U.S. patent application number 14/286102 was filed with the patent office on 2014-09-11 for system and method for pharmacovigilance.
This patent application is currently assigned to Aetna, Inc.. The applicant listed for this patent is Aetna, Inc.. Invention is credited to Rajesh Revachand MEHTA, Gregory Brian STEINBERG, Henry George WEI.
Application Number | 20140257844 14/286102 |
Document ID | / |
Family ID | 50781363 |
Filed Date | 2014-09-11 |
United States Patent
Application |
20140257844 |
Kind Code |
A1 |
MEHTA; Rajesh Revachand ; et
al. |
September 11, 2014 |
SYSTEM AND METHOD FOR PHARMACOVIGILANCE
Abstract
A method, computer-readable storage medium, and system for
analyzing a relationship between one or more agents and one or more
clinical outcomes. The method includes: receiving a selection of
one or more agents; receiving a selection of one or more clinical
outcomes; for each of the one or more agents, analyzing clinical
data stored in a database to determine a number of occurrences of
each of the one or more clinical outcomes when the agent is
administered; for each of the one or more agents, calculating a
risk score for each clinical outcome corresponding to the number of
occurrences of the clinical outcome; and outputting the risk scores
to a graphical display.
Inventors: |
MEHTA; Rajesh Revachand;
(New York, NY) ; WEI; Henry George; (New York,
NY) ; STEINBERG; Gregory Brian; (Dingmans Ferry,
PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Aetna, Inc. |
Hartford |
CT |
US |
|
|
Assignee: |
Aetna, Inc.
Hartford
CT
|
Family ID: |
50781363 |
Appl. No.: |
14/286102 |
Filed: |
May 23, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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13733791 |
Jan 3, 2013 |
8744872 |
|
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14286102 |
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
G06F 19/00 20130101;
G16H 50/30 20180101; G16H 50/70 20180101 |
Class at
Publication: |
705/2 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. A method for analyzing a relationship between an agent and one
or more clinical outcomes, the method comprising: receiving a
selection of a first agent; receiving a selection of one or more
clinical outcomes; analyzing, by a processor included in a
computing device, clinical data stored in a database to determine a
number of occurrences of each of the one or more clinical outcomes
when the first agent is administered; calculating, by the
processor, for the first agent, a first set of risk scores for each
of the one or more clinical outcomes, wherein calculating the risk
score for a clinical outcome includes measuring a statistical
significance of a relationship between the first agent and the
clinical outcome based on a total number of patients in an entire
population, a number of patients to whom the first agent is
administered, a number of occurrences of the clinical outcome when
the first agent is administered, and a total number of patients in
the entire population that experienced the clinical outcome;
outputting the first set of risk scores to a graphical display
device, wherein, for the first agent, a first risk score indicates
that the first agent is not the cause of a first clinical outcome
for a first sub-population that is a subset of the entire
population; and generating a first hypothesis by the processor that
is based on the first risk score that indicates that the first
agent is not the cause of the first clinical outcome for the first
sub-population.
2. The method of claim 1, further comprising: generating and
outputting a message corresponding to the first hypothesis
generated by the processor.
3. The method of claim 1, further comprising: receiving a selection
of a second agent; analyzing, by the processor, the clinical data
to determine a number of occurrences of each of the one or more
clinical outcomes when the second agent is administered;
calculating, by the processor, for the second agent, a second set
of risk scores for each of the one or more clinical outcomes;
outputting the second set of risk scores to the graphical display
device, wherein, for the second agent, a second risk score
identifies a possible benefit of the second agent for a second
sub-population that is a subset of the entire population; and
generating a second hypothesis by the processor that is based on
the second risk score that indicates that identifies the possible
benefit of the second agent for the second sub-population that is a
subset of the entire population.
4. The method of claim 3, further comprising: generating and
outputting a message corresponding to the second hypothesis
generated by the processor.
5. The method of claim 1, further comprising: filtering the
clinical data stored in the database based on one or more filters,
such that the risk scores are calculated using data that satisfies
the one or more filters, wherein the one or more filters include
age, gender, clinical stratification scores and identified
conditions, and/or an indication of use of the agent.
6. The method of claim 1, further comprising calculating a
confidence level for the first risk score based on the equation: (
I ) [ ( IAO ) ( I - IO - IA + IAO ) - ( IO - IAO ) ( IA - IAO ) ] 2
( IA ) ( I - IA ) ( IO ) ( I - IO ) ##EQU00003## wherein: I
represents the total number of patients in the entire population;
IA represents the number of patients to whom the first agent is
administered; IAO represents the number of occurrences of the first
clinical outcome when the first agent is administered; and IO
represents the total number of patients in the entire population
that experienced the first clinical outcome.
7. The method of claim 1, wherein the first agent comprises a
prescription drug.
8. The method of claim 1, wherein the first agent exhibits a
relatively lower risk score for one of the clinical outcomes
compared to the other agents in a common class of agents.
9. The method of claim 1, wherein the clinical data stored in a
database includes demographic data, lab data, pharmacy data, claims
data, diagnostic codes, procedure codes, heath reference
information, medical news, standards-of-care, and/or
patient-entered data.
10. The method of claim 1, further comprising displaying, for each
combination of agent and clinical outcome, a circle corresponding
to the risk score, wherein a larger circle corresponds to a larger
risk score.
11. A non-transitory computer-readable storage medium storing
instructions that when executed by a processor cause a computer
system to analyze a relationship between an agent and one or more
clinical outcomes, by performing the steps of: receiving a
selection of a first agent; receiving a selection of one or more
clinical outcomes; analyzing, by a processor included in a
computing device, clinical data stored in a database to determine a
number of occurrences of each of the one or more clinical outcomes
when the first agent is administered; calculating, by the
processor, for the first agent, a first set of risk scores for each
of the one or more clinical outcomes, wherein calculating the risk
score for a clinical outcome includes measuring a statistical
significance of a relationship between the first agent and the
clinical outcome based on a total number of patients in an entire
population, a number of patients to whom the first agent is
administered, a number of occurrences of the clinical outcome when
the first agent is administered, and a total number of patients in
the entire population that experienced the clinical outcome;
outputting the first set of risk scores to a graphical display
device, wherein, for the first agent, a first risk score indicates
that the first agent is not the cause of a first clinical outcome
for a first sub-population that is a subset of the entire
population; and generating a first hypothesis by the processor that
is based on the first risk score that indicates that the first
agent is not the cause of the first clinical outcome for the first
sub-population.
12. The computer-readable storage medium of claim 11, further
comprising: generating and outputting a message corresponding to
the first hypothesis generated by the processor.
13. The computer-readable storage medium of claim 11, further
comprising: receiving a selection of a second agent; analyzing, by
the processor, the clinical data to determine a number of
occurrences of each of the one or more clinical outcomes when the
second agent is administered; calculating, by the processor, for
the second agent, a second set of risk scores for each of the one
or more clinical outcomes; outputting the second set of risk scores
to the graphical display device, wherein, for the second agent, a
second risk score identifies a possible benefit of the second agent
for a second sub-population that is a subset of the entire
population; and generating a second hypothesis by the processor
that is based on the second risk score that indicates that
identifies the possible benefit of the second agent for the second
sub-population that is a subset of the entire population.
14. The computer-readable storage medium of claim 13, further
comprising: generating and outputting a message corresponding to
the second hypothesis generated by the processor.
15. The computer-readable storage medium of claim 1, further
comprising: filtering the clinical data stored in the database
based on one or more filters, such that the risk scores are
calculated using data that satisfies the one or more filters,
wherein the one or more filters include age, gender, clinical
stratification scores and identified conditions, and/or an
indication of use of the agent.
16. The computer-readable storage medium of claim 11, further
comprising calculating a confidence level for the first risk score
based on the equation: ( I ) [ ( IAO ) ( I - IO - IA + IAO ) - ( IO
- IAO ) ( IA - IAO ) ] 2 ( IA ) ( I - IA ) ( IO ) ( I - IO )
##EQU00004## wherein: I represents the total number of patients in
the entire population; IA represents the number of patients to whom
the first agent is administered; IAO represents the number of
occurrences of the first clinical outcome when the first agent is
administered; and IO represents the total number of patients in the
entire population that experienced the first clinical outcome.
17. The computer-readable storage medium of claim 11, wherein the
first agent comprises a prescription drug.
18. The computer-readable storage medium of claim 11, wherein the
first agent exhibits a relatively lower risk score for one of the
clinical outcomes compared to the other agents in a common class of
agents.
19. The computer-readable storage medium of claim 11, wherein the
clinical data stored in a database includes demographic data, lab
data, pharmacy data, claims data, diagnostic codes, procedure
codes, heath reference information, medical news,
standards-of-care, and/or patient-entered data.
20. A system comprising: a clinical data database; and a healthcare
organization computing device executing one or more processors to
analyze a relationship between a prescription drug and one or more
clinical outcomes, by performing the steps of: receiving a
selection of a first prescription drug; receiving a selection of
one or more clinical outcomes; analyzing, by a processor included
in a computing device, clinical data stored in a database to
determine a number of occurrences of each of the one or more
clinical outcomes when the first prescription drug is administered;
calculating, by the processor, for the first prescription drug, a
first set of risk scores for each of the one or more clinical
outcomes, wherein calculating the risk score for a clinical outcome
includes measuring a statistical significance of a relationship
between the first prescription drug and the clinical outcome based
on a total number of patients in an entire population, a number of
patients to whom the first prescription drug is administered, a
number of occurrences of the clinical outcome when the first
prescription drug is administered, and a total number of patients
in the entire population that experienced the clinical outcome;
outputting the first set of risk scores to a graphical display
device, wherein, for the first prescription drug, a first risk
score indicates that the first prescription drug is not the cause
of a first clinical outcome for a first sub-population that is a
subset of the entire population; and generating a first hypothesis
by the processor that is based on the first risk score that
indicates that the first prescription drug is not the cause of the
first clinical outcome for the first sub-population.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This patent application is a continuation of U.S. patent
application Ser. No. 13/733,791, filed on Jan. 3, 2013, which is
incorporated by reference herein in its entirety.
FIELD
[0002] This disclosure relates generally to the field of health
care management and, more specifically, to a system and method for
pharmacovigilance.
BACKGROUND
[0003] Pharmacovigilance is the science of collecting, monitoring,
researching, assessing, and evaluating information from healthcare
providers and patients on the adverse effects of medications with a
view towards identifying hazards associated with the medications
and preventing harm to patients.
[0004] A typical health care system includes a variety of
participants, including doctors, hospitals, insurance carriers, and
patients, among others. These participants frequently rely on each
other for the information necessary to perform their respective
roles because individual care is delivered and paid for in numerous
locations by individuals and organizations that are typically
unrelated. As a result, a plethora of health care information
storage and retrieval systems are required to support the heavy
flow of information between these participants related to patient
care. Critical patient data is stored across many different
locations using legacy mainframe and client-server systems that may
be incompatible and/or may store information in non-standardized
formats. To ensure proper patient diagnosis and treatment, health
care providers often request patient information by phone or fax
from hospitals, laboratories, or other providers. Therefore,
disparate systems and information delivery procedures maintained by
a number of independent health care system constituents lead to
gaps in timely delivery of critical information and compromise the
overall quality of clinical care. Since a typical health care
practice is concentrated within a given specialty, an average
patient may be using services of a number of different specialists,
each potentially having only a partial view of the patient's
medical status.
[0005] Moreover, pharmacovigilance is facing increased pressure
from regulators and academics who are mining real-world databases
for safety signals. Some factors affecting the pharmacovigilance
landscape include: an increasing use of real-world data by
regulators; heightened expectations of manufacturers from the FDA
(Food and Drug Administration), public, and
academics/investigators; externalization of safety data (e.g., EMR
(electronic medical records); and emergence of pharmacovigilance as
an applied science.
[0006] There are certain limitations to the way in which
pharmacovigilance is currently being implemented. Firstly,
pharmacovigilance, or drug surveillance, is typically done by "ad
hoc" reporting, where a physician independently identifies patients
that have a problem with a certain drug and report this singular
instance to the FDA. The FDA then accumulates this information and
communicates with pharmaceutical manufacturers. This process is
inefficient and ineffective. To overcome some of the drawbacks of
the ad hoc approach, the FDA has implemented the "Sentinel" and
"Mini Sentinel" initiatives. However, these initiatives look at
retrospective and/or historical data to perform drug
surveillance.
[0007] Accordingly, there remains a need in the art for a system
and method for pharmacovigilance that overcomes the drawbacks and
limitations of current approaches.
SUMMARY
[0008] Some embodiments of the disclosure provide a method,
computer-readable storage medium, and system for analyzing a
relationship between one or more agents and one or more clinical
outcomes. The method includes: receiving a selection of one or more
agents; receiving a selection of one or more clinical outcomes; for
each of the one or more agents, analyzing clinical data stored in a
database to determine a number of occurrences of each of the one or
more clinical outcomes when the agent is administered; for each of
the one or more agents, calculating a risk score for each clinical
outcome corresponding to the number of occurrences of the clinical
outcome; and outputting the risk scores to a graphical display.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a schematic diagram illustrating an overview of a
system for analyzing a relationship between one or more agents and
one or more clinical outcomes, in accordance with an embodiment of
the disclosure.
[0010] FIG. 2 is a flow diagram illustrating a method for analyzing
a relationship between one or more agents and one or more clinical
outcomes, in accordance with an embodiment of the disclosure.
[0011] FIG. 3 is a screenshot of a user interface displaying an
average relative risk for plurality of agents versus a plurality of
outcomes, in accordance with an embodiment of the disclosure.
[0012] FIG. 4 is a screenshot of a user interface displaying an
average relative risk for different agents in the same class of
agents relative to a particular outcome, in accordance with an
embodiment of the disclosure.
[0013] FIG. 5 is a screenshot of a user interface displaying an
average relative risk for one agent relative to one outcome, where
the data is sorted by one or more filters, in accordance with an
embodiment of the disclosure.
[0014] FIGS. 6-8 are screenshots of user interfaces displaying an
average relative risk for a plurality of outcomes for one agent
relative to other agents in the same class of agents, in accordance
with several embodiments of the disclosure.
[0015] FIG. 9 is a screenshot of a user interface displaying an
average relative risk for two agents relative to a plurality of
outcomes, where the data is filtered by gender and age, in
accordance with an embodiment of the disclosure.
[0016] FIG. 10 is a schematic diagram illustrating an overview of a
system for analyzing a relationship between one or more agents and
one or more clinical outcomes, in accordance with an embodiment of
the disclosure.
DETAILED DESCRIPTION
[0017] Embodiments of the disclosure provide a system and method
for pharmacovigilance. According to some embodiments, health
related clinical data is stored in one or more databases. The
clinical data may include, for each patient, demographic data,
diagnostic codes, procedure codes, medication and prescription
data, and lab data, among others. Clinical data may include also
data from electronic medical records (EMRs). A processor is
configured to receive a selection of one or more agents (e.g.,
drugs) and one or more clinical outcomes (e.g., adverse events).
The processor is configured to calculate a risk score for the one
or more clinical outcomes in relation to the one or more agents.
According to various embodiments, the risk score may be an absolute
risk or a relative risk. A chi-squared statistical analysis and a
p-value statistical analysis may also be performed to confirm or
reject the observed calculations.
[0018] Accordingly, some embodiments provide a proactive,
prospective, and ongoing approach to pharmacovigilance. The
database from which the analysis is performed is continuously being
updated with new clinical data. For example, medical claims data
may be entered into the database within 48 hours of an insurance
carrier receiving information about the treatment.
[0019] Some embodiments disclosed herein provide a proactive and
automated signal detection and surveillance system with
standardized reporting. Some embodiments provide real-time
monitoring due to rapid adjudication and incorporation of claims
data into analytic database, and a signal validation system that
can exonerate or stratify risk in near real-time and identify
potential benefits, versus an industry average of six to nine
months.
[0020] Turning to the figures, FIG. 1 is a schematic diagram
illustrating an overview of a system for analyzing a relationship
between one or more agents and one or more clinical outcomes, in
accordance with an embodiment of the disclosure. A health care
organization 100 collects and processes a wide spectrum of medical
care information relating to a patient 102 in order to analyze the
relationship between one or more agents and one or more clinical
outcomes. A personal health record (PHR) 108 of a patient 102 may
be configured to solicit the patient's input for entering
additional pertinent medical information, tracking follow-up
actions, and allowing the health care organization 100 to track the
patient's medical history.
[0021] When the patient 102 utilizes the services of one or more
health care providers 110, a medical insurance carrier 112 collects
the associated clinical data 114 in order to administer the health
insurance coverage for the patient 102. Additionally, a health care
provider 110, such as a physician or nurse, enters clinical data
114 into one or more health care provider applications pursuant to
a patient-health care provider interaction during an office visit
or a disease management interaction. Clinical data 114 originates
from medical services claims, pharmacy data, as well as from lab
results, and includes information associated with the
patient-health care provider interaction, including information
related to the patient's diagnosis and treatment, medical
procedures, drug prescription information, in-patient information,
and health care provider notes, among other things. The medical
insurance carrier 112 and the health care provider 110, in turn,
provide the clinical data 114 to the health care organization 100,
via one or more networks 116, for storage in one or more medical
databases 118. The medical databases 118 are administered by one or
more server-based computers associated with the health care
provider 100 and comprise one or more medical data files located on
a computer-readable medium, such as a hard disk drive, a CD-ROM, a
tape drive, or the like. The medical databases 118 may include a
commercially available database software application capable of
interfacing with other applications, running on the same or
different server based computer, via a structured query language
(SQL). In an embodiment, the network 116 is a dedicated medical
records network. Alternatively, or in addition, the network 116
includes an Internet connection that comprises all or part of the
network.
[0022] In some embodiments, an on-staff team of medical
professionals within the health care organization 100 consults
various sources of health reference information 122, including
evidence-based preventive health data, to establish and
continuously or periodically revise a set of clinical rules 120
that reflect best evidence-based medical standards of care for a
plurality of conditions. The clinical rules 120 are stored in the
medical database 118.
[0023] To supplement the clinical data 114 received from the
insurance carrier 112, the PHR 108 allows patient entry of
additional pertinent medical information that is likely to be
within the realm of patient's knowledge. Examples of
patient-entered data include additional clinical data, such as
patient's family history, use of non-prescription drugs, known
allergies, unreported and/or untreated conditions (e.g., chronic
low back pain, migraines, etc.), as well as results of
self-administered medical tests (e.g., periodic blood pressure
and/or blood sugar readings). Preferably, the PHR 108 facilitates
the patient's task of creating a complete health record by
automatically populating the data fields corresponding to the
information derived from the medical claims, pharmacy data and lab
result-based clinical data 114. In one embodiment, patient-entered
data also includes non-clinical data, such as upcoming doctor's
appointments. In some embodiments, the PHR 108 gathers at least
some of the patient-entered data via a health risk assessment tool
(HRA) 130 that requests information regarding lifestyle, behaviors,
family history, known chronic conditions (e.g., chronic back pain,
migraines, etc.), and other medical data, to flag individuals at
risk for one or more predetermined medical conditions (e.g.,
cancer, heart disease, diabetes, risk of stroke, etc.) pursuant to
the processing by a calculation engine 126. Preferably, the HRA 130
presents the patient 102 with questions that are relevant to his or
her medical history and currently presented conditions. The risk
assessment logic branches dynamically to relevant and/or critical
questions, thereby saving the patient time and providing targeted
results. The data entered by the patient 102 into the HRA 130 also
populates the corresponding data fields within other areas of PHR
108. The health care organization 100 aggregates the clinical data
114 and the patient-entered data, as well as the health reference
and medical news information 122, into the medical database 118 for
subsequent processing via the calculation engine 126.
[0024] The health care organization 100 includes a
multi-dimensional analytical software application including a
calculation engine 126 comprising computer-readable instructions
for performing statistical analysis on the contents of the medical
databases 118 in order to analyze a relationship between one or
more agents and one or more clinical outcomes. The relationships
identified by the calculation engine 126 can be presented in a
graphical display 104, e.g., to the healthcare provider 110 and/or
medical insurance carrier 112 and/or to the government (e.g.,
FDA).
[0025] After collecting the relevant data, the calculation engine
126 receives a selection of one or more agents. In one example
implementation, the agents are prescription drugs. The calculation
engine calculates a risk of occurrence of one or more clinical
outcomes for each of the one or more agents. In one implementation,
a drug may be exonerated from causing a clinical outcome that is
detected, for example, in spontaneous reports or for specific
subgroups (or possibly overall). In another example implementation,
the calculation engine 126 may determine that certain adverse
events occur mostly in off-label use. "Off-label" use refers to
non-recommended uses of a drug, such as non-FDA approved uses. In
another implementation, calculation engine 126 may determine how a
drug's safety profile compares to other drugs within the same class
of drugs. Other use cases are also within the scope of embodiments
of the disclosure, as described in greater detail herein.
[0026] For example, embodiments disclosed herein can provide
"comparative effectiveness" information by directly comparing
multiple pharmacologically similar agents against varied and
multiple health outcomes of interest, allowing for inferences to be
made about the comparative risks and benefits of these agents. This
analysis may lead to the identification of new therapeutic
indications for existing agents.
[0027] While the entity relationships described above are
representative, those skilled in the art will realize that
alternate arrangements are possible. In one embodiment, for
example, the health care organization 100 and the medical insurance
carrier 112 is the same entity. Alternatively, the health care
organization 100 is an independent service provider engaged in
collecting, aggregating, and processing medical care data from a
plurality of sources to provide a personal health record (PHR)
service for one or more medical insurance carriers 112. In yet
another embodiment, the health care organization 100 provides PHR
services to one or more employers by collecting data from one or
more medical insurance carriers 112.
[0028] FIG. 2 is a flow diagram illustrating a method 200 for
analyzing a relationship between one or more agents and one or more
clinical outcomes, in accordance with an embodiment of the
disclosure. As shown, the method 200 begins at step 202, where a
processor, such as a processor associated with the calculation
engine 126, receives a selection of an agent. In one embodiment,
the agent is a prescription drug. At step 204, the processor
receives selection of an adverse event. In some embodiments,
adverse events are clinic events. Non-limiting examples include
accidents, cancer, congestive heart failure, depression, diarrhea,
glaucoma, infection, liver dysfunction, lymphoma, major bleeding,
renal failure, seizures, sudden death, suicide, among many others.
In some embodiments, the adverse events are coded according to
standard external definitions (for example, by the government). In
other embodiments, the adverse events are coded according to
proprietary definitions.
[0029] At step 206, the processor analyzes clinical data in a
database to determine a number of occurrences of the adverse event
when the agent is administered. As described, the clinical data can
come from many sources, including demographic data, claims data,
procedure codes, diagnostic codes, pharmacy/prescription data,
patient-entered data, among others. The processor analyzes the data
to identify a number of patients that have exhibited the adverse
event when taking the drug for a predetermined minimum amount of
time (for example, 6 months).
[0030] At step 208, the processor applies one or more filters. The
clinical data can be filtered according to certain parameters, such
as patient age, gender, demographic info, clinical stratification
scores and identified conditions, and whether the use of the drug
was "on-label" or "off-label" (i.e., "on-label" refers to use in
the recommended or FDA approved manner; "off-label" refers to use
in a non-recommended or non-FDA approved manner), among others. The
analysis performed at step 206 can, therefore, be applied only to
the data that satisfies the filters. In some embodiments, step 208
is performed before step 206. Also, in some embodiments, step 208
is optional and is omitted. In such a case, no filter is applied,
and all the clinical data is analyzed.
[0031] At step 210, the processor calculates a risk score
corresponding to the adverse event and the agent. According to some
embodiments, the risk score can be an absolute risk or a relative
risk. Table 1 below illustrates occurrences of the adverse event
when a particular drug is administered, a total number of patients
that suffered the adverse event, a total number of patients to whom
the drug was administered, and a total number of patients to whom
the drug was not administered.
TABLE-US-00001 TABLE 1 Drug No Drug Total Adverse IAO IO Event No
Adverse Event Total IA I
[0032] In Table 1, "IAO" refers to the occurrence of the adverse
event when the drug is administered, "IO" refers to the total
number of patients that suffered the adverse event, "IA" refers to
the total number of patients to whom the drug was administered, and
"I" refers to the total number of patients to whom the drug was not
administered.
[0033] According to one embodiment, an "ON agent risk," "NO agent
risk," "Absolute Risk," and "Relative Risk" can be calculated using
Equations 1 to 4, respectively:
ONagentRisk = IAO IA , ( Equation 1 ) NOagentRisk = IO - IAO I - IA
, ( Equation 2 ) AbsoluteRisk = ONagentRisk - NOagentRisk , and (
Equation 3 ) RelativeRisk = ONagentRisk NOagentRisk . ( Equation 4
) ##EQU00001##
[0034] A "chi-squared" analysis can also be performed to calculate
a confidence level for the statistical analysis performed using
Equation 5:
.chi. 2 = ( I ) [ ( IAO ) ( I - IO - IA + IAO ) - ( IO - IAO ) ( IA
- IAO ) ] 2 ( IA ) ( I - IA ) ( IO ) ( I - IO ) . ( Equation 5 )
##EQU00002##
[0035] In some embodiments, a "P-value" may be calculated to test
the statistical significance of the calculations.
[0036] Table 2, below, illustrates an example where the adverse
event is congestive heart failure (CHF) and the drug is an ACE
inhibitor.
TABLE-US-00002 TABLE 2 Drug No Drug Total Adverse 568 2433 Event No
Adverse Event Total 179499 656938
[0037] As shown, a total of 179499 patients took the drug and 568
experienced the adverse effect. A total of 2433 patients
experienced the adverse effect. A total of 656938 patients did not
take the drug.
[0038] Using the Equations 1-4 above, the relative risk is
calculated at 0.81. The chi-squared value is calculated using
Equation 5 as 19.49.
[0039] At step 212, the processor determines whether there are more
adverse events to analyze for the selected agent/drug. If the
processor determines that there are more adverse events to analyze
for the selected agent/drug, then the method 200 returns to step
204, described above. If the processor determines that there are no
more adverse events to analyze for the selected agent/drug, then
the method 200 proceeds to step 214.
[0040] At step 214, the processor determines whether there are more
agents/drugs to analyze against adverse events. If the processor
determines that there are more agents/drugs to analyze, then the
method 200 returns to step 202, described above. If the processor
determines that there are no more agents/drugs to analyze, then the
method 200 proceeds to step 216.
[0041] At step 216, the processor outputs results (i.e., risk
scores) to a graphical display. In some embodiments, the results
may be graphically represented as a "heat map," where a circle
corresponds to the average relative risk of the drug-adverse event
combination, and where a greater size of the circle corresponds to
a greater average relative risk. Examples are provided below in
FIGS. 3-9.
[0042] FIG. 3 is a screenshot of a user interface displaying an
average relative risk for plurality of agents versus a plurality of
outcomes, in accordance with an embodiment of the disclosure. As
shown, a listing of different agents (for example, prescription
drugs) is shown along a vertical axis 304 and a listing of
different outcomes (for example, adverse clinical events) is shown
along a horizontal axis 306. A selection of which agents and/or
outcomes are shown in the user interface can be made via interface
element 308 via one or more checkboxes. Note, in FIG. 3, the
selection of different outcomes is not shown (i.e., a user would
need to "scroll down" to see the checkboxes for the different
outcomes).
[0043] As described above, a processor can calculate a risk score,
such as average relative risk, for each combination of agent and
outcome. In the example shown in FIG. 3, average relative risk is
graphically displayed such that an increase in the size 302 of the
circle shown for the particular agent-outcome combination
corresponds to an increase in the average relative risk. For
example, a high average relative risk is exhibited between the
agent "Clozapine" and the outcome "Hip fracture," displayed as
circle 310.
[0044] FIG. 4 is a screenshot of a user interface displaying an
average relative risk for different agents in the same class of
agents relative to a particular outcome, in accordance with an
embodiment of the disclosure. In the example shown in FIG. 4, three
different blood thinners are shown along a vertical axis 402
relative to a particular outcome (e.g., major bleeding) along a
horizontal axis 404. In the example shown, the three blood thinners
are "Dabigatran," "Prasugrel," and "oral antiplatelet agents other
than Prasugrel." With respect the particular outcome shown, it is
readily apparent from the sizes of the circles, that the agent
"oral antiplatelet agents other than Prasugrel" has the lowest
average relative risk of the three agents. Providing a graphical
representation of the average relative risk provides for a superior
user experience, when compared to conventional techniques.
[0045] FIG. 5 is a screenshot of a user interface displaying an
average relative risk for one agent relative to one outcome, where
the data is sorted by one or more filters, in accordance with an
embodiment of the disclosure. As described, the data can be
filtered using one or more filters prior to performing the
statistical analysis. In the example shown in FIG. 5, a single
outcome (e.g., major bleeding) is shown along a horizontal axis
504. Along the vertical axis 502, a single agent is shown (e.g.,
"Dabigatran"), where the data is first filtered by gender 506 and
then by indication 508. Filtering by "indication," in this example,
refers to whether the drug was used in an FDA approved manner
(i.e., "on-label") or a non-FDA approved manner (i.e.,
"off-label"). In the example in FIG. 5, A.Fib "Non-Valvular" refers
to the FDA approved mode of administering Dabigatran, and A.Fib
"Valvular" refers to the non-FDA approved mode of administering
Dabigatran. When comparing the average relative risk for the four
different combinations of gender 506 and indication 508, the
outcome has a similar average relative risk for both indications
(i.e., Non-Valvular and Valvular) for females. However, for males,
the Valvular (i.e., non-FDA approved) mode of administering the
drug has a significantly greater average relative risk. The outcome
shown in FIG. 5 may suggest that a blanket statement from the FDA
that prohibits Valvular treatment with Dabigatran (for both males
and females) is not necessary, and that the FDA should consider
allowing Valvular treatments for women. The results shown using
embodiments of the disclosure are not meant to be definitive proof
that certain drugs do not cause certain complications/outcomes, but
rather to generate a hypothesis for further investigation and/or
research.
[0046] In addition, in some embodiments, a user can click on or
hover a cursor over one of the circles, which causes a dialog box
510 to be displayed. The dialog box 510 includes various counts and
statistics for the particular agent-outcome pair.
[0047] FIGS. 6-8 are screenshots of user interfaces displaying an
average relative risk for a plurality of outcomes for one agent
relative to other agents in the same class of agents, in accordance
with several embodiments of the disclosure.
[0048] In FIG. 6, two different outcomes are shown along the
horizontal axis 604 (i.e., CHF (congestive heart failure) and
sudden death). Along the vertical axis 602, a single agent is shown
(i.e., "Lisinopril," an ACE inhibitor) along with an agent grouping
(i.e., "ACE-I"), which corresponds to all ACE inhibitors, including
the single agent shown separately. As shown in the example in FIG.
6 via circles 606, Lisinopril has a similar average relative risk
for CHF as all ACE inhibitors. However, as shown via circles 608,
Lisinopril has a higher average relative risk for sudden death
compared to all ACE inhibitors. This finding could cause physicians
and/or the FDA to place certain warnings on Lisinopril.
[0049] In FIG. 7, two different outcomes are shown along the
horizontal axis 704 (i.e., diarrhea and infections). Along the
vertical axis 702, five different agents from the same class are
shown. In this example, five different proton pump inhibitors are
shown. As shown in the example in FIG. 7 via circles 706, each of
the five proton pump inhibitors has a similar average relative risk
for diarrhea. However, with respect to infections, "Prevacid" has a
lower average relative risk as compared to the other four proton
pump inhibitors, as evidenced by the smaller size of circle 708. As
such, in one example, this information tends to show that Prevacid
may be superior to the other proton pump inhibitors since the risk
for diarrhea is roughly the same as for the other proton pump
inhibitors, but with a lower risk for infections.
[0050] In FIG. 8, five different outcomes are shown along the
horizontal axis 804. Along the vertical axis 802, two different
agents from the same class are shown. In this example, two
different antibiotics are shown, amoxicillin and azithromycin. As
shown in the example in FIG. 8 via circles 806, both antibiotics
have similar average relative risk for four of the five outcomes
shown. However, with respect to the outcome "sudden death,"
azithromycin has a relatively large average relative risk (as shown
via circle 808) and amoxicillin has a very low (or even calculated
"zero") average relative risk for sudden death. Further
investigation into this outcome can be performed by applying
filters, as shown in FIG. 9.
[0051] FIG. 9 is a screenshot of a user interface displaying an
average relative risk for two agents relative to a plurality of
outcomes, where the data is filtered by gender and age, in
accordance with an embodiment of the disclosure. In FIG. 9, five
different outcomes are shown along the horizontal axis 904. Along
the vertical axis 902, two different agents from the same class are
shown. In this example, two different antibiotics are shown,
amoxicillin and azithromycin. The agents are filtered first by
gender 906 and then by age band 908. For the particular outcome in
question, "Sudden Death" 910, filtering the data by gender and age
band reveals that azithromycin has a relatively high average
relative risk for sudden death for women ages 45-56. In one
example, the analysis and calculation shown in FIG. 9 may,
therefore, "exonerate" azithromycin from the risk of sudden death
for all males and for females outside the ages of 45-56.
[0052] In the additional embodiment illustrated in FIG. 10, the
system and method of the present disclosure implement a plurality
of modules for providing real-time processing and delivery of
calculated statistics about agents and outcomes. For example, the
statistics may be presented to a health care provider 110 via one
or more health care provider applications 756. In one
implementation, health care organization 100 includes a real-time
transfer module 758. The real-time transfer module 758 comprises
computer executable instructions encoded on a computer-readable
medium, such as a hard drive, of one or more server computers
controlled by the health care organization 100. Specifically, the
real-time transfer module 758 is configured to calculate
statistics, such a risk scores, for real-time information received
via a network 760 between the health care organization 100 and
external systems and applications. Preferably, the real-time
transfer module 758 employs a service-oriented architecture (SOA)
by defining and implementing one or more application
platform-independent software services to carry real-time data
between various systems and applications.
[0053] In one embodiment, the real-time transfer module 758
comprises web services 762, 764 that interface with external
applications for transporting the real-time data via a Simple
Object Access Protocol (SOAP) over HTTP (Hypertext Transfer
Protocol). The message ingest web service 762, for example,
receives real-time data that is subsequently processed in real-time
by the calculation engine 126. The message ingest web service 762
synchronously collects clinical data 114 from the medical insurance
carrier 112, patient-entered data 128, including patient-entered
clinical data 128, from the patient's PHR 108 and HRA 130, as well
as health reference information 122 and medical news information
124. In an embodiment, the message ingest web service 762 also
receives clinical data 114 in real-time from one or more health
care provider applications 756, such as an electronic medical
record (EMR) application and a disease management application. In
yet another embodiment, the message ingest service 762 receives at
least some of the patient-entered data 128 pursuant to the
patient's interaction with a nurse in disease management or an
integrated voice response (IVR) system. Incoming real-time data is
optionally stored in the medical database 118. Furthermore,
incoming real-time data associated with a given patient 102, in
conjunction with previously stored data at the database 118 and the
clinical rules 120, defines a rules engine run to be processed by
the calculation engine 126. Hence, the real-time transfer module
758 collects incoming real-time data from multiple sources and
defines a plurality of rules engine runs associated with one or
more agents (e.g., drugs) and one or more outcomes (e.g., adverse
events) for real-time processing.
[0054] The real-time transfer module 758 forwards the rules engine
runs to the calculation engine 126 to instantiate a plurality of
real-time rule processing sessions 772. The processing of the rule
processing sessions 772 by the calculation engine 126 can be
load-balanced across multiple logical and physical servers to
facilitate multiple and simultaneous requests for real-time
calculation of risk scores for one or more pairs of agents and
outcomes. In one embodiment, the load-balancing of sessions 772 is
accomplished in accordance with a J2EE (Java) specification. Each
rule processing session 772 makes calls to the medical database 118
by referring to a unique agent ID field for a corresponding agent
(e.g., drug) to receive data related to that agent for processing
of incoming real-time data. The results 1000 of the real-time
processing of the calculation engine may then be output to the
real-time transfer module 758 for distribution to one or more
health care provider applications 756 and/or to other servers
and/or services via message output service 764.
[0055] In sum, embodiments described herein provide a system and
method for pharmacovigilance, i.e., drug surveillance. The systems
and methods described herein may, in some implementations, be used
by drug companies or others (such as, for example, the FDA) to
monitor and test the safety and efficacy of drugs with respect to
certain outcomes. The systems and methods could be customized by
applying certain filters to analyze the data at finer
granularity.
[0056] Some embodiments compute the clinical context of a health
outcome or adverse event, rather than simply pairing a drug to a
health outcome of interest. In various implementations, this
includes analyzing the existence of an FDA-labeled indication for
the drug (i.e., on-label use versus off-label use), the relative
frequency of the symptoms for the outcome of interest (e.g.,
dizziness or palpitations may be symptoms of an arrhythmia), the
relative frequency of testing for the outcome of interest (e.g.,
Holter EKG monitoring may be used to detect arrhythmias) to
calibrate whether frequency of the outcome of interest (e.g., there
may appear to be more liver abnormalities just because more liver
function testing was being done), the relative frequency of the
outcome itself, and the relative frequency of "rescue treatments"
related to the outcome, e.g. for a drug that causes diarrhea, the
frequency of anti-diarrheal treatments (as opposed to episodes of
the diarrhea itself).
[0057] Embodiments aggregate this data in a manner not only to
detect new signals of drug-adverse event relationships, but can be
configured in a way to "exonerate" or provide data to suggest that
no agent-outcome relationship was detected, even though the sample
size suggests a high probability that the relationship. In this
way, drugs that may appear to be generating signals in the FDA AERS
(Adverse Event Reporting System) may be compared against the signal
confirmation versus exoneration findings calculated using
embodiments of the disclosure. For example, using the embodiments
disclosed herein, which are capable of updating on a near-real-time
basis by running analysis on a frequent repeated basis (e.g.,
weekly, monthly), signals are detected earlier and trend analysis
for emerging and/or fading signals can be performed more
quickly.
[0058] All references, including publications, patent applications
and patents, cited herein are hereby incorporated by reference to
the same extent as if each reference were individually and
specifically indicated to be incorporated by reference and were set
forth in its entirety herein.
[0059] The use of the terms "a" and "an" and "the" and similar
referents in the context of describing the disclosure (especially
in the context of the following claims) are to be construed to
cover both the singular and the plural, unless otherwise indicated
herein or clearly contradicted by context. The terms "comprising,"
"having," "including," and "containing" are to be construed as
open-ended terms (i.e., meaning "including, but not limited to,")
unless otherwise noted. Recitation of ranges of values herein are
merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range,
unless otherwise indicated herein, and each separate value is
incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the disclosure and does not
pose a limitation on the scope of the disclosure unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the disclosure.
[0060] Preferred embodiments of this disclosure are described
herein, including the best mode known to the inventors for carrying
out the disclosure. Variations of those preferred embodiments may
become apparent to those of ordinary skill in the art upon reading
the foregoing description. The inventors expect skilled artisans to
employ such variations as appropriate, and the inventors intend for
the disclosure to be practiced otherwise than as specifically
described herein. Accordingly, this disclosure includes all
modifications and equivalents of the subject matter recited in the
claims appended hereto as permitted by applicable law. Moreover,
any combination of the above-described elements in all possible
variations thereof is encompassed by the disclosure unless
otherwise indicated herein or otherwise clearly contradicted by
context.
* * * * *