U.S. patent application number 17/363242 was filed with the patent office on 2022-01-20 for method and system to optimize pharmacotherapy.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Xia CHEN, James GARSTECK, Dirk Ernest VON HOLLEN.
Application Number | 20220020463 17/363242 |
Document ID | / |
Family ID | 1000005740011 |
Filed Date | 2022-01-20 |
United States Patent
Application |
20220020463 |
Kind Code |
A1 |
CHEN; Xia ; et al. |
January 20, 2022 |
METHOD AND SYSTEM TO OPTIMIZE PHARMACOTHERAPY
Abstract
A method of optimizing pharmacotherapy includes monitoring one
or more characteristics of a patient associated with a condition of
the patient and evaluating a baseline severity of the condition of
the patient, recommending a selected therapy to the patient based
on a therapy efficacy-risk map, evaluating an actual severity of
the condition of the patient, evaluating an efficacy of the
selected therapy, evaluating a risk score of the selected therapy,
and updating the therapy efficacy-risk map based on the evaluated
efficacy and risk score of the selected therapy.
Inventors: |
CHEN; Xia; (Lorton, VA)
; VON HOLLEN; Dirk Ernest; (Clark, NJ) ; GARSTECK;
James; (Scottdale, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
|
NL |
|
|
Family ID: |
1000005740011 |
Appl. No.: |
17/363242 |
Filed: |
June 30, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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63054197 |
Jul 20, 2020 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 50/20 20180101;
G16H 40/67 20180101; G16H 50/30 20180101; G16H 10/20 20180101; A61B
5/4848 20130101; G16H 20/10 20180101; G16H 50/70 20180101 |
International
Class: |
G16H 20/10 20060101
G16H020/10; G16H 40/67 20060101 G16H040/67; G16H 50/30 20060101
G16H050/30; G16H 50/20 20060101 G16H050/20; G16H 50/70 20060101
G16H050/70; G16H 10/20 20060101 G16H010/20; A61B 5/00 20060101
A61B005/00 |
Claims
1. A method of optimizing pharmacotherapy, the method comprising:
monitoring one or more characteristics of a patient associated with
a condition of the patient and evaluating a baseline severity of
the condition of the patient; recommending a selected therapy to
the patient based on a therapy efficacy-risk map; evaluating an
actual severity of the condition of the patient; evaluating an
efficacy of the selected therapy; evaluating a risk score of the
selected therapy; and updating the therapy efficacy-risk map based
on the evaluated efficacy and risk score of the selected
therapy.
2. The method of claim 1, wherein the therapy efficacy-risk map
corresponds one or more therapies with one or more levels of
severity of the condition and includes efficacies and risk scores
of the one or more therapies, and wherein recommending the selected
therapy includes selecting a therapy from the therapy efficacy-risk
map corresponding to the severity of the condition of the patient
which maximizes efficacy and minimizes risk among the therapies
corresponding to the severity of the condition of the patient.
3. The method of claim 2, wherein updating the therapy
efficacy-risk map includes replacing the efficacy associated with
the selected therapy with the evaluated efficacy and replacing the
risk score associated with the selected therapy with the evaluated
risk score.
4. The method of claim 2, wherein the therapy efficacy-risk map is
a default map based on population data, and wherein updating the
therapy efficacy-risk map includes replacing the efficacy and risk
score associated in the default therapy efficacy-risk map with the
selected therapy with the evaluated efficacy and evaluated risk
score.
5. The method of claim 2, wherein the therapy that maximizes
efficacy and minimizes risk is determined using a composite cost
function.
6. The method of claim 1, wherein evaluating the efficacy of the
therapy is based on one or more of a comparison of the predicted
severity of the condition to the actual severity of the condition,
a sleep quality score, and an alertness score.
7. The method of claim 6, wherein evaluating the efficacy of the
therapy is based on a comparison of the baseline severity of the
condition to the actual severity of the condition, a sleep quality
score, and an alertness score, and wherein each of the comparison
of the baseline severity of the condition to the actual severity of
the condition, the sleep quality score, and the alertness score has
an associated weighting.
8. The method of claim 1, wherein monitoring one or more of the
characteristics of the patient includes monitoring one or more
objective physiological characteristics associated with Restless
Legs Syndrome (RLS), and wherein evaluating the actual severity of
the condition is based on the one or more objective physiological
characteristics.
9. The method of claim 1, wherein monitoring one or more
characteristics of the patient includes monitoring one or more of
side effects, augmentation metrics, and stressors, and wherein
evaluating the risk score of the selected therapy is based on one
or more of the monitored side effects, augmentation metrics, and
stressors.
10. The method of claim 9, wherein the side effects are monitored
using subjective inputs from the patient and natural language
processing of subjective inputs.
11. The method of claim 1, recommending the selected therapy to the
patient includes determining a risk score associated with a current
therapy of the patient and selecting the current therapy of the
patient if the risk score associated with the current therapy is
below a risk score threshold.
12. The method of claim 1, wherein the selected therapy includes a
drug type, a dosage, and a dose time.
13. The method of claim 1, wherein the condition is RLS.
14. A system for optimizing pharmacotherapy, the system comprising:
one or more sensing modules structured to monitor one or more
characteristics of a patient associated with a condition of the
patient; a baseline severity module structured to determine a
severity of the condition of the patient; a selected therapy module
structured to store a recommended therapy to the patient based on a
therapy efficacy-risk map; an actual severity evaluation module
structured to evaluate an actual severity of the condition of the
patient; a therapy efficacy evaluation module structured to
evaluate an efficacy of the recommended therapy; a risk evaluation
module structured to evaluate a risk score of the recommended
therapy; and a therapy efficacy-risk map update module structured
to update the therapy efficacy-risk map based on the evaluated
efficacy and risk score of the recommended therapy.
15. A non-transitory computer readable medium storing one or more
programs, including instructions, which when executed by a
computer, causes the computer to perform a method of optimizing
pharmacotherapy, the method comprising: monitoring one or more
characteristics of a patient associated with a condition of the
patient and evaluating a baseline severity of the condition of the
patient; recommending a selected therapy to the patient based on a
therapy efficacy-risk map; evaluating an actual severity of the
condition of the patient; evaluating an efficacy of the selected
therapy; evaluating a risk score of the selected therapy; and
updating the therapy efficacy-risk map based on the evaluated
efficacy and risk score of the selected therapy.
Description
CROSS-REFERENCE TO PRIOR APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 63/054,197, filed on 20 Jul. 2020. This application
is hereby incorporated by reference herein.
BACKGROUND OF THE INVENTION
1. Field of the Invention
[0002] The disclosed concept generally relates to therapy and, more
particularly, to optimizing pharmacotherapy.
2. Description of the Related Art
[0003] Restless Legs Syndrome (RLS) has been estimated to impact
between 3.9 to 14.3% of the adult population. Diagnosis of RLS
requires the patient to express an urge to move the legs, typically
associated with accompanying unpleasant sensations that begin to
worsen as the patient remains still for an extended period of time.
Typically the urge to move the legs is worse during the evening and
night than during the day (most often at sleep onset) and is
temporarily relieved by movement. RLS symptoms typically need to
occur at least 2x/week and have significant side effects (e.g.
daytime sleepiness, mood issues, etc.) in order to be considered
for clinical treatment. The prevalence of RLS is significantly
higher in women than in men and is a common disorder during
pregnancy, especially toward the third trimester.
[0004] RLS can be treated with iron supplementation, dopamine
agonists, alpha-2-delta calcium channel ligands, as well as
non-pharmacologic interventions, like ensuring good sleep hygiene,
avoiding caffeine, and getting regular low-intensity exercise.
Also, avoiding exacerbating factors, like sleep deprivation or
antidepressants.
[0005] Patients with milder symptoms that occur intermittently may
be treated with non-pharmacologic therapy. For more severe
symptoms, or when non-pharmacologic therapy is not successful,
pharmacotherapy (i.e., drug therapy) is often used. Although there
is continuing research on the best methods of treatment of RLS, the
efficacy of pharmacotherapy tends to decrease over time and/or lead
to augmentation. Lowered efficacy and augmentation may result in a
physician altering drug dosing and/or dose timing, or changing the
drug completely. Some subjectivity or trial and error may be
involved in altering pharmacotherapy regimes for an RLS
patient.
[0006] There remains room for improvement in pharmacotherapy for
RLS or other conditions.
SUMMARY OF THE INVENTION
[0007] Accordingly, it is an object of the disclosed concept to
provide a system and method to optimize pharmacotherapy treatment
of conditions such as RLS or other conditions.
[0008] As one aspect of the disclosed concept, a method of
optimizing pharmacotherapy comprises: monitoring one or more
characteristics of a patient associated with a condition of the
patient and evaluating a baseline severity of the condition of the
patient; recommending a selected therapy to the patient based on a
therapy efficacy-risk map; evaluating an actual severity of the
condition of the patient; evaluating an efficacy of the selected
therapy; evaluating a risk score of the selected therapy; and
updating the therapy efficacy-risk map based on the evaluated
efficacy and risk score of the selected therapy.
[0009] As one aspect of the disclosed concept, a system for
optimizing pharmacotherapy comprises: one or more sensing modules
structured to monitor one or more characteristics of a patient
associated with a condition of the patient; a baseline severity
module structured to determine a severity of the condition of the
patient; a recommendation module structured to recommend a selected
therapy to the patient based on a therapy efficacy-risk map; an
actual severity evaluation module structured to evaluate an actual
severity of the condition of the patient; a therapy efficacy
evaluation module structured to evaluate an efficacy of the
selected therapy; a risk evaluation module structured to evaluate a
risk score of the selected therapy; and a therapy efficacy-risk map
update module structured to update the therapy efficacy-risk map
based on the evaluated efficacy and risk score of the selected
therapy.
[0010] As one aspect of the disclosed concept, a non-transitory
computer readable medium storing one or more programs, including
instructions, which when executed by a computer, causes the
computer to perform a method of optimizing pharmacotherapy. The
method comprises: monitoring one or more characteristics of a
patient associated with a condition of the patient and evaluating a
baseline severity of the condition of the patient; recommending a
selected therapy to the patient based on a therapy efficacy-risk
map; evaluating an actual severity of the condition of the patient;
evaluating an efficacy of the selected therapy; evaluating a risk
score of the selected therapy; and updating the therapy
efficacy-risk map based on the evaluated efficacy and risk score of
the selected therapy.
[0011] These and other objects, features, and characteristics of
the disclosed concept, as well as the methods of operation and
functions of the related elements of structure and the combination
of parts and economies of manufacture, will become more apparent
upon consideration of the following description and the appended
claims with reference to the accompanying drawings, all of which
form a part of this specification, wherein like reference numerals
designate corresponding parts in the various figures. It is to be
expressly understood, however, that the drawings are for the
purpose of illustration and description only and are not intended
as a definition of the limits of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 is a flowchart of a method of optimizing
pharmacotherapy in accordance with an example embodiment of the
disclosed concept;
[0013] FIG. 2 is schematic diagram of a system for optimizing
pharmacotherapy in accordance with an example embodiment of the
disclosed concept;
[0014] FIG. 3 is a schematic diagram of a system in a build phase
for evaluating risk of pharmacotherapy in accordance with an
example embodiment of the disclosed concept; and
[0015] FIG. 4 is a schematic diagram of a system in a deployment
phase for evaluating risk of pharmacotherapy in accordance with an
example embodiment of the disclosed concept.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0016] As required, detailed embodiments of the disclosed concept
are disclosed herein; however, it is to be understood that the
disclosed embodiments are merely exemplary of the invention, which
may be embodied in various forms. Therefore, specific structural
and functional details disclosed herein are not to be interpreted
as limiting, but merely as a basis for the claims and as a
representative basis for teaching one skilled in the art to
variously employ the disclosed concept in virtually any
appropriately detailed structure.
[0017] As used herein, the singular form of "a", "an", and "the"
include plural references unless the context clearly dictates
otherwise.
[0018] Directional phrases used herein, such as, for example and
without limitation, top, bottom, left, right, upper, lower, front,
back, and derivatives thereof, relate to the orientation of the
elements shown in the drawings and are not limiting upon the claims
unless expressly recited therein.
[0019] In accordance with an embodiment of the disclosed concept,
optimized pharmacotherapy recommendations are provided. For
example, a personalized therapy efficacy-risk map is generated to
recommend therapies corresponding to the severity of the patient's
condition. Risks and efficacies of the therapies are included in
the therapy efficacy-risk map. In building the personalized therapy
efficacy-risk map, characteristics of a patient are continuously
monitored and the risk and efficacy of treatments are evaluated. As
the patient is continuously monitored and therapies are tried, the
personalized efficacy-risk map is updated to reflect efficacies and
risks of the therapies, as well as changes in efficacies and risks
of the therapies. In this manner, pharmacotherapy efficacy and risk
reduction can be optimized.
[0020] FIG. 1 is a flowchart of a method of optimizing
pharmacotherapy in accordance with an example embodiment of the
disclosed concept. The method begins at 100 where a number of
behaviors and/or biometrics of a patient are monitored. The
monitored behaviors and/or biometrics may include RLS objective
metrics such as, without limitation, leg movement frequency,
intensity, and duration. The monitored behaviors and/or biometrics
may include sleep metrics such as, without limitation, sleep
efficiency, wake after sleep onset, sleep onset latency, total
sleep time, a number of awakenings, and sleep stages. The monitored
behaviors and/or biometrics may include alertness metrics such as,
without limitation, nap frequency, nap duration, movement, movement
intensity, and behaviors. The monitored behaviors and/or biometrics
may include behaviors such as, without limitation, sleep hygiene,
caffeine intake frequency and timing, and medication intake. The
monitored behaviors and/or biometrics may be monitored with one or
more sensors such as, without limitation, under mattress and/or
wearable sensors. The monitored behaviors and/or biometrics may
also be monitored by patient provided subjective responses. The
patient response may be gathered through any suitable means such
as, without limitation, a digital survey, a chat bot, or any other
suitable means for gathering subjective responses.
[0021] As part of the monitoring behaviors and biometrics, a
severity of the patient's RLS may be determined. In an embodiment,
the severity may be defined by pre-determined levels (i.e., mild,
moderate, severe, and very severe) based on the International RLS
Rating Scale. Objective RLS related physiological measures such as
movement intensity, duration, and frequency may be used to
determine the patient's RLS severity level.
[0022] At 102 a therapy is recommended to the patient. As part of
recommending a therapy, a therapy efficacy-risk map is generated.
The therapy efficacy-risk map corresponds a therapies to the
severity of the patient's condition. Additionally, the therapy
efficacy-risk map provides an efficacy and a risk of the therapy.
An example therapy efficacy-risk map is provided below in Table 1
and an example of a more detailed therapy efficacy-risk map is
provided below in Table 2.
TABLE-US-00001 TABLE 1 RLS Severity Therapy Past Efficacy Past Risk
Score Level ID (F.sub.1) (F.sub.2) very severe 7 78 10 very severe
9 65 5 moderate 4 80 15 moderate 11 70 15 moderate 2 50 5 severe 1
68 15 severe 8 75 25 severe 6 60 20 mild 3 90 20 mild 5 84 16 mild
10 77 20
TABLE-US-00002 TABLE 2 RLS Past Severity Therapy Past Risk Dose
Dose Level ID Efficacy Score Drug mg/d Time Side Effects
Augmentation very 7 78 10 Pramipexole 0.25 8:00 PM none mild severe
very 9 65 5 Pramipexole 0.5 7:00 PM somnolence mild severe moderate
4 80 15 Ropinirole 0.5 8:00 PM none none moderate 11 70 15
Gabapentin 600 mg 8:00 PM somnolence, mild enacarbil dizziness
moderate 2 50 5 Ropinirole 0.25 8:00 PM nausea moderate severe 1 68
15 Ropinirole 0.25 8:00 PM nausea mild severe 8 75 25 Pramipexole
0.25 7:00 PM somnolence moderate severe 6 60 20 Ropinirole 0.5 7:00
PM nausea moderate mild 3 90 20 Ropinirole 0.25 8:00 PM none none
mild 5 84 16 Ropinirole 0.5 7:00 PM nausea none mild 10 77 20
Gabapentin 600 mg 8:00 PM none mild enacarbil
[0023] As shown in the example therapy efficacy-risk map, each
severity level includes a number of corresponding therapies (e.g.,
very severe has corresponding therapy IDs 7 and 9). In an example
embodiment, each therapy ID corresponds to a particular drug, dose,
and dose time. For example, therapy ID 7 corresponds to a 0.25 mg/d
dose of Pramipexole at 8:00 PM and therapy ID 9 corresponds to a
0.5 mg/d dose of Pramipexole at 7:00 PM. Each therapy also has a
corresponding efficacy and risk score. The efficacy of score
represents the efficacy of the therapy and the risk score
represents risks such as side effects and augmentation. Evaluation
of the efficacy and risk of therapies will be described in more
detail. When initially generated, the therapy efficacy-risk map may
use population data such as clinical study results, review
articles, or questionnaires to generate the efficacy and risk
scores of therapies. However, as will be described in more detail
herein, through continuous monitoring of the patient through
various therapies, the therapy efficacy-risk map is updated to be
personalized to the patient. For example, the efficacy and risk
scores of therapies may be evaluated and updated based on patient
monitoring.
[0024] In an embodiment, the therapy recommended to the patient
will be selected from the therapies corresponding to the severity
of the patient's condition. The selected therapy will maximize
efficacy while minimizing the risk score. In an embodiment, to
maximize efficacy while minimizing the risk score, a composite cost
function may be used such as
F(x)=W.sub.1*F.sub.1(x)-W.sub.2F.sub.2(x) where x is a vector in
the solution space (i.e., it includes medication, dosing, dosing
timing and duration), F.sub.1(x) is the corresponding efficacy of
the therapy, F.sub.2(x) is the corresponding risk score of the
therapy, W.sub.1 is a weighting of the efficacy, and W.sub.2 is a
weighting of the risk score. By finding the maximum of the
composite cost function, the corresponding solution x will be the
best therapy in the list (i.e., the therapy with maximum efficacy
and minimum risk). While using a composite cost function is one
example of selecting the recommended therapy to maximize efficiency
and minimize risk, it will be appreciated that other suitable
methods may also be used to select the recommended therapy.
[0025] In some example embodiments, a risk score threshold may be
employed in determining a recommended therapy. For example, the
patient's current therapy may be recommended if the risk score is
below the risk score threshold. If the risk score of the patient's
current therapy is above the risk score threshold, a new therapy
may be recommended to the patient. The new therapy may be
recommended using the composite cost function described above or
any other suitable method.
[0026] At 104 the actual severity of the patient's condition during
therapy is evaluated. In an embodiment, the actual severity of the
patient's condition is evaluated based on monitored biometrics
during a specified monitoring period. For example, objective RLS
physiological measures such as, without limitation, movement
intensity, duration, and frequency may be monitored using one or
more sensors. The physiological measures may be monitored while the
recommended therapy is applied to the patient. For example, the
physiological measures may be monitored at night when the therapy
is applied to the patient. From the monitored physiological
measures, the actual severity of the patient's condition may be
evaluated.
[0027] At 106, the efficacy of the therapy is evaluated. The
efficacy of the therapy may be based on a number of factors such
as, without limitation, a reduction score of severity, a sleep
quality score, and an alertness score. The reduction score may
include a comparison of the baseline severity (e.g., determined
before therapy) and the actual severity (e.g., determined while a
therapy is in use). The reduction score may be based on a ratio of
the actual to baseline severity or a difference between the
baseline and actual severity. In an example embodiment, the
reduction score may be within a range of 0 to 100, with a score of
100 being the most effective.
[0028] The sleep quality score may be based on a number of sleep
metrics such as, without limitation, sleep efficiency, wake after
sleep onset, number of awakenings, sleep onset latency, deep sleep
percentage, and total sleep time. The sleep metrics may be
monitored while the therapy is in use. In an example embodiment,
the sleep quality score may be within a range of 0 to 100, with a
score of 100 being the most effective.
[0029] The alertness score may be based on a number of metrics such
as, without limitation, number of naps, duration of naps,
movements, movement intensity, and behaviors. The metrics for the
alertness score may be monitored during the day. In an example
embodiment, the alertness score may be within a range of 0 to 100,
with a score of 100 being the most effective.
[0030] While a severity reduction score, sleep quality score, and
alertness score are provided as examples of factors that can be
used to evaluate the efficacy of a therapy, it will be appreciated
that different factors may be employed. The various factors may be
weighted differently to determine the therapy efficacy. For
example, in an embodiment, the reduction score and sleep quality
score are weighted higher than the alertness score. However, it
will be appreciated that different weightings may be used without
departing from the disclosed concept.
[0031] At 108, the risk score of the therapy is evaluated. As part
of monitoring the patient, factors associated with the risk score
may be monitored. Factors associated with the risk score may
include therapy side effects (e.g., dizziness, somnolence, nausea,
etc.), therapy augmentation metrics (e.g., movement intensity,
duration and frequency), and relevant stressors (e.g., health
condition changes and medication changes). In an embodiment, the
side effects may be monitored via subjective patient inputs such as
text inputs provided to a chat bot, web chat bubble, or other
suitable input mechanism, and processed using natural language
processing or other suitable processing mechanisms.
[0032] For initial risk scores, population data such as clinical
guidelines or clinical studies may be used to determine the risk
score. As the patient is continuously monitored and uses therapies,
the initial risk scores may be replaced with personalized risk
scores based on the monitoring and risk evaluation of the
patient.
[0033] At 110, the therapy efficacy-risk map is updated. In an
embodiment, the efficacy and/or risk scores evaluated based on
monitoring of the patient replace their corresponding existing
efficacy and/or risk scores in the therapy efficacy-risk map. In
this manner, over time as the patient is monitored and uses
therapies, the initial therapy efficacy-risk map based on
population data is transformed into a personalized efficacy-risk
map. The personalized therapy efficacy-risk map captures efficacy
and risks of therapies personal to the patient, such as when a
therapy is more or less effective or present more or less risk to
the patient than what the population data suggests. Additionally,
through continuous monitoring and evaluation, changes in efficacy
and risk of therapies over time are captured in the therapy
efficacy-risk map, such as when augmentation begins to appear after
using a therapy for a time or when a therapy loses some efficacy
after a period of use. In this manner, the therapy recommended to
the patient can continually be optimized.
[0034] In some embodiments, one or more steps of the method may be
omitted or modified. For example, the method may have a build phase
where therapies are recommended by a therapy provider. As the
patient uses therapies, the efficacies and risks of the therapies
may be evaluated through monitoring of the patient and the therapy
efficacy-risk map for the patient evolves from a default
therapy-severity map to a personalized therapy-severity map. Over
time, as the patient has tried different therapies, the method may
move to a deployment phase where the personalized therapy
efficacy-risk map is used to recommend a therapy.
[0035] The method may be implement by one or more computing
devices, memories, and sensors. For example, monitoring the
behaviors and/or biometrics of the patient may be implemented with
sensors such as under mattress and/or wearable sensors as well as
any suitable computing device for inputting subjective patient
responses. The method may be implemented on a localized or
distributed system. For example, one or more parts of the method
may be implemented on a user device, such as a mobile phone,
tablet, or computer, as well, parts of the method may be
implemented on a remote device such as a server. For example, the
personalized therapy efficacy-risk map may be stored on the user
device or on a server. Some examples of systems for implementing
parts of the method will be described in more detail herein.
[0036] FIG. 2 is schematic diagram of a system 200 for optimizing
pharmacotherapy in accordance with an example embodiment of the
disclosed concept. The system 200 includes an alertness sensing
module 202, a sleep metrics sensing module 204, a baseline RLS
severity module 206, an RLS metrics monitoring module 208, a
selected therapy module 210, a therapy efficacy evaluation module
212, an actual RLS severity evaluation module 214, a build
personalized therapy efficacy-risk map module 216, and an RLS
therapy risk evaluation module 218.
[0037] The alertness sensing module 202 may sense metrics
associated with alertness such as nap frequency, nap duration,
movement, movement intensity, and behaviors. The alertness sensing
module 202 may include or receive outputs from one or more sensors
such as under mattress and/or wearable sensors. The sleep metrics
sensing module 204 may sense metrics associated with sleep quality
such as sleep efficiency, wake after sleep onset, sleep onset
latency, total sleep time, number of awakenings, and sleep stages.
The sleep metrics sensing module may include or receive outputs
from one or more sensors such as under mattress and/or wearable
sensors. The RLS metrics monitoring module 208 may sense objective
metrics associated with RLS such as leg movement frequency,
intensity, and duration. The RLS metrics monitoring module 208 may
include or receive output from one or more sensors such as under
mattress and/or wearable sensors. Together, the alertness sensing
module 202, sleep metrics sensing module 204, and RLS metrics
monitoring module 208 may be used to monitor biometrics of a
patient.
[0038] The baseline RLS severity module 206 is structured to
estimate a baseline severity of the patient's RLS. In an
embodiment, the baseline severity may be defined by pre-determined
levels (i.e., mild, moderate, severe, and very severe) based on the
International RLS Rating Scale. Objective RLS related physiological
measures such as movement intensity, duration, and frequency may be
used to determine the patient's RLS severity level. The baseline
RLS severity module 206 may include or be connected to one or more
sensors structured to monitor the objective RLS related
physiological measures.
[0039] The selected therapy module 210 may identify the selected
therapy for the patient. For example, the selected therapy module
210 may receive information on the selected therapy. The actual RLS
severity module 214 is structured to estimate the actual severity
of the patient's RLS. The actual severity may be estimated based on
the objective RLS metrics monitored by the RLS metrics monitoring
module 208.
[0040] The therapy efficacy evaluation module 212 is structured to
evaluate the efficacy of the therapy. The efficacy may be evaluated
as described above with respect to FIG. 1. The RLS therapy risk
evaluation module 218 is structured to evaluate the risk of the
selected therapy. The risk may be evaluated as described above with
respect to FIG. 1.
[0041] The build personalized therapy efficacy-risk map module 216
is structured to build a personalized therapy risk-efficacy map for
the patient based on the actual severity, the selected therapy, the
evaluated efficacy of the therapy, and the evaluated risk of the
therapy. As described above, the therapy efficacy-risk map
corresponds therapies with levels of severity and includes an
evaluation of the efficacy of the therapy and an evaluation of the
risk of the therapy. The initial therapy efficacy-risk map may be a
default map generated by population data. Over time, the therapy
efficacy-risk map may be updated based on monitoring and evaluation
of the patient to become a personalized therapy efficacy-risk
map.
[0042] The system 200 may be implemented as one or more computing
devices and sensors. The system 200 may be a localized or
distributed system.
[0043] In some embodiments, the selected therapy module 210 may
also store recommendations for therapy. The recommendation may be
based on maximizing efficacy and minimizing risk, as is described
above with respect to FIG. 1.
[0044] FIG. 3 is a schematic diagram of a system 300 in a build
phase for the personal risk history of pharmacotherapy in
accordance with an example embodiment of the disclosed concept. The
system 300 includes an RLS side effects monitoring module 302, an
RLS augmentation metrics monitoring module 304, a stressor sensing
module 306, a population risk history module 308, an RLS therapy
risk evaluation module 310, and a build personal risk history
module 312.
[0045] The RLS side effects monitoring module 302 is structured to
monitor side effects associated with the selected therapy. The RLS
side effects monitoring module 302 may include or be connected to
one or more devices structured to obtain subjective inputs from the
patient associated with side effects. For example and without
limitation, the devices may include one more computing devices with
a user interface to prompt a user to provide subjective inputs such
as free text inputs (e.g., via a chat bot or web chat bubble)
related to side effects. The devices may also include a natural
language processing module to process the inputs. For example, the
devices may be structured to prompt a user to answer a question
associated with side effects and to process the answer to determine
whether a side effect is present.
[0046] The RLS augmentation metrics monitoring module 304 is
structured to monitor metrics associated with augmentation
associated with the selected therapy. The augmentation metrics may
include, without limitation, movement intensity, duration and
frequency. The RLS augmentation metrics monitoring module may
include or be connected to one or more sensors structured to
monitor the augmentation metrics.
[0047] The stressor sensing module 306 is structured to monitor
stressors associated with the selected therapy. The stressors may
include, without limitation, health condition changes and
medication changes. The stressor sensing module 306 may include or
be connected to one or more sensors structured to monitor the
stressors. The stressor sensing module 306 may also include or be
connected to one or more devices structured to receive inputs
associated with the stressors.
[0048] The population risk history module 308 is structured to
gather and/or provide a risk history of a selected therapy from
population data such as clinical studies or guidelines. As has been
described herein, an initial risk score associated with a therapy
may be generated from population data. The population risk history
module 308 may include or be connected to a storage device
including risks associated with therapies.
[0049] The RLS therapy risk evaluation module 310 is structured to
determine a risk score of a therapy based on one or more of the
side effects, augmentation metrics, stressors, and risk history.
The build personal risk history module 312 is structured to build
and store a personal risk history based on risk scores determined
by the RLS therapy risk evaluation module 310. For example, the
build personal risk history module 312 may include or be connected
to a storage device that stores therapies and their associated risk
scores. Initially, the build personal risk history module 312 may
use an initial risk history based on population data. As the
patient is monitored and evaluated, the build personal risk history
module 312 may update the initial risk history by replacing initial
risk scores with risk scores determined by the RLS therapy risk
evaluation module.
[0050] The system 300 may be implemented as one or more computing
devices and sensors. The system 300 may be a localized or
distributed system. In an embodiment, the system 300 may be used in
a build phase when little monitoring of the patient has been
performed and population risk history has to be used to evaluate
therapy risk. Over time, the system 300 may transition to or be
replaced by a system in a deployment phase where the personal risk
history is more developed based on monitoring and evaluation of the
patient. An example of a system in the deployment phase will be
described in more detail with respect to FIG. 4.
[0051] In some embodiments of the disclosed concept, the system 300
may include one or more interfaces. The interfaces may be, for
example and without limitations, user interfaces available in
device applications to a patient and/or care provider. In some
embodiments, the interfaces may provide for input from multiple
sources. The sources may include direct input from the user (e.g.,
questionnaires, free text input, etc.), direct input from a care
provider, data such as articles and studies, and input from
sensors. Sensors may include sensors on a user device (e.g.,
temperature, time, position, weather, or other sensors), wearable
sensors (e.g., watch, ring, patch, etc.), or other sensors
positioned within a home or other area (e.g., temperature,
mattress, blanket, pillow, etc.). The sensors may be used to gather
real-time data associated with the patient and their environment.
The information from multiple sources may be used to continuously
monitor the patient for symptoms and exacerbations associated with
their condition and/or their current therapy. The system 300 may
also include one or more storage and processing device to store and
evaluate the collected data.
[0052] FIG. 4 is a schematic diagram of a system 400 in a
deployment phase for evaluating risk of pharmacotherapy in
accordance with an example embodiment of the disclosed concept. The
system includes an RLS side effects monitoring module 402, an RLS
augmentation metrics monitoring module 404, and a stressor sensing
module 406 which operate similar to their corresponding components
in FIG. 3. The system 400 also includes a personal risk history
module 408 and an RLS therapy risk evaluation module 410.
[0053] The personal risk history module 408 is structured to store
a personal risk history associated with the patient. The personal
risk history may be the same or similar to one built using the
system 300 of FIG. 3, and includes risk scores determined based on
monitoring and evaluation of the patient. The RLS therapy risk
evaluation module 410 is structured to determine a risk score
associated with a selected therapy based on one or more of the side
effects, augmentation metrics, stressors, and the personal risk
history. The RLS therapy risk evaluation module 410 is also
structured to update the personal risk history with each new
determined risk score. In this manner, the personal risk history is
continually update as the patient is monitored and evaluated while
utilizing therapies.
[0054] The risk scores determined by the system 400 may be employed
in the systems or methods of FIG. 1 or 2 to build and/or update the
therapy efficacy-risk map. For example, the risk score determined
based on monitoring and evaluation of the patient may be used to
replace an existing risk score in the therapy efficacy-risk
map.
[0055] While some embodiments have been described above related to
optimizing treatment of RLS, it will be appreciated that the
disclosed concept is also applicable to treatment of other
conditions. Additionally, while some embodiments have been
described in association with pharmacotherapy, it will be
appreciated that the disclosed concept may also be applied to
different types of therapies.
[0056] It will also be appreciated that an embodiment of the
disclosed concept may be embodied on a non-transitory computer
readable medium storing one or more programs, including
instructions, which when executed by a computer, causes the
computer to perform the method described with respect to FIG.
1.
[0057] Although the invention has been described in detail for the
purpose of illustration based on what is currently considered to be
the most practical and preferred embodiments, it is to be
understood that such detail is solely for that purpose and that the
invention is not limited to the disclosed embodiments, but, on the
contrary, is intended to cover modifications and equivalent
arrangements that are within the spirit and scope of the appended
claims. For example, it is to be understood that the present
invention contemplates that, to the extent possible, one or more
features of any embodiment can be combined with one or more
features of any other embodiment.
[0058] In the claims, any reference signs placed between
parentheses shall not be construed as limiting the claim. The word
"comprising" or "including" does not exclude the presence of
elements or steps other than those listed in a claim. In a device
claim enumerating several means, several of these means may be
embodied by one and the same item of hardware. The word "a" or "an"
preceding an element does not exclude the presence of a plurality
of such elements. In any device claim enumerating several means,
several of these means may be embodied by one and the same item of
hardware. The mere fact that certain elements are recited in
mutually different dependent claims does not indicate that these
elements cannot be used in combination.
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