
phases of the study (96.4% overall). The percentage of successful
insertions was similar for the arm (95.7%) and abdomen (97.4%)
insertion sites. Abdomen insertions may have been more successful
because it is easier to see the entire insertion process at the
abdomen site when inserting a sensor on oneself.
[0138] The success rate for the initial Sensor Calibration process
was evaluated from results recorded in the receiver log data for
each successful sensor insertion attempt. The time required to
complete the first sensor calibration was evaluated in addition to
the overall success or failure. The percentage of sensors that were
successfully calibrated and produced glucose results within the
first 12 hours was calculated. Sensor calibration is not allowed
within the first 10 hours. Sensors that could not be calibrated
because conditions were out of range were excluded, e.g., if the
glucose was changing too rapidly for calibration. The percentage of
sensors that were successfully calibrated within 12 hours, when
used as directed, was similar for the blinded (90.5%) and unblinded
(92.6%) phases of the study (91.5% overall).
[0139] Sensor duration was evaluated as the time duration from
sensor insertion to the last CM glucose result reported for the
sensor. Some sensors were removed early by user error or
discretion, or because of protocol logistics such as the end of the
trial. These sensors are excluded from analysis, unless the sensor
reached the nominal 5-day sensor life (>108 hours). The median
sensor life was similar for the blinded (119.9 hours) and unblinded
(120.0 hours) phases of the study. The percentage of sensors, used
as directed, that produced glucose results for 108 hours or more
was similar for the blinded (83.5%) and unblinded (83.0%) phases of
the study. Sensors on the arm tended to have slightly longer
duration (86.2% for >108 hours) than those on the abdomen
(79.4%), because there is somewhat less flexing and folding of the
skin at the posterior arm insertion site than on the abdomen,
improving the effectiveness of the skin adhesive that holds the
sensor in place.
Glycemic Analysis
[0140] The change in glycemic status between the blinded and
unblinded phases of the study was stratified by type 1 and type 2
diabetes. During the unblinded phase when alarms were set, subjects
were instructed to perform a BG test when alarms were triggered.
Some important differences in controlling glucose concentration
with insulin administration between the two types of diabetes are
the following: [0141] Subjects with type 2 diabetes are less likely
to induce hypoglycemia with insulin because they are insensitive to
insulin. Type 1 subjects, with normal insulin sensitivity are much
more likely to induce hypoglycemia. [0142] Subjects with type 2
diabetes can reduce hyperglycemia by reducing carbohydrate
ingestion and allowing endogenous insulin to reduce blood glucose.
Patients with type 1 diabetes produce no endogenous insulin, so a
reduction of carbohydrates is not a viable strategy for controlling
glucose. Controlling glucose with injected insulin is much more
difficult than control with endogenous insulin.
[0143] The time spent in hypoglycemic (<70 mg/dL), euglycemic
(70-180 mg/dL) and hyperglycemic ranges is illustrated in FIG. 8
for type 1 and 2 subjects in the blinded and unblinded phases of
the study.
[0144] The type 1 subjects improved in the unblinded phase by
reducing time in hypoglycemia. The time spent below the 70 mg/dL
threshold for hypoglycemia was reduced by 42% from 1.4 hours to 0.8
hours (p<0.0001). The time spent in hyperglycemia (>180
mg/dL) did not change.
[0145] For type 2 subjects, the duration of hyperglycemia improved
in the unblinded phase. The time spent in the euglycemic range
increased by 12% (p=0.0027) and the time spent >180 mg/dL
decreased by 18% (p=0.0057). As anticipated, the measures of
hypoglycemia for type 2 subjects, which were low in the blinded
phase, were largely unchanged in the unblinded phase.
[0146] Accordingly, a continuous analyte monitoring system in one
embodiment includes an analyte sensor having at least about 80% of
its paired data points within zone A and at least about 95% of its
paired data points within zone A and zone B of the Clarke Error
Grid, a transmitter capable of receiving information from the
sensor, and a receiver capable of receiving information from the
transmitter.
[0147] In one aspect, analyte sensor has at least about 85% of its
paired data points within zone A of the Clarke Error Grid.
[0148] In a further aspect, the analyte sensor has at least about
90% of its paired data points within zone A of the Clarke Error
Grid.
[0149] In still a further aspect, the analyte sensor has more than
approximately 90% of its paired data points within zone A of the
Clarke Error Grid.
[0150] Additionally, in another aspect, the analyte sensor has at
least about 85% of its paired data points within zone A of the
Consensus Error Grid, and further, where the analyte sensor has at
least about 90% of its paired data points within zone A of the
Continuous Glucose Error Grid Analysis.
[0151] The analyte sensor may be a glucose sensor.
[0152] In yet another aspect, the system may not require
confirmation of analyte data obtained by the system.
[0153] The system may include a drug delivery device, where one or
more of the transmitter and the receiver may be adapted to transmit
analyte information to the drug delivery device.
[0154] In another aspect, the analyte sensor may be calibrated
using single point calibration.
[0155] A continuous analyte monitoring system in accordance with
another embodiment includes an analyte sensor having at least about
85% of its paired data points within zone A and at least about 95%
of its paired data points within zone A and zone B of the Consensus
Error Grid, a transmitter capable of receiving information from the
sensor, and a receiver capable of receiving information from the
transmitter.
[0156] The analyte sensor may have at least about 85% of its paired
data points within zone A of the Consensus Error Grid.
[0157] The analyte sensor may have at least about 90% of its paired
data points within zone A of the Consensus Error Grid.
[0158] The analyte sensor may have more than approximately 90% of
its paired data points within zone A of the Consensus Error
Grid.
[0159] In another aspect, the system may not require confirmation
of analyte data obtained by the system.
[0160] The system may include a drug delivery device, where one or
more of the transmitter and the receiver may be adapted to transmit
analyte information to the drug delivery device.
[0161] Also, the analyte sensor may be calibrated using single
point calibration.
[0162] A method of monitoring glucose levels in accordance with
still another embodiment includes determining glucose concentration
using a first transcutaneously positioned analyte sensor, reporting
glucose concentration to a user, where a second sensor is not used
to confirm the accuracy of the first transcutaneously positioned
analyte sensor.
[0163] In one aspect, determining may include over a period of time
ranging from about 1 day to about 7 days.
[0164] The first transcutaneously positioned analyte sensor may
have at least about 85% of its paired data points within zone A of
the Clarke Error Grid.
[0165] The first transcutaneously positioned analyte sensor may
have at least about 90% of its paired data points within zone A of
the Clarke Error Grid.
[0166] The first transcutaneously positioned analyte sensor may
have more than about 90% of its paired data points within zone A of
the Clarke Error Grid.
[0167] The first transcutaneously positioned analyte sensor may be
a glucose sensor.
[0168] The method in a further aspect may include determining
health related information based on the reported glucose
concentration, where the health related information may include a
bolus amount, or one or more of a food intake, medication dosage
level, or activity level.
[0169] Also, the medication dosage level may include insulin dosage
level.
[0170] In a further aspect, the method may include transmitting the
reported glucose concentration, and where transmitting may include
one or more of a wired transmission or a wireless transmission.
[0171] In still another aspect, the method may include calibrating
the first transcutaneously positioned analyte sensor using single
point calibration.
[0172] The first transcutaneously positioned analyte sensor may
have at least about 95% of its paired data points within zone A and
zone B of the Clarke Error Grid.
[0173] The first sensor may have at least about 85% of its paired
data points within zone A.
[0174] A method of monitoring glucose levels in accordance with yet
another embodiment includes determining glucose concentration using
a first transcutaneously positioned analyte sensor, reporting
glucose concentration to a user, where accuracy of the first
transcutaneously positioned analyte sensor is established other
than with a second sensor.
[0175] In one aspect, the first transcutaneously positioned analyte
sensor has at least about 85% of its paired data points within zone
A of the Clarke Error Grid.
[0176] In another aspect, the first transcutaneously positioned
analyte sensor has at least about 90% of its paired data points
within zone A of the Clarke Error Grid.
[0177] In still another aspect, the first transcutaneously
positioned analyte sensor has more than about 90% of its paired
data points within zone A of the Clarke Error Grid.
[0178] The first transcutaneously positioned analyte sensor may be
a glucose sensor.
[0179] A method of monitoring glucose levels using a single glucose
sensor in accordance with still yet a further embodiment includes
transcutaneously positioning a glucose sensor in a patient for a
period of time, determining glucose concentration of the patient
using the transcutaneously positioned glucose sensor, and using one
or more additional devices during the period of time only to
calibrate the glucose sensor but not to confirm the accuracy of the
transcutaneously positioned glucose sensor.
[0180] The glucose sensor in one embodiment has at least about 85%
of its paired data points within zone A and at least about 95% of
its paired data points within zone A and zone B of the Clarke Error
Grid.
[0181] The glucose concentration may be determined over a period of
time ranging from about 1 day to about 7 days.
[0182] In a further aspect, the glucose sensor has at least about
85% of its paired data points within zone A of the Clarke Error
Grid.
[0183] In yet another aspect, the glucose sensor has at least about
90% of its paired data points within zone A of the Clarke Error
Grid.
[0184] The glucose sensor in still another aspect has more than
approximately 90% of its paired data points within zone A of the
Clarke Error Grid.
[0185] In still a further aspect, the method may include
determining a health related information based on the determined
glucose concentration, where the health related information
includes one or more of a food intake, medication dosage level, or
activity level, and further, where medication dosage level includes
insulin dosage level.
[0186] The method may include transmitting data associated with the
determined glucose concentration, where transmitting may include
one or more of a wired transmission or a wireless transmission.
[0187] Also, calibration of the glucose sensor may include
performing single point calibration.
[0188] An analyte monitoring system in accordance with still yet
another embodiment includes an analyte sensor configured to detect
one or more analyte levels of a patient, a transmitter unit
operatively coupled to the analyte sensor, the transmitter unit
configured to transmit one or more signals associated with the
detected one or more analyte levels, and a receiver unit configured
to receive the transmitted one or more signals associated with the
detected one or more analyte levels, where the accuracy of the
detected one or more analyte levels relied upon to make a
clinically relevant decision is established without using a blood
glucose measurement.
[0189] In one aspect, the clinically relevant decision may include
healthcare decision.
[0190] The clinically relevant decision may include a bolus amount
determination.
[0191] The blood glucose measurement may include a confirmatory
blood glucose measurement.
[0192] The detected one or more analyte level may be calibrated,
for example, using single point calibration.
[0193] The transmitter unit may be configured to wirelessly
transmit the one or more signals to the receiver unit.
[0194] The analyte sensor in one embodiment has at least about 85%
of its paired data points within zone A and at least about 95% of
its paired data points within zone A and zone B of the Clarke Error
Grid.
[0195] An analyte monitoring device in accordance with still yet a
further embodiment includes a receiver unit for receiving one or
more signals related to an analyte level detected by an
electrochemical sensor, the receiver unit including a display to
display an indication of the analyte level, where the
electrochemical sensor has at least about 85% of its paired data
points within zone A and at least about 95% of its paired data
points within zone A and zone B of the Clarke Error Grid.
[0196] The electrochemical sensor may have at least about 85% of
its paired data points within zone A of the Clarke Error Grid.
[0197] The electrochemical sensor may have at least about 90% of
its paired data points within zone A of the Clarke Error Grid.
[0198] The electrochemical sensor may have more than approximately
90% of its paired data points within zone A of the Consensus Error
Grid.
[0199] The receiver unit may be configured to calibrate the one or
more signals related to the analyte level, and further, where the
receiver unit may be configured to display the calibrated one or
more signals related to the analyte level without a confirmatory
blood glucose measurement.
[0200] In another aspect, the receiver unit may be configured to
calibrate the one or more signals related to the analyte level
using single point calibration.
[0201] The receiver unit may be configured to display the one or
more signals related to the analyte level without a confirmatory
blood glucose measurement.
[0202] The receiver unit in one embodiment may include one of an rf
receiver or an rf transceiver.
[0203] The receiver unit in still a further aspect may be
configured to calibrate the one or more signals related to the
analyte level using a calibration value of less that about one
microliter of body fluid, where the body fluid includes blood.
[0204] The receiver unit may include an alarm configured to
indicate when the analyte level is at or near a threshold
level.
[0205] The threshold level may include one of hypoglycemia,
impending hypoglycemia, hyperglycemia, or impending
hyperglycemia.
[0206] The alarm may include one or more of an audible signal, a
visual display, or a vibratory signal.
[0207] The alarm may be configured to automatically deactivate
after a predetermined time period.
[0208] The receiver unit in one aspect may be a portable handheld
unit.
[0209] The receiver unit may be configured for wearing on or under
an article of clothing.
[0210] The receiver unit may include an rf transceiver configured
to receive or transmit the one or more signals related to an
analyte level.
[0211] In still another aspect, the display may be configured to
display one or more of analyte level trend information, rate of
change information associated with the analyte level, basal profile
information, bolus amount information, or therapy related
information.
[0212] In a further aspect, the receiver may include a blood
glucose meter.
[0213] The display may be configured to display the indication of
the analyte level at least one or more of once per minute, once per
five minutes, once per ten minutes, or over a predetermined time
period, where the predetermined time period may include one or more
of less than 24 hour period, one day, three days, seven days,
fourteen days, twenty one days, twenty eight days, less than thirty
days, or greater than thirty days.
[0214] A monitoring device in a further embodiment includes a
portable housing, an rf receiver coupled to the portable housing,
the rf receiver configured to wirelessly receive one or more
signals related to an analyte level of a patient detected by an
electrochemical sensor, a processing unit coupled to the portable
housing and to the rf receiver, the processing unit configured to
process the one or more signal received by the rf receiver, and a
display unit coupled to the portable housing and the processing
unit, the display unit configured to display an indication
associated with the one or more signals related to the analyte
level of the patient, where the electrochemical sensor has at least
about 85% of its paired data points within zone A and at least
about 95% of its paired data points within zone A and zone B of the
Consensus Error Grid.
[0215] The electrochemical sensor may have at least about 85% of
its paired data points within zone A of the Consensus Error
Grid.
[0216] The electrochemical sensor may have at least about 90% of
its paired data points within zone A of the Consensus Error
Grid.
[0217] An analyte monitoring device in accordance with still
another embodiment includes a receiver unit for receiving one or
more signals related to an analyte level detected by an
electrochemical sensor, the receiver unit including a display to
display an indication of the analyte level, and the receiver unit
further configured to process one or more signals related to
analyte related therapy for communication with a drug
administration system, where the electrochemical sensor has at
least about 85% of its paired data points within zone A and at
least about 95% of its paired data points within zone A and zone B
of the Clarke Error Grid.
[0218] In one aspect, the electrochemical sensor has at least about
90% of its paired data points within zone A of the Clarke Error
Grid.
[0219] Various other modifications and alterations in the structure
and method of operation of this disclosure will be apparent to
those skilled in the art without departing from the scope and
spirit of the present disclosure. Although the present disclosure
has been described in connection with specific embodiments, it
should be understood that the embodiments of the present disclosure
as claimed should not be unduly limited to such specific
embodiments. It is intended that the following claims define the
scope of the present disclosure and that structures and methods
within the scope of these claims and their equivalents be covered
thereby.
* * * * *