Note: Descriptions are shown in the official language in which they were submitted.
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
Noninvasive Predictive and/or Estimative Blood Pressure Monitoring
STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0001] This invention was made with government support under grant number
0535269 awarded by the National Science Foundation; grant number FA8650-07-C-
7702 awarded by the Air Force Research Laboratory; and grant numbers W81XWH-
09-C-1060 and W81XWH-09-1-0750 awarded by Army Medical Research Material
and Command. The government has certain rights in the invention.
COPYRIGHT STATEMENT
[0002] A portion of the disclosure of this patent document contains
material
that is subject to copyright protection. The copyright owner has no objection
to the
facsimile reproduction by anyone of the patent document or the patent
disclosure as it
appears in the Patent and Trademark Office patent file or records, but
otherwise
reserves all copyright rights whatsoever.
FIELD
[0003] The present disclosure relates, in general, tools and techniques
for
medical monitoring, and more particularly, to tools and techniques that can
monitor,
estimate, and/or predict a patient's blood pressure.
BACKGROUND
[0004] Blood pressure is a widely used indicator for a variety of
cardiovascular conditions and is considered a primary vital sign. Generally,
however,
blood pressure is relatively difficult to measure. There are several
measurement
techniques, but the most accurate techniques are invasive, while noninvasive
techniques require relative expertise and specialized equipment to perform.
For
example, using the auscultatory technique, a blood pressure cuff will be
applied to a
1
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
patient's arm (or other extremity), and a clinician will inflate the cuff to
apply pressure
to the patient's blood vessels in the extremity, to the point where blood flow
through
the vessel is occluded. By determining (e.g., by listening with a stethoscope)
when
the blood begins to flow through the vessel as pressure is decreased, the
clinician can
estimate the systolic pressure in the vessel by measuring the pressure with a
sphygmomanometer at that point. By continuing to listen as pressure continues
to
decrease until the flowing blood no longer makes a sound, the clinician can
estimate
the diastolic pressure in the vessel by measuring the pressure at that point.
Without a
sphygmomanometer, pressure cuff, stethoscope, and training, however, such
techniques are difficult or impossible to perform. Further, in emergent
situations, the
mechanics of performing this technique can distract the clinician from other
important
duties.
[0005] Hence, there is a need for a simpler technique to provide accurate
estimates of blood pressure without requiring a blood pressure cuff or undue
attention
from the clinician; it would be helpful if the technique could predict future
changes in
blood pressure as well.
BRIEF SUMMARY
[0006] Various embodiments can monitor, estimate and/or predict a
patient's
current or future blood pressure noninvasively. In various aspect, such
embodiments
can perform one or more of the following functions: estimating if a patient's
blood
pressure is low; estimating if a patient's blood pressure is high; estimating
if a
patient's blood pressure is normal; identifying when a patient's blood
pressure
changes significantly over a time period; predicting when a patient's blood
pressure
will increase to a specific value; predicting when a patient's blood pressure
will
decrease to a specific value; predicting when a patient's blood pressure will
increase
by a specified amount; and/or predicting when a patient's blood pressure will
decrease
by a specified amount.
[0007] The tools provided by various embodiments include, without
limitation, methods, systems, and/or software products. Merely by way of
example, a
method might comprise one or more procedures, any or all of which are executed
by a
computer system. Correspondingly, an embodiment might provide a computer
system
2
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
configured with instructions to perform one or more procedures in accordance
with
methods provided by various other embodiments. Similarly, a computer program
might comprise a set of instructions that are executable by a computer system
(and/or
a processor therein) to perform such operations. In many cases, such software
programs are encoded on physical, tangible and/or non-transitory computer
readable
media (such as, to name but a few examples, optical media, magnetic media,
and/or
the like).
[0008] For example, one set of embodiments provides methods. An
exemplary method might comprise monitoring, with one or more sensors,
physiological data of a patient. The method might further comprise analyzing,
with a
computer system, the physiological data. Many different types of physiological
data
can be monitored and/or analyzed by various embodiments, including without
limitation, blood pressure waveform data, plethysmograph waveform data,
photoplethysmograph ("PPG") waveform data (such as that generated by a pulse
oximeter), and/or the like. In some cases, the method can further comprise
predicting
and/or estimating a blood pressure of the patient, and/or displaying (e.g., on
a display
device) an estimate and/or prediction of the blood pressure value of the
patient.
[0009] An apparatus, in accordance with yet another set of embodiments,
might comprise a computer readable medium having encoded thereon a set of
instructions executable by one or more computers to perform one or more
operations.
In some embodiments, the set of instructions might comprise instructions for
performing some or all of the operations of methods provided by certain
embodiments.
[0010] A system, in accordance with yet another set of embodiments, might
comprise one or more processors and a computer readable medium in
communication
with the one or more processors. The computer readable medium might have
encoded
thereon a set of instructions executable by the computer system to perform one
or
more operations, such as the set of instructions described above, to name one
example. In some embodiments, the system might further comprise one or more
sensors and/or a therapeutic device, either or both of which might be in
communication with the processor and/or might be controlled by the processor.
Such
sensors can include, but are not limited to, a blood pressure sensor, an
intracranial
pressure monitor, a central venous pressure monitoring catheter, an arterial
catheter,
3
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
an electroencephalograph, a cardiac monitor, a transcranial Doppler sensor, a
transthoracic impedance plethysmograph, a pulse oximeter, a near infrared
spectrometer, a ventilator, an accelerometer, an electrooculogram, a
transcutaneous
glucometer, an electrolyte sensor, and/or an electronic stethoscope.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] A further understanding of the nature and advantages of particular
embodiments may be realized by reference to the remaining portions of the
specification and the drawings, in which like reference numerals are used to
refer to
similar components. In some instances, a sub-label is associated with a
reference
numeral to denote one of multiple similar components. When reference is made
to a
reference numeral without specification to an existing sub-label, it is
intended to refer
to all such multiple similar components.
[0012] Fig. lA is a schematic diagram illustrating a system for
estimating
compensatory reserve, in accordance with various embodiments.
[0013] Fig. 1B is a schematic diagram illustrating a sensor system that
can be
worn on a patient's body, in accordance with various embodiments.
[0014] Fig. 2A is a process flow diagram illustrating a method estimating
a
patient's blood pressure and/or predicting future changes in a patient's blood
pressure,
in accordance with various embodiments.
[0015] Fig. 2B illustrates a technique for estimating and/or predicting a
blood
pressure value for a patient, in accordance with various embodiments.
[0016] Fig. 3A is a process flow diagram illustrating a method estimating
a
patient's compensatory reserve and/or dehydration state, in accordance with
various
embodiments.
[0017] Fig. 3B illustrates a technique for estimating and/or predicting a
patient's compensatory reserve index, in accordance with various embodiments.
[0018] Fig. 4 is a process flow diagram illustrating a method of
generating a
model of a physiological state, in accordance with various embodiments.
4
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0019] Fig. 5 is a generalized schematic diagram illustrating a computer
system, in accordance with various embodiments.
DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS
[0020] The following disclosure illustrates a few exemplary embodiments
in
further detail to enable one of skill in the art to practice such embodiments.
The
described examples are provided for illustrative purposes and are not intended
to limit
the scope of the invention.
[0021] In the following description, for the purposes of explanation,
numerous
specific details are set forth in order to provide a thorough understanding of
the
described embodiments. It will be apparent to one skilled in the art, however,
that
other embodiments of the present may be practiced without some of these
specific
details. In other instances, certain structures and devices are shown in block
diagram
form. Several embodiments are described herein, and while various features are
ascribed to different embodiments, it should be appreciated that the features
described
with respect to one embodiment may be incorporated with other embodiments as
well.
By the same token, however, no single feature or features of any described
embodiment should be considered essential to every embodiment of the
invention, as
other embodiments of the invention may omit such features.
[0022] Unless otherwise indicated, all numbers used herein to express
quantities, dimensions, and so forth should be understood as being modified in
all
instances by the term "about." In this application, the use of the singular
includes the
plural unless specifically stated otherwise, and use of the terms "and" and
"or" means
"and/or" unless otherwise indicated. Moreover, the use of the term
"including," as
well as other forms, such as "includes" and "included," should be considered
non-
exclusive. Also, terms such as "element" or "component" encompass both
elements
and components comprising one unit and elements and components that comprise
more than one unit, unless specifically stated otherwise.
[0023] Overview
[0024] A set of embodiments provides methods, systems, and software that
can be used, in many cases noninvasively, to estimate a patient's blood
pressure
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
(and/or to predict future changes in the patient's blood pressure) without the
need for a
sphygmomanometer or labor-intensive procedures. In a particular set of
embodiments, a device, which can be worn on the patient's body, can include
one or
more sensors that monitor a patient's physiological parameters. The device (or
a
computer in communication with the device) can analyze the data captured by
the
sensors and compare such data with a model (which can be generated in
accordance
with other embodiments) to estimate the patient's blood pressure (e.g., low,
normal, or
high) and/or to predict whether (and, in some cases when and/or by how much) a
patient's blood pressure will increase or decrease.
[0025] Different embodiments can measure a number of different
physiological parameters from the patient, and the analysis of those
parameters can
vary according to which parameters are measured (and which, according to the
generated model, are found to be most predictive of blood pressure and/or
changes in
blood pressure). In some cases, the parameters themselves (e.g., continuous
waveform data captured by a photoplethysmograph) can be analyzed against the
model to make estimates or predictions of blood pressure. In other cases,
physiological parameters can be derived from the captured data, and these
parameters
can be used Merely by way of example, the '483 Application (already
incorporated
by reference) describes techniques for estimating a patient's compensatory
reserve
index ("CRI," also referred to in the Related Applications as a Cardiac
Reserve Index
or Hemodynamic Reserve Index ("HDRI"), all of which should be considered
equivalent terms), and changes in CRI values over time can be used to estimate
and/or
predict blood pressure.
[0026] For example, the '483 Application describes a hemodynamic reserve
monitor that is able to estimate the compensatory reserve of a patient. In an
aspect,
this monitor quickly, accurately and/or in real-time can determine the
probability of
whether a patient is bleeding. In another aspect, the device can
simultaneously
monitor the patient's compensatory reserve by tracking the patient's CRI, to
appropriately and effectively guide fluid resuscitation and ongoing patient
care. The
same device (or a similar device) can also include advanced functionality to
estimate
or predict a patient's blood pressure based on the monitored CRI values, as
explained
in further detail below.
6
CA 02929819 2016-05-05
WO 2015/069940 PCT/US2014/064413
[0027] The Compensatory reserve index (CRI) is a hemodynamic parameter
that is indicative of the individual-specific proportion of intravascular
fluid reserve
remaining before the onset of hemodynamic decompensation. CRI has values that
range from 1 to 0, where values near 1 are associated with normovolemia
(normal
circulatory volume) and values near 0 are associated with the individual
specific
circulatory volume at which hemodynamic decompensation occurs.
[0028] The mathematical formula of CRI, at some time "t" is given by the
following equation:
CRI(t)¨ 1¨ AIM
(Eq. 1)
BLV.,
[0029] Where BLV(t)is the intravascular volume loss ("BLV," also referred
to as "blood loss volume" in the Related Applications) of a person at time
"t," and
BLV pis the intravascular volume loss of a person when they enter hemodynamic
decompensation ("HDD"). Hemodynamic decompensation is generally defined as
occurring when the systolic blood pressure falls below 70 mmHg. This level of
intravascular volume loss is individual specific and will vary from subject to
subject.
[0030] Lower body negative pressure (LBNP) in some linear or nonlinear
relationship X with intravascular volume loss:
BLV = A = LBNP (Eq. 2)
[0031] can be used in order to estimate the CRI for an individual
undergoing a
LBNP experiment as follows:
BLv(t) A =LBNP(t) LBNP(t)
C RI = 1 _____________________ P=-=-= 1 __ = 1 (Eq. 3)
BLV HDD il=LBNPHDD LBNPHDD
[0032] Where LBNP (t) is the LBNP level that the individual is
experiencing
at time "t", and, LBNPHDD is the LNPB level that the individual will enter
hemodynamic decompensation.
[0033] A measure of CRI is useful in a variety of clinical settings,
including
but not limited to: 1) acute blood loss volume due to injury or surgery; 2)
acute
circulatory volume loss due to hemodialysis (also called intradialytic
hypotension);
and 3) acute circulatory volume loss due to various causes of dehydration
(e.g.
reduced fluid intake, vomiting, dehydration, etc.). A change in CRI can also
herald
7
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
other conditions, including without limitation changes in blood pressure,
general
fatigue, overheating and certain types of illnesses. Accordingly, the tools
and
techniques for estimating and/or predicting CRI can have a variety of
applications in a
clinical setting, including without limitation diagnosing such conditions.
[0034] In various embodiments, a compensatory reserve monitor can
include,
but is not limited to, some or all of the following functionality, as
described in further
detail herein:
[0035] A. Estimating and/or displaying intravascular volume loss to
hemodynamic decompensation (or cardiovascular collapse).
[0036] B. Estimating, predicting and/or displaying a patient's
compensatory reserve as an index that is proportional to an approximate
measure of
intravascular volume loss to CV collapse, recognizing that each patient has a
unique
reserve capacity.
[0037] C. Estimating, predicting and/or displaying a patient's
compensatory reserve as an index with a normative value at euvolemia (for
example,
CRI = 1), representing a state in which the patient is normovolemic; a minimum
value
(for example, CRI = 0) which implies no circulatory reserve and that the
patient is
experiencing CV collapse; and/or an excess value (for example, CRI > 1)
representing
a state in which the patient is hypervolemic; the patient's normalized
compensatory
reserve can be displayed on a continuum between the minimum and maximum values
(perhaps labeled by different symbols and/or colors depending on where the
patient
falls on the continuum).
[0038] D. Determining and/or displaying a probability that bleeding or
intravascular volume loss has occurred.
[0039] E. Displaying an indicator that intravascular volume loss has
occurred and/or is ongoing; as well as other measures of reserve, such as
trend lines.
[0040] F. Estimating and/or predicting a patient's blood pressure,
and/or
future changes to a patient's blood pressure.
[0041] G. Displaying an estimate and/or prediction of a patients current
and/or
future blood pressure status.
8
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0042] In various embodiments, CRI estimates can be (i) based on a fixed
time history of patient monitoring (for example a 30 second or 30 heart beat
window);
(ii) based on a dynamic time history of patient monitoring (for example
monitoring
for 200 minutes may use all sensor information gathered during that time to
refine and
improve CRI estimates); (iii) based on either establishing a baseline estimate
of CRI
when the patient is normovolemic (no volume loss has occurred); and/or (iv)
based on
NO baselines estimates when patient is normovolemic.
[0043] Certain embodiments can also recommend treatment options, based on
the analysis of the patient's condition (including the estimated/predicted
blood
pressure, probability of bleeding, state of dehydration, and/or the patient's
estimated
and/or predicted CRI). Treatment options can include, without limitation, such
things
as optimizing hemodynamics, ventilator adjustments, IV fluid adjustments,
transfusion of blood or blood products, infusion of volume expanders,
medication
changes, changes in patient position and surgical therapy.
[0044] As a specific example, certain embodiments can be used as an input
for
a hemodialysis procedure. For example, certain embodiments can predict how
much
intravascular (blood) volume can be safely removed from a patient during a
hemodialysis process. For example, an embodiment might provide instructions to
a
human operator of a hemodialysis machine, based on estimates or predictions of
the
patient's CRI. Additionally and/or alternatively, such embodiments can be used
to
continuously self-adjust the ultra-filtration rate of the hemodialysis
equipment,
thereby completely avoiding intradialytic hypotension and its associated
morbidity.
[0045] As another example, certain embodiments can be used to estimate
and/or predict a dehydration state (and/or the amount of dehydration) in an
individual
(e.g., a trauma patient, an athlete, an elder living at home, etc.) and/or to
provide
treatment (either by providing recommendations to treating personnel or by
directly
controlling appropriate therapeutic equipment). For instance, if an analytical
model
indicates a relationship between CRI (and/or any other physiological phenomena
that
can be measured and/or estimated using the techniques described herein and in
the
Related Applications) and dehydration state, an embodiment can apply that
model,
using the techniques described herein, to estimate a dehydration state of the
patient.
9
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0046] Exemplary Systems and Methods
[0047] Fig. lA provides a general overview of a system provided by
certain
embodiments. The system includes a computer system 100 in communication with
one or more sensors 105, which are configured to obtain physiological data
from the
subject (e.g., animal or human test subject or patient) 110. In one
embodiment, the
computer system 100 comprises a Lenovo THINKPAD X200, 4GB of RAM with
Microsoft WINDOWS 7 operating system and is programmed with software to
execute the computational methods outlined herein. The computational methods
can
be implemented in MATLAB 2009b and C++ programming languages. A more
general example of a computer system 100 that can be used in some embodiments
is
described in further detail below. Even more generally, however, the computer
system 100 can be any system of one or more computers that are capable of
performing the techniques described herein. In a particular embodiment, for
example,
the computer system 100 is capable of reading values from the physiological
sensors
105, generating models of physiological state from those sensors, and/or
employing
such models to make individual-specific estimations, predictions, or other
diagnoses,
displaying the results, recommending and/or implementing a therapeutic
treatment as
a result of the analysis, and/or archiving (learning) these results for use in
future,
model building and predictions.
[0048] The sensors 105 can be any of a variety of sensors (including
without
limitation those described herein) for obtaining physiological data from the
subject.
An exemplary sensor suite might include a Finometer sensor for obtaining a
noninvasive continuous blood pressure waveform, a pulse oximeter sensor, an
Analog
to Digital Board (National Instruments USB-9215A 16-Bit, 4 channel) for
connecting
the sensors (either the pulse oximeter and/or the finometer) to the computer
system
100. More generally, in an embodiment one or more sensors 105 might obtain,
e.g.,
using one or more of the techniques described herein, continuous physiological
waveform data, such as continuous blood pressure. Input from the sensors 105
can
constitute continuous data signals and/or outcomes that can be used to
generate,
and/or can be applied to, a predictive model as described below.
[0049] In some cases, the structure might include a therapeutic device
115
(also referred to herein as a "physiological assistive device"), which can be
controlled
by the computer system 100 to administer therapeutic treatment, in accordance
with
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
the recommendations developed by analysis of a patient's physiological data.
In a
particular embodiment, the therapeutic device might comprise hemodialysis
equipment (also referred to as a hemodialysis machine), which can be
controlled by
the computer system 100 based on the estimated CRI of the patient, as
described in
further detail below. Further examples of therapeutic devices in other
embodiments
can include a cardiac assist device, a ventilator, an automatic implantable
cardioverter
defibrillator ("AICD"), pacemakers, an extracorporeal membrane oxygenation
circuit,
a positive airway pressure ("PAP") device (including without limitation a
continuous
positive airway pressure ("cPAP") device or the like), an anesthesia machine,
an
integrated critical care system, a medical robot, intravenous and/or intra-
arterial
pumps that can provide fluids and/or therapeutic compounds (e.g., through
intravenous injection), a heating/cooling blanket, and/or the like.
[0050] Fig. 1B illustrates in more detail an exemplary sensor device 105,
which can be used in the system 100 described above. (It should be noted, of
course,
that the depicted sensor device 105 of Fig. 1B is not intended to be limiting,
and
different embodiments can employ any sensor that captures suitable data,
including
without limitation sensors described elsewhere in this disclosure and in the
Related
Applications.) The illustrated sensor device 105 is designed to be worn on a
patient's
wrist and therefore can be used both in clinical settings and in the field
(e.g., on any
person for whom monitoring might be beneficial, for a variety of reasons,
including
without limitation estimation/prediction of blood pressure).
[0051] Hence, the exemplary sensor 105 device includes a finger cuff 125
and
a wrist unit 130. The finger cuff 125 includes a fingertip sensor 135 (in this
case, a
PPG sensor) that captures data based on physiological conditions of the
patient, such
as PPG waveform data. The sensor 135 communicates with an input/output unit
140
of the wrist unit 130 to provide output from the sensor 135 to a processing
unit 145 of
the wrist unit 130. Such communication can be wired (e.g., via a standard¨such
as
USB¨or proprietary connector on the wrist unit 130) and/or wireless (e.g., via
Bluetooth, such as Bluetooth Low Energy ("BTLE"), near field connection
("NFC"),
WiFi, or any other suitable radio technology).
[0052] In different embodiments, the processing unit can have different
types
of functionality. For example, in some cases, the processing unit might simply
act to
store and/or organize data prior to transmitting the data through the I/O unit
140 to a
11
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
monitoring computer 100, which might perform data analysis, control a
therapeutic
device 115, etc. In other cases, however, the processing unit 145 might act as
a
specialized computer (e.g., with some or all of the components described in
connection with Fig. 5, below and/or some or all of the functionality ascribed
to the
computer 100 of Figs. lA and 1B), such that the processing unit can perform
data
analysis onboard, e.g., to estimate and/or predict a patient's current and/or
future
blood pressure. As such, the wrist unit 105 might include a display, which can
display any output described herein, including without limitation estimated
and/or
predicted values (e.g., of CRI, blood pressure, hydration status, etc.), data
captured by
the sensor (e.g., heart rate, pulse ox, etc.), and/or the like.
[0053] In some cases, the wrist unit 130 might include a wrist strap 155
that
allows the unit to be worn on the wrist, similar to a watch. Of course, other
options
are available to facilitate transportation of the sensor device 105 with a
patent. More
generally, the sensor device 105 might not include all of the components
described
above, and/or various components might be combined and/or reorganized; once
again,
the embodiment illustrated by Fig. 1B should be considered only illustrative,
and not
limiting, in nature.
[0054] Figs. 2A, 2B, 3A, 3B and 4 illustrate methods and screen displays
in
accordance with various embodiments. While the methods of Figs. 2A, 2B, 3A, 3B
and 4 are illustrated, for ease of description, as different methods, it
should be
appreciated that the various techniques and procedures of these methods can be
combined in any suitable fashion, and that, in some embodiments, the methods
depicted by Figs. 2A, 2B, 3A, 3B and 4 can be considered interoperable and/or
as
portions of a single method. Similarly, while the techniques and procedures
are
depicted and/or described in a certain order for purposes of illustration, it
should be
appreciated that certain procedures may be reordered and/or omitted within the
scope
of various embodiments. Moreover, while the methods illustrated by Figs. 2A,
2B,
3A, 3B and 4 can be implemented by (and, in some cases, are described below
with
respect to) the computer system 100 of Fig. 1 (or other components of the
system,
such as the sensor 105 of Figs. lA and 1B), these methods may also be
implemented
using any suitable hardware implementation. Similarly, while the computer
system
100 of Fig. 1 (and/or other components of such a system) can operate according
to the
methods illustrated by Figs. 2A, 2B, 3A, 3B and 4 (e.g., by executing
instructions
12
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
embodied on a computer readable medium), the system 100 can also operate
according to other modes of operation and/or perform other suitable
procedures.
[0055] Merely by way of example, a method might comprise one or more
procedures, any or all of which are executed by a computer system.
Correspondingly,
an embodiment might provide a computer system configured with instructions to
perform one or more procedures in accordance with methods provided by various
other embodiments. Similarly, a computer program might comprise a set of
instructions that are executable by a computer system (and/or a processor
therein) to
perform such operations. In many cases, such software programs are encoded on
physical, tangible and/or non-transitory computer readable media (such as, to
name
but a few examples, optical media, magnetic media, and/or the like).
[0056] By way of non-limiting example, various embodiments can comprise a
method for using sensor data to estimate and/or predict a patient's current
and/or
future blood pressure. Fig. 2 illustrates an exemplary method 200 in
accordance with
various embodiments. The method 200 might comprise generating a model, e.g.,
with
a computer system, against which patient data can be analyzed to estimate
and/or
predict various physiological states (block 205). In a general sense,
generating the
model can comprise receiving data pertaining to a plurality of more
physiological
parameters of a test subject to obtain a plurality of physiological data sets.
Such data
can include PPG waveform data to name one example, and/or any other type of
sensor
data including without limitation data captured by other sensors described
herein and
in the Related Applications.
[0057] Generating a model can further comprise directly measuring one or
more physiological states of the test subject with a reference sensor to
obtain a
plurality of physiological state measurements. The one or more physiological
states
can include, without limitation, a state of low blood pressure, a state of
normal blood
pressure, and a state of high blood pressure. (In other embodiments, different
states
can include a state of hypervolemia, a state of euvolemia, and/or a state of
cardiovascular collapse (or near-cardiovascular collapse)). Generating the
model can
further comprise correlating the states with the measured physiological
parameters.
There are a variety of techniques for generating a model in accordance with
different
embodiments, using these general functions. One exemplary technique for
generating
a model of a generic physiological state is described below with respect to
Fig. 4,
13
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
below, which provides a technique using a machine-learning algorithm to
optimize
the correlation between measured physiological parameters (such as PPG
waveform
data, to name one example) and physical states (e.g., various blood pressure
values,
either numeric or qualitative). It should be appreciated, however, that any
suitable
technique or model may be employed in accordance with various embodiments.
[0058] A number of physiological states can be modeled, and a number of
different conditions can be imposed on test subjects as part of the model
generation.
[0059] Merely by way of example, in one set of embodiments, a number of
physiological parameters of a plurality of test subjects might be measured. In
some
cases, subject might have a variety of blood pressure values, including
without
limitation, low blood pressure, normal blood pressure, and high blood
pressure.
Using the method described below with respect to Fig. 4 (or other, similar
techniques,
many of which are described in the Related Applications), the system can
determine
which sensor information most effectively differentiates between subjects with
low
blood pressure and those with high blood pressure. Using a similar technique,
the
system can further determine what sensor information best differentiates
between
subjects with low blood pressure and those with normal blood pressure, and
what
sensor information best differentiates between subjects with normal blood
pressure
and those with high blood pressure. Using these different sensor information
sets, the
techniques described with regard to Fig. 4 (and in the Related Applications)
can
develop a model that classifies sensor signals into those correlating with
low, normal,
and high blood pressure values, respectively.
[0060] In a similar model, sensor information might be captured, over
time,
for subjects whose blood pressure decreases and increases over time. Using the
techniques of Fig. 4 (and the Related Applications), models can be generated
that
classify sensor signals into various levels of blood pressure increase or
decrease over
time.
[0061] Additionally and/or alternatively to using direct sensor data to
build
such models, some embodiments might construct a model based on data that is
derived from sensor data. Merely by way of example, one such model might use,
as
input values, CRI values of test subjects with low, normal, and high blood
pressure,
respectively, and/or those with blood pressure that is increasing or
decreasing over
14
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
time. Accordingly, the process of generating a model might first comprise
building a
model of CRI, and then, from that model, building a model of blood pressure.
[0062] A CRI model can be generated in different ways. For example, in
some
cases, one or more test subjects might be subjected to LBNP. In an exemplary
case,
LBNP data is collected from human subjects being exposed to progressively
lower
levels of LBNP, until hemodynamic decompensation, at which time LBNP is
released
and the subject recovers. Each level of LBNP represents an additional amount
of
blood loss. During these tests, physiological data (including without
limitation
waveform data, such as continuous non-invasive blood pressure data)) can be
collected before, during, and/or after the application of the LBNP. As noted
above, a
relationship (as expressed by Equation 2) can be identified between LBNP and
intravascular volume loss, and this relationship can be used to estimate CRI.
Hence,
LBNP studies form a framework (methodology) for the development of the
hemodynamic parameter referred to herein as CRI and can be used to generate
models
of this parameter.
[0063] More generally, several different techniques that induce a
physiological state of reduced volume in the circulatory system, e.g., to a
point of
cardiovascular collapse (hemodynamic decompensation) or to a point near
cardiovascular collapse, can be used to generate such a model. LBNP can be
used to
induce this condition, as noted above. In some cases, such as in a study
described
below, dehydration can be used to induce this condition as well. Other
techniques are
possible as well. Similarly, data collected from a subject in a state of
euvolemia,
dehydration, hypervolemia, and/or other states might be used to generate a CRI
model
in different embodiments.
[0064] At block 210, the method 200 comprises monitoring, with one or
more
sensors, physiological data of a patient. As noted above, a variety of
physical
parameters can be monitored, invasively and/or non-invasively, depending on
the
nature of the anticipated physiological state of the patient. In an aspect,
monitoring
the one or more physical parameters might comprise receiving, e.g., from a
physiological sensor, continuous waveform data, which can be sampled as
necessary.
Such data can include, without limitation, plethysmograph waveform data, PPG
waveform data (such as that generated by a pulse oximeter), and/or the like.
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0065] The method 200 might further comprise analyzing, with a computer
system (e.g., a monitoring computer 100 and/or a processing unit 135 of a
sensor unit,
as described above), the physiological data (block 215). In some cases, the
physiological data is analyzed against a pre-existing model (which might be
generated
as described above and which in turn, can be updated based on the analysis, as
described in further detail below and in the Related Applications).
[0066] Merely by way of example, in some cases, sensor data can be
analyzed
directly against a generated model to estimate and/or predict blood pressure
levels.
For example, the sensor data can be compared to determine similarities with
models
that (i) estimate whether the patient's current blood pressure is low, normal,
or high;
(ii) identify a situation in which a patient's blood pressure has changed
significantly
over a period of time, in some cases several minutes to several hours, or in
other
cases, days, weeks, months, or years; (iii) predict when a patient's blood
pressure will
increase or decrease to a specific value; or (iv) predict when a patient's
blood pressure
will increase or decrease by a specific amount. Merely by way of example, an
input
waveform captured by a sensor from a patient might be compared with sample
waveforms generated by models for each of the above conditions to estimate or
predict present or future blood pressure conditions, for example, using the
technique
265 illustrated in Fig. 2B.
[0067] The technique 265 provides one method for deriving an estimate of
a
blood pressure value in accordance with some embodiments. The illustrated
technique 265 comprises sampling waveform data (e.g., any of the data
described
herein and in the Related Applications, including without limitation arterial
waveform
data, such as continuous PPG waveforms and/or continuous noninvasive blood
pressure waveforms) for a specified period, such as 32 heartbeats (block 270).
That
sample is compared with a plurality of waveforms of reference data
corresponding to
different blood pressure values (block 275), which can be qualitative values
(such as
low, medium, and high, as illustrated) or might be quantitative (e.g.,
numeric) values
(such as BP=60, BP=70, BP=80, ... BP=180, etc.). (These reference waveforms
derived as part of the model developed using the algorithms described in this
and the
Related Applications, might be the result of experimental data, and/or the
like).
Merely by way of example, the sample might be compared with waveforms
corresponding to a low blood pressure (block 275a), a normal blood pressure
(block
16
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
275b), and a high blood pressure (block 275c), as illustrated. From the
comparison, a
similarity coefficient is calculated (e.g., using a least squares or similar
analysis) to
express the similarity between the sampled waveform and each of the reference
waveforms (block 280). These similarity coefficients can be normalized (if
appropriate) (block 285), and the normalized coefficients can be summed (block
390)
to produce an estimated blood pressure value of the patient. For example,
numerical
values (such as 0, 1, and 2) might be assigned to low, normal, and high
qualitative
blood pressure values, respectively, and by summing these normalized
coefficients,
the resulting value can be expressed numerically and then categorized based on
the
numeric values assigned to the qualitative values.
[0068] In other cases, similar techniques can be used to analyze data
against a
model based on parameters derived from direct sensor measurements. (In one
aspect,
such operations can be iterative in nature, by generating the derived
parameters¨such
as CRI, to name one example¨by analyzing the sensor data against a first
model, and
then analyzing the derived parameters against a second model.
[0069] For example, Fig 3A illustrates a method 300 of calculating a
patient's
CRI, which can be used (in some embodiments) as a parameter that can be
analyzed
to estimate and/or predict a patient's blood pressure. The method 300 includes
generating a model of CRI (block 305), monitoring physiological parameters
(310)
and analyzing the monitored physical parameters (block 315) , using techniques
such
as those described above and in the '483 Application, for example.
[0070] Based on this analysis, the method 300, in an exemplary
embodiment,
includes estimating, with the computer system, a compensatory reserve of the
patient,
based on analysis of the physiological data (block 320). In some cases, the
method
might further comprise predicting, with the computer system, the compensatory
reserve of the patient at one or more time points in the future, based on
analysis of the
physiological data (block 325). The operations to predict a future value of a
parameter can be similar to those for estimating a current value; in the
prediction
context, however, the applied model might correlate measured data in a test
subject
with subsequent values of the diagnostic parameter, rather than
contemporaneous
values. It is worth noting, of course, that in some embodiments, the same
model can
be used to both estimate a current value and predict future values of a
physiological
parameter.
17
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0071] The estimated and/or predicted compensatory reserve of the patient
can
be based on several factors. Merely by way of example, in some cases, the
estimated/predicted compensatory reserve can be based on a fixed time history
of
monitoring the physiological data of the patient and/or a dynamic time history
of
monitoring the physiological data of the patient. In other cases, the
estimated/predicted compensatory reserve can be based on a baseline estimate
of the
patient's compensatory reserve established when the patient is euvolemic. In
still
other cases, the estimate and/or prediction might not be based on a baseline
estimate
of the patient's compensatory reserve established when the patient is
euvolemic.
[0072] Merely by way of example, Fig. 3B illustrates one technique 365
for
deriving an estimate of CRI in accordance with some embodiments similar to the
technique 265 described above with respect to Fig. 2B for deriving an estimate
of
blood pressure values directly from sensor data. The illustrated technique
comprises
sampling waveform data (e.g., any of the data described herein and in the
Related
Applications, including without limitation arterial waveform data, such as
continuous
PPG waveforms and/or continuous noninvasive blood pressure waveforms) for a
specified period, such as 32 heartbeats (block 370). That sample is compared
with a
plurality of waveforms of reference data corresponding to different CRI values
(block
375). (These reference waveforms might be derived using the algorithms
described in
the Related Applications, might be the result of experimental data, and/or the
like).
Merely by way of example, the sample might be compared with waveforms
corresponding to a CRI of 1 (block 375a), a CRI of 0.5 (block 375b), and a CRI
of 0
(block 375c), as illustrated. From the comparison, a similarity coefficient is
calculated (e.g., using a least squares or similar analysis) to express the
similarity
between the sampled waveform and each of the reference waveforms (block 380).
These similarity coefficients can be normalized (if appropriate) (block 385),
and the
normalized coefficients can be summed (block 390) to produce an estimated
value of
the patient's CRI.
[0073] Returning to Fig. 3A, the method 300 can comprise estimating
and/or
predicting a patient's dehydration state (block 330). The patient's state of
dehydration
can be expressed in a number of ways. For instance, the state of dehydration
might be
expressed as a normalized value (for example, with 1.0 corresponding to a
fully
hydrated state and 0.0 corresponding to a state of morbid dehydration). In
other
18
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
cases, the state of dehydration might be expressed as a missing volume of
fluid or as a
volume of fluid present in the patient's system, or using any other
appropriate metric.
[0074] A number of techniques can be used to model dehydration state.
Merely by way of example, as noted above (and described in further detail
below), the
relationship between a patient's compensatory reserve and level of dehydration
can be
modeled. Accordingly, in some embodiments, estimating a dehydration state of
the
patient might comprise estimating the compensatory reserve (e.g., CRI) of the
patient,
and then, based on that estimate and the known relationship, estimating the
dehydration state. Similarly, a predicted value of compensatory reserve at
some point
in the future can be used to derive a predicted dehydration state at that
point in the
future. Other techniques might use a parameter other than CRI to model
dehydration
state.
[0075] The method 300 might further comprise normalizing the results of
the
analysis (block 335), such as the compensatory reserve, dehydration state,
and/or
probability of bleeding, to name a few examples. Merely by way of example, the
estimated/predicted compensatory reserve of the patient can be normalized
relative to
a normative normal blood volume value corresponding to euvolemia, a normative
excess blood volume value corresponding to circulatory overload, and a
normative
minimum blood volume value corresponding to cardiovascular collapse. Any
values
can be selected as the normative values. Merely by way of example, in some
embodiments, the normative excess blood volume value is >1, the normative
normal
blood volume value is 1, and the normative minimum blood volume value is 0. As
an
alternative, in other embodiments, the normative excess blood volume value
might be
defined as 1, the normative normal blood volume value might be defined as 0,
and the
normative minimum blood volume value at the point of cardiovascular collapse
might
be defined as -1. As can be seen from these examples, different embodiments
might
use a number of different scales to normalize CRI and other estimated
parameters.
[0076] In an aspect, normalizing the data can provide benefits in a
clinical
setting, because it can allow the clinician to quickly make a qualitative
judgment of
the patient's condition, while interpretation of the raw estimates/predictions
might
require additional analysis. Merely by way of example, with regard to the
estimate of
the compensatory reserve of the patient, that estimate might be normalized
relative to
a normative normal blood volume value corresponding to euvolemia and a
normative
19
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
minimum blood volume value corresponding to cardiovascular collapse. Once
again,
any values can be selected as the normative values. For example, if the
normative
normal blood volume is defined as 1, and the normative minimum blood volume
value is defined as 0, the normalized value, falling between 0.0 and 1.0 can
quickly
apprise a clinician of the patient's location on a continuum between euvolemia
and
cardiovascular collapse. Similar normalizing procedures can be implemented for
other estimated data (such as probability of bleeding, dehydration, and/or the
like).
[0077] The method 300 might further comprise displaying data with a
display
device (block 340). Such data might include an estimate and/or prediction of
the
compensatory reserve of the patient and/or an estimate and/or prediction of
the
patient's dehydration state. A variety of techniques can be used to display
such data.
Merely by way of example, in some cases, displaying the estimate of the
compensatory reserve of the patient might comprise displaying the normalized
estimate of the compensatory reserve of the patient. Alternatively and/or
additionally,
displaying the normalized estimate of the compensatory reserve of the patient
might
comprise displaying a graphical plot showing the normalized excess blood
volume
value, the normalized normal blood volume value, the normalized minimum blood
volume value, and the normalized estimate of the compensatory reserve (e.g.,
relative
to the normalized excess blood volume value, the normalized normal blood
volume
value, the normalized minimum blood volume value).
[0078] In some cases, the method 300 might comprise repeating the
operations of monitoring physiological data of the patient, analyzing the
physiological
data, and estimating (and/or predicting) the compensatory reserve of the
patient, to
produce a new estimated (and/or predicted) compensatory reserve of the
patient.
Thus, displaying the estimate (and/or prediction) of the compensatory reserve
of the
patient might comprises updating a display of the estimate of the compensatory
reserve to show the new estimate (and/or prediction) of the compensatory
reserve, in
order to display a plot of the estimated compensatory reserve over time.
Hence, the
patient's compensatory reserve can be repeatedly estimated and/or predicted on
any
desired interval (e.g., after every heartbeat), on demand, etc.
[0079] In further embodiments, the method 300 can comprise determining a
probability that the patient is bleeding, and/or displaying, with the display
device, an
indication of the probability that the patient is bleeding (block 345). For
example,
CA 02929819 2016-05-05
WO 2015/069940 PCT/US2014/064413
some embodiments might generate a model based on data that removes fluid from
the
circulatory system (such as LBNP, dehydration, etc.). Another embodiment might
generate a model based on fluid removed from a subject voluntarily, e.g.,
during a
blood donation, based on the known volume (e.g., 500cc) of the donation. Based
on
this model, using techniques similar to those described above, a patient's
physiological data can be monitored and analyzed to estimate a probability
that the
patient is bleeding (e.g., internally).
[0080] In some cases, the probability that the patient is bleeding can be
used
to adjust the patient's estimated CRI. Specifically, give a probability of
bleeding
expressed as Pr Bleed at a time t, the adjusted value of CRI can be expressed
as:
CRI d( 0= 1¨ ((1¨ 0,11(0)x Pr Bleed (0)
Adjuste"
(Eq. 4)
[0081] Given this relationship, the estimated CRI can be adjusted to
produce a
more accurate diagnosis of the patient's condition at a given point in time.
[0082] The method 300 might comprise selecting, with the computer system,
a
recommended treatment option for the patient, and/or displaying, with the
display
device, the recommended treatment option (block 355). The recommended
treatment
option can be any of a number of treatment options, including without
limitation,
optimizing hemodynamics of the patient, a ventilator adjustment, an
intravenous fluid
adjustment, transfusion of blood or blood products to the patient, infusion of
volume
expanders to the patient, a change in medication administered to the patient,
a change
in patient position, and surgical therapy.
[0083] In a specific example, the method 300 might comprise controlling
operation of hemodialysis equipment (block 360), based at least in part on the
estimate of the patient's compensatory reserve. Merely by way of example, a
computer system that performs the monitoring and estimating functions might
also be
configured to adjust an ultra-filtration rate of the hemodialysis equipment in
response
to the estimated CRI values of the patient. In other embodiments, the computer
system might provide instructions or suggestions to a human operator of the
hemodialysis equipment, such as instructions to manually adjust an ultra-
filtration
rate, etc.
21
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0084] In some embodiments, the method 300 might include assessing the
tolerance of an individual to blood loss, general volume loss, and/or
dehydration
(block 365). For example, such embodiments might include estimating a
patient's
CRI based on the change in a patient's position (e.g., from lying prone to
standing,
lying prone to sitting, and/or sitting to standing). Based on changes to the
patient's
CRI in response to these maneuvers, the patient's sensitivity to blood loss,
volume
loss, and/or dehydration can be measured. In an aspect, this measurement can
be
performed using a CRI model generated as described above; the patient can be
monitored using one or more of the sensors described above, and the changes in
the
sensor output when the subject changes position can be analyzed according to
the
model (as described above, for example) to assess the tolerance of the
individual to
volume loss. Such monitoring and/or analysis can be performed in real time.
[0085] Returning to Fig. 2, based on the analysis of the data (whether
data
collected directly by sensors or derived data, such as CRI), the method 200
can
include estimating a current blood pressure of the patient (block 220). As
noted
above, the analysis of the data can include analyzing the data against models
of low,
normal, and blood pressure conditions to identify whether the data indicates
that the
current blood pressure of the patient is low, normal, or high, and using
similar
techniques, the data can be analyzed against models of specific numeric blood
pressure values. In different embodiments, the blood pressure estimate (and/or
prediction) can be expressed in terms of systolic pressure, diastolic
pressure, mean
arterial pressure, or any combination of these values. (Each value can be
modeled
differently if desired, or a model might include sub-models for all three
values.) In
certain embodiments, the value of the blood pressure might merely be estimated
and/or predicted as "low," "normal," or "high," while in other cases, an
actual
quantitative value of the current blood pressure might be estimated or a
quantitative
value of a future blood pressure can be predicted. (For instance, the models
might be
constructed more specifically to correlate to specific numeric values of blood
pressure, or they might be constructed more generally to correlate with low,
normal,
and high ranges of blood pressure.)
[0086] Additionally, estimating a patient's blood pressure can include
identifying whether a patient's blood pressure has changed (increased or
decreased)
significantly over a specified period of time. For example, using readings
over a
22
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
period of minutes or hours, a change in blood pressure over that time can be
estimated. In other cases, readings taken periodically over a period of weeks,
months,
or years can be used to detect long-term changes in blood pressure. Using such
techniques, a patent's response to treatment or degrading/improving health
(either on
an acute or a chronic basis) can be monitored.
[0087] In some cases, the estimate of a patient's blood pressure will be
based
on the analysis of a plurality of measured (or derived) values of a particular
physiological parameter (or plurality of parameters). Hence, in some cases,
the
analysis of the data might be performed on a continuous waveform, either
during or
after measurement of the waveform with a sensor (or both), and the estimated
blood
pressure can be updated as measurements continue. Further, the patient's blood
pressure can be measured directly (using conventional techniques), and these
direct
measurements (at block 235) can be fed back into the model to update the model
and
thereby improve performance of the algorithms in the model (e.g., by refining
the
weights given to different parameters in terms of estimative or predictive
value).
[0088] At block 230, the method 200 can include predicting a patient's
future
blood pressure. Similar to the estimate of the patient's current blood
pressure, the
prediction of the patient's future blood pressure is based on analysis of the
monitored
sensor data (either analysis of the monitored data itself, analysis of
parameters derived
from the monitored data, such as CRI, or both). A number of different
predictions can
be made by various embodiments, again depending on the types of models
generated
to analyze the data. For instance, embodiments can predict when a patient's
blood
pressure will increase or decrease to a specified value. Alternatively and/or
additionally, embodiments can predict when a patient's blood pressure will
increase or
decrease by a specified amount.
[0089] At block 235, the method 200 might include updating the model(s)
based on a comparison of the patient's directly-measured (or estimated) blood
pressure at a given time with the predictions made at past times. Once again,
such
direct measurements can be fed back into the model(s) to improve their
predictive
value. After models have been updated, the models can be used for further
analysis of
measured/derived physiological parameters, as shown by the broken lines on
Fig. 2.
23
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0090] In some cases, the method 200 comprises displaying data (block
240)
indicating the blood pressure estimates and/or predictions. In some cases, the
data
might be displayed on a display of a sensor device (such as the device 105
illustrated
by Fig. 1B). Alternatively and/or additionally the data might be displayed on
a
dedicated machine, such as a compensatory reserve monitor, or on a monitor of
a
generic computer system. Different techniques can be used to display the data;
in
some cases, a set of colors may be used to display data (e.g., red for high
blood
pressure, green for normal blood pressure, and yellow for low blood pressure).
In
other cases, a textual and/or digital display of the data (e.g., a numeric
reading of a
quantitative estimated blood pressure value, a textual indicator of the
estimated blood
pressure value as low, normal, or high, an alphanumeric indication of
when¨either
relative to the current date/time, such as "Two Hours" or "Three Weeks," or
absolute,
such as "10:37AM" or "November 15, 2014"¨the patient's blood pressure is
predicted to increase or decrease to a specific level, or the like). There are
many
different ways that the data can be displayed, and any estimates or
predictions
generated by the method 200 can be displayed in any desired way, in accordance
with
various embodiments.
[0091] In certain embodiments, the method 200 can include selecting
and/or
displaying treatment options for the patient (block 245) and/or controlling a
therapeutic device (block 250) based on the estimates and/or predictions of
the
patient's blood pressure. For example, a number of different therapeutic
devices
(including without limitation those described above) can be controlled to
address
abnormal (e.g., high or low) blood pressure conditions. As another example, if
the
system were being used in an outpatient or home setting and the subject's
blood
pressure were acutely low, a variety of treatment options could be suggested,
such as:
sit down if dizzy and stay at rest until symptoms resolve, confirm blood
pressure
result with an alternative method (e.g. cuff method), hold any anti-
hypertensive
medications until evaluated by a care provider, seek immediate medical
attention, etc.
If the system were being used in an inpatient setting, the recommendations may
span
a variety of potential treatment options, such as: give an estimated volume of
isotonic
IV fluid at a certain rate, start a specified pressor medication at a certain
dosage, etc.
Various therapeutic devices, such as an intravenous pump, could be directed by
the
system to run at certain rate to provide a certain dosage in response to the
estimated
24
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
blood pressure value (and/or a predicted future value). Alternatively and/or
additionally, the system might continuously and/or automatically adjust such
devices
to achieve a desired therapeutic effect, such as blood pressure in a certain
range.
[0092] Further, in certain embodiments, the method 200 can include
functionality to help a patient (or a clinician) to monitor blood pressure.
For example,
in some cases, any blood pressure trends outside of the normal range would set
off
various alarm conditions, such as an audible alarm, a message to a physician,
a
message to the patient, an update written automatically to a patient's chart,
etc. Such
messaging could be accomplished by electronic mail, text message, etc., and a
sensor
device or monitoring computer could be configured with, e.g., an SMTP client,
text
messaging client, or the like to perform such messaging.
[0093] Similarly, if an alarm condition were met for another
physiological
parameter, that alarm could trigger a check of the current blood pressure via
this the
method 200, to determine whether the first alarm condition has merit or not.
If not,
perhaps there could be an automated silencing of the original alarm condition,
since
all is well at present. More generally, the blood pressure monitoring
technique could
be added to an ecosystem of monitoring algorithms (including without
limitation
those described in the Related Applications), which would inform one another
or
work in combination, to inform one another about how to maintain optimal
physiological stability.
[0094] Fig. 4 illustrates a method 400 of employing such a self-learning
predictive model (or machine learning) technique, according to some
embodiments.
In particular, the method 400 can be used to correlate physiological data
received
from a subject sensor with a measured physiological state. More specifically,
with
regard to various embodiments, the method 400 can be used to generate a model
for
predicting and/or estimating various physiological parameters, such as
estimated
and/or predicted blood pressure, CRI, the probability that a patient is
bleeding, a
patient's dehydration state, and/or the like from one or more of a number of
different
physiological parameters, including without limitation those described above
and in
the Related Applications.
[0095] The method 400 begins at block 405 by collecting raw data
measurements that may be used to derive a set of D data signals sl, ... , SD
as indicated
CA 02929819 2016-05-05
WO 2015/069940 PCT/US2014/064413
at block 410 (each of the data signals s being, in a particular case, input
from one or
many different physiological sensors). Embodiments are not constrained by the
type
of measurements that are made at block 405 and may generally operate on any
data
set. For example, data signals can be retrieved from a computer memory and/or
can
be provided from a sensor or other input device. As a specific example, the
data
signals might correspond to the output of the sensors described above (which
measure
the types of waveform data described above, such as continuous, non-invasive
PPG
data and/or blood pressure waveform data).
[0096] A set of K current or future outcomes 6 = (or, ...,oK) is
hypothesized
at block 415 (the outcomes o being, in this case, past and/or future
physiological
states, such as blood pressure values (either quantitative values or
qualitative levels,
such as low, normal, or high), CRI, dehydration state, probability of
bleeding, etc.).
The method autonomously generates a predictive model M that relates the
derived
data signals g with the outcomes 6. As used herein, "autonomous," means
"without
human intervention."
[0097] As indicated at block 420, this is achieved by identifying the
most
predictive set of signals Sk, where Sk contains at least some (and perhaps
all) of the
derived signals sl, , SD for each outcome ok, where k E {1, , KJ. A
probabilistic
predictive model 5k = Mk (Sk) is learned at block 425, where 5k is the
prediction of
outcome okderived from the model Mk that uses as inputs values obtained from
the set
of signals Sk, for all k E {1, ..., KJ. The method 400 can learn the
predictive models
OK = Mk (Sk) incrementally (block 430) from data that contains example values
of
signals sl, sE, and the corresponding outcomes ol, oK. As the data become
available, the method 400 loops so that the data are added incrementally to
the model
for the same or different sets of signals Sk, for all k E {1, ..., KJ.
[0098] While the description above outlines the general characteristics
of the
methods, additional features are noted. A linear model framework may be used
to
identify predictive variables for each new increment of data. In a specific
embodiment, given a finite set of data of signals and outcomes {(gi, 51), (g2,
52), ... },
a linear model may be constructed that has the form, for all k E {1, , KJ,
ak = fk (ao + Eci1.1 aisi)
(Eq. 5)
26
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
where ftis any mapping from one input to one output, and c/o, al, , ad are the
linear
model coefficients. The framework used to derive the linear model coefficients
may
estimate which signals s, sl, , sd are not predictive and accordingly sets the
corresponding coefficients ao, al, , ad to zero. Using only the predictive
variables,
the model builds a predictive density model of the data, {(gi, 61), (g2, 52),
... }. For
each new increment of data, a new predictive density models can be
constructed.
[0099] In some embodiments, a prediction system can be implemented that
can predict future results from previously analyzed data using a predictive
model
and/or modify the predictive model when data does not fit the predictive
model. In
some embodiments, the prediction system can make predictions and/or to adapt
the
predictive model in real-time. Moreover, in some embodiments, a prediction
system
can use large data sets not only to create the predictive model, but also
predict future
results as well as adapt the predictive model.
[0100] In some embodiments, a self-learning, prediction device can include
a
data input, a processor and an output. Memory can include application software
that
when executed can direct the processor to make a prediction from input data
based on
a predictive model. Any type of predictive model can be used that operates on
any
type of data. In some embodiments, the predictive model can be implemented for
a
specific type of data. In some embodiments, when data is received the
predictive
model can determine whether it understands the data according to the
predictive
model. If the data is understood, a prediction is made and the appropriate
output
provided based on the predictive model. If the data is not understood when
received,
then the data can be added to the predictive model to modify the model. In
some
embodiments, the device can wait to determine the result of the specified data
and can
then modify the predictive model accordingly. In some embodiments, if the data
is
understood by the predictive model and the output generated using the
predictive
model is not accurate, then the data and the outcome can be used to modify the
predictive model. In some embodiments, modification of the predictive model
can
occur in real-time.
[0101] Particular embodiments can employ the tools and techniques described
in the Related Applications in accordance with the methodology described
herein
perform the functions of a cardiac reserve monitor, as described herein. These
27
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
functions include, but are not limited to monitoring, estimating and/or
predicting a
subject's (including without limitation, a patient's) current or future blood
pressure
and/or compensatory reserve, estimating and/or determining the probability
that a
patient is bleeding (e.g., internally) and/or has been bleeding, recommending
treatment options for such conditions, and/or the like. Such tools and
techniques
include, in particular, the systems (e.g., computer systems, sensors,
therapeutic
devices, etc.) described in the Related Applications, the methods (e.g., the
analytical
methods for generating and/or employing analytical models, the diagnostic
methods,
etc.), and the software programs described herein and in the Related
Applications,
which are incorporated herein by reference.
[0102] Hence, Fig. 5 provides a schematic illustration of one embodiment of
a
computer system 500 that can perform the methods provided by various other
embodiments, as described herein, and/or can function as a monitoring
computer, CRI
monitor, processing unit of sensor device, etc. It should be noted that Fig. 5
is meant
only to provide a generalized illustration of various components, of which one
or
more (or none) of each may be utilized as appropriate. Fig. 5, therefore,
broadly
illustrates how individual system elements may be implemented in a relatively
separated or relatively more integrated manner.
[0103] The computer system 500 is shown comprising hardware elements that
can be electrically coupled via a bus 505 (or may otherwise be in
communication, as
appropriate). The hardware elements may include one or more processors 510,
including without limitation one or more general-purpose processors and/or one
or
more special-purpose processors (such as digital signal processing chips,
graphics
acceleration processors, and/or the like); one or more input devices 515,
which can
include without limitation a mouse, a keyboard and/or the like; and one or
more
output devices 520, which can include without limitation a display device, a
printer
and/or the like.
[0104] The computer system 500 may further include (and/or be in
communication with) one or more storage devices 525, which can comprise,
without
limitation, local and/or network accessible storage, and/or can include,
without
limitation, a disk drive, a drive array, an optical storage device, solid-
state storage
device such as a random access memory ("RAM") and/or a read-only memory
("ROM"), which can be programmable, flash-updateable and/or the like. Such
28
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
storage devices may be configured to implement any appropriate data stores,
including without limitation, various file systems, database structures,
and/or the like.
[0105] The computer system 500 might also include a communications
subsystem 530, which can include without limitation a modem, a network card
(wireless or wired), an infra-red communication device, a wireless
communication
device and/or chipset (such as a BluetoothTM device, an 802.11 device, a WiFi
device,
a WiMax device, a WWAN device, cellular communication facilities, etc.),
and/or the
like. The communications subsystem 530 may permit data to be exchanged with a
network (such as the network described below, to name one example), with other
computer systems, and/or with any other devices described herein. In many
embodiments, the computer system 500 will further comprise a working memory
535,
which can include a RAM or ROM device, as described above.
[0106] The computer system 500 also may comprise software elements, shown
as being currently located within the working memory 535, including an
operating
system 540, device drivers, executable libraries, and/or other code, such as
one or
more application programs 545, which may comprise computer programs provided
by
various embodiments, and/or may be designed to implement methods, and/or
configure systems, provided by other embodiments, as described herein. Merely
by
way of example, one or more procedures described with respect to the method(s)
discussed above might be implemented as code and/or instructions executable by
a
computer (and/or a processor within a computer); in an aspect, then, such code
and/or
instructions can be used to configure and/or adapt a general purpose computer
(or
other device) to perform one or more operations in accordance with the
described
methods.
[0107] A set of these instructions and/or code might be encoded and/or
stored
on a non-transitory computer readable storage medium, such as the storage
device(s)
525 described above. In some cases, the storage medium might be incorporated
within a computer system, such as the system 500. In other embodiments, the
storage
medium might be separate from a computer system (i.e., a removable medium,
such
as a compact disc, etc.), and/or provided in an installation package, such
that the
storage medium can be used to program, configure and/or adapt a general
purpose
computer with the instructions/code stored thereon. These instructions might
take the
form of executable code, which is executable by the computer system 500 and/or
29
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
might take the form of source and/or installable code, which, upon compilation
and/or
installation on the computer system 500 (e.g., using any of a variety of
generally
available compilers, installation programs, compression/decompression
utilities, etc.)
then takes the form of executable code.
[0108] It will be apparent to those skilled in the art that substantial
variations
may be made in accordance with specific requirements. For example, customized
hardware (such as programmable logic controllers, field-programmable gate
arrays,
application-specific integrated circuits, and/or the like) might also be used,
and/or
particular elements might be implemented in hardware, software (including
portable
software, such as applets, etc.), or both. Further, connection to other
computing
devices such as network input/output devices may be employed.
[0109] As mentioned above, in one aspect, some embodiments may employ a
computer system (such as the computer system 500) to perform methods in
accordance with various embodiments of the invention. According to a set of
embodiments, some or all of the procedures of such methods are performed by
the
computer system 500 in response to processor 510 executing one or more
sequences
of one or more instructions (which might be incorporated into the operating
system
540 and/or other code, such as an application program 545) contained in the
working
memory 535. Such instructions may be read into the working memory 535 from
another computer readable medium, such as one or more of the storage device(s)
525.
Merely by way of example, execution of the sequences of instructions contained
in
the working memory 535 might cause the processor(s) 510 to perform one or more
procedures of the methods described herein.
[0110] The terms "machine readable medium" and "computer readable
medium," as used herein, refer to any medium that participates in providing
data that
causes a machine to operation in a specific fashion. In an embodiment
implemented
using the computer system 500, various computer readable media might be
involved
in providing instructions/code to processor(s) 510 for execution and/or might
be used
to store and/or carry such instructions/code (e.g., as signals). In many
implementations, a computer readable medium is a non-transitory, physical
and/or
tangible storage medium. Such a medium may take many forms, including but not
limited to, non-volatile media, volatile media, and transmission media. Non-
volatile
media includes, for example, optical and/or magnetic disks, such as the
storage
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
device(s) 525. Volatile media includes, without limitation, dynamic memory,
such as
the working memory 535. Transmission media includes, without limitation,
coaxial
cables, copper wire and fiber optics, including the wires that comprise the
bus 505, as
well as the various components of the communication subsystem 530 (and/or the
media by which the communications subsystem 530 provides communication with
other devices). Hence, transmission media can also take the form of waves
(including
without limitation radio, acoustic and/or light waves, such as those generated
during
radio-wave and infra-red data communications).
[0111] Common forms of physical and/or tangible computer readable media
include, for example, a floppy disk, a flexible disk, a hard disk, magnetic
tape, or any
other magnetic medium, a CD-ROM, any other optical medium, a RAM, ROM, a
PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a
carrier wave as described hereinafter, or any other medium from which a
computer
can read instructions and/or code.
[0112] Various forms of computer readable media may be involved in
carrying
one or more sequences of one or more instructions to the processor(s) 510 for
execution. Merely by way of example, the instructions may initially be carried
on a
magnetic disk and/or optical disc of a remote computer. A remote computer
might
load the instructions into its dynamic memory and send the instructions as
signals
over a transmission medium to be received and/or executed by the computer
system
500. These signals, which might be in the form of electromagnetic signals,
acoustic
signals, optical signals and/or the like, are all examples of carrier waves on
which
instructions can be encoded, in accordance with various embodiments of the
invention.
[0113] The communications subsystem 530 (and/or components thereof)
generally will receive the signals, and the bus 505 then might carry the
signals (and/or
the data, instructions, etc. carried by the signals) to the working memory
535, from
which the processor(s) 505 retrieves and executes the instructions. The
instructions
received by the working memory 535 may optionally be stored on a storage
device
525 either before or after execution by the processor(s) 510.
[0114] Conclusion
31
CA 02929819 2016-05-05
WO 2015/069940
PCT/US2014/064413
[0115] This document discloses novel tools and techniques for estimating
compensatory reserve and similar physiological states. While certain features
and
aspects have been described with respect to exemplary embodiments, one skilled
in
the art will recognize that numerous modifications are possible. For example,
the
methods and processes described herein may be implemented using hardware
components, software components, and/or any combination thereof. Further,
while
various methods and processes described herein may be described with respect
to
particular structural and/or functional components for ease of description,
methods
provided by various embodiments are not limited to any particular structural
and/or
functional architecture but instead can be implemented on any suitable
hardware,
firmware and/or software configuration. Similarly, while certain functionality
is
ascribed to certain system components, unless the context dictates otherwise,
this
functionality can be distributed among various other system components in
accordance with the several embodiments.
[0116] Moreover, while the procedures of the methods and processes
described
herein are described in a particular order for ease of description, unless the
context
dictates otherwise, various procedures may be reordered, added, and/or omitted
in
accordance with various embodiments. Moreover, the procedures described with
respect to one method or process may be incorporated within other described
methods
or processes; likewise, system components described according to a particular
structural architecture and/or with respect to one system may be organized in
alternative structural architectures and/or incorporated within other
described systems.
Hence, while various embodiments are described with¨or without¨certain
features
for ease of description and to illustrate exemplary aspects of those
embodiments, the
various components and/or features described herein with respect to a
particular
embodiment can be substituted, added and/or subtracted from among other
described
embodiments, unless the context dictates otherwise. Consequently, although
several
exemplary embodiments are described above, it will be appreciated that the
invention
is intended to cover all modifications and equivalents within the scope of the
following claims.
32
