U.S. patent application number 10/334283 was filed with the patent office on 2004-07-08 for system and method for predicting patient health within a patient management system.
Invention is credited to Fogoros, Richard, Kenknight, Bruce H., Manicka, Yatheendhar, Mazar, Scott Thomas, Pederson, Michael J..
Application Number | 20040133079 10/334283 |
Document ID | / |
Family ID | 32680799 |
Filed Date | 2004-07-08 |
United States Patent
Application |
20040133079 |
Kind Code |
A1 |
Mazar, Scott Thomas ; et
al. |
July 8, 2004 |
System and method for predicting patient health within a patient
management system
Abstract
Systems and Methods for predicting patient health and patient
relative well-being within a patient management system are
disclosed. A preferred embodiment utilizes an implantable medical
device comprising an analysis component and a sensing component
further comprising a three-dimensional accelerometer, a
transthoracic impedance sensor, a cardio-activity sensor, an oxygen
saturation sensor and a blood glucose sensor. Some embodiments of a
system disclosed herein also can be configured as an Advanced
Patient Management System that helps better monitor, predict and
manage chronic diseases.
Inventors: |
Mazar, Scott Thomas; (Inver
Grove Heights, MN) ; Fogoros, Richard; (Pittsburgh,
PA) ; Manicka, Yatheendhar; (Woodbury, MN) ;
Kenknight, Bruce H.; (Maple Grove, MN) ; Pederson,
Michael J.; (Minneapolis, MN) |
Correspondence
Address: |
MERCHANT & GOULD PC
P.O. BOX 2903
MINNEAPOLIS
MN
55402-0903
US
|
Family ID: |
32680799 |
Appl. No.: |
10/334283 |
Filed: |
January 2, 2003 |
Current U.S.
Class: |
600/300 |
Current CPC
Class: |
A61B 5/0031 20130101;
A61B 5/14546 20130101; A61B 5/14532 20130101; A61B 5/0205 20130101;
A61B 5/0809 20130101; A61B 5/11 20130101; A61B 5/363 20210101; A61B
5/352 20210101; A61B 2562/0219 20130101 |
Class at
Publication: |
600/300 |
International
Class: |
A61B 005/00 |
Claims
What is claimed is:
1. A system for predicting patient health and well-being within a
patient management system comprising a medical device further
comprising: a. a sensing component in electronic communication with
other components of the system including one or more sensors
adapted to sense physiological function data; b. an analysis
component in electronic communication with other components of the
system adapted to analyze the sensed physiological data; and c. a
communications component in electronic communication with other
components of the system adapted to communicate the sensed or
analyzed physiological data.
2. The medical device of claim 1, wherein the device is an
implantable medical device.
3. The sensing component of claim 1, wherein the sensing component
comprises an accelerometer.
4. The accelerometer of claim 3, wherein the accelerometer
comprises a one-dimensional accelerometer.
5. The accelerometer of claim 3, wherein the accelerometer
comprises a two-dimensional accelerometer.
6. The accelerometer of claim 3, wherein the accelerometer
comprises a three-dimensional accelerometer.
7. The sensing component of claim 1, wherein the sensing component
comprises a transthoracic impedance sensor.
8. The sensing component of claim 1, wherein the sensing component
comprises a cardio-activity sensor.
9. The sensing component of claim 1, wherein the sensing component
comprises an oxygen saturation sensor.
10. The sensing component of claim 1, wherein the sensing component
comprises a blood glucose sensor.
11. The sensing component of claim 1, wherein the sensing component
comprises a cardiac output/ejection fraction sensor.
12. The sensing component of claim 1, wherein the sensing component
comprises a chamber pressure sensor.
13. The sensing component of claim 1, wherein the sensing component
comprises a temperature sensor.
14. The sensing component of claim 1, wherein the sensing component
comprises a sodium sensor.
15. The sensing component of claim 1, wherein the sensing component
comprises a potassium sensor.
16. The sensing component of claim 1, wherein the sensing component
comprises a calcium sensor.
17. The sensing component of claim 1, wherein the sensing component
comprises a magnesium sensor.
18. The sensing component of claim 1, wherein the sensing component
comprises a pH sensor.
19. The sensing component of claim 1, wherein the sensing component
comprises a partial oxygen sensor.
20. The sensing component of claim 1, wherein the sensing component
comprises a partial CO2 sensor.
21. The sensing component of claim 1, wherein the sensing component
comprises a cholesterol sensor.
22. The sensing component of claim 1, wherein the sensing component
comprises a triglyceride sensor.
23. The sensing component of claim 1, wherein the sensing component
comprises a catecholamine sensor.
24. The sensing component of claim 1, wherein the sensing component
comprises a creatine phosphokinase sensor.
25. The sensing component of claim 1, wherein the sensing component
comprises a lactate dehydrogenase sensor.
26. The sensing component of claim 1, wherein the sensing component
comprises a troponin sensor.
27. The sensing component of claim 1, wherein the sensing component
comprises a prothrombin time sensor.
28. The sensing component of claim 1, wherein the sensing component
comprises a complete blood count sensor.
29. The sensing component of claim 1, wherein the sensing component
comprises a blood urea nitrogen sensor.
30. The sensing component of claim 1, wherein the sensing component
comprises a body weight sensor.
31. The sensing component of claim 1, wherein the sensing component
comprises a blood (systemic) pressure sensor.
32. The sensing component of claim 1, wherein the sensing component
comprises a adrenocorticotropic hormone sensor.
33. The sensing component of claim 1, wherein the sensing component
comprises a thyroid marker sensor.
34. The sensing component of claim 1, wherein the sensing component
comprises a gastric marker sensor.
35. The sensing component of claim 1, wherein the sensing component
comprises a creatinine sensor.
36. The accelerometer of claim 3, wherein the accelerometer is
adapted to sense the fine and gross body position of a person.
37. The fine and gross body position of claim 36, wherein the
sensed body position of the person comprises standing, sitting,
lying on the back, lying on the stomach, lying upon the left side
and lying on the right side.
38. The accelerometer of claim 3, wherein the accelerometer is
adapted to sense the fine and gross body motion of a person.
39. The fine and gross body motion of claim 38, wherein the sensed
body motion comprises a baseline measurement of patient
activity.
40. The fine and gross body motion of claim 38, wherein the sensed
body motion comprises a measure of well-being.
41. The fine and gross body motion of claim 38, wherein the sensed
body motion comprises a measure of lethargy.
42. The measure of lethargy of claim 41, wherein the measure
comprises the magnitude of activity and the frequency of
activity.
43. The accelerometer of claim 3, wherein the accelerometer is
adapted to detect a cough.
44. The detected cough of claim 43, wherein the cough is analyzed
to detect the onset of a common cold.
45. The detected cough of claim 43, wherein the cough is analyzed
to detect the onset of influenza.
46. The detected cough of claim 43, wherein the cough is analyzed
to titrate a drug.
47. The titrated drug of claim 46, wherein the drug is an
angiotensin converting enzyme inhibitor.
48. The titrated drug of claim 46, wherein the drug comprises a
near-term drug delivery system.
49. The near-term drug delivery system of claim 48, wherein the
system comprises communication with a clinician.
50. The near-term drug delivery system of claim 48, wherein the
system comprises communication with a patient.
51. The analysis component of claim 1, wherein the analysis is
performed internal to the patient.
52. The analysis component of claim 1, wherein the analysis is
performed external to the patient.
53. The analysis component of claim 1, wherein the analysis is
performed, in part, internal to the patient.
54. The analysis component of claim 1, wherein the analysis is
performed, in part, external to the patient.
55. The analysis component of claim 1, wherein the analysis
includes detecting changes in sensed data patterns that are
indicative of early occurrence of a new disease state.
56. The analysis component of claim 1, wherein the analysis
includes detecting changes in sensed data patterns that are
indicative of onset of illness.
57. The analysis component of claim 1, wherein the analysis
includes detecting changes in sensed data patterns that are
indicative of progression of a disease.
58. The analysis component of claim 1, wherein the analysis
includes detecting changes in sensed accelerometer patterns that
are indicative of early occurrence of a new disease state.
59. The analysis component of claim 1, wherein the analysis
includes detecting changes in sensed accelerometer patterns that
are indicative of onset of illness.
60. The analysis component of claim 1, wherein the analysis
includes detecting changes in sensed accelerometer patterns that
are indicative of progression of a disease.
61. The analysis component of claim 1, wherein the analysis
includes detecting changes in transthoracic impedance variation
patterns that are indicative of early occurrence of a new disease
state.
62. The new disease state of claim 61, wherein the new disease
state is chronic obstructive pulmonary disease.
63. The analysis component of claim 1, wherein the analysis
includes detecting changes in transthoracic impedance variation
patterns that are indicative of onset of illness.
64. The onset of illness of claim 63, wherein the illness comprises
asthma.
65. The analysis component of claim 1, wherein the analysis
includes detecting changes in transthoracic impedance variation
patterns that indicate progression of a disease.
66. The progression of disease of claim 65, wherein the disease
comprises heart failure.
67. The analysis component of claim 1, wherein the analysis
includes combining sensed data to cross-validate sensed
conclusions.
68. The combined sensed data of claim 67, wherein the combined data
includes a change in accelerometer data pattern coincident with
inhalation/exhalation time ratio measured by transthoracic
impedance.
69. The combined data of claim 68, wherein the combined data
indicates progression of asthma.
70. The analysis component of claim 1, wherein the analysis
includes monitoring left and right intracardial R-wave
amplitude.
71. The analysis of claim 70, wherein the analysis includes singly
reporting changes.
72. The analysis of claim 71, wherein the analysis comprises an
early and confident indication of onset of pulmonary edema.
73. The analysis of claim 70, wherein the analysis includes
correlating left and right intracardial R-wave amplitude data with
accelerometer and transthoracic impedance data.
74. The analysis of claim 73, wherein the analysis comprises an
early and confident indication of onset of pulmonary edema.
75. The analysis component of claim 1, wherein the analysis
includes combining accelerometer, transthoracic impedance and blood
oxygen saturation data to form an early and confident indication of
onset of pulmonary edema.
76. The analysis component of claim 1, wherein the analysis
includes combining accelerometer, transthoracic impedance and blood
oxygen saturation data to form an early and confident indication of
progression of pulmonary edema.
77. The analysis component of claim 1, wherein the analysis
includes combining accelerometer, transthoracic impedance, blood
oxygen saturation, cardio-activity and blood glucose data for an
early and confident indication of onset of cardiac and pulmonary
disease states.
78. The analysis component of claim 1, wherein the analysis
includes combining accelerometer, transthoracic impedance, blood
oxygen saturation, cardio-activity and blood glucose data for an
early and confident indication of changes in cardiac and pulmonary
disease states.
79. The analysis component of claim 1, wherein the analysis
includes combining data from other base sensors for an early and
confident indication of onset of diseases other than
cardio-pulmonary diseases.
80. The analysis component of claim 1, wherein the analysis
includes combining data from other sensors for an early and
confident indication of progression of diseases other than
cardiopulmonary diseases.
81. The communications component of claim 1, wherein the
communications are wired electronic communications.
82. The communications component of claim 1, wherein the
communications are wireless electronic communications.
83. The communications component of claim 1, wherein the
communications are a combination of wired and wireless electronic
communications.
84. A method for predicting patient health and well-being within a
patient management system comprising a medical device comprising
the steps of: a. sensing physiological function data with one or
more sensor components in electronic communication with other
components of the system and adapted to sense such data; b.
analyzing the sensed physiological data with an analysis component
in electronic communication with other components of the system and
adapted to analyze the sensed data; and c. communicating the sensed
and analyzed physiological data with a communications component in
electronic communication with other components of the system and
adapted to communicate the sensed and analyzed data to the
components of the system.
85. The method of claim 84, wherein the step of sensing
physiological function data comprises the further step of sensing a
fine and gross body position of a person with an accelerometer.
86. The method of claim 84, wherein the step of sensing
physiological function data comprises the further step of sensing
respiration function data of a person with an transthoracic
impedance sensor.
87. The method of claim 84, wherein the step of sensing
physiological function data comprises the further step of sensing
cardiac activity of a person with a cardio-activity sensor.
88. The method of claim 84, wherein the step of sensing
physiological function data comprises the further step of oxygen
saturation of a person with an oxygen saturation sensor.
89. The method of claim 84, wherein the step of sensing
physiological function data comprises the further step of oxygen
saturation of a person with a blood glucose sensor.
90. The method of claim 84, wherein the step of analyzing sensed
physiological function data comprises the further step of analyzing
changes in sensed data patterns that are indicative of early
occurrence of a new disease state.
91. The method of claim 84, wherein the step of analyzing sensed
physiological function data comprises the further step of analyzing
changes in sensed data patterns that are indicative of onset of
illness.
92. The method of claim 84, wherein the step of analyzing sensed
physiological function data comprises the further step of analyzing
changes in sensed data patterns that are indicative of progression
of a disease.
93. The method of claim 84, wherein the step of analyzing sensed
physiological function data comprises the further step of analyzing
the sensed physiological function data by using clinically derived
algorithms.
94. The step of analyzing sensed physiological function data of
claim 93, wherein the step comprises the further step of analyzing
the sensed physiological data by using algorithms reflecting a
standard of medical care of a medical institution.
95. The method of claim 84, wherein the step of analyzing sensed
physiological function data comprises the further step of analyzing
the sensed physiological function data with an Advanced Patient
Management system.
96. The method of claim 84, wherein the step of communicating the
sensed and analyzed physiological function data comprises the
further step of electronically communicating the sensed and
analyzed data to other components of the system.
97. The method of claim 96, wherein the step of electronically
communicating the sensed and analyzed physiological function data
comprises the further step of wirelessly communicating the sensed
and analyzed data.
98. The step of communicating the sensed and analyzed physiological
function data of claim 96, wherein the step comprises the further
step of communicating the sensed and analyzed data to a patient
management system.
99. The step of communicating the sensed and analyzed physiological
function data of claim 97, wherein the step comprises the further
step of communicating the sensed and analyzed data to a patient
management system.
100. The step of communicating the sensed and analyzed
physiological function data of claim 96, wherein the step comprises
the further step of communicating the sensed and analyzed data to
an Advanced Patient Management system.
101. The step of communicating the sensed and analyzed
physiological function data of claim 97, wherein the step comprises
the further step of communicating the sensed and analyzed data to
an Advanced Patient Management system.
Description
TECHNICAL FIELD
[0001] The present system relates generally to a Patient Management
System and particularly, but not by way of limitation, to such a
system that can determine patient health, relative well-being and
predictive degradation by using the sensing functions of an
implantable medical device and analyzing the sensed patient data to
predict patient health.
BACKGROUND
[0002] Implantable medical devices are becoming increasingly
versatile and able to perform many different physiological sensing
functions that enable a clinician to quickly and accurately assess
patient health. Traditionally, an accurate assessment of patient
health required the clinician to synthesize often divergent or
seemingly unrelated indications of patient health. For example, a
diagnosis of congestive heart failure might include not only an
assessment and evaluation of cardiac function data, but also an
evaluation of other physiological factors like patient fatigue or
respiration data.
[0003] Typically, a clinician will assess patient health by
inquiring how the patient feels or asking about the patient's
activities and then make an indirect assessment based on the
patient's response and the clinician's observation of the patient's
appearance. However, these measures are very subjective and are
limited to the time of the patient/clinician interaction and the
quality of patient recall or willingness to divulge information.
These factors affect the quality of the assessment.
[0004] Modern implantable medical devices offer objective data to
help the clinician assess patient health. Modern medical devices
can sense and analyze physiological factors with improved accuracy
and report that sensed and analyzed information to the clinician or
the patient. The data or information that a medical device reports
in the form of a sensed physiological parameter can be
characterized as either derived or non-derived data. Non-derived
data can be understood as raw biometric information sensed by the
medical device that has not been processed to any meaningful
degree. For example, non-derived biometric information may comprise
the quantified measurement of a patient's heart rate or blood
pressure. In contrast, derived data is biometric information that
has been analyzed and perhaps assigned some qualitative or
quantitative value. For example, as a medical device senses a
patient's cardiac cycle and clinically analyzes that information,
the medical device may report that an arrhythmia has occurred as
the result of sensing and analyzing a cardiac rhythm outside
expected parameters. Other derived sensors may include, the
cumulative calories burned by daily activity, a weight loss
monitor, a participation in activities monitor, a depression
monitor or determining the onset of cancer, all of which may be
ascertained by sensing physiological data and analyzing that data
by using clinically derived algorithms or other analytical
tools.
[0005] An example of a sensor component of a medical device is an
accelerometer. An accelerometer is essentially a device capable of
measuring an object's relative orientation in a gravity field. It
can directly sense patient movement (non-derived data) and present
that information for analysis and perform as a derived sensor. Such
derived information might include whether a patient is fatigued by
reason of illness or because of overexertion. Thus, relative
activity may correspond to relative patient health. In addition to
simply determining whether a patient is ambulatory, a sensitive or
finely-tuned accelerometer can also determine a patieht's relative
position, i.e., whether the patient is sitting, standing, sleeping
or distinguish whether the patient is prone because he decided to
lie down instead of abruptly falling down. A sensitive
accelerometer can also detect fine body movement, like the physical
reflexes of a person coughing or sneezing.
[0006] Coughing is often more than an indication of a respiratory
irritation or condition like asthma or the onset of the common
cold, but may also be a common side effect of certain drugs. For
example, Angiotensin Converting Enzyme ("ACE") inhibitors may cause
a patient to cough when the patient's dosage is too high. Thus,
coughing may be used to titrate the appropriate dose of a drug like
an ACE inhibitor.
[0007] Implantable medical devices comprising cardio-sensors, i.e.,
pacemakers, can also monitor and sense a patient's cardiac activity
and provide remedial therapy. In addition, such medical devices can
sense and measure transthoracic impedance as a means to evaluate
patient respiration data.
[0008] As a measurement of respiration, modern implantable medical
devices often employ a sensor that measures transthoracic
impedance. Transthoracic impedance is essentially the measure of a
voltage across some known spacing or distance. To measure this
voltage, the medical device drives a current from the device to the
tip of a lead and voltage is measured from another area proximate
to the device and another area proximate to the lead. For example,
as a person's heart pumps, the transthoracic impedance changes
because the heart is moving relative to the implanted device.
Similarly, as a person's lung inflates and deflates as he breathes,
the geometry of the current flowing between the device and the tip
of the lead changes. In measuring respiration, the spacing or
distance is situated in such a way that the distance crosses over
either a person's left or right lung. Thus, when the geometry
changes, the resistance also changes. In the context of breathing,
the periodicity of the resistance also can serve as an indication
of the relative depth or shallowness of breathing. In other words,
a transthoracic impedance sensor can determine the symmetrical
relationship between inhalation and exhalation. The symmetry of
inhalation to exhalation can establish a pattern of respiration
that may have clinical meaning, like determining asthma, apnea or
chronic obstructive pulmonary disease ("COPD"). Within the context
of detecting an asthma attack, a symmetrical breathing pattern
recognized by a transthoracic impedance monitor may comprise the
forced expiratory volume over one second ("FEV1"). Modern medical
devices that measure transthoracic impedance can be configured to
filter out the cardiac component and other impedance noise and
concentrate on measuring the breathing component.
[0009] An implantable medical device may also employ a sensor that
measures blood glucose levels. In this way, the medical device may
predict the need for insulin therapy before the patient or
clinician observes acute symptoms of hyperglycemia.
[0010] However, the data sensed by modern implantable medical
devices is often presented in a form that merely reduces the data
to some numerical or relative value that requires the clinician to
further analyze the numerical or relative value output to make a
meaningful clinical assessment. In addition, current implantable
medical devices frequently are not analytically robust enough to
provide meaningful diagnostic assessments or predictions of patient
health beyond the mere reporting of physiological data. Merely
reporting physiological data can be of limited value due to a
person's natural ability to initially compensate for nascent
changes in health status. Because of such analytical and perceptual
limitations, sensing cardiac activity or transthoracic impedance
data through a single implantable medical device may only provide
the clinician with a useful starting point for further clinical
analysis.
[0011] Thus, for these and other reasons, there is a need for a
Patient Management System comprising an implantable medical device
further comprising various physiological sensors that sense and
report patient data. The system is further adapted to analyze the
sensed data in a manner that yields an accurate assessment or
prediction of patient health or relative well-being. In this way,
the system can be configured to not only report a relative state of
patient health and detect early stage disease progression, but also
alert the clinician to patient health degradation before the onset
of an acute episode or symptomatic illness.
SUMMARY
[0012] According to one aspect of the invention, there is provided
a system and method for predicting patient health and relative
well-being within a Patient Management System using an implantable
medical device configured with multiple physiological sensors in
communication with other components of the system via a
communications network.
[0013] The Patient Management System further includes an analytical
component contained within the medical device or outside the device
or a combination of internal and external analytical components. A
non-limiting example of such an analytical component is an
externally-based Advanced Patient Management System. As used
herein, "physiological function data," "physiology data," "patient
data" and "patient health data" are substantively synonymous terms
and relate to a measurable or relative physiological parameter. In
addition to physical parameters like heart rate, respiration and
blood chemistry, physiological parameters may include, for example,
subjective evaluations of well-being, perceived emotional state and
other psychological attributes. Also, as used herein, a "clinician"
can be a physician, physician assistant (PA), nurse, medical
technologist, or any other patient health care provider.
[0014] In one embodiment of a system for predicting patient health
and relative well-being within a patient management system, the
system comprises a medical device further comprising a sensing
component, an analysis component and a communications component.
The sensing component includes one or more base sensors adapted to
sense physiological function data. The analysis component is
adapted to analyze physiological data sensed by the sensing
component and detect subtle, early indications of changes in
disease state. The communications component is adapted to
communicate sensed and analyzed physiological data to the
components of the system.
[0015] In another embodiment of the system for predicting patient
health and relative well-being within a patient management system,
the medical device comprising sensing, analysis and communications
components is implanted within a patient, and the sensing component
includes an accelerometer. The accelerometer can be configured to
detect a patient's fine and gross body motion, and can be a one-,
two- or three-dimensional accelerometer. Example analysis includes
detecting changes in measured accelerometer patterns that are
indicative of early occurrence of a new disease state or onset of
illness or indicate progression of a disease.
[0016] In a further embodiment of the system for predicting patient
health and relative well-being within a patient management system,
the sensing component of the implantable medical device comprises
an accelerometer and a transthoracic impedance sensor. In this
embodiment, the implantable medical device is adapted to detect a
patient's fine and gross body motion and respiration parameters.
Example analysis includes detecting changes in transthoracic
impedance variation patterns that are indicative of early
occurrence of a new disease state (such as COPD) or onset of
illness (such as asthma) or indicate progression of a disease (such
as DC impedance indicating lung fluid accumulation which
corresponds to progression of heart failure). Further, the sensed
data can be used in combination to cross-validate sensed
conclusions, such as a change in accelerometer data pattern
coincident with inhalation/exhalation time ratio measured by
transthoracic impedance to indicate progression of asthma.
[0017] In yet another embodiment of the system for predicting
patient health and relative well-being within a patient management
system, the sensing component of the implantable medical device
comprises an accelerometer, a transthoracic impedance sensor and a
cardio-activity sensor. In this embodiment, the implantable medical
device is adapted to detect a patient's fine and gross body motion,
respiration parameters, and cardiac-activity parameters. Example
analysis includes monitoring left and right intracardial R-wave
amplitude and either singly reporting changes or correlating
changes with changes in accelerometer and transthoracic impedance
to form an early and confident indication of onset of pulmonary
edema.
[0018] In yet a further embodiment of the system for predicting
patient health and relative well-being within a patient management
system, the sensing component of the implantable medical device
comprises an accelerometer, a transthoracic impedance sensor, and
an oxygen saturation sensor. In this embodiment, the implantable
medical device is adapted to detect a patient's fine and gross body
motion, respiration parameters, cardiac-activity parameters and
blood gas data. Example analysis includes combining changes in
accelerometer and transthoracic impedance with blood oxygen
saturation to form an early and confident indication of onset or
progression of pulmonary edema.
[0019] In a preferred embodiment of the system for predicting
patient health and relative well-being within a patient management
system, the sensing component of the implantable medical device
comprises a three-dimensional accelerometer, a transthoracic
impedance sensor, a cardio-activity sensor, an oxygen saturation
sensor and a blood glucose sensor. In this embodiment, the
implantable medical device is adapted to detect a patient's fine
and gross body motion, respiration parameters, cardiac-activity
parameters, blood gas data and episodes of hyper- and hypoglycemia.
Example analysis includes combining changes in accelerometer data,
transthoracic impedance, blood oxygen saturation, cardio-activity
and blood glucose for an early and confident indication of onset
and changes in cardiac and pulmonary disease states.
[0020] By selecting other base sensors, early and confident
indications of onset or progression of diseases beyond
cardiopulmonary can be made. By way of non-limiting example only,
other base sensors might include a cardiac output/ejection fraction
sensor; a chamber pressure sensor; a temperature sensor; sodium,
potassium, calcium and magnesium sensors; a pH sensor; a partial
oxygen sensor; a partial CO2 sensor; a cholesterol and triglyceride
sensor; a catecholamine sensor; a creatine phosphokinase sensor; a
lactate dehydrogenase sensor; a troponin sensor; a prothrombin time
sensor; a complete blood count sensor; a blood urea nitrogen
sensor; a body weight sensor; a blood (systemic) pressure sensor; a
adrenocorticotropic hormone sensor; a thyroid marker sensor; a
gastric marker sensor and a creatinine sensor. Data from these
sensors can be analyzed to predict or detect, by way of
non-limiting example only, the early onset of stroke, pain
quantification/determination, chronic depression, cancer tissue
(onset, progression, recurrence), syncope, autonomic tone,
myocardial infarct, ischemia and seizure.
[0021] The various embodiments described above are provided by way
of illustration only and should not be construed to limit the
invention. Those skilled in the art will readily recognize various
modifications and changes that may be made to the present invention
without following the example embodiments and applications
illustrated and described herein, and without departing from the
true spirit and scope of the present invention, which is set forth
in the following claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In the drawings, which are not necessarily drawn to scale,
like numerals describe substantially similar components throughout
the several views. Like numerals having different letter suffixes
represent different instances of substantially similar components.
The drawings illustrate generally, by way of example, but not by
way of limitation, various embodiments discussed in the present
document.
[0023] FIG. 1 is a schematic/block diagram illustrating generally,
among other things, one embodiment of the system and method for
predicting patient health within a patient management system.
[0024] FIG. 2 is a schematic/block diagram illustrating generally,
among other things, another embodiment of the system and method for
predicting patient health within a patient management system
comprising an accelerometer.
[0025] FIG. 3 is a schematic/block diagram illustrating generally,
among other things, another embodiment of the system and method for
predicting patient health within a patient management system
comprising an accelerometer and a transthoracic impedance
sensor.
[0026] FIG. 4 is a schematic/block diagram illustrating generally,
among other things, another embodiment of the system and method for
predicting patient health within a patient management system
comprising an accelerometer, a transthoracic impedance sensor and
an oxygen saturation sensor.
[0027] FIG. 5 is a schematic/block diagram illustrating generally,
among other things, another embodiment of the system and method for
predicting patient health within a patient management system
comprising an accelerometer, a transthoracic impedance sensor, an
oxygen saturation sensor and a cardio-activity sensor.
[0028] FIG. 6 is a schematic/block diagram illustrating generally,
among other things, another embodiment of the system and method for
predicting patient health within a patient management system
comprising an accelerometer, a transthoracic impedance sensor, an
oxygen saturation sensor, a cardio-activity sensor and a blood
glucose sensor.
[0029] FIG. 7 is a schematic/block diagram illustrating generally,
among other things, another embodiment of the system and method for
predicting patient health within an Advanced Patient Management
system.
[0030] FIG. 8 is a flow diagram illustrating generally, among other
things, the interactive functions of the system and method for
predicting patient health within a patient management system.
DETAILED DESCRIPTION
[0031] In the following detailed description, reference is made to
the accompanying drawings that form a part hereof, and in which are
shown by way of illustration, specific embodiments or examples.
These embodiments may be combined, other embodiments may be
utilized, and structural, logical and electrical changes may be
made without departing from the spirit and scope of the present
invention. The following detailed description is, therefore, not to
be taken in a limiting sense, and the scope of the present
invention is defined by the appended claims and their
equivalents.
[0032] The present system and method are described with respect to
an implantable medical device as a component of a Patient
Management System capable of predicting patient health and relative
well-being by the comprehensively analyzing sensed physiological
data.
[0033] FIG. 1 is a schematic/block diagram illustrating generally
an embodiment of the system and method for predicting patient
health and relative well-being within a patient management system
100. The system comprises a medical device further comprising a
sensing component 101, an analysis component 102 and a
communications component 103. The medical device can be implantable
104 within a patient 105.
[0034] The sensing component 101 includes one or more sensors
adapted to sense physiological data. The sensors may comprise an
accelerometer, a transthoracic impedance sensor, an oxygen
saturation sensor, and a cardio-activity sensor.
[0035] The analysis component 102 is adapted to analyze
physiological data sensed by the sensing component. Analysis may be
internal and/or external to the patient. Analysis may include the
use of clinically derived algorithms to analyze the biometric data
in a way that yields a clinically relevant output. The algorithms
can be the result of the extraction, codification and use of
collected expert knowledge for the analysis or diagnosis of medical
conditions. For example, the algorithms can comprise institutional
analytical or diagnostic techniques used in specific clinical
settings. By reducing the analytical or diagnostic methodologies of
institutions like the Cleveland Clinic, the Mayo Clinic or the
Kaiser Permanente system to algorithmic expression, a patient will
enjoy the benefit of the medical expertise of a leading medical
institution without having to visit the institution. The analysis
and sensing components are further adapted to electronically
communicate with the communications component.
[0036] The communications component 103 is adapted to communicate
sensed and analyzed physiological data to the components of the
system, whether the components are internal or external to the
patient.
[0037] FIG. 2 is a schematic/block diagram illustrating generally
an embodiment of the accelerometer 200 component of the system and
method for predicting patient health and relative well-being within
a patient management system. The accelerometer 200 can be
configured to detect a patient's fine and gross body motion. A
suitable accelerometer includes a one-dimensional, two-dimensional
200 or three-dimensional accelerometer. Typically, a
one-dimensional accelerometer only measures movement along a single
axis 201 as further illustrated in FIG. 2. A two-dimensional
accelerometer typically measures movement along two orthogonal axes
202. A three-dimensional accelerometer measures movement along
three orthogonal axes 203. When the system comprises a
three-dimensional accelerometer, the system can determine person's
body position with greatest accuracy. Thus, in addition to
detecting gross body movement, a sensitive accelerometer may be
adapted to detect fine body movement, like a person coughing. When
the system is configured to analyze accelerometer data to determine
whether a person is coughing, a clinician can utilize that
derivative information two assist in determining the onset of a
common cold, influenza or the proper dosage of a drug, like an ACE
inhibitor, that may cause a coughing side effect when the dosage is
too high. In addition, coughing or other activity sensed by the
accelerometer 200 may be used to titrate the dosage of other drugs
as a component of a near-term drug delivery system, wherein the
titration analysis is communicated to the patient or the
clinician.
[0038] FIG. 3 is a schematic/block diagram illustrating generally
an embodiment of the transthoracic impedance sensor 300 component
of the system and method for predicting patient health and relative
well-being within a patient management system. In one embodiment,
as illustrated in FIG. 3, the transthoracic impedance sensor 300 is
a component of an implantable medical device 301. In this
embodiment, the implantable medical device comprises an
accelerometer 200 as illustrated in FIG. 2 and a transthoracic
impedance sensor 300. A transthoracic impedance sensor 300 may be
adapted to sense impedance changes in the heart or lungs or both.
The transthoracic impedance sensor can be configured to filter out
the cardiac component and other impedance noise and focus on
respiration measurement. In such a filtered embodiment, the
transthoracic impedance sensor 300 can assist the clinician in
predicting the onset or presence of an asthma attack, apnea, COPD
and FEV1. Further, in this embodiment, the transthoracic impedance
sensor 300 may also be configured to detect the accumulation of
fluid in the lungs. Such detection may also serve to predictively
indicate the onset or existence of pulmonary disease.
[0039] FIG. 4 is a schematic/block diagram illustrating generally
an embodiment of the oxygen saturation sensor 400 component of the
system and method for predicting patient health and relative
well-being within a patient management system. In one embodiment,
as illustrated in FIG. 4, the oxygen saturation sensor 400 is a
component of an implantable medical device 301. In this embodiment,
the implantable medical device comprises an accelerometer 200 as
illustrated in FIG. 2, a transthoracic impedance sensor 300 and an
oxygen saturation sensor 400. An oxygen saturation sensor 400
determines the ratio between the deoxygenated hemoglobin and
oxygenated hemoglobin. In a healthy person, breathing air at sea
level, the level of saturation is between 96% and, 98%. Abnormal
levels may indicate a respiratory or environmental problem. When
combined with other measurements of patient health, a patient's
oxygen saturation level may provide further evidence of patient
health or relative well-being.
[0040] FIG. 5 is a schematic/block diagram illustrating generally
an embodiment of the cardio-activity sensor 500 component of the
system and method for predicting patient health and relative
well-being within a patient management system. In one embodiment,
as illustrated in FIG. 5, the cardio-activity sensor 500 is a
component of an implantable medical device 301. In this embodiment,
the implantable medical device comprises an accelerometer 200 as
illustrated in FIG. 2, a transthoracic impedance sensor 300, an
oxygen saturation sensor 400, and a cardio-activity sensor 500. The
cardio-activity sensor 500 may be configured to detect cardiac
arrhythmias. Depending on the nature of the arrhythmia, the
cardio-activity sensor 500 may cause therapy to be directed to the
patient in the form of a low energy electrical stimuli, i.e., pace
pulse, or a defibrillation countershock. The cardio-activity sensor
500 may also be used to signal a clinician that an arrhythmia
requires further analysis or medical intervention. The
cardio-activity sensor 500 in this embodiment may also assist in
predicting stroke by measuring ST-segment changes in an
electrocardiogram and conveying that information to the analysis
component 102 to confirm ST-segment elevations or
abnormalities.
[0041] FIG. 6 is a schematic/block diagram illustrating generally
an embodiment of the blood glucose sensor 600 component of the
system and method for predicting patient health and relative
well-being within a patient management system. In one embodiment,
as illustrated in FIG. 6, the blood glucose sensor 600 is a
component of an implantable medical device 301. In this embodiment,
the implantable medical device comprises an accelerometer 200 as
illustrated in FIG. 2, a transthoracic impedance sensor 300, an
oxygen saturation sensor 400, a cardio-activity sensor 500 and a
blood glucose sensor 600. The blood glucose sensor 600 may be
configured to detect elevations or de-elevations in blood glucose.
Depending on the nature of the blood glucose level, the blood
glucose sensor 600 may cause therapy to be directed to the patient
in the form of insulin administration or be used to signal an alert
to the patient or clinician.
[0042] FIG. 7 is a schematic/block diagram illustrating generally
an embodiment of the system and method for predicting patient
health and relative well-being within a patient management system
100 illustrating the analysis of patient data by an
externally-based Advanced Patient Management System ("APM")
700.
[0043] APM is a system that helps patients, their physicians and
their families to better monitor, predict and manage chronic
diseases. In the embodiment shown in FIG. 7, the APM system 700
consists of three primary components: 1) an implantable medical
device 301 with sensors adapted to monitor physiological data, 2) a
Data Management System ("DMS") 701, adapted to process and store
patient data 701a collected from the sensors, patient population
data 701b, medical practice data 601c further comprising clinically
derived algorithms, and general practice data 701d, and 3) an
analytical engine 702 adapted to analyze data from the DMS. APM is
designed to support physicians and other clinicians in using a
variety of different devices, patient-specific and non-specific
data, along with medication therapy, to provide the best possible
care to patients. Currently, implanted devices often provide only
limited sensing, analysis and therapy to patients. APM moves the
device from a reactive mode into a predictive one that allows a
clinician to use APM to predict patient health.
[0044] FIG. 8 is a flow diagram illustrating generally the
interactive functions of the system and method for predicting
patient health and relative well-being within a patient management
system 100. As illustrated in FIG. 8, the sensing 800, analysis 701
and communications 802 components are interactive, thus allowing
the components to communicate and share data. By way of
non-limiting example only, the sensing component 800 would first
sense physiological function data from a patient 105. The sensing
component 800 may be further adapted to provide therapy to the
patient 105. That data would then be transmitted to analysis
component 801 for analysis. Analysis may comprise the use of
clinically derived algorithms and may be performed internal and/or
external to the patient 105. Based on the analysis, the sensing
component 800 may be further adapted to provide therapy to the
patient 105. The analyzed data is then received by communications
module 802, which reports the analyzed data in the form of a
determination of patient health or relative well-being to a patient
105 or clinician 105a. The communications component 802 may also be
in communication with a patient management system, including an
externally based Advanced Patient Management system 803.
Communication can be in the form of wired or wireless electronic
communication.
[0045] The various embodiments described above are provided by way
of illustration only and should not be construed to limit the
invention. Those skilled in the art will readily recognize various
modifications and changes that may be made to the present invention
without following the example embodiments and applications
illustrated and described herein, and without departing from the
true spirit and scope of the present invention, which is set forth
in the following claims.
[0046] It is to be understood that the above description is
intended to be illustrative, and not restrictive. For example, the
above-described embodiments may be used in combination with each
other. Many other embodiments will be apparent to those of skill in
the art upon reviewing the above description. The scope of the
invention should, therefore, be determined with reference to the
appended claims, along with the full scope of equivalents to which
such claims are entitled. In the appended claims, the terms
"including," "includes" and "in which" are used as the
plain-English equivalents of the respective terms "comprising,"
"comprises" and "wherein."
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