Prediction Models for Postoperative Pneumonia in Elderly Hip Fracture Patients: A Systematic Review and Critical Appraisal
Journal of Clinical Nursing,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 14, 2025
ABSTRACT
Background
Although
several
models
have
been
developed
to
predict
postoperative
pneumonia
in
elderly
hip
fracture
patients,
no
systematic
review
of
the
model
quality
and
clinical
applicability
has
reported.
Objective
To
systematically
critically
appraise
existing
for
patients.
Design
Systematic
meta‐analysis.
Methods
10
databases
were
searched
from
inception
April
15,
2024,
updated
on
August
26.
Two
reviewers
independently
performed
literature
selection,
information
extraction
assessment.
A
narrative
synthesis
was
employed
summarise
characteristics
models.
Meta‐analysis
using
Stata
17.0.
Results
13
studies
containing
25
included.
The
prevalence
9.62%
(95%
CI:
7.62%–11.62%).
Age
(53.8%),
hypoproteinemia
(46.2%),
chronic
obstructive
pulmonary
disease
(COPD,
30.8%),
gender
(30.8%),
activity
daily
living
score
(ADL,
30.8%)
American
Society
Anesthesiologists
(ASA,
top
six
predictors.
All
reported
area
under
curve
(AUC:
0.617–0.996).
9
(69.2%)
used
Hosmer‐Lemeshow
(H‐L)
test,
calibration
curves,
or
Brier
scores
evaluate
calibration.
5
(38.5%)
internal
validation,
4
(30.8%)
external
validation.
had
a
high
risk
bias
due
single
sample
source,
inappropriate
data
processing,
inadequate
evaluation,
negligence
(76.9%)
good
applicability.
Conclusions
Prediction
patients
are
still
developing
stage.
validation
evaluation
poor.
Future
should
focus
robust
updating.
Additionally,
Transparent
Reporting
Multivariable
Model
Individual
Prognosis
Diagnosis
+
artificial
intelligence
(TRIPOD+AI)
statement
be
followed.
Relevance
Clinical
Practice
effective
discriminating
but
further
adjustment
warranted.
Язык: Английский
Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture
Clinical Interventions in Aging,
Год журнала:
2025,
Номер
Volume 20, С. 217 - 230
Опубликована: Фев. 1, 2025
Hip
fractures
have
become
a
significant
health
concern,
particularly
among
super-aged
patients,
who
were
at
high
risk
of
postoperative
pneumonia
due
to
their
frailty
and
the
presence
multiple
comorbidities.
This
study
aims
establish
validate
model
predict
patients
with
hip
fracture.
Data
derived
from
Chinese
PLA
General
Hospital
(PLAGH)
Fracture
Cohort
Study,
we
included
555
(≧80
years
old)
fracture
treated
surgery.
Patient's
demographics,
comorbidities,
laboratory
tests,
surgery
types
collected
for
analysis.
All
randomly
splitting
into
training
group
validation
according
ratio
7:3.
The
majority
used
train
models,
which
was
tuned
using
series
algorithms,
including
decision
tree
(DT),
random
forest
(RF),
extreme
gradient
boosting
machine
(eXGBM),
support
vector
(SVM),
neural
network
(NN),
logistic
regression
(LR).
incidence
7.2%
(40/555).
Among
six
developed
eXGBM
demonstrated
optimal
model,
area
under
curve
(AUC)
value
0.929
(95%
CI:
0.900-0.959),
followed
by
RF
(AUC:
0.916,
95%
0.885-0.948).
LR
had
an
AUC
0.720
0.662-0.778).
In
addition,
prediction
performance
in
terms
accuracy
(0.858),
precision
(0.870),
F1
score
(0.855),
Brier
(0.104),
log
loss
(0.349).
It
also
showed
favorable
calibration
ability
clinical
net
benefits
across
various
threshold
risk.
develops
validates
reliable
learning-based
specifically
following
can
serve
as
useful
tool
identify
guide
strategies
Язык: Английский