predictors of mortality and the provision of dialysis in patients with
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BRIEF COMMUNICATION
Predictors of Mortality and the Provision of Dialysis in
Patients with Acute Tubular Necrosis
GLENN M. CHERTOW,* J. MICHAEL LAZARUS,* EMIL P. PAGANINI,t
ROBIN L. ALLGREN,� RICHARD A. LAFAYETTE,� and MOHAMED H. SAYEGH,*
FOR THE AURICULIN#{174} ANARITIDE ACUTE RENAL FAILURE STUDY GROUP
*Re,icil Division, Department of Medicine, Brigham and Women ‘s Hospital. Harvard Medical School, Boston,
Massachusetts; �Section of Dialysis (hid Extracorporeal Therapy. Cleveland Clinic Foundation, Cleveland,
Ohio; �Dis’isioiz of Nephrologv, Department of Medicine, Stanford University Medical Center, Palo Alto,
California; (t�1(i�.S�cios, Jar,, !‘ilou,itainVie%i’, Caiift’r,iia.
fl/)%�(�#{149}.(#{149}#{149}(�1. To explore the l�ItLlril1 history of critically i II l)atienls
with ilc’tlIC relLil IUI lure titie to acute tubular flct..’fl)SIS. we
CV�lILliltC(1 256 J)al ieflts enrol led in the pIaceh� arni of a ran-
doiniicd clinical trial . l)eLth and the c()lnpositc OUICOIUC. death
or the provision f l�Ij were determined with lollow-up to
60 d. ‘I’he relative risks ( RR ) and 95�X confidence intervals
(()5(/(� (‘1 ) UssoCialeti with routinely available (len�)graphic.
ci illical , ilfl(l laboratory variables were esti niated using propor-
I it ma I l�iiar�ls regression . N I nety -I lii’ee ( 36�X ) deaths were doe-
uluenteci; an additiot�al 52 (20�f ) patients who survived re-
cci ved dialysis. Predictors of lnort4llity included nude gei�ler
(RR. 2.01 ; t)5(�/�, (‘J, I .2 1 to 3.3(�. oliguria (RR. 2.25; 95% Cl.
I .43 to 3.55 ), niec’lianical ventilation ( HR. I .86; 95�X (‘1. 1 . I 8
IC) 2.93 ), actile niyocardial infarction ( RR. 3. I 4; t)5(% Cl, I .85
to 5.3 I ). acute stroke or seizure (KR. 3.08: 95% Cl, I .56 to
6.06), chronic ilul�unosuppressit)n ( RR. 2.37: 95�/ (‘I. I . I 6 to
4.88 ). hyperhiliruhineinia ( RR. I .06; 95(4 (], I .03 �o I .08 per
I nig/di increase in total hilirtihii�. al�l l�etaholie acidosis (RR.0.’)5; 95(/( CJ� 0.9() to (1.99 per I mEq/L increase in serum
bicarbonate concentration ). Predictors of death or the provision
01 dialysis were oligtiria (RR. 5,95; 95(4 Cl. 3.96 to 8,95).
mechanical ventilation (RR. I .53: 95’7 CI. I .07 to 2.2 1 ). acute
Inyocardial inlaretion ( RR. I .95; ‘)5�* Cl. I .24 to 3.07 . ar-
rhyihlnia (RR. I .5 1 ‘. 95”f (‘1. I .04 to 2. I 9), and hypoalhtIl�in-
eflula (RR. 0.56: 95(7( Cl. 0.42 to 0.74 per I g/dl increase in
sertiin 4ilhuniin concentration). Neither mortality nor the pro-
vision Of dialysis was related to patient age. These observations
C�lfl he used to estin�iIe risk early in the course of acute tubular
necrosis. Furtherniore, these aliti related niodels may he used to
adjust f�r case-nh x variat ion in qutli ity il�provel�ent efforts,
LIfl(.l to objectively strati l’y pat jents in future intervention trials
ail�e(l at favorably altering the course of hospital-acquired
acute renal failure. (J Am SOC Nephrol 9: 692-698, 1998)
Acute renal failure (ARF) occurs in approxil�ately 2 to 5�* of
liospitalii.ed patients ( I .2 ). Hospital-acquired ARF is associ-
ated with a 25 It) 90�/( risk of in-hospital iuortal ity. de�)elflliflg.
at least IU part. 011 case flu ,( and the severity of renal injury
( 3�22 ) . A nuniber ol’ previ�ts studies have attelnj)ted to
identify clinical risk factors associated with adverse outconies
ifl this patient 1)�PIIl�ti�fl. IViost have been derived Iron) single
iflstittltU)fl eXpelience. which Iii�its generalii.ahility. and the
niajority l�ive iLItI insufficient s1ti�ple site to perfi’irni i�ii1ti-
variable analyses. 1-kIrthern�ore. the ln��jority of’ studies were
retrosl)ect i ye. and w ithout hI inded recorti reviewers. �int1 there-
kre subjeci It) illf()rn�st ion bias.
Vv’e recent ly P�Ll’t icipated in il placebo-control led. n�ult icenter
R�’ceived July 2’). I 1)1)7, Accept�’tl ( )oIIher I . I
lhi� 5’IOFk ‘��I’I P1’CS�’t1I�’(l I Ii Il)SII’�b.t I t�tiii #{149}Ilil)�’ 28th AItIiIIaI �v1eet PUg I (I TheAn)L’ricIn S1)tiCIy of Nephrlllogy. Sin I )iego, C‘A. Nivetuhcr S lu 8, I 995
C‘� )(It’S1)(1Ii(l�’Ii�(.’ III I )r. ( il�’iiii 1�vI� ( ‘heiit��. I )ial�ss� I tilt Adini,ii�itssi v�’ Oi�
t’ie�’. l3riglsiiii intl \�Voi��’n � s I lospilal . 75 Francis Slr�’el. l3osion, �V1A 02 I I
I O46�607 �/‘#)4()(i’)2�$�() �(X)/()
.111tIrI)�Il i d’ Ihe Antericitn Si �ciet v i�I N�’Plir11l( �
( ��yrigl1l () I 1)98 t)Y h�’ A l1h’li�.(Ii Sis:k’iy ol Nephrolllgy
II’kII. the prinlary t*ijcctive of’ which was to evaluate the effect
of’ intravenous Auriculinok Anaritide ( synthetic atrial natriuretic
peptide. ANP1� l’(,� ()l� the need for dialysis in 504 people
with acute tubular necrosis (ATN) of’ ischemic and/or toxic
origin. For the purpose of this report. we restricted our analysis
It) placebo recipients (n = 256). This strategy affor(led us the
opportunity tC) examine the i nipact of’ deniographic, clinical
( historic and current ). �int1 laboratory variables, derived f’roni
lBLllt i-illstittltit)llal experience, on the ivitural history of pn’-
gressive ARF due to ATN.
Materials and MethodsPalle�its
POtI.’flti.tI 5lLid� subjects were #{149}i�lultswith ARF tltic to ATN. who
l�i�l a progressive risc in sertun ercatinine concentration of at least I .0
tiig/tIl 0VC� 24 II) 48 h. without CVi(Ietlc’e Of recovery or stabili/alion,
Microscopic urinary SCdiI�CIU exLInutlatiC)n, renal ultrasonography.
LU1C.l�lCtCt’I11i flLII101) 1)1 I he Itact innul exeret ion of’ soditum weuc OfiiI)t)�
the diaguiC)stic �iiethods tused by investigators IC) identify stuhjecls with
ATN. Subjects whose ARF was titue to eatises other than ATN. stich
8% prereuial �itotenhia. urinary tract obstruction. glonieruloncphritis.
interstit ial nephritis. �CIheuoeuiihol Ic liscasc. nialignant hypertension.
Mortality and Dialysis in Acute Tubular Necrosis 693
renovasculan thrombosis on dissection, on hepatonenal syndrome, were
excluded. Subjects with chronic renal failure (usual serum creatinine
greaten than 3.0 mg/dl). prior renal transplantation. circulatory shock
(systolic BP less than 90 mmHg with presson support). and those in
whom dialysis was anticipated within 24 h or who were deemed
unsuitable candidates for dialysis were also excluded from the study.
Diuretic agents. vasoactive agents, hyperalimentation. and all other
therapies were administered at the discretion of the treating physician.
The need for dialysis and its timing. modality, membrane, duration,
and/on intensity were determined on a case-by-case basis by thetreating nephrologist. Outcomes were assessed for up to 60 d after
randomization. Oligunia was defined as an average urine output of less
than 0.4 L /d at the time of study entry.
Statistical Analyses
Two-by-two contingency tables were evaluated with Fisher’s exact
test. Correlation among variables was described with the Pearson
product moment correlation coefficient. Time-to-event analyses were
performed using proportional hazards regression, with censoring at
day 60 after study entry. Continuous and categorical variables were
examined for univariate associations with mortality and the need for
dialysis. Variables with univaniate associations at the P < 0.05 level
were considered candidates for multivariable analysis. Proportional
hazards regression was used to simultaneously adjust for multiple
covaniates. using stepwise selection and entry and exit criteria set at
the P < 0.05 level (23). Variables not included by the automated
technique were reentered individually to evaluate for residual con-
founding and model fit. Multiplicative interaction terms with oliguria
were considered for all other model covariates. Relative risks (RR)
and 95% confidence limits (95% CI) were calculated based on the
model parameter coefficients and standard errors, respectively. Plots
of log (-log lsurvival rate]) against log (survival time) were per-
formed to establish the validity of the proportionality assumption (24).
Competing models were compared with the log likelihood test. Lo-
gistic regression analysis (yielding odds ratio [OR] as the analogous
effect estimate) was performed for the mortality outcome, using death
at 30 d as the dependent variable. The area under the receiver
operating characteristic (ROC) curve was calculated to assess model
discrimination. There were no missing data for the categorical van-
ables analyzed. Five percent or less of the continuous observations
were missing. Median values of missing continuous variables were
imputed in multivariable models. Statistical analyses were conducted
using SAS (The SAS Institute, Cany, NC). All P values are two-tailed.
ResultsThe mean age of study subjects was 62.0 ± 16.9 yr. Twenty-
five percent of the study sample was age 75 on above. Thirty-
five percent of subjects were women. There were 1 82 (7 1 %)
Caucasian, 43 (17%) African-American, 25 (10%) Hispanic,
and six (3%) people of Asian-American or other race or eth-
nicity. The large majority (85%) of patients were cared for in
intensive care units at the time of study entry. Table I outlines
the baseline clinical characteristics of the study population.
The mean serum ereatinine and urea nitrogen concentrations at
study enrollment were 4.6 ± 2.0 mg/dl and 66.2 ± 28.5 mg/dl,
respectively, indicating a marked degree of renal dysfunction
in the majority of eases.
Three patients (1 %) were lost to follow-up: one each at I I,
22, and 33 d. Ninety-three (36%) deaths were documented over
the 60-d study period. An additional 52 (20%) patients who
survived received dialysis.
Univariate Analyses
The relations among demographic, clinical, and laboratory
variables, and the outcomes of interest (mortality, and the
composite outcome death or the provision of dialysis) were
initially assessed using unadjusted proportional hazards regres-
sion. Table 2 lists those variables significantly associated with
mortality on univaniate analysis. Although there were too few
patients with leukemia (ti = 4) or lymphoma (ii = 2) to
accurately estimate RR, it should be noted that six of six
(100%) patients with hematologie malignancy died within 21 d
of study entry (P = 0.001).
Outcome: Mortality or Diah’sis
It would be desirable to identify risk factors for requiring
dialysis among those patients with early ATN. The difficulty in
doing so is related to the fact that some individuals with ARF
may die before their renal failure is so severe as to require the
initiation of dialysis. As a result, an “adverse” risk factor for
the outcome “provision of dialysis” might be favorable with
regard to survival; i.e., the provision ofdialysis is dependent on
surviving the early days of ATN. Therefore, we fit our models
using the composite outcome: death on dialysis. Table 3 lists
those variables significantly associated with mortality or dial-
ysis on univariate analysis.
Multivariable Analyses
To determine significant multivariable predictors of the out-
comes of interest, we fit proportional hazards regression mod-
els, using time to death, on time to death or dialysis, as the
dependent variables. Several key explanatory variables were
highly correlated, as expected (e.g. , serum albumin and cal-
cium concentrations, r = 0.53, P < 0.0001 ; serum bicarbonate
and chloride concentrations, r = -0.43, P < 0.0001 ; meehan-
ical ventilation and sepsis, r = 0.35, P < 0.0001; arrhythmias
and acute congestive heart failure, r = 0.25, P < 0.0001). The
potential for collineanity was accounted for in the model build-
ing process.
The results of the multivaniable proportional hazards model
derived for mortality are outlined in Table 4. Although the
association between albumin and mortality did not reach con-
ventional statistical significance, it was included in the final
model due to its large effect estimate, and improved model fit.
The results of the multivaniable proportional hazards model
derived for the composite outcome death or dialysis are out-
lined in Table 5. The coefficients of the regression models are
summarized in the Appendix.
The multivaniable models were validated using the bootstrap
procedure. The bootstrap randomly selects, with replacement, a
predetermined number of observations from the original data
set; /3-coefficients and their respective SEM are then reesti-
mated with each successive procedure (25). Each successive
iteration of the bootstrap yields a sample of distinct composi-
tion, on which the derived model is tested. In theory, this
process is akin to prospective validation of a model, assuming
Category Value
Table 1. Demographic
characteristics
and baseline clinical and laboratory Table 2. Significant predictors of mortality: univariate
of study subjects analysisa
Parameter RR 95% CI
Male gender 2.20 1.35 to 3.57
Oliguria 2.20 1.43 to 3.38
Mechanical ventilation 2.43 1 .58 to 3.74
Acute myocardial infarction 2.02 1 .23 to 3.31
Arrhythmias 1.77 1.16 to 2.69
Acute congestive heart failure 2. 14 1 . 1 1 to 4.13
Gastrointestinal bleeding 2.68 1 .61 to 4.45
Infection 2.16 1.42 to 3.29
Sepsis 1.72 1.14 to 2.61
Acute stroke or seizure 2.80 1 .49 to 5.26
Chronic liver disease 2.42 1.22 to 4.81
History of hypertension 0.64 0.43 to 0.97
Chronic immunosuppression 2.57 1.29 to 5.11
Albumin (per g/dl increase) 0.54 0.38 to 0.77
Bicarbonate (per mEqfL increase) 0.93 0.89 to 0.97
Calcium (per mg/dl increase) 0.75 0.60 to 0.94
Chloride (per mEqIL increase) 1.04 1.01 to 1.07
Creatinine (per mg/dl increase) 0.83 0.72 to 0.96
Glucose (per mg/dl increase) 1.13 1.01 to 1.27
Sodium (per mEqIL increase) 1 .05 1 .0 1 to 1.08
Total bilirubin (per mg/dl increase) 1 .06 1 .03 to 1.06
Total protein (per g/dl increase) 0.79 0.63 to 0.98
Urea nitrogen (pen mg/dl increase) 1.08 1.01 to 1.16
a RR, relative risk; CI, confidence interval.
Table 3. Significant predictors of mortality or dialysis:
univariate analysisa
Parameter RR 95% CI
Oliguria 4.81 3.35 to 6.89
Mechanical ventilation 2.06 1 .47 to 2.89
Acute myocardial infarction 1.58 1.04 to 2.41
Arrhythmias 1.70 1.20 to 2.38
Acute congestive heart failure 1 .90 1 .09 to 3.31
Albumin (pen g/dl increase) 0.61 0.46 to 0.79
Bicarbonate (per mEqfL increase) 0.96 0.92 to 0.99
Calcium (per mg/dl increase) 0.80 0.67 to 0.96
Phosphorus (per mg/dl increase) 1 . 10 1 .00 to 1.21
Potassium (per mEqfL increase) 1 .25 1 .02 to 1.53
Total bilirubin (per mg/dl increase) 1.04 1.02 to 1.06
Total protein (per g/dl increase) 0.78 0.65 to 0.93
Urea nitrogen (per mg/dl increase) 1 .09 1.02 to 1.15
a Abbreviations as in Table 2.
that the sample from which the model was derived and the
sample on which the model was tested are drawn from the
same population. One-hundred bootstrapped samples per out-
come measure, each with 256 observations, were analyzed.
The point estimates of RR were ±5% compared with the RR
estimates derived from the original data set. The 95% CI were
694 Journal of the American Society of Nephrology
a Variable highly skewed, expressed as median (intenquartile
range), otherwise mean ± SD.
Demographic factors
age (yr)
gender (% female)
race or ethnicity (%)
Caucasian
African-American
Hispanic
Asian-American
other
Acute medical conditions
mechanical ventilation (%)infection, with on without sepsis (%)sepsis (%)
arrhythmias (%)oliguria (%)
acute myocandial infarction (%)gastrointestinal bleeding (%)
acute congestive heart failure (%)acute stroke on seizure (%)
panereatitis (%)Chronic medical conditions
hypertension (%)
coronary artery disease (%)congestive heart failure (%)diabetes mellitus (%)liver disease (%)immunosuppression (%)
obstructive lung disease (%)
Solid nonhematogenous cancer (%)leukemia or lymphoma (%)
Laboratory values
albumin (g/dl)
alanine aminotnansferase (UIL)
aspartate aminotnansfenase (UIL)
bicarbonate (mEq/L)
calcium (mg/dl)
chloride (mEqfL)
eneatinine (mg/dl)
glucose (mg/dl)
lactate dehydrogenase (U/L)
phosphorus (mg/dl)
potassium (mEqfL)
sodium (mEqIL)
total bilirubin (mg/dl)
total protein (g/dl)
urea nitrogen (mg/dl)
leukocyte count (Xl000)
hematoenit (%)
platelet count (Xl000)
62.0 ± 16.9
35
71
17
10
2
50
4730
29
23
15
11
7
6
5
59
48
29
28
6
6
4
3
2
2.7 ± 0.7
41 (19 to 87y’
58 (28 to l45)�
21.4 ± 4.8
7.9 ± 0.9
101.2 ± 7.6
4.6 ± 2.0
167.5 ± 81.8
460 (280 to 9l6)�
5.2 ± 1.9
4.5 ± 0.7136.1 ± 5.9
3.5 ± 6.6
5.3 ± 1.0
66.2 ± 28.5
13.7 ± 8.9
29.7 ± 5.0
154 ± 96
Mortality and Dialysis in Acute Tubular Necrosis 695
Table 4. Multivariable proportional hazards regression analysis: mortality’1
Parameter RR 95% CI P Value
Total bilirubin 1 .06 1 .03 to 1 .08 18.42 <0.0001
Acute myocardial infarction 3.14 1.85 to 5.31 18.11 <0.0001
Oligunia 2.25 1.43 to 3.55 12.26 0.0005
Acute stroke on seizure 3.08 1.56 to 6.06 10.57 0.001
Mechanical ventilation 1 .86 1 . 1 8 to 2.93 7.24 0.007
Male gender 2.01 1.21 to 3.36 7.15 0.008
Chronic immunosuppression 2.37 1.16 to 4.88 5.53 0.02
Bicarbonate 0.95 0.90 to 0.99 4.82 0.03
Albumin 0.73 0.51 to 1.04 3.04 0.08
a Total bilinubin, per mg/dl increase; bicarbonate, per mEqfL increase; albumin, pen g/dl increase; other factors (yes/no). x2 ranked by
level of statistical significance after simultaneous adjustment for other model covariates. Abbreviations as in Table 2.
Table 5. Multivaniable proportional hazards regression analysis: mortality or dialysisa
Parameter RR 95% CI x� P Value
Oliguria 5.95 3.96 to 8.95 73.43 <0.0001
Albumin 0.56 0.42 to 0.74 15.74 <0.0001
Acute myocardial infarction 1.95 1 .24 to 3.07 8.29 0.004
Mechanical ventilation 1.53 1.07 to 2.21 5.30 0.02
Arrhythmias 1.51 1.04 to 2.19 4.71 0.03
a Albumin, pen g/dl increase; other factors (yes/no). � ranked by level of statistical significance after simultaneous adjustment for other
model covaniates. Abbreviations as in Table 2.
Table 6. Multivariable logistic regression analysis: mortality�’
Parameter OR 95% CI P Value
Male gender 3.70 1.75 to 7.82 15.67 <0.0001
Mechanical ventilation 2.95 1.53 to 5.68 15.53 <0.0001
Oliguria 4.39 2.09 to 9.24 13.29 0.0003
Acute myocardial infarction 5.90 2.43 to 14.4 1 1 .07 0.0009
Acute stroke or seizure 7.35 1.92 to 28.1 8.32 0.004
History of hypertension 0.44 0.23 to 0.86 5.68 0.02
Bicarbonate 0.93 0.86 to 1.00 4.22 0.04
a Bicarbonate, pen mEqfL increase; other factors (yes/no). x2 ranked by level of statistical significance after simultaneous adjustment for
other model covariates. OR, odds ratio. Other abbreviations as in Table 2.
up to 5% wider than in the primary regression models. No
covaniates were eliminated from the bootstrapped predictive
models due to nonsignificance.
To test whether the predictors were influenced by the chosen
analytical method and to determine model discrimination,
companion logistic regression models were fit using 30-d mor-
tality as the dependent variable. Seventy-four patients died
within 30 d. Multivariable logistic regression yielded a model
with seven significant explanatory variables, outlined in Table
6. The area under the ROC curve was 0.8 1 , indicating very
good model discrimination. The logistic regression model was
qualitatively similar to the model based on proportional haz-
ards regression. Total bilirubin was a prominent feature of the
hazard function, but not significantly associated with 30-d
mortality using the multivaniable logit function. This disenep-
ancy suggests that elevated levels of total bilirubin were asso-
ciated with death early in the post-ARF course.
Validation of the Cleveland Clinic Model
In an effort to refine outcome prediction in this population,
we aimed to validate an existing model on these data. We
chose the Cleveland Clinic Foundation Mortality in Acute
Renal Failure model (CCF Model) of Paganini et a!. (26),
because: (1) it captured a similar array of variables; (2) it had
been developed using logistic regression analysis; and, most
importantly, (3) it had performed extremely well in its own
institution. Because the CCF Model was restricted to patients
who required dialysis, we arbitrarily modified the points as-
696 Journal of the American Society of Nephrology
Table 7. Validation of the modified
et al.’1
CCF model of Paganini
SconeNo. of Deaths/
Total No. in GroupCCF Derivation (%)
No. of Deathsfl’otalNo. in Group ANP
Validation (%)
0 to 4 9/37 (24) 1/29 (3)
5 to 7 49/99 (49) 14/87 (16)
8 to 14 245/325 (75) 42/1 18 (36)
�15 40/45 (89) 17/22 (77)
a CCF, Cleveland Clinic Foundation; ANP, atrial natniuretic
peptide.
signed to the ereatinine and urea nitrogen variables (i.e. , pa-
tients with serum ereatinine �2 mg/dl were assigned one rather
than two points, and patients with blood urea nitrogen >75
mg/dl were assigned one point; all others were assigned 0
points). This resulted in a score range of 0 to 1 8 (rather than 0
to 20 for the original CCF Model). For other variables, points
were assigned according to those of Paganini et a!. Patients
were grouped in a similar manner (i.e. , 0 to 4 points, 5 to 7
points, 8 to 14 points, �15 points).
Using the modified CCF Model, the number of points as-
signed was 8.7 ± 3.5 (median. 9; range, 3 to 16). Table 7
shows the predictive performance of the modified CCF Model
on the ANP study data, compared with the original derivation
set reported by Paganini et al. Although the overall mortality
rates were lower in the ANP Study (in which all patients did
not require dialysis), the modified CCF Model performed
extremely well on these data. The area under the ROC curve
for the modified CCF score was 0.75.
DiscussionPrognostic stratification in ARF has been the subject of
numerous previous reports (see above). An ARF-speeifie in-
strument is needed, because generic severity-of-illness mea-
sures, such as the Acute Physiology and Chronic Health Eval-
uation (APACHE) II scone, have not consistently predicted
short-term mortality in critically ill patients with ARF
(14,18,22). Furthermore, such an index could be used to pro-
mote quality improvement in ARF management by providing
an objective means of comparing differences in case mix and
outcomes across multiple institutions.
Several observations from the mortality analysis are note-
worthy. First, there was a striking difference in risk by gender,
with men experiencing approximately twice the mortality nate
of women, even after covariate adjustment. The reasons for this
increased risk are unclear. Second, several factors that were
associated with mortality on univaniate analysis (arrhythmias,
acute congestive heart failure, gastrointestinal bleeding,
chronic liver disease, infection, and numerous laboratory van-
ables) were not independently associated with mortality after
adjustment for the model covariates. This observation does not
suggest that these variables were unimportant, but rather that
they have insignificant predictive power over and above that of
other related factors, such as mechanical ventilation and total
bilirubin. Finally, advanced age was notably absent from both
the univariate results and the multivariable regression models.
Despite a broad age range (18 to 93, interquartile range, 50 to
75) and relatively large sample size, we were unable to detect
any relation between advanced age and the RR of death.
The analysis of the composite outcome death or dialysis also
provided important prognostic insights. The RR of death or
dialysis was markedly increased with oliguria, to nearly three
times the magnitude of the RR of death alone (sixfold com-
pared with twofold). This finding emphasizes the importance
of oliguria in influencing the timing of dialysis initiation. The
relation between oliguria and outcome is complex, however.
We have shown previously that once dialysis is required, the
presence or absence of oliguria predating the initiation of
dialysis is of little or no prognostic value (22). These findings
suggest that oliguria itself may not increase mortality risk;
rather, the dialysis procedure, on another associated factor
unrelated to the severity of renal disease, may directly influ-
ence outcomes.
There was a striking association between the serum albumin
concentration and the risk of death or dialysis. Although the
association between low serum albumin and mortality in pa-
tients with end-stage renal disease (27-30), and other disease
states (3 1 ,32), has been well established, the prognostic impor-
tance of the serum albumin in ARF has not been previously
demonstrated. Likewise, others have failed to show an association
between metabolic acidosis and adverse outcomes in ARF. The
therapeutic implications of these findings are unknown; survival
or renal recovery may or may not be enhanced with correction of
acidosis, or with administration of hyperalimentation or growth
factors aimed at reversing catabolism (33).
There are several potential limitations to this study. The
exclusion of subjects who were thought to require dialysis
imminently, or who were not considered candidates for dialy-
sis, might have biased the study sample toward a less critically
ill population. Furthermore, if practitioners precluded from
study participation subjects of a particular age, sex, race, on
disease category, particularly those who they thought were at
higher risk, the study sample might not have been representa-
tive. Conversely, restriction to patients with ATN might bias
the study sample toward a more critically ill population, com-
pared with the ARF population at lange. Although ATN is the
most common pathologic correlate of ARF in seriously ill
patients, the predictive models may not be valid for individuals
with ARF due to atheroembolie disease, prerenal azotemia,
interstitial nephritis, and other causes of renal disease. With
regard to the statistical analyses, models were fit with contin-
uous independent variables. This approach assumes a linear
relation between risk factors and outcomes. Categorization of
these variables obviates the linearity assumption, but markedly
reduces the power to detect associations. On the basis of
companion analyses (data not shown), linearity assumptions
were reasonable for the continuous variables of major interest
(i.e. , total bilirubin, bicarbonate, and albumin) over the ranges
encountered in clinical practice. It is noteworthy that results
obtained from proportional hazards and logistic regression
analyses were qualitatively similar.
Mortality and Dialysis in Acute Tubular Necrosis 697
In summary, using data collected prospectively as part of a
randomized clinical trial, we found that male gender, oliguria,
mechanical ventilation, acute myocardial infarction, acute
stroke or seizure, chronic immunosuppression, hyperbiliru-
binemia, and metabolic acidosis were significantly associated
with the RR of death after progressive ARF due to ATN. A
similar model was developed for the composite outcome, death
or dialysis. An existing model derived at a single institution
(CCF Model) was validated on these data. These observations
suggest that the risk of death or dialysis can be determined
relatively early in the course of ARF. Continued efforts must
be directed at refining predictive models in ARF (1) to provide
accurate prognosis for patients with this devastating complica-
tion; (2) to adjust for ease-mix variation in quality improve-
ment efforts; and (3) to objectively stratify patients in future
intervention trials aimed at favorably altering the course of
hospital-acquired ARF.
AppendixModel 1: Mortality
log hazard ratio
= 0.6991 (MALE) + 0.8128 (OLIGURIA)
+ 0.0557 (TOTAL BILIRUBIN)
+ 0.6215 (VENTILATION)
+ 1 . 1 245 (STROKE OR SEIZURE)
+ 1.1432 (ACUTE MI)
+ 0.8643 (IMMUNOSUPPRESSION)
- 0.0555 (BICARBONATE)
- 0.3139 (ALBUMIN)
Model 2: Mortality or Dialysis
log hazard ratio
= 1.7836 (OLIGURIA)
+ 0.6662 (ACUTE MI)
+ 0.4107 (ARRHYTHMIA)
+ 0.4279 (VENTILATION)
- 0.5851 (ALBUMIN)
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