Este
livro
traz
trabalhos
sobre
um
dos
sistemas
vitais
para
nossa
vida,
responsável
pelas
principais
incidências
de
mortalidade
e
morbidades,
o
sistema
cardiovascular.
Ele
possui
coração
como
ator
principal,
caracterizado
por
batidas
frequências
Abstract
Study
Objectives
To
evaluate
wearable
devices
and
machine
learning
for
detecting
sleep
apnea
in
patients
with
stroke
at
an
acute
inpatient
rehabilitation
facility
(IRF).
Methods
A
total
of
76
individuals
wore
a
standard
home
test
(ApneaLink
Air),
multimodal,
wireless
sensor
system
(ANNE),
research-grade
actigraphy
device
(ActiWatch)
least
1
night
during
their
first
week
after
IRF
admission
as
part
larger
clinical
trial.
Logistic
regression
algorithms
were
trained
to
detect
using
biometric
features
obtained
from
the
ANNE
sensors
ground
truth
rating
ApneaLink
Air.
Multiple
evaluated
different
combinations
detection
criteria
based
on
apnea–hypopnea
index
(AHI
≥
5,
AHI
15).
Results
Seventy-one
(96%)
participants
multiple
nights.
In
contrast,
only
48
(63%)
could
be
successfully
assessed
obstructive
by
ApneaLink;
28
(37%)
refused
testing.
The
best-performing
model
utilized
photoplethysmography
(PPG)
finger-temperature
moderate-severe
15),
88%
sensitivity
positive
likelihood
ratio
(LR+)
44.00.
This
was
tested
additional
nights
data
achieving
71%
(10.14
LR+)
when
considering
each
independently
86%
accuracy
averaging
multi-night
predictions.
Conclusions
research
demonstrates
feasibility
accurately
early
recovery
process
techniques.
These
findings
can
inform
future
efforts
improve
post-stroke
disorders,
thereby
enhancing
patient
long-term
outcomes.
Clinical
Trial
SIESTA
(Sleep
Inpatients:
Empower
Staff
Act)
Acute
Stroke
Rehabilitation,
https://clinicaltrials.gov/study/NCT04254484?term=SIESTA&checkSpell=false&rank=1,
NCT04254484
BMJ Open,
Год журнала:
2019,
Номер
9(7), С. e030559 - e030559
Опубликована: Июль 1, 2019
Rationale
Sleep-disordered
breathing
(SDB)
is
strongly
linked
to
adverse
cardiovascular
outcomes
(cardiovascular
diseases
(CVD)).
Whether
heart
rate
changes
measured
by
nocturnal
R-R
interval
(RRI)
dips
(RRI
dip
index
(RRDI))
adversely
affect
the
CVD
unknown.
Objectives
To
test
whether
RRDI
predicts
incidence
and
mortality
in
Wisconsin
Sleep
Cohort
study
(WSCS),
independent
of
known
effects
SDB
on
beat-to-beat
variability.
Methods
The
analysed
electrocardiograph
obtained
from
polysomnography
assess
total
(the
number
RRI
divided
recording
time)
sleep
time).
A
composite
risk
as
a
function
was
estimated
Cox
proportional
hazards
WSCS.
Results
sample
consisted
569
participants
WSCS
with
no
prior
at
baseline
were
followed
up
for
15
years.
Nocturnal
(10-unit
change)
associated
event(s)
(HR,
1.24
per
10-unit
increment
(95%
CI
1.10
1.39),
p<0.001).
After
adjusting
demographic
factors
(age
58±8
years
old;
53%
male;
body
mass
31±7
kg/m
2
),
apnoea–hypopnoea
(AHI
4%),
individuals
highest
category
(≥28
vs<15
dips/hour)
had
significant
HR
increased
7.4(95%
1.97
27.7),
p=0.003).
significantly
new-onset
1.21
1.09
1.35),
p<0.001)
which
remained
after
factors,
AHI
4%,
hypoxemia
other
comorbidities.
Conclusion
Increased
morbidity,
frequency
higher
men
than
women,
but
not
women.
Expert Review of Molecular Diagnostics,
Год журнала:
2021,
Номер
21(2), С. 223 - 233
Опубликована: Янв. 6, 2021
Introduction:
This
study
aimed
to
define
and
characterize
current
literature
describing
salivary
biomarker
changes
with
the
goal
of
improving
diagnosis
treatment
outcomes
for
sleep
apnea.Area
Covered:
A
search
six
databases
yielded
401
peer-reviewed
articles
published
through
October
2019
corresponded
221
unique
references
following
deduplication.
Twenty
studies
were
selected.
The
sample
size
ranged
from
17
99.
samples
mostly
whole
saliva
selected
glandular
areas.Expert
Opinion:
Most
targeted
focused
on
level
cortisol
ɑ-amylase.
One
used
RNA
transcriptome
analysis
96
genes.
Only
two
explored
novel
targets
using
mass
spectrometry.
ɑ-amylase,
myeloperoxidase,
IL-6
among
those
biomarkers
found
associated
OSA.
Cytokeratin,
CystatinB,
calgranulin
A,
alpha-2-HS-glycoprotein
are
upregulated
in
OSA
patients
based
non-targeting
Salivary
ɑ-amylase
others
appeared
be
severity
treatment.
There
inconsistencies
collection
processing
protocols.
More
needed
exploring
examine
if
these
capable
diagnosing
monitoring
proteomics
or
transcriptomics.
have
a
potential
noninvasive
measure
disease
outcome
apnea.
Journal of Clinical Sleep Medicine,
Год журнала:
2020,
Номер
16(10), С. 1753 - 1760
Опубликована: Июль 9, 2020
This
analysis
determined
∼5-year
incident
hypertension
rates
using
the
2017
American
College
of
Cardiology/American
Heart
Association
blood
pressure
(BP)
guidelines
in
individuals
with
obstructive
sleep
apnea
(OSA)
hypopneas
defined
by
a
≥
3%
oxygen
desaturation
or
arousal
but
not
hypopnea
criterion
4%
(4%
only).Data
were
analyzed
from
participants
Sleep
Health
Study
exam
2
(n
=
1219)
who
normotensive
(BP
≤
120/80
mm
Hg)
at
1.
The
AHI
1
was
classified
into
4
categories
OSA
severity:
<
5,
5
15,
15
30,
and
30
events/h
both
only
definitions.
Three
definitions
hypertension-elevated
BP
(>
Hg),
stage
130/80
140/90
Hg)-were
used
to
determine
incidence
2.Five-year
follow-up
available
for
476
as
having
standard
Incident
Association-defined
these
discordantly
15%
(elevated
BP),
(stage
1),
6%
2).
Hypertensive
medications
normotensive.
overall
rate
least
an
elevated
40%
(191/476)
those
criterion.Use
definition
resulted
failure
identify
significant
number
eventually
developed
could
have
benefited
earlier
diagnosis
treatment.
Frontiers in Neurology,
Год журнала:
2019,
Номер
10
Опубликована: Авг. 13, 2019
Context:
Accurate
discrimination
between
obstructive
and
central
hypopneas
requires
quantitative
assessments
of
respiratory
effort
by
esophageal
pressure
(OeP)
measurements,
which
preclude
widespread
implementation
in
sleep
medicine
practice.
Mandibular
Movement
(MM)
signals
are
closely
associated
with
diaphragmatic
during
sleep.
Objective:
We
aimed
at
reliably
detecting
off
events
using
MM
statistical
characteristics.
Methods:
A
bio-signal
learning
approach
was
implemented
whereby
raw
fragments
corresponding
to
normal
breathing
(NPB;
n
=
501),
(n
263),
1861)
were
collected
from
28
consecutive
patients
(mean
age
54
years,
mean
AHI
34.7
n/h)
undergoing
in-lab
polysomnography
(PSG)
coupled
a
magnetometer,
OeP
recordings.
Twenty
three
input
features
extracted
data
explore
distinctive
changes
signals.
Random
Forest
model
built
upon
those
classify
the
hypopnea
events.
External
validation
interpretive
analysis
performed
evaluate
model's
performance
contribution
each
feature
output.
Results:
Obstructive
characterized
longer
duration
(21.9
vs.
17.8
s,
p
<
10-6),
more
extreme
low
values
(p
negative
trend
reflecting
mouth
opening
amplitude,
wider
variation,
asymmetrical
distribution
amplitude.
showed
reliable
features-based
classification
rule
(Kappa
coefficient
0.879
balanced
accuracy
0.872).
The
revealed
that
event
duration,
lower
percentiles,
tendency,
amplitude
most
important
determinants
Conclusions:
can
be
used
as
surrogate
markers
differentiate
Journal of Clinical Sleep Medicine,
Год журнала:
2022,
Номер
18(11), С. 2673 - 2680
Опубликована: Авг. 11, 2022
Obstructive
sleep
apnea
(OSA)
remains
a
highly
prevalent
disorder
that
can
lead
to
multiple
adverse
outcomes
when
undiagnosed
and/or
left
untreated.
There
continue
be
gaps
and
variations
in
the
provision
of
care
for
adult
patient
population
with
OSA,
which
emphasizes
importance
measure
maintenance
initiative
The
Quality
Measures
Care
Adult
Patients
Sleep
Apnea
(originally
developed
2015).
American
Academy
Medicine
(AASM)
convened
Task
Force
2018
review
current
medical
literature,
other
existing
quality
measures
focused
on
same
population,
any
performance
data
or
literature
show
care,
inform
potential
revisions
set.
These
revised
will
implemented
AASM
Clinical
Data
Registry
(Sleep
CDR)
capture
encourage
continuous
improvement
associated
diagnosing
managing
OSA
population.Lloyd
R,
Morgenthaler
TI,
Donald
et
al.
patients
obstructive
apnea:
2022
update
after
maintenance.
J
Clin
Med.
2022;18(11):2673-2680.
Journal of Clinical Sleep Medicine,
Год журнала:
2018,
Номер
14(12), С. 1971 - 1972
Опубликована: Дек. 14, 2018
Once
upon
a
time
there
was
30-apnea
rule
that
used
by
Medicare
to
determine
candidacy
for
positive
airway
pressure
(PAP)
treatment.The
origin
of
this
is
somewhat
obscure
but
probably
based
on
the
initial
studies
obstructive
sleep
apnea
(OSA)
in
early
1970s.
1
To
characterize
manifestations
OSA
comparison
control
group,
inclusion
criterion
established
be
at
least
5
apneas/h
during
6-hour
polysomnography-thus,
30
apneas.This
did
not
recognize
existence
hypopneas,
and
it
complicated
practical
definition
sleep-disordered
breathing
as
rapidly
became
clear
many
patients
expressed
polysomnographic
patterns
were
characterized
hypopneas-whatever
they
were-rather
than
apneas.
2While
performing
split-night
studies,
technicians
would
literally
count
apneas
until
threshold
reached
which
point
titration
PAP
could
commence.Often
insufficient
remaining
successfully
identify
effective
treatment.There
also
occasions
when
hypopneas
scored
facilitate
needed
treatment
with
hope
records
audited.Most
importantly,
using
rule,
qualify
payment
through
their
insurance
provider.The
first
comprehensive
effort
define
published
1999.
3This
report
concluded
necessary
"distinguish
from
because
both
types
events
have
similar
pathophysiology."This
rather
vague
perhaps
overly
flexible
resolve
controversy
related
rule.In
2001,
Clinical
Practice
Review
Committee
American
Academy
Sleep
Medicine
(AASM)
position
paper
recommended
specific
criteria
hypopnea.
4The
advanced
Heart
Health
Study:
30%
or
greater
reduction
airflow
chest
wall
movement
accompanied
decrease
oxyhemoglobin
desaturation
≥
4%.While
rigorously
evidence-based
current
standards,
felt
these
result
high
interobserver
agreement,
seemed
reasonable
findings
Study.This
ultimately
recognized
Centers
Medicaid
Services
(CMS)
reimbursement
resulting
welcome
passing
rule.
International Journal of Knowledge-Based Organizations,
Год журнала:
2020,
Номер
10(4), С. 13 - 23
Опубликована: Сен. 23, 2020
Early
diagnosis
in
the
case
of
sleep
apnea
has
its
own
set
benefits
for
treating
cases.
However,
there
are
many
challenges
and
limitations
that
impact
current
conditions
testing.
In
this
manuscript,
a
model
is
proposed
early
OSA,
using
non-conventional
metrics.
Profoundly,
metrics
used
combination
symptoms,
causes,
effects
problem.
Using
machine
learning
two
sets
classifiers,
inputs
collected
as
part
training
datasets
analysis.
The
data
classifiers
tests
NB
SVM.
comparative
analysis
results,
it
imperative
SVM
classifier-based
algorithm
giving
more
effective
performance.