Note: Descriptions are shown in the official language in which they were submitted.
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INDIVIDUALIZED DIALYSIS WITH INLINE SENSOR
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This
patent application claims the benefit of U.S. Patent Application No.
16/811,533, filed on March 6, 2020. This patent application also claims the
benefit of U.S.
Provisional Patent Application No. 62/967,349, filed January 29, 2020. Both of
which are
incorporated by reference herein in their entirety.
BACKGROUND
[0002] Patients
with kidney failure or partial kidney failure typically undergo hemodialysis
treatment at hemodialysis treatment centers, clinics, or in the home. When
healthy, kidneys
maintain the body's internal equilibrium of water and minerals (e.g., sodium,
potassium,
chloride, calcium, phosphorous, magnesium, and sulfate). In hemodialysis,
blood is taken from
a patient through an intake needle (or catheter) which draws blood from a vein
located in a
specific access location (arm, thigh, subclavian, etc.). The blood is then
pumped through
extracorporeal tubing via a peristaltic or other pump, and then through a
special filter called a
dialyzer. The blood passes through the dialyzer in contact with an internal
semipermeable
membrane, typically in a countercurrent direction to the flow of a dialysate
solution on the
opposite side of the membrane. The dialyzer is intended to remove unwanted
toxins such as
urea, creatinine and exchange essential electrolytes like potassium and/or
sodium. Further, the
dialyzer is intended to remove excess water from the blood by diffusion and/or
convective
transport, depending on the specific type of dialysis ordered. The dialyzed
blood then flows
out of the dialyzer via additional tubing and through a needle (or catheter)
back into the patient.
[0003] During
dialysis, an excess of electrolytes in the patient's blood may be lost. Also
in some cases, dialysis may result in insufficient removal of electrolytes.
For example, blood
contains sodium ions (Nat), potassium ions (K ), and calcium ions (Ca2 ). Too
much sodium
in the blood can contribute to the patient feeling an increase in thirst or
can lead to hypertension.
Losing too much sodium can lead to decline in blood volume, chest pain,
nausea, vomiting,
headache, and muscle cramps. Too much potassium in the blood can lead to
muscle pain,
weakness, and numbness. Losing too much potassium can lead to heart rhythm
disturbances.
Having too much calcium in the blood can lead to vascular calcification.
Losing too much
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calcium can lead to bone disorders and/or uncontrollable secondary parathyroid
hormone
(PTH) secretion.
[0004]
Electrolyte composition in the blood is a highly dynamic function, dependent
on
many physiological and nutritional inputs, and subject to significant
variability between
patients. In most dialysis settings, one or only a small number of dialysate
compositions (i.e.,
"recipes") is available to treat patients, regardless of individual variations
in electrolyte profiles
that exist between patients or even between the same patient on different
days. This "one-size-
fits-all" approach to treatment may be reasonable for the majority of
patients, but some patients
do not tolerate it well. Accordingly, a method and system for preparing a
patient-specific
dialysate would be advantageous, and one that can adapt to real-time changes
in patient needs
between and even during dialysis treatments.
SUMMARY
[0005] In an
exemplary embodiment, the present application provides an individualized
and on-demand dialysis system for determining individualized dialysis
prescriptions. The
system comprises a prescription recommendation server and an on-demand
dialysis machine.
The prescription recommendation server is configured to: receive, from a
prescriber computing
device, patient information associated with a new patient; determine, based on
the patient
information, an individualized dialysis prescription for the new patient,
wherein the
individualized dialysis prescription indicates a particular patient cluster
associated with the
new patient; and transmit, to an on-demand dialysis machine, the
individualized dialysis
prescription for the new patient. The on-demand dialysis machine is configured
to: receive,
from the prescription recommendation server, the individualized dialysis
prescription for the
new patient; and perform a dialysis treatment on the new patient based on the
individualized
dialysis prescription.
[0006] In some
instances, the prescription recommendation server is configured to
determine the individualized dialysis prescription for the new patient based
on using one or
more dialysis prescription machine learning and/or artificial intelligence (AI-
ML) models.
[0007] In some
examples, the prescription recommendation server is configured to
determine the individualized dialysis prescription based on using the one or
more dialysis
prescription AI-ML models by: inputting the patient information into the one
or more dialysis
prescription AI-ML models to determine the particular patient cluster, wherein
the particular
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patient cluster is associated with a medical condition of the new patient; and
determining the
individualized dialysis prescription based on the particular patient cluster.
[0008] In some
variations, the prescription recommendation server is further configured to:
train the one or more dialysis prescription AI-ML models based on received
training
information to determine associations within the received training
information.
[0009] In some
instances, the prescription recommendation server is further configured to:
receive the training information, wherein the training information comprises
past prescriptions
provided to a plurality of patients, outcomes associated with performing
dialysis treatment
using the past prescriptions, and a plurality of recommended dialysis
prescriptions.
[0010] In some
examples, the one or more dialysis prescription AI-ML models comprises
a supervised AI-ML model. The supervised AI-ML model is a support vector
machine (SVM)
model or a K Nearest Neighbor (kNN) model.
[0011] In some
variations, the prescriber computing device and the on-demand dialysis
machine are both physically located at a prescriber' s office.
[0012] In some
instances, the prescriber computing device is physically located at a
prescriber's office associated with a first geographical location, and the on-
demand dialysis
machine is physically located at a residence of the new patient. The residence
is associated
with a second geographical location that is different from the first
geographical location.
[0013] In some
examples, the prescription recommendation server is further configured to:
transmit, to the prescriber computing device, the individualized dialysis
prescription for the
new patient; receive, from the prescriber computing device, prescriber
information indicating
one or more adjustments to the individualized dialysis prescription. The
prescription
recommendation server is configured to transmit the individualized dialysis
prescription for the
new patient by transmitting the individualized dialysis prescription with the
one or more
adjustments indicated by the prescriber information.
[0014] In
another exemplary embodiment, the present application provides a method for
determining individualized dialysis prescriptions. The method comprises:
receiving, by a
prescription recommendation server and from a prescriber computing device,
patient
information associated with a new patient; determining, based on the patient
information, an
individualized dialysis prescription for the new patient, wherein the
individualized dialysis
prescription indicates a particular patient cluster associated with the new
patient; and
transmitting, to an on-demand dialysis machine, the individualized dialysis
prescription for the
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new patient. The on-demand dialysis machine performs a dialysis treatment on
the new patient
based on the individualized dialysis prescription.
[0015] In some
instances, determining the individualized dialysis prescription for the new
patient is based on using one or more dialysis prescription machine learning
and/or artificial
intelligence (AI-ML) models.
[0016] In some
examples, the method further comprises: inputting the patient information
into the one or more dialysis prescription AI-ML models to determine the
particular patient
cluster, wherein the particular patient cluster is associated with a medical
condition of the new
patient; and determining the individualized dialysis prescription based on the
particular patient
cluster.
[0017] In some
variations, the method further comprises: training the one or more dialysis
prescription AI-ML models based on received training information to determine
associations
within the received training information.
[0018] In some
instances, the method further comprises: receiving the training information,
wherein the training information comprises past prescriptions provided to a
plurality of
patients, outcomes associated with performing dialysis treatment using the
past prescriptions,
and a plurality of recommended dialysis prescriptions.
[0019] In some
examples, the one or more dialysis prescription AI-ML models comprises
a supervised AI-ML model. The supervised AI-ML model is a support vector
machine (SVM)
model or a K Nearest Neighbor (kNN) model.
[0020] In some
variations, the prescriber computing device and the on-demand dialysis
machine are both physically located at a prescriber' s office.
[0021] In some
instances, the prescriber computing device is physically located at a
prescriber's office associated with a first geographical location and the on-
demand dialysis
machine is physically located at a residence of the new patient. The residence
is associated
with a second geographical location that is different from the first
geographical location.
[0022] In some
examples, the method further comprises: transmitting, to the prescriber
computing device, the individualized dialysis prescription for the new
patient; and receiving,
from the prescriber computing device, prescriber information indicating one or
more
adjustments to the individualized dialysis prescription. The method further
comprises
transmitting the individualized dialysis prescription with the one or more
adjustments indicated
by the prescriber information.
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[0023] In yet another exemplary embodiment, a non-transitory computer-
readable medium
having processor-executable instructions stored thereon is provided. The
processor-executable
instructions, when executed, facilitate: receiving, by a prescription
recommendation server and
from a prescriber computing device, patient information associated with a new
patient;
determining, based on the patient information, an individualized dialysis
prescription for the
new patient, wherein the individualized dialysis prescription indicates a
particular patient
cluster associated with the new patient; and transmitting, to an on-demand
dialysis machine,
the individualized dialysis prescription for the new patient. The on-demand
dialysis machine
performs a dialysis treatment on the new patient based on the individualized
dialysis
prescription.
[0024] In some instances, determining the individualized dialysis
prescription for the new
patient is based on using one or more dialysis prescription machine learning
and/or artificial
intelligence (AI-ML) models.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] FIG. 1 illustrates a front perspective view of a hemodialysis system
that includes an
electrolyte composition monitor according to some embodiments of the
disclosure;
[0026] FIG. 2 is a block diagram illustrating use of an electrolyte
composition monitor with
a patient, according to embodiments of the disclosure;
[0027] FIG. 3 is a flow diagram for managing electrolytes in blood of a
dialysis patient
during dialysis, according to an embodiment of the disclosure;
[0028] FIG. 4 is an example timeline for managing electrolytes in blood of
a dialysis patient
during dialysis, according to an embodiment of the disclosure;
[0029] FIG. 5 is a flow diagram for determining individualized dialysate
recipes or
prescriptions for a patient;
[0030] FIG. 6 is a block diagram of an example computer system;
[0031] FIGs. 7a and 7b are graphical representations of real-time
electrolyte concentration
measurements using an NMR sensor;
[0032] FIG. 8 is a simplified block diagram depicting an exemplary
individualized and on-
demand dialysis system with network capabilities in accordance with an
exemplary
embodiment of the present application; and
[0033] FIG. 9 illustrates an exemplary process for using machine learning
to provide
individualized dialysis prescriptions and treatments to patients.
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DETAILED DESCRIPTION
[0034] During
dialysis, an electrolyte composition monitor according to embodiments of
the disclosure can employ a dialysate mixing system to make an amount of
dialysate on demand
using, among other things, a plurality of chemical concentrates. The dialysate
generated will
have a formula, recipe, or prescription that differs from dialysate previously
used during
dialysis. The dialysate' s formula will thus be adjusted during dialysis based
on the electrolyte
composition monitor detecting an elevated or depressed level of one or more
electrolytes in a
patient's blood during dialysis.
[0035] In an
embodiment, the dialysate used during dialysis is made in batches. Each batch
follows a prescription, formula, or recipe chosen by the electrolyte
composition monitor based
on receiving electrolyte concentration levels from the patient's blood. The
electrolyte
composition monitor may continually adjust a next dialysate batch's recipe and
task its
dialysate mixing system to follow the prescribed recipe. For example, the
dialysate mixing
system may receive a recipe indicating particular chemical constituents and
amounts of each
chemical constituent to be included in the dialysate. Based on the
prescription, the dialysate
mixing system can determine, for example, a number of tablets, mass of powder,
or volume of
concentrated electrolyte solution required for each chemical constituent.
Tablets, powders
and/or concentrated electrolyte solutions, can be automatically dispensed and
mixed with
purified water, bicarbonate, and/or sodium chloride in a mixing chamber to
produce the
dialysate according to the desired dialysate recipe.
[0036]
Embodiments of the disclosure allow for chemical constituents to be delivered
and
stored in a tablet form or in a concentrated form, thus requiring minimal
storage space and
oversight. Mixing the dialysate in batches throughout dialysis suggests less
storage space is
required since the volume of dialysate made can be fully exhausted during a
treatment session.
[0037]
Embodiments of the disclosure allow for the dialysate composition used during
dialysis to be personalized, whereby a patient's individual responses to
dialysis are taken into
account by monitoring his electrolyte responses to the dialysis treatment
throughout the
treatment session. In this way, a one-size-fits-all rule or a coarse heuristic
is not applied during
the treatment. The electrolyte composition monitor, through its continuous
adjustments of
dialysate composition, can effectively personalize treatment to the individual
patient, ensuring
that the patient does not leave the dialysis treatment with deficient levels
or elevated levels of
certain monitored electrolytes, and improving long-term outcomes and patient
satisfaction.
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[0038]
Embodiments of the disclosure allow for an electrolyte composition monitor
that
can, over time, learn a dialysis recipe or formula most appropriate for the
patient. By
continually adjusting the dialysate in batches, the electrolyte composition
monitor can
determine which electrolytes the patient is typically sensitive to; thus, in
further treatments, the
electrolyte composition monitor can suggest a starting dialysate recipe that
is more appropriate
for the patient. In this way, embodiments of the electrolyte composition
monitor will allow for
a learning model tailored to adapt to evolving patient needs.
[0039]
Embodiments of the disclosure provide individualized dialysis treatment based
on
online monitoring of electrolytes in the patient's blood by generating
individualized dialysate
as electrolyte conditions in the patient's blood change during treatment. This
improvement
solves a problem in current treatment practice where dialysate formulas and
recipes for
individual patients are based on monthly lab blood test results. Dialysis
patients are rarely in
steady state, so a lab blood test may be outdated by the time the patient
enters the clinic for
dialysis. Thus, reliance on monthly lab testing may prove harmful or of
limited benefit to
individual patients.
[0040] FIG. 1
shows a dialysis system, in particular, a hemodialysis system 100. Although
the system described herein is largely described in connection with
hemodialysis systems by
way of example, it is explicitly noted that the system described herein may be
used in
connection with other types of medical devices and treatments, including
peritoneal dialysis
systems. The hemodialysis system 100 includes a hemodialysis machine 102
connected to a
disposable blood component set 104 that partially forms a blood circuit.
During hemodialysis
treatment, an operator connects an arterial patient line 106 and a venous
patient line 108 of the
blood component set 104 to a patient. The blood component set 104 includes an
air release
device 112. As a result, if blood passing through the blood circuit during
treatment contains
air, the air release device 112 will vent the air to atmosphere.
[0041] The
blood component set 104 is secured to a module 130 attached to the front of
the hemodialysis machine 102. The module 130 includes a blood pump 132 capable
of
circulating blood through the blood circuit. The module 130 also includes
various other
instruments and sensors, e.g., electrolyte sensors, capable of monitoring the
blood flowing
through the blood circuit. The module 130 includes a door that when closed, as
shown in FIG.
1, cooperates with the front face of the module 130 to form a compartment that
is sized and
shaped to receive the blood component set 104.
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[0042] The
blood pump 132 is part of a blood pump module 134. The blood pump module
134 includes a display window, a start/stop key, an up key, a down key, a
level adjust key, and
an arterial pressure port. The display window displays the blood flow rate
setting during blood
pump operation. The start/stop key starts and stops the blood pump 132. The up
and down keys
increase and decrease the speed of the blood pump 132. The level adjust key
raises a level of
fluid in an arterial drip chamber.
[0043] The
hemodialysis machine 102 further includes a dialysate circuit formed by the
dialyzer 110, various other dialysate components, and dialysate lines
connected to the
hemodialysis machine 102. Many of these dialysate components and dialysate
lines are inside
the housing 103 of the hemodialysis machine 102 and are thus not visible in
FIG. 1. During
treatment, while the blood pump 132 circulates blood through the blood
circuit, dialysate
pumps (not shown) circulate dialysate through the dialysate circuit.
[0044] The
dialysate is created by the hemodialysis machine 102 in batches. That is, the
hemodialysis machine 102 is configured to mix various chemical constituents of
the dialysate
together to form a dialysate batch having requisite characteristics based on
measurements of
electrolyte concentration in the patient's blood. In this way, dialysate used
during the dialysis
treatment can be optimized for the specific patient for different phases of
the treatment based
on how the patient is responding to the treatment.
[0045] The
hemodialysis machine 102 includes an electrolyte composition monitor (200 of
FIG. 2), which is made up of a controller 101 and a dialysate mixing system
105 for mixing
dialysate. During dialysis, the controller 101 is configured to receive
electrolyte measurements
from the patient's blood, and the controller 101 is configured to provide
signals for adjusting
the dialysate recipe for dialysate batches throughout the dialysis treatment.
The dialysate
mixing system 105 is internal to the housing 103 of the hemodialysis machine
102. In an
embodiment, water, sodium chloride (NaCl), bicarbonate (NaHCO3), and a
plurality of
chemical concentrates are mixed together to form the dialysate. The dialysate
mixing system
105 provides already mixed dialysate to the dialyzer 110 via at least a
dialysate supply line,
which is also internal to the housing 103 of the hemodialysis machine 102. A
drain line 128
and an ultrafiltration line 129 extend from the hemodialysis machine 102. The
drain line 128
and the ultrafiltration line 129 are fluidly connected to the various
dialysate components and
dialysate lines inside the housing 103 of the hemodialysis machine 102 that
form part of the
dialysate circuit. During hemodialysis, the dialysate supply line carries
fresh dialysate through
various dialysate components, including the dialyzer 110. As the dialysate
passes through the
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dialyzer 110, it collects toxins from the patient's blood. The resulting spent
dialysate is carried
from the dialysate circuit to a drain via the drain line 128. When
ultrafiltration is performed
during treatment, a combination of spent dialysate and excess fluid drawn from
the patient is
carried to the drain via the ultrafiltration line 129.
[0046] In an
embodiment, the controller 101 determines the chemical composition of each
batch of dialysate. For example, a batch of dialysate may be 12 liters (L) and
the chemical
composition may include a plurality of chemical concentrates. The chemical
concentrates may
be liquid concentrates of varying viscosity and/or may be solid concentrates
in the form of
tablets, pills, or powders. The controller 101 may compute the chemical
composition (e.g., an
amount of each of the plurality of chemical concentrates such as a number of
tablets) for each
12 L batch of dialysate based on a prescription issued by a physician /
doctor.
[0047] In an
embodiment, the controller 101 may use a reduced volume of the dialysate for
the dialysis treatment of the patient. For example, the controller 101 may
reduce the dialysate
to blood flow ratio for the dialysis treatment. By reducing the dialysate to
blood flow ratio, the
dialysis treatment may consume less dialysate (e.g., 40 L of the dialysate per
dialysis treatment
may be used instead of 120L).
[0048] A drug
pump 192 also extends from the front of the hemodialysis machine 102. The
drug pump 192 is a syringe pump that includes a clamping mechanism configured
to retain a
syringe 178 of the blood component set 104. The drug pump 192 includes a
stepper motor
configured to move the plunger of the syringe 178 along the axis of the
syringe 178. The drug
pump 192 can thus be used to inject a liquid drug (e.g., heparin) from the
syringe 178 into the
blood circuit via a drug delivery line 174 during use, or to draw liquid from
the blood circuit
into the syringe 178 via the drug delivery line 174 during use.
[0049] The
hemodialysis machine 102 includes a user interface with input devices such as
a touch screen 118 and a control panel 120. The touch screen 118 and the
control panel 120
allow an operator to input various different treatment parameters to the
hemodialysis machine
102 and to otherwise control the hemodialysis machine 102. The touch screen
118 allows an
operator to select between user profiles, and the control panel 120 can allow
the operator to
select between user profiles by scanning the patient's membership card. The
touch screen 118
displays information to the operator of the hemodialysis system 100. The
controller 101 is also
configured to receive and transmit signals to the touch screen 118 and the
control panel 120.
The controller 101 can control operating parameters of the hemodialysis
machine 102, e.g.,
providing signals at appropriate times for adjusting composition of dialysate
throughout a
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dialysis treatment. The dialysate mixing system can be, e.g., the dialysate
mixing system in
Kalaskar et al., US 2018/0326138, which is hereby incorporated herein in its
entirety.
[0050] FIG. 2
is a block diagram illustrating use of an electrolyte composition monitor 200
with a patient 210 during dialysis, according to embodiments of the
disclosure. Components
of the hemodialysis system 100 of FIG. 1 are used as an example, but as
previously stated, the
electrolyte composition monitor 200 can be used in peritoneal dialysis. The
electrolyte
composition monitor 200 is configured to receive electrolyte measurements from
one or more
electrolyte sensors 212. The electrolyte composition monitor 200 is also
configured to use the
electrolyte measurements to adjust dialysate recipe, mix a new batch of
dialysate, and provide
fresh dialysate to the dialyzer 110.
[0051] The
electrolyte composition monitor 200 includes the controller 101 and the
dialysate mixing system 105. The controller 101 is configured to interface
with the electrolyte
sensors 212 to receive the electrolyte measurements. Examples of the
controller 101 include a
field programmable gate array (FPGA), an application specific integrated
circuit (ASIC), and
a processor with non-transitory computer-readable medium.
[0052] The
dialysate mixing system 105 includes a dispenser 202 and a mixing chamber
204. The dispenser 202 can include chemical concentrates in chemical sources
206. The
chemical concentrates are used as ingredients of the dialysate mixture. The
chemical
concentrates can be liquid concentrates of varying viscosity or can be solid
concentrates in the
form of tablets, pills, or powders. The chemical sources 206 are containers
that hold these
chemical concentrates. Chemical sources 206 can thus hold concentrates of
potassium chloride
(KC1), calcium chloride (CaCl2), magnesium chloride (MgCl2), citric acid,
dextrose, sodium
chloride (NaCl), sodium bicarbonate (NaHCO3), acetic acid, glucose, and so on.
Not all
chemical concentrates available need to be used in a dialysate formula or
recipe. For example,
one recipe may only call for acetic acid, NaCl, CaCl2, KC1, MgCl2, and
glucose. Another recipe
may call for bicarbonate, NaCl, CaCl2, KC1, and MgCl2, without glucose.
Regardless, the
dialysis composition should meet the intended prescription for a given patient
and meet
biocompatibility standards (e.g., pH of the dialysate).
[0053] The
dispenser 202 can further include actuators 208 that aid in dispensing
specific
amounts of the chemical concentrates to a mixing chamber 204 for mixing a
batch of dialysate.
The actuators 208 not only control the amount of chemical concentrates
provided to the mixing
chamber 204, but also control an amount of water used in mixing the batch of
dialysate. A
water source 205 can be a water connection for receiving filtered water or
water suitable for
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use in dialysis treatment. The water source 205 can connect to the
hemodialysis system 100
via an inlet tube.
[0054] The
dispenser 202 provides the chemical concentrates and water to the mixing
chamber 204. Contents in the mixing chamber 204 are agitated for an
appropriate amount of
time until chemical concentrates are sufficiently distributed throughout. In
some embodiments,
the mixing chamber 204 increases its temperature to assist in dissolving
and/or distributing the
chemical concentrates to yield a homogenous solution. After realizing a
homogenous solution,
the mixing chamber 204 can be brought to an appropriate temperature for
dialysis treatment.
[0055] The
mixing chamber 204 of the dialysate mixing system 105 provides the mixed
dialysate to the dialyzer 110. In an embodiment, the mixing chamber 204 is
multi-chambered
where a first chamber is used for mixing dialysate and a second chamber is
used for storing
and delivering dialysate to the dialyzer 110. In an embodiment, the mixing
chamber 204
includes sensors for sensing levels of dialysate in both the first and second
chambers. The
mixing chamber 204 may also alert the controller 101 when a batch of dialysate
has been mixed
and when fresh dialyzer from the batch of dialysate is provided to the
dialyzer 110.
[0056] The
dialyzer 110 receives blood from the patient 210 via the arterial patient line
106. Electrolyte sensors along the arterial patient line 106 may be provided
for measuring
electrolyte concentration in blood upstream of the dialyzer 110. These
electrolyte sensors are
identified as arterial electrolyte sensors 212-1 in FIG. 2. The dialyzer 110
returns blood to the
patient 210 via the venous patient line 108. Electrolyte sensors along the
venous patient line
108 may be provided for measuring electrolyte concentration in blood
downstream of the
dialyzer 110. These electrolyte sensors can be identified as venous
electrolyte sensors 212-2.
Furthermore, electrolyte sensors not interrupting the dialysis circuit can be
interfaced with the
patient 210. For example, a sensor can be placed along a peripherally inserted
central catheter
(PICC) line to measure electrolyte concentration. These sensors are identified
as non-dialysis
circuit electrolyte sensors 212-3.
[0057] The
electrolyte sensors 212 are configured to measure electrolyte concentration in
the blood of the patient 210. The electrolyte sensors 212 can be, for example,
conductivity
sensors, nuclear magnetic resonance (NMR) sensors and/or optical sensors. NMR
sensors may
detect, determine, and/or obtain real-time electrolyte concentrations (e.g.,
sodium
concentrations) in the blood of the patient 210. Additionally, and/or
alternatively, NMR
sensors may be modified (e.g., re-tuned to different radio frequencies) to
detect, determine,
and/or obtain real-time potassium and/or phosphorous concentrations.
Additionally, and/or
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alternatively, in some embodiments, an NMR sensor measures concentrations of
free sodium
in both the dialysate and the blood, each sampled separately, and the
concentration of free
sodium is reported to the controller 101. Furthermore, the sodium and other
electrolyte
concentrations may vary from patient to patient and even for a given patient
between
consecutive dialysis sessions. Accordingly, using an NMR sensor or other
sensor to measure
these concentrations in real-time to adjust the electrolyte concentrations and
even using
individualized recipes (described in FIGs. 3 and 5) may be beneficial to
provide the optimal
treatment for the patient during dialysis. Additionally, and/or alternatively,
the electrolyte
sensors 212 may include optical sensors configured to detect real-time
electrolyte
concentrations such as calcium concentrations and/or magnesium concentrations.
[0058] Examples
of an NMR sensor usable with exemplary embodiments of the present
application are described in further detail in U.S. Patent 10,371,775 (Titled:
Dialysis System
With Radio Frequency Device Within A Magnet Assembly For Medical Fluid Sensing
And
Concentration Determination), granted on Aug. 6, 2019, and U.S. Provisional
Patent
Application No. 62/967,349 (Titled: Individualized And On-Demand Dialysis
System With
Networking Capabilities), filed on Jan. 29, 2019, both of which are
incorporated by reference
herein in its entirety.
[0059]
Furthermore, FIG. 7 shows graphical representations of real-time measurements
obtained using an NMR sensor. For example, FIG. 7a shows real-time electrolyte
concentration (e.g., sodium concentration) measurements using the NMR sensor.
Line 702
indicates the sodium concentration and the shaded area 704 represents the
accuracy margin.
FIG. 7b also shows real-time electrolyte concentration measurements using the
NMR sensor.
For example, portion 704 of line 702 indicates the baseline sodium
concentration. Then,
portion 708 indicates a first adjustment of the baseline sodium concentration
(e.g., introducing
or injecting sodium boluses to increase the sodium concentration). Portion 710
shows another
injection of sodium boluses for increasing the sodium concentration again.
[0060] FIG. 3
is a flow diagram for managing electrolytes in blood of a dialysis patient
during dialysis, according to an embodiment of the disclosure. FIG. 3 is a
flow diagram
illustrating a process 300 that an electrolyte composition monitor, e.g.,
electrolyte composition
monitor 200, can perform in managing electrolytes in blood of patient 210. At
302, the
controller 101 of the electrolyte composition monitor 200 receives (e.g.,
obtains) electrolyte
measurements from electrolyte sensors 212. The obtained electrolyte
measurements may
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include sodium, potassium, phosphorous, magnesium, and/or calcium electrolyte
concentrations in the blood of the patient 210.
[0061] At 304,
the controller 101 determines from the electrolyte measurements whether
electrolyte concentrations in the blood are within predefined ranges. In an
example, electrolyte
concentration of sodium in the blood should be within 135-145 mEq/L range,
electrolyte
concentration of potassium should be within 3.5-5 mEq/L range, electrolyte
concentration of
calcium should be within 8.5-10.2 mg/dL (2-2.6 mmol/L) range, and so on. The
electrolyte
measurements received at the controller 101 are prepared in a manner to obtain
electrolyte
concentrations. For example, if a sodium NMR sensor provides radio frequency
(RF) energy
level at a resonant frequency of sodium as measurement signals, then the
controller 101
analyzes the RF energy level provided to determine the concentration of sodium
in the blood.
This concentration of sodium is compared to the upper and lower bounds of the
predefined
range for sodium to determine whether sodium concentration in the blood is
within the
predefined range.
[0062] In some
examples, the predefined ranges are clinically defined ranges such as
clinically known ranges. In other examples, the predefined ranges may be
individualized. For
example, as described below in 502 of FIG. 5, the dialysate recipe is a recipe
determined based
on historical trend analysis on electrolyte measurements from the patient's
previous dialysis
treatments. In other words, the dialysate recipe is individualized for the
patient 210 based on
the previous dialysis treatments performed on the patient 210. The dialysate
recipe associated
with the patient may include electrolyte ranges (e.g., an electrolyte
concentration range for
sodium, potassium, calcium, magnesium, and/or phosphorous). Further, as
described below in
FIG. 5, the controller 101 may load the dialysate recipe prior to beginning
the dialysis
treatment. At 302, the controller 101 determines the predefined ranges based
on the loaded
dialysate recipe and may compare these predefined ranges with the electrolyte
concentrations
from the electrolyte measurements.
[0063] At 306,
the controller 101 determines adjustment values, based on the plurality of
electrolyte measurements, for one or more electrolyte concentrations outside
the predefined
ranges. The controller 101 determines, for each electrolyte concentration
outside of the
predefined ranges, whether to increase or decrease concentration of the
electrolyte. Increasing
or decreasing the concentration provides directionality to the adjustment
values. The controller
101 then determines the magnitude of the adjustment value by determining a
target amount by
which the concentration of the electrolyte should be increased.
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[0064] In an
embodiment, the controller 101 determines adjustment values by unit
increments. That is, after determining whether to increase or decrease a
concentration of an
electrolyte that is not within a predefined range, the controller 101
determines that the
concentration of the electrolyte should be adjusted by a given unit. In an
embodiment where
chemical constituents of dialysate are adjusted by tablets, each unit
represents an electrolyte
concentration provided by a chemical concentrate's respective tablet. In an
embodiment where
chemical constituents of dialysate are adjusted by liquid concentrates, each
unit represents an
expected electrolyte concentration provided by opening its respective valve
for a predetermined
amount of time. Although one unit increments are described, adjustment values
can be
determined as multiple unit increments. For example, the controller 101 can
determine that the
concentration of the electrolyte that is not within its predefined range
should be increased by
three units which correspond to an amount of electrolytes expected from three
tablets.
[0065] In an
embodiment, the controller 101 determines adjustment values based on pre-
programmed dialysate recipes, formulas or prescriptions. The controller 101
can store one or
more recipes for various electrolyte conditions in its memory. For example,
the memory may
include a recipe for low sodium, high sodium, low potassium, high potassium,
and so on. Each
of these dialysate recipes can be tagged as being effective in reducing or
raising one or more
electrolyte concentrations. That way, based on a combination of electrolytes
determined to be
outside their respective predefined ranges, the controller 101 can select a
recipe from one of
these predefined recipes for the next batch of dialysate.
[0066] In some
examples and referring to FIG. 5 and process 500 below, the controller 101
determines the magnitude and/or directionality of the adjustment values (e.g.,
unit increments)
based on the loaded dialysate recipe from 502. For example, the controller 101
may determine
and load the dialysate recipe based on historical trend analysis on
electrolyte measurements
from the patient's previous dialysis treatments (e.g., based on the most
effective recipe from
the historical trend analysis, the dialysate recipe with the greatest number
of batches in the
patient profile, and/or the most recent dialysate recipe used during the
dialysis treatment). For
instance, if the high sodium recipe has the greatest number of made batches in
the patient
profile (e.g., the dialysate solution was created/adjusted the greatest number
of times using the
recipe), the controller 101 may determine the high sodium recipe as the most
effective dialysate
recipe and load that recipe at 502. Then, at 306, the controller 101 may
determine the
magnitude of the adjustment values using this recipe.
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[0067] In an
embodiment, the controller 101 determines that one or more electrolyte
concentrations outside the predefined ranges deviates significantly from the
predefined ranges.
For example, at 304, a potassium concentration of 6.0 mEq/L is determined, and
the predefined
range for potassium is between 3.5 and 5.0 mEq/L. The potassium concentration
is then
determined by the controller 101 to be too high. The controller 101 can
determine that the next
dialysate batch should decrease the potassium concentration. Thus, the
controller 101 can
determine an adjustment value for potassium that reduces the potassium ion
concentration in
the next batch of dialysate as prescribed. Although potassium is used as an
example, the
controller 101 can determine that concentration of more than one electrolyte
in the blood is too
high and determine adjustment values to make a next batch of dialysate. In
other words, the
controller 101 may determine that an electrolyte concentration (e.g.,
potassium) is outside of
the predefined ranges and determine one or more adjustment values for the next
dialysate batch.
The one or more adjustment values may be a single adjustment value for the
electrolyte
concentration (e.g., potassium) or may include multiple adjustments values for
multiple
different electrolyte concentrations (e.g., potassium, calcium, and so on).
[0068]
Additionally, and/or alternatively, the controller 101 may determine multiple
electrolyte concentrations (e.g., potassium and calcium) are outside of the
predefined ranges
and may determine one or more adjustment values for the next dialysate batch.
The one or
more adjustment values may be a single adjustment value for the electrolyte
concentration (e.g.,
potassium) or may include multiple adjustments values for multiple different
electrolyte
concentrations (e.g., calcium, potassium, and so on).
[0069] In an
embodiment, the controller 101 determines that a majority or all of the
electrolyte concentrations are outside the predefined ranges and adjustment
values of all the
electrolyte concentrations have a same direction. The controller 101 can
determine adjustment
values based on the amounts of chemicals supplied by the chemical sources 206
and the amount
of water to include in the dialysate. In some instances, each batch of the
dialysate may be 12
L. In other instances, the batches may be greater than 12 L such as 24 L. The
controller 101
can determine adjustment values based on the amount of chemicals supplied by
the chemical
sources 206, the amount of water to include in the dialysate, and the volume
of the batch of the
dialysate. For instance, if the prescription indicates that 2 potassium
tablets are used for a 12
L batch, the controller 101 may determine to use 4 potassium tablets for a 24
L batch.
[0070] At 308,
the controller 101 provides instructions to the dispenser 202 to adjust the
composition of the dialysate during dialysis based on the determined
adjustment values of 306.
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The composition of the dialysate includes chemicals from the chemical sources
206. In an
embodiment, the controller 101 generates adjustment signals for changing the
composition of
the dialysate during dialysis based on the determined adjustment values. The
controller 101
then provides actuating signals to actuators 208 for changing how much of each
chemical
concentrate to release into the mixing chamber 204. By effecting a change in
an amount of
any of the chemical concentrates released into the mixing chamber 204, the
controller 101
causes the dispenser 202 to change proportions of the chemicals in the
dialysate.
[0071] In an
embodiment, when a respective adjustment value for an electrolyte
concentration outside the respective predefined range indicates an increase,
the controller 101
generates a respective adjustment signal for dispensing a higher proportion of
a respective
chemical, thus increasing a chemical contribution of a respective chemical
source in the
chemical sources 206. When the respective adjustment value for the electrolyte
concentration
outside the respective predefined range is a decrease, the controller 101
generates a respective
adjustment signal for dispensing a lower proportion of the respective chemical
of the respective
chemical source in the chemical sources 206.
[0072] In an
embodiment, the adjustment signals the controller 101 provides to the
dispenser 202 are encoded as a number of electrical pulses. Electrical pulses
can be voltage or
current pulses. For example, a number of pulses provided by the controller 101
to a respective
actuator in the actuators 208 can encode an amount of a respective chemical in
the chemical
sources 206 to release into the mixing chamber 204. In a previous dialysate
batch, if 5 pulses
were provided to an actuator that controls a release of CaCl2 tablets into the
mixing chamber
204, then for a next dialysate batch, if 4 pulses are provided to the actuator
then a lower number
of CaCl2 tablets will be released into the mixing chamber 204. Thus, the
adjustment signals
generated by the controller 101 can be encoded as a change in a number of
electrical pulses
provided to one or more actuators. The change in number of electrical pulses
can be an increase
in the number of electrical pulses or a decrease in the number of electrical
pulses. Furthermore,
all but one actuator in the actuators 208 can receive a reduced number of
electrical pulses.
Conversely, all but one actuator in the actuators 208 can receive an increased
number of
electrical pulses.
[0073] After
completing 308, the controller 101 cycles back to 302 and receives new
electrolyte measurements (e.g., second electrolyte measurements from the
sensors 212). The
process 300 is performed by the electrolyte composition monitor 200 until the
dialysis
treatment of patient 210 ends.
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[0074] For
example, in subsequent iterations, at 304, the controller 101 determines from
the electrolyte measurements whether the first adjustment values caused the
new electrolyte
measurements to be within the predefined ranges. If no electrolyte
concentrations in the blood
are outside the predefined ranges, then the controller 101 determines at 310
that no adjustment
is necessary. The controller 101 keeps the most recent recipe for the new
dialysate batch and
the process 300 returns to 302. If there are electrolyte concentrations that
are still outside of
the predefined ranges, the controller 101 may determine new adjustment values
based on the
recipe and provide additional instructions to adjust the composition of the
dialysate during
dialysis. Furthermore, the controller 101 may determine the effectiveness of
the previous
recipe used and/or determine a new recipe to use for the adjustment values.
[0075] For
example, in some instances, the controller 101 may determine the effectiveness
of the recipe using process 300. As described above, the controller 101 may
determine the
directionality and/or magnitude of the adjustment values based on the loaded
recipe. For
example, the controller 101 may obtain a first and a second electrolyte
measurement from the
electrolyte sensors 212. The first electrolyte measurement may be obtained in
the first iteration
of process 300 and the second electrolyte measurement may be obtained in the
second iteration
of process 300 (e.g., the second electrolyte measurement may be subsequent to
adjusting the
composition of the dialysate during dialysis). The controller 101 may compare
the first
electrolyte measurement, the second electrolyte measurements, and/or the
predefined ranges to
determine the effectiveness of the recipe. For instance, if the electrolyte
concentration is within
the predefined ranges after the adjustment, the controller 101 may determine
the recipe used
for the adjustment values at 306 is effective. If the electrolyte
concentration is still not within
the predefined ranges, the controller 101 may determine the recipe is not
effective.
[0076]
Additionally, and/or alternatively, the controller 101 may determine the
effectiveness of the recipe based on how close the second electrolyte
measurement is to the
predefined range. For instance, if the second electrolyte measurement is
within the predefined
range, the controller 101 may determine the recipe is very effective. If the
second electrolyte
measurement is close to the predefined range, but is not within the predefined
range it, the
controller 101 may determine the recipe is effective. If the second
electrolyte measurement is
not close to the predefined range, the controller 101 may determine the recipe
is not effective.
If the second electrolyte measurement is even further away from the predefined
range
compared to the first electrolyte measurement, the controller 101 may
determine the recipe is
extremely ineffective.
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[0077] In some
variations, the controller 101 may dynamically rank recipes during the
dialysis treatment (e.g., during process 300). For example, after creating
each batch of the
dialysate solution using the recipe, the controller 101 may determine the
effectiveness of the
recipe. Then, the controller 101 may determine whether to load a new dialysate
recipe based
on the updated effectiveness of the recipe. If the controller 101 loads a new
dialysate recipe,
the controller 101 may use the new dialysate recipe to determine the
adjustment values. In
other words, during the dialysis treatment, the controller 101 may use
multiple different recipes
to determine the adjustment values based on the determined effectiveness of
the recipes during
the treatment of the patient.
[0078] In some
instances, the controller 101 may rank the recipes after the dialysis
treatment for the patient has concluded (e.g., after process 300 has
concluded). For example,
the controller 101 may determine the effectiveness of the one or more recipes
used during the
dialysis treatment based on comparing the electrolyte concentration after the
adjustment with
the predefined ranges. Then, the controller 101 may store the associated
effectiveness of the
recipes in memory and/or rank the recipes based on the effectiveness. The next
time the patient
undergoes dialysis treatment, the controller 101 may load the highest ranking
stored recipe for
the predefined ranges and/or the adjustment values.
[0079] In some
examples, process 300 may be used for peritoneal dialysis (PD solutions).
For peritoneal dialysis, process may further include a sterilization step. For
example, prior to
308, the controller 101 may provide instructions to the dispenser 202 to
sterilize the
composition of the dialysate including the chemicals from the chemical sources
206. Then, at
308, the controller 101 provides instructions to the dispenser 202 to
sterilize the chemicals from
the chemical sources.
[0080] FIG. 4
illustrates an example timeline 400 for managing electrolytes in blood of a
dialysis patient during dialysis. As described above with respect to FIG. 3,
process 300 is
cyclic or periodic, so with respect to the timeline 400, one period of
activities is highlighted
via timestamps tf, t1, t2, t3, t4, and ts. The timestamps are defined as
follows:
tf ¨ Time when the controller 101 receives a fresh dialysate signal indicating
that a new
batch of dialysate is mixed and ready for use
¨ Time when the controller 101 receives electrolyte measurements from the
electrolyte sensors 212
t2 ¨ Time when the controller 101 sends adjustment signals to the actuators
208 of the
dispenser 202
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t3 ¨ Time when the actuators 208 allow chemicals and water to migrate from the
chemical sources 206 and water source 205, respectively, to the mixing chamber
204
t4 ¨ Time when the mixing chamber 204 starts mixing the new batch of dialysate
ts ¨ Time when an old batch of dialys ate is depleted
[0081] FIG. 4
organizes activities in FIG. 3 according to the example timeline 400. In
Period 1, at the start of dialysis, a fresh batch of dialysate is mixed and
ready for use. At this
point, the mixing chamber 204 provides a fresh dialysate signal to the
controller 101 at
timestamp tf. After a time duration 402, the controller 101 receives, at
timestamp t1,
electrolyte measurements from the electrolyte sensors 212. During a time
duration 404, the
controller 101 determines adjustment signals to provide to the actuators 208,
and at timestamp
t2, sends the adjustment signals to the actuators 208. The actuators 208
respond to the
adjustment signals after a time duration 406, so at timestamp t3, the
actuators 208 allow
chemicals and water to migrate from their respective sources into the mixing
chamber 204.
After a time duration 408, the mixing chamber 204 then mixes its contents, at
timestamp t4, to
form a new batch of dialysate.
[0082] At
timestamp ts, the old batch of dialysate is completely depleted from the
mixing
chamber 204, so time duration 410 indicates a time between when the mixing
chamber 204
begins mixing contents for the new batch of dialysate and when the old batch
of dialysate is
depleted. In some embodiments, an error is not generated by the controller 101
when the new
batch of dialysate is ready before the old batch of dialysate is depleted.
This condition is
indicated in FIG. 4 by showing that a fresh dialysate signal is provided at
timestamp tf during
time duration 410.
[0083] In an
embodiment, the controller 101 can optimize the process 300 by trying to
reduce the time duration 412 between timestamps tf and ts. That way, the new
batch of
dialysate is ready at a same time that the old batch of dialysate is depleted
so that when the
fresh dialysate signal is received at the controller 101, the controller 101
can determine an
appropriate time duration 402 to wait before obtaining electrolyte
measurements from the
electrolyte sensors 212. That way, the controller 101 gives enough time to be
able to view the
effects of the new batch of dialysate on the electrolytes in the blood.
[0084] Put
another way, the controller 101 can monitor tpõp , a time duration between
when the controller 101 sends adjustment signals to the actuators 208 and when
the controller
101 receives the fresh dialysate signal from the mixing chamber 204. The
controller 101 can
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try to optimize tpõp such that its duration is substantially the same as the
sum of durations
406, 408, and 410.
[0085] In an
embodiment, the controller 101 determines that if an adjustment signal is sent
at a certain time, then there would be a violation of tpõp , that is,
timestamp ts would be reached
before the new batch of dialysate is mixed and ready. The controller 101 can
determine in this
case to delay the adjustment signal, mix a new batch of dialysate based on an
old recipe, and
then provide the buffered adjustment signal in a next period. This indicates
that after timestamp
tf, there is a maximum time tmax that the controller 101 can wait before
sending the adjustment
signals to the actuators 208 at timestamp t2. In an embodiment tina, can be
determined to be
the sum of durations 402, 404, 406, 408, and 410 minus tpõp. Since tina,
depends on
timestamp ts, in some embodiments, tmax is determined by the controller 101
based on flow
rate of dialysate exiting the mixing chamber 204 and a volume of dialysate in
the mixing
chamber 204.
[0086] In an
embodiment, the controller 101 can also monitor and try to regularize tc, a
time duration between when the controller 101 sends an adjustment signal and
when the
controller 101 obtains electrolyte measurements to ascertain effects of the
adjustment signals
on electrolyte concentration in the blood.
[0087] FIG. 5
is a flow diagram for determining individualized dialysate recipes or
prescriptions for a patient, according to an embodiment of the disclosure.
FIG. 5 is a flow
diagram illustrating a process 500 performed by a dialysis system, e.g., the
hemodialysis
system 100, to determine the patient's dialysate recipes. At 502, the
hemodialysis system 100
loads a dialysate recipe from a patient profile.
[0088] In an
embodiment, the hemodialysis system 100 may receive a chip card or a
computer memory storage like a flash drive that contains dialysate recipes for
the patient 210.
In an embodiment, the patient profile may be obtained from a database or
centralized storage.
By way of example, for a description of a system for securely distributing
information,
including medical prescriptions, within a connected health network, reference
is made to US
Pub. No. 2018/0316505A1 to Cohen et al., which is incorporated herein by
reference.
[0089] The
dialysate recipe for treatment is selected and loaded from the patient
profile.
In an embodiment, the dialysate recipe selected is a last recipe used from a
previous treatment
that the patient 210 went through. In another example, the dialysate recipe
selected is a default
recipe especially when the patient 210 has never undergone dialysis at the
specific location. In
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another example, the dialysate recipe selected is a recipe determined based on
trend analysis
of previous dialysate recipes from the patient profile. In another example,
the dialysate recipe
selected is a recipe determined based on historical trend analysis on
electrolyte measurements
from the patient's previous dialysis treatments.
[0090] At 504,
the hemodialysis system 100, via the dialysate mixing system 105, mixes a
first batch of dialysate based on the loaded recipe from 502.
[0091] At 506,
the hemodialysis system 100 via the electrolyte composition monitor 200,
monitors blood electrolytes and adjusts dialysate recipes based on electrolyte
measurements
according to various embodiments of the disclosure. For example, the
electrolyte composition
monitor 200 monitors blood electrolytes and adjusts dialysate recipes as
provided in the process
300. During treatment, the hemodialysis system 100 creates a folder or a
collection of dialysate
entries within the patient profile for the current dialysis treatment. Within
the folder, the
hemodialysis system 100 can store one or more of dialysate recipe used, number
of batches
mixed that correspond to the dialysate recipe, and electrolyte measurements
that led the
dialysate recipe.
[0092] At 508,
after the dialysis treatment is completed, the hemodialysis system 100 ranks
the dialysate recipes stored at 506. In an embodiment, the dialysate recipes
are ranked based
on a number of dialysate batches made per recipe. In other words, if the
hemodialysis system
100 determines the dialysate batches are effective (e.g., effective in
reducing the electrolyte
concentration(s) to the predefined range in 304), the hemodialysis system 100
may use the
recipe again, which would increase the number of dialysate batches made using
the recipe and
would cause the hemodialysis system 100 to rank the dialysate recipe higher.
In an
embodiment, the dialysate recipes are ranked based on a trend analysis that
compares similar
dialysate recipes, then combines the number of batches for the similar
dialysate recipes, and
then ranks groups of dialysate recipes based on the combined number of
batches.
[0093] In an
embodiment, the similar dialysate recipes with the highest combined number
of batches are analyzed to determine one representative recipe. The
representative recipe can
be determined via one or more statistical means, e.g., can be determined using
an average, a
median, a random selection, and so on.
[0094] At 510,
the hemodialysis system 100 stores the dialysate recipes with the highest
number of batches in the patient profile. In an embodiment, a representative
recipe determined
according to embodiments of the disclosure is stored along with the dialysate
recipes.
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[0095] FIG. 6
is a block diagram of an example computer system 600. For example, the
controller 101 is an example of the system 600 described here. The system 600
includes a
processor 610, a memory 620, a storage device 630, and an input/output device
640. Each of
the components 610, 620, 630, and 640 can be interconnected, for example,
using a system bus
650. The processor 610 processes instructions for execution within the system
600. The
processor 610 can be a single-threaded processor, a multi-threaded processor,
or a quantum
computer. The processor 610 can process instructions stored in the memory 620
or on the
storage device 630. The processor 610 may execute operations that facilitate
performing
functions attributed to the electrolyte composition monitor 200.
[0096] The
memory 620 stores information within the system 600. In some
implementations, the memory 620 is a computer-readable medium. The memory 620
can, for
example, be a volatile memory like synchronous random access memory (SRAM) or
a non-
volatile memory like flash.
[0097] The
storage device 630 is capable of providing mass storage for the system 600. In
some implementations, the storage device 630 is a non-transitory computer-
readable medium.
The storage device 630 can include, for example, a hard disk device, an
optical disk device, a
solid-date drive, a flash drive, magnetic tape, or some other large capacity
storage device. The
storage device 630 may alternatively be a cloud storage device, e.g., a
logical storage device
including multiple physical storage devices distributed on a network and
accessed using a
network. In some implementations, the information stored on the memory 620 can
also be
stored on the storage device 630.
[0098] The
input/output device 640 provides input/output operations for the system 600.
In some implementations, the input/output device 640 includes one or more of
network
interface devices (e.g., an Ethernet card), a serial communication device
(e.g., an RS-232 10
port), and/or a wireless interface device (e.g., a short-range wireless
communication device, an
802.11 card, a 3G wireless modem, or a 4G wireless modem). In some
implementations, the
input/output device 640 includes driver devices configured to receive input
data and send
output data to other input/output devices, e.g., a keyboard, a printer, and
display devices (such
as the touch screen 118). In some implementations, the input/output device 640
receives
dialysate prescription (e.g., wirelessly) for processing by the hemodialysis
system 100. In some
implementations, mobile computing devices, mobile communication devices, and
other
devices are used for sending dialysate prescriptions.
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[0099] As
described above, a dialysate mixing system according to embodiments of the
disclosure can generate and/or make an amount of dialysate on demand using,
among other
things, a plurality of chemical concentrates based on a formula, recipe, or
prescription. In some
examples, the formula, recipe, or prescription may be generated using one or
more artificial
intelligence and/or machine learning (AI-ML) algorithms, datasets, or models.
In some
instances, the artificial intelligence (Al) algorithms may include sub-
disciplines such as
machine learning (ML) algorithms and/or deep learning (DL) algorithms. The Al,
ML, and/or
DL algorithms as well as additional and/or alternative mathematical models /
modeling may be
used to generate the individualized dialysis prescriptions for the patient.
The generation of
dialysis prescriptions will be described in further detail below.
[0100] In
particular, a goal of hemodialysis is to achieve electrolyte homeostasis.
However,
utilizing fixed electrolyte concentrations to remove solutes, based on once-a-
month
measurements of serum chemistry, may be associated with and/or potentially
cause un-
physiologic rapid electrolyte shifts during dialysis, provoking cardiac
arrhythmias and sudden
cardiac death, which is the leading cause of death among dialysis patients.
The ability to
individualize and continuously tailor the dialysate composition in response to
real-time
changes in serum electrolyte levels during treatment is needed to address the
hazards arising
from the rapid intradialytic electrolyte shifts.
[0101] There
are currently nephrologist / medical staff shortages worldwide, which makes
it difficult for a patient to obtain their own individualized dialysis
prescription. This is
especially true as the renal disease patient population worldwide is
increasing, which is also
causing nephrologists to spend less time with their patients. Furthermore,
even if patients were
treated with their own individualized dialysis prescription, there are
currently few tools, if any,
to analyze how the individualized dialysis prescriptions are impacting the
patient's outcome.
Additionally, there is a vast amount of previous treatment information and
outcomes for
dialysis patients available, but there is no implemented mechanism to use this
vast amount of
data in gaining the insight to generate new dialysis prescriptions. Today's
dialysis treatments
have largely remained unchanged over the past decades. The present disclosure
describes
technology tools and methods such as using AI-ML algorithms and/or models to
generate
individualized dialysis prescriptions. By using
AI-ML algorithms, more accurate
individualized dialysis prescriptions may be generated and provided to treat
patients, which
may in turn better prevent cardiac arrhythmias, sudden cardiac death, and/or
other medical
complications. In addition to the improved patient benefits and greater
accuracy, the
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individualized dialysis prescription may be generated and controlled in real
time in an
automated manner using AI-ML algorithms, which may help with the growing
shortage of
nephrologists as well as the increase in patients with renal diseases.
[0102] As
described herein, a dialysis system (e.g., the hemodialysis system 100 that
includes the hemodialysis machine 102) is designed to generate completely
customizable
dialysate prescriptions that can be individualized for each treatment and/or
patient.
Furthermore, this prescription can be continuously adjusted throughout
treatment. In other
words, the dialysis system, married with on-line point-of-care serum
electrolyte assessments,
may allow for precise control of electrolyte levels during treatment,
potentially reducing
mortality due to inadequate electrolyte homeostasis. Additionally, the ability
to generate small
batches of dialysate solution on demand helps solve an important and common
barrier to home
hemodialysis, the necessity of delivering and storing large volumes of
concentrates used for
preparing the dialysis prescription.
[0103] The
dialysis system also aims to achieve an appropriate fluid balance to allow
normalization of blood-water volumes and alleviate symptoms of fluid overload
(swelling,
hypertension, pulmonary edema). Sodium, the major determinant of extracellular
fluid
volume, obtained via dietary sources or dialysate, can significantly alter
fluid balance. Excess
sodium intake from dialysate may lead to fluid overload and even
hospitalization or death.
Therefore, clinicians try to limit sodium concentration. However, higher
dialysate sodium also
improves symptoms - hypotension and/or muscle cramps. The clinician may need
to make a
difficult choice of balancing excess sodium delivery with symptom relief when
determining
dialysate sodium prescriptions. Complicating the problem, pre-dialysis plasma
sodium levels
vary from patient-to-patient and even from treatment-to-treatment in the same
patient. For
improved volume regulation, an ideal dialysis machine would have the capacity
to measure
plasma sodium and adjust dialysate-to-serum concentration gradients within a
single dialysis
session. This, combined with effective fluid removal by controlled and safe
ultrafiltration (UF)
rate may allow major enhancements in personalizing volume regulation at the
patient and
treatment level. Furthermore, patients should be provided with tools to view
and manage the
volume status, implied by real-time sodium values in plasma and prescribed
dialysate.
[0104] FIG. 8
is a simplified block diagram depicting an exemplary individualized and on-
demand dialysis system 800 with network capabilities. The system 800 includes
a prescriber's
office 804, a network 802, a data storage system 808, an enterprise web server
810, and a
patient's residence 812. The entities and devices within system 800 may be
connected to and/or
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communicate over a network 802. The network 802 may be a global area network
(GAN) such
as the Internet, a wide area network (WAN), a local area network (LAN), or any
other type of
network or combination of networks. The network 802 may provide a wireline,
wireless, or a
combination of wireline and wireless communication between the entities within
the system
800. In some instances, the web server 810 may be in communication with the
data storage
system 808 without using the network 802. For instance, the web server 810 may
use one or
more communication protocols such as WI-Fl or BLUETOOTH and/or a wired
connection to
communicate with the data storage system 808. In some examples, the data
storage system
808 may be included within the web server 810.
[0105] In
operation, the prescriber's office (e.g., nephrologist' s office) 804 may use
the
network 802 to communicate with one or more devices, residences, and/or other
systems within
the system 800. In some examples, a prescriber such as a nephrologist may
prescribe a dialysis
prescription for a patient. The dialysis prescription may be individualized.
For example, the
prescription may indicate different levels of electrolytes such as sodium,
potassium,
magnesium, calcium, and/or other prescriptions / parameters for the particular
patient.
[0106] The
prescriber may use a computing device (e.g., computing device 805) to input
the information indicating the dialysis prescription. For instance, the
prescriber' s office may
include a prescriber computing device 805. The computing device 805 may
include one or
more processors, memory, and/or additional components to communicate with one
or more
devices within the system 800. In some examples, after inputting the
prescription, the
prescriber computing device 805 may provide information to the data storage
system 808.
[0107] The data
storage system 808 may be any system or set of systems that stores
information and/or communicates with one or more devices within system 800.
For example,
the data storage system 808 may store the individualized prescriptions for one
or more patients
after receiving the information from the prescriber computing device 805
and/or other
computing devices including computing devices from other prescribers /
nephrologists. The
data storage system 808 may also store past patient information, prescriptions
given, and/or
may receive information tracking the prescription / patient outcomes. For
example, a new
patient entering dialysis for the first time may be put into an applicable
cluster (e.g., by using
ML algorithms described below), and a prescription may be suggested by the
machine learning
and/or artificial intelligence (AI-ML) algorithms engine 816. The nephrologist
may choose to
accept, edit or reject the recommendation.
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[0108] The
patient's residence 812 may be a dwelling or home of the patient. The
prescriber's office 804 and/or the patient's residence 812 may include an on-
demand dialysis
machine 806 and 814. In some instances, the on-demand dialysis machine 806
and/or 814 may
be the dialysis system shown in FIG. 1 (e.g., the hemodialysis system 100
and/or the
hemodialysis machine 102) and may be used to perform dialysis treatment,
including
hemodialysis treatment, on a patient. For example, the patient may receive
treatment at the
prescriber's office using the first on-demand dialysis machine 806.
Additionally, and/or
alternatively, the patient may receive treatment at the patient's residence
812 using the second
on-demand dialysis machine 814. The treatment performed by the first and/or
second on-
demand dialysis machines 806 and/or 812 may be based on the individualized
dialysis
prescription. For example, as described above, the controller 101 of the
dialysis machine 102
may compute the chemical composition for a dialysate and create the batch of
dialysate for the
patient based on the dialysis prescription. In some instances, the dialysis
machines 806 and/or
812 may be capable of generating the 'standard' dialysate with fixed
formulations. In some
examples, the batches needed for the entire treatment duration may be made
sequentially
following the prescriber's program / information sent to the machine. The
total number of
batches to be made may depend on the duration of the dialysis treatment and
dialysate flow
(milliliter/minute (ml/min)). In some variations, the on-demand dialysis
machines 806 and/or
814 may be peritoneal dialysis (PD) machines. In instances where the on-demand
dialysis
machines 806 and/or 814 are PD machines, the PD fluid may be sterilized with a
specific filter
at the point of care. In some examples, the on-demand dialysis machines 806
and/or 814 may
include a water purification system that is used to prepare the dialysate.
[0109] The
first and second on-demand dialysis machine 806 and/or 814 may communicate
and receive the individualized prescriptions for the patients from one or more
devices within
the system 800 such as the prescriber computing device 805, and/or the data
storage system
808. For example, the first and/or second on-demand dialysis machine 806, 814
may provide
patient identification information to the data storage system 808. In
response, the first and/or
second on-demand dialysis machine 806, 814 may receive, from the data storage
system 808,
the individualized prescriptions. The first and/or second on-demand dialysis
machine 806, 814
may use the individualized prescriptions to provide an individualized dialysis
treatment for the
patient. In other words, by including communication devices such as databases,
servers, cloud
computing platforms, and so on, the patients may receive their individualized
dialysis
treatments in their own residence 812 and/or in clinics such as at the
prescriber's offices 804.
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[0110] After
the treatment has been completed, the first and/or second on-demand dialysis
machine 806, 814 may provide treatment information including the treatment
outcomes to the
data storage system 808. For example, the data storage system 808 may store
previous dialysis
treatments for the patient and/or other patients.
[0111] In some
instances, the prescriber may be recommended one or more dialysis
treatments for the patient using an AI-ML algorithms and/or dataset. The
prescriber may
accept, modify, and/or decline the recommended treatment from the machine
learning and/or
artificial intelligence algorithm. For example, the web server 810 may be one
or more servers
and/or other systems within the system 800. The web server 810 may include one
or more
processors which execute the AI-ML algorithm engine 816.
[0112] The AI-
ML algorithm engine 816 may use one or more AI-ML algorithms and/or
datasets to determine and recommend an individualized dialysis prescription
for the patient to
a nephrologist. For instance, the dataset used to determine the individualized
dialysis
prescription for the patient may be utilized to develop a supervised ML
algorithm (e.g., a person
and/or another device instructs the engine 816 of what the AI-ML dataset
should learn and
provides the data to train and test the dataset). Additionally, and/or
alternatively, the AI-ML
dataset may be utilized to develop an unsupervised AI-ML algorithm. For
unsupervised AI-
ML datasets, a person and/or another device provides vast data to the engine
816 to train and
test the AI-ML algorithm, but does not instruct the engine 816 on what the
algorithm and/or
dataset should learn. Based on the vast data, the engine 816 may use the
trained AI-ML dataset
to determine and form similar patient clusters. The patient clusters may be
associated with one
or more individualized dialysis prescriptions.
[0113] In other
words, the engine 816 may receive information (e.g., vast data) such as past
prescriptions (e.g., previously prescribed individualized dialysis
prescriptions), outcomes, and
potential recommended prescriptions. Using this information, the engine 816
may train, test,
and/or implement an AI-ML algorithm. The engine 816 may input information
associated with
a new or existing patient into the trained AI-ML dataset and the trained AI-ML
dataset may
output a patient cluster associated with the new or existing patient. For
instance, the patient
cluster may be a cluster or grouping of similar patients such as a cluster of
hyperkalemic
patients. Each of these patient clusters may be associated with one or more
individualized
dialysis prescriptions.
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[0114] In some
variations, the dataset may be used in a Reinforced AI-ML algorithm. For
Reinforced AI-ML, the engine 816 may use a reward state to train the AI-ML
algorithm (e.g.,
if the response improves the current state, then reward and if not, then
penalize).
[0115] The AI-
ML algorithm engine 816 may receive treatment information from the data
storage system 808. This information may include, but is not limited to, past
prescriptions,
outcomes, and/or recommended prescriptions. Based on the received information,
the AI-ML
algorithm engine 816 may train the AI-ML dataset for use in determining
patient clusters and/or
individualized prescriptions for patients. In some instances, the AI-ML
algorithm engine 816
may receive training information from one or more devices such as one or more
dialysis
machines (e.g., the first and/or second on-demand dialysis machines 806, 814).
For example,
a large chunk of data ("big data") may be divided into training data (-70%)
and test data
(-30%). The engine 816 may have a specific objective (e.g., finding
associations between
input i.e., patient disease conditions and output patient condition due to
dialysis prescriptions).
The engine 816 may use methods such as Support Vector Machine (SVM) and/or K
Nearest
Neighbor (kNN) to accomplish this objective. For instance, the engine 816 may
use SVM and
kNN to form patient clusters based on the associations or similarities of the
different patients
within the data.
[0116] After
training the AI-ML module, the AI-ML algorithm engine 816 may input new
data (e.g., data associated with a new or existing patient) into the trained
AI-ML module to
determine and/or identify a particular patient cluster and/or treatment
cluster for the patient.
The patient cluster or treatment cluster may be a cluster or grouping of
patients with similar
symptoms, medical conditions, and/or undergoing similar medical treatments.
For instance,
the patient / treatment cluster may be a cluster of patients with medical
conditions such as
common electrolyte imbalances in end-stage renal disease (ESRD) hyponatremia,
hyperkalemia, and so on. The AI-ML algorithm engine 816 may recommend
individualized
prescriptions for the new or existing patient using the past dataset on
patient outcomes.
[0117] The AI-
ML algorithm engine 816 may provide the output from the AI-ML dataset
(e.g., the identified patient cluster and/or the individualized prescription)
to a device such as
the prescriber computing device 805, the first on-demand dialysis machine 806,
and/or the
second on-demand dialysis machine 814. The first and/or second on-demand
dialysis machines
806, 814 may perform dialysis treatment on the patient using the
individualized dialysis
prescription that was determined by the AI-ML algorithm engine 816. For
instance, referring
to FIG. 5 and 502, the controller 101 may load the dialysate recipe (e.g., the
individualized
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dialysis prescription) that was generated by the AI-ML algorithm engine 816.
Then, the
process 500 may proceed as described above in FIG. 5.
[0118] In some
examples, the prescriber (e.g., nephrologist) may review, examine, modify,
revise, accept, reject, and/or override the output (e.g., the prescription)
recommended by AI-
ML. For example, the prescriber computing device 805 may receive the output of
the AI-ML
dataset (e.g., the identified data cluster and/or the individualized
prescription) and cause display
of the output. The prescriber may provide user input to the prescriber
computing device 805
indicating whether the output is acceptable and/or additional modifications /
revisions to the
output. The prescriber computing device 805 may provide the user input (e.g.,
whether the
output from the AI-ML dataset is acceptable and/or modifications / revisions
to the output) to
the AI-ML algorithm engine 816. The AI-ML algorithm engine 816 may use this
data to train
and/or further train the AI-ML dataset.
[0119] In some
instances, the engine 816 may provide an identified patient cluster or
treatment cluster for the patient to the prescriber computing device 805. The
prescriber, using
the device 805, may provide input indicating whether the identified patient
cluster or treatment
cluster is acceptable. If the patient / treatment cluster is acceptable, the
prescriber computing
device 805 may determine an individualized dialysis prescription for the
patient (e.g., the
prescriber may prescribe a dialysis prescription based on the patient /
treatment cluster).
Additionally, and/or alternatively, if the identified patient / treatment
cluster is not acceptable,
the prescriber may provide additional input indicating a new or modified
patient / treatment
cluster for the patient. The input indicating whether the patient / treatment
cluster is acceptable
as well as the additional input indicating a new or modified patient /
treatment cluster may be
provided back to the engine 816 and the engine 816 may use this input to train
/ further train
the AI-ML dataset.
[0120] In some
examples, the engine 816 may provide an individualized dialysis
prescription for the patient to the prescriber computing device 805. The
prescriber, using the
device 805, may provide input indicating whether the determined individualized
dialysis
prescription is acceptable. Additionally, and/or alternatively, if the
determined individualized
dialysis prescription is not acceptable, the prescriber may provide additional
input indicating a
new or modified individualized dialysis prescription for the patient. The
input indicating
whether the individualized dialysis prescription is acceptable as well as the
additional input
indicating a new or modified individualized dialysis prescription may be
provided back to the
engine 816 and the engine 816 may use this input to train / further train the
AI-ML dataset.
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[0121] After
determining the individualized dialysis prescription, the prescriber computing
device 805 may provide information indicating the individualized dialysis
prescription to the
first and/or second on-demand dialysis machines 806 and/or 814. The first
and/or second on-
demand dialysis machines 806 and/or 814 may provide dialysis treatment to the
patient based
on the individualized dialysis prescription.
[0122] In some
variations, the patient may be receiving the treatment at home (e.g., using
the second on-demand dialysis machine 814). The second on-demand dialysis
machine 814
may require a patient log-in prior to performing the dialysis treatment. For
example, the second
on-demand dialysis machine 814 may receive user input indicating a
fingerprint, code (e.g.,
QR code), and/or other identification indicating the identity of the patient.
The second on-
demand dialysis machine 814 may verify or deny the patient identification.
Based on verifying
the patient, the second on-demand dialysis machine 814 may perform dialysis
treatment on the
patient using the individualized dialysis prescription.
[0123] FIG. 9
illustrates an exemplary process 900 for using AI-ML to provide
individualized dialysis prescriptions and treatments to patients. In
operation, at block 902, the
past treatments and/or other information (e.g., previously determined dialysis
prescriptions
and/or patient outcomes based on the prescriptions) may be stored in the data
storage system
808. The AI-ML algorithm engine 816 may retrieve this information from the
data storage
system 808.
[0124] At block
904, the AI-ML algorithm engine 816 (e.g., an artificial intelligence (Al)
module) uses one or more AI-ML algorithms to place a patient into a particular
patient cluster,
compares past prescription outcomes / other information, and recommends an
individualized
prescription.
[0125] At block
906, the AI-ML algorithm engine 816 generates a digital patient profile
and/or additional data based on the output from the AI-ML algorithm. The
profile includes
information such as the patient belongs to a particular patient cluster and a
recommended
individualized dialysis prescription.
[0126] At block
908, the individualized dialysis prescription and/or cluster from the digital
patient profile may be reviewed, modified/revised, accepted / rejected /
overridden. For
example, the AI-ML algorithm engine 816 may provide the individualized
dialysis prescription
/ cluster to the prescriber computing device 805. The prescriber computing
device 805 may
display the dialysis prescription and/or cluster. Then, the nephrologist may
review, modify,
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revise, accept, reject, and/or override the output from the AI-ML algorithm
engine 816 (e.g.,
the recommended dialysis prescription / cluster).
[0127] At block
910, the prescription is sent to one or more dialysis machines such as a
dialysis machine 814 in the patient's residence 812. For instance, the
prescriber computing
device 805 may provide the prescription / cluster to the dialysis machines 806
and/or 814.
Additionally, and/or alternatively, the prescription may first be sent to a
database (e.g., the data
storage system 808) and/or a cloud computing service. The database / cloud
computing service
may then forward this to the dialysis machine such as a home hemodialysis
machine.
[0128] At block
912, the dialysis machines (e.g., 806 and/or 814) verifies the patient's
identity, prepares dialysate based on the prescription, and performs the
dialysis treatment. For
example, after receiving the individualized dialysis prescription, the
dialysis machine may
prompt the patient to log-in and verify their identity. Treatment will be
denied / cancelled if
the patient is not verified. If the patient is verified, the dialysis machine
may prompt the patient
to ensure the power is on, water is connected, cassettes are loaded, other pre-
treatment
predations are completed, and/or it is connected to the blood circuit. Then
the dialysate is
prepared based on the individualized dialysis prescription. The machine may
make any fluid
based on the chemicals contained in the tableted form in cassettes. After the
preparations are
completed, the patient may undergo dialysis treatment as described above.
[0129] Although
millions of hemodialysis treatments were given in the past and prior to
using AI-ML, the collected data was rarely used in understanding the trends
between the patient
morbidities, prescribed prescriptions, and outcomes. With the advent of AI-ML,
data science
methods such as kNN and/or SVM may be used to define patient clusters (e.g.,
common
electrolyte imbalances in ESRD hyponatremia, hyperkalemia). For example, a new
patient
entering stage 5 ESRD needing dialysis may be classified into an applicable
cluster. Since past
prescriptions and outcomes have been examined to understand the trend, it is
possible use this
information in prescribing an individualized dialysis prescription for the
patient.
[0130] In some
instances, the engine 816 may examine calcium (Ca) prescriptions used in
various clinics. In some examples, the engine 816 may determine associations
between co-
morbidities (e.g., hypokalemic patient may also have hypomagnesemia) that may
not be readily
apparent and use the determined associations to determine individualized
dialysis
prescriptions.
[0131] All
references, including publications, patent applications, and patents, cited
herein
are hereby incorporated by reference to the same extent as if each reference
were individually
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and specifically indicated to be incorporated by reference and were set forth
in its entirety
herein.
[0132] The use
of the terms "a" and "an" and "the" and "at least one" and similar referents
in the context of describing the invention (especially in the context of the
following claims) are
to be construed to cover both the singular and the plural, unless otherwise
indicated herein or
clearly contradicted by context. The use of the term "at least one" followed
by a list of one or
more items (for example, "at least one of A and B") is to be construed to mean
one item selected
from the listed items (A or B) or any combination of two or more of the listed
items (A and B),
unless otherwise indicated herein or clearly contradicted by context. The
terms "comprising,"
"having," "including," and "containing" are to be construed as open-ended
terms (i.e., meaning
"including, but not limited to,") unless otherwise noted. Recitation of ranges
of values herein
are merely intended to serve as a shorthand method of referring individually
to each separate
value falling within the range, unless otherwise indicated herein, and each
separate value is
incorporated into the specification as if it were individually recited herein.
All methods
described herein can be performed in any suitable order unless otherwise
indicated herein or
otherwise clearly contradicted by context. The use of any and all examples, or
exemplary
language (e.g., "such as") provided herein, is intended merely to better
illuminate the invention
and does not pose a limitation on the scope of the invention unless otherwise
claimed. No
language in the specification should be construed as indicating any non-
claimed element as
essential to the practice of the invention.
[0133]
Preferred embodiments of this invention are described herein, including the
best
mode known to the inventors for carrying out the invention. Variations of
those preferred
embodiments may become apparent to those of ordinary skill in the art upon
reading the
foregoing description. The inventors expect skilled artisans to employ such
variations as
appropriate, and the inventors intend for the invention to be practiced
otherwise than as
specifically described herein. Accordingly, this invention includes all
modifications and
equivalents of the subject matter recited in the claims appended hereto as
permitted by
applicable law. Moreover, any combination of the above-described elements in
all possible
variations thereof is encompassed by the invention unless otherwise indicated
herein or
otherwise clearly contradicted by context.
