U.S. patent application number 17/181888 was filed with the patent office on 2021-06-10 for systems of centralized data exchange for monitoring and control of blood glucose.
This patent application is currently assigned to UNIVERSITY OF VIRGINIA PATENT FOUNDATION. The applicant listed for this patent is UNIVERSITY OF VIRGINIA PATENT FOUNDATION. Invention is credited to Boris P. KOVATCHEV.
Application Number | 20210169409 17/181888 |
Document ID | / |
Family ID | 1000005417643 |
Filed Date | 2021-06-10 |
United States Patent
Application |
20210169409 |
Kind Code |
A1 |
KOVATCHEV; Boris P. |
June 10, 2021 |
SYSTEMS OF CENTRALIZED DATA EXCHANGE FOR MONITORING AND CONTROL OF
BLOOD GLUCOSE
Abstract
A flexible system capable of utilizing data from different
monitoring techniques and capable of providing assistance to
patients with diabetes at several scalable levels, ranging from
advice about long-term trends and prognosis to real-time automated
closed-loop control (artificial pancreas). These scalable
monitoring and treatment strategies are delivered by a unified
system called the Diabetes Assistant (DiAs) platform. The system
provides a foundation for implementation of various monitoring,
advisory, and automated diabetes treatment algorithms or methods.
The DiAs recommendations are tailored to the specifics of an
individual patient, and to the patient risk assessment at any given
moment. A central data exchange node or server collects patient
data from individual DiAs devices and provides safety assurance,
monitoring, telemedicine and database building for the DiAs
system.
Inventors: |
KOVATCHEV; Boris P.;
(Charlottesville, VA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
UNIVERSITY OF VIRGINIA PATENT FOUNDATION |
Charlottesville |
VA |
US |
|
|
Assignee: |
UNIVERSITY OF VIRGINIA PATENT
FOUNDATION
Charlottesville
VA
|
Family ID: |
1000005417643 |
Appl. No.: |
17/181888 |
Filed: |
February 22, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15109682 |
Jul 5, 2016 |
10925536 |
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PCT/US2015/010167 |
Jan 5, 2015 |
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17181888 |
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61923361 |
Jan 3, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/0022 20130101;
A61B 5/7275 20130101; A61B 5/389 20210101; A61M 2230/201 20130101;
A61B 5/11 20130101; A61M 2205/52 20130101; A61M 2230/30 20130101;
A61B 5/021 20130101; A61B 5/316 20210101; A61B 5/02416 20130101;
A61B 5/14532 20130101; A61B 5/022 20130101; A61B 5/4839 20130101;
A61M 2230/005 20130101; A61B 5/746 20130101; A61M 5/142 20130101;
A61M 2230/04 20130101; A61M 2205/50 20130101; G16H 40/63 20180101;
A61B 5/024 20130101; A61M 2205/3592 20130101; A61M 5/14276
20130101; A61B 2562/0219 20130101; A61M 2205/3584 20130101; A61M
2230/06 20130101; A61B 5/0024 20130101; G16H 40/67 20180101; G16H
50/30 20180101; A61M 5/1723 20130101; A61B 5/318 20210101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/11 20060101 A61B005/11; A61B 5/145 20060101
A61B005/145; G16H 50/30 20060101 G16H050/30; G16H 40/63 20060101
G16H040/63; G16H 40/67 20060101 G16H040/67; A61B 5/318 20060101
A61B005/318; A61B 5/316 20060101 A61B005/316; A61B 5/021 20060101
A61B005/021; A61M 5/142 20060101 A61M005/142; A61M 5/172 20060101
A61M005/172 |
Claims
1. A system for managing glycemic control of a patient, comprising:
a hub device configured to accept input data from one or more of a
plurality of diverse blood glucose measurement devices and one or
more of a plurality of diverse insulin delivery devices over a
wireless communications connection; a user device configured to
receive data from said hub device over a wired or wireless
connection, and including a) a data classifier module configured to
classify data accepted by said hub device and to determine
appropriate processing of said input data according to its
classification; b) a patient state estimation module configured to
process input data in accordance with at least one data processing
algorithm corresponding to the classification of the input data as
determined by the data classifier module; c) a patient risk status
module configured to determine a level of risk of said patient with
respect to abnormal glycemic states using processed data from said
patient state estimation module; d) an output module configured to
output advisory messages, patient alerts, and control signals for
said blood glucose measurement devices and said insulin delivery
devices based on the level of risk determined by said patient risk
status module; and e) a wireless communications connection to a
central data exchange node arranged to collect patient data from a
plurality of user devices and to provide monitoring of said user
device.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority from U.S. Provisional
Application No. 61/923,361 filed Jan. 3, 2014, under 35 U.S.C.
.sctn. 119 (e) and PCT Article 8
BACKGROUND OF THE INVENTION
[0002] Diabetes mellitus (DM), often simply referred to as
diabetes, is a group of metabolic diseases characterized by high
glucose levels in the blood (i.e. hyperglycemia), either because
the body does not produce enough insulin (Type 1 DM or T1DM), or
because cells do not respond to the insulin that is produced (Type
2 DM or T2DM). Intensive treatment with insulin and with oral
medications to maintain nearly normal levels of glycemia (i.e.
euglycemia) markedly reduces chronic complications in both T1DM and
T2DM [1,2,3], but may risk symptomatic hypoglycemia and potentially
life-threatening severe hypoglycemia. Therefore, hypoglycemia has
been identified as the primary barrier to optimal diabetes
management [4,5]. People with T1DM and T2DM face a lifelong
optimization problem: to maintain strict glycemic control without
increasing their risk for hypoglycemia. However, the struggle for
close glycemic control could result in large blood glucose (BG)
fluctuations over time. This process is influenced by many external
factors, including the timing and amount of insulin injected, food
eaten, physical activity, etc. In other words, BG fluctuations in
diabetes are the measurable result of the interactions of a complex
and dynamic biological system, influenced by many internal and
external factors.
[0003] The optimization of this system depends largely on
self-treatment behavior, which has to be informed by glucose
monitoring and has to utilize data and technology available in the
field. The currently accessible data sources include
self-monitoring of blood glucose (SMBG), continuous glucose
monitoring (CGM), as well as assessment of symptoms and
self-treatment practices. The available treatments include
medication (exclusively for T2DM), multiple daily insulin
injections (MDI), and insulin pumps (CSII--continuous subcutaneous
insulin injection). Currently, these treatments are at various
stages of development and clinical acceptance, with SMBG now a
routine practice, CGM rapidly developing, and emerging integrated
systems that combine CGM with CSII and pave the way for the
artificial pancreas of the near future.
[0004] Self-Monitoring of Blood Glucose
[0005] Contemporary home BG meters offer convenient means for
frequent and accurate BG determinations through SMBG [6,7]. Most
meters are capable of storing BG readings (typically over 150
readings) and have interfaces to download these readings into a
computing device such a PC. The meters are usually accompanied by
software that has capabilities for basic data analysis (e.g.
calculation of mean BG, estimates of the average BG over the
previous two weeks, percentages in target, hypoglycemic and
hyperglycemic zones, etc.), logging of the data, and graphical
representations of the BG data (e.g. histograms, pie charts, etc.).
In a series of studies we have shown that specific risk analysis of
SMBG data could also capture long-term trends towards increased
risk for hypoglycemia [8, 9,10], and could identify 24-hour periods
of increased risk for hypoglycemia [11,12]. The basics of the risk
analysis are presented below. The methods outlined here have been
applied to both SMBG and CGM data.
[0006] Evaluating Risk for Hypoglycemia and Hyperglycemia: These
methods are based on the concept of Risk Analysis of BG data [13],
and on the recognition of a specific asymmetry of the BG
measurement scale that can be corrected by a mathematical data
transformation [14]. The risk analysis steps are as follows:
[0007] 1. Symmetrization of the BG scale: A nonlinear
transformation is applied to the BG measurements scale to map the
entire BG range (20 to 600 mg/dl, or 1.1 to 33.3 mmol/l) to a
symmetric interval. The BG value of 112.5 mg/dl (6.25 mmol/l) is
mapped to zero, corresponding to zero risk for hypo- or
hyperglycemia. The analytical form of this transformation is
f(BG,.alpha.,.beta.)=[(ln (BG)).sup..alpha.-.beta.], .alpha.,
.beta.>0, where the parameters are estimated as .alpha.=1.084,
.beta.=5.381, .gamma.=1.509, if BG is measured in mg/dl and
.alpha.=1.026, .beta.=1.861, .gamma.=1.794 if BG is measured in
mmol/l [14].
[0008] 2. Assignment of a risk value to each SMBG reading: We
define the quadratic risk function r(BG)=10f(BG).sup.2. The
function r(BG) ranges from 0 to 100. Its minimum value is achieved
at BG=112.5 mg/dl (a safe euglycemic BG reading), while its maximum
is reached at the extreme ends of the BG scale. Thus, r(BG) can be
interpreted as a measure of the risk associated with a certain BG
level. The left branch of this parabola identifies the risk of
hypoglycemia, while the right branch identifies the risk of
hyperglycemia.
[0009] 3. Computing measures of risk for hypoglycemia and glucose
variability: Let x.sub.1, x.sub.2, . . . x.sub.n be a series of n
BG readings, and let rl(BG)=r(BG) if f(BG)<0 and 0 otherwise;
rh(BG)=r(BG) if f(BG)>0 and 0 otherwise. Then the Low Blood
Glucose Index (LBGI) is computed as:
LBGI = 1 n i = 1 n rl ( x i ) ##EQU00001##
[0010] In other words, the LBGI is a non-negative quantity that
increases when the number and/or extent of low BG readings
increases. In studies, the LBGI typically accounted for 40-55% of
the variance of future significant hypoglycemia in the subsequent
3-6 months [8,9,10], which made it a potent predictor of
hypoglycemia based on SMBG. Similarly, we compute the High Blood
Glucose Index (HBGI) as follows:
HBGI = 1 n i = 1 n rh ( x i ) ##EQU00002##
[0011] The HBGI is a non-negative quantity that increases when the
number and/or extent of high BG readings increases.
[0012] Continuous Glucose Monitoring
[0013] Since the advent of continuous glucose monitoring technology
10 years ago [15,16,17], which initially had limited performance
particularly in the hypoglycemic range [18,19], significant
progress has been made towards versatile and reliable CGM devices
that not only monitor the entire course of BG day and night, but
also provide feedback to the patient, such as alarms when BG
reaches preset low or high levels. A number of studies have
documented the benefits of continuous glucose monitoring
[20,21,22,23] and charted guidelines for clinical use and its
future as a precursor to closed-loop control [24,25,26,27].
However, while CGM has the potential to revolutionize the control
of diabetes, it also generates data streams that are both
voluminous and complex. The utilization of such data requires an
understanding of the physical, biochemical, and mathematical
principles and properties involved in this new technology. It is
important to know that CGM devices measure glucose concentration in
a different compartment--the interstitium. Interstitial glucose
(IG) fluctuations are related to BG presumably via the diffusion
process [28,29,30]. To account for the gradient between BG and IG,
CGM devices are calibrated with capillary glucose, which brings the
typically lower IG concentration to corresponding BG levels.
Successful calibration would adjust the amplitude of IG
fluctuations with respect to BG, but would not eliminate the
possible time lag due to BG-to-IG glucose transport and the sensor
processing time (instrument delay). Because such a time lag could
greatly influence the accuracy of CGM, a number of studies were
dedicated to its investigation, yielding various results
[31,32,33,34]. For example, it was hypothesized that if glucose
fall is due to peripheral glucose consumption the physiologic time
lag would be negative, i.e. fall in IG would precede fall in BG
[28,35]. In most studies IG lagged behind BG (most of the time) by
4-10 minutes, regardless of the direction of BG change [30,31]. The
formulation of the push-pull phenomenon offered reconciliation of
these results and provided arguments for a more complex BG-IG
relationship than a simple constant or directional time lag
[34,36]. In addition, errors from calibration, loss of sensitivity,
and random noise confound CGM data [37]. Nevertheless, the accuracy
of CGM is increasing and may be reaching a physiological limit for
subcutaneous glucose monitoring [38,39,40].
[0014] The Artificial Pancreas
[0015] The next step in the progression of diabetes management is
automated glucose control, or the artificial pancreas, which links
a continuous glucose monitor with an insulin pump. A key element of
this combination is a closed-loop control algorithm or method,
which monitors blood glucose fluctuations and the actions of the
insulin pump, and recommends insulin delivery at appropriate
times.
[0016] The artificial pancreas idea can be traced back to
developments that took place over thirty years ago when the
possibility for external BG regulation in people with diabetes had
been established by studies using intravenous (i.v.) glucose
measurement and i.v. infusion of glucose and insulin. Systems such
as the Biostator.TM. have been introduced and used in hospital
settings to maintain normoglycemia (or euglycemia) by exerting both
positive (via glucose or glucagon) and negative (via insulin)
control [51,52,53,54,55]. Detailed descriptions of the major early
designs can be found in [56,57,58,59,60,61]. More work followed,
spanning a broader range of BG control techniques, powered by
physiologic mathematical modeling and computer simulation control
[62,63,64,65]. A review of methods for i.v. glucose control can be
found in [66]. However, i.v. closed-loop control remains cumbersome
and unsuited for outpatient use. An alternative to extracorporeal
i.v. control has been presented by implantable intra-peritoneal
(i.p.) systems employing intravenous BG sampling and i.p. insulin
delivery [67,68]. The implementation of these systems, however,
requires considerable surgery. Thus, with the advent of
minimally-invasive subcutaneous (s.c.) CGM, increasing academic and
industrial effort has been focused on the development of s.c.-s.c.
systems, using CGM coupled with an insulin infusion pump and a
control algorithm or method [69,70,71,72]. In September 2006, the
Juvenile Diabetes Research Foundation (JDRF) initiated the
Artificial Pancreas Project and funded a consortium of centers to
carry closed-loop control research [73]. So far, encouraging pilot
results have been reported by several centers [74,75,76,77,78].
[0017] Thus, in the past 30 years the monitoring and control of BG
levels in diabetes has progressed from assessment of average
glycemia once in several months, through daily SMBG, to minutely
CGM. The increasing temporal resolution of the monitoring
technology has enabled increasingly intensive diabetes treatment,
from daily insulin injections or oral medication, through insulin
pump therapy, to the artificial pancreas of the near future.
BRIEF SUMMARY OF THE INVENTION
[0018] As evident from the discussion above, a multitude of methods
exist for BG monitoring and control in diabetes, ranging from
traditional SMBG, medication, and MDI treatment, to CGM and
artificial pancreas. These methods are currently dissimilar and
there is no system that can handle more than one monitoring or
control method at a time. An aspect of an embodiment of the present
invention introduces the first flexible system capable of utilizing
data from different monitoring techniques and capable of providing
assistance to patients with diabetes at several scalable levels,
ranging from advice about long-term trends and prognosis to
real-time automated closed-loop control (artificial pancreas).
These scalable monitoring and treatment strategies are delivered by
a unified system--named by the present inventors as the Diabetes
Assistant (DiAs) platform--that provides a foundation for
implementation of various monitoring, advisory, and automated
diabetes treatment algorithms or methods. The DiAs recommendations
are tailored to the specifics of an individual patient, and to the
patient risk assessment at any given moment. Some non-limiting and
exemplary unique characteristics of DiAs are: [0019] Informed by a
Body Sensor Network; [0020] Modular--layered architecture
distributes data processing tasks across various application
modules; individual modules are easily replaceable; [0021]
Scalable--naturally support new and expanded functionality,
multiple data sources, and multiple data utilization strategies;
[0022] Portable--DiAs can run easily on portable computing devices,
such as a cell phone, tablet computer, portal digital assistant
(PDA), etc; thus it is deployable on a wide variety of rugged,
inexpensive, and readily available devices; [0023] Local and Global
modes of operation--certain processes and patient interactions are
available through the portable device; other services and remote
monitoring of subject and system states are available via wireless
communications (e.g. 3G, WiFi, etc.).
[0024] According to one aspect of the invention, a system is
provided for managing glycemic control of a patient, comprising an
input module configured to accept input data from one or more of a
plurality of diverse blood glucose measurement devices and one or
more of a plurality of diverse insulin delivery devices; a data
classifier module configured to classify data accepted by said
input module and to determine appropriate processing of said input
data according to its classification; a patient state estimation
module configured to process input data in accordance with at least
one data processing algorithm corresponding to the classification
of the input data as determined by the data classifier module; a
patient risk status module configured to determine a level of risk
of said patient with respect to abnormal glycemic states using
processed data from said patient state estimation module; and an
output module configured to output advisory messages, patient
alerts, and control signals for said blood glucose measurement
devices and said insulin delivery devices based on the level of
risk determined by said patient risk status module.
[0025] According to another aspect of the invention, a
non-transitory computer-readable storage medium is provided
containing computer-executable instructions for performing
functions to carry out the system.
BRIEF SUMMARY OF THE DRAWINGS
[0026] FIG. 1 is a schematic illustration of the DiAs platform
inputs and outputs according to an aspect of the invention;
[0027] FIG. 2 is a schematic illustration of DiAs processes and
services according to an aspect of the invention;
[0028] FIG. 3 is a block diagram of the DiAs system including
applications and communication functions according to an embodiment
of the invention;
[0029] FIG. 4 shows an example implementation of the DiAs system on
a cell phone platform according to an embodiment of the
invention;
[0030] FIG. 5 is a schematic illustration of an implementation of
the DiAs system as a hub for a body sensor network;
[0031] FIG. 6 is a schematic block diagram of an example data
processing system for implementation of the present invention in
whole or in part; and
[0032] FIG. 7 is a schematic diagram showing another example
implementation of the DiAs system on a central server over a
network, enabling remote monitoring, safety assurance, telemedicine
and database building to a further embodiment of the invention.
DETAILED DESCRIPTION OF THE INVENTION
Overview
[0033] As shown in FIG. 5, a principal application of the DiAs
system is the dynamic aggregation of body sensor network (BSN) data
toward the goal of supporting long-term and efficient treatment of
diabetes. DiAs is based on a wearable or handheld Diabetes
Assistant platform that collects and pre-processes data from each
individual's BSN, and uploads summary statistics to a remote
location. The interface/algorithmic/methodology framework of DiAs:
(i) ensures plug and play functionality with different metabolic
sensors, (ii) allows for a general framework for prioritization of
sensor data, making it clear how scarce computational, memory, and
communication resources will be allocated to various sensing
modalities, (iii) manages access to multiple uplink channels of
varying reliability to a remote site, and (iv) resolves tradeoffs
relating to where heavy computations should be performed (e.g.,
locally within the DiAs platform or remotely).
DiAs Inputs and Outputs
[0034] FIG. 1 presents the data sources available to the DiAs
platform and the output services that DiAs provides. The data
sources include SMBG, CGM, insulin delivery data (MDI and CSII),
and other BSN data inputs such as heart rate, body accelerometer
data, blood pressure, respiration, EKG data, etc. Depending on data
availability (intermittent or continuous, blood glucose alone, or a
multivariate data stream), DiAs provides different types of
services that can be generally classified as: [0035] Local
Services: Applications that run on a portable device (e.g. a cell
phone or tablet computer) communicating with an array of self-SMBG
monitoring and CGM devices, an array of insulin delivery devices,
and with other sensors in a BSN. The local service of DiAs is
equipped with intelligent processing to provide an array of patient
services, including safety supervision, local alerts, patient
advisory functions, and closed-loop control (described further
below); [0036] Global Services: A centralized server communicating
with multiple local services to provide different levels of data
processing, advice, and training to patients; enable remote
monitoring of glucose control profiles (e.g. parents monitoring
remotely their children with diabetes); enable global alerts (e.g.
a 911 call with GPS service to pinpoint a patient in need of
emergency assistance), and to provide a physician-oriented
information service presenting key data for multiple patients at a
glance.
DiAs Processes
[0037] The general flow of DiAs processes is presented in FIG. 2
and includes the following steps: [0038] 1. Incoming data are
directed to a Data Availability Classifier (DAC), which assesses
the frequency, dimensionality, and quality of the incoming data.
Based on the assessment, the DAC recommends different classes of
data processing algorithms for the incoming data. Many of these
algorithms already exist and are generally known, and can be
classified as follows: [0039] SMBG: This is currently the most
established algorithmic class, including methods for the retrieval
of SMBG data, evaluation of glycemic control, estimation of the
risk for hypoglycemia, and information displays. SMBG acquisition
and processing methods are described in several U.S. patents and
published patent applications (see references [79-85] incorporated
herein by reference). A 5-year clinical trial testing a SMBG-based
system in 120 people with T1DM was recently completed, resulting in
improved glycemic control, reduction of the risk for severe
hypoglycemia, and high patient approval rating (results published
in [86]); [0040] CGM: Key elements applicable to these methods have
been defined (references [87-92]). These methods are currently
under development and testing in a large NIH-funded research
project (Grant RO1 DK 085623, Principal Investigator Dr. Boris
Kovatchev); [0041] CGM+insulin pump: Most of the methods applicable
to CGM alone have extensions capable of dealing with input/output
to/from an insulin pump. We have recently completed an extensive
series of clinical trials of closed-loop control to date. [0042]
Other: Heart rate changes can be used to indicate periods of
physical activity, and more specifically periods of increased
insulin sensitivity associated with exercise. These data inform
diabetes control at several levels, including risk assessment for
hypoglycemia and closed-loop control [93,94]. [0043] 2. The first
step of data processing is Patient State Estimation, given
available data and using one of the methods described above. The
state estimation results in assessment of the patient's risk
status, which can be based on the risk analysis metrics presented
in the background discussion above, and on biosystem observers or
sensors, which process physiologic (and possibly behavioral) data
to produce quantitative biosystem state estimators.
[0044] These algorithms or methods are based on underlying
mathematical models of the human metabolism and a Kalman filter,
which produces system state estimation. Each system state estimator
is a physiological or behavioral parameter of importance to the
functioning of a person. The ensemble (vector) of biosystem
estimators for a particular person represents the status of this
person in terms of the blood glucose trend, availability of
insulin, and risk for hypoglycemia. In essence, biosystem observers
personalize the metabolic observation to a specific subject and
extract composite information from the vast array of raw data that
allows the precise evaluation of the subject's condition. It is
anticipated that the biosystem observers will reside within a
wearable DiAs system, while their summarized output will be sent to
both the local predictive and control algorithms or methods and to
remote observers as follows: [0045] The primary output from the
Patient State Estimation will be assessment of the patient's risk
status for hypo- or hyperglycemia, based on the risk analysis and
the LBGI/HBGI presented above. If the data quality and density is
adequate for the risk status of the patient (e.g. the patient is in
a steady state performing regular SMBG resulting in LBGI and HBGI
lower than certain preset thresholds), then DiAs refers the data to
algorithms that maintain the current patient status or fine-tune
the patient's glycemic control. These algorithms can work in either
an advisory or automated (closed-loop control) mode as follows:
[0046] In advisory mode, DiAs activates the following services
modules: [0047] Advisory Module 1: Prediction of elevated risk for
hypoglycemia (24 hours ahead); [0048] Advisory Module 2: Bolus
calculator suggesting pre-meal insulin doses; [0049] Advisory
Module 3: Suggestion of basal rate profiles for the next 24 hours.
[0050] In closed-loop control mode, DiAs activates the following
service modules: [0051] Control Module 1: Real-time detection and
prevention of hypoglycemia; [0052] Control Module 2: Stochastic
control of pre-meal insulin boluses, and [0053] Control Module 3:
Deterministic control of basal rate and overnight steady state.
[0054] If the data quality and density is inadequate for the risk
status of the patient (e.g. the patient is at high risk for
hypoglycemia, hyperglycemia, or both as indicated by the LBGI and
HBGI exceeding certain preset thresholds), then: [0055] In advisory
mode, DiAs recommends enhanced monitoring (e.g. more frequent SMBG
or switching to CGM for a certain period of time); [0056] In
automated control mode, DiAs switches the monitoring device to
higher frequency SMBG measurement or to CGM mode (Note: such
flexible monitoring devices are not currently manufactured, but are
anticipated to be available in the future).
[0057] FIG. 3 presents a detailed schematic of the DiAs
architecture: [0058] Central to this architecture is the Biometric
State Estimator, which is the hub for exchange of data between the
DiAs monitoring devices and algorithmic services or related
methods. The Biometric State Estimator may also exchange data with
remote physicians and/or patient care centers over the Internet
through a network interface; [0059] The inputs used for state
estimation are provided by various peripheral devices that monitor
blood glucose fluctuations (SMBG Service, CGM Service), execute
insulin delivery (Pump Service), or monitor other physiological
parameters (Heart Rate Service, Esc. Service) as shown in FIG. 3;
[0060] In turn, the Biometric State Estimator provides feedback to
these devices as determined by a Safety Service, which assesses the
integrity of the received data and judges whether the peripheral
input/output devices are functioning properly. Methods employed by
the Safety Service include previously introduced detection of CGM
sensor errors [91] or judging the safety of insulin delivery [92];
[0061] DiAs Applications may include various advisory and/or
control algorithms, system and patient state alarms and indicators.
These applications may be external to the DiAs system, and may be
developed by third parties. Such applications may use DiAs services
provided that they comply with the data exchange standards of the
system. For example, a Hyperglycemia Mitigation Service (HMS) is a
closed-loop control algorithm or method included in one of the
embodiments of DiAs;
[0062] The user interface with the DiAs system can be custom
designed to meet the needs of specific DiAs implementations. One
such implementation of a user interface is shown in FIG. 4: [0063]
Two "traffic lights" signify the patient's present risk status for
hypoglycemia and hyperglycemia, respectively, indicating low risk
(green light), moderate risk/system action to mitigate the risk
(yellow light) and high risk/necessity for immediate human
intervention (red light); [0064] Several system/patient status
inquiry icons open additional interfaces allowing the patient to
access graphical and numerical representation of his/her glucose
control, or inform the system of events (such as carbohydrate
intake or exercise), which are treated as additional inputs by the
DiAs analytical system; [0065] Network service (described in the
next section) ensures remote monitoring and transmission of alerts
and critical information in high-risk states.
Implementation of DiAs
[0066] FIG. 5 shows two major components of a DiAs implementation
as a Body Sensor Network: [0067] Local Services (within the
wearable/portable DiAs device) use predictive and control
algorithms or methods based on simplified models of the human
metabolic system that are trackable in real time. These are simple,
typically linearized macro-level models that focus only on the
principal system components. One example of such a model is the
classic Minimal Model of Glucose Kinetics developed 30 years ago
[95]. Available algorithms or methods include assessment,
prediction, and control of glucose fluctuations in diabetes: [0068]
Risk analysis of metabolic state with respect to normative limits;
[0069] Detection of abrupt system changes, i.e. transitions of the
system (person) from a stable to a critical state; [0070]
Prediction of trends and gradual system changes, and outcome
evaluation; [0071] Estimation of the probability for abrupt
critical transitions; [0072] Warnings, alarms, and advisory
messages when critical thresholds are approached; [0073] Automated
intervention to prevent critical events; [0074] Communication to
remote location and global algorithms or methods.
[0075] As shown in FIG. 5, a portable DiAs device (such as shown in
FIG. 4) is communicatively connected (e.g. wirelessly through a
wireless communication protocol such as Bluetooth, IEEE 802.11,
etc.) to a plurality of BSN sensors, such as an ICP sensor, ECG
sensor, blood pressure sensor, pulse oximetry sensor, inertial
sensor, EMG sensor, artificial pancreas sensor, etc. Additionally,
the DiAs device may have an interface to accept SMBG data. [0076]
Global services rely on predictive and control algorithms or
methods deployed at a central location and receiving information
from an array of individual system observers. These algorithms or
methods will be based on large-scale probability models, risk
analysis, clustering, and discriminant algorithms or methods. The
output of these algorithms or methods will allow: [0077] The
monitoring of vital signs and metabolic processes by health care
providers; [0078] The detection of critical cases that require
immediate intervention; [0079] Collection of population-level
anonymous public health statistics of interest to health care
organizations. [0080] Software/Hardware Implementation: Central to
DiAs is a scalable software stack with a modular design that can be
efficiently adapted to a variety of hardware platforms. The
software architecture, the availability of suitable hardware
platforms and opportunities to transfer software modules to
commercial partners will factor into the choice of DiAs operating
systems. For clinical trials and ambulatory implementation,
hardware is needed that is portable, rugged, reliable, inexpensive
and easily available. In this regard, a cell phone or a tablet
computer could be selected. Consequently, the DiAs system may run
within a customized version of the Android operating system.
Android has a robust development environment, is available with
source code, is backed by Google and runs on an ever-increasing
array of cell phones and tablets from a variety of manufacturers.
Android is being adopted by many commercial developers for new
embedded software projects. Although many current products with
embedded control software either have no operating system at all or
use a simple control loop the trend is towards basing new embedded
software projects on Android and embedded Linux. Since Android is
built on top of Linux, an Android-based operating system for DiAs
would allow transfer of software code to industry partners for
commercial use. Android also provides a rich software development
kit that supports multi-touch graphical user interface design, data
communications, geo-location and telephony. Specifically: [0081] At
the highest level the AAPP Software Stack is composed of three
major functional blocks: Device I/O Services, Core Services, and
Control. As described above, FIG. 3 presents a diagram of the
software stack depicting these blocks. [0082] Device I/O Services
handles all communication with sensors, pumps and other devices and
provides a data interface to other elements of the system. The
Device I/O modules store SMBG, CGM, and delivered insulin data and
provide it to other components upon request. [0083] Device I/O
modules also implement a sensor and pump command service that
validates and delivers commands received from the Safety Service.
[0084] Core Services is responsible for providing a runtime
environment for applications such as the Closed-Loop Control App or
the User Advice App and for supervising their operation. It
generates state estimates based upon available data and provides
this data to applications upon request. [0085] Safety Service
screens insulin bolus commands for safety before delivering them to
the pump module and monitors the functioning of I/O devices
detecting errors and potentially unsafe deviations.
[0086] While a preferred operating system has been discussed above,
it will be recognized by those skilled in the art that the DiAs
system may be implemented using any operating system that has
features necessary to implement the DiAs system as contemplated
above.
[0087] FIG. 7 shows a further example embodiment of a DiAs system
in accordance with the invention. According to this embodiment,
local patient functions including a user interface and control
algorithms as disclosed above are performed on a smart-phone
platform 701. Additionally, the smart-phone 701 connects to a
central server or data exchange node 708 via a wireless connection
702 (e.g., a Wi-Fi, 3G or other similar wireless network
connection). In a preferred embodiment, the connection 702 is a
one-way connection from the smart-phone 701 to the central node or
server, i.e. the central node or server does not transmit any
substantive content to the smart-phone 701.
[0088] The smart-phone 701 sends patient data from the DiAs system
user interface and control application(s) running on the
smart-phone to the central node or server over the wireless network
connection 702. The data from a plurality of smart-phones 701 each
provided to an individual patient are collected by the central
server 708 and used for safety assurance, monitoring, telemedicine
and database building purposes.
[0089] The smart-phone 701 further has a communications connection
703 (either wired or wireless) to a DiAs hub device 704. Hub 704 is
a meter-based platform that connects via one or more wireless
connections 705 to a number of peripheral devices 706, for example,
an insulin pump, a continuous glucose monitoring (CGM) device, etc.
Hub 704 functions to ensure proper inter-device connection between
the peripheral devices 706 and the smart-phone 701 running the DiAs
local applications. Meter-based Hub 704 further functions to
confirm SMBG readings from the peripheral devices 706.
[0090] In a preferred example embodiment, the smart-phone 701 and
DiAs hub 704 run an operating system (OS) such as Android or other
equivalent OS modified to meet medical application requirements,
such as may be mandated by relevant authorities such as the
FDA.
[0091] Turning now to FIG. 6, a functional block diagram is shown
for a computer system 600 for exemplary implementation of an
embodiment or portion of an embodiment of the present invention.
For example, a method or system of an embodiment of the present
invention may be implemented using hardware, software or a
combination thereof and may be implemented in one or more computer
systems or other processing systems, such as personal digit
assistants (PDAs) equipped with adequate memory and processing
capabilities. In an example embodiment, the invention was
implemented in software running on a general purpose computer 600
as illustrated in FIG. 6. The computer system 600 may includes one
or more processors, such as processor 604. The Processor 604 is
connected to a communication infrastructure 606 (e.g., a
communications bus, cross-over bar, or network). The computer
system 600 may include a display interface 602 that forwards
graphics, text, and/or other data from the communication
infrastructure 606 (or from a frame buffer not shown) for display
on the display unit 630. Display unit 630 may be digital and/or
analog.
[0092] The computer system 600 may also include a main memory 608,
preferably random access memory (RAM), and may also include a
secondary memory 610. The secondary memory 610 may include, for
example, a hard disk drive 612 and/or a removable storage drive
614, representing a floppy disk drive, a magnetic tape drive, an
optical disk drive, a flash memory, etc. The removable storage
drive 614 reads from and/or writes to a removable storage unit 618
in a well known manner. Removable storage unit 618, represents a
floppy disk, magnetic tape, optical disk, etc. which is read by and
written to by removable storage drive 614. As will be appreciated,
the removable storage unit 618 includes a computer usable storage
medium having stored therein computer software and/or data.
[0093] In alternative embodiments, secondary memory 610 may include
other means for allowing computer programs or other instructions to
be loaded into computer system 600. Such means may include, for
example, a removable storage unit 622 and an interface 620.
Examples of such removable storage units/interfaces include a
program cartridge and cartridge interface (such as that found in
video game devices), a removable memory chip (such as a ROM, PROM,
EPROM or EEPROM) and associated socket, and other removable storage
units 622 and interfaces 620 which allow software and data to be
transferred from the removable storage unit 622 to computer system
600.
[0094] The computer system 600 may also include a communications
interface 624. Communications interface 124 allows software and
data to be transferred between computer system 600 and external
devices. Examples of communications interface 624 may include a
modem, a network interface (such as an Ethernet card), a
communications port (e.g., serial or parallel, etc.), a PCMCIA slot
and card, a modem, etc. Software and data transferred via
communications interface 624 are in the form of signals 628 which
may be electronic, electromagnetic, optical or other signals
capable of being received by communications interface 624. Signals
628 are provided to communications interface 624 via a
communications path (i.e., channel) 626. Channel 626 (or any other
communication means or channel disclosed herein) carries signals
628 and may be implemented using wire or cable, fiber optics, blue
tooth, a phone line, a cellular phone link, an RF link, an infrared
link, wireless link or connection and other communications
channels.
[0095] In this document, the terms "computer program medium" and
"computer usable medium" are used to generally refer to media or
medium such as various software, firmware, disks, drives, removable
storage drive 614, a hard disk installed in hard disk drive 612,
and signals 628. These computer program products ("computer program
medium" and "computer usable medium") are means for providing
software to computer system 600. The computer program product may
comprise a computer useable medium having computer program logic
thereon. The invention includes such computer program products. The
"computer program product" and "computer useable medium" may be any
computer readable medium having computer logic thereon.
[0096] Computer programs (also called computer control logic or
computer program logic) are may be stored in main memory 608 and/or
secondary memory 610. Computer programs may also be received via
communications interface 624. Such computer programs, when
executed, enable computer system 600 to perform the features of the
present invention as discussed herein. In particular, the computer
programs, when executed, enable processor 604 to perform the
functions of the present invention. Accordingly, such computer
programs represent controllers of computer system 600.
[0097] In an embodiment where the invention is implemented using
software, the software may be stored in a computer program product
and loaded into computer system 600 using removable storage drive
614, hard drive 612 or communications interface 624. The control
logic (software or computer program logic), when executed by the
processor 604, causes the processor 604 to perform the functions of
the invention as described herein.
[0098] In another embodiment, the invention is implemented
primarily in hardware using, for example, hardware components such
as application specific integrated circuits (ASICs). Implementation
of the hardware state machine to perform the functions described
herein will be apparent to persons skilled in the relevant
art(s).
[0099] In yet another embodiment, the invention is implemented
using a combination of both hardware and software.
[0100] In an example software embodiment of the invention, the
methods described above may be implemented in SPSS control language
or C++ programming language, but could be implemented in other
various programs, computer simulation and computer-aided design,
computer simulation environment, MATLAB, or any other software
platform or program, windows interface or operating system (or
other operating system) or other programs known or available to
those skilled in the art.
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