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Medical Patent Abstract
A medical risk assessment method and computer program product resident
on a computer or a hand-held device that allows a physician to determine
the best strategy for primary and secondary cardiovascular disease
prevention utilizing current guidelines and published medical literature.
The computer program product evaluates a number of risk factors
to determine specific recommendations for an individual patient,
including Framingham risk scoring (FRS), pertinent medical history,
individual lipid panel and advanced lipoprotein profiling, patient
laboratory test results, and published literature on the effects
of anti-lipid medicines on plasma concentration and/or composition
of lipoprotein molecules and clinical outcomes. The risk assessment
method establishes a cardiovascular treatment therapy strategy for
a patient by determining a cardiac risk classification group, determining
a cardiovascular treatment therapy based on the patient's lipoprotein
profile and the patient's cardiac group risk classification, and
presenting the cardiovascular treatment therapy for the patient
to a medical practitioner on a patient evaluation display.
Medical Patent Claims
What is claimed is:
1. A computer-implemented method for establishing a cardiovascular
treatment therapy strategy for a patient, comprising the steps of:
determining a cardiac risk classification group for the patient
based on a predetermined set of cardiac risk factors; determining
a cardiovascular treatment therapy based on the patient's determined
cardiac group risk classification and lipoprotein profile; and presenting
the determined cardiovascular treatment therapy for the patient
to a medical practitioner on a patient evaluation display.
2. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 1 wherein the predetermined
set of cardiac risk factors comprises a first subset and a second
subset of cardiac risk factors.
3. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 2 wherein the first subset of
cardiac risk factors includes coronary heart disease, peripheral
vascular disease, type II diabetes mellitus, an abdominal aortic
aneurysm and symptomatic carotid disease.
4. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 2 wherein the second subset
of cardiac risk factors includes a patient's age, a smoking status,
a family history of cardiac disease, a diagnosis of systolic arterial
hypertension and a systolic blood pressure treated condition.
5. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 1 wherein the step of determining
a cardiac risk classification group comprises evaluation of the
patient's medical history to determine if the patient has at least
one medical condition in a first subset of cardiac risk factors.
6. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 5 wherein the step of determining
a cardiac risk classification group further comprises determining
a Framingham risk score for the patient.
7. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 5 wherein the step of determining
a cardiac risk classification group further comprises evaluation
of the patient's medical history to determine if the patient has
at least two medical conditions in a second subset of cardiac risk
factors.
8. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 5 wherein the patient is classified
in a first risk classification group if the patient has at least
one medical condition in a first subset of cardiac risk factors.
9. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 6 wherein the patient is classified
in a first risk classification group if the patient has a Framingham
risk score greater than 15.
10. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 7 wherein the patient is classified
in a separate risk classification group if the patient has at least
two medical conditions in the second subset of cardiac risk factors
and has a Framingham risk score less than 12.
11. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 7 wherein the patient is classified
in a separate risk classification group if the patient has at least
two medical conditions in the second subset of cardiac risk factors
and has a Framingham risk score of 12 or greater.
12. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 7 wherein the patient is classified
in a separate risk classification group if the patient has less
than two medical conditions in the second subset of cardiac risk
factors.
13. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 5 wherein the first subset of
cardiac risk factors includes coronary heart disease, peripheral
vascular disease, type II diabetes mellitus, an abdominal aortic
aneurysm and symptomatic carotid disease.
14. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 5 wherein the second subset
of cardiac risk factors includes a patient's age, a smoking status,
a family history of cardiac disease, a diagnosis of systolic arterial
hypertension and a systolic blood pressure treated condition.
15. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 1 wherein the cardiac group
risk classification group and cardiovascular treatment therapies
are based on published medical guidelines and published medical
literature.
16. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 15 wherein the published medical
literature includes the effects of anti-lipid medicines on plasma
concentration.
17. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 15 wherein the published medical
literature includes the effects of anti-lipid medicines on composition
of lipoprotein molecules and clinical outcomes.
18. The computer-implemented method for establishing a cardiovascular
treatment therapy strategy of claim 1 further comprising presenting
a medical guidelines recommendation for the patient to the medical
practitioner on the patient evaluation display.
19. A computer program product for establishing a cardiovascular
treatment therapy strategy, the computer program product comprising:
a recording medium; program instructions recorded on the recording
medium for determining a cardiac risk classification group for the
patient based on a predetermined set of cardiac risk factors; program
instructions recorded on the recording medium for determining a
cardiovascular treatment therapy based on the patient's cardiac
group risk classification and lipoprotein profile; and program instructions
recorded on the recording medium for presenting the determined cardiovascular
treatment therapy for the patient to a medical practitioner on a
patient evaluation display.
20. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 19 wherein the program instructions
for determining a cardiac risk classification group comprise program
instructions for evaluation of the patient's medical history to
determine if the patient has at least one medical condition in a
first subset of cardiac risk factors.
21. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 20 wherein program instructions
for determining a cardiac risk classification group further comprise
program instructions for determining a Framingham risk score for
the patient.
22. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 20 wherein the program instructions
for determining a cardiac risk classification group further comprise
program instructions for evaluation of the patient's medical history
to determine if the patient has at least two medical conditions
in a second subset of cardiac risk factors.
23. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 20 further comprising program
instructions for classifying the patient in a first risk classification
group if the patient has at least one medical condition in a first
subset of cardiac risk factors.
24. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 21 further comprising program
instructions for classifying the patient in a first risk classification
group if the patient has a Framingham risk score greater than 15.
25. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 22 further comprising program
instructions for classifying the patient in a separate risk classification
group if the patient has at least two medical conditions in the
second subset of cardiac risk factors and has a Framingham risk
score less than 12.
26. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 22 further comprising program
instructions for classifying the patient in a separate risk classification
group if the patient has at least two medical conditions in the
second subset of cardiac risk factors and has a Framingham risk
score of 12 or greater.
27. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 22 further comprising program
instructions for classifying the patient in a separate risk classification
group if the patient has less than two medical conditions in the
second subset of cardiac risk factors.
28. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 20 wherein the first subset
of cardiac risk factors includes coronary heart disease, peripheral
vascular disease, type II diabetes mellitus, an abdominal aortic
aneurysm and symptomatic carotid disease.
29. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 20 wherein the second subset
of cardiac risk factors includes a patient's age, a smoking status,
a family history of cardiac disease, a diagnosis of systolic arterial
hypertension and a systolic blood pressure treated condition.
30. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 19 wherein program instructions
for determining the cardiac group risk classification group and
program instructions for determining cardiovascular treatment therapies
are based on published medical guidelines and published medical
literature.
31. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 30 wherein the published medical
literature includes the effects of anti-lipid medicines on plasma
concentration.
32. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 30 wherein the published medical
literature includes the effects of anti-lipid medicines on composition
of lipoprotein molecules and clinical outcomes.
33. The computer program product for establishing a cardiovascular
treatment therapy strategy of claim 19 further comprising presenting
a medical guidelines recommendation for the patient to the medical
practitioner on the patient evaluation display.
Medical Patent Description
BACKGROUND OF THE INVENTION
The present invention relates generally to analysis of patient
specific medical history and laboratory results, and more particularly
to computer-implemented methods and products for assessing a patient's
medical risks and formulating a treatment strategy.
Coronary heart disease is the leading cause of morbidity and mortality
in the United States, accounting for approximately 500,000 deaths
per year, and an associated annual morbidity cost of more than $200
billion. Over the past several decades, numerous clinical and epidemiological
studies have shown that an elevated blood cholesterol level is one
of the major modifiable risk factors associated with the development
of coronary heart disease. These studies have demonstrated that
low-density lipoprotein (LDL) cholesterol is a primary lipoprotein
mediating atheroscelorsis. Cigarette smoking, hypertension, diabetes,
and a low level of high-density lipoprotein (HDL) cholesterol are
other risk factors that have been implicated in coronary heart disease.
The National Institutes of Health established the National Cholesterol
Education program in 1985. The National Cholesterol Education Program
Adult Treatment Panel I (NCEP-ATP I) developed its first set of
guidelines in 1988. The guidelines establish clear goals for patients
with lipid abnormalities. Revised guidelines were developed in 1993
(NCEP-ATP II) and in 2001 (NCEP-ATP III). Risk stratification continues
to determine LDL goals and the intensity of LDL-lowering therapy.
Dietary therapy remains the first line of treatment with drug therapy
reserved for use in patents at high risk for coronary heart disease
or patients who do not respond to non-pharmacological therapy.
Under the current guidelines, optimal cholesterol screening now
includes a lipoprotein profile, preferably using blood drawn in
a fasting state. The lipoprotein profile includes total cholesterol,
LDL cholesterol, HDL cholesterol, and triglycerides. The lipoprotein
profile cannot be interpreted without knowledge of the patient's
risk factors. The major risk factors that modify low-density lipoprotein
goals include age (men over 45, women over 55 having normal onset
menopause), smoking status (current tobacco user or within the last
5 years), hypertension (blood pressure exceeding 140/90 mmHg), high-density
lipoprotein levels, and family history of coronary artery disease.
Patients with diabetes and those with a ten year cardiac event risk
of 20% or greater are considered coronary heart disease equivalents.
An additional step in the determination of coronary heart disease
risk involves the calculation of the Framingham risk score (FRS)
for persons with two or more risk factors. The ATP III Guidelines
also raise the threshold of low HDL cholesterol from less than 35
mg/dL to less than 40 mg/dL. An HDL level of 60 mg/dL or higher
is considered to be a negative risk factor.
The FRS is a risk assessment tool that has been derived from data
collected in the Framingham heart study. The ATP III Guidelines
recommend that patients with two or more risk factors have their
FRS calculated. The FRS consist of points that are allocated based
on various degrees of risk associated with five categories: age,
total cholesterol level, HDL cholesterol level, tobacco smoking
status and hypertension and whether this condition is treated. The
FRS point total results in a percent risk of having a cardiac event
in the next ten years.
In the ATP III Guidelines, the target LDL level for patients with
established coronary heart disease is still 100 mg/dL or less. Patients
with diabetes and patients with an FRS of 20% or higher are considered
coronary heart disease equivalents. Since patients with diabetes
and patients with an FRS of 20% or higher are in the same risk category
as coronary heart disease patients, it is recommended that they
maintain an LDL level of 100 mg/dL.
The extent of LDL lowering therapy depends on the patient's coronary
heart disease risk. The two major modalities for lowing the LDL
level advocated by the ATP III Guidelines are therapeutic lifestyle
changes and drug therapy. Patients are classified in one of three
categories of risk: (1) coronary heart disease (CHD) and CHD equivalents,
(2) two or more risk factors, or (3) zero or one risk factors. The
two or more risk factors category can be further subdivided into
patients having an FRS score 12 or higher and patients having an
FRS score less than 12.
Therapeutic lifestyle changes encompass diet, physical activity
and weight loss. The ATP III Guidelines continue to stress the importance
of non-pharmacological treatment, but recognize its limitations
by reducing the trial of these modalities from six months to twelve
weeks before considering the use of medications to assist in achieving
recommended LDL goals.
The failure of therapeutic lifestyle changes to modify LDL cholesterol
levels or the presence of high CHD risk levels warrants the use
of drug therapy. Several drugs have specific effects on lipoprotein
metabolism, including bile acid sequestrants (resins), fibric acids,
nicotinic acid, and statins. Bile acid sequestrants include cholestyramine,
colestipol, and colesevelam. Fibric acids include gemfibrozil and
fenofibrate. Nicotinic acid includes extended-release nicotinic
acid (Niaspan) and sustained release nicotinic acids. The statins
include lovastatin (Levitor), pravastatin (Pravachol), fluvastatin
(Lescol), atorvastatin (Mevacor), synvastatin (Zorcor) and rosuvastatin
(Crestor). If the LDL goal based on established risk is not achieved,
therapy should be intensified with an increase in drug dosage or
the addition of another LDL-lowering drug.
The ATP III Guidelines recognized the increasing number of studies
correlating elevated triglyceride levels with increased coronary
artery disease risk. The ATP III Guidelines lowered the acceptable
triglyceride level from the ATP II Guidelines. The primary modes
of treating hypertriglyceridemia are diet and exercise. If indicated,
nicotinic acid and fibric acid derivatives are the most effective
drugs in lowering triglyceride levels. Triglyceride reduction is
also a secondary benefit of statins.
Niapsan is one of only two products in the United States approved
for increasing HDL levels. Low HDL cholesterol levels have been
shown to be one of the highest risk factors contributing to coronary
heart disease. Most of the drugs for treating cholesterol problems
are designed to reduce LDL levels. Niaspan's active ingredient,
niacin, is most effective in elevating HDL levels. The increase
in HDL has been shown to significantly reduce the chances of coronary
heart disease.
Statins work by slowing down the liver's production of cholesterol,
high levels of which are implicated in atheroschelorsis, a disease
process that leads to clogging of the arteries. Heart attack patients
typically are given one of the FDA-approved statins at the time
of discharge or within months of discharge from the hospital as
a preventative measure against future heart attacks. Because of
their effectiveness as a class for reducing LDL cholesterol and
their favorable safety profile, statins are by far the most frequently
used drugs as first line therapy for patients with high LDL cholesterol.
Statins generally raise HDL cholesterol levels and lower plasmatriglycerides.
Three bile acid-binding sequestrants are currently available in
the United States: cholestyramine (Questran), colestipol (Colestid),
and colesevelan hydrochloride (Welchol). These resins significantly
decrease LDL cholesterol and can produce small increases in HDL
cholesterol. Bile acid sequestrants should generally not be used
in patients with triglyceride levels above 200 mg/dL, and should
not even be considered for use in patients with triglyceride levels
exceeding 400 mg/dL.
Fibrates that are currently available in the United States include
gemfibrozil (Lopid) and fenofibrate (TriCor). Gemfibrozil and fenofibrate
can decrease triglyceride levels, increase HDL cholesterol levels
and shift small, dense LDL particles toward larger, more buoyant
sizes, improving and potentially correcting the lipoprotein abnormalities
commonly found in Type II diabetes. Since neither fibrate drug adversely
affects glycemic control, either can by used in patients with diabetes.
There is a need for a computer-implemented method and computer
program product that can enable a physician to quickly assimilate
all the pertinent medical data and guidelines recommendations necessary
to evaluate a patient's medical risk for cardiovascular disease
and determine a patient care management program that is best suited
for an individual patient.
SUMMARY OF THE INVENTION
The present invention is a medical risk assessment method and computer
program product that is resident on a computer or a hand-held device
and that allows a physician to determine the best strategy for primary
and secondary cardiovascular disease prevention utilizing current
guidelines and published medical literature. The computer software
program evaluates a number of risk factors to determine specific
recommendations for an individual patient, including Framingham
risk scoring (FRS), pertinent medical history, individual lipid
panel and advanced lipoprotein profiling, patient laboratory test
results, and published literature on the effects of anti-lipid medicines
on plasma concentration and/or composition of lipoprotein molecules
and clinical outcomes.
In one exemplary embodiment of the invention, a computer-implemented
risk assessment method establishes a cardiovascular treatment therapy
strategy for a patient by determining a cardiac risk classification
group for the patient based on a predetermined set of cardiac risk
factors, determining a cardiovascular treatment therapy based on
the patient's lipoprotein profile and the patient's cardiac group
risk classification; and presenting the cardiovascular treatment
therapy for the patient to a medical practitioner on a patient evaluation
display.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention is better understood by reading the following detailed
description of the invention in conjunction with the accompanying
drawings, wherein:
FIG. 1 illustrates the high level processing logic of the medical
risk assessment software in accordance with an exemplary embodiment
of the present invention.
FIGS. 2A 2E illustrates the Group 1 processing logic of the medical
risk assessment software in accordance with an exemplary embodiment
of the present invention.
FIGS. 3A 3B illustrate the Group 2 processing logic of the medical
risk assessment software in accordance with an exemplary embodiment
of the present invention.
FIGS. 4A 4B illustrate the Group 3 processing logic of the medical
risk assessment software in accordance with an exemplary embodiment
of the present invention.
FIGS. 5A 5B illustrate the Group 4 processing logic of the medical
risk assessment software in accordance with an exemplary embodiment
of the present invention.
FIG. 5C illustrates the LDL reducing and HDL reducing power algorithms
included in the Groups 1 4 processing logic of the medical risk
assessment software.
FIGS. 6 15 illustrate displays of sample results including specific
patient therapy recommendations derived from use of the medical
risk assessment software in accordance with an exemplary embodiment
of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
The following description of the present invention is provided
as an enabling teaching of the invention in its best, currently
known embodiment. Those skilled in the relevant art will recognize
that many changes can be made to the embodiment described, while
still obtaining the beneficial results of the present invention.
It will also be apparent that some of the desired benefits of the
present invention can be obtained by selecting some of the features
of the present invention without using other features. Accordingly,
those who work in the art will recognize that many modifications
and adaptations to the present invention are possible, and may even
be desirable in certain circumstances, and are a part of the present
invention. Thus, the following description is provided as illustrative
of the principles of the present invention and not in limitation
thereof, since the scope of the present invention is defined by
the claims.
The medical risk assessment software and method of the invention
allows a physician to establish an accurate strategy for primary
and secondary cardiovascular disease prevention utilizing current
guidelines and published medical literature. The software includes
artificial intelligence for making clinical decisions in specific
situations. The medical risk assessment software takes into consideration
a number of key factors including Framingham risk scoring, pertinent
medical history, individual lipid panel and advanced lipoprotein
profiling, and pertinent individual laboratory values such as serum
creatine glucose. Pertinent medical history includes existing conditions
such as coronary heart disease (CHD), symptomatic carotid disease
(SCD), peripheral vascular disease (PVD), Type II Diabetes Mellitus
(Type II DM), abdominal aortic aneurysm (AAA), systolic blood pressure
(SPB) treatment, systolic arterial hypertension (SAH), family history
of premature coronary artery disease, and whether the patient is
a smoker. Individual lipid panel and advanced lipoprotein panel
includes cholesterol, systolic blood pressure, triglycerides, high
density lipoproteins (HDL), low density lipoprotein (LDL), and non-HDL.
The medical risk assessment software also takes into consideration
the effects of currently available anti-lipid medicines on plasma
concentration and/or composition of lipoprotein molecules and clinical
outcomes as published in medical literature. The medical risk assessment
software makes specific management recommendations for individuals,
with or without existing cardiovascular conditions, integrating
their clinical and laboratory profiles with results in current published
literature and displaying all of this pertinent information and
recommended treatment therapies on a patient evaluation display.
The present invention can be implemented on a general purpose computer,
a computer network, an Internet-based system, a personal digital
assistant (PDA) or as an embedded system. The present invention
can be implemented as a portable product to operate on a personal
computer, a computer workstation or a mainframe computer. The computer
utilized can be of conventional design, having a processing unit,
an input device, an output device, and a memory unit interconnected
by a communications bus. The memory unit can be a conventional random-access
memory (RAM) and a hard disk drive. The memory unit stores a plurality
of databases and a plurality of computer instructions that implement
the methods and program products of the invention. Two databases
are used to implement the invention in one exemplary embodiment.
The first contains data extracted from published medical guidelines
such as the ATP III guidelines. The second contains data extracted
from published medical literature that documents the effects of
anti-lipid medicines. In an alternative embodiment, these databases
can be integrated to form a single database. The computer program
instructions may be implemented in various computer programming
languages such as C, C++, Java and can incorporate extracted guidelines
and extracted test results into processing algorithms. The program
instructions provide the processing logic to evaluate patient-specific
information to determine cardiovascular treatment recommendations
to display to the physician.
The high level processing logic for the medical risk assessment
software is illustrated in FIG. 1. After the medical risk assessment
process is initiated in logic block 100, a test is made in decision
block 102 to determine if the patient has any existing CHD, SCD,
PPD, DM or AAA condition checked. If the patient has any of these
conditions, then Group 1 processing is performed as indicated in
logic block 120. Group 1 processing logic is shown in FIGS. 2A 2E.
If the patient does not have any of the conditions tested for in
decision block 102, then in decision block 104, a test is made to
determine if the patient has a Framingham risk score greater than
15. If the patient has an FRS>15, then Group 1 processing is
performed as indicated in logic block 122. If the patient has an
FRS.ltoreq.15, then a determination is made if the patient's age
is greater than 45, whether the patient smokes, has a pertinent
family history of coronary artery disease (CAD), has systolic arterial
hypertension, or an HDL less than 40. A test is then made in decision
block 108 to determine if the number of conditions met is less than
2. If it is, then Group 4 processing is performed as indicated in
logic block 124. In decision block 108, if two or more conditions
are met, a test is then made in decision block 110 to determine
if the Framingham risk score is less than 12. If the FRS is less
than 12, then Group 2 processing is performed as indicated in logic
block 126. If FRS is 12 or higher, then Group 3 processing is performed
as indicated in logic block 128.
FIGS. 2A 2E illustrate the Group 1 processing logic of the medical
risk assessment software. For Group 1 patients, the target HDL level
is less than 130, and the target LDL level is less than 100. If
the patient has a history of CHD, SCD, PPD, DM or AAA, or if the
patient has a Framingham risk score exceeding 15, then the processing
logic in FIGS. 2A 2E is executed to provide recommended treatment
therapies for the patient. The processing logic is divided into
four sections: 200, 210, 220, and 230. If the patient has a triglycerides
(TRG) level that is greater than 500, then processing logic section
200 is performed (FIG. 2A). In this case, the recommendation would
be to prescribe a fibrate and a statin. If the patient's TRG level
is less than 200, processing logic 210 is performed (FIGS. 2A 2B).
The medical risk assessment software provides a patient therapy
recommendation to the physician based on the patient's TRG, LDL,
and HDL levels. If the TRG level is greater than 200, but the LDL
is not measured, processing logic section 220 is performed as illustrated
in FIG. 2C. An LDL equal to zero in the processing logic code means
that the LDL level is not measured. The recommended patient therapy
in FIG. 2C is based upon non-HDL level, HDL level, and total cholesterol
level. Based on the total cholesterol level, the HDL level and the
non-HDL level, a specific drug is recommended for patient therapy
such as Lipitor, Advicor, Niaspan, or a fibrate. Advicor is a combination
product containing both extended-release niacin and lovastatin.
This drug has been approved for the treatment of primary hypercholesterolemia
dyslypidemia. Advicor is indicated for patients who were previously
treated with either of its component, but who require additional
lipid modification for LDL or HDL cholesterol and triglycerides.
Advicor lowers total cholesterol and LDL cholesterol, while raising
the amount of HDL cholesterol.
FIGS. 2D 2E illustrate the processing logic performed when the
Group 1 patient's TRG is greater than 200 and the patient's LDL
level is measured. The patient therapy recommendations provided
by the processing logic section 230 are based on TRG level, LDL
level, non-HDL level, and Lipoprotein (a) (LPA) level.
Group 2 processing is illustrated in FIGS. 3A 3B. The Group 2 patient
category is for those patients without a history of CHD, SCD, PPD,
DM or AAA, meeting two or more of the conditions shown in logic
block 106 of FIG. 1, but having a Framingham risk score less than
12. The conditions that are determined in logic block 106 of FIG.
1 are whether the patient's age is greater than 45, whether the
patient smokes, whether the patient has a history of systolic arterial
hypertension, whether the patient's HDL level is less than 40, and
the patient's family history of CAD. The processing logic in FIGS.
3A 3B are divided into three sections: 300, 310, and 320. The processing
logic section 300 is performed if the patient's triglycerides level
exceeds 200 and the patient's LDL level is not measured. Processing
logic section 310 is performed if the patient's TRG level exceeds
200 and the patient's LDL level is measured. Logic processing section
320 is performed if the patient's TRG level is less than 200. In
processing logic 300, TRG, HDL and cholesterol levels are used to
determine a treatment therapy for the patient. In processing logic
310, TRG, LDL, and non-HDL levels are used to determine a recommended
patient treatment therapy. In processing logic 320, TRG, LDL and
HDL levels are used to determine a recommended treatment therapy
for the patient.
FIGS. 4A 4B illustrate the Group 3 processing logic. Patients in
Group 3 are those without a history of CHD, SCD, PPD, DM, or AAA,
meeting more than two conditions in logic block 106 of FIG. 1 and
having a Framingham risk score greater than or equal to 12. The
target HDL is less than 160 and the target LDL is less than 130
for patients in Group 3. Group 3 processing logic is divided into
processing logic sections 400, 410, and 420. Processing logic section
400 is performed if the patient's TRG level exceeds 200 and the
patient's LDL level is not measured. This processing logic uses
TRG level, HDL level and total cholesterol level to determine a
recommended treatment therapy for the patient. Processing logic
section 410 is performed if the patient's TRG level exceeds 200
and the patient's LDL level is measured. The patient's TRG, LDL,
HDL and non-HDL levels are used to determine a recommended treatment
therapy. Processing logic section 420 is performed if the patient's
TRG level is less than 200. The patient's TRG, LDL, and HDL levels
are taken into consideration in determining a recommended treatment
therapy.
Group 4 processing logic is illustrated in FIGS. 5A 5B. Group 4
processing logic is executed if the patient has no history of CHD,
SCD, PPD, DM, or AAA, and has either none or one of the conditions
listed in logic block 106 of FIG. 1. For Group 4 patients, the target
HDL level is less than 190 and the target LDL level is less than
160. Group 4 processing logic is divided into sections 500, 510,
520, and 530. If the patient's TRG level exceeds 500, then the recommended
therapy is to prescribe a fibrate and Niaspan as illustrated in
processing logic section 500. If the patient's TRG exceeds 200,
but the LDL is not measured, then processing logic section 510 is
performed. TRG, total cholesterol and HDL levels are used to determine
a recommended treatment therapy for the patient. If the patient's
TRG level exceeds 200 and LDL is measured, then processing logic
section 520 is performed. A recommended treatment therapy is determined
based on the patient's TRG level, LDL level, non-HDL level and HDL
level. If the patient's TRG level is less than 200 and LDL is measured,
then processing logic section 530 is performed. The patient's TRG
level, LDL level and HDL level are used to determine a recommended
treatment therapy.
FIG. 5C illustrates the LDL reducing and HDL reducing power algorithm
included in the Groups 1 4 processing logic of the medical risk
assessment software. The reducing power algorithms are performed
when the patient's TRG level exceeds 200 and the patient's LDL is
measured. The reducing power algorithms are divided into sections
540 and 550. The LDL reducing power algorithm section 540 is further
subdivided into subsection 544 for TRG greater than 292 and subsection
548 for TRG less than 293. The HDL reducing power algorithm section
550 is further subdivided into subsection 554 for TRG greater than
432 and subsection 558 for TRG less than 433. The LDL and HDL reducing
power algorithm are for use in processing logic sections 230, 310,
410 and 520 of FIGS. 2 5, respectively.
Patient evaluation display examples are provided in FIGS. 6 15.
The patient evaluation display is divided into a number of sections.
Section 154 enables entry of the patient's age, whether the patient
is a new patient, and where calculated, a Framingham risk score.
Section 158 lists current patient therapies. A "0" indicates
that the patient is not receiving the particular therapy. A "1"
indicates that the patient is receiving the corresponding therapy.
Section 162 is used to identify specific drugs that the patient
may currently be using, including a specific statin, fibrate, or
resin. Laboratory results for the patient are entered in section
166. Laboratory results include total cholesterol level, systolic
blood pressure, HDL level, LPA level, triglycerides level, and LDL
level. The non-HDL level is determined by subtracting the HDL level
from the total cholesterol level. Sections 170 and 174 indicate
the patient's pertinent medical history. Section 170, in particular,
lists the five key risk factors, i.e., CHD, SCD, PVD, Type II DM,
and AAA, that will classify the patient in Group 1 for processing
by the medical risk assessment software. Section 174 lists the risk
factors that are used to determine whether the patient should be
classified in Group 2, Group 3, or Group 4 for processing by the
medical risk assessment software. The guidelines recommendations
are listed in Section 178 and are based on the patient's laboratory
results and pertinent medical history. Section 182 displays the
recommended patient therapy that is determined on the basis of the
underlying processing logic that is performed based on the patient's
age, laboratory results, key risk factors, and other risk factors.
Section 186 is a reference section that provides a reference to
specific published literature where appropriate. Section 190 of
the patient evaluation display contains three selectable buttons
to (1) compute a recommended patient treatment therapy, (2) clear
the screen, or (3) quit the medical risk assessment software program.
In the example of FIG. 6, the patient is 55 years old and has a
TRG of 295, an LDL of 162 and an HDL of 63. There is an indication
in section 170 that the patient has a CHD condition. Corresponding
to decision block 102 in FIG. 1, this results in the patient being
classified in Group 1 and the processing logic of FIGS. 2A 2E being
executed. The guideline recommendations for this specific patient
are a target non-HDL cholesterol level less than 130 and a target
LDL cholesterol less than 100. Since the patient has a TRG level
greater than 200, and a measured LDL level, the processing logic
section 230 will be performed by the medical risk assessment software
to determine the recommended treatment therapy. For this patient,
the recommended treatment therapy is a statin as indicated in section
182 of FIG. 6.
In the example of FIG. 7, the patient is 75 years old and has a
TRG of 327, an LDL of 133 and an HDL of 41. Non of the high risk
factors in section 170 are checked. The FRS is computed for the
patient and is determined to be less than 15. This leads to a determination
in logic block 106 whether one or more of the conditions of age,
smoking, family history, SAH, or HDL less than 40, are met. None
of the conditions in section 174 are checked, however, the patient
is older than 45 as indicated in section 154. Since the number of
conditions met is less than 2, the patient is classified in Group
4 and the processing logic section of FIGS. 5A 5B is performed.
The guideline recommendations for this specific patient are a target
non-HDL cholesterol level less than 190 and a target LDL cholesterol
less than 160. Since the patient's TRG level exceeds 200 and the
LDL is measured, processing logic section 520 is performed to determine
the recommended patient therapy. In this example, the recommended
patient therapy is Niaspan as indicated in Section 182.
In the example of FIG. 8, the patient is 52 years old and has a
TRG of 527, and LDL of 133 and an HDL of 41. The CHD risk factor
is checked in section 170, therefore the patient is classified in
Group 1 and the processing logic of FIGS. 2A 2E is executed. The
guideline recommendations for this specific patient are a target
non-HDL cholesterol level less than 130 and a target LDL cholesterol
less than 100. Since the patient has a TRG level greater than 500,
processing logic section 200 is performed to determine the recommended
patient therapy. Section 182 indicates to the physician the recommended
patient therapy.
In the example shown in FIG. 9, the patient is 44 years old and
has a TRG of 232, an LDL of 153 and an HDL of 39. The patient does
not have any of the key risk factors in section 170 checked. Therefore
the Framingham risk score is determined and is less than 15. The
number of risk factor conditions is then determined as listed in
logic block 106 of FIG. 1. Since none of the conditions in section
174 are checked and the patient's age is less than 45, the patient
is classified in Group 4. The processing logic of FIGS. 5A 5B is
then executed to determine a recommended patient therapy. The guideline
recommendations for this specific patient are a target non-HDL cholesterol
level less than 190 and a target LDL cholesterol less than 160.
In this example, the TRG level exceeds 200 and the LDL level is
measured. Therefore, processing logic section 520 is performed to
determine the recommended treatment therapy displayed in section
182.
In the example shown in FIG. 10, the patient is 64 years old and
has a TRG of 207, an LDL of 145 and an HDL of 36. None of the key
risk factors in section 170 are checked; therefore, the Framingham
risk score for the patient is determined. Since the patient's FRS
score is not greater than 15, the patient's age and the number of
other risk factors checked in section 174 are used to determine
the patient's group classification. The number of conditions met
is 3 in this example. Since the patient's FRS score is greater than
12, the patient is classified in Group 3 and the processing logic
of FIG. 4 is executed. The guideline recommendations for this specific
patient are a target non-HDL cholesterol level less than 160 and
a target LDL cholesterol less than 130. The patient's TRG level
exceeds 200, therefore, processing logic section 410 is performed
to determine the recommended patient therapy. The medical risk assessment
software determines that the recommended patient therapy is Niaspan
as indicated in section 182.
In the example shown in FIG. 11, the patient is 75 years old and
has a TRG of 224, an LDL of 129, and an HDL of 41. None of the high
risk factors in section 170 are checked and the FRS score is not
greater than 15. Therefore, the conditions listed in logic block
106 of FIG. 1 are determined for the patient. The only condition
met is that the patient's age is greater than 45, therefore the
patient is classified in Group 4 and the processing logic of FIGS.
5A 5B is executed. The guideline recommendations for this specific
patient are a target non-HDL cholesterol level less than 190 and
a target LDL cholesterol less than 160. Since the patient's TRG
level exceeds 200, processing logic section 520 is performed. In
this example, the patient's HDL exceeds 40, but the LDL level is
less than 130. Therefore, the recommended patient therapy is determined
to be diet, as indicated in section 182.
In the example shown in FIG. 12, the patient is 55 years old and
has a TRG of 166, an LDL of 182 and an HDL of 46. None of the key
risk factors in section 170 are checked and the FRS score is less
than 15; therefore a determination is made of the number of conditions
met by the patient that are listed in logic block 106 of FIG. 1.
Since none of the conditions in section 174 are checked, and the
patient's age exceeds 45, the patient is classified into Group 4
and the processing logic of FIGS. 5A 5B is executed. The guideline
recommendations for this specific patient are a target non-HDL cholesterol
level less than 190 and a target LDL cholesterol less than 160.
In this example, the TRG level is less than 200 and the LDL level
is measured; therefore processing logic section 530 is performed.
For this patient, the LDL level exceeds 160, but the HDL level is
greater than 45. Since the FRS score is greater than 12, the medical
risk assessment software determines that the patient's treatment
therapy should be a statin.
In the example shown in FIG. 13, the patient is 30 years old and
has a TRG of 57, an LDL of 170, and an HDL of 46. None of the key
risk factors are checked in section 170 and the Framingham risk
score is 0 for this patient. Since none of the key risk factors
were checked, and the FRS score is less than 15, the conditions
listed in logic block 106 of FIG. 1 are examined to determine how
many of the conditions are met. Since the patient's age is less
than 45 and none of the conditions in section 174 are checked, the
patient is classified in Group 4 and processing logic in FIGS. 5A
5B is executed. The guideline recommendations for this specific
patient are a target non-HDL cholesterol level less than 190 and
a target LDL cholesterol less than 160. This patient has a TRG level
less than 200 and a measured LDL level, therefore, processing logic
section 530 is performed to determine a recommended treatment therapy
for the patient. The recommended treatment therapy is shown in section
182.
In the example shown in FIG. 14, the patient is 65 years old and
has a TRG of 232, an LDL of 117, and an HDL of 54. CHD is checked
in section 170. Since the patient has a key risk factor checked,
the patient is classified in Group 1 and the processing logic of
FIGS. 2A 2E is executed. The guideline recommendations for this
specific patient are a target non-HDL cholesterol level less than
130 and a target LDL cholesterol less than 100. Since the patient
has a TRG level less than 200 and a measured LDL level, processing
logic section 210 is performed. The recommended patient treatment
therapy is indicated in section 182.
In the example shown in FIG. 15, the patient is 65 years old and
has a TRG of 232, an LDL of 117 and an HDL of 54. The patient does
not have any of the key risk factors checked in section 170. The
Framingham risk score for this patient is 14. Since none of the
key risk factors are checked and the FRS score is less than 15,
the number of conditions met by the patient that are listed in logic
block 106 of FIG. 1 is determined to classify the patient. Since
the patient's age is greater than 45 and two additional conditions
are checked in section 174, the Framingham risk score is used to
determine whether the patient is classified in Group 2 or in Group
3. In this instance, since the FRS is greater than 12, the patient
is classified in Group 3. The processing logic of FIGS. 4A 4B is
executed. The guideline recommendations for this specific patient
are a target non-HDL cholesterol level less than 160 and a target
LDL cholesterol less than 130. Since the patient's TRG level is
greater than 200 and the patient's LDL level is measured, processing
logic section 410 is performed to determine a recommended treatment
therapy. In this example, the recommended treatment therapy is Niaspan,
as indicated in section 182.
It is important to note that the present invention has been described
in the context of a fully functioning data processing system, although
those skilled in the art will appreciate that the mechanisms of
the invention are capable of being distributed in the form of computer
program instructions in a variety of forms, which when executed
on the data processing system, perform the methods described herein.
The present invention applies regardless of the type of signal bearing
medium used to carry out the distribution. Examples of signal bearing
mediums include non-volatile hard-coded mediums, such as read-only
memories; recordable type mediums such as floppy disks, hard disk
drives, and CD-ROMs; and transmission type mediums such as digital
and analog communication links.
While the invention has been particularly shown and described with
reference to a preferred embodiment thereof, it will be understood
by those skilled in the art that various other changes in form and
detail may be made without departing from the spirit and scope of
the invention. |