U.S. patent application number 14/290872 was filed with the patent office on 2015-03-26 for method for selecting a clinical treatment plan tailored to patient defined health goals.
This patent application is currently assigned to HEALTH OUTCOMES SCIENCES, LLC. The applicant listed for this patent is HEALTH OUTCOMES SCIENCES, LLC. Invention is credited to John Albert Spertus.
Application Number | 20150088534 14/290872 |
Document ID | / |
Family ID | 29710537 |
Filed Date | 2015-03-26 |
United States Patent
Application |
20150088534 |
Kind Code |
A1 |
Spertus; John Albert |
March 26, 2015 |
METHOD FOR SELECTING A CLINICAL TREATMENT PLAN TAILORED TO PATIENT
DEFINED HEALTH GOALS
Abstract
The invention discloses a method by which the health care
professional or patient may draw upon historical medical data
concerning patients similarly situated in medical condition, to
assist him/her in deciding on a clinical intervention procedure to
select. This method is specifically tailored to the patient, as
data is provided and evaluated from only similarly situated
patients, and provides an expectation of potential outcome of the
patient should one or the other of the options be selected. The
invention further provides a database that may be used in order to
provide this comparison based evaluation method. A computer based
software system is further disclosed that implements the method.
The invention more specifically provides a method by which a
post-coronary event patient may make an informed decision of which
post-coronary revascularization procedure to undergo in the future
management of his disease. This method employs the patient's health
status date (symptoms, function and quality of life), and provides
projections of the patient's expected survival, risk, and 1-year
health status outcome from the selection of revascularization
procedure, such as Coronary Artery Bypass Grafting (CABG) or
Percutaneous Coronary Intervention (PCI).
Inventors: |
Spertus; John Albert;
(Kansas City, MO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HEALTH OUTCOMES SCIENCES, LLC |
Los Angeles |
CA |
US |
|
|
Assignee: |
HEALTH OUTCOMES SCIENCES,
LLC
Los Angeles
CA
|
Family ID: |
29710537 |
Appl. No.: |
14/290872 |
Filed: |
May 29, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10165855 |
Jun 7, 2002 |
8744867 |
|
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14290872 |
|
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
G16H 20/00 20180101;
G06Q 10/10 20130101; G16H 50/30 20180101 |
Class at
Publication: |
705/2 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. (canceled)
2. A non-transitory computer readable storage medium storing
instructions that when executed by a computer system, cause the
computer system to perform operations comprising: retrieving from
non-transitory memory clinical information regarding a first
individual patient; assessing health status parameters of the first
individual patient; identifying, from clinical information for a
plurality of patients stored in a database, other patients having
similar health status parameters as the first individual patient,
the health status parameters comprising disease states and
demographics; identifying health outcomes associated with the
identified other patients and corresponding treatments received by
the identified other patients, wherein a first set of the
identified other patients received a first treatment and a second
set of the identified other patients received a second treatment;
identifying projected health outcomes for the first individual
patient based at least in part on health outcomes of the first set
of the identified other patients that received the first treatment
and on health outcomes of the second set of the identified other
patients that received the second treatment; generating a
presentation of the identified projected health outcomes and
enabling a selection of a treatment corresponding to one of the
identified projected health outcomes.
3. The non-transitory computer readable storage medium as defined
in claim 2, the operations further comprising: projecting a
survival probability or a quality of life probability for the first
individual patient; predicting health status outcomes for at least
one individual patient suffering from at least one of a condition
of: coronary artery disease, or peripheral vascular disease, or
congestive heart failure, or chronic obstructive pulmonary disease,
or cancer, by isolating treatment regimens for at least one of the
conditions.
4. The non-transitory computer readable storage medium as defined
in claim 2, wherein the clinical information for the plurality of
patients includes economic burden for at least one patient.
5. The non-transitory computer readable storage medium as defined
in claim 2, wherein the clinical information for the plurality of
patients includes economic burden and health status assessment for
at least one patient.
6. The non-transitory computer readable storage medium as defined
in claim 2, wherein the first individual patient's demographics
include living situation, and the identified projected health
outcomes are identified based at least in part on the first
individual patient's living situation.
7. The non-transitory computer readable storage medium as defined
in claim 2, wherein the first individual patient's demographics
include social support and, the identified projected health
outcomes are identified based at least in part on the first
individual patient's social support.
8. The non-transitory computer readable storage medium as defined
in claim 2, wherein the first individual patient's demographics
include employment status, and the identified projected health
outcomes are identified based at least in part on the first
individual patient's employment status.
9. The non-transitory computer readable storage medium as defined
in claim 2, wherein the first individual patient's demographics
include type of employment, and the identified projected health
outcomes are identified based at least in part on the first
individual patient's type of employment.
10. The non-transitory computer readable storage medium as defined
in claim 2, wherein the first individual patient's demographics
include education level, and the identified projected health
outcomes are identified based at least in part on the first
individual patient's education level.
11. The non-transitory computer readable storage medium as defined
in claim 2, wherein identifying projected health outcomes for the
first individual patient is based in part on a preference of the
first individual patient and on a goal of the first individual
patient.
12. The non-transitory computer readable storage medium as defined
in claim 2, the operations further comprising projecting a survival
probability or a quality of life probability for the first
individual patient.
13. The non-transitory computer readable storage medium as defined
in claim 2, the operations further comprising indicating an
appropriate post-cardiac clinical regime using a statistical
analysis on a quality of life profile associated with the first
individual patient.
14. The non-transitory computer readable storage medium as defined
in claim 2, the operations further comprising indicating an
appropriate post-cardiac clinical regime by performing a
statistical analysis on the first individual patient's quality of
life profile, computing a statistical average based on the
statistical analysis, and computing a statistical deviation about
the average.
15. The non-transitory computer readable storage medium as defined
in claim 2, the operations further comprising: predicting health
status outcomes for at least one individual patient suffering from
one or more of the following conditions: arthritis, chronic
obstructive pulmonary disease, cancer, or a peripheral vascular
disease, by isolating treatment regimens for the one or more
conditions that resulted historically in health status outcomes and
goals corresponding to those of the first individual patient to
thereby provide the first individual patient with a corresponding
set of health care options from which to select.
16. A computer implemented method comprising: retrieving from
non-transitory memory clinical information regarding a first
individual patient; identifying, from clinical information for a
plurality of patients stored in a database, other patients having
similar health status parameters as the first individual patient,
the health status parameters comprising disease states and
demographics; identifying, by the computer system, health outcomes
associated with the identified other patients and corresponding
treatments received by the identified other patients, wherein a
first set of the identified other patients received a first
treatment and a second set of the identified other patients
received a second treatment; identifying, by the computer system,
projected health outcomes for the first individual patient based at
least in part on health outcomes of the first set of the identified
other patients that received the first treatment and on health
outcomes of the second set of the identified other patients that
received the second treatment; generating, by the computer system,
a presentation of the identified projected health outcomes and
enabling a selection of a treatment corresponding to one of the
identified projected health outcomes.
17. The method as defined in claim 16, the method further
comprising: projecting, by the computer system, a survival
probability or a quality of life probability for the first
individual patient; predicting, by the computer system, health
status outcomes for at least one individual patient suffering from
at least one of a condition of: coronary artery disease, or
peripheral vascular disease, or congestive heart failure, or
chronic obstructive pulmonary disease, or cancer, by isolating
treatment regimens for at least one of the conditions.
18. The method as defined in claim 16, wherein the first individual
patient's demographics include education level, and the identified
projected health outcomes are identified based at least in part on
the first individual patient's education level.
19. The method as defined in claim 16, wherein identifying
projected health outcomes or the first individual patient is based
in part on a preference of the first individual patient and on a
goal of the first individual patient.
20. The method as defined in claim 16, the method further
comprising further comprising projecting a survival probability or
a quality of life probability for the first individual patient.
21. The method as defined in claim 16, the method further
comprising indicating an appropriate post-cardiac clinical regime
using a statistical analysis on a quality of life profile
associated with the first individual patient.
22. The method as defined in claim 16, the method further
comprising indicating an appropriate post-cardiac clinical regime
by performing a statistical analysis on the first individual
patient's quality of life profile, computing a statistical average
based on the statistical analysis, and computing a statistical
deviation about the average.
23. The method as defined in claim 16, the method further
comprising: predicting health status outcomes for at least one
individual patient suffering from one or more of the following
conditions: arthritis, chronic obstructive pulmonary disease,
cancer, or a peripheral vascular disease, by isolating treatment
regimens for the one or more conditions that resulted historically
in health status outcomes and goals corresponding to those of the
first individual patient to thereby provide the first individual
patient with a corresponding set of health care options from which
to select.
24. A computer system comprising: a processing device;
non-transitory computer readable storage medium storing
instructions that when executed by the processing device, cause the
computer system to perform operations comprising: retrieving from
non-transitory memory clinical information regarding a first
individual patient; assessing health status parameters of the first
individual patient; identifying, from clinical information for a
plurality of patients stored in a database, other patients having
similar health status parameters as the first individual patient,
the health status parameters comprising disease states and
demographics; identifying health outcomes associated with the
identified other patients and corresponding treatments received by
the identified other patients, wherein a first set of the
identified other patients received a first treatment and a second
set of the identified other patients received a second treatment;
identifying projected health outcomes for the first individual
patient based at least in part on health outcomes of the first set
of the identified other patients that received the first treatment
and on health outcomes of the second set of the identified other
patients that received the second treatment; generating a
presentation of the identified projected health outcomes and
enabling a selection of a treatment corresponding to one of the
identified projected health outcomes.
25. The system as defined in claim 24, the operations further
comprising: projecting a survival probability or a quality of life
probability for the first individual patient; predicting health
status outcomes for at least one individual patient suffering from
at least one of a condition of: coronary artery disease, or
peripheral vascular disease, or congestive heart failure, or
chronic obstructive pulmonary disease, or cancer, by isolating
treatment regimens for at least one of the conditions.
26. The system as defined in claim 24, wherein the first individual
patient's demographics include education level, and the identified
projected health outcomes are identified based at least in part on
the first individual patient's education level.
27. The system as defined in claim 24, wherein identifying
projected health outcomes for the first individual patient is based
in part on a preference of the first individual patient and on a
goal of the first individual patient.
28. The system as defined in claim 24, the operations further
comprising further comprising projecting a survival probability or
a quality of life probability for the first individual patient.
29. The system as defined in claim 24, the operations further
comprising indicating an appropriate post-cardiac clinical regime
using a statistical analysis on a quality of life profile
associated with the first individual patient.
30. The system as defined in claim 24, the operations further
comprising indicating an appropriate post-cardiac clinical regime
by performing a statistical analysis on the first individual
patient's quality of life profile, computing a statistical average
based on the statistical analysis, and computing a statistical
deviation about the average.
31. The system as defined in claim 24, the operations, the
operations further comprising: predicting health status outcomes
for at least one individual patient suffering from one or more of
the following conditions: arthritis, chronic obstructive pulmonary
disease, cancer, or a peripheral vascular disease, by isolating
treatment regimens for the one or more conditions that resulted
historically in health status outcomes and goals corresponding to
those of the first individual patient to thereby provide the first
individual patient with a corresponding set of health care options
from which to select.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] Any and all applications for which a foreign or domestic
priority claim is identified in the Application Data Sheet as filed
with the present application, are hereby incorporated by reference
in their entirety under 37 CFR 1.57.
FIELD OF THE INVENTION
[0002] The present invention lies in the field of clinical
treatment plan assessment and selection, as a method for selecting
the particular regimen best suited for a particular patient's
individual health goals and desired outcomes is disclosed. The
invention also relates to the field of computer software programs,
as the method for clinical treatment assessment and selection may
be implemented through the use of a computer program and associated
software program. The invention further relates to the field of
cardiac patient care, as the described method of assessment and
treatment selection is particularly defined in some aspects for use
in the treatment of a patient that has cardiac disease, such as
coronary artery disease.
BACKGROUND OF THE INVENTION
Statement of the Problem
[0003] Patient-reported health status has been used as been used as
an endpoint in clinical trials. Health status measures quantify the
patient's perception of how a disease affects them. Specifically,
these developed parameters have been created to allow the patient
to report on a subjective level how their disease has affected
their everyday function, disease or other symptoms, and generally
their perceived quality of life. Apart from being reported as an
endpoint, the information obtained from patients on these factors
has not been examined or proposed for any other use.
[0004] Cardiovascular disease continues to be common in a large
percentage of the population, despite improvements made in general
in towards improvement in life-style to enhance heart heath. The
patient with coronary artery disease, for example, after a cardiac
event such as a heart attack, is and the heath-care professional
are then faced with selecting a post-cardiac event treatment
regimen or clinical intervention procedure, such as Coronary Artery
Bypass grafting (CABG) or Percutaneous Coronary Intervention (PCI).
Post-cardiac event treatment decisions are presented for the most
part in a vacuum to the patient, as no statistical data or
factually based decision tree criteria that can be of a full range
of clinical outcomes including health status has to this been
available against which the patient may make an informed and
conscious decision
[0005] Current clinical evidence collected from patients having had
one of two revascularization procedures have shown no difference in
the patient survival data. However, survival data is only one
criterion in examining and evaluating a selected revascularization
procedure. Improving a patients' health status outcomes (symptoms,
function, and quality of life) is an important critical goal of
treatment selection, yet essentially no data about the health
status outcomes after revascularization exists. With limited
outcomes data to differentiate between the relative risks and
benefits of CABG and PCI, for example, selecting a mode of coronary
revascularization is currently determined almost exclusively by
technical considerations and procedural risks. These factors in
essence exclude patient participation in the decision making
process.
[0006] A need continues to exist in the medical arts of a method
that may be used that would allow both the patient and the
attending health care professional to make a clinical health care
treatment decision that would optimize the patient's desired heath
goals and quality of life concerns. Such a method would incorporate
the patient's individual age, sex, socioeconomic, demographic and
clinical characteristics, and provide a patient-tailored set of
options with the associated relative risk and success outcomes that
are likely to be expected. Such a method does not currently exist
in the art.
SUMMARY OF THE INVENTION
[0007] The invention in a general and overall sense provides a
method for preparing a disease-specific database for use in
assessing health care options available to a patient.
[0008] In some embodiments, the methods of the invention are
employed in the evaluation and decision making process for
treatment plan in post-cardiac event patients. Currently, no such
method for the post-coronary event patient is described or
available.
[0009] The database described as part of the present invention may
in another aspect provide a method by which the post-coronary or
other clinical patient, or his attending health care professional,
may create a decision matrix that can be used to consider and
select the most appropriate revascularization or other
post-coronary event intervention procedure for the patient. This
provides the patient with a set of options that is specifically
tailored for that patient, and provides the patient with an
assessment of the relative benefits and disadvantages associated
with selecting one or another of the options being presented.
[0010] In another aspect, the invention provides a computer based
software system that is devised so as to indicate relative risk
associated with the selection of a particular revascularization
protocol, given the specific health status of a given patient
considering potential options for the treatment of their coronary
disease.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIGS. 1A and 1B. FIG. 1A is a graph of angina frequency
post-PCI (Percutaneous Coronary Intervention) (diamond is
economically burdened patient; square is not economically burdened
patient). Patients characterized as not economically burdened
demonstrate a higher frequency of angina after PCI
revascularization procedure. FIG. 1B is a graph of angina frequency
in patients post-CABG revascularization. Patients characterized as
economically burdened had about the same frequency of angina post
CABG as did the patient population characterized as economically
burdened.
[0012] FIGS. 2A and 2B--FIG. 2A is a graph demonstrating the
frequency of angina in a post-coronary attack patient having had
bypass surgery. The complications monitored in these patients were
death (0.9%), stroke (1.5%), readmit (2%) and PTCA (less than 1%).
FIG. 2B is a graph demonstrating the frequency of angina in a
post-coronary event patient that had an angioplasty. The same
complications were monitored in these patients, with a reported
frequency of 0.1% death, 0.02% stroke, 30% readmission to the
hospital, and 20% Re PTCA.
[0013] FIG. 3 block diagram illustrating a computer system
according to one embodiment of the present invention.
[0014] FIG. 4 is a medical system according to one embodiment of
the present invention.
[0015] FIG. 5 is a flow diagram illustrating one method of the
medical system to the present invention.
[0016] FIG. 6 is another diagram illustrating one method of the
present invention.
[0017] FIG. 7 is a flow diagram illustrating one step of the method
of the present invention.
[0018] FIG. 8 is a flow diagram illustrating another step of the
present invention.
[0019] FIG. 9 is a flow diagram illustrating another step of the
present invention.
[0020] FIG. 10 is a flow diagram illustrating another step of the
present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0021] FIG. 3 shows computer system 300 configured adapted for use
in a medical or clinical health care facility to identify an
appropriate post-cardiac event regimen for an individual patient
considering options for post-coronary event treatment, in accord
with one embodiment of the invention. Computer system 300 may
include processor 302, computer memory 304, and storage unit 306.
In computer system 300, processor 302 is communicatively connected
to computer memory 304 and to storage unit 306 for operating in
accord with the invention. In one embodiment, computer system 300
is configured for identifying a disease state and demographics of
the individual patient. Computer system 300 may assess health
status parameters from the individual patient to provide a first
data assessment profile and identify the projected health outcome
desired by the individual patient based upon said individual
preferences and goals. Computer system 300 may assess health status
parameters from a population of patients having similar
demographics to said individual patient, said population of
patients having received different treatments, thereby providing a
library of specific projected health outcomes for each different
treatment. Upon assessing the health status parameters from the
population of patients, Computer system 300 may select preferred
outcomes from the library of specific projected health outcomes
that similarly coincide with preferences and goals of the
individual patient, present the preferred outcomes to the patient,
and select a clinical treatment for the patient based on the
preferred outcomes. In one embodiment of the invention, software
303 is configured for operatively controlling computer system 300
and may initially reside in storage unit 306. Upon initializing
computer system 300, software 303 may be loaded in computer memory
304. Processor 302 may then software run 303.
[0022] FIG. 4 shows medical system 400 configured for configured
identifying an appropriate post-cardiac event regimen for an
individual patient considering options for post-coronary event
treatment, in accord with one embodiment of the invention. Medical
system 400 may include processor 404, storage unit 406, and
interface 408. In medical system 400, storage unit 406 is
configured for configured for storing group data in a database. The
group data may comprise responses to a questionnaire having a
plurality of questions regarding quality of life and demographic
information. The response may be derived from a plurality of
patients having survived a coronary event. A first group of the
patients have received a post-coronary event revascularization
procedure. A second group of the patients had not received the
post-coronary event revascularization procedure. Demographics of
the first and second groups of patients may be similar to those of
the individual patient. In medical system 400, interface 408 is
configured for receiving responses to the questions from the
individual patient. In medical system 400, processor 404 is
communicatively connected to interface 408 and to storage unit 306
for performing statistical analysis on the responses from the
plurality of patients and from the individual patient. A comparison
of the statistical analysis of the responses from the group of
patients and from the individual patient may provide a basis upon
which the individual patient may select a post-cardiac event
treatment appropriate to preferences and goals of the individual
patient.
[0023] With further regard to FIGS. 3 and 4, those skilled in the
art should appreciate that storage unit 406 and storage unit 306
may illustratively represent the same storage memory and/or one or
a combination of storage unit 306 and computer memory 304 within
computer system 300. Processor 302 may incorporate functionality
including processor 404, for example.
[0024] FIG. 5 shows a flow chart illustrating operation 500 of
medical system 400, in accord with one method of the invention.
Operation 500 commences in step 502. Processor 404 identifies a
disease state and demographics of the individual patient, in step
504. Processor 404 assesses health status parameters of the
individual patient, in step 506. Processor 404 identifies the
projected health outcome desired by the individual patient based
upon said individual preferences and goals, in step 508. Processor
404 assesses health status parameters from a population of patients
having similar demographics to the individual patient to provide a
library of specific projected health outcomes for each different
treatment, in step 510. Processor 404 selects preferred outcomes
from the library of specific projected health outcomes that
similarly coincide with preferences and goals of the individual
patient, in step 512. Interface 408 presents the preferred outcomes
to the patient, in step 514. Processor 404 selects a clinical
treatment for the patient based on the preferred outcomes, in step
516. Operation 500 ends in step 518.
[0025] Instructions that perform the operation discussed in FIG. 5
may be stored in storage media or computer memory. The instructions
may be retrieved and executed by processor 404. Some examples of
instructions include software, program code, and firmware. Some
examples of storage media include memory devices, tapes, disks,
integrated circuits, and servers. The instructions are operational
when executed by processor 404 to direct processor 404 to operate
in accord with the invention. Those skilled in the art are familiar
with instructions and storage media.
[0026] FIG. 6 shows a flow chart illustrating operation 600 of
medical system 400, in accord with one method of the invention.
Operation 500 commences in step 502. Processor 404 identifies a
disease state and demographics of the individual patient, in step
504. The demographics may include age, sex, economic burden, living
situation, social support, employment status, type of employment,
and education level of the individual patient. Processor 404
assesses set of health status parameters from the patient to
provide a first data assessment profile, in step 605. Processor 404
assesses set of health status parameters from a first population of
patients to provide a first reference data assessment profile, in
step 610. Processor 404 assesses set of health status parameters
from a second population of patients to provide a second reference
data assessment profile, in step 612. Processor 404 assesses set of
health status parameters from a third population of patients to
provide a third reference data assessment profile, in step 608. The
first, second, and third populations may have similar demographics
as the individual patient and differing treatments and/or
revascularization procedures. Processor 404 projects the survival
and quality of life probability of the individual patient from the
first, second, and third reference data assessment profiles to
respectively provide first, second, and third projected
post-procedural outcomes of the revascularization procedures and/or
treatments, in steps 614, 616, and 618. The revascularization
procedures may include a coronary artery bypass grafting (CABG)
procedure and a Percutaneous Coronary Intervention (PCI) procedure.
The treatment may include anti-coronary disease medication, diet
modification, herbal remedy, and other non-surgical intervention
procedure. Processor 404 compares the first projected
post-procedural outcome to the second projected post-procedural
outcome, in step 620. Processor 404 selects an appropriate
revascularization procedure for the individual patient in response
to the step of comparing, in step 622. Operation 600 ends in step
624.
[0027] FIG. 7 shows a flow chart illustrating step 605 of operation
600, in accord with one method of the invention. Step 605 enters
through entry point 701. Processor 404 assess the individual
patient's data, in step 402. Step 605 exits through exit point
703.
[0028] FIG. 8 shows a flow chart illustrating step 610 of operation
600, in accord with one method of the invention. Step 610 enters
through entry point 801. Processor 404 may collect the first data
assessment profile of the first group of patients, in step 802.
Processor 404 may select the CABG revascularization procedure, in
step 804. Step 610 exits through exit point 803.
[0029] FIG. 9 shows a flow chart illustrating step 612 of operation
600, in accord with one method of the invention. Step 612 enters
through entry point 901. Processor 404 may collect the second data
assessment profile of the second group of patients, in step 902.
Processor 404 may select the PCI revascularization procedure, in
step 904. Step 612 exits through exit point 903.
[0030] FIG. 10 shows a flow chart illustrating step 620 of
operation 600, in accord with one method of the invention. Step 620
enters through entry point 1001. Processor 404 may perform a
statistical analysis on the individual patient quality of life
profile, in step 1002. The statistical analysis may indicate to the
individual patient an appropriate post-cardiac clinical regimen.
Processor 404 may compute a statistical average that indicates the
appropriate post-cardiac clinical regimen based on the statistical
analysis, in step 1004. Processor 404 may compute a statistical
deviation about the average, in step 1006. Step 620 exits through
exit point 1003.
[0031] Those skilled in the art should appreciate that operation
500 and 600 are shown for illustrative purposes and that certain
changes or step sequences, such as those found in steps 504, 506,
and 508, may be altered as a matter of design choice.
Example 1
[0032] The present example demonstrates the utility of the present
invention for assembly and stratifying patient data as part of a
tool that can be used by a patient or attending health care
professional in making a choice of clinical treatment to pursue. In
a general sense, the invention creates a patient outcome management
system.
[0033] Using a collected, consecutive cohort of patients undergoing
coronary revascularization, the critical patient and procedural
characteristics that predict health status outcomes after a cardiac
revascularization procedure, such as Coronary Artery Bypass
Grafting (CABG) or Percutaneous Coronary Intervention (PCT), can be
identified. The method of the present invention may be used in
conjunction with any group of patients and collected
disease-burdened population of people, such as a population of
patients that suffer from arthritis, chronic obstructive pulmonary
disease, cancer (brain, prostate, breast, skin, etc.), any type of
peripheral vascular disease, by way of historical data may be
collected and stratified according to a defined demographic profile
of an individual patient considering his/her options for treatment
of a disease or condition.
[0034] This demographically sorted data may then be screened to
isolate those treatment regimens that resulted historically in the
spectrum of health status outcomes and goals/priorities identified
to be most important to the patient. From the treatment regimens
that had provided at least the majority of results/consequences
important to the patient, the patient may be presented with a
focused set of health care treatment options to consider in making
his/her decision.
[0035] By using the types of data outlined below, the patient and
procedural characteristics that most influence a patients'
peri-procedural and 1-year outcomes will be determined.
TABLE-US-00001 TABLE 1 Patient Data Clinical Data Demographics
Comorbidities Economic burden Depression (MOS-D) Living Situation
Rx setting - i.e. AMI Social Support Cardiac risk factors
Employment Smoking Status Education SF-12/EuroQOL
[0036] For a patient having had a coronary disease, such as a heart
attack, that is considering a revascularization procedure, the
following specific data will also be tabulated and PCI (1639
patients), the present investigation identified specific
post-coronary event outcomes that were identifiable with a specific
selection of one revascularization event over another. The outcome
data that was characteristic of the patients that were
statistically analyzed for this system had been tallied one-year
after the particular revascularization procedure was performed on
them.
[0037] A substudy of some of these patients has been completed on
495 patients. Of these 224 patients received CABG revascularization
procedure and 271 patients received a PCI revascularization
procedure. All of these patients were administered baseline and
monthly follow-up assessments for 6 months to model the recovery of
health status after revascularization. Preliminary analysis of this
substudy revealed identifiable trends in health status outcome that
were linked to the treatment protocol elected.
[0038] Previous clinical trials had reported no survival
differences between PCI and CABG. Presently published results
however, clearly showed that after PCI, patients are more dependent
on anti-anginal mediations than CABG. Recently published clinical
trial data demonstrates that 21.1% of PCI patients as compared with
41.5% of CABG patients were free of anti-anginal medications 1 year
after treatment (P<0.001). Given the greater need for
medications after PCI, the present investigation considers the
variable that patient may have difficulty in affording their
healthcare, and that these patients may have difficulty in
affording their healthcare, and that these patients may have a
worse health status aft PCI as compared with CABG.
Example 2
Angina Frequency
[0039] The present example demonstrates the utility of the
invention for considering health status rather than survival for
managing the health care options to be presented to a patient. The
present example also illustrates the utility of a new set of
risk-stratification variables that are important in the medical
decision variables that are important in the medical decision
making process. The present example also demonstrates the utility
of the present example as an efficient mechanism of collecting data
about patient's current health status and of new
risk-stratification variables that are useful in projecting
anticipated outcomes.
[0040] The present invention further presents the inclusion of
interdual patient data that is being accumulated through each new
decision making event, back into the pool of data or population
data that may be used/is used in a subsequent pool of patients. In
this manner, the population database is constantly being updated,
as well as opportunities for new treatment regimens becoming part
of the decision-making process system.
[0041] The need to integrate multiple sources of data and to depict
multiple types of outcomes has led to the present inventors'
development of yet another aspect of the invention, a decision
making tool PREDICT.TM., that is to be used in tailoring treatment
choices to individual patients.
[0042] At the time of revascularization, the 34.3% of patients
reporting an economic burden had significantly more frequent angina
than those who did not (SAQ Angina Frequency score (range=0-100
where higher scores indicate less angina)=60+-26 vs. 69+-25 for
CABG; 52+-30 vs. 67+-25 for PCI (p<0.01 for both)).
[0043] During the 6 months of follow-up, however, a persistent
disparity in angina control was noted after PCI (Repeated Measures
ANOVA controlling for all baseline differences between groups:
F=6.6, p=0.009) but not after CABG (F=0.06, p=0.8). Similar
findings were noted for SAQ physical function and quality of life
domains as well. The mechanism by which economically disadvantaged
patients are unable to attain the same health status after PCI as
economically secure patients is unclear. The absence of such a
disparity in CABG may indicate that patients who have difficulty
affording medical care might preferentially select surgical
revascularization.
Prophetic Example 3
Identification of Determinants of Health Status
[0044] The key determinants of health status (symptoms, function,
quality of life) after PCI and CABG through robust analyses of an
existing database. Using the types of data described in the Table
above, we will determine the key predictor variables for angina
frequency, physical limitation and quality of life as measured by
12-month, post-procedure SAQ scores will be tabulated and
statistically analyzed.
TABLE-US-00002 TABLE 2 Procedural Data Outcome Data Number of
diseased vessels Seattle Angina Questionnaire Percent Stenosis
Short Form-12 Ejection Fraction EuroQOL Technique of revasc
variation Hospitalizations Treatment success Repeat procedures
Complications Survival
[0045] Data reduction will be done with clustering, stepwise
variable selection and factor analysis techniques to identify the
most parsimonious set of data that needs to be collected. Internal
(bootstrap) validation and comparisons with external data sources
will be used to validate selected variables. Given the anticipated
error in predicting outcomes with any statistical model (due to
unmeasured patient variability and the role of chance) the patient
will not be presented with a single projected outcome for each SAQ
domain. Rather, these data will be used to stratify patients and
then generate the range of observed outcomes seen in similar
patients treated with both PCI and CABG (see example below). This
will make concrete the range of previously observed outcomes (among
similar patients) and allow patients and their physicians to choose
a treatment strategy that has the best trade-off between projected
distributions of outcome and risk (the latter coming from the
models of STS, ACC, Emory, NY State, Northern New England,
etc.).
[0046] While the format and elements of outcomes projections will
change, an example of the types of data that we envision presenting
is shown below. In this example, a 72 year old women with diabetes,
normal LV function, and difficulty affording her healthcare can see
the trade-offs between the better symptom distribution, greater
peri-operative risk and lower likelihood of repeat admissions and
revascularization procedures of bypass surgery as compared with
PCI. Such presentations of outcomes data will allow patients (and
their physicians) to be more involved and, ultimately, satisfied
with the process of selecting a revascularization strategy.
Prophetic Example 4
Computer Program Using Observational Data Bases for
Revascularization Decision Making Processes
[0047] Observational databases will be used to facilitate treatment
decisions for patients considering revascularization. The
PREDICT.TM. instrument provides the vehicle that will be employed
to accomplish this task. Four distinct components create
PREDICT.TM.. First, an interface for data collection is required.
Second, a software program takes collected created so that
collected data may be transformed into clinically meaningful
distributions of projected outcomes. Third, a mechanism for
customizing PREDICT.TM.'s output so that patient-valued and readily
interpretable outcomes may be displayed. And finally, the
infrastructure for tracking outcomes of patients using PREDICT.TM.
must be created so that the system can continue to grow as new
treatment technologies are introduced.
[0048] PREDICT.TM.'s networked software architecture optimization
algorithms. To assist in keeping this project appraised of the most
recent developments in the rapidly evolving field of software and
computing design.
[0049] The first step in designing PREDICT.TM. is to create a
mechanism for collecting the data elements identified in Example 1.
The present decision tool will be seamlessly integrated into the
flow of patient care. This is particularly important in the setting
of coronary revascularization where the decision to perform
revascularization may be made with the first injection of contrast
during diagnostic angiography. The ultimate design of data
collection will depend on the number and types of data needed, the
current plan is to write a Palm Pilot.RTM. application for the
collection of critical data elements, to identify the optimal point
in patient care for their acquisition, and to synchronize collected
data to a server so that we can generate the needed outcomes
reports.
[0050] The second step in developing PREDICT.TM. is to build a
software application for generating the observed outcomes
distributions and predictions. Given the need to link multiple
potential data sources and to incorporate these data into a series
of models for output generation, a distributed network-based
software system will be developed using an extensible information
system (XML) to represent critical data elements. The open
architecture, scalability and cross-platform utility of XML make it
ideal for creating PREDICT.TM.. Our approach will involve creating
and implementing the architectural specifications for a flexible,
scalable system to include data collection and representation,
compute engine development, and mechanisms for generating
customizable output.
[0051] The third step in creating PREDICT.TM. is to make the output
readily interpretable. Current evidence suggests that patient's
better recall and understand facts presented in numeric,
probabilistic terms. In fact, the lack of access to numeric
estimates has been shown to encourage patients to overestimate
treatment benefits and to underestimate risk. Furthermore, the use
of numeric summaries of expected outcomes improves the accuracy of
physician-patient communication. In addition to the format of data,
the frame of the message, in terms of health benefits (gains) or
costs (losses) is also important. Research on message framing has
produced mixed results with some health behaviors being influenced
more by loss-oriented messages (e.g., breast self-exams) and others
by emphasizing health gains (e.g. smoking cessation). Alternative
ways of framing numeric, probabilistic outcomes presentations and
then conduct a series of focus groups with different data
formulations (e.g., negatively vs. positively framed, different
visual formats) to finalize the present approach will be developed.
Insights from these exercises will provide invaluable feedback in
perfecting PREDICT.TM.'s output. Ultimately, a customizable menu of
choices will be created for patients and physicians to select those
outcomes that are most relevant to them. This will allow one
patient, who is most concerned about returning to work, to select
that outcome whereas another may choose angina relief or quality of
life as the outcome that most concerns them. This will maximize the
likelihood that feedback will address the goals and values of each
individual patient.
[0052] The final step in creating PREDICT.TM. is to build an
infrastructure for follow-up. As patient data is entered into
PREDICT.TM., it can provide the baseline assessment for following
that individual's outcome over time. For this reason, we will
design PREDICT.TM.'s software to allow maximal use of all collected
data. Once accomplished, collected baseline data will be
synchronized with procedural and follow-up databases so that 1 year
after initial treatment, patients can be contacted for follow-up.
As follow-up data is captured, those patients' data will enter the
repository from which future patients will see the distribution of
outcomes associated with patients who were similar to them. This
creates a continuously evolving system that minimizes the delay in
updating outcomes projections in an era of rapid technological
change.
Prophetic Example 5
Pilot Test of Predict.TM.
[0053] Throughout the process of developing PREDICT.TM., ongoing
feedback from patients and physicians will be acquired through
individual interviews and focus groups. Ultimately, however, an
explicit demonstration of its feasibility will be needed. The final
goal of this proposal will be to conduct a 1-month pilot test. The
complete design of such testing cannot be definitively described
because the precise parameters that will dictate usage will evolve
from the steps outlined in Specific Aims 1 and 2. Conceptually,
however, we plan to adopt the following basic approach: PREDICT.TM.
will be implemented among a consecutive cohort of patients and
describe the time required for implementation, the percent of
patients eligible in whom the tool was used, the physicians'
assessment whether the tool provides value to outweigh the time
required in its use and patients' satisfaction with decision making
as assessed by the Satisfaction with Decision scale..sup.9 Once the
insights from this pilot study are reviewed and analyzed, a
multicenter trial of PREDICT.TM. will be created to assess its
impact on patient choices for revascularization, satisfaction, cost
and outcome.
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