U.S. patent application number 16/109517 was filed with the patent office on 2018-12-20 for systems and methods for monitoring, managing, and treating asthma and anaphylaxis.
The applicant listed for this patent is President and Fellows of Harvard College. Invention is credited to Samuel Berry, Alan Dunne, Aymeric Guy, Olivier Henry, Premananda Pai Indic, Donald E. Ingber, Christoph Matthias Kanzler, Mustafa Karabas, Huy Lam, Andy H. Levine, Benjamin Matthews, Daniel Leo Miranda, Joseph Mooney, James Niemi, John Osborne, Jonathan Sabate del Rio, Adam Zapotok.
Application Number | 20180361062 16/109517 |
Document ID | / |
Family ID | 58717961 |
Filed Date | 2018-12-20 |
United States Patent
Application |
20180361062 |
Kind Code |
A1 |
Levine; Andy H. ; et
al. |
December 20, 2018 |
Systems And Methods For Monitoring, Managing, And Treating Asthma
And Anaphylaxis
Abstract
A physiologic sensor module includes at least one wearable
sensor that is configured for wearing on a human body part and for
measuring at least one biological signal. The module further
includes at least one controller communicatively coupled to the
wearable sensor and configured to receive the biological signal
from the wearable sensor. The controller is further configured to
process the biological signal in real-time, extract one or more
clinical features from the biological signal, and based on the
clinical features, determine detection of anaphylaxis.
Inventors: |
Levine; Andy H.; (Newton,
MA) ; Kanzler; Christoph Matthias; (Brookline,
MA) ; Guy; Aymeric; (Somerville, MA) ;
Miranda; Daniel Leo; (Natick, MA) ; Mooney;
Joseph; (Sudbury, MA) ; Zapotok; Adam;
(Hanover Township, PA) ; Berry; Samuel; (Seattle,
WA) ; Lam; Huy; (Germantown, MD) ; Sabate del
Rio; Jonathan; (Roxbury, MA) ; Osborne; John;
(Winchester, MA) ; Karabas; Mustafa; (Chestnut
Hill, MA) ; Dunne; Alan; (Cambridge, MA) ;
Niemi; James; (Concord, MA) ; Matthews; Benjamin;
(Newton, MA) ; Ingber; Donald E.; (Boston, MA)
; Henry; Olivier; (Brookline, MA) ; Indic;
Premananda Pai; (Whitehouse, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
President and Fellows of Harvard College |
Cambridge |
MA |
US |
|
|
Family ID: |
58717961 |
Appl. No.: |
16/109517 |
Filed: |
August 22, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15591329 |
May 10, 2017 |
10080841 |
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16109517 |
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PCT/US2016/062920 |
Nov 18, 2016 |
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15591329 |
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62257190 |
Nov 18, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/411 20130101;
A61M 5/14248 20130101; A61B 5/0826 20130101; A61B 5/1486 20130101;
A61M 2205/581 20130101; A61K 31/137 20130101; A61M 2205/587
20130101; A61M 2230/04 20130101; A61M 2230/63 20130101; A61M 5/1723
20130101; A61M 37/00 20130101; A61B 5/113 20130101; A61B 5/7278
20130101; A61M 5/2053 20130101; A61M 2230/30 20130101; A61B 5/00
20130101; A61B 5/1135 20130101; A61M 2005/1726 20130101; A61B
5/14546 20130101; A61M 5/00 20130101; A61M 5/20 20130101; A61M
2205/50 20130101; A61M 37/0015 20130101; A61M 2230/65 20130101;
A61B 5/7264 20130101; A61B 5/1451 20130101; A61B 5/0452 20130101;
A61B 5/02 20130101; A61B 5/08 20130101; A61M 2005/206 20130101;
A61M 2205/18 20130101; A61M 2205/8206 20130101; A61M 2230/005
20130101; A61B 5/0809 20130101; A61B 5/145 20130101; A61B 5/4839
20130101; A61M 2230/50 20130101 |
International
Class: |
A61M 5/172 20060101
A61M005/172; A61M 5/142 20060101 A61M005/142; A61B 5/02 20060101
A61B005/02; A61B 5/08 20060101 A61B005/08; A61B 5/145 20060101
A61B005/145; A61B 5/00 20060101 A61B005/00; A61K 31/137 20060101
A61K031/137; A61M 5/00 20060101 A61M005/00; A61M 5/20 20060101
A61M005/20; A61M 37/00 20060101 A61M037/00 |
Claims
1-22. (canceled)
23. A physiologic module for detecting and treating symptoms of
anaphylaxis, the physiologic module comprising: at least one
wearable sensor for measuring at least one biological signal; a
wearable injector having an enclosure for housing a movable needle
in a retracted position and a reservoir for storing epinephrine,
the needle being movable at least in part outside the enclosure to
an injecting position; and at least one controller communicatively
coupled to the wearable sensor and to the wearable injector, the at
least one controller configured to receive the biological signal
from the wearable sensor, process the biological signal in
real-time, extract one or more clinical features from the
biological signal, based on the clinical features, determine
presence of an anaphylaxis symptom, and in response to the
anaphylaxis symptom, automatically cause the needle to move to the
injecting position and deliver a bolus of the epinephrine to a
human body part.
24. The physiologic module of claim 23, further comprising a
communication port adapted to communicate wirelessly with a mobile
device, the at least one controller being further configured to
contact a caregiver or emergency services in response to
determining the presence of the anaphylaxis symptom.
25. The physiologic module of claim 23, further comprising a manual
activation button for causing the needle to move to the injecting
position and to deliver the bolus of the epinephrine to the human
body part.
26. The physiologic module of claim 23, wherein the biological
signal includes biosensor data indicative of measured histamine
levels.
27. A method for detecting and treating symptoms of anaphylaxis,
the method comprising: measuring, via a wearable sensor, at least
one biological signal; sending the biological signal to a
controller coupled to the wearable sensor; processing, via the
controller, the biological signal in real-time; extracting, via the
controller, one or more clinical features from the biological
signal; based on the clinical features, determine, via the
controller, presence of an anaphylaxis symptom; in response to the
anaphylaxis symptom, automatically cause the needle to inject
epinephrine in a human body part.
28. The method of claim 27, wherein the measuring of the at least
one biological signal includes taking a sample of blood or
interstitial fluid.
29. The method of claim 27, further comprising automatically
contacting a caregiver or emergency services in response to the
anaphylaxis symptom.
30. The method of claim 27, further comprising providing a GPS
position of the wearable sensor to a caregiver or emergency
services.
31. The method of claim 30, wherein the GPS position is
communicated via a mobile device.
32. The method of claim 31, where the GPS position is communicated
in the form of a text message, an e-mail, and/or a voice
communication.
33. The method of claim 27, further comprising wirelessly
communicating the clinical features with a mobile device.
34. The method of claim 27, further comprising automatically moving
the needle to a retracted position after injecting the epinephrine
in the human body part.
35. The method of claim 27, wherein the biological signal is
responsive to one or more of an electrocardiogram (ECG), a skin
temperature, a respiration rate, a galvanic skin response, and
biosensor data.
36. A sensor module comprising: a histamine sensor configured for
measuring a histamine level of a patient, the histamine sensor
outputting a signal indicative of the measured histamine level; and
at least one controller communicatively coupled to the histamine
sensor and configured to receive the signal from histamine sensor,
process the signal in real-time, extract one or more clinical
features from the signal, and based on the clinical features,
determine detection of an allergic reaction.
37. The sensor module of claim 36, wherein the histamine sensor
continuously monitors the patient.
38. The sensor module of claim 36, further comprising an alarm
feature that provides an alert when the allergic reaction is
detected.
39. The sensor module of claim 36, wherein the alarm feature
includes transmitting the alert to a clinician.
40. The sensor module of claim 36, wherein the sensor module is
located in a clinical or hospital setting.
41. The sensor module of claim 36, wherein the histamine level is
measured in blood or interstitial fluid of the patient.
42. A manual injector module comprising: a wearable injector having
an enclosure for housing a movable needle in a retracted position
and a reservoir for storing a therapeutic agent, the needle being
movable at least in part outside the enclosure to an injecting
position; and a manual activator coupled to the injector and
configured to, upon activation, cause the needle to move to the
injecting position.
43. The manual injector module of claim 42, wherein the wearable
injector is configured for wearing on a thigh, upper arm, or
abdomen.
44. The manual injector module of claim 42, further comprising a
communication feature for alerting a caregiver or emergency
services.
45. The manual injector module of claim 44, wherein the
communication feature is communicatively coupled to a mobile
device.
46. The manual injector module of claim 44, wherein the
communication feature sends an alert indicative of one or more of
an injection occurrence, low batteries, and expired therapeutic
agent.
47. The manual injector module of claim 42, wherein the therapeutic
agent is epinephrine.
48. The manual injector module of claim 42, further comprising a
GPS feature indicative of a position of the wearable injector.
49. The manual injector module of claim 42, wherein the needle
automatically retracts to the retracted position from the injecting
position.
50. A method for detecting and treating symptoms of anaphylaxis or
asthma, the method comprising: sensing data via one or more
non-invasive sensors; sending the data to a controller configured
to with an anaphylaxis detection algorithm; based on the data, and
in response to the controller causing the anaphylaxis detection
algorithm to determine a high likelihood of anaphylaxis or an
asthmatic attack, triggering a biosensor to take a biological
sample; in response to the biosensor confirming that anaphylaxis or
an asthmatic attack is occurring, trigger a needle to insert an
auto-injection of epinephrine.
51-54. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority to and benefit of U.S.
Provisional Patent Application Ser. No. 62/257,190, filed on Nov.
18, 2015, which is hereby incorporated by reference herein in its
entirety.
FIELD OF THE INVENTION
[0002] The present invention relates to compact wearable devices
for management and treatment of asthma or anaphylaxis and
components to provide objective measures of allergic reactions.
BACKGROUND OF THE INVENTION
[0003] Asthma is a common chronic condition affecting children and
adults and is characterized by inflammation of the lower
respiratory tract, cough, breathlessness, and recurrent episodes of
polyphonic (musical) expiratory wheezing. The inherent defect in
asthma is of airway smooth muscle or the inflammatory milieu which
renders the lower airway smooth muscles hyper-reactive. Asthma
exacerbation is defined as a sudden worsening of asthma symptoms
that can last days to weeks. Patients with asthma are prone to
acute exacerbations secondary to a variety of triggers, including
viral or bacterial infections, pollens, smoke, aeroallergens, mold,
chemicals, and fluctuations in air temperature. Although mortality
from asthma is decreasing worldwide, it remains one of the most
common causes of death in both children and adults, and morbidity
remains a significant problem. Generally, deaths from asthma
exacerbation occur prior to or shortly after patients are seen by
emergency medical personnel suggesting that the timing of when
asthmatics seek medical attention profoundly determines
outcome.
[0004] Currently, there are no commercially available technologies
to monitor and analyze breathing in asthma that could provide
patients warning of impending respiratory failure. Commercially
available peak flow meters provide snapshots of pulmonary function,
but are quite unreliable. Patients and their families generally
recognize they are "unwell", and often initiate "sick" asthma care
plans that include frequent inhalation of bronchodilator medicines,
and occasionally initiation of enteral steroid therapy. Generally,
these patients will contact their primary care physician in the
acute phase, and seek advice as to whether and when they should be
seen in the office, clinic, or emergency room. Commonly, patients
receiving "sick" asthma care plan management improve at home and
are not seen during the acute illness by a physician. However, it
is not uncommon that patients who remain at home and who
self-administer frequently inhaled bronchodilator therapy (more
frequently than every 2-3 hours) for prolonged periods of time
(>24 hours) abruptly (within minutes to hours) worsen prompting
calls to 911 for emergency services in the home. A small percentage
of these patients require resuscitation and die in the home or
prior to arrival in the emergency room. An early warning signal
instructing asthma patients to seek medical attention for advancing
respiratory distress prior to them becoming critically ill would be
of monumental importance in preventing asthma morbidity and
mortality. In addition, detecting and treating asthma attacks early
have important therapeutic value in that each asthma attack makes
the underlying disease worse. Thus, a major challenge in pulmonary
medicine is to design a technology enabling outpatient monitoring
of asthma severity in real time. In addition to asthma, this
technology is useful in diagnosing the progression of Chronic
Obstructive Pulmonary Disease ("COPD"), which includes chronic
bronchitis and emphysema.
[0005] Anaphylaxis, according to another example, is a severe and
potentially life threatening allergic reaction to foods, insect
venom, medications, and other allergens. The symptoms of
anaphylaxis are numerous, complex and confusing. Many people do not
recognize the early symptoms, including teachers and child
caregivers, or choose to downplay or ignore the danger out of fear
or denial. Denial is a common coping mechanism for stress, and may
cause a person to delay or fail to react to the situation. Time is
critical when experiencing anaphylaxis.
[0006] The only treatment for anaphylaxis is the injection of
epinephrine. One in 50 Americans are at risk of experiencing
anaphylaxis in their lifetime, with estimates of 500-1000 people
dying from anaphylaxis every year.
[0007] After contact with an allergen, a person can have as little
as 10 minutes (bee sting) to 30 minutes (food allergy) until
cardiac arrest and death. Chances of survival increase the sooner
they receive a dose of epinephrine, commonly applied using an
EpiPen.RTM., which can reverse life-threatening airway
constriction. This is an especially difficult problem in children
and their parents, and in many situations lives have been lost
because epi-pens aren't available, can't be found, or have expired,
or the sufferer has simply lost consciousness before they can
inject themselves. Additionally, allergy testing is performed in
physicians' offices by providing a small amount of allergen to the
patient and asking the patient how they feel. There is no objective
measure to provide the physician to either gauge the degree of
allergic response or even its presence. Patients allergic to foods
and drugs, such as penicillin and chemotherapy drugs, are treated
by desensitizing them, giving the patients small amounts of
allergen in increasing doses. Again, the only feedback to the
physician is to ask the patient if they feel an allergic response.
Thus, lives could be saved if it were possible to detect the early
onset of anaphylaxis, and to initiate treatment automatically.
[0008] Accordingly, present embodiments are directed to solving the
above and other needs, including providing technological components
combined and configured into various different device embodiments
for the treatment of acute conditions, such as anaphylaxis and
asthma, as described herein.
SUMMARY OF THE INVENTION
[0009] According to one aspect of the present invention, a
physiologic sensor module includes at least one wearable sensor
that is configured for wearing on a human body part and for
measuring at least one biological signal. The module further
includes at least one controller communicatively coupled to the
wearable sensor and configured to receive the biological signal
from the wearable sensor. The controller is further configured to
process the biological signal in real-time, extract one or more
clinical features from the biological signal, and based on the
clinical features, determine detection of respiratory failure or
probability of future occurrence. Optionally, the biological signal
includes biosensor data indicative of measured levels of an
inflammatory mediator. Optionally, yet, the physiologic sensor
module further includes a wearable injector of epinephrine or other
therapeutic agent coupled to the at least one controller, the
wearable injector being mounted to a human body part and including
a movable needle, the needle delivering a bolus of the epinephrine
or other therapeutic agent, in response to the detection of
anaphylaxis, to treat anaphylaxis symptoms.
[0010] According to another aspect of the present invention, a
medication injector module includes a wearable injector on a human
body part, the wearable injector having an enclosure for housing a
movable needle in a retracted position and a reservoir for storing
epinephrine. The needle is movable at least in part outside the
enclosure in an injecting position. The module further includes at
least one controller communicatively coupled to the wearable
injector and configured to receive a biological signal. The
controller is also configured to process the biological signal in
real-time, extract one or more clinical features from the
biological signal, and based on the clinical features, determine
presence of anaphylaxis symptoms. The controller is further
configured to automatically cause the needle to move to the
injecting position and deliver a bolus of the epinephrine to the
human body part.
[0011] According to an alternative aspect of the present invention,
a physiologic module is directed to detecting and treating symptoms
of anaphylaxis. The physiologic module includes at least one
wearable sensor for measuring at least one biological signal, and a
wearable injector. The wearable injector has an enclosure for
housing a movable needle in a retracted position, and a reservoir
for storing epinephrine. The needle is movable at least in part
outside the enclosure in an injecting position. The physiologic
module further includes at least one controller communicatively
coupled to the wearable sensor and to the wearable injector. The
controller is configured to receive the biological signal from the
wearable sensor, process the biological signal in real-time, and
extract one or more clinical features from the biological signal.
Based on the clinical features, the controller is further
configured to determine the presence of an anaphylaxis symptom, and
in response to the anaphylaxis symptom, automatically cause the
needle to move to the injecting position and deliver a bolus of the
epinephrine to a human body part.
[0012] According to another alternative aspect of the present
invention, physiologic monitoring modules are directed to detecting
early signs of anaphylaxis, COPD, and/or asthma, and to present
associated data to a physician.
[0013] According to another alternative aspect of the present
invention, a sensor module includes a histamine sensor configured
for measuring a histamine level of a patient, the histamine sensor
outputting a signal indicative of the measured histamine level. The
sensor module further includes at least one controller
communicatively coupled to the histamine sensor and configured to
receive the signal from histamine sensor, process the signal in
real-time, extract one or more clinical features from the signal,
and based on the clinical features, determine detection of an
allergic reaction.
[0014] According to another alternative aspect of the present
invention, a manual injector module includes a wearable injector
having an enclosure for housing a movable needle in a retracted
position and a reservoir for storing a therapeutic agent. The
needle is movable at least in part outside the enclosure to an
injecting position. The manual injector module further includes a
manual activator coupled to the injector and configured to, upon
activation, cause the needle to move to the injecting position.
[0015] According to another alternative aspect of the present
invention, a method is directed to detecting and treating symptoms
of anaphylaxis, the method including sensing data via one or more
non-invasive sensors, and sending the data to a controller
configured to with an anaphylaxis detection algorithm. Based on the
data, and in response to the controller causing the anaphylaxis
detection algorithm to determine a high likelihood of anaphylaxis,
triggering a biosensor is triggered to take a biological sample,
and, in response to the biosensor confirming that anaphylaxis is
occurring, a needle is triggered to insert an auto-injection of
epinephrine.
[0016] Additional aspects of the invention will be apparent to
those of ordinary skill in the art in view of the detailed
description of various embodiments, which is made with reference to
the drawings, a brief description of which is provided below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIG. 1 is a plot illustrating respiration signals indicative
of chest wall movement over time under different levels of
obstructed breathing.
[0018] FIG. 2A is a plot showing raw respiratory data for time
frequency decomposition of a breathing signal.
[0019] FIG. 2B is a plot showing wavelet-based decomposition for
the time frequency decomposition of FIG. 2A.
[0020] FIG. 2C is a plot showing an empirical model decomposition
for the time frequency decomposition of FIG. 2A.
[0021] FIG. 3A is a schematic representation of Vinyl-SAM addition
in a copolymerization process.
[0022] FIG. 3B is a schematic representation of drop addition of
HRP+DAO+Fc+PEGDA monomer+AIBN, in the copolymerization process of
FIG. 3A.
[0023] FIG. 3C is a schematic representation of addition of
coverslip with Teflon.TM. monolayer and UV light exposure for 5
minutes, in the copolymerization process of FIG. 3A.
[0024] FIG. 3D is a schematic representation of immersing in DMSO
to remove coverslip, in the copolymerization process of FIG.
4A.
[0025] FIG. 4 is a plot showing interferences of ascorbic acid.
[0026] FIG. 5 is a plot showing sensor sensitivity increased
.about.100-fold.
[0027] FIG. 6A is an image showing a Gold-Silver alloy co-deposited
on a plain gold substrate.
[0028] FIG. 6B is an image showing the sample of FIG. 6A after
complete removal of silver (i.e., de-alloying).
[0029] FIG. 6C is an enlarged view of FIG. 6B.
[0030] FIG. 7 is a diagram illustrating a standard DAO histamine
detection mechanism with planar electrodes.
[0031] FIG. 8 is a plot showing a calibration curve of flat vs. NPG
gold sensors.
[0032] FIG. 9A is a plot showing features from normal breathing
signals that are derived from Hospital Asthma Severity Scores
("HASS").
[0033] FIG. 9B is a plot showing features from obstructed breathing
signals that are derived from HASS scores.
[0034] FIG. 10A is a plot showing an example of respiration signals
for normal breathing.
[0035] FIG. 10B is a plot showing an example of respiration signals
for obstructed breathing.
[0036] FIG. 11A is a plot showing electrocardiogram ("ECG")
features for normal breathing.
[0037] FIG. 11B is a plot showing ECG features for obstructed
breathing.
[0038] FIG. 12 is a plot showing a visual separation between normal
and obstructed breathing.
[0039] FIG. 13 is a diagram showing an airway obstruction severity
("AOS") algorithm.
[0040] FIG. 14 is a diagram showing the AOS algorithm of FIG. 13
containing two machine learning pipelines.
[0041] FIG. 15 is a diagram showing a classical machine learning
pipeline for the AOS algorithm of FIG. 13.
[0042] FIG. 16 is a table showing physiologic features of a
respiration signal.
[0043] FIG. 17 is a table showing features of an ECG &
plethysmograph ("PLETH") signal.
[0044] FIG. 18 is a plot showing a 4 RR interval along with power
calculated at four different frequency ranges.
[0045] FIG. 19 is a chart showing an average power estimated using
a point process model at four different frequency ranges.
[0046] FIG. 20A is a plot showing the distribution of power at a
first time scale, which follows a skewed distribution that is
characterized by a Gamma function (solid line).
[0047] FIG. 20B is a plot showing the distribution of power of FIG.
20A at a second time scale.
[0048] FIG. 20C is a plot showing the distribution of power of FIG.
20A at a third time scale.
[0049] FIG. 21 is a chart showing the shape of the distribution
estimated using a Gamma function of power at different time
scales.
[0050] FIG. 22A is a perspective view of a smart auto-injector in a
pre-operation position, according to one embodiment.
[0051] FIG. 22B is a side view of the smart auto-injector of FIG.
22A.
[0052] FIG. 22C is a perspective view of the smart auto-injector of
FIG. 22A in a mid-operation position.
[0053] FIG. 22D is a side view of the smart auto-injector of FIG.
22C in the mid-operation position.
[0054] FIG. 22E is a perspective view of the smart auto-injector of
FIG. 22A in a post-operation position.
[0055] FIG. 22F is a side view of the smart auto-injector of FIG.
22A in the post-operation position.
[0056] FIG. 23A is a perspective view of a smart auto-injector in a
pre-operation position, according to another embodiment.
[0057] FIG. 23B is a side view of the smart auto-injector of FIG.
23A.
[0058] FIG. 23C is a perspective view of the smart auto-injector of
FIG. 23A in a post-operation position.
[0059] FIG. 23D is a side view of the smart auto-injector of FIG.
23C in the post-operation position.
[0060] FIG. 23E is a side view of the smart auto-injector of FIG.
23A in an initial state.
[0061] FIG. 23F is a side view of the smart auto-injector of FIG.
23A in a needle insertion state.
[0062] FIG. 23G is a side view of the smart auto-injector of FIG.
23A in a medication injection state.
[0063] FIG. 24 is a perspective illustration of a CO2
cartridge-based actuator for a smart auto-injector, according to an
alternative embodiment.
[0064] FIG. 25A is a perspective illustration of a lock/latch
mechanism for a smart auto-injector, according to another
alternative embodiment.
[0065] FIG. 25B shows the lock-latch mechanism of FIG. 25A after
applying a push force.
[0066] FIG. 26 is a perspective illustration showing a
rack-and-pinion drive for a smart auto-injector, according to
another alternative embodiment.
[0067] FIG. 27 is a perspective illustration of a pulley mechanism
for a smart auto-injector, according to another alternative
embodiment.
[0068] FIG. 28 is a side illustration of a worm-gear mechanism for
a smart auto-injector, according to another alternative
embodiment.
[0069] FIG. 29A is a perspective illustration of a squeezable pouch
for a smart auto-injector, according to another alternative
embodiment.
[0070] FIG. 29B shows the squeezable pouch of FIG. 29A in a
squeezed position.
[0071] FIG. 30 is a perspective illustration of a friction drive
for a smart auto-injector, according to another alternative
embodiment.
[0072] While the invention is susceptible to various modifications
and alternative forms, specific embodiments have been shown by way
of example in the drawings and will be described in detail herein.
It should be understood, however, that the invention is not
intended to be limited to the particular forms disclosed. Rather,
the invention is to cover all modifications, equivalents, and
alternatives falling within the spirit and scope of the invention
as defined by the appended claims.
DETAILED DESCRIPTION
[0073] While this invention is susceptible of embodiment in many
different forms, there is shown in the drawings and will herein be
described in detail preferred embodiments of the invention with the
understanding that the present disclosure is to be considered as an
exemplification of the principles of the invention and is not
intended to limit the broad aspect of the invention to the
embodiments illustrated. For purposes of the present detailed
description, the singular includes the plural and vice versa
(unless specifically disclaimed); the words "and" and "or" shall be
both conjunctive and disjunctive; the word "all" means "any and
all"; the word "any" means "any and all"; and the word "including"
means "including without limitation."
[0074] Various unique and novel technologies are currently being
developed at the Wyss Institute, in collaboration with Boston
Children's Hospital and UMASS Medical School. These technologies
are being developed and integrated into medical devices for the
management and treatment of asthma and anaphylaxis. Each of the
underlying technological components is described separately, based
on respective unique and novel features. These technological
components can be combined and configured into various different
device embodiments for the treatment of acute conditions, such as
anaphylaxis and/or asthma conditions.
[0075] Generally, the description below describes a sensor module
that is configured to detect various acute conditions, including
asthma and anaphylaxis. According to one example, the sensor module
includes a wearable device that monitors breathing, assesses asthma
severity, and alerts to dangerous changes. According to another
example, the sensor module includes a wearable device that alerts
upon early detection of anaphylaxis, auto-injects epinephrine, and
calls emergency services (e.g., initiates 911 call) and/or family.
According to yet another example, the sensor module includes one or
more monitors for use in a hospital or a physician's office to
provide objective measures of a patient's physiologic response to
an allergen.
[0076] Symptom Detection, Alarming, and Auto-Injection Device
[0077] Generally, an auto-injection device is described below in
reference to the detection of, but not limited to, asthma and
anaphylaxis. The auto-injection device detects and/or provides an
alarm when detecting symptoms of such acute conditions as asthma
and/or anaphylaxis. For example, the device is a non-invasive,
wearable device that senses chest wall movement and analyses user
breathing pattern and asthma severity in real time, and alerts the
user (or guardian) of critical asthma severity.
[0078] According to one aspect of the present disclosure, a
non-invasive wearable device is directed to monitoring and alarming
for changes in asthma severity. The system is comprised of a
non-invasive breathing sensor that gathers physiologic signals from
the user's body, and extracts a set of features relevant to the
user's respiration. It then passes these variables into a novel
algorithm in order to calculate a unique indicator of asthma
severity, called the Airway Obstruction Severity Score ("AOS"). The
software alarms when the calculated severity significantly deviates
from historical or patient normal values. The device will be
effective even in patients with rapid onset and worsening of
bronchospasm who are alone or who lose consciousness before being
able to call for help.
[0079] An algorithm is based on a machine learning framework and
will consider different features from the respiration signals, such
as the Inspiration Time (i) to Expiration time ratio (e) ratio, or
i:e ratio, to assess the severity of bronchoconstriction, which is
one of the most significant symptoms of anaphylaxis. This risk is
the AOS, and the algorithm is referred to as the AOS algorithm
(described in more detail below in the respective section of the
disclosure). By way of example, the device operates to alert a user
that their breathing has reached a certain severity threshold in
accordance with the following exemplary device operation for
detecting asthma severity: [0080] A. Sensing chest wall movement of
a subject using a non-invasive device with a breathing sensor,
[0081] B. Determining measures representative of active exhalation
time and total exhalation time for each breath using the sensed
physiologic signal, and [0082] C. Generating an indication of
asthma severity using AOS according to the respiratory measures
generated from the sensed physiologic signal.
[0083] The device may consist of a wearable breathing sensor placed
on the subject's chest, and a processor attached to or embedded
within it, or housed externally within a smartphone, smartwatch or
other device. In other embodiments, the wearable device may perform
all of the operations (sensing, data acquisition & algorithm
execution) and use a smartphone or smartwatch only as a method to
alert the user.
[0084] According to one example, a method of operating a device to
detect asthma severity includes having a physiologic signal (e.g.,
chest wall movement) sensed using a respiration sensor. The
physiologic signal provides surrogate information of respiration of
the subject. Values of active exhalation time and total exhalation
time for each subject breath are then calculated on the mobile
device using the sensed physiologic signal, and fed into the AOS
algorithm. An indication of asthma severity, or AOS, of the subject
is generated according to the features extracted from the breathing
data, including an awareness of historical trends and likelihood of
getting worse or improving, possibly with machine learning
approaches. If an AOS threshold is exceeded, an alert is sent to
the user on the mobile device.
[0085] The dynamic features as well as statistical features are
incorporated in a machine learning framework tailored specifically
to an individual subject, which is then employed to assess the
pathological fluctuations in the breathing signal related to the
risk of bronchoconstriction. The assessed risk score from the
algorithm is compared to the clinician rating risk score of asthma
(such as the first study described below).
[0086] Onset of an anaphylactic event is marked by several
physiologic signals. The present disclosure is directed to a
wearable sensor providing these data points. By taking these
variables into account, accurate prediction of an anaphylactic
event is performed.
[0087] Furthermore, the present disclosure also describes an
integrated wearable device that detects the early onset of
anaphylaxis and, then, automatically injects epinephrine. Using
sensors on or inside the body, the wearable device carefully
monitors the biology and physiology of the wearer, in possible
combination with location or environmental measurements, and
activates an alarm when the early stages of anaphylaxis are
detected. If required, the device automatically injects epinephrine
and potentially notifies emergency services (e.g., dialing 911) or
family members.
[0088] The present disclosure further describes a wearable device
and system that monitors the wearer's physiology and detects the
early onset of anaphylaxis. In the event of detection, the system
alerts the user and, if needed, auto-inject epinephrine. The system
includes non-invasive and/or indwelling biosensors that stream data
to a processor, which runs software that processes the data in real
time and executes an anaphylaxis detection algorithm, as well as a
wearable auto-injector including a needle and syringe containing a
dose of epinephrine.
[0089] Anaphylaxis causes a systemic reaction, which may present in
a variety of symptoms. Because of this, other types of physiologic
sensors are optionally incorporated into the system in addition to
a breathing sensor. For example heart rate, blood pressure,
galvanic skin response (GSR) and/or skin temperature sensors are
optionally used. Based on their relevance to a diagnosis of
anaphylaxis, these sensors allow the disclosed AOS algorithm to
more accurately detect the onset of an anaphylactic event.
[0090] Accordingly, the AOS algorithm is based on a machine
learning framework and considers these features, taking into
account historical trends, to assess the severity of anaphylaxis.
If a threshold is exceeded, an alert is sent to the user on their
mobile device and epinephrine is automatically injected by the
device. The device optionally alerts emergency services, family, or
caregivers automatically upon injection of epinephrine.
[0091] According to a specific example, a method operates a device
to detect anaphylaxis onset. The physiologic signals are measured
using wearable sensors on the body, or using indwelling chemical
biosensors within the body. The physiologic signals are related,
for example, to one or more of breathing data, ECG data, BP data,
skin temperature, microphone data, GSR data, and biosensor data.
Specific features of the user's physiologic status are then
extracted from these raw signals and fed into an anaphylaxis
detection algorithm (e.g., the AOS algorithm). If detected, the
user is alerted to the anaphylactic episode and epinephrine is
auto-injected, if needed.
[0092] Wearable Physiologic Sensors
[0093] Wearable physiologic sensors are directed to the detection
of, but are not limited to, asthma and anaphylaxis. Two exemplary
sensory modes utilize one or more non-invasive physiologic sensors
to generate the signals used for feeding into the detection
algorithms. For an asthma detection sensory mode, reliance is
optionally based solely on respiration signals. However, for an
anaphylaxis sensory mode, additional sensors are used, such as:
[0094] ECG, [0095] blood pressure, [0096] skin temperature, [0097]
skin conductance, [0098] pulse oximeter, [0099] microphones, and/or
[0100] biosensors for histamine and other chemical markers of
allergic response.
[0101] These sensors are optionally off-the-shelf physiologic
sensors. For respiration sensing, various sensing methods are used,
such as [0102] a) impedance pneumography, a common way to
electrically measure respiration using electrodes placed on the
chest; [0103] b) respiratory inductance plethysmography (RIP), a
system where belts or straps are placed around the subject's chest
in order to measure the expansion and contraction of the thorax;
[0104] c) flexible soft-sensors that can be placed in straps around
the chest to monitor chest wall expansion, similar to RIP belts but
more elastic and less restrictive); [0105] d) ECG Derived
Respiration (EDR) (respiration waveform acquired using signals from
ECG skin leads); [0106] e) nasal thermistors or thermocouples
(respiration waveforms acquired by measuring changes in nostril air
temperature); and/or [0107] f) proximity sensors on
anterior/posterior chest that measure thorax expansion, and [0108]
g) g) acoustic sensors that measure breathing sounds.
[0109] According to one benefit of the described devices, a
capability of "two-step" authentication of anaphylaxis is provided,
as follows: the first step is to confirm anaphylaxis using
non-invasive physiologic sensors. If this test is passed, a
biosensor will take a biological sample to confirm that anaphylaxis
is occurring. This two-step authentication ensures that wearers are
never injected with epinephrine based on a false alarm.
Alternatively, the patient is monitored continuously for levels of
biomarkers such as histamine.
[0110] According to another benefit, one or more of the described
devices use Wyss Institute-developed "soft sensors" for respiration
sensing and biosensors for histamine sensing.
[0111] Airway Obstruction Severity Score ("AOS") Algorithm
[0112] The AOS algorithm is directed to using an incoming
continuous respiration waveform to calculate the severity of
asthmatic breathing, i.e., on a percentage scale of 0 to 1 where
0=healthy and 1=severe asthma attack. The algorithm is based on a
machine learning framework and considers different features from
the respiration signals to assess the severity of
bronchoconstriction, as well as historical data for the person
wearing the device. The dynamic features, such as amplitude and
frequency fluctuations, are derived from the breathing signal using
a time-frequency decomposition either using wavelet based
decomposition or empirical model decomposition. The statistical
features, such as instantaneous mean and instantaneous variances,
are derived from the breathing signal using a point process
modeling approach. The dynamic features as well as statistical
features are incorporated in a machine learning framework tailored
specifically to an individual subject, which is then employed to
assess the pathological fluctuations in the breathing signal
related to the risk of bronchoconstriction. This risk is the
AOS.
[0113] In reference to FIG. 1, patients with asthma exacerbation
experience expiratory flow limitation leading to a prolonged
exhalation phase of breathing. Breathing symptoms include wheezing
(change in ratio of inspiration to expiration), change in breathing
rate (breaths/minutes), and/or breathing becomes more regular as it
becomes more difficult to do so (e.g., the person will generally
stop eating or speaking to concentrate on breathing). Cardiac
symptoms include a sudden change in heart rate, which usually is
presented as bradycardia (slower heart rate), or, sometimes, as
tachycardia (faster heart rate). Other cardiac symptoms include
dysrhythmia, or unpredictability in inter-beat interval, or a
sudden decrease in blood pressure (hypotension). In addition, other
symptoms that are often reported, but that can vary greatly between
individuals (and some can be difficult to accurately quantify)
include flushing (increased skin temperature), itchiness of the
throat, or difficulty in swallowing.
[0114] Historically, the inspiratory to expiratory (I:E) time ratio
(where the inspiratory and expiratory times refer to the periods
during which a subject inhales ("B" in FIG. 1) and exhales ("C" in
FIG. 1), respectively, has been used in the past as a single
component of clinical asthma severity scores to roughly gauge
asthma severity of patients seen in emergency rooms and hospital
wards. During an asthma exacerbation, as bronchoconstriction
worsens, the i:e ratio reduces (expiration prolongs relative to
inspiration) due to difficulty exhaling.
[0115] However, during normal breathing at low resting rates, the
i:e ratio may also appear equally short as to that seen in asthma
(see dashed line). Physicians recognize worsening asthma clinically
when a reduced i:e ratio is accompanied by difficulty exhaling and
respiratory distress along with a history suggestive of asthma
exacerbation. Therefore, technologies to measure i:e ratio alone
cannot be reliably used to estimate asthma severity. Asthma
severity is accurately and sensitively scored by measuring and
calculating the ratio of the active component of exhalation (when
airflow out of the lungs is above zero) as a function of the entire
expiration phase of breathing.
[0116] According to one aspect of the AOS algorithm, a method is
directed to calculating asthma severity in real-time, from
breath-to-breath, and averaged over time. According to another
aspect of the AOS algorithm, a method is directed to calculating
i:e ratio (in contrast to current methods), which better reflects
the real severity of breathing. According to another aspect of the
AOS algorithm, a feature is directed to the ability to predict the
onset of an asthmatic episode even before breathing severity
worsens.
[0117] An anaphylaxis detection algorithm expands upon the AOS
algorithm described above, to detect the early onset of
anaphylaxis. Inputs to the algorithm include the respiration
signal, and also a collection of other physiologic signals gathered
from wearable non-invasive sensors, such as: [0118] ECG, [0119]
blood pressure, [0120] skin temperature, [0121] skin conductance,
[0122] pulse oximeter, [0123] microphones, and/or [0124] Global
Positioning System ("GPS") (to determine, for example, if the
patient is running or is stationary).
[0125] In addition, this algorithm optionally uses input from
biosensors (described in the following section) that acquire
signals from biological samples. These signals are fed into the
machine learning algorithm. This algorithm considers different
features from the input signals to assess the likelihood of an
imminent anaphylactic attack. The dynamic features of the signals,
as well as statistical features, are incorporated in a machine
learning framework tailored specifically to an individual subject,
which is then employed to assess the pathological fluctuations in
the signals related to the risk of anaphylaxis.
[0126] According to one aspect of the anaphylaxis algorithm, a
feature is directed to the ability to detect the early onset of
anaphylaxis.
[0127] Biosensors for Symptom Detection
[0128] Biosensors are directed to detecting, but are not limited
to, asthma and anaphylaxis. By way of example, a biosensor detects
the early stages of anaphylaxis by measuring levels and rates of
change of levels of physiological mediators of anaphylaxis, such as
histamine, tryptase, and platelet activation factor, in
interstitial fluids, blood, or other biological samples (e.g.,
saliva, tears).
[0129] An allergic reaction is often triggered by an uncontrolled
production of IgE antibody followed by the release of histamine.
Detecting sudden changes in histamine levels of blood are
potentially good indicators of a life threatening allergic
reaction. An electrochemical histamine biosensor for use in
detecting the sudden changes in histamine levels is based on
current glucose monitors used in diabetes monitoring. A proof of
concept sensor based on the enzyme diamine oxidase has been
demonstrated. The anaphylaxis detector leverages glucose monitor
designs and utilizes an indwelling sensor or an injectable sensor
that is inserted on demand or when non-invasive sensors (e.g.,
physiologic monitors described above) detect the potential for
development of an allergic reaction.
[0130] Detection of a high level or a rapid rise in histamine
serves as a measure of early anaphylaxis to warn a physician or
patient of the existence of an allergic reaction, or to trigger
actuation of an epinephrine auto-injector. Histamine sensors
require access to blood or interstitial fluids. This is achieved in
several ways, by way of example. For physician use, a sensor
electrode is placed under the skin with a needle. For periodic
measurements, blood is taken from the patient and applied to the
sensor. Access to subcutaneous fluid is also obtained with
micro-needle patches, e.g., small needles penetrate the skin. Each
needle is connected to an electrode to gain sufficient signals.
[0131] Another subcutaneous access device is directed to burning
small holes through the epidermis. In this device, interstitial
fluid, then, leaks into small chambers in which detection
electrodes are located. Numerous cells are optionally placed on a
patch such that serial measurements are performed over time as each
cell is energized.
[0132] Miniaturized Wearable Auto-Injector
[0133] In accordance with some aspects of the present disclosure,
the sensor module includes a miniaturized wearable auto-injector
that is directed to the injection of, but not limited to,
epinephrine. In contrast to present-use injectors, and according to
some aspects of the present disclosure, compact and miniaturized
wearable injectors are stand-alone, manually activated, or
configured to communicate with a central processor and wearable
sensors. According to one exemplary aspect, the injectors of the
present disclosure allow the user to attach the device to multiple
sites on the body, such as the thigh, stomach, lower back, or upper
arm.
[0134] In further contrast to some of the present-use injectors
that are manually administered auto-injectors for injecting the
drug intramuscularly, the injectors of the present disclosure are
capable of injecting the drug either intramuscularly or
subcutaneously depending on the physiology of the wearer, the need
of the patient, and the drug being injected. Using a detection
algorithm (such as one or more of the algorithms described above),
a system in accordance with the present disclosure automatically
injects epinephrine with varying dose options if the system detects
the onset of anaphylaxis. If the onset of anaphylaxis continues, a
second dose is injected automatically. The device may have
disposable medication cartridges that are optionally replaceable,
thereby making the device reusable. In addition, the device is
capable of informing the users of battery status, and the
expiration status of the medication, through a user interface or
through communication with a smartphone.
[0135] According to some aspects of the present disclosure, a
device is wearable on the body of a person and includes one or more
of the following features: [0136] device is always present, [0137]
device is discreetly hidden under clothes, [0138] device includes
adjustable sizes for different body shapes, [0139] device is
suitable to multiple sites on body, and/or [0140] device consists
of hypoallergenic materials.
[0141] According to some aspects of the present disclosure, a
device is wearable on the body of a person and includes one or more
of the following features: [0142] a needle, made of upper-elastic
materials, such as nitinol, [0143] capability of trigger manually,
as a back-up or safety feature, [0144] disposable cartridges,
[0145] multiple doses (0.15 milliliters, 0.3 milliliters, 0.5
milliliters, etc.), [0146] capability of multiple injections, based
on duration anaphylactic episode, [0147] injection is either
intramuscularly or subcutaneously, [0148] period expiration
feedback is provided to the user by light-emitting diode (LED)
indicator and/or audio indicator, [0149] miniaturized
configuration, including, for example, micro-actuators, such as
mechanical actuators (e.g., springs, pistons, jets, etc.),
electromechanical actuators (soft actuators, piezo-actuators, micro
motors, solenoids, etc.), and/or custom actuator, [0150]
replaceable cartridges, [0151] integrated sensors to inject without
any user interaction, and/or [0152] integrated with smartphone to
notify emergency services (e.g., 911), family, and/or friends when
injection occurs.
[0153] Referring to FIGS. 2A-2C, the algorithm includes different
features that are based on respiration signals to assess the
severity of bronchoconstriction. The dynamic features, such as
amplitude and frequency fluctuations, are derived from the
breathing signal using a time-frequency decomposition either using
wavelet based decomposition or empirical model decomposition. The
statistical features such as instantaneous mean and instantaneous
variances will be derived from the breathing signal using a point
process modeling approach.
[0154] Referring to FIGS. 3A-3D, a copolymerization method includes
addition of Vinyl-SAM to the electrode surface (FIG. 3A) and a drop
addition of HRP+DAO+Fc+PEGDA monomer+AIBN (FIG. 3B). The method
further includes the addition of a coverslip with Teflon monolayer
and UV light exposure for five minutes (FIG. 3C), and the immersing
in DMSO to remove the coverslip and non-polymerized monomers (FIG.
3D).
[0155] More specifically, the copolymerization method is directed
to a sensor modification process, in which the first step (FIG. 3A)
chemically modifies the electrode to introduce a variety of
functional groups that are known to participate in the
polymerization process. The modification provides a robust
immobilization of the subsequent polymer layer at the electrode
surface.
[0156] In a second step (FIG. 3C), a mixture of enzyme, monomers,
electroactive moiety, and a polymerization initiator is deposited
onto the electrode surface. In a third step (FIG. 3C), the
deposited mixture is spread over the electrode surface using a
photomask to define the patterns to be polymerized. The assembly is
further exposed to UV light to imitate polymerization. In a final
step (FIG. 3D), the polymerization is stopped, the photomask
removed, and the polymerized surface is washed to remove any
non-polymerized and weakly-bound material.
[0157] Referring to FIG. 4, a plot shows interferences of ascorbic
acid, with ascorbic acid strongly interfering electrochemistry at
positive detection potentials of greater than 0.2 Volts. A -0.36
Volt detection potential shows no interference from ascorbic/uric
acid that is normally present in blood. The plot includes a curve
for PBS-no ascorbic acid (i.e., absence of ascorbic acid) and a
curve for PBS+100 mM ascorbic acid (i.e., presence of ascorbic
acid).
[0158] More specifically, the plot of FIG. 4 demonstrates that the
sensor is not sensitive to electroactive interferents, such as
ascorbic acid or uric acid, which are commonly known as the main
electrochemical interferents present in biological samples. The
curve representing PBS+100 mM ascorbic acid represents the current
measure at the electrode surface while applying an increasing
potential. Passed 0.2 Volts, the presence of ascorbic acid is
apparent. However, below 0.2 Volts, there is no difference between
the presence or absence of ascorbic acid. The sensor operates below
the potential threshold and is insensitive to this type of
electrochemical interferents.
[0159] According to one embodiment, the sensor is a physiology
sensor that uses or modifies an off-the-shelf sensor to generate
respiratory waveform capturing chest wall movement. The sensor,
according to another embodiment, is an anaphylaxis continuous
biosensor that detects one or more of tryptase, histamine, IgE, and
a platelet activating factor (PAF).
[0160] Referring to FIG. 5, a plot shows a sensor sensitivity that
is increased approximately 100 times over two versions, 1 and 2.
The target sensitivities, for example, are 10 nM.
[0161] Referring generally to FIGS. 6A-6C, a method and device is
directed specifically to the sensitive electrochemical detection of
histamine in biological fluids. In accordance with one aspect of
this method and device, an electrode is modified with an enzyme
specific to histamine by entrapment in an electroactive
polymer.
[0162] More specifically, an electroactive polymer is prepared in
situ, i.e., a mixture of monomers and enzyme are deposited together
onto the electrode and exposed to a UV light to initiate
polymerization. The electroactive polymer is optionally prepared
prior to deposition, mixed with the enzyme, and finally deposited
onto the electrodes. The electroactive polymer is then left to dry
in controlled atmosphere to cure.
[0163] The electroactive polymer allows the wiring of the enzyme
core directly to the electrode. In doing so, the detection
potential required to test the enzyme is considerably lowered,
which allows keeping background signals from potential interferents
low. Known electrochemical interferents are, for example, ascorbic
acid and uric acid, both typically found in large concentration in
biological samples.
[0164] To improve sensitivity, the electrode is nanostructured.
Silver and gold are co-deposited during fabrication of the device.
Upon immersion in nitric acid, the silver will dissolve, leaving
nanometer-size cavities. The resulting nanostructured electrode
possess a much higher surface area, as illustrated in FIGS.
6A-6C.
[0165] Gold-Silver alloy is electrochemically deposited onto a
plain gold electrode or co-sputtered on a plain gold substrate. The
surface area of the resulting electrode is, then, electrochemically
assessed. According to one example, the area of a plain electrode
is improved by a factor of 10, based on introducing nanoporous gold
structures. In one experiment, cyclic voltamogram in dilute
sulphuric acid has demonstrated the enhancement in surface area of
a nanoporous gold electrodes (NPG) in comparison to a plain
electrode. The electrode potential was scanned from negative to
positive to induce the formation of an oxide layer (at
approximately 1.2 Volts). The electrode potential is scanned back
to the original negative potential. The reduction of the oxide
formed at the electrode surface is seen as a sharp peak at
approximately 0.9 Volts. A roughness factor was calculated by
normalizing the area under the reduction peak against the geometric
area of the electrode
[0166] The enhanced surface area allows reaching very low detection
limits for the detection of histamine using a co-polymer consisting
of polyethylenglycol diacrylate, vinylferrocene, diamine oxidase
and horseradish peroxidase. The enzymes DAO and HRP are polymerized
in situ together with the electrochemical mediator vinyl ferrocene
in a matrix of poly(ethylene glyclol diacrylate). While first
histamine sensitivity tests conducted in a model solution showed
poor performances, the lower limit of detection achievable is
considerably enhanced by increasing the surface area of the sensor
through nanoporous gold (NPG) layer formation.
[0167] In one example, the preparation of the NPG layer includes a
plating solution including 0.1M Na.sub.2S.sub.2O.sub.3/0.6M Ag/0.3M
Au prepared in double distilled water fresh before each deposition
round. A bare gold electrode is first electrochemically cleaned in
0.5M sulfuric acid, rinse in water, dried and immersed in the
plating solution. A potential of 0.25 Volts with respect to Ag/AgCl
reference electrode is applied for 60 minutes. Silver is removed
from the resulting layer by immersing the electrode in 70% nitric
acid for 60 minutes.
[0168] In a further example, the preparation of the sensing layer
includes a 1% vinyl ferrocene solution containing 2% AIBN and 0.5%
glutaraldehyde prepared in poly(ethylene glycol diacrylate), which
is sonicated to dissolve vinyl ferrocene and vortexed to ensure
proper mixing. The enzyme solution is prepared by mixing 22
milligrams (mg) of diamine oxidase (DAO) and 1 mg of horseradish
peroxidase (HRP) in 50 microliters (.mu.L) of PBS to result in a 22
U/milliliters (mL) DAO and 3000 U/mL HRP mixture. A stir bar is
added and 200 .mu.L of the polymerization solution is added
dropwise to the DAO/HRP mixture to form a uniform paste. The
mixture is then constantly mixed for 2 hours at 4.degree. C. A drop
of the polymerization solution is deposited onto a 3 millimeter
(mm) in a diameter gold electrode that is modified with a
self-assembled monolayer of allyl mercaptan, and which is spread
evenly across the electrode surface with a fluorinated glass cover
slip. The electrode is exposed to UV light for 5 minutes to
initiate polymerization and to entrap the enzymes in a crosslinked
ferrocene-modified PEG network. The electrode is rinsed in 40% DMSO
prepared in water to remove any non-polymerized monomer and loosely
trapped enzyme. The electrode is finally thoroughly rinsed in water
and stored in PBS at 4.degree. C.
[0169] The fabricated sensors show very good ferrocene-enzyme
communication. Histamine is measured by following the ferrocene
reduction current as DAO catalyzes histamine and produces hydrogen
peroxide, which is further used by HRP. However, to increase
sensitivity, the sensor surface area is increased, using NPG. The
fabricated electrodes are optionally further modified with the
enzymes polymerization mixture. According to an alternative
embodiment, the electrodes are interdigitated for enhanced
transduction.
[0170] One benefit of the above described biomolecular sensor,
which is directed to the detection of early signs of allergic
reaction and anaphylaxis, is related to the direct wiring of
histamine oxidase onto nanoporous gold electrodes. The direct
wiring results in the electrodes exhibiting great sensitivity that
is relevant to the measurement of histamine in whole blood. Another
benefit of the sensor is that one of its applications is in the
food industry for measuring product freshness of, for example, meat
and fish.
[0171] According to an alternative embodiment, the biosensor is
integrated with an interstitial fluid-sampling device. For example,
the sampling device is in the form of an array of plain and/or
hollow micro-needles that collect interstitial fluid passively.
Alternatively, the array of micro-needles generate and/or collect
interstitial fluids actively via an electric field, such as in
iontophoresis or by heat (to degrade biological tissue and extract
the fluid).
[0172] In another alternative embodiment, the biosensor is a
different entity than the micro-needle array. Hollow micro-needles
are used to drive interstitial fluid to the biosensor, which is
located at the back of the micro-needles. The micro-needles drive
the interstitial fluid either passively, by diffusion, and/or
actively, via an electric field, such as in iontophoresis or by
heat (to degrade biological tissue and extract the fluid).
[0173] In yet another alternative embodiment, the biosensor is a
part of the micro-needle array, with each micro-needle being an
individually addressable self-contained biosensor. In the
preparation of an electrochemical micro-needle biosensor, each
needle includes an independently addressable working macro- or
micro-electrode. All micro-needles optionally share a common
counter and/or a common reference electrode to perform the
measurement.
[0174] In a further alternative embodiment, the biosensor is
inserted under the skin with an insertion device. The insertion
device is, for example, a device similar or identical to those used
for insertion of glucose sensors in continuous glucose monitoring
devices.
[0175] In another further alternative embodiment, the biosensor is
not part of a wearable device. Instead, the biosensor is a
different entity than the sampling device. Optionally, the
biosensor is integrated in a portable device to enable
point-of-care monitoring of the patient, for example, at home or in
clinical settings.
[0176] For an exemplary sensor construction, the detection of
histamine relies on the production of hydrogen peroxide by diamine
oxidase in the presence of histamine, followed by subsequent
oxidation of the enzyme HRP when reacting with the hydrogen
peroxide produced. The redox state of HRP is measured using the
mediator ferrocene. Enzymed horseradish peroxidase and diamine
oxidase are copolymerized with poly(etyleneglycol) diacrylate,
vinyldferrocene and photoinitiator at the electrode surface. The
modified electrode is tested in the presence of the various
concentration of histamine, and potential interferents, such as
ascorbic acid. The sensitivity of the sensor is enhanced by
increasing the surface area of the electrode, by forming a layer of
nonoporous gold.
[0177] Referring to FIG. 7, a standard DAO histamine detection
mechanism has planar electrodes that, by way of example, require
high-detection potential for Pt electrodes which make the sensors
very susceptible to interferences from other electrochemically
active compounds that might be found in biological fluids. The
electrodes are not limited by electrode material and optionally
include a mediating layer to reduce or eliminate contribution from
interfering substances. According to one example, a detection
potential is at -0.36V vs. AgAgCl reference electrodes.
[0178] Referring to FIG. 8, histamine sensitivity of NPG on planar
electrodes is illustrated in a flat vs. NPG gold sensors
calibration curve. For a low histamine concentration, the
sensitivity is -8.49e-07 A/mM, with R.sup.2=0.989. For a higher
histamine concentration, the sensitivity is -4.35e-05 A/mM, with
R.sup.2=0.981. The curve has x100 sensitivity enhancement and
limits of detection of approximately 100 nM.
[0179] According to one exemplary embodiment, the sensor is
optionally an automated breathing and bio-sensed auto-injector of
epinephrine. To detect the asthma severity estimation, a two-step
process includes the detection of artifacts in the recorded signals
and the subsequent estimation of the HASS score is applied. The
first step is the windowing of BCH, PPG, ECG, or RESP data, and the
second step is the artifact detection, after which data is
discarded and the HASS estimation is performed.
[0180] For processing pipelines, the detection of artifacts and the
estimation of the HASS score are both implemented as machine
learning pipelines. The performance is assessed by comparing the
estimated HASS score to a ground truth HASS score given by a
physician. Thus, initially a feature extraction is performed from
the BCH, PPG, ECG, or RESP data, and, then, a feature selection is
performed. From the selected features, a classification model is
obtained, and a target score is compared to a ground truth
score.
[0181] For artifact detection and labeling of ECG and RESP signals,
features are derived to identify corrupted signals. Those features
are designed to represent, by way of example, signal
characteristics indicative of clipping, high-frequency noise,
baseline drift, periodicity, unusual shape, and missing
segments.
[0182] For artifact detection ECG, artifacts in the ECG signal are
detected with high reliability. For example, prediction outcomes
show an accuracy of at least about 81%, a sensitivity of at least
about 72%, and a specificity of at least about 83.8%.
[0183] Referring to FIGS. 9A and 9B, HASS estimation is illustrated
in reference to features respiration. The features from the
respiration signals are derived to calculate the HASS scores, with
normal breathing being illustrated in FIG. 9A and obstructed
breathing being illustrated in FIG. 9B.
[0184] Referring to FIGS. 10A and 10B, HASS estimation is
illustrated in reference to respiration signals. The illustrated
plots shows an example of respiration signals for normal breathing
(FIG. 10A) and obstructed breathing (FIG. 10B).
[0185] Referring to FIGS. 11A and 11B, HASS estimation is
illustrated in reference to features ECG. The illustrated plots
shows an example of ECG signal for normal breathing (FIG. 11A) and
obstructed breathing FIG. 11B).
[0186] Referring to FIG. 12, a plot illustrates a clear visual
separation between normal and obstructed breathing. The separation
is achieved by reducing multiple features derived from a
respiration signal to two dimensions (e.g., Reduced Dimension 1 and
Reduced Dimension 2).
[0187] Referring to FIG. 13, an AOS algorithm uses various
physiologic signals as input to calculate the severity of airway
obstruction in a person wearing the medical device described above.
The AOS algorithm outputs an obstruction severity score on a scale
of 0 to 1, where 0=healthy and 1=extremely obstructed. The AOS
algorithm is embedded onto the medical device. Because airway
obstruction is a major symptom of anaphylaxis, the AOS algorithm is
also a key module in the anaphylaxis detection device.
Additionally, airway obstruction is a symptom of asthma and is
optionally used in an asthma monitoring device.
[0188] Referring to FIG. 14, the physiologic input signals to the
AOS algorithm include (but are not limited to) the following:
electrocardiogram (ECG), respiration (chest wall movement), and
pulse plethysmograph (PLETH) waveforms. The AOS algorithm contains
two classical machine learning pipelines operating in series, as
illustrated in FIG. 14.
[0189] Referring to FIG. 15, the first pipeline is used to filter
outliers and noise from the incoming physiologic signals, and the
second pipeline is used to calculate the AOS score. Each pipeline
uses a classical machine learning technique.
[0190] After filtering the signal, the first step in an AOS
Calculation Pipeline is to extract features that can be expressed
numerically and that correlate with obstructed breathing. The
features are calculated on a segment of the physiologic input
signals and plugged into a feature selection model. The goal of the
feature selection model is to optimize the performance of the AOS
algorithm to effectively predict the severity of airway
obstruction. This is achieved by selecting a subset of features
that are sufficient to accurately describe the intrinsic behavior
of the observed breathing patterns. A supervised learning approach
using the reduced feature set in conjunction with ground truth
information about the presence and severity of obstructed breathing
(e.g., derived from a clinical expert) allows the AOS algorithm to
generate a predictive model which can be applied for the autonomous
and objective evaluation of breathing obstruction severity.
[0191] Referring to FIG. 16, exemplary physiologic features are
used in the AOS algorithm. The features include inspiratory to
expiratory time ratio (I:E), the inspiratory and expiratory times
referring to the periods during which a patient inhales (I) and
exhales (E.sub.total).
[0192] Another way of characterizing the structural changes of the
respiratory waveforms associated with obstructed breathing is
established by calculating statistical features like the mean,
standard deviation, range, skewness, kurtosis and the entropy of
each breath. These additional statistical features are not included
in the table of FIG. 16, but are used in the machine learning
framework.
[0193] Through statistical analyses of the features seen in the
table of FIG. 16, several features are identified, such as the
upper respiratory slope that shows statistically significant
differences (p-value <0.05) between normal and obstructed
breathing. Additionally, referring to FIG. 12, the information
content of multiple features derived from the respiration signal is
reduced into two dimensions, which shows a clear separation between
normal and obstructed breathing. Furthermore, a machine learning
classification model is applied that enables the autonomous
discrimination between respiration signals having normal and
obstructed breathing with high reliability (accuracy, sensitivity,
and specificity above 82%). These results indicate that the
features extracted from the respiration waveform are able to
represent physiologic changes associated with airway obstructions.
This demonstrates that the AOS algorithm reliably detects
obstructed breathing, and is a relevant part of the anaphylaxis
detection and treatment device.
[0194] Referring to FIG. 17, the ECG and PLETH waveforms change
during periods with obstructed breathing due to the associated
stress on the body. Therefore, a variety of physiologic
characteristics, such as those displayed in the table of FIG. 17,
are derived from the ECG and the PLETH waveforms. Similar
statistical characteristics (mean, standard deviation, etc.) used
for the respiratory waveforms are also calculated to characterize
the structural changes in the ECG and PLETH waveforms. These
characteristics are optionally used as input for a machine learning
model to automatically classify the severity of airway
obstruction.
[0195] Additional features from the respiration, ECG, and PLETH
waveforms are calculated using a point-process method, which is a
stochastic process that continuously characterizes the intrinsic
probabilistic structure of discrete events and that has been
successfully applied to study a wide range of phenomena, analyzing
data such as earthquake occurrences, traffic modeling, and neural
spiking activity. More recently, the utility of point process
theory has been validated as a powerful tool to estimate heart beat
and respiratory dynamics--including instantaneous measures of
variability and stability--even in short recordings under
nonstationary conditions.
[0196] In contrast, the commonly used standard methods are
primarily applicable for stationary data or provide only
approximate estimates of the dynamic signatures that are not
corroborated by goodness-of-fit methods. Few methods are available
for time-frequency analysis of nonstationary data (e.g.,
Hilbert-Huang and Wavelet transforms). However, these methods need
to be applied to short batches of data, making them less suitable
for tracking dynamics in real time. Finally, the point process
framework allows for inclusion of any covariate at any sampling
rate, and we will take advantage of this property to generate
instantaneous indices as well as power spectrum indices.
[0197] To effectively characterize the variability in ECG R wave
peak intervals (RR interval), the power spectrum is calculated at
different frequency ranges. FIG. 18 represents the estimation of
instantaneous power at different frequency ranges along with the RR
interval.
[0198] Referring to FIG. 19, the average power spectrum is
calculated in each of the frequency ranges, with the results
showing that the power spectrums vary significantly different
between "Low Risk" and "High Risk" groups. Thus, power spectrum at
different frequency ranges is a relevant feature in the machine
learning framework.
[0199] To obtain additional relevant features from the PLETH
signal, a wavelet transform technique is further applied. The
wavelet transform technique is a powerful tool for extracting
amplitude or power instantaneously at multiple time scales from a
nonstationary data. The power is estimated at multiple time scales
based on a wavelet transform with the Morlet function as the mother
wavelet. Using translational and scaling of the mother wavelet, the
power is estimated at multiple time scales with a dyadic
representation of scales.
[0200] Referring to FIGS. 20A-20C, it is determined that the shape
parameters of the distribution at two of the time scales of the
PLETH signal are significantly different between "Low Risk" vs
"High Risk" group. Specifically, the distribution of power at
different time scales follows a skewed distribution that is
characterized by a Gamma function. Thus, the distribution of power
is a relevant feature in the machine learning framework.
[0201] Referring to FIG. 21, the shape of the distribution of the
PLETH signal estimated using the Gamma function of power at
different time scales shows the significant difference between the
"Low Risk" and the "High Risk" groups. Specifically, the
distribution is shown in the 0-0.3 seconds range as well as the
0.5-1.2 seconds range.
[0202] Thus, a benefit of the AOS algorithm include calculating
breathing obstruction severity in real-time using a combination of
many breath-to-breath and heartbeat-to-heartbeat features that are
averaged over time. Other benefits of the AOS algorithm include the
abilities to continuously and immediately generate a breathing
obstruction severity score (e.g., no calibration or "learning time"
necessary). Yet other benefits of the AOS algorithm include
providing a breathing obstruction severity score without a human
(e.g., a clinician) and to calculate the I:E ratio, in contrast to
flawed current methods. Another benefit of the AOS algorithm is the
measurement of obstructed breathing, which is a symptom of many
conditions, including asthma and anaphylaxis, as well as other
ailments.
[0203] Referring to FIGS. 22A-22F, the sensor module includes a
Smart Auto-injector device 100 with an external housing 101 that
includes a motor 102, a latch/locking mechanism 104, a drive spring
106, a nitinol needle 108, a reservoir and actuator for medication
delivery 110, and an adhesive patch 112. The device 100 receives a
signal from a sensing module, in response to which the motor 102
unlatches the spring 106. Alternatively, instead of the motor 102,
the spring 106 is unlatched in response to a manual action provided
by a user.
[0204] As specifically illustrated in FIGS. 22C and 22D, the spring
106 drives the needle 108 through a pre-shaped curve 114 for
intramuscular ("IM") injection. The pre-shaped curve 114, which
according to some examples is in the shape of an anvil or a
channel, reshapes and drives the needle 108 for the IM injection to
an IM injection depth. A relief valve (e.g., a fluid outlet) opens
up due to built-up pressure or mechanical trigger. The spring 106
injects a predetermined dosage (e.g., 0.15 mg, 0.3 mg, or 0.5 mg)
and the device 100 remains still for a predetermined time (e.g., 5
sec., 10 sec. or 15 sec.). A user removes the device away from the
body, or the motor 102 retracts a mobile housing 116 containing the
needle 108 by pulling it backwards, retracting the needle 108 into
the device 100 (as illustrated in FIGS. 22E and 22F).
[0205] Some benefits of the device 100 include that it is fully
wearable on the body, is discreetly hidden under clothing, has an
adjustable size for different body shapes, and is suitable for
multiple sites on the body. Optionally, the device 100 is
configured to include hypoallergenic materials and is applicable
for IM and/or subcutaneous injections. Optionally, yet, the device
100 is compatible with a smartphone for notifying emergency
services, family members, and/or friends when the device 100 has
made an injection.
[0206] Other benefits of the device 100 include having the needle
108 being driven through the pre-shaped curve 114 for being
reshaped for IM or subcutaneous insertion at different angles.
Another benefit of the needle 108 includes the super-elasticity
and, potentially, the additional shape memory properties of the
nitinol material for IM injections. Because one objective of this
design is to minimize the height of the injector, using a
super-elastic nitinol needle enables the use of a straight needle
that bends 90 degrees to enter the body as it is advanced through
the pre-shaped curve 114. Further, this design minimizes the height
required of the injector 101, making it more likely to be worn
under clothes. Optionally, the needle 108 is configured to provide
a dual functionality as the needle and the medication reservoir.
Additionally, the needle 108 is designed in a way that it drives
itself for insertion and is retracted by an electromechanical or
mechanical actuator.
[0207] According to further benefits of the device 100, a dual
actuation feature is achieved by fully automating the needle
insertion, the medication delivery, and the needle retraction.
Additionally, the dual actuation is optionally triggered manually
for the needle insertion and the medication delivery, and/or
double-manually triggered for the needle insertion and medication
delivery. Furthermore, the device 100 is beneficial for using
hydrostatic forces for reshaping the needle through the pre-shaped
curve 114 with different angles for the IM insertion.
[0208] Referring to FIGS. 23A-23G, the sensor module includes a
Smart Auto-injector device 200, in accordance with an alternative
embodiment, with an external housing 201 that includes a motor 202,
a latch/locking mechanism 204, a drive spring 206, a nitinol needle
208, a reservoir and actuator for medication delivery 210, an
adhesive patch 212, and a mobile housing 216. The device 200
receives a signal from a sensing module, in response to which the
motor 202 unlatches the spring 206. Alternatively, instead of the
motor 202, the spring 206 is unlatched in response to a manual
action provided by a user.
[0209] As specifically illustrated in FIGS. 23C and 23D, the spring
206 drives the mobile housing 216, which contains the motor 202 and
the needle 208, driving the needle 208 through a pre-shaped curve
214 for IM injection. The pre-shaped curve 214, which according to
some examples is in the shape of an anvil or a channel, reshapes
and drives the needle 208 for the IM injection to an IM injection
depth.
[0210] As specifically illustrated in FIGS. 23E-23G, a motor shaft
rotates and reels-in a cable 220 that is coupled to the passive
actuator 210 for medication delivery. The cable 220 moves the
passive actuator 210 downwards, injecting a predetermined dosage
(e.g., 0.15 mg, 0.3 mg, or 0.5 mg) and the device 200 remains still
for a predetermined time (e.g., 5 sec., 10 sec. or 15 sec.). The
motor 202 retracts the needle 208 into the device 200.
[0211] Referring to FIG. 24, the sensor module includes an
alternative Smart Auto-injector device in the form of a CO.sub.2
cartridge-based actuator 300 that includes a housing 302 enclosing
mechanisms for a needle driver, a medication reservoir, and a
medication delivery. The housing 302 is coupled to a first CO.sub.2
cartridge 303a and a second CO.sub.2 cartridge 303b. The actuator
300 further includes a lock/latch mechanism 304, which is operated
manually or via a motor.
[0212] In operation, the actuator 300 receives a signal from a
sensing module and the motorized or manual action unlatches a
spring mechanism as previously described above in reference to the
Smart Auto-injector devices 100, 200. The spring mechanism, motor,
or other electromechanical actuator engages the first CO.sub.2
cartridge 303a, which releases pressurized CO.sub.2 gas to actuate
the internal mechanism and drive a nitinol needle through a
pre-shaped curve (as described above). The pre-shaped curve
reshapes and helps drive the needle for IM injection (as described
above), and the CO.sub.2 cartridge 303a actuates the internal
mechanism to deliver the predetermined dosage (e.g., 0.15 mg, 0.3
mg, or 0.5 mg) of medication when the needle insertion is
completed. The second CO.sub.2 cartridge 303b is, then, engaged, to
reverse the internal mechanism and retract the needle back into the
device immediately after the medication delivery ends. A benefit of
the CO.sub.2 cartridges 303a, 303b is that they act as actuators
for driving one or more of the medication insertion, the needle
insertion, and the needle retraction.
[0213] Referring to FIGS. 25A and 25B, the sensor module includes
another alternative Smart Auto-injector device that has a
lock/latch mechanism 400 with a driver spring 402, a motor 404
mounted along a pivot axis 406, and a lock spring 407. The
lock/latch mechanism 400 allows the device (as described above) to
be triggered by the motor 404 in a fully automatic manner.
Optionally, as a safety measure, the device is also triggered
manually by using a multiple-layered safety switch 408.
[0214] Referring to FIG. 26, the sensor module includes an
alternative Smart Auto-injector device that has a rack and pinion
mechanism 500 with a motor 502, a reservoir 504, a spring for
medication delivery 506, and a needle 508. The motor 502 drives the
rack and pinion mechanism 500 to drive the needle 508 through a
pre-shaped curve for reshaping the needle 508 for IM injection. At
the end of the needle insertion motion, the spring 506 is released
and the predetermined dosage of medication is delivered. The device
remains still for a predetermined time period and, then, the motor
502 retracts the needle 508 back into the device from the human
body.
[0215] Referring to FIG. 27, the sensor module includes another
alternative Smart Auto-injector device that has a pulley mechanism
600 with a motor 602, a plurality of pulleys 604, and a needle 606.
The motor 602 drives one or more cables 608 to drive the needle 606
through a pre-shaped curve for reshaping the needle 606 for IM
injection. At the end of the needle insertion motion, a spring for
medication delivery is released and a predetermined dosage of
medication is delivered. The device remains still for a
predetermined time period and, then, the motor 602 triggers a
mechanism to retract the needle 606 back into the device from the
human body.
[0216] Referring to FIG. 28, the sensor module includes another
alternative Smart Auto-injector device that has a worm-gear
mechanism 700 with a motor 702, a worm gear 704, a linkage 706, a
driving mechanism 708 (including a reservoir and medication
delivery), and a mechanical stop 710. The motor 702 drives the
worm-gear mechanism 700 to drive a needle through a pre-shaped
curve for reshaping the needle for IM injection. Then, the driving
mechanism 708 hit the mechanical stop 710 and a predetermined
dosage of medication starts to be delivered. The device remains
still for a predetermined time period, and, then, the motor 702
retracts the needle back into the device from the human body.
[0217] Referring to FIGS. 29A and 29B, the sensor module includes
another alternative Smart Auto-injector device that has a
collapsible pouch 800 as a reservoir. The pouch 800 is wrapped with
a wire mesh 802 that is made of nitinol or stainless steel. The
pouch 800 is fixed at a position in a reservoir housing and the
wire is attached to a mechanical or electromechanical actuator.
When the actuator starts working for delivering the medication, the
array of wires is pulled to squeeze the pouch 800. The pressure
inside the pouch 800 is increased, and based on the increased
pressure, a fluid outlet 804 opens to deliver the medication into
the body through a nitinol needle. The pouch 800 further includes a
fluid inlet 803 for refilling. Alternatively, the pouch 800 lacks
the wire mesh 802, but includes other physical features for
applying pressure to the pouch 800 to collapse and open the fluid
outlet 804.
[0218] Referring to FIG. 30, the sensor module includes another
alternative Smart Auto-injector device that has a friction drive
900 with a motor 902, a friction wheel 904, a guide wheel 906, and
a needle 908. The motor 902 drives the friction wheel 904 that is
coupled by surface friction to the needle 908. The rotation of the
friction wheel 904, thus, drives the needle 908 through a
pre-shaped curve for reshaping the needle 908 for IM injection. The
needle 908 is guided at the motor level by the guide wheel 906,
which is freely rotating. At the end of the needle insertion
motion, the predetermined dosage of medication is delivered through
electromechanical or mechanical means. The device remains still for
a predetermined time period, and, then, the motor 902 drives the
friction wheel 904 in an opposite direction to retract the needle
908 back into the device.
[0219] Consistent with the above disclosure, benefits of the
described devices include retracting a needle with
electromechanical components (e.g., motors, solenoids,
piezoelectric actuators, linear motors, etc.) or with mechanical
actuators (e.g., springs, pistons, jets, CO.sub.2 cartridges,
etc.). By way of example, electromechanical drives include cables
pulled by a motor that drives the needle for insertion and
retraction through a pulley system that provides a lower profile
and a mechanical advantage, as illustrated in FIG. 27. In another
example, an electromechanical drive includes a needle driven by a
two-way motor for the insertion, medication delivery, and
retracted, as illustrated in FIGS. 23A-23G. In yet another example,
an electromechanical drive includes a direct drive with a
rack-and-pinion and a mechanical actuator-driven medication
injection, as illustrated in FIG. 26. In yet another example, an
electromechanical drive includes a direct drive with a friction and
a mechanical actuator-driven medication injection, as illustrated
in FIG. 30. In yet another example, an electromechanical drive
includes a direct drive with a worm gear to drive the needle for
insertion and retraction, and a mechanical actuator-driven
medication injection, as illustrated in FIG. 28.
[0220] A further benefit of the described devices includes having
an adjustable dosage for medication delivery (e.g., 0.15 mg, 0.30
mg, 0.5 mg), which is adjustable manually or via software. Yet
another benefit includes having refillable, replaceable, or
disposable cartridges and/or a needle assembly for epinephrine
injection. A further benefit includes having reliable indicators
(e.g., an electronic indicator or a visual check) for providing
feedback to a patient on medication.
[0221] Other benefits of the described devices include a reservoir
design that is collapsible, as illustrated in FIGS. 29A and 29B;
that includes electromechanical components (e.g., rotary and linear
motors, and solenoids) driven with pulleys, as illustrated in FIGS.
23A-23G; and that is pre-pressurized and released by a trigger, as
illustrated in FIGS. 25A and 25B. Further benefits of the reservoir
design include having a squeezable pouch, as illustrated in FIGS.
29A and 29B, driven by mechanical or electromechanical components
(e.g., rotary or linear motors, pulley systems, springs, pistons,
jets, CO.sub.2 cartridges); having a nitinol or stainless steel net
around the flexible pouch driven by the mechanical and/or
electromechanical actuators; and having a fluid inlet for refilling
and a fluid outlet for delivery.
[0222] Exemplary Device Embodiments for Sensor Module
[0223] According to one embodiment A of the sensor module described
above, the sensor module is an all-in-one wearable anaphylaxis
device. The wearable device is worn, for example, on the thigh,
upper arm, or abdomen. The wearable device detects the early onset
of anaphylaxis using non-invasive physiological sensors, detection
algorithms (e.g., the AOS algorithm), and a histamine biosensor.
Optionally, upon detection, the wearable device alerts the user,
dials emergency services (e.g., dials "911"), and/or auto-injects
epinephrine.
[0224] According to another embodiment B of the sensor module
described above, the sensor module is a non-invasive wearable
device directed to anaphylaxis detection and/or alarm, with no
injection and no biosensor. The wearable device is worn, for
example, on the thigh, upper arm, or abdomen. The wearable device
detects the early onset of anaphylaxis using only non-invasive
physiological sensors and detection algorithms (e.g., the AOS
algorithm). Optionally, upon detection, the wearable device alerts
the user and/or emergency services.
[0225] According to an alternative embodiment C of the sensor
module described above, the sensor module is a minimally-invasive
wearable device for anaphylaxis detection and alarm, with no
injection (including only a biosensor). The wearable device is
worn, for example, on the thigh, upper arm, or abdomen. The
wearable device detects the early onset of anaphylaxis using only a
histamine sensor. Optionally, upon detection, the wearable device
alerts the user and/or emergency services.
[0226] According to another alternative embodiment D of the sensor
module described above, the sensor module is a sensor device for
continuous monitoring of allergic reactions in a clinical or
hospital setting. The sensor device is a real-time histamine sensor
that continuously monitors a histamine level in a person's blood or
interstitial fluid. The sensor device provides alarms and/or alerts
if an allergic reaction is detected.
[0227] According to a further alternative embodiment E of the
sensor module described above, the sensor module is a wearable
manual injector with no sensors. The wearable manual injector is an
epinephrine auto-injector device that is worn on the thigh, upper
arm, or abdomen (for example). The wearable manual injector is
manually activated and, optionally, includes mobile device (e.g.,
smart phone) integration to notify emergency services (e.g., "911"
services) and/or caregivers upon injection. Other options include
notifications that the wearable manual injector has a depleted
energy level (e.g., the device is in a low battery mode), that the
epinephrine has expired or is depleted, etc.
[0228] According to a further alternative embodiment F of the
sensor module described above, the sensor module is a non-invasive
wearable device for continuous asthma monitoring and/or detection
(with no injection and no biosensor). The wearable device is worn,
for example, on the chest, upper arm, or abdomen, and continuously
monitors the breathing of a user. The wearable device assesses the
severity of airway obstruction and, upon the early detection of
asthmatic conditions, alerts the user and/or others (e.g.,
caregivers, emergency services, hospital, clinician, family
members, etc.). Optionally the wearable device is configured to
record airway obstruction severity over time, to detect trends in
historical severity data, to alert the user to worsening
conditions, and/or to upload the data to a server for analysis by a
clinician or other trained personnel.
[0229] Each of these embodiments and obvious variations thereof is
contemplated as falling within the spirit and scope of the
invention. Moreover, the present concepts expressly include any and
all combinations and sub-combinations of the preceding elements and
aspects.
* * * * *