Constructing a nomogram model for patients with cervical spondylotic myelopathy
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 14, 2025
This
study
aims
to
construct
a
nomogram
predict
the
prognosis
of
patients
with
cervical
spondylotic
myelopathy
(CSM).
included
358
diagnosed
myelopathy.
We
collected
serological
indicators
at
admission,
including
routine
blood
tests,
biochemical
liver
and
kidney
function,
coagulation
function
other
laboratory
tests.
used
t-test,
rank-sum
test,
chi-square
test
or
Fisher
for
inter-group
comparison.
univariate
multivariate
logistic
regression
analysis
obtain
independent
predictors
poor
patients,
constructed
into
using
R
language,
verified
predictive
performance
nomogram.
The
results
showed
that
Platelets
(PLT)
(1.005
[1.001,
1.009],
p
=
0.021),
Albumin
(ALB)
(0.891
[0.818,
0.97],
0.008),
Aspartate
aminotransferase
(AST)
(1.031
[1.002,
1.061],
0.035),
Alanine
(ALT)
(0.958
[0.92,
0.998],
0.037),
Fibrinogen
(FIB)
(0.654
[0.464,
0.921],
0.015)
were
predictors.
Good
prediction
modest
errors
was
shown
by
in
both
training
validation
groups.
ALB,
AST,
ALT,
FIB,
PLT
admission
may
be
efficacy
ACDF
CSM
patients.
these
factors
has
good
performance.
Serological
can
as
supplement
spine-related
imaging
indicators,
allowing
clinicians
better
complete
diagnosis
treatment
process
preoperative
evaluation
process,
so
more
postoperative
benefit.
Язык: Английский
A nomogram for predicting poor sleep quality in patients with systemic lupus erythematosus
Frontiers in Neurology,
Год журнала:
2025,
Номер
16
Опубликована: Май 21, 2025
To
construct
a
nomogram
for
poor
sleep
quality
in
patients
with
systemic
lupus
erythematosus
(SLE).
Clinical
data
from
218
SLE
who
visited
tertiary
hospital's
rheumatology
and
immunology
department
Chengdu,
Sichuan
Province,
China,
between
2021
2022
were
analyzed.
LASSO
analysis
multivariate
logistic
regression
used
to
identify
independent
risk
factors,
was
integrate
model
the
various
factors.
The
evaluated
using
receiver
operating
characteristic
curves,
calibration
decision
curve
(DCA).
Internal
validation
conducted
bootstrap
method,
clinical
impact
(CIC)
assess
effectiveness
of
predictive
model.
In
total,
104
(47.7%)
had
quality,
while
114
did
not
have
(52.3%).
predicting
an
area
under
0.789,
sensitivity
51.92%,
specificity
93.86%.
closely
approximated
ideal
curve;
DCA
showed
threshold
probability
35%;
C-index
0.789;
CIC
60%.
These
results
indicate
that
has
good
accuracy
utility.
We
constructed
validated
SLE,
providing
convenient
reliable
tool
prediction
these
patients.
Further
multicenter
studies
are
warranted
validate
findings
further
elucidate
underlying
mechanisms
disturbances
SLE.
Язык: Английский