Immune Cell-Based versus Albumin-Based Ratios as Outcome Predictors in Critically Ill COVID-19 Patients
Journal of Inflammation Research,
Год журнала:
2025,
Номер
Volume 18, С. 73 - 90
Опубликована: Янв. 1, 2025
Purpose:
The
aim
of
the
retrospective,
single-center
study
was
to
assess
prognostic
value
immune
cell-based
and
albumin-based
ratios
regarding
lethal
outcome
in
critically
ill
COVID-19
patients.
Patients
Methods:
We
analyzed
612
adult
patients
admitted
intensive
care
unit
(ICU)
between
April
2020
November
2022.
Blood
measurement
on
admission
ICU
encompassed
complete
blood
count
(CBC),
IL-6,
C-reactive
protein
(CRP),
albumin,
lactate,
lactate
dehydrogenase
(LDH),
serum
bicarbonate,
arterial
base
deficit/excess
(BD/E),
D-dimer.
All
measured
calculated
parameters
were
compared
survivors
nonsurvivors,
with
measure
being
hospital
mortality.
Results:
Immune
[NLR
-
Neutrophil-to-Lymphocyte
Ratio,
MLR
Monocyte-to-Lymphocyte
PLR
Platelet-to-Lymphocyte
MPV
Mean
Platelet
Volume,
MPV/PC
Volume-to-Platelet
Count
Derived
(d-)NLR
ratio
neutrophil
divided
by
result
white
cell
(WBC)
–
count),
N/LP
Neutrophil
x
100/Lymphocyte
count,
CLR
(CRP)-to-Lymphocyte
CPR
CRP-to-Platelet
LLR
Lactate
(LDH)-to-Lymphocyte
Systemic
Inflammation
Index
(SII)
platelet
neutrophil/lymphocyte
Response
(SIRI)
monocyte/lymphocyte
count]
investigated.
White
counts
significantly
higher,
while
lymphocyte
lower
nonsurvivors.
MPV,
MPV/PC,
NLR,
d-NLR,
MLR,
N/LP,
CRP,
LDH,
CPR,
CLR,
LLR,
SII,
SIRI
values
higher
Monocyte
did
not
differ
groups.
Albumin-based
included
CRP-to-Albumin
Ratio
(CAR),
Lactate-to-Albumin
(LAR)
LDH-to-Albumin
(LDH/ALB).
Conclusion:
only
independent
predictor
outcomes
at
is
LDH/ALB
ratio.
Most
other
moderate,
although
highly
significant
predictors
mortality
Keywords:
LDH/albumin
ratio,
ill,
COVID-19,
Язык: Английский
Advances in Understanding Inflammation and Tissue Damage: Markers of Persistent Sequelae in COVID-19 Patients
Journal of Clinical Medicine,
Год журнала:
2025,
Номер
14(5), С. 1475 - 1475
Опубликована: Фев. 22, 2025
This
review
explores
the
crucial
role
of
established
and
emerging
biomarkers
in
diagnosis,
management,
understanding
post-COVID-19
conditions.
With
COVID-19
affecting
multiple
organ
systems,
have
been
instrumental
identifying
ongoing
inflammation
tissue
damage,
facilitating
early
diagnosis
prognostication.
Specifically,
markers
like
C-reactive
protein
(CRP),
interleukin-6
(IL-6),
novel
entities
such
as
soluble
urokinase
plasminogen
activator
receptor
(suPAR)
neutrophil
extracellular
traps
(NETs)
provide
insights
into
pathophysiological
mechanisms
predict
long-term
outcomes.
highlights
integration
these
clinical
workflows
their
implications
for
personalized
medicine,
emphasizing
potential
guiding
therapeutic
interventions
monitoring
recovery.
Future
directions
suggest
a
focus
on
longitudinal
studies
to
explore
biomarker
trajectories
interaction
with
outcomes,
aiming
enhance
management
conditions
refine
public
health
strategies.
Язык: Английский
Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 7, 2025
Abstract
Objective:
We
aimed
to
develop
an
interpretable
model
predict
the
mortality
risk
for
patients
with
severe
pneumonia.
Methods:
The
study
retrospectively
employed
data
from
pneumonia
hospitalized
at
First
Affiliated
Hospital
of
Henan
University
Chinese
Medicine
and
Provincial
between
January
2008
November
2021
as
training
set
development.
Patients
admitted
same
two
hospitals
December
2024
were
prospectively
included
test
evaluation.
The
demographic
characteristics,
clinical
manifestations
upon
admission,
factors
comorbidities,
complications,
laboratory
results,
treatment
during
hospitalization,
other
features,
fatal
outcomes
collected.
In
set,
all
analyzed
in
comparison
survivors
non-survivors.
least
absolute
shrinkage
selection
operator
(LASSO)
regression
was
applied
select
features
establishment
five
models:
logistic
(LR),
support
vector
machine
(SVM),
decision
tree
(DT),
random
forest
(RF),
extreme
gradient
boosting
(XGBoost).
performance
models
assessed
discrimination,
calibration
practicability.
optimal
screened
out,
SHapley
Additive
exPlanation
(SHAP)
method
used
explain.
Results:
A
total
323
eligible
enrolled,
including
226
97
set.
four
models,
XGBoost
demonstrated
third
highest
AUROC
(0.853),
along
SHAP
value
indicated
that
retention
catheterization
applicationhad
strongest
predictive
prediction
horizons,
closely
followed
by
variables
oral
herbal
decoction,
BUN
level,
age,
tracheotomy
application,
complication
septic
shock,
TCM
syndrome
(pathogenic
qi
falling
into
prostration
syndrome).
Conclusions:
Older
increased
BNU
syndrome)
may
be
potential
affect
pneumonia,
among
which
application
decoction
are
protective
factors.
exhibits
superior
overall
predicting
hospital
greater
than
traditional
scoring
systems
such
PSI,
SOFA,
APACHE
II,
assists
clinicians
prognostic
assessment,
resulting
improved
therapeutic
strategies
resource
allocation
patients.
Язык: Английский
Risk factors for thrombocytopenia associated with intravenous valproic acid therapy in pediatric patients undergoing neurosurgical operations
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 21, 2025
Thrombocytopenia
is
one
of
the
side
effects
VPA.
This
study
aimed
to
evaluate
incidence
and
risk
factors
thrombocytopenia
after
intravenous
VPA
treatment
in
children
with
neurosurgical
operations.
Pediatric
patients
undergoing
operations
treated
were
enrolled
this
retrospective
study.
According
platelet
count
injection
VPA,
pediatric
divided
into
group
non-thrombocytopenia
group.
Binary
logistic
regression
analysis
was
used
explore
for
thrombocytopenia.
A
total
252
included
study,
12.3%
(31/252).
Univariate
showed
that
baseline
count,
duration
therapy,
blood
loss
associated
occurrence
administration
revealed
(OR
0.995,
95%
CI
0.991-0.999)
independent
Our
data
show
common
an
factor
Regular
monitoring
important
whether
short-term
prophylactic
use
Язык: Английский