Netherlands;. 'Departmeizt of Intensive. Care,. Academic. Medical. Center, ...... of futile dialysis treatment, greater consideration of the choice of outcome.
Predicting Mortality in Intensive Care Renal Failure Treated with Dialysis CAROLINE RUDOLF RAYMOND *Re,ial
Unit,
Department
methods
study
tify
a group
was
a near
treatment. patients
This retrospective who received a first study
mortality
ICU
models assessed
vival
for
and with
The
mortality
their
will
its initiation
die
requiring
even
The
on an intensive
predict,
before
Numerous
from
sur-
observed
mortality inhospital
unit
(ICU)
with
ICU
(4-9).
veloped
specifically
Several
general
the risk
of patient
Other
for
based
on
only
the
devastating
models
use
with
have
ARF
primary
mentioned predict
aim
of this
prognostic mortality
study
methods
in ICU
patients
was in
been
developed
have
needing
were
able
This
those
did
some
not,
to identify
In
in
this
patients
a group
models
of patients
indicates that these the decision not to
of patients.
study
models,
with
ARF
to determine
(1 Am
where
examined
as well
both
Soc
patients
only.
The
these
models
dialysis
certainty
Materials
and
general
as prediction
whether
a near-l00%
been
de-
have
patients
been
de-
(10-13),
receiving
dialysis
of
their
dialysis
the
Nephrol
ICU
models
clinical
goal
could
mor-
designed
of this
identify
would
provide
no benefit
of death,
despite
dialysis
study
a group
because
of
treatment.
and
December
study
were
1993 patients
not included ARF in our to
treatment
Thirty-six 90%
various
methods are shown for the general ICU
0.66
for
the
238
patients
in-
.
by cardiopulmonary
0.50
mortality
in this study was 76%. Descriptive information on these is shown in Table 1 Most of the patients were treated
curves
extensively.
patient
clinical
of 126 patients
were
Analysis
The above-mentioned when possible, a mortality assigned
clinical variables
a clinical survival index consisting Schaefer et a!. (13) analyzed data
with ARF requiring dialysis in a medical analysis of the data collected immediately a model with six variables.
Statistical
regression
of the two, the latter
for
for each
with
standardized
0.64
chronic
of the
the performance
We also examined
characteristics,
(the model’s
of a clinical standardized
was
was
gastrointestinal,
personal
equation
component
nents.
dialysis.
to be the better Lobs et a!. (12)
into
we calculated
components
regression
mortality
10 groups
respiratory,
diseases,
were
risk for
of the various
in our
variables
cardiovascular,
chronic
of the clinical
relative
Because
if the
Patients
into
scores using all variables in the model (the model’s well as the model’s scores using only the variables
the selection of 10 variables. Liano et a!. (1 1) developed both a linear regression model and a logistic regression model based on a study of 228 patients with acute tubular necrosis, 50 percent of whom required reported present study.
separation
prediction
grouped
the
been
and
mortality
predicted
contribution
by review
discriminant
the
Whereas deciles of risk are (1 8), the scores from some of
ties that
the relative
matologicaL/malignancy, resuscitation,
the
of the variables.
model
reason,
(16).
of predicted
mortality. instances
glucose), 0.98 for the cardiovascular heart failure), 0.32 for the respiratory
a stepwise
power,
of patients
sized quintiles
Acute renal failure models. Rasmussen et a!. (10) studied 148 patients referred to the renal unit because of ARF. Fifty percent of these patients required dialysis. Predictors of mortality were selected of the literature;
(or
as the proportion
has no discriminatory
compared with the observed recommended for use in these
fit a linear
and stepwise
mortality
by the sum
Teres
the model
into five equally
Model
of organ
specificity
the model can correctly identify the patient who a higher score to that patient). If the area under the
is 0.5,
relative weights by dividing the
(MPM).
the
iden-
the ROC
(17)
minus
(correct
where
curve
1.0
sensitivity
under
linear discriminant analysis on a large number of variables for mortality prediction of general ICU patients at the time of ICU admission and 24 and 48 h after admission to create a Mortality Prediction number
between
and
be
analysis.
the area
of the pairs where died (by assigning
die)
can
curve
of those who survive). If all possible pairs of one patient survived and the other died were identified,
22 variables
with
relationship
who
(ROC)
to distin-
die,
identification
ICU admission.
in a model
characteristic
who
incorrect patients
components
by
SAPS
resulted
the
of a model
patients
of those
adding or removing model components. From the 14 origmeasurements, five variables were selected for the SAPS-R (R = reduced) model using multiple logistic regression analysis. The SAPS-E (E = extended) model was derived from the SAPS variables plus eight more measurements. Stepwise regression analysis of these SAPS
inal
operating
and
tification
grouped
predictive
receiver
the ability
survive
and
in the APACHE the
using
who
patients
are excluded.
to improve
assessed
patients
be calculated.
Simplified Acute Physiology Score (SAPS) consists of a selection of measurements of the original APACHE system without an adjustment for chronic health problems (6). Le Gall et a!. selected routinely available measurements to avoid the possibility of a systematic bias in using the APACHE score, which may arise because Viviand
Discrimination,
between
area is 1.0, the model discriminates perfectly. Calibration. Calibration refers to the degree of correspondence between the estimated probabilities of mortality produced by a model
The
missing
guish
(SAPS-R),
0.58
(SAPS),
under increasing
the
0.71
ROC order):
(35%),
for
or general
the ROC order):
.
(SAPS-E),
0.63
(32%),
in Figure 1 Areas under models were (in increasing 0.62 and
(MPM),
curve
internal
The etiology of the ARF was acute of the patients. ROC curves for the
the
(55),
0.74
0.62
ARF-specific
(Rasmussen),
(APACHE
(APACHE
III).
models 0.65
(Lohr),
II), Areas
were
(in 0.69
Prediction
Table
1. Patient
characteristics
(N
Schaefer
238)
=
Patients
Proportion
161 (y, mean
Specialty
± SD)
61
of the Treating
Cardiopulmonary
Internal General
±
surgery
and was
with ARF
of the
according
1 13
prognostic
to the
variables
different
clinical
Liano
75
32
metabolic
45 14 15
19
(71%
and
Another
Schaefer and
6
accorded (48%).
the
quintiles
Neurology
0
the
4
2
Discussion
2
I
hospital
for
outcome
of predicted was
high
ICU
poor
between
in the
the relative
weight
were
and alive
(Table
of
model
was
who
risk
the
weight
Schaefer
admission
of patients
model received
relative
III model,
mortality
also
difference the
variables
reason
long-term
The
0
was
In the APACHE
to
Gynecology
striking
models
cardiovascular
in total).
6
2
in the Shaefer model. The Schaefer using only patients with ARF who
treatment.
35
4
categorized
not included developed
83
Neurosurgery
An examination
models
dialysis
medicine surgery
Cardiology Vascular
68 15y
Physician
surgery
in ICU Patients
components revealed some interesting differences (Table 2). The relative weight of renal failure was only 4% of the total weight in the APACHE III model but 21 % in the Liano model
(%)
(no.)
Males Age,
models.
of the
Models
very
high
in the highest
at discharge
from
3).
Etiology Glomerular
performance
of
interstitial
ATN,
postischemic
ATN,
nephrotoxic
5
2
in patients
with
ATN,
pigment
5
2
academic
hospital.
ATN,
multicausal
ATN,
unknown
Other
reasons
nephritis
The
Acute
73
examined
120
50
17
7
models
8
3
evaluated
4
2
models
origin
Unknown
Status
3 1
were
hospital
Death
182
76
appropriate et a!. (19).
such
near-l00%
ficity
of mortality
chance
whether
any
of the models
in combination
0%.
In ROC curve in the tively
good
terms, bottom
despite could
dialysis,
achieve
with
a sensitivity
this left
meant corner
performance
clinical goal of idenbe futile because of a we determined
a near-lOO%
appreciably
speci-
greater
than
focusing on the slope of the of the square. Note the rela-
of the APACHE
corner of the graph. Figure 2 presents the observed
III and Liano
five
Liano
the risk
estimates
results
quintiles
of risk
mortality
risk
mortality.
Table
the different
of
Table
APACHE
on the
2 also
for
and
or higher) includes
as well the
performance
Patients
had the
III,
mortality
developed by Lien after the start of this study, II (21).
observed
in our
study
of some studies of patients but higher than the results
is comwith that
instead
mortality
die
in hospital. an area
under the APACHE
of inhospital
ROC well
None
under
renot
ARF
other for use of
mortality.
analysis showed that no model in its ability to identify patients
of the
the ROC
models
curve
tested
of >0.78.
in this
study
In contrast
to
ROC curve observed in our population of II, others have reported larger areas when
was applied to patients with Parker et al.-0.79 (22)-and
receiving dialysis, Chertow et al.-0.74
ARF
(3).
The would
clinical goal of this study was to determine be possible to use these models to identify
patients
with
(predicted (predict-
chance
weights Liano,
of in
of the and
method
a near-lOO%
chance
to ascertain
the
of mortality
predicted observations the patients
risk
models’
whether a group
despite
ability
it of
dialysis.
patients is to examine the lower left corner of the ROC curve, which represents sensitivity while maintaining a near- 100% specificity. With the APACHE III model, 33 of the 143 patients (23%) who died had a score of >71 %, the highest
of parameters
MPM,
models were
, the model
study-e.g.
inhospital
of these therefore,
in the highest model
relative
this
Some and,
One
a near-100%
number
as the
APACHE
Liano
the Liano overesti-
ARF-specific
the inhospital
III model
the
of
et a!..
Schaefer
2 summarizes
based
or higher)
Liano,
risks followed Schaefer method
quintile.
categories,
categories
The
that
mortality closely, the
of 84%
MPM,
than
of the highest of 59%
risk
III,
of mortality.
better
of mortality.
mortality
APACHE
risk
was
observed
the risk
ed mortality
the
the
model
Whereas
for
Obviously,
in this study. data collection
The results from the performed exceptionally
the model including
underestimated
clinical
in
groups.
they were specifically developed for in patients with ARF. Not all existing
only
and
of risk
the
mated
mortalities
models
(13) and 44% (22). One explanation noted by Schaefer et al. (1 3) is their
models, the best performing prognostic models acto ROC analysis. Both of the general prognostic meth-
ods
model
predicted
ARF-specific
studies report-57% the lower mortality ICU
was
because
with the results dialysis (3,12)
the area 0.62 for
quintiles
Schaefer cording
76%
showed and
for
and
Others were published as MPM II (20) and SAPS
parable receiving
who
models
in this
used
were tested prospective
The (Schaefer), and 0.78 (Liano). Given the tifying patients for whom dialysis would
patient
mortality
models quired
24
models
The general ICU prediction they have been developed and
because
large
were
prediction
general
examination.
included in
14 10
32
to another
Both
for
were
predicting
at Discharge
Home Transfer
selected
I 1 mortality
study for the prediction of inhospital mortality ARF receiving acute dialysis in the ICU of an
in this
to identify
such
of a surviving patient. This is rather close by Dobkin et al. (23), who observed that 39%
who
died
and
no patients
who
survived
had
scores
to of
I 14
of the American
Journal
Society
of Nephrology
> Cl)
ci) U)
0
20
40
with
1. ROC acute
curve
renal
analysis
failure
80
Specificity
1 Figure
60
for seven
(right
general
20 1
(%)
mortality
models
prediction
(left
40
80
Specificity
-
panel)
60
and
four
models
100
(%)
specifically
developed
for patients
panel).
Mortality
100
100
t
0
100
Prediction
Model
80
0
::
60
40
40 # -
-
-U,
U,
20
20
#{149}
.___
0
,
0-20
20-40
‘
100
40-60
‘
‘
60-80
‘
0
80-100
,
-
0-20
20-40
Liano
‘
40-60
#{149} 80-100
60-80
Schaefer
100
U
...
U.
>.
t
__._
-
80
80
60
60
40
40
20
20
-
0
0’ 0-20
‘
20-40
40-60
‘
60-80
‘
20-40
0-20
80-100
#{149}
40-60
#{149}
‘
60-80
Percentiles(%) Figure
2. The observed
Prediction
of a >70%
Model)
cutoff
(-)
and
two
point
and predicted ARF-specific
for
(-
- -)
models
the APACHE
Schaefer et al. (1 3) questioned the usefulness point according to the APACHE II model.
mortality
in each
(Liano,
Schaefer).
II score.
80-100
#{149}
Percentiles(%) quintile
However,
of the 70% cutoff They reported that
of risk
for two
certain-death APACHE
70%.
general
patients II
model
prognostic
could based
on
models
not
be
risk
cutoff
(APACHE
III, Mortality
identified
using
the
points
of 50%
and
Prediction
Table
2.
Number
different
of patients
models,
quintile
with
available
relative
confidence
for
(%)
weight intervals,
different
of these
and
models
variables
lower
limit
(N),
number
in each
of the
area
quintile
APACHE (N
of parameters,
model,
highest
the
of the
curve, mortality
ARF
I 15
components
of
in the
highest
risk”
Schaefer
Liano (N
234)
=
with
mortality
MPM (N
Patients
of clinical
ROC
predicted
III
in ICU
(no.)
under
b82)hPb
=
Models
(N
231)
=
233)
=
Parameter 1 (1 1)
1 (28)
1 (16)
2 (43)
2(5)
1 (19)
1 (14)
Renal
3(4)
2(21)
Neurological
1 (4)
1
Hematology/malignancy
1 (0)
1 (14)
2(3)
1 (14)
Metabolic
5 (8)
Cardiovascular
2 (6)
Respiratory
Infection Cardiopulmonary Chronic
Area
for
under
Lower
receiver bFor
limit
III,
and
the
100%
mortality
III
Comparisons of
by
all
models
mated
risk
risks
in
group,
this
this
risk.
by the
patients
with
average
between
tients
are
general
ARF
3.
the
receiving
mortality
Long-term
the
in the
risk
dialysis
is much
a
may higher
and and
by
that
MPM,
period
the
the
of the
lack
in highest
pa-
of cal-
quintibes’
III MPM
Liano
Schaefer
1
4
1 y after
Discharge
0
1 (0)
1 (1)
2 (2)
without
dialysis)
intensive
care
unit;
ROC,
fact
for
ICU.
have
after
possible the
assessment.
To
assessments
of model
we reassessed observed
total
after
exclusion.
weight III
score,
score, but
most
of the this
those
other
not
have
ICU noted,
previous
24-h
based
on
the
of the
removing in
Another
remarkable for
ICU
provides do
not
models
were
not
included
exclusion
after
models
h after
asin its
from
influenced
changes reason
that
performance
their
variable
recalimodels
of the other
were had
models major
24
surgery
thus,
whether
No
their
the
than
some
performance
other
first
ICU
Others
CABG
and,
analyses.
relative
APACHE
risks
Lemebe
(25).
with
determine the
general
for the better
receiving III
after
mortality
It is likely
from
admission
compared
patients
APACHE
must
the
value
explanation
III model
the
ICU
increases,
study h
of
ICU
patients.
scores
24
model the
assign dialysis
in this first
model.
had the
predictive
the
to the
poorer
APACHE
a better
period
that
probability
of
in scoring
that
show
signed
the MPM
term
been
used
included in
in the
the
to
24 h before
use
performances
example,
was
stay
that
a constant
been
One
of
noted
used
the
models
Because
duration
(24)
information
within
of the
adding
APACHE
the
some
probably
24-h
that
collected
the these
the
results, patients
performance
were difference
admission.
In the
almost
50%
even
include
of
the this
variable.
Physiology
Prediction
ICU,
particularly
as the by
first
differICU
the
the
et a!.
would for
5
Mortality
Model;
were
to
admission
the
be
discriminative
be ex-
1
Acute
97
whereas
brated
among
predictors
for
of
than
general
the
explanation
of patients
risk
(no.)
APACHE,
84 Prediction
designed
show the
overesti-
of mortality
ICU
represented
five of
high even
in calibration
ARF
treatment,
is the
(alive
(0.04)
graft.
admission
ARF-specific
the
average
APACHE
Alive
0.69
49
may
were
underestimation
model
outcome
Survivors
(0.03)
93 (82-98)
of mortality
from
Hospital
0.78
98 (89-100)
bypass
increases
with
Another
of risk. a near-
risks
consistent
in the
artery
risks
quintiles
experienced
Schaefer’s
with
completely models.
(0.04)
89 (77-97)
Mortality
ibration
exam-
quintile
In contrast,
risk
patients
to identify
in the highest
a gross
differences
that
ICU
not ICU
and
coronary
involves
predicted
showed more
MPM,
respectively). and
models.
These fact
overall
ences
Table
ICU
approach
models
98%,
generally
general
plained
Liano
observed
assigned
mortality
and
and
0.71
excluded.
in the highest
the patients
(97%
(0.04)
(86-100)
Evaluation: CABG,
of mortality
that
of the
error;
relevant,
chance
of patients
APACHE
1 (22)
59
(%)
Health
standard
were
more
risk
Chronic
patients
clinically
we found
using
of predicted and SE,
a near-l00%
study,
quintiles
quintibe
CABG
97
2 (25)
% (no.)
Physiology
of the group
In this
quintile,
interval,
characteristics;
with
ination
in highest
Acute
Another,
0.74
( 1 5)
1
3 (48)
(SE)
of highest
operating APACHE
patients
risk
curve
confidence
APACHE,
1 (9) 1 (6)
( 1 2)
1
admission
mortality
95%
a
ICU
ROC
Observed
2 (9)
7 (8)
characteristics
Reason
( 1 0)
I (9)
recuscitation
diseases
Personal
1 (16)
Model.
and Chronic
Health
Evaluation;
In examining greater
consideration
the
identification of
the
of futile choice
of
dialysis outcome
treatment, measure
is
1 16
Journal
worthwhile.
Inhospital
most
widely.
ward
the
from in the
predicted
risk
In terms
survival
of long-term
demonstrated
renal
function (28)
that
after
discharge,
care
facilities
models
applied
identify
pital
mortality
can
required
the
predictive
examined
all
with
use
models
risk
dialysis,
of dialysis
way,
it can
subjective
opinion”
by the
the highest mates
might,
than
a decision
and
unpleasant
support
when
considering
an
III and
of risk,
the factors In this
serve
the
need
for
models
in a large
their
informed
when
the
model
whether The
number
rather costly
of
this
evaluation
present
of these
14.
HH,
15.
study
demic
supported
16.
by a grant
Medical
Center,
for
from
Good
Amsterdam,
the
Clinical The
Committee
Practice
for the
of the
Aca-
Netherlands.
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renal
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