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Kidney biopsy in systemic lupus erythematosus.

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ARTHRITIS & RHEUMATISM Volume 37
Number 4, April 1994, pp 559-567
0 1994, American College of Rheumatology
559
KIDNEY BIOPSY IN
SYSTEMIC LUPUS ERYTHEMATOSUS
111. Survival Analysis Controlling for Clinical and Laboratory Variables
JOHN R. McLAUGHLIN, CLAIRE BOMBARDIER, VERNON T. FAREWELL,
DAFNA D. GLADMAN, and MURRAY B. UROWITZ
Objective. To examine the importance of renal
biopsy as a predictor of death due to any cause in
patients with systemic lupus erythematosus (SLE).
Methods. The study included 123 SLE patients
who had a renal biopsy between 1970 and 1984 and were
followed up as part of a prospective study. Data were
initially analyzed to identify clinical and laboratory
features that were significantly associated with the risk
of dying. Renal biopsy variables were then examined to
determine whether they contributed additional information about prognosis.
Results. The clinical and laboratory factors most
closely associated with the risk of dying in multivariate
analyses were the serum creatinine level and the SLE
Disease Activity Index score. The presence of chronic
renal lesions on biopsy contributed significantly to the
prognostic information offered by clinical and laboratory factors in the subset of patients who had normal
serum creatinine levels-the majority (85%) of patients
in this study.
Conclusion. These results indicate that renal
biopsy serves an important role in the assessment of
Supported by grants from the Arthritis Society and the
Ontario Ministry of Health. Dr. McLaughlin's work was supported
by a PhD Fellowship from the National Health Research and
Development Program of Health and Welfare, Canada. Dr. Bombardier's work was supported by a National Health and Welfare
Scholarship.
John R. McLaughlin, PhD: University of Toronto, Toronto,
Ontario, Canada; Claire Bombardier, MD, FRCP(C): Wellesley
Hospital, Toronto, and the University of Toronto; Vernon T.
Farewell, PhD: University of Waterloo, Waterloo, Ontario, Canada;
Dafna D. Gladman, MD, FRCP(C): Wellesley Hospital; Murray B.
Urowitz, MD, FRCP(C): Wellesley Hospital.
Address reprint requests to Murray B. Urowitz, MD,
FRCP(C), Rheumatic Disease Unit, The Wellesley Hospital (TW
651A), 160 Wellesley Street East, Toronto, Ontario, M4Y 153,
Canada.
Submitted for publication October 8, 1992; accepted in
revised form August 24, 1993.
prognosis in patients who do not have advanced renal
disease.
There is an ongoing debate over whether renal
biopsy results are useful indicators of prognosis, in
terms of both renal outcome and overall mortality, in
systemic lupus erythematosus (SLE). In particular,
there has been controversy over whether renal biopsy
features add prognostic information over and above
that of routine clinical and laboratory data obtained at
the time of biopsy. Several previous studies examined
the prognostic value of renal biopsy observations
while controlling for the effects of clinical and laboratory factors by way of multivariate statistical analyses.
Chronic renal lesions were associated with a poor
prognosis in most (1-5) but not all (6) of these studies,
which assessed prognosis in terms of renal function
(2-4) or both renal function and patient survival
(1,5 A).
As part of an ongoing prospective study of the
determinants of SLE progression, we previously examined the relationship between renal biopsy findings
and clinical manifestations of SLE, showing that there
was no uniform correlation between the two (7). The
relationship between renal biopsy results and survival
was further examined by univariate analyses, which
demonstrated that the presence of chronic or proliferative lesions was associated with a higher risk of dying
(8). Renal outcomes were not employed in this analysis because they occurred rarely in this series: only 6
patients developed end-stage renal disease, and of
these, 5 died (8).
This study examines the relative importance of
renal biopsy compared with clinical and laboratory
features at the time of biopsy as indicators of progno-
McLAUGHLIN ET AL
560
sis in terms of mortality, among the SLE patients who
underwent biopsy. This aim was met by assessing the
role of clinical and laboratory measures, by examining
whether there was a further contribution by renal
biopsy observations, and finally, by assessing whether
there was confounding by differences in treatment.
PATIENTS AND METHODS
Sample selection. Study subjects were selected from
the University of Toronto Lupus Database. This database
contains observations made prospectively during the clinical
examinations of SLE patients seen at the SLE clinic of a
teaching hospital since 1970. Data collection has been standardized by the use of a data collection form, which guides
clinicians through the observation and definition of 207
demographic, historical, physical, laboratory, and therapeutic variables. To be included in the database, each patient
must be diagnosed as having SLE, either by fulfilling 4 or
more of the American College of Rheumatology (ACR;
formerly, the American Rheumatism Association) criteria
for the classification of SLE (9), or by having other manifestations of SLE in addition to several of the ACR criteria.
This study included the members of the SLE database who had a renal biopsy at the Wellesley Hospital from
1970 until 1984. There were 475 patients registered in the
database at that time. During this period, patients were
asked to have biopsies as part of the routine assessment of
their SLE. This period of inclusion was chosen because the
classification of renal biopsies obtained during this time was
standardized by means of an expert review committee, and
because it allowed a minimum of 2 years of followup for each
subject (7,8). These cases were followed from the time of the
patient’s first renal biopsy until January 1, 1987. Only those
whose renal tissue was first examined by biopsy prior to
their death were included. Thus, 123 patients were included
in the present investigation.
The outcome was patient death due to any cause.
End-stage renal disease occurred in only 6 of the 123
patients, 5 of whom died; therefore, end-stage renal disease
was not analyzed because it was too rare to be informative.
Patients who were not seen at the SLE clinic during the year
prior to the end-point date (i.e., 1986) were traced to verify
their final outcome status. Three of these patients could not
be located and were classified as lost to followup; their
end-point date was set as the date of their last examination at
the clinic.
This report focuses on the 3 methods of classifying
renal lesions that were shown in univariate analyses to be
indicators of prognosis (8). These methods include classification by the World Health Organization (WHO) criteria for
lupus nephritis (10,ll) and a regrouping of the WHO classes
that reflects the presence of proliferative lesions (WHO class
IV and subsets of classes I11 and V). Biopsies were also
classified according to the presence of chronic renal lesions
(glomerular sclerosis, interstitial fibrosis), as defined by
Austin et a1 (3). Further details about the sample selection,
data collection, and procedures employed in the review of
renal biopsy samples have been previously described (7,8).
In addition to the biopsy observations, prognostic
importance was examined for 51 variables previously identified as possible prognostic factors. These variables were
diagnostic criteria, demographic descriptors and indicators
of comorbidity, therapy, and SLE status. The indicators of
SLE status were based on both clinical and laboratory
measures and were defined according to the criteria established by the ACR (12,13). The SLE Disease Activity Index
(SLEDAI), a previously validated scale composed of 24
clinical variables (14-16), was also included in the analysis of
prognosis.
The question of whether treatment is associated with
mortality cannot be validly assessed in an observational
study such as this; however, it is still important to examine
whether treatment is a possible confounder of the relationship between prognostic factors and mortality. Particular
emphasis was placed on corticosteroid and immunosuppressive medications being taken at the time of biopsy, although
nonsteroidal antiinflammatory drugs (NSAIDs) were also
examined. Prednisone therapy at the time of biopsy was
classified as none, low dose (1-39 mg/day), and high dose
(240 mglday). The use of immunosuppressive medications
(e.g., azathioprine, cyclophosphamide) at the time of biopsy
was classified dichotomously (used or not used).
Survival analysis was used to study the relationship
between the possible prognostic factors and mortality. Survival time was defined as the interval from the time of biopsy
until death or until January 1, 1987, at which time surviving
patients were censored. Nonparametric life-table methods
were used to calculate Kaplan-Meier estimates of survival
probabilities (17). Proportional hazards regression models
(chapter 4, ref. 18) were used to estimate relative risk (RR)
and to examine the simultaneous effects of multiple prognostic factors. Relative risk was estimated as the antilogarithm
of the regression coefficient for each covariate. For individual parameters in the regression models, significance tests
and 95% confidence intervals (95% CI) were obtained based
on the asymptotic normality of the parameter estimates.
Where appropriate, the test of significance for the addition of
variables to a multivariate regression model employed the
likelihood ratio test, which is chi-square distributed.
The best-fit multivariate model of clinical and laboratory factors was obtained in 2 stages. First, multivariate
analyses were performed to identify the strongest predictors
of survival among all of the manifestations that related to a
single organ system (i.e., to reduce the number of highly
correlated variables). Second, a stepwise multivariate procedure was employed that began with the most highly
predictive variables from all of the organ systems and then
selected variables that were significant at the standard probability level (a = 0.05). To determine whether renal biopsy
observations provided important prognostic information beyond the clinical and laboratory variables, the biopsy variables were added to a multivariate model containing the
most highly predictive clinical and laboratory variables.
Treatment variables were then added to these multivariate
models in order to determine whether there was confounding, as would be indicated by shifts in the regression coefficients for the main prognostic factors.
561
KIDNEY BIOPSY IN SLE
Table 1. Features of the 123 SLE patients at the time of biopsy*
28 (23)
4.7 (0, 15.9)
Total deaths, no. (%)
Duration of followup, median years
(minimum, maximum)
SLE duration before biopsy, median years
(minimum, maximum)
Age, mean (SD)
Sex ratio, fema1e:male
WHO renal biopsy class, no. (%)
I, normal
11, mesangial glomerulonephritis
111, segmental glomerulonephritis
IV, diffuse glomerulonephritis
V, membranous glomerulonephritis
VI, sclerosing glomerulonephritis
Serum creatinine, mean pmoleshiter (SD)
Treatment, no. (%)
Acetylsalicylic acid
Nonsteroidal antiinflammatory agents
Corticosteroids
High-dose corticosteroids (240 mg/day)
Immunosuppressive agents
* SLE = systemic lupus erythematosus; WHO
Organization.
0.8 (0, 26)
33.9 (12.6)
9: 1
8 (6.5)
52 (42.3)
20 (16.3)
32 (26.0)
7 (5.7)
4 (3.3)
109 (69)
31 (25)
10 (8)
96 (78)
28 (23)
13 (11)
=
World Health
The appropriateness of the proportional hazards
model was evaluated by testing for violations in the proportional hazards assumption using time-dependent covariates
and diagnostic plots (18). There was no evidence that the
proportional hazards assumption was violated, which indicated that models containing simple fixed-time covariates
provided a valid depiction of the impact of the main prognostic factors. Accordingly, the results that follow were
obtained from simple proportional hazards models.
The SAS program (19) was used for descriptive
analyses and to fit simple proportional hazards models.
Regression analyses for models including time-dependent
variables were performed using the Fortran program for Cox
regression analysis described by Kalbfleisch and Prentice (18).
RESULTS
The features of the 123 study subjects are
summarized in Table 1, The duration of disease before
biopsy was quite short, with a median of 0.8 years,
whereas the length of followup after biopsy was relatively long, with a median of 4.7 years. The age and
sex distributions are similar to those usually reported
for SLE patients seen in rheumatology clinics: predominantly women in their child-bearing years. Twentyeight patients (23%) died during the study period. The
certified cause of death was cardiopulmonary disease
in 10 patients, septicemia in 8, central nervous system
disease in 4, pneumonia in 3, and unknown in 3
patients.
Table 1 also shows that 49% of the patients had
mild lesions on renal biopsy (WHO classes I and 11),
whereas 42% had proliferative lesions (classes I11 and
IV). At the time of biopsy, the number of patients
receiving specific treatments was 31 (25%) taking
acetylsalicylic acid, 10 (8%) taking NSAIDs, 96 (78%)
taking corticosteroids, 28 (23%) taking high-dose corticosteroids (240 mg/day), and 13 (11%) taking immunosuppressive medications. During the full period of
followup after biopsy, an additional 25 patients received high-dose corticosteroids and an additional 38
received immunosuppressive agents. For all patients
combined, the survival rates at 5 and 10 years were
82% and 74%, respectively.
In univariate analyses, whereby relative risks
and significance levels were estimated using proportional hazards models, several of the 51 clinical and
laboratory variables were significant prognostic factors (Table 2). Several other variables were analyzed
but not listed in Table 2 because they were not
associated with mortality and they occurred rarely in
this sample.
The variables in Table 2 with the highest relative risks were indicators of renal disease (e.g., low
creatinine clearance, elevated serum creatinine, presence of urinary casts). Among the variables with
relative risks that were significantly different from 1.O
was a SLEDAI score 220; the cut-off value of 20 was
chosen because this identified subjects in the upper
quartile of the range of SLEDAI values. Variables
indicating the presence of cushingoid features, infections, and muscle weakness also had relative risks that
were significantly different from 1 .O. Features that
achieved borderline significance (0.05 < P < 0.1)
included the presence of vasculitic lesions, fever,
myositis, and alopecia.
For the following analyses, serum creatinine
was selected as the renal manifestation of greatest
prognostic importance. This was done even though
creatinine clearance had a larger relative risk estimate,
because creatinine clearance data were missing for a
large proportion of cases (30%).
Table 3 shows that 2 variables were contained
in the best-fit multivariate model that was based on all
of the clinical and laboratory factors: indicators of
elevated SLEDAI and serum creatinine. The relative
risk estimates demonstrate that with an increasing
SLEDAI score, there was an increasing RR, and that
there were large and statistically significant RRs
among patients with SLEDAI scores >I9 (RR = 4.7)
and elevated serum creatinine levels (RR = 5.9).
Survival curves provide an alternative depic-
562
McLAUGHLIN ET AL
Table 2. Univariate relative risk (RR) estimates and level of significance for the relationship between
mortality and clinical and laboratory variables at the time of biopsy in 123 patients with systemic lupus
erythematosus (SLE)*
Variablet
Total
no.
No. of
deaths
RR
P
95%
CI
14
9
8.5
<0.001
3.3,21
18
12
9
6
4.7
4.0
<0.001
0.003
2.1,11
1.6,25
48
46
29
50
5
22
25
27
5
40
34
17
9
15
4
6
43
8
8
12
18
3
10
12
9
3
13
10
7
4
6
2
2
13
15
16
1.o
1.4
3.9
2.7
4.4
2.3
2 .o
2.1
3.0
1.9
1.9
2.0
2.3
2.0
2.8
2.5
1.6
1.5
-
1
0.4
1.6
1.4
1.6
0.7
1.8
0.5,3.7
1.6,7.4
1.3J.9
1.3J3.1
1.1,5.1
0.9,4.4
0.9,4.6
0.9,lO
0.9,4.0
0.9,4.2
0.8,4.6
0.7,6.9
0.8,4.8
0.7,12
0.6,ll
0.8,3.3
0.7,3.2
0.6,3.7
0.1,2.6
0.6,4.2
0.7,3.0
0.6,4.7
0.3,1.7
0.4,7.5
0.5,4.1
0.6,3.4
0.4,6.3
0.5,2.6
0.4,3.9
Creatinine clearance <SO% of
normalt
Serum creatinine > 120 pmoles/liter
Urinary cellular casts
SLEDAI score
0-9
10-19
220
Cushingoid features
Infection
Muscle weakness
Vasculitic lesions
Fever
Myositis
Alopecia
Hematuria (>5 RBC/hpD
Age 250
Proteinuria (>3.0 gm/day)
Pulmonary embolism
Cardiovascular disease
Thrombocytopenia (<100 x 10’iliter)
Arthritis
Recent period of biopsy (1978-1984)
SLE duration 2 I year
Lupus headache
Serositis
Low complement
Leukopenia (<3.5 x 10’iliter)
DNA binding
Avascular necrosis
Sex (males)
Mucous membrane lesions
Visual disturbance
Pyuria
Pleurisy
64
43
13
17
58
14
41
6
12
22
6
39
12
5
14
4
7
2
4
6
2
9
3
1.5
1.5
1.4
1.5
1.2
1.2
0.51
0.004
0.01
0.02
0.03
0.06
0.07
0.07
0.09
0.11
0.13
0.13
0.15
0.17
0.22
0.22
0.28
0.29
0.30
0.36
0.37
0.37
0.42
0.43
0.43
0.48
0.59
0.71
0.78
* Variables were excluded if they occurred in fewer than 10 patients and had P values greater than
0.80. These were: the presence of psychosis, seizures, peripheral neuropathy, pericarditis, organic
brain syndrome, cerebrovascular accident, infertility, cranial neuropathy, and diabetes after steroid
therapy. 95% CI = 95% confidence interval; SLEDAI = SLE Disease Activity Index; RBC/hpf = red
blood cells/high power field.
t Creatinine clearance data were available for 86 of the 123 patients. Cardiovascular disease was
defined as a history of myocardial infarction, angina, or congestive heart failure; serositis as the
presence of pleuritis or pericarditis; low complement as a CH5O value <120 hemolytic units or BlC
<0.6 gdliter or C4 <0.16 gdliter.
Table 3. Relative risk estimates and level of significance from the
best-fit multivariate proportional hazards model containing clinical
and laboratory predictors of mortality among all patients (n = 123)*
95%
Variable
SLEDAI score
0-9
10-19
220
Serum creatinine
(>120 pmolesfliter)
P
RR
1.O
1.5
4.7
0.50
0.002
5.9
<0.001
* See Table 2 for definitions of abbreviations.
CI
0.6,4.0
1.9,i1.9
2S713.8
tion of the effect of these prognostic factors. Figure 1
shows that the survival fraction of patients with elevated serum creatinine dropped sharply during the first
year of followup relative to patients with normal
creatinine levels. Figure 2 indicates that, in general, a
higher SLEDAI score was associated with poorer
prognosis (lower survival), and that after about 5
years, the survival fractions for patients with low
(score of 0-9) and intermediate (score of 10-19)
SLEDAI scores were very similar.
The statistics shown in Table 4 refer to 3
563
KIDNEY BIOPSY IN SLE
P
P
R
0
R
0
B
A 0.8
B
I
I
L 0.7.
I
T 0.6.
Y
B
A
B
I
L 0.7.
I
T 0.6.
Y
0 0.5
Serum Creatinine 5 120
SLEDAI > 19
F
0.4.
0.4 t
1I
S
U
S
U
Serum Creatinine > 120
R 0.3.
R 0.3
v
I 0.2
A
L
I
0 0.5.
3
F
v
I
v
I1
I 0.2.
A
L 0.1 *
i
0.01
0
v
- NO cases observed beyond 8.2 years
l
1
2
3
4
5
8
7
.1
8
9
10
f
0.0
0
1
2
3
4
5
7
6
8
9
10
TIME AFTER BIOPSY (years)
TIME AFTER BIOPSY (years)
Figure 1. Survival curves for 123 patients with systemic lupus
erythernatosus, according to the presence of elevated serum creatinine at the time of renal biopsy (5120 pmolesfliter in 105 patients;
>I20 pmolesfliter in 18 patients).
Figure 2. Survival curves for 123 patients with systemic lupus
distinct models that arose by adding each of the 3
biopsy variables to the baseline model that contained
only clinical and laboratory variables (as shown in
Table 3). The biopsy variables did not significantly
improve the predictive ability of the models, as shown
by significance levels of 0.32, 0.10, and 0.18 for the
erythematosus, according to the Systemic Lupus Erythernatosus
Disease Activity Index (SLEDAI) class at the time of renal biopsy
(score <10 for 48 patients, 10-19 for 46, and >19 for 29 patients).
Table 4. Contribution of the renal biopsy variables to the best-fit multivariate model containing
clinical and laboratory variables, among all patients (n = 123)*
Model
no.
Variable added
to model
~
1.
~
3.
RR
95% CI
2
df
P
1.O
0.6
0.7
1.2
1.9
4.5
0.1J.2
0.1,7.3
0.1,ll.Z
0.1,34.6
0.4,54.3
5.88
5
0.32
1.O
2.1
-
2.72
1
0.10
0.9,4.9
2.32
1
0.18
~~
WHO class
I
I1
111
2.
Effect of adding biopsy
variable
IV
V
VI
Proliferative lesions
Absent
Present
Chronic lesions
Absent
Present
1.O
2.3
0.7,7.8
* Best-fit clinical-laboratory model for all patients, as defined in Table 3. See Table 1 for WHO class
definitions; df = degrees of freedom (see Table 2 for definitions of other abbreviations).
McLAUGHLIN ET AL
564
Table 5. Relative risk estimates and level of significance from the
best-fit multivariate proportional hazards model containing clinical
and laboratory predictors of mortality in the subgroup of patients
with normal serum creatinine (n = 105)*
Variable
SLEDAI score
0-9
10-19
220
RR
P
95% CI
I .o
0.76
0.02
0.4,4.0
1.2,10.4
1.2
3.5
* See Table 2 for definitions of abbreviations.
WHO class, proliferative lesion, and chronic lesion
classifications, respectively.
Further analyses were performed on the subgroup of patients who had normal serum creatinine at
the time of biopsy, because a statistical interaction
was detected between the chronic lesion and the
serum creatinine variables. Furthermore, it was clinically relevant to consider whether a renal biopsy
contributed important prognostic information in patients with normal renal function. In this study, only 18
patients (15%) had an elevated serum creatinine value
at the time of biopsy (Table 2). Among the remaining
105 patients (85%) with normal levels of serum creatinine, 19 deaths occurred. Of these patients, 74 (70%)
had no indication of clinical renal disease: proteinuria,
hematuria, and urinary casts were absent. The best-fit
model based on all of the clinical and laboratory
variables (including indicators of renal disease other
than serum creatinine) in this subgroup contained only
the SLEDAI variable (Table 5), for which the relative
risk estimates were slightly reduced from the level for
all patients shown in Table 3.
Biopsy variables were then added to the baseline model for the subgroup (as in Table 5). Table 6
shows that statistical significance was achieved by
both the proliferative (P = 0.03) and chronic (P =
0.004) lesion variables, but not for the WHO class
variable (P = 0.12). The presence of proliferative and
chronic lesions in this subset of patients was also
associated with large relative risks (RR = 3.0 and 7.9,
respectively). The presence of proliferative lesions did
not contribute additional prognostic information to
that obtained by the model containing the indicators
for SLEDAI and chronic lesions (2 = 1.37, 1 degree
of freedom, P = 0.24), whereas the chronic lesion
variable was significant when added to the model
containing the SLEDAI and proliferative lesion variables (2 = 5.08, 1 degree of freedom, P = 0.02).
The role of treatment as a potential confounder
of the relationship between the major predictors and
mortality was then assessed for the patient subgroup
by adding variables that represented treatment at the
time of biopsy to the best-fit multivariate model that
was identified in Table 6, namely, that which contained the SLEDAI and chronic lesion variables. Table 7 shows that there were only slight shifts in the
relative risk estimates (<lo%) for the SLEDAI and
chronic lesion variables, which indicated that these
estimates were not confounded by the treatment variables considered.
Table 6. Contribution of the renal biopsy variables to the best-fit model containing clinical and
laboratory variables, among patients with normal serum creatinine (n = 105)*
Model
no.
1.
Variable added
to model
WHO class
I
I1
2.
3.
111
IV
V
VI
Proliferative lesions
Absent
Present
Chronic lesions
Absent
Present
Effect of adding biopsy
variable
RR
95% CI
1.o
0.7
0.6
1.8
2.1
10.6
0.1,6.0
0.1,7.9
0.3,38.8
0.1,40.8
0.9,132
1.o
3.0
2.6,3.5
1.o
7.9
2.5,25.5
2
df
P
8.84
5
0.12
4.72
1
0.03
8.43
1
0.004
-
-
* Best-fit clinical-laboratory model for the subgroup, as defined in Table 5 ; WHO = World Health
Organization (see Table 4 for other definitions).
565
KIDNEY BIOPSY IN SLE
Table 7. Relative risk estimates for the SLEDAI and chronic lesion variables, among patients with
normal serum creatinine (n = 105) before (baseline model) and after adding indicators of treatment at
time of biopsy*
Treatment variable added to baseline model
~~~~
SLEDAI score
0-9
10-19
220
Chronic lesions
Absent
Present
Baseline
model
Steroids
High-dose
steroids
Immunosuppressives
1 .o
1.4
3.6
1 .o
1.3
3.2
I .o
1.4
3.9
1.4
3.6
1.O
1.o
1.9
1.6
I .o
8.6
8.3
* Baseline model for patient subgroup, as developed in Table 6. SLEDAI
Erythematosus Disease Activity Index.
DISCUSSION
The major findings in this study were as follows: I) information obtained by clinical and laboratory investigations, especially serum creatinine and
the SLEDAI, had prognostic significance in SLE
patients who had undergone renal biopsy; 2) findings
on renal biopsies, especially the presence of chronic
lesions, contributed additional information about prognosis in patients who had normal serum creatinine
levels at the time of biopsy; and 3) the importance of
the main prognostic factors was maintained even after
controlling for differences in treatment.
As reported previously (8), the description of
the case series indicated that in relation to series
reported from other centers, this group of patients had
a larger proportion with normal biopsy findings (WHO
class I) or mesangial lesions (class 11), a lower average
value of serum creatinine, and a smaller proportion
who were prescribed prednisone. In conjunction with
the rarity of end-stage renal disease (6 cases), these
results indicate that in this case series, there was a
milder level of renal disease at the time of biopsy than
in series reported from other centers. Compared with
previously published studies of biopsied SLE patients,
overall patient survival at 5 and 10 years was slightly
higher (5,20) or similar (6,21) in this study, suggesting
that patients died of conditions other than renal disease that were associated with SLE or its treatment, as
we have previously reported (22).
Several clinical and laboratory variables observed at the time of biopsy were found to be associated with an increased risk of death. The largest
relative risks were obtained for indicators of renal
1 .o
1.o
=
Systemic Lupus
dysfunction (low creatinine clearance, elevated serum
creatinine, and urinary casts). Other clinical observations that achieved statistical significance included the
presence of cushingoid features, infection, and muscle
weakness. The SLEDAI, as a summary measure of
multiple manifestations of active SLE, was also significantly associated with mortality. These findings are
consistent with previous studies that focused on clinical and laboratory features, which have identified
associations between mortality and renal disease manifestations (23) and, in particular, elevated serum
creatinine (24).
Only 2 of the clinical and laboratory variables
were contained in the best-fit multivariate model for
mortality. These were indicators of higher SLEDAI
(scores of 10-19 or 220) and elevated serum creatinine
(>120 pmoleslliter). The relative risk estimates were
1.5 (95% CI = 0.6,4.0) and 4.7 (95% CI = 1.9,11.9) for
the 2 SLEDAI classes, and 5.9 (95% CI = 2.5,13.8) for
the serum creatinine class. Each of these relative risk
estimates was slightly greater than the value obtained
in the univariate analyses.
The ability of the SLEDAI to summarize information about a large number of variables, some of
which were correlated, made this analysis of prognosis
much more feasible. It should be noted, however, that
SLEDAI and serum creatinine are not independent,
since renal manifestations (i.e., casts, hematuria, proteinuria) are components of the SLEDAI, and serum
creatinine is one of the standard measures of renal
function. Even though the 2 variables were related,
their correlation was not so great that the regression
models failed to converge. The fact that the serum
566
creatinine variable had a significant positive coefficient
in the model that also contained the SLEDAI, indicates that SLEDAI underestimated the risk associated
with the presence of renal disease in this case series.
In contrast to the univariate analyses (8), renal
biopsy cla.sses were not significantly associated with
mortality when added to the multivariate clinicallaboratory model for the complete patient sample.
Relative risk estimates of 2.1 (95% CI = 0.9,4.9) and
2.3 (95% CI = 0.7,7.8) were obtained for the proliferative and chronic lesion classes, respectively. In general, these models suggested that the biopsy observations did not contribute significantly to the prognostic
information provided by the SLEDAI and serum creatinine at the time of biopsy.
Previous studies that reported multivariate
models of the relationship between SLE prognosis and
clinical and laboratory variables (2,3,25-28) each derived slightly different final models; however, there
was general consistency in the finding that renal function (2,3,25-27) and SLE disease activity (2) are
among the most important indicators of prognosis. The
SLEDAI was not included in previous multivariate
studies of prognosis; however, an activity index that
included only laboratory test results was a significant
predictor of renal function in the model reported by
Whiting-O’Keefe et a1 (2). Of the previous studies that
examined prognosis in terms of patient survival
(1,5,6,25,27,29,30),3 considered the impact of a renal
biopsy (1,5,6). Among previous studies that examined
the importance of a renal biopsy while controlling for
other factors in multivariate analyses ( M ) , 3 of which
considered patient survival as an outcome (1,5,6),
chronic renal lesions were associated with poor prognosis in all but one (6). A major difference from
previous studies is that the present study considered
prognosis in a sample of patients with less advanced
renal disease and among whom end-stage renal disease
occurred rarely; previous studies focused on SLE
patients with lupus nephritis (1-6,28).
The focus of previous studies on patients with
severe renal disease would imply that they probably
included a large number of study patients with elevated serum creatinine. The results of this study
suggest that in such patients, prognosis in terms of
mortality can be adequately assessed by clinical and
laboratory measures, and therefore biopsies would
provide little additional information. The present
study demonstrated that biopsy variables provided
important prognostic information beyond that available from clinical and laboratory measures in patients
McLAUGHLIN ET AL
who had normal levels of serum creatinine at the time
of biopsy. In addition, it should be noted that in
patients with renal dysfunction, a renal biopsy may
sometimes be necessary to distinguish between active
and chronic renal lesions as an aid in making therapeutic decisions.
Among the strengths of this study were that
data were collected prospectively using a standardized
protocol, that a fixed date of entry into the study was
defined, that complete and detailed clinical information was obtained on each patient, and that a concerted effort was made to obtain complete followup
information. This study shares some of the usual
limitations of clinic-based cohort studies. For example, it is difficult to assess how representative the
patient series was of cases in the population, although
it should be noted that the study included patients
referred from a wide range of sources (e.g., family
physicians, specialists, other patients). Whereas this
study succeeded in detecting factors associated with
large relative risks of dying, it did not allow variables
to be ruled out as prognostic factors with certainty
since confidence intervals were wide for many variables. In order to obtain more precise estimates of
prognostic significance, larger studies are required;
such studies could be accomplished by pooling samples of patients from different research centers.
In conclusion, several factors were significantly
associated with mortality in univariate analyses, and
the relative importance of these factors was examined
in multivariate analyses. The magnitude of the relative
risk estimates demonstrated that the indicators of
renal disease were strong predictors of mortality, and
that the presence of active disease (e.g., high SLEDAI
score) was a weaker, but still significant, indicator of
risk. The result of the subgroup analysis has important
clinical implications, because it suggests that the contribution of new prognostic information by renal biopsy is limited to patients who have normal renal
function. In patients with renal dysfunction, as indicated by an elevated level of serum creatinine, the
biopsy contributed no new information about the risk
of dying. Since the majority of SLE patients have
normal levels of serum creatinine (e.g., 85% in this
study), these results indicate that renal biopsies do
serve an important role in the assessment of prognosis
in SLE. If this relationship is confirmed in other
centers, further studies that examine the balance between the costs and utilities of renal biopsies in the
majority of SLE patients will be required.
KIDNEY BIOPSY IN SLE
567
15. Gladman D, Goldsmith C, Urowitz M, Bacon P, Bombardier C,
The assistance of Dr. C. H. Chang is gratefully
acknowledged.
16.
REFERENCES
1. Ballou S, Chung-Park M, Waggoner DM, Kushner I: Prognostic
value of clinical and renal biopsy findings in lupus nephritis
(abstract). Arthritis Rheum 23:651-652, 1980
2. Whiting-O’Keefe Q, Henke JE, Shearn MA, Hopper J Jr: The
information content from renal biopsy in systemic lupus erythematosus: stepwise linear regression. Ann Intern Med 96:718723, 1982
3. Austin HA, Muenz LR, Joyce KM, Antonovych lT,Kullick
ME, Klippel JH, Decker JL, Balow JE: Prognostic factors in
lupus nephritis: contribution of renal histologic data. Am J Med
75:382-391, 1983
4. Balow JE: Therapeutic trials in lupus nephritis. Nephron 27:
5. !\lossent HC, Henzen-Logmans SC, Vroom TM, Berden JHM,
Swaak TJG: Contribution of renal biopsy data in predicting
outcome in lupus nephritis: analysis of 116 patients. Arthritis
Rheum 33:970-977, 1990
6. Esdaile JM, Levinton C, Federgreen W, Hayslett JP, Kashgarian M: The clinical and renal biopsy predictors of long-term
outcome in lupus nephritis: a study of 87 patients and review of
the literature. Q J Med 72:779-833, 1989
7. Gladman DD, Urowitz MB, Cole E, Ritchie S, Chang CH,
Churg J: Kidney biopsy in SLE. I. A clinical-morphologic
evaluation. Q J Med 73: 1125-1 133, 1989
8. McLaughlin JR, Gladman DD, Urowitz MB, Bombardier C,
Farewell V, Cole E: Kidney biopsy in systemic lupus erythematosus. 11. Survival analyses according to biopsy results. Arthritis Rheum 34:1268-1273, 1 9 9 1
9. Tan EM, Cohen AS, Fries JF, Masi AT, McShane DJ, Rothfield
NF, Schaller JG, Tala1 N, Winchester RJ: The 1982 revised
criteria for the classification of systemic lupus erythematosus.
Arthritis Rheum 25:1271-1277, 1982
10. Churg J, Sobin LN: Renal Disease Classification and Atlas. Vol.
I: Glomerular Diseases. Tokyo, Igaku-Shoin, 1982
1 1 . Kashgarian M: New approaches to clinical pathologic correlation in lupus nephritis. Am J Kidney Dis 2:164-169, 1982
12. American Rheumatism Association Glossary Committee: Dic13. American Rheumatism Association Glossary Committee: Dictionary of the Rheumatic Diseases. Vol. 11: Diagnostic Testing.
New York, Contact Associates, 1985
14. Bombardier C, Gladman DD, Urowitz MB, Caron D, Chang
CH, and the Committee on Prognosis Studies in SLE: Derivation of the SLEDAI: a disease activity index for lupus patients.
Arthritis Rheum 35:630640, 1992
17.
18.
19.
20.
21.
22.
23.
24.
25.
Isenberg D, Kalunian K, Liang MH, Maddison P, Nived 0 ,
Richter M, Snaith M, Symmons D, Zoma A: Cross cultural
validation and reliability of three disease activity indices in
systemic lupus erythematosus. J Rheumatol 19:608-611, 1992
Petri M, Genovese M, Engle E, Hochberg M: Definition,
incidence, and clinical description of flare in systemic lupus
erythematosus: a prospective cohort study. Arthritis Rheum
34:937-944, 1991
Kaplan EL, Meier P: Nonparametrk estimation from incomplete observations. J Am Stat Assoc 53:457481, 1958
Kalbfleisch JD, Prentice RL: The Statistical Analysis of Failure
Time Data. New York, John Wiley, 1979
SAS Institute: SAS User’s Guide: Version 5 . Cary NC, SAS
Institute, 1985
Appel GB, Cohen DJ, Pirani CL, Meltzer JI, Estes D: Longterm follow-up of patients with lupus nephritis: a study based on
the classification of the World Health Organization. Am J Med
83:877485, 1987
Tateno S, Kobayashi Y, Shigematsu H, Hiki Y: Study of lupus
nephritis: its classification and the significance of subendothelial
deposits. Q J Med 52:311-331, 1983
Rubin L, Urowitz MB, Gladman DD: The bimodal pattern
revisited. Q J Med 55:87-98, 1985
Wallace DJ, Podell T, Weiner J , Klinenberg JR, Forouzesh S,
Dubois EL: Systemic lupus erythematosus survival patterns:
experience with 609 patients. JAMA 245:934-948, 1981
Fries JF, Weyl S, Holman HR: Estimating prognosis in systemic lupus erythematosus. Am J Med 57561-565, 1974
Ginzler EM, Diamond HS, Weiner M, Schlesinger M, Fries JF,
26. Magi1 AB, Ballou HS, Chan V, Lirenman DS, Rae A, Sutton
RA: Diffuse proliferative lupus glomerulonephritis: determination of prognostic significance of clinical, laboratory and pathologic factors. Medicine (Baltimore) 63:21&220, 1984
27. Reveille JD, Bartolucci A, Alarc6n GS: Prognosis in systemic
lupus erythematosus: negative impact of increasing age at onset,
black race, and thrombocytopenia, as well as causes of death.
Arthritis Rheum 33:3748, 1990
28. Ward MM, Studenski S: Clinical prognostic factors in lupus
nephritis: the importance of hypertension and smoking. Arch
Intern Med 152:2082-2088, 1992
29. Seleznick MJ, Fries JF: Variables associated with decreased
survival in systemic lupus erythematosus. Semin Arthritis
Rheum 21:73-80, 1991
30. Studenski S, Allen NB, Caldwell DS, Rice JR, Polisson RP:
Survival in systemic lupus erythematosus: a multivariate analysis of demographic factors. Arthritis Rheum 30: 1326-1332, 1987
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