U.S. patent application number 14/460231 was filed with the patent office on 2015-03-12 for systems and methods for revascularization assessment.
The applicant listed for this patent is Nanyang Technological University. Invention is credited to Renzhe Bi, Justin Dauwels, Jing Dong, Kijoon Lee.
Application Number | 20150073271 14/460231 |
Document ID | / |
Family ID | 52468749 |
Filed Date | 2015-03-12 |
United States Patent
Application |
20150073271 |
Kind Code |
A1 |
Lee; Kijoon ; et
al. |
March 12, 2015 |
SYSTEMS AND METHODS FOR REVASCULARIZATION ASSESSMENT
Abstract
Disclosed herein are systems and methods for revascularization
assessment. The methods can in some cases include one or more of
the steps of measuring blood perfusion as a function of time to
obtain time series data, mathematically transforming the time
series data into a power spectrum, calculating at least one
parameter of the power spectrum within a specific frequency range,
and using the at least one calculated parameter as a discriminator
for the first population and the second population.
Inventors: |
Lee; Kijoon; (Singapore,
SG) ; Bi; Renzhe; (Singapore, SG) ; Dong;
Jing; (Singapore, SG) ; Dauwels; Justin;
(Singapore, SG) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nanyang Technological University |
Singapore |
|
SG |
|
|
Family ID: |
52468749 |
Appl. No.: |
14/460231 |
Filed: |
August 14, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61865977 |
Aug 14, 2013 |
|
|
|
61888790 |
Oct 9, 2013 |
|
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Current U.S.
Class: |
600/427 ;
600/475; 600/477; 600/479; 600/504 |
Current CPC
Class: |
A61B 5/7257 20130101;
G16H 50/20 20180101; A61B 5/7246 20130101; A61B 5/0265 20130101;
A61B 5/6831 20130101; A61B 5/726 20130101; A61B 5/02007 20130101;
A61B 5/0075 20130101; A61B 5/7282 20130101; A61B 5/6829 20130101;
A61B 5/0261 20130101 |
Class at
Publication: |
600/427 ;
600/504; 600/479; 600/477; 600/475 |
International
Class: |
A61B 5/026 20060101
A61B005/026; A61B 5/02 20060101 A61B005/02; A61B 5/00 20060101
A61B005/00 |
Claims
1. A computer-implemented method for discriminating between at
least a first population and a second population, the method
comprising: measuring blood perfusion as a function of time to
obtain time series data; mathematically transforming the time
series data into a power spectrum; calculating at least one
parameter of the power spectrum within a specific frequency range;
and using the at least one calculated parameter as a discriminator
for the first population and the second population.
2. The method of claim 1, wherein at least the first population and
the second population comprise two patient populations.
3. The method of claim 1, wherein the first population comprises a
healthy control group and the second population comprises an
ischemic population.
4. The method of claim 1, wherein measuring blood perfusion as a
function of time comprises using an optical measurement method.
5. The method of claim 4, wherein the optical method is diffuse
correlation spectroscopy.
6. The method of claim 4, wherein the optical method is diffuse
speckle contrast analysis.
7. The method of claim 4, wherein the optical method is diffuse
optical tomography.
8. The method of claim 4, wherein the optical method is
near-infrared spectroscopy.
9. The method of claim 4, wherein the optical method is laser
Doppler flowmetry.
10. The method of claim 1, wherein measuring blood perfusion as a
function of time comprises using a non-optical measurement
method.
11-44. (canceled)
45. A system for discriminating between at least a first population
and a second population, the system comprising: a processor
configured to receive blood perfusion measurements as a function of
time to obtain time series data; mathematically transform the time
series data into a power spectrum; calculate at least one parameter
of the power spectrum within a specific frequency range; and use
the at least one calculated parameter as a discriminator for the
first population and the second population.
46. The system of claim 45, wherein at least the first population
and the second population comprise two patient populations.
47. The system of claim 45, wherein the first population comprises
a healthy control group and the second population comprises an
ischemic population.
48. The system of claim 45, further comprising at least one optical
sensor configured to measure blood perfusion as a function of
time.
49. The system of claim 48, wherein the optical sensor comprises a
diffuse optical flow sensor.
50-59. (canceled)
60. A computer-implemented method for discriminating between at
least a first population and a second population, the method
comprising: measuring blood perfusion as a function of time to
obtain time series data; calculating statistical parameters from
the time series data; and using at least one of the statistical
parameters as a discriminator for the first population and the
second population.
60. The method of claim 60, wherein the time series is between
about 30 seconds and about 15 minutes.
62. The method of claim 60, wherein the first population is a
non-ischemic population and the second population is an ischemic
population.
63. The method of claim 60, wherein at least one of the statistical
parameters is a standard deviation calculation.
64. The method of claim 60, wherein blood perfusion is measured
noninvasively.
65-84. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit under 35 U.S.C.
.sctn.119(e) as a nonprovisional application of U.S. Prov. App. No.
61/865,977 filed on Aug. 14, 2013, as well as U.S. Prov. App. No.
61/888,790 filed on Oct. 9, 2013. This application is also related
to U.S. application Ser. No. 13/967,298 filed on Aug. 14, 2013.
Each of the foregoing applications is hereby incorporated by
reference in their entireties.
BACKGROUND
[0002] 1. Field
[0003] This disclosure relates to the measurement of blood flow in
tissue, in particular measurement of blood flow in the foot or
other extremities.
[0004] 2. Description of the Related Art
[0005] The rapidly aging population in the developed world has led
to an increasing prevalence of aging-associated degenerative
diseases such as peripheral arterial disease and Type 2 diabetes.
The manifestations of these include tissue ischemia, chronic wounds
and diabetic foot ulcers, where lack of appropriate treatment may
lead to infection, gangrene and, in the case of foot ischemia,
partial or complete amputation of one or both feet.
[0006] Peripheral arterial disease (PAD) is a progressive disease
in which narrowed or obstructed arteries reduce blood flow to the
limbs. PAD can result from atherosclerosis, inflammatory processes
leading to stenosis, an embolism, or thrombus formation, and is
associated with smoking, diabetes, dyslipidemia, and hypertension.
PAD can if untreated result in critical limb ischemia (CLI), in
which blood flow to the limb (usually the legs and feet) is
compromised to such an extent that tissue damage ensues with
consequent ulceration, gangrene or loss of the limb. Patients with
PAD are also at a disproportionately high risk of other
cardiovascular diseases like myocardial infarction and stroke and
of death as a result of these conditions. With the incidence of
diabetes increasing worldwide, treatment of CLI and prevention of
disability and of limb loss from it has become a significant health
priority.
[0007] Peripheral vascular intervention procedures using
endovascular (minimally invasive) intervention, open surgery or a
combination of the two are currently the only methods available to
restore perfusion to the limbs in patients with PAD. Medical
management can help only to delay the progression of the disease,
if at all. However, clinicians currently lack the intraoperative
tools to properly assess perfusion in the affected tissue, usually
in the feet, in real-time to reliably guide the conduct of the
interventional procedure. Existing technologies that measure blood
perfusion include skin perfusion pressure (SPP), duplex ultrasound
(DUX), and transcutaneous oxygen monitoring (TCOM). Each of these
techniques suffers from one or more disadvantages. SPP only
provides perfusion data at the skin dermis level, requires the skin
temperature to be normalized to 44.degree. C., is affected by skin
pigmentation and is unreliable with patients with edema. SPP also
requires the use of a pressure cuff, which further limits its
utility as a real-time perfusion assessment tool during peripheral
vascular interventions. DUX does not assess tissue perfusion but
instead measures blood flow in large vessels (>1.5 mm). TCOM
requires the patient to be placed on hyperbaric oxygen, making it
incompatible with the cath lab/operating room. Furthermore, TCOM
does not provide real time revascularization data as it takes about
4 to 6 weeks for the measurements to equilibrate.
[0008] Accordingly, there is a need for noninvasive, real-time
measurement of blood perfusion in a range of blood vessel sizes and
in the tissue supplied by these vessels. In particular, there is a
need for noninvasive, real-time measurement of blood perfusion in
the foot that can be reliably performed as the interventional
procedure proceeds and be used to inform the decision making during
the procedure.
[0009] Ischemia is a condition where a restriction of blood supply
to tissues leads to a shortage in oxygen and glucose, resulting in
irreversible damage to tissues. If discovered too late, reperfusion
of blood by various treatment options, thrombolytic or surgical,
will only further increase the damage to the tissue as opposed to
rescuing the tissue. For example, one of the most common sites of
ischemia is the foot. In this case, early detection and diagnosis
of an ischemic foot at risk is imperative, before the damage
becomes irreversible. Currently, the most common way to diagnose an
ischemic foot is ABI (Ankle Brachial Index) which compares the
blood pressure in the arm with that at the ankle. An ABI
measurement less than 0.9, in some cases, is indicative of an
ischemic foot. However, ABI measurements are highly dependent on
operator protocol, i.e. different values can be obtained when
measurements are obtained with the subject in a seated or supine
position, or when the operator uses a different measurement
protocol/equipment. ABI also produces falsely elevated measurements
in calcified vessels of patients who have diabetes mellitus, are
receiving hemodialysis, or if there is an extensive distal arterial
lesion below the ankle (Yamada et al, J Vasc Surg 2008; 47:
318-23).
[0010] A chronic wound is a non-healing wound that shows little or
no improvement after four weeks or does not heal in eight weeks. In
practice, patients may present with chronic wounds that remain open
for over a year. Around the world, there are 37 million people who
suffer from chronic wounds, mostly on the lower limbs. In the US
alone, chronic wounds have affected 6.5 million patients and
accounted for $1.4 billion in spending in 2010. Since chronic
wounds are associated with the diseases of aging, such as diabetes
and obesity, the healthcare need for chronic wound management is
rising together with the rise in aged populations in the developed
world. The early diagnosis of a chronic ischemic wound on lower
limbs is particularly important, as it has a major impact in
determining whether conservative wound management (e.g., bandages
and moist dressings) would be sufficient, or whether more
aggressive therapies are required to forestall further wound
deterioration that may culminate in amputation.
[0011] Conservative therapy for wounds (e.g., bandages, moist
dressings) can suffice to facilitate wound healing if the blood
perfusion around the wound tissue is not compromised beyond the
minimal threshold for passive healing to occur. In cases where the
perfusion is compromised, however, the inappropriate use of
conservative wound therapy causes a time lag between the first
presentment of a wound in a clinical setting to an effective
therapy commensurate with the seriousness of the wound
condition.
[0012] The single most important determinant of tissue viability in
a wound is its blood supply. The ability to assess the blood
perfusion around the wound bed allows clinical decisions to be made
regarding either (a) continuation of conservative therapy if tissue
is viable or, (b) if blood perfusion is too severely compromised
for successful conservative therapy, to progress early to more
advanced wound care products like chemical debriding agents, or
advanced wound therapies such as topical negative pressure,
hyperbaric oxygen therapy ("HBOT"), etc. In appropriate cases, the
patient can be directed to revascularization by peripheral
interventional procedures. Hence, a blood perfusion monitor that
can facilitate the early streaming of patients into conservative
versus aggressive wound therapies is highly desirable.
[0013] HBOT involves the administering of oxygen at levels 2-2.5
times sea level in a hyperbaric chamber. A patient may be
prescribed up to 40 sessions of HBOT, with typically 3-4 sessions
per week, in order to maximize the delivery of oxygen to chronic
wound tissue. Such therapy is expensive and is not without risk;
its side effects include ear and sinus barotrauma, paranasal
sinuses and oxygen toxicity of the central nervous system. (Aviat
Space Environ Med. 2000; 71(2):119-24.) Moreover, a retrospective
study of 1144 patients (Wound Rep Reg 2002; 10:198-207) indicated
that 24.4% of chronic wound patients received no benefit from it.
Therefore, a diagnostic device to better predict the success of
HBOT in chronic wound treatment will help to avoid unnecessary and
unhelpful therapy, and obtain significant cost savings in the
healthcare system.
[0014] In foot ischemia cases where amputation is required, there
is a need for a new diagnostic tool that can better guide decisions
regarding the amputation level, by predicting the potential success
of amputation wound healing. Amputation is typically performed on
patients with severe limb ischemia who cannot be treated with
reconstructive vascular surgery, patients with diabetic foot ulcers
or venous ulcerations. Approximately 85-90% of lower limb
amputations in the developed world are caused by peripheral
vascular disease and poor wound healing accounts for 70% of the
complication cases that arise from amputation. Due to the lack of
optimal tools to predict amputation healing, physicians have to
make subjective judgments on the best site for amputation, and
since the bias is to maximize limb preservation, it is not uncommon
for a patient to require a subsequent amputation higher up the leg
when the first amputation wound is unable to heal. The healing rate
of below-knee amputation ranges between 30 and 92%, with a
re-amputation rate of up to 30%. Thus, an accurate tool for
predicting successful amputation healing is needed to help doctors
more accurately determine the site of amputation that will result
in maximal limb preservation while avoiding the trauma and cost of
a revision amputation.
[0015] Generally in surgical procedures, particularly in plastic
and reconstructive surgery, tissue flaps are used to cover wound
defects. These may be either pedicled flaps (i.e. have a vascular
pedicle of their own that supplies blood to the flap) or free-flaps
that need microvascular connections with the recipient site to
ensure adequate blood supply. Both types of flaps are crucially
dependent on the blood perfusion within them for the flaps to
survive. Flap perfusion needs close monitoring especially in the
first few hours to days after the reconstruction procedure and
early detection of loss of perfusion will help to direct the
patient for further surgical procedures as needed to ensure
continued flap viability. It will thus be useful if a diagnostic
tool can potentially be used to monitor flap blood perfusion
continuously in the post-operative period and prevent flap loss due
to delayed detection of flap ischemia.
[0016] Currently, diagnostic devices on the market for wound care
include duplex ultrasound (for example, as described in EP0814700
A1), transcutaneous oxygen monitoring (TCOM or TcPO.sub.2) (for
example, as described in WO1980002795 A1), and skin perfusion
pressure (SPP) (for example, as described in CA2238512 C), each of
which suffer severe disadvantages that limits their effectiveness
in administering the right therapy to chronic wound patients.
Duplex ultrasound only measures blood flow in large vessels
(>1.5 mm). TCOM measurements are not optimally correlated with
the status of the wound (Wounds 2009; 21(11):310-316). This is
especially so as TCOM measurements are influenced by many factors
including local edema, anatomical localization, thickness of the
epidermal stratum corneum, and leg dependency (Figoni et al, J.
Rehab Research Development 2006; 43 (7) 891-904). In addition, test
results are heavily affected by moisture and temperature levels
(Podiatry Today 2012; 25(7) 84-92). Lo et al. (Wounds 2009:21(11)
310-316) report that skin perfusion pressure (measured by laser
Doppler) appears to be a more accurate predictor of wound healing
versus TcPO.sub.2; however SPP is only able to provide data at
limited depth and requires skin temperature to be normalized to
44.degree. C., is sensitive to skin pigmentation and unreliable
with edema.
[0017] Most recently, the use of diffuse speckle contrast analysis
(DSCA) has been developed to measure real-time blood perfusion in
tissue depths of up to two centimeters (2 cm), in absolute BFI
("blood flow index") units (as described in more detail in U.S.
Provisional App. Nos. 61/755,700, filed Jan. 23, 2013, and
61/830,256, filed Jun. 3, 2013, each of which are hereby
incorporated by reference in their entirety). The present
disclosure centers on the use of DSCA to generate additional
information, such as low frequency oscillation data that forms the
basis of a calibrated index that can guide clinical decisions in
treating ischemia.
SUMMARY
[0018] Disclosed herein is a system for assessment of peripheral
blood flow during peripheral vascular intervention, the system
including: a support structure configured to be positioned onto a
patient's foot; a diffuse optical flow (DOF) sensor carried by the
support structure; an analyzer configured to analyze data from the
DOF sensor to determine absolute and/or relative blood flow at a
location near the DOF sensor when the support structure is
positioned onto a patient's foot; and a feedback device configured
to provide a signal indicative of the absolute and/or relative
blood flow determined by the analyzer.
[0019] In some embodiments, the support structure can include a
retention ring and an adhesive material, or simply an adhesive
material. In some embodiments, the support structure can include a
strap having the DOF sensor attached thereto. In some embodiments,
the DOF sensors can be arranged such that when the support
structure is positioned onto the patient's foot, at least two of
the DOF sensors are over different topographical locations in the
foot including different pedal angiosomes. In some embodiments, the
DOF sensors can be arranged such that when the support structure is
positioned onto the patient's foot, at least five of the DOF
sensors are over different topographical locations in the foot
including different pedal angiosomes. In some embodiments, the
analyzer can include a software autocorrelator. In some
embodiments, the analyzer can include a hardware autocorrelator. In
some embodiments, the signal indicative of the absolute and/or
relative blood flow can be visual, audible, or tactile. In some
embodiments, the system can be configured to provide the signal
indicative of the absolute and/or relative blood flow in
substantially real-time. In some embodiments, the system can be
configured to provide the signal indicative of the absolute and/or
relative blood flow within 1 second from measurement.
[0020] Also disclosed herein is a method for real-time assessment
of peripheral blood flow during peripheral vascular intervention
procedures, the method including: disposing at least one diffuse
optical flow (DOF) sensor adjacent to a location on a foot of a
patient; obtaining measurements of intensity fluctuation from the
DOF sensor; analyzing the obtained measurements to determine an
absolute and/or relative blood flow rate at the location; and
signaling the determined absolute and/or relative blood flow rate
to an operator.
[0021] In some embodiments, disposing the at least one DOF sensor
can include placing a support structure onto the foot of the
patient, the DOF sensor being carried by the support structure. In
some embodiments, the method can further comprise disposing a
plurality of DOF sensors adjacent to a respective plurality of
locations on the foot of the patient. In some embodiments, the
plurality of locations can include at least two, three, four, five,
or more locations corresponding to different topographical
locations in the foot including different pedal angiosomes. In some
embodiments, plurality of locations can include at least five
locations corresponding to five different topographical locations
in the foot including different pedal angiosomes. In some
embodiments, signaling can include providing visual, audible, or
tactile indicia of absolute and/or relative blood flow. In some
embodiments, signaling the determined absolute and/or relative
blood flow rate to an operator can be performed in less than 1
second from measurement.
[0022] Further disclosed is a method for assessment of peripheral
blood flow during peripheral vascular intervention procedures, the
method including: disposing a plurality of diffuse optical flow
(DOF) sensors adjacent to a respective plurality of locations on an
extremity of a patient, wherein at least two of the locations
correspond to different topographical locations in the foot
including different pedal angiosomes; determining an absolute
and/or relative blood flow rates at each of the plurality of
locations in the extremity of the patient; and signaling the
determined absolute and/or relative blood flow rates to an
operator.
[0023] In some embodiments, the extremity can be a foot. In some
embodiments, the extremity can be a hand. In some embodiments, the
signaling can be performed in substantially real-time. In some
embodiments, the determined absolute and/or relative blood flow
rates can be utilized to assess the efficacy of an interventional
procedure.
[0024] Also disclosed herein is a patient interface, for supporting
a plurality of diffuse optical flow (DOF) sensors in optical
communication with a patient's foot, comprising: a support,
configured to be mountable on and carried by the foot; at least
three sensors carried by the support, each sensor corresponding to
a separate topographical location in the foot including an
angiosome selected from the group consisting of: the angiosome of
the medial plantar artery; the angiosome of the lateral plantar
artery; the angiosome of the calcaneal branch of the posterior
tibial artery; the angiosome of the calcaneal branch of the
peroneal artery; and the angiosome of the dorsalis pedis
artery.
[0025] In some embodiments, the patient interface can include at
least four sensors carried by the support, each sensor
corresponding to a separate topographical location in the foot
including a pedal angiosome. In some embodiments, the support can
comprise a retention ring and adhesive material. In some
embodiments, the support can comprise an optical source fiber and
an optical detector fiber. In some embodiments, the optical source
fiber and the optical detector fiber can further comprise at least
one coupling for releasably coupling the sensor to an analyzer. In
some embodiments, the patient interface can comprise a cable, which
includes a plurality of pairs of source fibers and detector fibers,
each pair connected to a separate sensor. In some embodiments, each
sensor can be releasably carried by the support.
[0026] Also disclosed herein is a system for assessment of
peripheral blood perfusion, the system including: a support
structure configured to be positioned onto a patient's foot; a
diffuse optical sensor carried by the support structure; an
analyzer configured to analyze data from the diffuse optical sensor
to characterize the composition or flow of blood at a location near
the diffuse optical sensor when the support structure is positioned
onto a patient's foot; and a feedback device configured to provide
a signal indicative of composition or flow of blood determined by
the analyzer.
[0027] Further disclosed herein is a method for real-time
assessment of peripheral blood, the method including: disposing at
least one diffuse optical sensor adjacent to a location on a foot
of a patient; obtaining measurements of diffused light; analyzing
the obtained measurements to characterize the composition and/or
flow rate of blood at the location; and signaling the determined
composition and/or flow rate to an operator. In some embodiments,
sensors disclosed herein do not take pressure measurements, e.g.,
blood pressure measurements.
[0028] Also disclosed is a method for assessment of peripheral
blood flow during peripheral vascular intervention procedures, the
method including: disposing a plurality of diffuse optical sensors
adjacent to a respective plurality of locations on an extremity of
a patient, wherein at least two of the locations correspond to
different topographical locations in the foot including different
pedal angiosomes; characterizing the composition and/or blood flow
rates at each of the plurality of locations in the extremity of the
patient; and signaling the composition and/or blood flow rates to
an operator.
[0029] Further disclosed herein is a patient interface, for
supporting a plurality of diffuse optical sensors in optical
communication with a patient's foot, comprising: a support,
configured to be mountable on and carried by the foot; at least
three sensors carried by the support, each sensor corresponding to
a separate topographical location in the foot including angiosome
selected from the group consisting of: the angiosome of the medial
plantar artery; the angiosome of the lateral plantar artery; the
angiosome of the calcaneal branch of the posterior tibial artery;
the angiosome of the calcaneal branch of the peroneal artery; and
the angiosome of the dorsalis pedis artery.
[0030] Disclosed herein is a system for using Low Frequency
Oscillation Index ("LFI") in blood flow measurements as a
diagnostic index for ischemic tissue management. In some
embodiments, blood perfusion can be measured as a function of time
to provide time series data. Measurement of blood perfusion can be
accomplished by a number of different techniques, including,
without limitation, diffuse correlation spectroscopy (DCS), diffuse
speckle contrast analysis (DSCA), diffuse optical tomography,
near-infrared spectroscopy, or Doppler flowmetry. In some
embodiments, blood perfusion can be measured by non-optical
techniques, for example via electrical or magnetic blood flow
measurement techniques. In some embodiments, blood perfusion can be
measured at a depth of at least about 1 mm below the skin. In some
embodiments, blood perfusion can be measured at a depth of at least
about 3 mm below the skin. In some embodiments, blood perfusion can
be measured at a depth of at least about 5 mm below the skin. The
tissue of interest can generally be in the lower limbs especially
if the system is used to assess peripheral vascular disease. In
other applications, the tissue of interest may be surgical tissue
flaps used in plastic and reconstructive surgery.
[0031] The obtained time series data may then be analyzed to obtain
relevant parameters for use in clinical applications. For example,
the time series data may be transformed into a power spectrum. In
some embodiments, a Fourier transform may be used to transform the
time series data into a power spectrum. In some embodiments, a Fast
Fourier Transform may be used. In other embodiments, a wavelet
transform can be used to obtain the power spectrum.
[0032] Once the power spectrum is obtained, one or more parameters
may be calculated and used to guide clinical judgment. In some
embodiments, parameters can be calculated from the power spectrum
over a specific frequency range. In some embodiments, the frequency
range can be between about 0.001 Hz to about 1000 Hz, between about
0.001 Hz and about 0.1 Hz, between about 0.045 Hz and about 0.01
Hz, or between 0.001 Hz to about 0.045 Hz. The calculated parameter
can be the area under the curve of the power spectrum within the
specified frequency range. In some embodiments, the calculated
parameter can be the local maximum power of the power spectrum
within the specified frequency range.
[0033] In other embodiments, the calculated parameter may be the
Pearson correlation coefficient calculated between the time-series
data of blood flow obtained from at least two locations on the
patient. In some instances, these two locations may be the
calcaneal and the arm respectively. In other instances, the
locations may be the medial plantar and the arm.
[0034] In still another embodiment, the calculated parameter may be
the Pearson correlation coefficient calculated between the
frequency domain spectrum obtained from at least two locations on
the patient
[0035] In some embodiments, the calculated parameter may be the
relative power.
[0036] In some embodiments, the calculated parameters may be
processed by a State Vector Machine (SVM).
[0037] The calculated parameter may then be used for any one of a
number of clinical evaluations. For example, the calculated
parameter can be used to distinguish between healthy and ischemic
limbs, such as healthy and ischemic feet. The parameter may be used
in some embodiments to identify patients who may have endothelial
or other vascular dysfunction that may impact wound healing. In
some embodiments, the calculated parameter can be used to screen
claudicant patients for interventional therapy. In some
embodiments, the calculated parameter can be used to predict the
likelihood of success for conservative wound therapy. In some
embodiments, the calculated parameter can be used to determine the
need for advanced wound therapy or interventional procedures, such
as balloon angioplasty or vascular surgery. The calculated
parameter may also be used in some embodiments to predict the
likelihood of success of an amputation site healing. In some
embodiments, the calculated parameter can be used to predict the
likelihood of success of a hyperbaric oxygen therapy for chronic
wound healing. In some embodiments, the calculated parameter can be
used to predict the likelihood of success of surgical flaps. In
some embodiments, the calculated parameter can be used to predict
the uptake of drugs.
[0038] The measurement can be obtained locally, such as at the
target site such as on the patient's foot. In some embodiments,
mathematically transforming the time-series data and/or calculating
a parameter can also be conducted locally. In some embodiments, the
mathematical transform and/or calculating a parameter can be
conducted remotely from the measurement site. For example, the
measurement may be obtained locally, and the obtained time-series
data may be transmitted to a remote location for further
processing. In some embodiments, this can enable remote monitoring
of a patient. Time series data can be obtained by a probe worn by
the patient, while the monitoring physician or other individual can
be located remotely, and can receive the obtained time series data
for further processing and evaluation. In various embodiments, the
processing (e.g., mathematical transform and calculation of
parameters) can be conducted in software, in hardware, or some
combination thereof. In some embodiments, the processing can be
conducted on a local device such as a general purpose computer,
while in other embodiments the processing can be conducted via a
distributed network.
[0039] Also disclosed herein are systems for discriminating between
at least a first population and a second population. The systems
can include one or more of a processor configured to receive blood
perfusion measurements as a function of time to obtain time series
data; mathematically transform the time series data into a power
spectrum; calculate at least one parameter of the power spectrum
within a specific frequency range; and/or use the at least one
calculated parameter as a discriminator for the first population
and the second population. The first population and the second
population can comprise two patient populations, such as, for
example, a healthy control group and an ischemic population. The
system can also include at least one optical and/or non-optical
sensor configured to measure blood perfusion as a function of time.
The optical sensor can include a diffuse optical flow sensor. The
processor can be configured to mathematically transform the time
series data into a power spectrum using a Fourier transform, a fast
Fourier Transform, or a wavelet transform. The specific frequency
range can be, for example, between about 0.001 Hz and about 1000
Hz, between about 0.001 Hz and about 0.1 Hz, between about 0.045 Hz
and about 0.1 Hz, or between about 0.001 Hz and 0.045 Hz. The
parameter could be, for example, an area under the curve of the
power spectrum within the specific frequency range, or the local
maximum power of the power spectrum within the frequency range of
interest.
[0040] Also disclosed herein is a method for discriminating between
at least a first population and a second population. The method can
include the steps of: measuring blood perfusion as a function of
time to obtain time series data; calculating statistical parameters
from the time series data; and using at least one of the
statistical parameters as a discriminator for the first population
and the second population.
[0041] In some embodiments, various statistical parameters can be
determined from data obtained relevant to blood flow of one, two,
or more patients or patient populations, including one or more of a
standard deviation, a mean, a median, a mode, a correlation
coefficient, a linear regression, a Z score, a p value, a
Chi-Squared test, and a Fisher's exact test.
[0042] Blood flow can be measured using optical (e.g., diffuse
optical) and/or non-optical flow sensors. Systems are also
disclosed for discriminating between at least a first population
and a second population. The systems can include a processor module
configured to receive blood perfusion measurements as a function of
time to obtain time series data; calculate at least one statistical
parameter from the time series data; frequency range; and use the
at least one calculated parameter as a discriminator for the first
population and the second population. The sensors can be configured
to send blood perfusion measurements through a wired or wireless
connection to the processor.
[0043] Also disclosed herein are computer-implemented methods for
discriminating between at least a first population and a second
population. The methods can include any number of the following
steps: sensing blood flow rate at a first anatomical location;
sending data relating to the blood flow rate to a module; sensing
blood perfusion at a second anatomical location; determining a
second blood flow index at the second anatomical location;
calculating the ratio of the first blood flow index to the second
blood flow index; and determining whether the ratio corresponds to
a characteristic of the first population or the second population
by comparing the ratio to a predetermined threshold value. The
method can also include the step of providing a signal to an
operator related to the ratio. The first anatomical location can be
the foot, and the second anatomical location can be a location that
is not directly perfused by an artery of the foot. The second
anatomical location can be selected from the group consisting of:
the thumb, the earlobe, the upper arm, and the thenar eminence. The
first population can be, for example, an ischemic population, and
the second population can be, for example, a non-ischemic
population.
[0044] Also disclosed herein is a system for discriminating between
at least a first population and a second population. The system can
include a processor configured to perform one or more of the
following steps: receive blood perfusion data from a first sensor
at a first anatomical location; determine a first blood flow index
at the first anatomical location; receive blood perfusion data from
a second sensor at a second anatomical location; determine a second
blood flow index at the second anatomical location; calculate the
ratio of the first blood flow index to the second blood flow index;
and determine whether the ratio corresponds to a characteristic of
the first population or the second population by comparing the
ratio to a predetermined threshold value. The system can also
include the first sensor configured to obtain blood perfusion data
from a first anatomic location, and the second sensor configured to
obtain blood perfusion data from the second anatomic location.
[0045] Also disclosed herein is a computer-implemented method for
discriminating between at least a first population and a second
population. The method can include any number of the following
steps: sensing a blood flow rate at a first anatomical location;
sending data relating to the blood flow rate to a module configured
to analyze the data relating to the blood flow rate; calculating a
numerical value derived from the data relating to the blood flow
rate; determining whether the calculated value corresponds to a
characteristic of the first population or the second population by
comparing the ratio to a predetermined threshold value; and
providing a signal to an operator relating to the calculated value.
The first anatomical location can be the foot. Calculating the
numerical value can comprise calculating a statistical parameter
from the data relating to the blood flow rate, or characterizing
the blood flow rate as a function of a specified time interval. The
statistical parameter can be, for example, a standard deviation.
The method can also include calculating the numerical value
comprises calculating a power spectrum parameter from the data
relating to the blood flow rate, or calculating a ratio derived
from the data relating to the blood flow rate.
[0046] Also disclosed herein is a system for discriminating between
at least a first population and a second population. The system can
include any number of the following: a module configured to receive
blood flow rate data from a first sensor at a first anatomical
location; calculate a numerical value derived from the data
relating to the blood flow rate; determine whether the calculated
value corresponds to a characteristic of the first population or
the second population by comparing the ratio to a predetermined
threshold value; and provide a signal to an operator relating to
the calculated value.
BRIEF DESCRIPTION OF THE DRAWINGS
[0047] FIG. 1A illustrates the pedal angiosomes.
[0048] FIG. 1B illustrates five measurement points on the foot,
each corresponding to one of the angiosomes shown in FIG. 1A.
[0049] FIG. 1C illustrates the branching of the arteries supplying
the pedal angiosomes.
[0050] FIGS. 1D-1H illustrate measurement using diffuse optical
flow (DOF) sensors at each of the five measurement positions of
FIG. 1B.
[0051] FIG. 2 is a block diagram of a system for measuring flow of
turbid media.
[0052] FIG. 3 is a schematic illustration of diffuse light
penetration and detection in multi-layer tissue.
[0053] FIG. 4 is a graph of autocorrelation functions for different
flow rates.
[0054] FIG. 5A is a graph of two blood flow indices (BFIs) during
cuff occlusion protocol.
[0055] FIG. 5B is a graph of autocorrelation functions illustrating
the derivation of the two BFIs of FIG. 5A.
[0056] FIG. 6 is a graph of two BFIs during cuff occlusion
protocol.
[0057] FIG. 7 illustrates various elements of a perfusion
monitoring system, according to some embodiments.
[0058] FIG. 7A illustrates an embodiment of a DSCA perfusion
monitor console and instrumentation box.
[0059] FIG. 7B illustrates embodiments of low-profile sensors.
[0060] FIG. 8A shows the raw BFI data (raw time series BFI data)
measured at the medial plantar section of the foot of two
individuals, one healthy versus one with indications of limb
ischemia, while FIG. 8B shows the equivalent power spectrum data of
the same individuals (Fourier transform of raw time series BFI
data.
[0061] FIGS. 9A and 9B illustrate boxplots of low frequency
oscillation index (LFI) from 26 healthy and 26 ischemic patients,
assessed in two different methods: maximum-based (LFI.sub.M) in
FIG. 9A, and area-based (LFI.sub.A) in FIG. 9B.
[0062] FIGS. 10A and 10B illustrate receiver operating
characteristic (ROC) curves for LFI.sub.M and LFI.sub.A
respectively measured in the medial plantar (MP) region.
[0063] FIG. 11 illustrates a ROC curve for a 5-dimensional SVM
utilizing patient BFI input parameters.
[0064] FIG. 12 shows The Flow Transform Level (FTL) relating to the
time series BFI, e.g., derivation of FTL from time series DSCA
blood flow index (BFI) data, where intensity is measured at a frame
rate of 60 Hz.
[0065] The standard deviation of 5 minutes of Medial Plantar BFI
data sampled at 1 Hz and 2 Hz was calculated, and the resulting ROC
curves are shown in FIGS. 13A and 13B. FIG. 13A illustrates the ROC
of Standard Deviation of BFI @ 1 Hz; FIG. 13B illustrates the ROC
of Standard Deviation of BFI @ 2 Hz.
[0066] The Standard Deviation of BFI from calcaneal and arm also
shows significant difference between healthy and ischemic patients,
but not strongly as with the medial plantar. The p-values of three
positions are compared in FIGS. 14A-14C, which are box plots of
FTLs in the medial plantar, calcaneal, and arm regions,
respectively. FIG. 14D illustrates FTL values for a number of
patients including healthy and ischemic patient populations.
[0067] FIG. 15A is a schematic illustration of a side-firing DOF
sensor.
[0068] FIG. 15B illustrates a cover sock.
[0069] FIG. 15C illustrates a cover sock having a plurality of
embedded side-firing DOF sensors.
[0070] FIG. 15D illustrates another embodiment of a DOF sensor,
with a retention ring and adhesive material.
[0071] FIG. 15E illustrates a detail view of the DOF sensor head
shown in FIG. 15D.
[0072] FIG. 16 is a flow diagram of a method for analyzing absolute
and/or relative blood flow.
[0073] FIGS. 17A-17C illustrate an embodiment of a DOF sensor, with
a horizontal sensor head.
[0074] FIGS. 18A-18D illustrate another embodiment of a DOF sensor
with a horizontal sensor head.
[0075] FIG. 19 illustrates a DOF sensor attached to a patient's
foot.
[0076] FIG. 20 illustrates a DOF sensor attached to a patient's
hand.
DETAILED DESCRIPTION
Diffuse Optical Flow Sensors
[0077] A number of techniques exist for characterizing blood flow
(which may also be referred to herein as blood perfusion), relying
on measuring of diffusion of light. Such techniques include Diffuse
Correlation Spectroscopy (DCS) and Diffuse Speckle Contrast
Analysis (DSCA). Both DCS and DSCA can be used to measure relative
and/or absolute blood flow. Other techniques rely on measuring
diffusion of light to detect other characteristics of tissue, such
as biochemical composition, concentrations of oxyhemoglobin and
deoxyhemoglobin, etc. Such techniques include Diffuse Optical
Spectroscopy (DOS), Diffuse Optical Tomography (DOT), and
Near-Infrared Spectroscopy (NIRS).
[0078] As used herein, "diffuse optical sensor" includes any sensor
configured to characterize properties of blood in tissue via
measurement of diffuse light. As such, diffuse optical sensors
include DCS, DSCA, DOS, DOT, and NIRS sensors. As used herein, the
term "diffuse optical flow sensor" includes any sensor configured
to characterize blood flow in tissue. As such, diffuse optical flow
(DOF) sensors include both DCS and DSCA sensors.
[0079] Near-infrared diffuse correlation spectroscopy (DCS) is an
emerging technique for continuous noninvasive measurement of blood
flow in biological tissues. In the last decade or so, DCS
technology has been developed to noninvasively sense the blood flow
information in deep tissue vasculature such as brain, muscle, and
breast. In contrast to some other blood flow measurement
techniques, such as positron emission tomography (PET), single
photon emission computed tomography (SPECT), and xenon-enhanced
computed tomography (XeCT), DCS uses non-ionizing radiation and
requires no contrast agents. It does not interfere with commonly
used medical devices such as pacemakers and metal implants. It
therefore has potential in cancer therapy monitoring and bedside
monitoring in clinical settings.
[0080] A DCS system can include a light source such as a laser with
a long coherence length, a detector such as a photon-counting
avalanche photodiode (APD) or photomultiplier tube (PMT), and an
autocorrelator. In various embodiments, the autocorrelator may take
the form of hardware or software. As one of the central components
of the DCS system, the autocorrelator computes the autocorrelation
function of the temporal fluctuation of the light intensity
obtained from the detector.
[0081] However, DCS can suffer from a long integration time, high
cost, and low channel number of simultaneous measurements. One
factor contributing to these limitations is dependence on very
sensitive photodetector(s) and subsequent autocorrelation
calculation. Diffuse Speckle Contrast Analysis (DSCA) is a newer
technology that provides an improved flowmetry system enabling
cost-effective, real-time measurements using statistical analysis
without having to rely on autocorrelation analysis on fast
time-series data. This statistical analysis can be implemented
either in spatial domain using a multi-pixel image sensor, or in
the time domain using slow counter. A multi-pixel image sensor can
also be used for time domain analysis such that single or multiple
pixels act as an individual detector, which is especially suitable
for multi-channel application. In various embodiments, this
approach can be used to measure blood flow, whether absolute,
relative, or both.
[0082] DSCA can be implemented in both spatial and time domains.
For spatial DSCA (sDSCA), a raw speckle image is first obtained
from the sample surface. The raw speckle images may first be
normalized by the smooth intensity background, which can be
averaged over a number of speckle images. The speckle contrast,
K.sub.s is defined as the ratio of the standard deviation to the
mean intensity across many detectors or pixels,
K.sub.s=.sigma..sub.s/<I>, where subscript s refers to the
spatial, as opposed to temporal, variations. The quantity K.sub.s
is related to the field autocorrelation function g.sub.1(.tau.) as
follows:
V ( T ) = [ K s ( T ) ] 2 = 2 T .intg. 0 T ( 1 - .tau. / T ) [ g 1
( .tau. ) ] 2 .tau. ##EQU00001##
[0083] where V is the intensity variance across the image, and T is
the image sensor exposure time. By using the known solution of the
correlation diffusion equation in the semi-infinite medium, the
formal relationship between the flow rate and K.sub.s can be
derived. The relationship between the flow and 1/K.sup.2 turns out
to be substantially linear in the range of flow seen in body
tissue, with 1/K.sub.s.sup.2 increasing with increasing flow
rate.
[0084] Another way to implement this speckle contrast rationale for
flowmetry is to use statistical analysis on time series data
obtained by integrating over a certain time. This temporal domain
analysis is referred to herein as tDSCA. The integrating time for
tDSCA can be regarded as analogous to the exposure time of the
image sensor in sDSCA. In the case of tDSCA, a detector with
moderate sensitivity with an integrating circuit can be used. For
example, each pixel on a CCD chip can be used for this purpose as
each CCD pixel keeps accumulating photoelectrons for a given
exposure time. Therefore, a number of single-mode fibers can be
directly positioned on some locations on a single CCD chip,
resulting in a multi-channel tDSCA system without losing any time
resolution. The number of channels is only limited by the CCD chip
size, pixel size, and the area of each fiber tip. In some
embodiments, tDSCA can use sensitive detectors such as avalanche
photodiode (APD) and/or photomultiplier tube (PMT) with a slow
counter such as a counter included in a DAQ card with USB
connection, but scaling this embodiment to multichannel instrument
is costly and bulky. Time-series data taken either way can be
obtained by repeat measurements, for example 25 measurements can be
made consecutively, after which the data can be analyzed
statistically to determine the flow rate. In a configuration with
an exposure time of 1 ms, one flow index would be obtained every 25
ms, resulting in approximately 40 Hz operation.
[0085] The statistical analysis of the time-series data can be
substantially identical to that described above with respect to
sDSCA, except that the statistics (average intensity and standard
deviation of intensity) are calculated in the time domain, rather
than the spatial domain. As a result, tDSCA may provide lower time
resolution than sDSCA. However, the detector area for tDSCA may be
significantly smaller than with sDSCA. As with the spatial domain
counterpart, tDSCA provides an approach with instrumentation and
analysis that are significantly simpler and less computationally
intensive than traditional DCS techniques.
[0086] Both DCS and DSCA technology can be used to evaluate on a
real-time basis the absolute and/or relative blood flow in the
foot, thereby providing an important tool for interventional
radiologists and vascular surgeons treating ischemia in the foot.
With current tools in the operating room, the physician can usually
assess via X-ray fluoroscopy whether an intervention such as a
balloon angioplasty procedure has succeeded in opening up and
achieving patency of a limb artery. However, the clinical
experience has been that structural patency as observed with
fluoroscopy is not a reliable indicator of successful reperfusion
of the topographical region of the foot where the ulcer wound,
ischemic tissue (e.g., blackened toes) or other clinical
manifestation is located. To augment fluoroscopic data on arterial
patency, a plurality of DOF sensors used in either DCS or DSCA
systems can be positioned at different topographical regions of the
foot to assess absolute and/or relative blood flow in the different
regions. For example, the topographical regions may correspond to
different pedal angiosomes.
[0087] An angiosome is a three-dimensional portion of tissue
supplied by an artery source and drained by its accompanying veins.
It can include skin, fascia, muscle, or bone. Pedal angiosomes are
illustrated in FIG. 1A. Below the knee, there are three main
arteries: the anterior tibial artery, the posterior tibial artery,
and the peroneal artery. The posterior tibial artery gives at least
three separate branches: the calcaneal artery, the medial plantar
artery, and lateral plantar artery, which each supply distinct
portions of the foot. The anterior tibial artery supplies the
anterior ankle and continues as the dorsalis pedis artery, which
supplies much of the dorsum of the foot. The calcaneal branch of
the peroneal artery supplies the lateral and plantar heel. The
anterior perforating branch of the peroneal artery supplies the
lateral anterior upper ankle. As a result, the pedal angiosomes
include: the angiosome of the medial plantar artery, the angiosome
of the lateral plantar artery, the angiosome of the calcaneal
branch of the posterior tibial artery, the angiosome of the
calcaneal branch of the peroneal artery, the angiosome of the
dorsalis pedis artery. There is some debate as to whether there is
a separate sixth pedal angiosome corresponding to the anterior
perforating branch of the peroneal artery.
[0088] FIG. 1B illustrates five measurement points on the foot,
each corresponding a pedal angiosome identified in FIG. 1A. By
detecting blood flow in each of these positions, blood flow from
the various arteries can be evaluated independently. For example,
measurement of blood flow at point A (see FIG. 1D) is indicative of
blood flow from the dorsalis pedis artery, and also the anterior
tibial artery. Similarly, measurement of blood flow at point B (see
FIG. 1E) corresponds to the medial plantar artery, while point C
(see FIG. 1F) corresponds to the lateral plantar artery, point D
(see FIG. 1G) corresponds to the calcaneal branch of the posterior
tibial artery, and point E (see FIG. 1H) corresponds to the
calcaneal branch of the peroneal artery.
[0089] FIG. 1C is a branching diagram of the arteries supplying the
pedal angiosomes. The blood flow measurement points A-E are
illustrated as terminating respective artery branches, though in
practice the measurement points need not be at the distal-most end
of the respective arteries. As noted above, measurements at any of
the points A-E may provide valuable clinical information regarding
local perfusion.
[0090] Topographical-based peripheral vascular interventions, such
as angiosome-directed peripheral vascular interventions, have been
developed relatively recently, and show promising performance
compared with traditional intervention, particularly in terms of
improved limb salvage rates. A system employing a plurality of DOF
sensors can provide real-time feedback on changes in perfusion of
different topographical locations in the foot, e.g. angiosome by
angiosome, so that interventional radiologists or vascular surgeons
may immediately evaluate whether specific intervention at a target
artery has succeeded in restoring sufficient blood perfusion to the
targeted topographical region of the foot where the ulcer wound,
ischemic tissue or other clinical manifestation is located.
[0091] FIG. 2 is a block diagram of a system for measuring flow of
turbid media. A sample 102 includes a heterogeneous matrix therein.
Within this matrix is an embedded flow layer with randomly ordered
microcirculatory channels through which small particles 207 move in
a non-ordered fashion. For example, in some embodiments the sample
may be body tissue, with a complex network of peripheral arterioles
and capillaries. A source 108 injects light into the sample 102. A
detector 110 can detect light scattered by the moving particles 207
in the microcirculatory channels. The detector 110 can be
positioned to receive light that passes from the source into the
sample, and diffuses through the sample. In some embodiments, the
detector can be coupled to the sample by a single-mode optical
fiber. In some embodiments, the detector may be a multi-pixel image
sensor, for example a CCD camera, used to image an area of the
sample. In other embodiments, the detector may be a photon-counting
avalanche photodiode (APD) or photomultiplier tube (PMT). As the
particles flow in random direction, the scattering of light from
the source 108 will vary, causing intensity fluctuations to be
detected by the detector 110.
[0092] An analyzer 112 is coupled to detector 110 and configured to
receive a signal from the detector 110. The analyzer 112 may
comprise an autocorrelator, which measures the temporal intensity
autocorrelation function of light received by the detector 110. The
autocorrelation function can be used to obtain the scattering and
flow characteristics of the small particles flowing in the sample
102. The time-dependent intensity fluctuations reflect the
time-dependent density fluctuations of the small particles 207, and
accordingly the autocorrelation function can be used to determine
the flow rate within the sample 102. In some embodiments, a
hardware autocorrelator may be employed, while in other embodiments
a software autocorrelator can be used. The flow rate or other
characteristic determined by the analyzer 112 may be outputted to a
display 114. The measured quantity may therefore be provided to an
operator via the display 114. In various embodiments, the operator
may be a clinician, diagnostician, surgeon, surgical assistant,
nurse, or other medical personnel. In some embodiments, the
measurement may be provided via display 114 in substantially
real-time. In some embodiments, the measurement may be provided via
display 114 within about 1 second from measurement, e.g., within
about 1 second of the time that the scattered light is detected by
the detector, the measurement may be provided via display 114. In
various embodiments, the measurement may be provided within less
than about 10 minutes, within less than about 5 minutes, within
less than about 1 minute, within less than about 30 seconds, within
less than about 10 seconds, or within less than about 1 second from
detection.
[0093] In some embodiments, as noted above, a software
autocorrelator may be used. This may advantageously provide
additional flexibility compared with a hardware autocorrelator, as
it allows for data pre-processing. A software autocorrelator may
also reduce the cost of a DCS system, while also reducing size and
improving form factor. The ability to pre-process data can also
improve the accuracy of measurements.
[0094] FIG. 3 is a schematic illustration of diffuse light
penetration and detection in multi-layer tissue. As illustrated, a
source 202 and a detector 204 are both positioned adjacent a
portion of tissue 206. As noted above, in some embodiments optical
fibers may be used to couple one or both of the source and detector
to the tissue. The tissue 206 is multi-layer, including an upper
layer 208 with no flow, and a deeper layer 210 with flow. A
plurality of light-scattering particles 212 flow within capillaries
in flow layer 210, and may include, for example, red blood cells.
As light 214 is emitted from the source 202, it diffuses as it
penetrates the tissue 206. As illustrated, a portion of the light
214 is diffused such that it is incident on the detector 204. The
light 214 may follow a roughly crescent-shaped path from the source
202 to the detector 204. The depth of penetration of the light 214
detected by the detector 204 depends on the separation between the
source and the detector. As the distance increases, penetration
depth generally increases. In various embodiments, the separation
distance may be between about 0.5 cm and about 10 cm, or in some
embodiments between about 0.75 cm and about 5 cm. Preferably, in
other embodiments the separation distance may be between about 1 cm
and about 3 cm. In various embodiments, the separation distance may
be less than about 10 cm, less than about 9 cm, less than about 8
cm, less than about 7 cm, less than about 6 cm, less than about 5
cm, less than about 4 cm, less than about 3 cm, less than about 2
cm, less than about 1 cm, less than about 0.9 cm, less than about
0.8 cm, less than about 0.7 cm, less than about 0.5 cm, less than
about 0.4 cm, less than about 0.3 cm, less than about 0.2 cm, or
less than about 0.1 cm. The penetration depth may vary, for example
in some embodiments the penetration depth of the sensor may be
between about 0.5 cm and about 5 cm, or in some embodiments between
about 0.75 cm and about 3 cm. Preferably, in other embodiments the
penetration depth may be between about 5 mm and about 1.5 cm. Of
course, the tissue optical properties of the various layers also
contribute to the penetration depth of the light, as does the
intensity, wavelength, or other characteristics of the light
source. These variations can allow for the depth of measurement to
be adjusted based on the part of the body being analyzed, the
particular patient, or other considerations. Measurements obtained
by the detector 204 may then be processed and analyzed to calculate
the autocorrelation function. As seen in FIG. 4, the
autocorrelation function may be used to determine the flow rate in
the tissue.
[0095] FIG. 4 is a graph of autocorrelation functions for different
flow rates, with steeper decay of the autocorrelation curve
indicating faster flow rates. The autocorrelation curves are
plotted on a semi-logarithmic scale in the graph. As is generally
known in the art, blood flow data can be analyzed by fitting each
autocorrelation curve to a model, such a semi-infinite, multi-layer
diffusion model. The fitted autocorrelation curves can then provide
relative blood flow rates, which can be usefully applied during
peripheral interventional procedures such as balloon angioplasty or
surgery, or as a diagnostic tool.
[0096] Diffuse optical flow (DOF) sensors (which, as described
above, can include either or both DCS and DSCA sensors) can be
particularly useful in measuring microcirculation, for example in
measuring blood perfusion in the foot. This technique can be
additionally improved by employing the concept of pedal topography.
One example of a topographical analysis of blood flow in the foot
incorporates the concept of pedal angiosomes, as described
above.
[0097] In many cases, prior to vascular intervention, an
interventional radiologist or vascular surgeon will image the
vasculature of interest, for example using fluoroscopy, computed
tomography, ultrasound, or other imaging technique. With such
imaging, several potential occlusions or lesions may be identified.
Peripheral intervention, such as balloon angioplasty, atherectomy,
or surgical bypass/grafts can be employed to re-open one or more of
the identified occlusions or lesions ("the target lesions"), in an
effort to restore perfusion to the affected region(s) of the foot.
For these peripheral interventions to result in successful limb
salvage, blood perfusion must reach a sufficient level that permits
healing of the foot wound. Without a real-time perfusion monitor, a
physician has no way of knowing for sure if an intervention has
achieved an improvement in perfusion sufficient for wound healing,
or at all. The use of real-time measurement of blood perfusion at
various topographic locations of the foot, as described herein,
addresses this problem. It provides objective quantitative
perfusion data in real-time so that the physician can know with
certainty whether a specific intervention at a target lesion has
succeeded in restoring perfusion to the topographic region of the
foot on which the wound is located. If a determination has been
made that an acceptable level of perfusion at the desired
topographic region has been achieved, the physician can avoid the
additional risk associated with further intervention, and bring the
procedure to a close. Alternatively, if a specific intervention at
a target lesion has not resulted in any perfusion improvement as
measured by a real-time perfusion monitor, the physician will
thereby be guided to undertake the additional risk of proceeding
onto secondary target lesions. The use of a real-time perfusion
monitor thus averts the situation where a peripheral intervention
procedure is ended prematurely prior to achieving the desired
improvement in perfusion. It also guides physicians as to which
target lesion (when revascularized) resulted in the greatest
perfusion improvement at the desired topographic region of the
foot. This real-time knowledge would in turn inform the physician
as to the optimal placement for use of a drug-eluting balloon or
other means to prolong the patency of the vessel in which the said
lesion is located.
[0098] Although changes in perfusion can be seen directly from the
change in shape of the autocorrelation function, potentially more
useful ways to define a blood flow index (BFI), which may also be
referred to herein as a blood perfusion index (BPI) have been
developed. FIG. 5A is a graph of two such BFIs over time during a
cuff occlusion protocol. The dashed vertical lines indicate the
starting and stopping times of the cuff inflation. The top chart
illustrates a BFI calculated from vertical crossing of the
autocorrelation curve, while the lower chart illustrates a BFI
calculated from horizontal crossing of the autocorrelation curve.
FIG. 5B is a graph illustrating these two different methods of
calculating BFI. The solid line represents the zero flow reference
data, while the dotted line represents real-time autocorrelation
data. The vertical crossing indicator compares the y-axis value
(g.sub.2) of the real-time autocorrelation data and the reference
data at a given time. For example, the first indicator can be
calculated as 1/g.sub.2 or 1.5-g.sub.2. The horizontal crossing
indicator compares the time difference between the autocorrelation
data and the reference data at a given flow rate. For example, the
second indicator can be calculated as log(t2/t1).
[0099] Charts such as those shown in FIG. 5A, or other such indicia
of blood flow, can be displayed to an operator in real-time via
audible, visual, or tactile feedback. A physician may thereby be
provided with substantially real-time feedback on the efficacy of a
peripheral intervention. For example, during balloon angioplasty, a
physician can monitor the BFI as measured on a specific location of
the foot. The BFI will decrease while the balloon is inflated, and
increase after deflation. After repeated inflation of the balloon
to perform the angioplasty, the BFI should increase relative to the
pre-angioplasty baseline, indicating that the angioplasty procedure
has resulted in an improvement in perfusion at the target foot
tissue. A BFI that does not increase relative to the
pre-angioplasty baseline indicates that the balloon angioplasty was
not successful in restoring perfusion. Providing such feedback in
real-time is an enormous benefit to physicians performing vascular
intervention. Rather than waiting post-operatively for hours or
days to determine whether perfusion has been improved, during which
time the foot may deteriorate to the point of requiring amputation,
the use of DOF sensors at select pedal locations during the
angioplasty procedure can provide immediate feedback, allowing the
physician to continue, modify, or conclude the procedure as needed.
As noted above, in various embodiments, the feedback may be
provided, in some cases, within less than about 10 minutes, within
less than about 5 minutes, within less than about 1 minute, within
less than about 30 seconds, within less than about 10 seconds, or
within less than about 1 second from measurement. In some
embodiments, success of a revascularization procedure can be
indicated by an increase in BFI of about or at least about 5%, 10%,
15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%,
80%, or more compared to a BFI value prior to the procedure.
[0100] While the example above relates to balloon angioplasty, the
use of DOF sensors to assess blood flow (whether relative,
absolute, or both) in the foot can be advantageously applied
before, during, or after a number of different interventions. For
example, DOF sensors can be used to aid interventions such as
rotational atherectomy, delivery of lytic substances including but
not limited to tPA, bypass procedures, stent and/or graft
placement, or any other intervention.
[0101] In addition to the above-described real-time monitoring of
blood perfusion in the operating room, derivative indices based on
the raw blood perfusion data generated via DCS or DSCA can also
serve as tools in an inpatient or outpatient setting, for example,
to direct appropriate wound or ulcer therapy based on the patient's
level of tissue perfusion, or to screen for critical thresholds of
peripheral arterial disease, by measuring blood perfusion in the
extremities (e.g. the foot). Such derivative indices include the
Foot Thumb Index ("FTI"), the Low Frequency Oscillation Index
("LFI") and its two parameters of "LFI.sub.A" and "LFI.sub.M", as
well as the Support Vector Machines Index ("SVM") and the Flow
Transform Level ("FTL"). These derivative indices are described
below and will jointly be referred to as "the Derivative Indices."
In some embodiments, the function of time references in one or more
of the derivative indices can be, for example, between about 15
seconds and about 15 minutes, between about 30 seconds and about 5
minutes, between about 30 second and about 2 minutes, or about 30
seconds, 45 seconds, 1 minute, 1.5 minutes, 2 minutes, 2.5 minutes,
3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9
minutes, 10 minutes, or ranges involving any two of the foregoing
values.
The Foot-Thumb Index ("FTI")
[0102] As described elsewhere in this application, blood flow
measurements may be made via absolute BFI measurements, or relative
BFI readings before and after an intervention, for example, before
and after angioplasty, such as balloon angioplasty. However, an
alternative measurement of perfusion may be obtained by taking the
ratio of the absolute BFI in the foot to the absolute BFI in
another reference location on the body such as but not limited to
the thumb, earlobe, upper arm (deltoid/shoulder region), or palm
(thenar eminence). For ease of reference, this alternative
measurement is referred to herein as the FTI (the Foot-Thumb
Index).
[0103] The FTI may address the difficulty in comparing absolute BFI
readings from one individual to another given the variability
occasioned by different physiological and environmental factors
such as room temperature, skin/tissue temperature, hemoglobin
concentration, time of day, skin pigmentation, etc. It thus allows
for calibration towards a standardized value or range of values
that serves as basis for differentiating ischemic and non-ischemic
tissue. This standardization would provide reference values for
populations, both normal and abnormal, and do so without the need
for standardized temperature or environmental pressure which is
required for laser Doppler or transcutaneous oxygen
measurements.
[0104] With reference to FIG. 6, the upper chart illustrates the
relative BFI chart reflecting perfusion in the medial plantar
angiosome of the foot of an individual undergoing a cuff occlusion,
as measured by DSCA, with the cuffed period marked out between the
vertical lines. The lower chart illustrates the FTI or medial
plantar BFI normalized against the thumb BFI of the same patient
during the cuff occlusion process. As shown in FIG. 6, the
unnormalized values reflect the absolute BFI readings while the FTI
(normalized) value (between the two vertical markers) reflects the
FTI during the cuffed period. The 0.91 listed in FIG. 6 refers to
the mean of the BFI prior to cuff occlusion (to the left of the
first vertical line).
[0105] In some embodiments, comparing the calculated FTI to a
predetermined threshold value is utilized to discriminate between a
first population (e.g., a population having a characteristic
disease or risk factor for the characteristic disease, e.g., an
ischemic population) and a second population (e.g., a population
not having a characteristic disease or risk factor for that
characteristic disease, e.g., a non-ischemic population; or a
different disease or risk factor for the different disease). In
some embodiments, it can be determined that a subject falls into a
characteristic population, e.g., an ischemic population, if the FTI
is less than about 0.90, 0.88, 0.86, 0.84, 0.82, 0.80, 0.78, 0.76,
0.74, 0.72, 0.70, 0.68, 0.66, 0.64, 0.62, 0.60, 0.58, 0.56, 0.54,
0.52, 0.50, 0.48, 0.56, 0.54, 0.52, 0.50, 0.48, 0.46, 0.44, 0.42,
0.40, or less.
Low Frequency Oscillation Index ("LFI")
[0106] LFI is a measurement index related to LFO, the low frequency
oscillation observed in hemodynamic measurements such as blood
flow, oxygenation, volume and pressure. The current literature
describes two different origins of LFO, namely those derived from
Mayer waves and vasomotion waves. Mayer waves are spontaneous
oscillations in arterial pressure, which oscillations have
significant correlation with oscillations of sympathetic nerve
activity. Vasomotion waves, on the other hand, are oscillations
generated by the blood vessel walls. The key distinction is that
Mayer waves are driven by nerve activity while vasomotion waves are
a characteristic of the vessel wall's autonomic behavior which is
not correlated to nerve activity.
[0107] Similar observations have been made in earlier studies,
albeit with more rudimentary laser Doppler tools (Schmidt et al, J
Vasc Surg 1993; 18:207-15 and Stansberry et al, Diabetes Care; July
1996; 19, 7: 715-21). In the context of blood flow, oscillations
caused by vasomotion can be measured by laser Doppler flowmetry,
but only in small spatial scale. In clinical parlance, this means
that laser Doppler cannot penetrate beyond skin level to measure
tissue perfusion at depth. With the development of diffuse speckle
contrast analysis (DSCA) which can utilize a CCD as a detector to
integrate transmitted light intensity, coupled with statistical
analysis to retrieve minute blood flow data, it is now possible to
overcome the limitations of laser Doppler to measure LFO at tissue
depths up to two centimeters, or greater in some embodiments.
Relative to laser Doppler, the tissue volume measured in DSCA is
several orders of magnitude larger, and hence the observed LFO is
much more sensitive to microcirculatory pathologies.
[0108] Studies by Rucker et al (Rucker et al in Am J Physiol Heart
Circ, 2000) showed that under critical perfusion conditions (when
arterial blood supply is reduced to the point of ischemia), it is
the vasomotion and flow motion in the skeletal muscle that preserve
nutritive function to surrounding tissue like skin, subcutis and
periosteum, which are incapable or less capable of this protective
mechanism. In addition, the impaired endothelial dysfunction as
seen in diabetes directly impairs vasomotor function (Kolluru et al
in Intl J of Vascular Med 2012) leading to delayed vascular
re-modeling and wound healing. It follows therefore that
measurement of either just partial pressure of oxygen (TcPO.sub.2)
or perfusion pressure in the skin alone (SPP) do not reflect the
critical nature of the ischemia in the underlying tissue to be able
to predict wound healing accurately. LFO evaluation of deep tissue
perfusion (e.g., up to 2 cm) is a direct measure of microvascular
vasomotor function in tissue and is likely to be a superior
predictor of wound healing.
[0109] The impact of these underlying pathologies also explains the
significant correlation between the Derivative Indices and
ischemia. In healthy patients, there a larger deviation and
variance in blood perfusion owing to healthier and more elastic
vessels. Also, their cardiovascular function is likely to be
stronger and as a consequence causing larger variation in blood
perfusion. In contrast, patients with ischemia may have a variety
of co-morbidities including diminished cardiac capacity and
calcified microvasculature resulting in less exuberant blood
perfusion fluctuations.
[0110] As described in more detail above as well as in U.S. Pub.
No. 2014/0052006 A1, which is hereby incorporated by reference in
its entirety, optical measurement techniques such as diffuse
speckle contrast analysis (DSCA) can be utilized to measure
real-time blood perfusion in tissue depths of up to two centimeters
(2 cm), in absolute BFI ("blood flow index") units. The BFI
readings, however, do not represent the full extent of the
information that can be obtained from the raw data. In addition to
BFI, it is possible to extract critical information about the
health of the microvascular blood vessels by evaluating
characteristics of the BFI signal, including, but not limited to,
analyzing the BFI signal's power spectrum and statistical
characteristics.
DSCA Measurements in Healthy v. Ischemic Feet
[0111] The BFI measurement of blood perfusion was taken of 68
individuals from two groups. The first group comprised 30 healthy
volunteers, while the second group comprised 38 patients who sought
treatment for claudication, amputation follow-up or general
podiatry. Of the healthy volunteers, 4 were excluded due to
BMI>28. For the patient group, 2 were excluded due to incorrect
fiber connection of the equipment, 2 were excluded for known venous
disease, and 8 were excluded due to normal readings of
ankle-brachial index (ABI) and/or toe-brachial index (TBI), coupled
with physician assessment of a lack of clinical indicators of
ischemia, or the presence of clinical indicators that pointed away
from ischemia. The resulting data was thus based on a comparison of
26 measurements in the healthy group, and 26 measurements in the
patient group.
[0112] In the healthy group, there were 11 men plus 15 women, with
an age range between 22 to 46, and a median age of 31. In the
patient group, there were 14 men plus 12 women, with an age range
between 53 and 82, and a median age of 68.
Data Acquisition
[0113] In some embodiments, a system for blood flow assessment
includes a support structure configured to be positioned on an
anatomical location of a patient, one or more sensors carried by
the support structure, an analyzer configured to analyze data from
the sensor(s) to determine absolute and/or relative blood flow at a
location near the sensor, and a feedback device configured to
provide a signal indicative of the absolute and/or relative blood
flow determined by the analyzer. In some embodiments, as
illustrated in FIG. 7, a system 700 may be arranged in a
distributed configuration comprising at least two sub-sections such
as a console 702 disposed on a movable cart, with an extension
umbilical adaptor 708 that is connected to the catheterization
table 701. The umbilical adaptor 708 will be configured to connect
(e.g., via sensor leads 706) with patient contacting sensors 704,
and can also connect via conduit 707 to the console 702. Placing
the umbilical adaptor 708 in proximity to the patient on the
catheterization table 701 can simplify the connection/disconnection
of sensors 704 to/from the system, and simplify the application of
the sensors 704 to the patient. Any of the connections described
and illustrated can be wired or wireless connections. The umbilical
adaptor 708 may be passive--providing only a remote connection
point for patient contacting sensors 704; or may be
active--comprising active circuitry and optics, which may include
(but is not limited to) sensor detection, identification,
authentication hardware/software; contact verification
hardware/software; one, two, or more laser sources (e.g., 1, 2, 3,
4, 5, or more laser sources); one, two, or more photodetectors
(e.g., 1, 2, 3, 4, 5, or more photodetectors); CPU; display;
touchscreen; keyboard/buttons; audio/visual annunciators; power
source; data storage; wireless/wired/optical networking interfaces;
input/output connectors/interfaces; gesture recognition interface,
and the like. The console 702 can include active circuitry and
optics, which may include (but is not limited to) sensor detection,
identification, authentication hardware/software; contact
verification hardware/software; one, two, or more laser sources
(e.g., 1, 2, 3, 4, 5, or more laser sources); one, two, or more
photodetectors (e.g., 1, 2, 3, 4, 5, or more photodetectors); CPU;
display; touchscreen; keyboard/buttons; audio/visual annunciators;
power source; data storage; wireless/wired/optical networking
interfaces; input/output connectors/interfaces; gesture recognition
interface, and the like.
[0114] The patient contacting sensors may be configured as single
(one-time) use disposables or multiple use devices. Single use
enforcement may be implemented using methods including, but not
limited to, time-limited activation based on unique serial numbers
on packaging, procedure limited activation based on embedded
identification circuitry, frangible connectors, frangible patient
contacting assemblies, light/time/air sensitive materials which
degrade mechanically, chemically, or optically, keyed
resistance/impedance circuits, custom keyed connectors, or any
anti-counterfeiting method that may be appropriately adapted for
this application, or any combination thereof.
[0115] The instrument used for measurement of BFI and LFO can be,
for example, a DSCA perfusion monitor, which may be a 3-channel
monitor in some embodiments. Each of the 3 channels can be
connected via a laser fiber to a sensor comprising a laser source
and detector. FIGS. 7A-7C show one embodiment of the device and the
low-profile sensors 1304 attached to the foot via adhesive Tegaderm
tape 1306 (3M, United States). FIG. 7A illustrates an embodiment of
the console 1300 and instrumentation box 1302. FIG. 7B illustrates
embodiments of low-profile sensors 1304.
[0116] Each volunteer/patient was asked to sit while sensor
locations were identified on the medial plantar and calcaneal areas
of the foot, avoiding calluses, and on the deltoid of the arm.
Local temperature readings were taken at the medial plantar,
calcaneal and deltoid using a non-contact dermal imager (Ti9, Fluke
Corporation). One sensor was then affixed to each of the medial
plantar, calcaneal and deltoid. Once the three sensors were
affixed, the BFI data was recorded for 5 minutes with the patient
in a seated position with both feet hanging down. Thereafter, the
patient was asked to lie down in a supine position and the BFI data
was recorded for another 5 minutes. Finally, the sensors were
detached, and temperature on the three sites was taken one more
time. All readings were taken on the right side of the body, unless
the right foot or forefoot had already been amputated, or where the
patient presented with clinical evidence of greater ischemia on the
left leg as compared to the right e.g. a chronic non-healing wound
on the left foot, extreme claudication on the left leg with no
symptoms on the right, angiographically defined vessel narrowing in
the left limb vessels, etc.
Power Spectrum Analysis:
[0117] The 5 minute time-series BFI data of patients in supine
positions at a sampling frequency of 1 Hz (total data set was 300
points) was normalized by dividing it by its mean value, and then
subjected to a Fast Fourier Transformation to obtain the Power
Spectrum.
[0118] By way of example, FIG. 8A shows the raw BFI data (raw time
series BFI data) measured at the medial plantar section of the foot
of two individuals, one healthy versus one with indications of limb
ischemia, while FIG. 8B shows the equivalent power spectrum data of
the same individuals (Fourier transform of raw time series BFI
data. Low frequency oscillation indices based on maximum peak
signal (LFI.sub.M) are shown with arrows).
[0119] There are several parameters/characteristics that can be
obtained from the Power Spectrum, and a few examples are listed in
the table below:
TABLE-US-00001 TABLE 1 Examples of parameters that can be
derived/extracted from BFI Power Spectra. LFI.sub.M The Low
Frequency oscillation Index (Maximum) relates to the peak signal
power in the frequency band between 0.045 Hz and 0.1 Hz LFI.sub.A
The Low Frequency oscillation Index (Area) relates to the area
under the normalized spectrum in the frequency band between 0.045
Hz and 0.1 Hz Absolute Power: The Absolute Power of a frequency
band, P.sub.A(f.sub.L, f.sub.H), is defined as the signal power
within that specific frequency band from f.sub.L to f.sub.H.
Mathematically: If a signal x(t) has Fourier transform X(f), its
power spectral density is |X(f)|.sup.2 = Sx(f). The absolute
spectral power in the band of frequencies from f.sub.L Hz to
f.sub.H is given by Absolute Spectral Power in Band = .intg. fH fL
Sx ( f ) df ##EQU00002## Relative Power: The Relative Power,
P.sub.R(f.sub.L, f.sub.H), of a frequency band is defined as the
ratio of the absolute power within that specific frequency band
(from f.sub.L to f.sub.H) divided by the total signal power across
the entire frequency spectrum. This is a dimensionless quantity.
P.sub.R(f.sub.L, f.sub.H) = P.sub.A(f.sub.L, f.sub.H)/P.sub.A(0,
.infin.) Mathematically: The relative spectral power measures the
ratio of the total power in the band (i.e., absolute spectral
power) to the total power in the signal. i.e., Relative Spectral
Power in Band = .intg. fL fH Sx ( f ) df .intg. 0 .infin. Sx ( f )
df ##EQU00003## Band-Pass Band-pass filtering refers to the
processing the Correlation original time series data to extract
signal components Coefficient: that exists within a specific
frequency band. For example a 0.01 Hz to 0.1 Hz band-pass filter
will only allow signal components between 0.01 Hz to 0.1 Hz to
pass; signal components frequencies lower than 0.01 Hz or higher
0.1 Hz will be blocked. The Pearson correlation coefficient between
two variables is defined as the covariance of the two variables
divided by the product of their standard deviations. The result is
a number between +1 and -1, where 0 represents that there is no
correlation, and +1 or -1 represent complete positive or negative
correlations respectively. For example a correlation coefficient
can be calculated between the time series BFI data from two
separate anatomical regions of the same patient (e.g. calcaneal BFI
correlation with medial plantar BFI), which will provide a measure
of how similar the two signals are. pass filters are known, such as
the 3rd order Butterworth filter etc. The band-pass correlation
coefficient of two signals is the Pearson correlation coefficient
calculated between two signals that have undergone band-pass
filtering. For example, one can calculate the correlation
coefficient between arm and medial plantar BFI signals that have
been band-pass filtered.
One Dimensional Data Analysis of Power Spectrum
[0120] Two parameters for one-dimensional analysis of the Power
Spectrum were evaluated, based on the Low Frequency Oscillation
Index ("LFI") characteristics within the frequency band between
0.045 Hz and 0.10 Hz, and are described above. LFI.sub.M is defined
as the maximum amplitude in the 0.045-0.10 Hz frequency band, and
assumes that most of the low frequency oscillation (LFO) signals
are explained by one single peak within the LFO frequency range of
0.045 to 0.10 Hz. In another words, it assumes that the frequency
of the LFO signal does not vary appreciably during 5 minutes of
data acquisition time. In contrast, LFI.sub.A is defined as the
area under the curve within the 0.045-0.10 Hz frequency band, is a
more suitable metric if one assumes that the frequency of LFO
changes significantly within this frequency range during the
acquisition time. In some embodiments, other frequency bands can be
utilized for a particular index depending on the desired clinical
result. For example, the frequency in some embodiments could be
less than 0.15 Hz, or less than about 0.10 Hz.
Results
[0121] Using the BFI data taken during this study, the LFI.sub.M
and LFI.sub.A measurements calculated for each volunteer/patient
are shown in FIGS. 9A and 9B, respectively. FIGS. 9A and 9B
illustrate boxplots of low frequency oscillation index (LFI) from
26 healthy and 26 ischemic patients, assessed in two different
methods: maximum-based (LFI.sub.M) in FIG. 9A, and area-based
(LFI.sub.A) in FIG. 9B. Double sided t-test p-values for medial
plantar (MP), calcaneal (C), and deltoid (Arm), respectively, are:
0.00027, 0.022, 0.20 for LFI.sub.M, and 0.0015, 0.016, and 0.41 for
LFI.sub.A. Boxplots are drawn using MatLab, where the horizontal
line within the boxes indicates the median value, while the boxes
indicate 25 to 75 percentile values, and crosses are outliers.
[0122] Receiver operating characteristic (ROC) curves were plotted
to assess the diagnostic accuracy of this test in distinguishing
ischemic from normal populations. In a ROC curve the true positive
rate (Sensitivity) is plotted as a function of the false positive
rate (100-Specificity) for different cut-off points. Each point on
the ROC curve represents a sensitivity/specificity pair
corresponding to a particular decision threshold. One metric used
to determine the accuracy of a test is the Area Under the Curve
(AUC) of an ROC plot: with an AUC of 0.9 to 1 representing
excellent discrimination, while an AUC of 0.5 representing a
worthless test. A test with perfect discrimination (no overlap in
the two distributions) has a ROC curve that passes through the
upper left corner (100% sensitivity, 100% specificity) and an AUC
of 1. Therefore the closer the ROC curve is to the upper left
corner, the higher the overall accuracy of the test.
[0123] FIGS. 10A and 10B illustrate receiver operating
characteristic (ROC) curves for LFI.sub.M and LFI.sub.A
respectively measured in the medial plantar (MP) region. Area under
the curve (AUC) is 0.7805 and 0.7322 for LFI.sub.M and LFI.sub.A,
respectively. Dashed curves are results of nonlinear curve
fitting.
[0124] As MP shows the smallest p-values for both LFI.sub.M and
LFI.sub.A cases in FIGS. 9A-9B, MP data were used to draw ROC
curves in FIGS. 10A-10B. AUC of the ROC curves of around 0.75 or
higher showing a decent discriminating power. By way of comparison,
Figoni et al (J. Rehab Res Dev 2006: 43 (7) 891-904) report that
TcPO.sub.2 has an AUC of 0.82 in discriminating between healthy
subjects, and ischemic patients (identified as prospective
candidates where unilateral transtibial amputation was imminent or
scheduled because of lower-limb ischemia). The ischemic group of
patients in the Figoni study however suffered from an extreme
degree of ischemia in that the decision for an amputation at a
level much above the site of TcPO.sub.2 measurement had already
been made. The patients in the study described above however were
typical patients in an out-patient setting, with none requiring
amputations at the time of testing. Despite this difference in the
degree of ischemia between subjects in this study and the Figoni
study, the AUC is similar between the studies suggesting a much
greater ability for LFI to distinguish subtle differences in the
degree of ischemia compared to TcPO.sub.2.
[0125] The data in FIGS. 9A-10B indicate that LFI.sub.M can be
superior to LFI.sub.A in some cases in its ability to distinguish
ischemic foot tissue from healthy foot tissue, and that the
distinction is particularly pronounced when the measurement is
taken at the medial plantar area of the foot, where the p-value is
statistically significant, and as small as 0.00027, or even
less.
[0126] The distinction between healthy and ischemic medial plantar
tissue is, in some cases, statistically more highly significant
when using LFI.sub.M as the relevant index. Not to be limited by
theory, a possible explanation for this may lie in the fact that
LFI.sub.M provides a more snapshot insight relative to LFI.sub.A.
In other words, LFI.sub.M is a measure of the maximal amplitude
change, and expect healthy vessels with higher elasticity and
better rheology of blood flow would be expected to manifest higher
LFI.sub.M values. In contrast, the LFI.sub.A is an averaged measure
of LFO, meaning that it averages out the multiple oscillatory
changes in a vessel into one averaged change represented by the
area under the curve. Given that LFI.sub.A is also capable of
distinguishing healthy versus ischemic tissue, it is possible that
LFI.sub.A does reflect overall functions of elasticity and rheology
over a period of time. It may simply be that, for a 5 minute
reading such as that used in this study, LFI.sub.M is a more
discriminatory index than LFI.sub.A. This hypothesis is supported
by smaller p-values associated with the use of LFI.sub.M versus
LFI.sub.A. In some embodiments, longer or shorter reading periods
can be utilized, such as about 1, 2, 3, 4, 6, 7, 8, 9, 10, 15, 20,
25, or 30 minutes as non-limiting examples.
[0127] This distinction, as shown in FIGS. 9A-9B, in some cases is
most clearly seen in the medial plantar, relative to the calcaneal
area of the foot, and the deltoid. The medial plantar vasculature
depends upon an intact pedal-plantar arch for blood supply and it
is at this level that occlusive arterial disease most commonly
presents. The medial plantar is therefore much more vulnerable to
ischemia, in contrast with the calcaneal circulation which is
dually supplied by the peroneal and the posterior tibial vessels.
The deltoid region is much less affected than the feet, if at all,
as significant upper limb arterial disease is rare in
atherosclerosis and/or diabetes.
[0128] In some embodiments, an LFI.sub.A value of less than about
130, 127.5, 125, 122.5, 120, 117.5, 115, 112.5, 110, 107.5, 105,
102.5, 100, 97.5, 95, 92.5, 90, 87.5, 85, 82.5, 80, 77.5, 75, 72.5,
70, 67.5, 65, 62.5, 60, 57.5, 55, 52.5, 50, or less can serve as a
predetermined discriminatory cut-off value between a first
population and a second population and indicate a risk factor for a
characteristic or a disease characteristic, e.g., ischemia, such as
severe ischemia, and notify the clinician by prompting an audible,
visual, or other signal, such as visually on the display, for
example.
[0129] In some embodiments, an LFI.sub.M value of less than about
15, 14.5, 14, 13.5, 13, 12.5, 12, 11.5, 11, 10.5, 10, 9.5, 9, 8.5,
8, 7.5, 7, 6.5, 6, or less can serve as a pre-determined
discriminatory cut-off value between a first population and a
second population and indicate a risk factor for a characteristic
or a disease characteristic, e.g., ischemia, such as severe
ischemia, and notify the clinician by prompting an audible, visual,
or other signal, such as visually on the display, for example.
Multi-Dimensional Data Analysis of Power Spectrum
[0130] In addition to the parameters LFI.sub.M and LFI.sub.A
described above, there are other parameters or methods of analyzing
the BFI data for the purposes of discriminating between the two
patient populations. In some embodiments, multiple independent
parameters can be utilized in conjunction in order to more
accurately discern to which population a patient belongs.
[0131] Analysis of multi-dimensional data sets can be facilitated
by the use of various strategies including, but not limited to, the
use of artificial neural networks (ANN), extreme learning machines
(ELM), and support vector machines (SVM). In particular, an SVM is
a means to define a hyperplane in multi-dimensional space that
discriminates between two populations. In some embodiments, a SVM
can be utilized to process multi-dimensional inputs comprising
parameters such as, but not limited to, relative signal powers in
specific frequency bands of a particular anatomical BFI signal,
absolute signal powers in specific frequency bands of a particular
anatomical BFI signal, and/or correlation coefficients between band
pass filtered BFI signals.
[0132] In some embodiments, an SVM can utilize one, two, or more of
the following five independent inputs (as described in Table 1)
from each patient from the data set described above: Band pass
(0.001 Hz to 0.110 Hz) relative power of the calcaneal BFI; Band
pass (0.001 Hz to 0.110 Hz) relative power of the medial plantar
BFI; Band pass (0.471 Hz to 0.478 Hz) absolute power of the deltoid
BFI; Band pass (0.471 Hz to 0.478 Hz) absolute power of the medial
plantar BFI; and/or Band pass (0.341 Hz to 0.351 Hz) correlation
coefficient between deltoid and medial plantar BFI.
[0133] When run against the same dataset of 26 healthy/26 ischemic
patients, the SVM achieved, in one embodiment, an accuracy of
0.961, a sensitivity of 0.961, and a specificity of 0.961. The ROC
of this SVM is shown in FIG. 11, which illustrates a ROC curve for
a 5-dimensional SVM utilizing patient BFI input parameters as noted
in the preceding paragraph.
Statistical Analysis of a BFI Signal:
[0134] In some embodiments, the statistical parameters of the BFI
signal can also be used as a discriminator. The Flow Transform
Level "FTL" is the standard deviation of the BFI signal calculated
at 2 Hz. FIG. 12 shows how this is derived from and relates to the
time series BFI, e.g., derivation of FTL from time series DSCA
blood flow index (BFI) data, where intensity is measured at a frame
rate of 60 Hz. Other frame rates, such as 30 Hz for example, can
also be utilized depending on the time duration selected.
[0135] The standard deviation of 5 minutes of Medial Plantar BFI
data sampled at 1 Hz and 2 Hz was calculated, and the resulting ROC
curves are shown in FIGS. 13A and 13B. FIG. 13A illustrates the ROC
of Standard Deviation of BFI @ 1 Hz; FIG. 13B illustrates the ROC
of Standard Deviation of BFI @ 2 Hz. As noted elsewhere herein, the
amount of time data sampled can be selected depending on the
desired clinical result, such as about 30 seconds, 45 seconds, 1
minute, 75 seconds, 90 seconds, 105 seconds, 2 minutes, 3 minutes,
4 minutes, 5 minutes, or another time interval. Other frequencies
other than 1 Hz or 2 Hz can be utilized as well, such as a
frequency of between about 0.5 Hz an about 10 Hz, or between about
1 Hz and about 10 Hz.
[0136] If the standard deviation of the BFI at 2 Hz is focused on,
and the data set shortened and analyzed, a slow degradation of the
AUC down to 2 minutes can be observed, and a precipitous drop at 1
minute. This result is shown in Table 2.
TABLE-US-00002 TABLE 2 Dependence of FTL AUC on sample time/data
set size. Sample time AUC for FTL 5 min 0.9645 4 min 0.9633 3 min
0.9554 2 min 0.9241 1 min 0.7428
[0137] The Standard Deviation of BFI from calcaneal and arm also
shows significant difference between healthy and ischemic patients,
but not strongly as with the medial plantar. The p-values of three
positions are compared in FIGS. 14A-14C, which are box plots of
FTLs in the medial plantar, calcaneal, and arm regions,
respectively.
Assessment of Results
[0138] An AUC of the ROC curves of around 0.75 or higher showing a
decent discriminating power, and an AUC exceeding 0.90 is
considered excellent in some embodiments. By way of comparison,
Figoni et al (J. Rehab Res Dev 2006: 43 (7) 891-904) report that
tcPO2 has an AUC of 0.82 in discriminating between healthy
subjects, and ischemic patients (identified as prospective
candidates where unilateral transtibial amputation was imminent or
scheduled because of lower-limb ischemia). The ischemic group of
patients in the Figoni study however suffered from an extreme
degree of ischemia in that the decision for an amputation at a
level much above the site of TcPO2 measurement had already been
made. In some embodiments, patients analyzed are typical patients
in an out-patient setting, with none requiring amputations at the
time of testing. FIG. 14D illustrates a graph showing FTL values
obtained in one study for healthy and ischemic patients on the Y
axis and the patient numerical identifier on the X axis.
[0139] Despite this difference in the degree of ischemia between
subjects with respect to the Figoni study, one-dimensional AUC
using LFI.sub.M can be similar to the Figoni study suggesting a
much greater ability for LFI to distinguish subtle differences in
the degree of ischemia compared to TcPO2. When utilizing multiple
parameters in our SVM, an AUC of 0.969 or better can be achieved,
far exceeding the performance reported for tcPO2.
[0140] Using FTL (Standard Deviation of BFI @ 2 Hz) an AUC of
0.9645 with a single parameter can be achieved from a single sensor
located at the medial plantar. This greatly simplifies the
measurement in some cases and can increase the utility and ease of
implementation of technique for clinical diagnostic and/or
screening applications.
[0141] In some embodiments, an FTL value of less than about 10,
9.75, 9.5, 9.25, 9, 8.75, 8.5, 8.25, 8, 7.75, 7.5, 7.25, 7, 6.75,
6.5, 6.25, 6, 5.75, 5.5, 5.25, 5, 4.75, 4.5, 4.25, 4, 3.75, 3.5,
3.25, 3, 2.75, 2.5, 2.25, 2, or less can serve as a pre-determined
discriminatory cut-off value between a first population and a
second population and indicate a risk factor for a characteristic
or a disease characteristic, e.g., ischemia, such as severe
ischemia, and notify the clinician by prompting an audible, visual,
or other signal, such as visually on the display, for example.
[0142] Referring back to FIGS. 1D-1H, DOF sensors can be separately
placed at different topographical regions of the foot, for example
the DOF sensors can be placed at each of the pedal angiosomes using
separate support structures. In another embodiment, however, a
plurality of DOF sensors can be incorporated into a single support
structure for simultaneous measurement of different pedal regions,
for example the pedal angiosomes. One such embodiment is
illustrated in FIGS. 15A-15C. A side-firing DOF sensor is shown in
FIG. 15A. As illustrated, light from a source can enter the sensor
602 through input cable 604, and can exit the sensor 602 through
the output cable 606 towards the detector. In some embodiments, the
input cable and the output cable can be bundled together. Rather
than having the cable oriented perpendicular to the surface of the
tissue to be measured, in this side-firing sensor the cable is
oriented substantially parallel, with an internal prism, mirror, or
other optical element redirecting light downwards towards the
tissue. As a result, the DOF sensor 602 can be laid flat against
the surface of the area to be measured, with the cables 604 and 606
extending substantially parallel to the surface. The overall effect
is a more low-profile DOF sensor, with improved comfort,
flexibility, and form-factor.
[0143] As used herein, the term "sensor" refers to the terminal end
of the DOF system that makes contact with the sample, for example
the patient's skin. The sensor may include an input optical fiber
coupled to a source and an output optical fiber coupled to a
detector. In other embodiments, the sensor may comprise receptacles
configured to removably receive such optical fibers. The sensor
defines the point at which input light is injected into the sample
surface and the point at which scattered light is detected from the
sample surface. In the illustrated embodiment, the DOF sensor 602
is substantially flat. However, in various embodiments, other
shapes are possible. For example, the DOF sensor may be provided
with a curved surface, for example contoured to correspond to
contours of a patient's body. A DOF sensor may include a concave
surface to correspond to the curvature of a wearer's plantar arch,
for example. In some embodiments, the DOF sensor can be malleable
to permit curvature and flexure to correspond to a patient's body.
As noted above, the distance of separation between the source and
the detector affects the penetration depth of measured light. More
specifically, the significant distance is that between the position
on the surface of the tissue at which light is injected, and
position on the surface of the tissue at which light is detected.
Accordingly, the side-firing DOF sensor 602 may be modified to
provide for different penetration depths depending on the part of
the body in which blood flow is to be measured. If the DOF sensor
is adapted for use in measuring relatively deep blood flow, the
source-detector separation can be greater than for a DOF sensor
adapted for use in measuring relatively shallow blood flow. In some
embodiments, this distance can be variable within an individual DOF
sensor. For example, a mechanism may be provided allowing for the
source input fiber and/or the detector output fiber to be moved
along the length of the DOF sensor to modify the distance
therebetween. For example, in some embodiments the source input
fiber may be substantially fixed in relation to the sensor, while
the detector output fiber is movable. Conversely, in some
embodiments the detector output fiber can be substantially fixed in
relation to the sensor, while the source input fiber can be
movable. In some embodiments, the movable fiber can be slidable
along the sensor, with a latch, screw, detent, or other structure
provided to releasably fix the location of the movable fiber after
a pre-selected distance has been set. In some embodiments, the
movable fiber can be mounted onto a support that is threadably
mated to a screw, such that rotation of the screw causes the
support, and thereby the movable fiber, to be advanced closer to or
further from the fixed fiber. Various other configurations are
possible. In other embodiments, various optical components within
the interior of the DOF sensor can be provided to alter the
effective source-detector distance. For example, the positions of
the fibers may be fixed, while internal prisms or mirrors or other
optical components can be adjusted to direct the light (incident
light from the source or scattered light to the detector) to or
from different locations.
[0144] FIG. 15B illustrates, as one example of a support structure,
a cover sock 608 designed to slip over the patient's foot. As shown
in FIG. 15C, a plurality of side-firing DOF sensors 602 can be
carried by a cover sock. In some embodiments, the side-firing DOF
sensors 602 are arranged at positions corresponding to different
pedal angiosomes. Since each DOF sensor 602 can be made thin and
flexible, they can be sewn or otherwise attached to the cover sock
608 at the appropriate positions. The optical fibers can be bundled
and guided outside the foot covering 608 and connected to an
analyzer. With this design, applying the multiple DOF sensors to a
patient's foot can be quick and essentially foolproof, which is
particularly advantageous in the hectic environment of an operating
room or catheterization lab.
[0145] FIGS. 15D and 15E illustrates another example of a support
structure and DOF sensor. As illustrated, the DOF sensor 610
includes bundled wires 612 extending therefrom. The bundled wires
612 include both the input and output optical fibers, as described
above. A retention ring 614 is configured to surround the
bottom-facing edge of the DOF sensor 610. The retention ring 614
can be affixed to a surface (e.g., a patient's skin) via adhesive
pads 616. The adhesive pads 616 can take a variety of forms,
including, for example Tegaderm.TM. Film. In other embodiments,
adhesive material is deposited onto the retention rings without the
use of separate adhesive pads.
[0146] As illustrated, the retention ring 614 can define an
aperture configured to receive the DOF sensor 610 therein. In
various embodiments, the retention ring 614 can include one or more
retention elements configured to releasably mate with corresponding
retention elements on the DOF sensor 610. The engagement of
corresponding retention elements thereby releasably locks the
sensor 610 into position with respect to the retention ring 614. In
various embodiments, a latch, screw, detent, or other structure can
be provided to releasably fix the DOF sensor 610 to the retention
ring 614.
[0147] Various other support structures are possible. For example,
in some embodiments the DOF sensors may be carried by a series of
straps configured to be wrapped around a patient's foot so as to
position the DOF sensors appropriately with respect to the desired
measurement regions of the pedal topography, for example different
pedal angiosomes. In some embodiments, the DOF sensors may be
carried by a sheet of flexible material to be wrapped around the
patient's foot. In some embodiments, the support structure may be
configured to carry one, two, three, four, five, or more DOF
sensors. In some embodiments, two or more support structures may be
provided for a single patient. For example a first support
structure may carry two DOF sensors and be positioned over a first
portion of a patient's foot, while a second support structure may
carry two additional DOF sensors and be positioned over a second
portion of the patient's foot. In various embodiments, the support
structure may be wearable, for example it may be a garment such as
a cover sock, shoe, etc. In some embodiments, the support structure
can include a strap or series of straps. In other embodiments, the
support structure can comprise an adhesive material by which one or
more DOF sensors can be attached to a patient's skin. For example,
in some embodiments, each of the DOF sensors can be provided with
an adhesive on the tissue-facing side so as to ensure that the
sensors contact the skin. In some embodiments, mechanical pressure
can be applied to the DOF sensors to ensure that they are pressed
against the skin--for example an external wrap may be used, or the
elasticity of a cover sock or other foot covering may itself be
sufficient to ensure that the DOF sensors are adequately held
against the skin. In some embodiments, DOF sensors can be embedded
into a foot plate sensor such as those used by podiatrists. An
individual may step onto the foot plate, and one or more DOF
sensors carried by the foot plate can measure absolute and/or
relative blood flow at various locations on the foot.
[0148] In some embodiments, each DOF sensor may be carried by a
different support structure. In other embodiments, a support
structure can be configured to carry any number of DOF sensors, for
example two, three, four, five, or more. In various embodiments,
the support structure can be configured such that, when the support
structure is positioned over a patient's foot, the position of the
DOF sensors correspond to different topographical locations in the
foot including selected pedal angiosomes. The support structure can
be configured to carry DOF sensors corresponding to any combination
of topographical locations in the foot including pedal angiosomes.
For example, in one embodiment a support structure may be
configured to carry DOF sensors adapted to measure blood flow at
the calcaneal branch of the posterior tibial artery and at the
calcaneal branch of the peroneal artery. In another embodiment a
support structure can be configured to carry DOF sensors adapted to
measure blood flow at the medial plantar artery, the lateral
plantar artery, and the calcaneal branch of the posterior tibial
artery. Various other configurations are possible, such that the
support structure can be tailored to provide DOF sensors at the
desired measurement locations.
[0149] FIG. 16 is a flow diagram of a method for analyzing relative
blood flow. The process 700 begins in block 702 with positioning at
least one DOF sensor on a patient's foot at a location
corresponding to a pedal angiosome. As noted above, in some
embodiments a plurality of such DOF sensors may be positioned at
various places on a patient's foot, or other places on the
patient's body. In some embodiments, a plurality of such DOF
sensors can be used to obtain simultaneous measurements from
different topographical locations in the foot including different
angiosomes. The process 700 continues in block 704 with obtaining
measurement of absolute and/or relative blood flow using the DOF
sensor. As noted above, DOF techniques can provide an
autocorrelation function indicative of the absolute and/or relative
blood flow within the tissue. The process 700 continues in block
706 with signaling the absolute and/or relative blood flow to the
operator. For example the signal may be provided via visual,
audible, or tactile communication. In some embodiments, the
absolute and/or relative blood flow can be signaled to the operator
in substantially real-time, for example within 1 second of
measurement. In some embodiments, a display may be provided that
shows the autocorrelation functions, a chart of blood flow indices
(BFIs), or other indicator of the absolute and/or relative blood
flow. Such a display can provide the operator with real-time
feedback to guide intra-operative decision-making.
[0150] As described above, sensor head designs for DOF sensors
traditionally employ fibers with either metal or ceramic ferrules
to protect the fiber tip, hence the typical sensor head design is
limited to a vertical contact scheme where light out of the fiber
is directly coupled into a sample. The vertical fiber design
suffers from a number of disadvantages when used in applications
for blood perfusion monitoring: it adds bulk, height and positional
instability to the sensor head; it may require additional means of
support to achieve stable and consistent contact with the skin; and
for these reasons, it may cause patient discomfort after prolonged
application.
[0151] Therefore, it is advantageous to implement a low profile
generally horizontal contact sensor head that is both simple and
cost-effective. FIGS. 17A-17C illustrate an embodiment of such a
DOF sensor head. FIG. 17A illustrates a schematic cross-section of
the sensor head 800, and FIGS. 17B and 17C illustrate plan views of
two possible embodiments for the sensor head 800. As illustrated, a
support structure includes a receptacle member 804 with a groove to
receive the optical fibers 806 therein, and a reflector member 808
with a reflecting surface. As illustrated, optical fibers 806 are
applied generally horizontally onto the surface of the sample 810,
and part of the fiber body is disposed within a groove in a
receptacle member 806, and the distal tip of the fiber 806
configured to be positioned between the surface of sample 810 and a
reflecting surface of the reflector member 808. Light coming out of
the source fiber tip will be reflected off of the reflecting
surface in this gap and will be directed towards the sample 810.
For the detector fiber, the reverse will happen: only those light
paths that fall within the acceptance cone will be reflected off of
the reflecting surface and collected by the fiber. In some
embodiments, the reflecting surface may comprise a sheet of
aluminum foil mounted onto a compliant backing such as a rubber,
silicone, or foam pad. It will be appreciated that a wide range of
materials may be utilized as reflectors including metal foils,
metal films, optically reflective coatings, interference gratings,
nanostructured meta-materials, or any other material with suitable
optical properties.
[0152] When applied to a sample, the planar DOF sensor places the
fiber in optical communication with the sample. In some embodiments
an optically transparent sterile barrier comprising at least one
optically transparent layer may be disposed between the fiber and
the sample. The at least one optically transparent layer may be
configured to have adhesive coatings to facilitate attachment of
the planar DOF sensor onto the surface of the sample/tissue. For
example, surgical tape may comprise a support configured to receive
the DOF sensor thereon, and to couple the DOF sensor to the
sample.
[0153] FIGS. 18A-18D show one embodiment of the supports fabricated
using 3D printing, with a support comprising an adhesive layer that
is disposed between the patient/tissue and the optical fibers.
FIGS. 18A and 18B illustrate the support member 902, with FIGS. 18C
and 18D showing top and bottom views, respectively, of the sensor
heads 900 prepared with a layer of surgical adhesive tape 912 to be
disposed between the patient's skin and the fibers. In FIGS. 18C
and 18D, the reflector pads 908 and tips of fibers 906 are obscured
by the adhesive liner of the surgical tape 912. In other
embodiments, the at least one optically transparent layer may not
have an adhesive coating, whereupon the planar DOF sensor may be
attached to the sample by the application of surgical tape, a
mechanical clamp, adjustable strap, or other means.
[0154] FIG. 19 illustrates a plurality of DOF sensors 1000 attached
to a patient's foot. With a source-detector separation of
approximately 1.5 cm on a healthy human foot, arterial cuff
occlusion protocol observations display typical blood perfusion
variations--e.g., a sudden decrease and plateauing during
occlusion, and sharp overshoot and subsequent recovery to baseline
value after release of the cuff pressure. FIG. 20 illustrates a DOF
sensor attached to a patient's hand. The computer screen indicates
a decrease in blood perfusion during arterial cuff occlusion and
subsequent reactive hyperemia, indicating healthy blood flow in the
hand. In the illustrated graph, two sets of cuff-occlusion are
shown with two distinct peaks of reactive hyperemia.
[0155] Advantages of the planar DOF sensor head include its low
weight, its stability during prolonged application, and a higher
level of patient comfort. Its performance is not compromised
compared to a vertical sensor head design, and it can be utilized
in any optical transmission measurement system in semi-infinite
geometry.
[0156] Some embodiments may also include memory to store measured
or computed data (such as but not limited to BFI, FTI, raw DOF
signals), and the capacity to transmit/receive measured or computed
data to/from at least one website/database. The at least one
website/database can provide patients and clinicians access to the
measured or computed data, process/analyze the data and provide
notifications to clinicians and/or patients. These notifications
may include, but are not limited to, alerts when patient should
seek medical attention, updates to clinicians that new patient data
is available for review, etc. The data can be stored in a manner
and compliant with standards applicable to electronic health
records of hospitals and diabetic/podiatry/geriatric/community care
centers. Such a system can enable clinicians, care givers, and
family members to remotely monitor patients, and can be especially
relevant in resource limited regions where access and travel to
clinical care centers are limited and/or difficult. By remotely
assessing patient's health, it will be possible to improve clinical
care by ensuring that only essential travel is undertaken.
[0157] In some embodiments, systems and components as described
herein can take the form of a computing system that is in
communication with one or more computing systems and/or one or more
data sources via one or more networks. The computing system may be
used to implement one or more of the systems and methods described
herein. While various embodiments illustrating computing systems
and components are described herein, it is recognized that the
functionality provided for in the components and modules (which may
also be referred to herein as engines) of computing system may be
combined into fewer components and modules or further separated
into additional components and modules. For example, a
communications engine may include a first module in communication
with a diagnostic imaging modality and a second module in
communication with a destination modality. Modules can include, by
way of example, components, such as software components,
object-oriented software components, class components and task
components, processes, functions, attributes, procedures,
subroutines, segments of program code, drivers, firmware,
microcode, circuitry, data, databases, data structures, tables,
arrays, and variables. Any modules can be executed by one or more
CPUs.
[0158] A software module may be compiled and linked into an
executable program, installed in a dynamic link library, or may be
written in an interpreted programming language such as, for
example, BASIC, Perl, or Python. It will be appreciated that
software modules may be callable from other modules or from
themselves, and/or may be invoked in response to detected events or
interrupts. Software instructions may be embedded in firmware, such
as an EPROM. It will be further appreciated that hardware modules
may be comprised of connected logic units, such as gates and
flip-flops, and/or may be comprised of programmable units, such as
programmable gate arrays or processors. The modules described
herein can be implemented as software modules, but may be also
represented in hardware or firmware. Generally, the modules
described herein refer to logical modules that may be combined with
other modules or divided into sub-modules despite their physical
organization or storage. In addition, all the methods described
herein may be executed as instructions on a CPU, and may result in
the manipulation or transformation of data.
[0159] In some embodiments, hardware components of the system
includes a CPU, which may include one, two, or more conventional
microprocessors. The system further includes a memory, such as
random access memory ("RAM") for temporary storage of information
and a read only memory ("ROM") for permanent storage of
information, and a mass storage device, such as a hard drive, flash
drive, diskette, or optical media storage device. Typically, the
modules of the system are connected using a standard based bus
system. In different embodiments, the standard based bus system
could be Peripheral Component Interconnect ("PCI"), Microchannel,
Small Computer System Interface ("SCSI"), Industrial Standard
Architecture ("ISA") and Extended ISA ("EISA") architectures, for
example.
[0160] In accordance with some embodiments, systems may be
operatively coupled to a destination modality, such as, for
example, an electronic medical record ("EMR"). EMRs may be any
software or hardware-software system configured to store and
provide access to electronic medical data. In accordance with
various embodiments, EMRs may be at least one of an electronic
medical record, an electronic health record, and the like. In some
embodiments, systems and components thereof can be operatively
coupled to a destination modality that can be an email or other
messaging modality; SAMBA, Windows, or other file sharing modality;
FTP or SFTP server modality; a VPN; a printer; and the like.
[0161] In accordance with some embodiments a system may comprise
one, two, or more software modules, a logic engine, numerous
databases and computer networks configured to provide a user with
access to various modalities as described herein and/or an EMR.
Systems may be configured such that patient data, or no patient
data is recorded by the system. While the system may contemplate
upgrades or reconfigurations of existing processing systems,
changes to existing databases and business information system tools
are not necessarily required. Systems may be implemented or
integrated into existing healthcare information management systems,
such as EMRs, without changes to the EMR system, and may interface
with other modalities without changes to the communication system
of the modality.
[0162] In accordance with some embodiments, systems may be software
or hardware-software systems. For example, systems can include a
communication engine configured to receive and transmit medical
information operatively coupled to an information converter
configured to render diagnostic medical information in a suitable
format for storage in a patient EMR; a work list engine configured
to create a user selectable task list from orders captured at an
EMR and selectable by a user at a medical diagnostic modality; and
an event log configured with a user selectable record of
transactions and/or errors in data transmission and/or data
conversion performed by the system.
[0163] In accordance with some embodiments, communication engine
may be any software or hardware software-system configured to
receive and/or transmit data. Communication engine may be
configured to transmit and receive data over a variety of network
interfaces including wired and wireless networks or a combination
thereof, such as via Ethernet, 802.11x, Bluetooth, FireWire, GSM,
CDMA, LTE, and the like. Communication engine may also be
configured to transmit and/or receive data with file transfer
protocols such as TCP/IP, as well as various encryption protocols,
such as, for example, WEP, WPA, WPA2, and/or the like.
[0164] Furthermore, in some embodiments, a communication engine may
be configured as an active or passive module. When communication
engine is passive, it may be configured to be discoverable by
various elements of a larger healthcare management system. In this
way, communication engine may be configured to receive a command or
request from a medical diagnostic modality for a user selected
patient, such that the communication engine may transmit the
request to an EMR, receive the patient data for a specific patient
from the EMR, and transfer the patient data from the EMR to the
medical diagnostic modality. As such, communication engine is only
configured to receive and transmit data. In some embodiments,
communication engine is not configured to collect, capture, or mine
data from, either, an EMR or a medical diagnostic modality.
Clinical Applications
[0165] Embodiments of Derivative Indices of DSCA provide a direct
assessment of microvascular vasomotion in the patient. Endothelial
dysfunction caused by diabetes (Kolluru et al in Intl J of Vascular
Med 2012) undermines normal vasomotion, leading to delayed vascular
re-modeling and wound healing. The Derivative Indices therefore can
in some embodiments provide means to better assess the healing
capacity of patients (both diabetic and non-diabetic) and hence
direct the optimal use of wound care therapy. Additional use for
the Derivative Indices could be for screening patients for
peripheral vascular disease, determining the efficacy of a
revascularization procedure, such as a bypass, stent, graft,
angioplasty, or other procedure, either intraoperatively or
postoperatively; predicting response to advanced wound therapies
such as HBOT, and determining the optimal sites for limb
amputation, for example. Other applications of this technique
include, for example, the assessment of plastic surgery grafts or
flaps for tissue viability. In some embodiments, DOF sensors can be
used to assess blood flow in the foot, ankle, calf, thigh, hand,
arm, neck, or other anatomical locations. In some embodiments, the
DOF sensors can be positioned within the body, for example within
natural orifices, such as the esophagus, stomach, small intestine,
colon, or uterus for example to assess blood flow. In various such
embodiments, DOF sensors can be disposed in accordance with
angiosome theory.
Ischemic Foot Screening
[0166] One, two, or more of the Derivative Indices may be used as a
tool to screen for ischemic feet, particularly for diabetic
patients where the presence of neuropathy as part of the diabetic
disease progression means that claudication is often not a reliable
manifestation of the severity of underlying peripheral arterial
disease, e.g., the patient feels no pain due to diabetic
neuropathy, rather than because there is no atherosclerotic
disease.
[0167] As a screening tool should ideally be small, compact,
inexpensive, and widely deployable and utilized by staff with
minimal training, in some embodiments the system for screening
ischemic feet may be implemented using a small, battery powered,
portable, blood perfusion monitor console comprising a single
sensor that is attached to the patient's foot for measurement
duration of, for example, 10 seconds to 10 minutes. The recorded
time series blood perfusion can then be processed into a power
spectrum via an internal processor. Alternatively, the time series
data may be telemetered to a distributed computational network for
processing. Results of the calculated one or more Derivative
Indices can then be reported directly to the physician's office or
care giver for further follow-up. Alternatively, caregivers or
clinicians may remotely access results via the internet, smart
phone, or other telecommunications device. Patients who present
with endothelial dysfunction and/or ischemia can then be referred
to primary care centers for more directed evaluation and
therapy.
[0168] Diabetic feet are also at risk of ulceration from a
combination of ischemia, high plantar pressures from bio-mechanical
change in the foot as well as neuropathy. In clinical practice, the
combination of these three factors leads to a diagnosis of a
diabetic foot at risk of ulceration ("DFAR"). Annually, 25% of
diabetics are thus diagnosed to be at risk of ulceration, and 50%
of such diagnosed patients subsequently undergo a major or minor
amputation of foot tissue.
[0169] Some approaches measure the three diagnostic indicators
separately--the ankle-brachial index ("ABI") can be used to measure
ischemia, while a pressure footplate can be used to measure plantar
pressure, and a pressure-sensitive monofilament that buckles at a
pre-determined pressure but is not felt on application by the
patient can be used to diagnose neuropathy. There are multiple
disadvantages of these approaches, including (a) ABI measurements
are highly variable depending on the procedural protocol that in
turn varies from hospital to hospital. The position of the patient
is highly material as ankle systolic pressure is affected by
posture--1 mmHg higher for each inch the ankle is below the heart;
(b) the presence of calcified vessels in diabetic feet can generate
falsely high readings of ABI; and (c) the clinic workflow can
become congested at the physician's desk as it takes a medically
qualified doctor to subjectively interpret on a case-by-case basis
three different reports for ischemia, plantar pressure, and
neuropathy in order to make a determination of a diabetic foot at
risk. It typically takes 30 minutes or more for a physician to run
these tests and make a diagnostic determination.
[0170] Some embodiments described herein include one, two, or more
flow sensors, such as diffuse optical flow (DOF) sensors configured
to measure one, two, or more parameters relevant to blood flow, and
operably connectable to one, two, or more anatomical regions of
interest, such as a foot or hand for example. The sensors are in
operative wired or wireless communication with a hardware console
unit configured to receive the parameters from the sensors and
perform predetermined calculations as described elsewhere herein.
Some embodiments described herein comprise a pressure-sensitive
footplate into which is embedded at least one diffuse optical flow
(DOF) sensor heads which will be in optical communication with an
angiosome or other topographic location of the patient's foot so as
to take a measurement based on one or more of the Derivative
Indices, and, optionally, at least one DOF reference sensor head
that can be applied to a suitable location on the patient such as
the thumb or the earlobe, to obtain a reference reading for
computation of the FTI. The device may generate a quantitative
readout per foot of the absolute BFI and/or FTI and/or any other
Derivative Index, as well as the plantar pressure, each with
objective threshold criteria for indicating whether a foot needs
further physician review and therapeutic or pre-emptive
intervention. The device represents a simple, objective and
intuitive method of diagnosing a diabetic foot at risk of ulcer in
a way that removes inter-operator variation and avoids multiple
tests. In some embodiments, to generate a report of the relevant
data, the patient merely has to stand on the footplate device for a
short period of time, for example approximately 30 seconds with an
adhesive sensor head affixed to one thumb or other reference point.
Such a simple outpatient tool can be easily used by nurses,
clinical technicians, physiotherapists etc. in the diabetes or
podiatry care community to more efficiently triage diabetic feet at
risk and thereby ease the workflow congestion caused by the chronic
shortage of physicians in many aging communities worldwide.
Guiding Wound Management
[0171] Current techniques utilized to assess wound healing
potential are sub-optimal. TcPO.sub.2 measurements have been shown
to be poor predictors of HBOT outcome (Fife et al, Wound Rep Reg
2002; 10: 198-207). Skin perfusion pressures are in fact better
predictors of wound healing than TcPO.sub.2 (Lo et al in Wounds
2009), though with a diagnostic accuracy of less than 80% for an
SPP cutoff value of <30 mmHg (Castruonuovo et al in JVS
1997).
[0172] It is possible that TcPO.sub.2 and SPP will never reach the
highest levels of diagnostic accuracy demanded by the clinical
community, as both are limited by the fact that measurements are
only skin deep. Studies by Rucker et al (Rucker et al in Am J
Physiol Heart Circ, 2000) showed that under critical perfusion
conditions, it is the vasomotion and flow motion in the skeletal
muscle that preserve nutritive function to surrounding tissue like
skin, subcutis and periosteum, which are incapable of this
protective mechanism. In addition, the impaired endothelial
dysfunction as seen in diabetes directly impairs vasomotor function
(Kolluru et al in Intl J of Vascular Med 2012) leading to delayed
vascular re-modeling and wound healing. It follows therefore that
measurement of either just partial pressure of oxygen (TcPO.sub.2)
or perfusion pressure in the skin alone (SPP) does not reflect the
critical nature of the ischemia in the underlying tissue, and hence
provides at best a partial indicator/predictor of wound
healing.
[0173] In contrast, the Derivative Indices directly measure the
vasomotor function in tissue at a depth much greater than skin (up
to 2 cm), and thus have the potential to be a superior predictor of
wound healing, and a powerful tool to guide the appropriate therapy
for wound healing. In some embodiments, blood flow can be measured
at a depth of greater than about 2 mm, 4 mm, 6 mm, 8 mm, 10 mm, 12
mm, 14 mm, 16 mm, 18 mm, 20 mm, or more.
[0174] Conservative therapy for wounds (e.g. bandages, moist
dressings) can suffice to facilitate wound healing if the blood
perfusion around the wound tissue is not compromised beyond the
minimal threshold for passive healing to occur. In cases where the
perfusion is thus compromised, however, the inappropriate use of
conservative wound therapy causes a time lag between the first
presentment of a wound in a clinical setting to an effective
therapy commensurate with the seriousness of the wound condition.
The TIME (Tissue viability, Infection control, Moisture,
Epithelialization) model of wound care emphasizes the need for
early diagnosis of tissue viability or otherwise in a wound, which
diagnosis will then drive the therapy pathway towards wound
healing. The single most important determinant of tissue viability
in a wound is its blood supply. The ability to assess the blood
perfusion around the wound bed allows clinical decisions to be made
regarding either (a) continuation of conservative therapy if tissue
is viable or, (b) if blood perfusion is too severely compromised
for successful conservative therapy, to progress to more advanced
wound care products like chemical debriding agents, or advanced
wound therapies such as topical negative pressure, hyperbaric
oxygen therapy etc. In more serious cases, the patient can be
directed to revascularization by peripheral interventional
procedures.
Guiding Amputation Levels
[0175] The Derivative Indices may also have a role in predicting
the success of amputation healing. Amputation is typically
performed on patients with severe limb ischemia who cannot be
treated with reconstructive vascular surgery, patients with
diabetic foot ulcers or venous ulcerations. Approximately, 85-90%
of lower limb amputations in the developed world are caused by
peripheral vascular disease and poor wound healing accounts for 70%
of the complication cases that arises from amputation. In spite of
the use of state of the art technologies to assess amputation
level, the healing rate of below-knee amputation ranges between 30
and 92%, with a re-amputation rate of up to 30%. Post-amputation
wounds fail to heal if the blood perfusion at the amputation level
is inadequate to support wound healing. When this occurs, the
surgical wound breaks down, often with superadded infection, and
can add to revision amputation where the leg is amputated at a
higher level, or to the morbidity of the patient as well delays in
patient rehabilitation and prosthetic fitting. The ability to
measure blood perfusion using one or more of the Derivative Indices
may enable the physician to better predict successful amputation
healing at different levels of the leg to be amputated. This will
guide the physician via objective criteria as to the appropriate
level of amputation to minimize patient pain and suffering while
maximizing limb preservation.
Screening for Hyperbaric Oxygen Therapy
[0176] Hyperbaric oxygen therapy to aid the healing of chronic
non-healing wounds is currently directed by the measurement of
TcPO.sub.2 in the skin surrounding the wound bed before and after
the administration of 100% oxygen. HBOT involves the administering
of oxygen at levels 2-2.5 times sea level in a chamber. The
administration of HBOT as a therapy over a long period of time is
not only expensive and comes with many undesirable side effects
such as ear and sinus barotrauma, paranasal sinuses and oxygen
toxicity of the central nervous system. (Aviat Space Environ Med.
2000; 71(2):119-24.) Moreover, a retrospective study of 1144
patients (Wound Rep Reg 2002; 10:198-207) indicated that 24.4% of
chronic wound patients who received HBOT obtained no benefit from
it. There is therefore a need to better predict the success of HBOT
for any given individual. Since measurements of the Derivative
Indices are taken at tissue depths well below skin level, it holds
potential for the ability to identify those patients for whom HBOT
may well be unsuitable.
Assessment of Surgical Flaps
[0177] A further use of the Derivative Indices in clinical practice
lies in surgical procedures, particular in plastic and
reconstructive surgery, where pedicled or free tissue flaps are
used to cover wound defects. Skin, myocutaneous,
fascio-myocutaneous and osseomyocutaneous flaps are used to
reconstruct tissue defects that may result from trauma, surgery for
tumors, infections or congenital diseases. These flaps depend upon
the blood supply from either their own blood vessels or from
micro-vascular reconstructions with the blood vessels in the
vicinity of the recipient tissue bed for their survival. Both types
of flaps (pedicled and free) are crucially dependent on the blood
perfusion within them for the flaps to survive. Flap perfusion
needs close monitoring especially in the first few hours to days
after the reconstruction procedure and early detection of loss of
perfusion will help to direct the patient for further surgical
procedures as needed to ensure continued flap viability. Monitoring
the perfusion of these flaps either via surface sensors or sensors
within the flap tissue may guide the physician towards an early
intervention that can preserve the viability of the flap. The
Derivative Indices can be potentially used to monitor flap blood
perfusion continuously in the post-operative period and prevent
flap loss due to delayed detection of flap ischemia.
Intravascular and/or Intra-Luminal Tissue Probes for Use in Guiding
Decisions for Various Therapies
[0178] In another embodiment, a DOF sensor for blood flow
assessment, e.g., intravascular use comprises at least two fibers
configured to emit/receive optical signals at their distal ends,
that is delivered via percutaneous and/or transluminal means into
an organ or tissue bed that allows for DCS or DSCA measurements of
blood perfusion in tissue volumes which are in optical
communication with the at least two fibers. Such an intravascular
sensor may be configured to have a small cross-section similar to a
guidewire of between about 0.01 to about 0.04 inches (or about 250
microns to about 1 mm). The intravascular sensor may be disposed
within a flexible sheath that will protect it during delivery, and
facilitate insertion of the probe into the target tissue, whereupon
the sheath may be partially retracted or the distal tip of the
probe partially extended beyond the end of the sheath, so as to put
the distal ends of the at least two fibers in optical communication
with the tissue whose perfusion is to be measured.
[0179] Intravascular and/or intra-luminal tissue probes can enable
the real-time measurement of blood perfusion in visceral organs or
tissue to guide decisions in various medical therapies, including
current treatment protocols for cancer therapy and vascular
malformations. These examples are described in greater detail
below. In some embodiments, systems and methods as disclosed herein
can be utilized for the diagnosis and assessment of the efficacy of
various therapeutic interventions for a wide variety of
indications, including transient ischemic attacks and acute
ischemic strokes (and the efficacy of a neurointerventional
revascularization procedure, such as angioplasty or stent
placement), ischemic bowel, pulmonary embolism, myocardial
infarction, and others. In some embodiments, systems and methods
can also measure active bleeding (such as GI bleeding) and
confirming the cessation thereof. Other indications are described
below.
[0180] (a) Measuring Tumor Vascularity and its Impact on
Photodynamic Therapy as Well as Tumor Sensitization Measurements
Before Radiofrequency Ablation
[0181] The following articles refer to the need for assessing tumor
blood flow in directing radiotherapy, chemotherapy, and
photodynamic therapy, and are hereby incorporated by reference in
their entireties. (Int. J. Radiation Oncology Biol. Phys 2003 V 55,
No 4, pp 1066-1073, "Nitric oxide-mediated increase in tumor blood
flow and oxygenation of tumors implanted in muscles stimulated by
electric pulses", B. F. Jordan, Bernard Gallez et al; The
Oncologist 2008, 13:631-644 "Use of H.sub.2 .sup.15O-PET and
DCE-MRI to Measure tumor blood flow", Adrianus J de Langen et al;
Radiat Res 2003 October 160 (4) 452-9 "Blood flow dynamics after
photodynamic therapy with verteporfin in the RIF-1 tumor" Chen B
Poque, et al) In brief, the potential for success for chemotherapy
is higher in well-perfused tumors. Prior knowledge of this can be
used to identify those patients likely to respond well to treatment
and stream such patients with greater confidence for chemotherapy
treatment. Quantitative measurement of tumor blood flow may also
help calculate doses of chemotherapeutic agents to be delivered,
especially when such chemotherapy is directly delivered into the
tumor via intra-luminal or endovascular means. This will help to
avoid the unnecessary and painful chemotherapy of patients who are
unlikely to benefit from treatment due to the poor vascularity of
their tumors.
[0182] Perfusion has also been shown to play a key role in the
success of hyperthermic treatments like radiotherapy and
photodynamic therapy. Oxygen deficiency in tumors has been shown to
reduce repose to non-surgical treatment modalities like
radiotherapy and chemotherapy. This oxygen deficiency may be caused
by decreased tumor perfusion (diffusion-related hypoxia) or changes
in red cell flux (acute hypoxia). Increasing tumor perfusion by
various methods such as use of vasoactive agents, carbogen
breathing and electrical stimulation of skeletal muscle surrounding
the tumor to increase tumor blood flow have been shown
experimentally to have radiosensitizing effects. Photo-dynamic
therapy (PDT) uses the principle of light at specific wavelengths
causing damage to tumor vasculature and rendering the tumor
ischemic, i.e. starving the tumor of its blood supply. Success of
PDT is thus assessed by the extent to which this ischemia is
achieved. The ability to measure tumor blood flow either by
endovascular or intra-luminal means can thus help direct the use of
these methods to enhance tumor response or to assess tumor response
to these non-surgical therapies.
[0183] (b) Intravascular and/or Intra-Tissue Probes to Guide
Injection of Sclerosing and Embolic Agents During Treatment of
Vascular Malformations
[0184] Vascular malformations ("VMs"), such as arterio-venous
malformations, are a network of abnormal small vessels that are
formed spontaneously or occur congenitally or following trauma to
create an alternate conduit of blood flow between arteries, veins
and capillaries, bypassing the normal blood flow that originates
from the artery through the capillary bed of an organ or tissue and
thence into the vein. Clinical indications for treatment of a VM
include local symptoms of pain, bleeding or ulceration at the site
of the VM, and significant cardiac strain (including high output
cardiac failure) from the high volumes of blood that flow within
these lesions. Superficial VMs may need treatment for cosmetic
reasons as well.
[0185] The treatment for VMs comprises injection via an
endovascular micro-catheter of a sclerosing agent such as absolute
alcohol or sodium tetradecylsulphate, which are toxic to blood
vessels and cause sclerosis or scarring that closes up the small
vessels within the VM. This may be the sole procedure or as part of
a surgical procedure wherein the volume of blood flowing within the
VM is reduced prior to surgical excision. Caution is required
during this procedure because excessive injection of the sclerosing
agent can lead to overflow into normal blood vessels, resulting in
significant damage such as skin necrosis, limb loss, acute
pulmonary hypertension, or even death. The challenge for the
physician is that a balance must be struck between injecting enough
sclerosing agent to completely close up the VM, but not so much
that the sclerosing agent leaks out and causes serious damage
elsewhere. Real-time perfusion monitoring of the VM can signal when
blood flow has ceased within the VM or reduced sufficiently to
allow surgical resection without significant loss of blood. This
may instruct the physician that enough sclerosing agent has been
injected and to avoid further injection, thereby reducing the risk
of an adverse outcome.
[0186] Various other modifications, adaptations, and alternative
designs are of course possible in light of the above teachings.
Therefore, it should be understood at this time that within the
scope of the appended claims the invention may be practiced
otherwise than as specifically described herein. It is contemplated
that various combinations or subcombinations of the specific
features and aspects of the embodiments disclosed above may be made
and still fall within one or more of the inventions. Further, the
disclosure herein of any particular feature, aspect, method,
property, characteristic, quality, attribute, element, or the like
in connection with an embodiment can be used in all other
embodiments set forth herein. Accordingly, it should be understood
that various features and aspects of the disclosed embodiments can
be combined with or substituted for one another in order to form
varying modes of the disclosed inventions. Thus, it is intended
that the scope of the present inventions herein disclosed should
not be limited by the particular disclosed embodiments described
above. Moreover, while the invention is susceptible to various
modifications, and alternative forms, specific examples thereof
have been shown in the drawings and are herein described in detail.
It should be understood, however, that the invention is not to be
limited to the particular forms or methods disclosed, but to the
contrary, the invention is to cover all modifications, equivalents,
and alternatives falling within the spirit and scope of the various
embodiments described and the appended claims. Any methods
disclosed herein need not be performed in the order recited. The
methods disclosed herein include certain actions taken by a
practitioner; however, they can also include any third-party
instruction of those actions, either expressly or by implication.
For example, actions such as "discriminating between two
populations" includes "instructing the discriminating between two
populations." The ranges disclosed herein also encompass any and
all overlap, sub-ranges, and combinations thereof. Language such as
"up to," "at least," "greater than," "less than," "between," and
the like includes the number recited. Numbers preceded by a term
such as "approximately", "about", and "substantially" as used
herein include the recited numbers (e.g., about 10%=10%), and also
represent an amount close to the stated amount that still performs
a desired function or achieves a desired result. For example, the
terms "approximately", "about", and "substantially" may refer to an
amount that is within less than 10% of, within less than 5% of,
within less than 1% of, within less than 0.1% of, and within less
than 0.01% of the stated amount.
* * * * *