Variables
Associated With Decreased Survival in Systemic Lupus Erythematosus Mitchel
J. Seleznick
Fifty-one deaths occurred among 310 patients with systemic lupus erythematosus (SLE) observed for 1,234 patient-years. Twenty-one of 97 entry variables at first clinic visit were associated with an increased risk of mortality. When corrected for multiple comparisons, the only risk factor that retained statistical significance was systolic blood pressure. Each millimeter unit increase in systolic blood pressure corresponded to a 2% increase in mortality risk. Stepwise covariance and recursive partitioning analyses
0
VER THE LAST few decades, numerous studies have documented an improvement in the prognosis of patients with systemic lupus erythematosus (SLE) and have identified risk factors for mortality.‘-‘3 Because of the nonuniformity of individual study populations and differing statistical methodology, results of prognostic analyses have not been generalizable. To address this deficiency, Ginzler et al pooled data from nine university centers and identified nine variables that predicted mortality.14 Many of these risk factors-such as urine protein, creatinine, and hematocrit-are strongly associated with the presence of clinically apparent renal disease. To identify variables predictive of mortality at the time the patient is first seen, possibly before the development of overt renal disease, we performed univariate and multivariate survival analyses using statistical methods not widely available at the time the multicenter data were analyzed. The Cox covariante analysis of censored survival data” allowed for identification of potential risk factors that may be amenable to therapeutic intervention and categorization of patients into prognostic subgroups. Stepwise analysis provided a better understanding of the independent contributions and interactions among predictor variables. Recursive partitioning analysis provided a more intuitive branching tree-like method to analyze subgroup variability and interactions among variables. The availability of a large data bank” allowed for consideration of variables not previously analyzed in other series and permitted comparison with previous analyses performed on this data bank in 1975.’ Semmars in ArthrtttsandRheumatism, Vol21. No 2 (October),
1991:
and James F. Fries tended to identify nonspecific prognostic variables, rather than the classic serological and diagnostic features of SLE. These data suggest that hypertension is a more significant risk factor for mortality in SLE than the more specific measures of disease severity. Copyright 0 1991 by W.B. Saunders Company INDEX WORDS: Systemic lupus erythematosus; prognosis; mortality; hypertension.
MATERIAL AND METHODS
Pertinent items from history, physical examination, and laboratory data were prospectively collected on patients with a clinical diagnosis of SLE seen in Stanford University Immunology Clinic, a tertiary care referral center, between January 1, 1970 and October 15, 1982. All patients fulfilled American College of Rheumatology (ACR) criteria”.” or had the clinical diagnosis confirmed by one of the authors according to methods previously described.’ The data were entered into the Arthritis, Rheumatism, and Aging Medical Information System (ARAMIS) data bank by a data bank manager, who attempted to contact all patients lost to follow-up by telephone and/or mail. Standard-
From the Department ofInternalMedicine and the Veteram Administration-Robert Wood Johnson Clinical Scholars Program, Stanford University College of Medicine, Stanford, CA, and Palo Alto Veterans Affairs Hospital, Palo Alto, CA; and the Department of Internal Medicine, University of South Florida College of Medicine, Tampa, FL. Supported by the Veterans Administration-Robert Wood Johnson Clinical Scholars Program, by the Northern California Chapter of the Arthritis Foundation, and by G-ant No. AM21393 from the National Institutes of Health to ARAMIS (Arthritis, Rheumatism, and Aging Medical Information System). Mitchel J. Seleznick, MD: Assistant Profe.s,wr of Internal Medicine, University of South Flotida College of Medicine; James F. Fries, MD: Associate Professor of Medicine, Stanford University College of Medicine. Address reprint requests Mitchel J. Seleznick, MD, Untversity of South Florida College of Medicine, Box 19, 12901 Bruce B. Downs Blvd, Tampa, FL, 33612. Copyright 0 1991 by W.B. Saunder.s Company 0049-017219112102-0002$5.0010
pp73.80
73
74
SELEZNICK AND FRIES
ized ARAMIS follow-up protocols were continued.16 Data collected within the first 30 days were considered to be baseline values. Ninetyseven variables chosen for adequacy for data analysis (listed in Appendix 1) were screened for statistical association with survival, using univariate Cox analysis. Variables that met the significance level of P < .Olwere selected for stepwise and recursive partitioning analysis. When missing, categorical data were assumed to be normal, and mean SLE data bank values were assigned to continuous variables. To protect against false associations due to missing data assumptions, univariate Cox analyses were performed for all significant variables using the missing value policy described above and also for the subgroup of patients with complete data. Missing data frequencies are given in Appendix 2. RESULTS
Fifty-one deaths occurred in 310 patients monitored for 1,234 patient-years. Patients known to be alive at the time of last observation were monitored for a mean of 3.8 years. Patients who died during the study period had a mean follow-up of 5.4 years. Mean duration of multisystem illness at study entry was 1.9 years. Fourteen patients were lost to follow-up, but they were included in the actuarial analyses as censored at time of last observation. Table 1 lists the demographic data at the time of study entry. Mean age was 34.4, and 86% were female. Survival 1, 5, and 10 years after the first symptom was 99%, 96%, and 89%, respectively. Survival 1,5, and 10 years after the first visit was 96%, 88%, and 64%. Although patients were initially evaluated at different points in their illness, we did not find a significant association between disease duration at first visit and survival following first visit. Twenty-one variables were found to be associated with increased mortality risk by univariate analysis (Table 2). These variables remained significant when patients with missing data were excluded. When corrected for multiple comparisons (significance adjusted to P < .OOOS),” only systolic blood pressure remained significant. To evaluate the possible prognostic contribution of other variables to systolic blood pressure, stepwise analysis was performed (Table 3). The exponen-
Table 1: Baseline Characteristics
of 310 SLE
Patients
I
Variable
Age W
Mean 34.4
Range 10 = 78
Sex (% female)
86
Systolic BP (mm Hg)
129
90 = 210
Diastolic BP (mm Hg)
83
40 = 130
BUN (mg/dL)
21
Creatinine (mg/dL)
1.1
ESR (mm/h)
39
C3 (mg/dL)
90
2 = 180 0.5 = 6.2 2=
125
10 = 212
Dipstick urine protein (O-3+)
1.2
0 = 3+
Hematocrit (%)
37
17 = 55
Albumin (g/dL)
3.6
0.8 = 5.3
Total protein (g/dL)
6.9
3.2 = 10
WBC (x 1 ,000/mm3)
6.2
1.3 = 35.9
% Bands
5.8
0 = 43
% Lymphs
20
0 = 64
268
5 = 900
Platelets (x 7 ,000/mm3) Alkaline phosphatase (U)
68
5=
195
FANA (titer)
295
0 = 640
DNA binding (U)
17.1
0 = 160
Abbreviations: BP, blood pressure; ESR, erythrocyte sedimentation rate.
tial of the coefficient of the hazard function corresponds to the relative risk per unit increase of the predictor variable. To illustrate the clinical significance of the statistical associations identified by stepwise analysis, life tables were constructed by comparing survival in patients stratified for each variable identified in the stepwise analysis. Excess mortality was seen more dramatically in later years in the patients with elevated systolic blood pressure (Fig 1). In contrast, patients with elevated blood urea nitrogen (BUN) exhibited excess mortality during the first year, followed by relatively parallel survival curves thereafter (Fig 2). Patients with mouth ulcers were at risk for both early and late mortality (Fig 3). An alternative method of multivariate analysis entailed repetitive division of patients into subgroups stratified by significant prognostic variables (recursive partitioning). Results of this analysis are given in Fig 4. Although survival rates appear to decrease monotonically in each of the terminal nodes, Fig 5 demonstrates that survival among the three middle groups was not significantly different. Therefore, although the algorithm defined five prognostic strata, significant differences in survival were
SURVIVAL
IN SLE
75
Table 2: Predictors of Mortality Systolic
in SLE
BP*
BUN+
90
Creatinine’ Mouth
ulcers7
Ankle edema’ WBCs in urine sediment* % Bands in differential* Diastolic BP* Total protein* FANA titer* C3f Hematocrit* Fatigue* Westergren
ESR’
30 0
Dipstick urine protein’ Alkaline
.o
40
Dry mouth*
phosphatase’
SYST
BP>150
0 SYST I
2
BP 5 150 I I
murmur’
Dyspnea’
i fP
urine protein’
Systolic
68’
4
YEARS
Serum albumin’ Z&Hour
I
Fig 1:
Survival rates stratified by systolic blood
pressure at the time of study entry.
< .OOOl.
‘P < ,001. ‘P < .Ol. ‘P < -05.
noted only among the best, worst, and intermediate groups. Patients with hypertension and azotemia formed the worst prognostic group. Normotensive, normocomplementemic patients without mouth ulcers formed the best prognostic group. DISCUSSION
The most significant finding of this study is the striking association of systolic blood pressure with mortality risk. Because systolic blood pressure is a major risk factor for mortality,‘” coronary disease,” congestive failure,” and stroke’? in the general population, the identification of this risk factor in a lupus population appears intuitive. Reveille et al identified both
Table 3: Results of Stepwise Cox Covariance
systohc and diastolic hypertension at the time of diagnosis as mortality risk factors by univariate, but not multivariate, analysis.” Although multivariate analysis may identify relationships and correlations within prognostic variables, variables selected by multivariate methods are not
100
r
1
I
I
1
I
8
10
90 80 70 60 50 40
l BUN
L 20
OBUN
>20
Analysis 30 :;::.:.I
1 0
2
4
6
12
YEARS Fig 2: Survival rates stratified time of study entry.
by BUN at the
SELEZNICK AND FRIES
s
50
5
MOUTH 40 60 r 0 MOUTH
necessarily more categorically important or clinically significant than univariate correlates. The importance of blood pressure as an indicator of disease severity and a risk factor for renal deterioration has been shown previously by Dinant et al; they observed renal deterioration in 5 of 13 patients with diastolic blood pressure greater than 9.5 mm Hg, and in only 1 of 24 normotensive patients.24 They suggested that uncontrolled hypertension may lead to deterioration of renal lupus and made a strong argument for vigorous antihypertensive therapy. In the series of Budman and Steinberg,“” two thirds of hypertensive lupus patients had creatinine clearance values greater than 60 mL/min and nonnephrotic proteinuria, which also indicates that hypertension may predate clinically apparent renal disease. Similar observations showing that hypertension may predate renal disease have been reported recently in a pediatric lupus population with a 68% prevalence of hypertension.2h Atherosclerosis and myocardial infarction
0 NO
30 1 0
2
ULCERS ULCERS
4
6
8
10
12
YEARS Survival rates stratified
Fig 3:
by mouth ulcers
at the time of study entry.
51
Deaths
(167%)
Mottal~ty
Rate
(310 Patients)
Svstolic B.P.
+
lam
Mouth Ulcers
-
A
A
(6 9%)
$$
(33 3%)
(40 4%)
;
;
(66.7%)
Fig 4:
G? 61
144, BUN < 40
Fig 5:
Survival rates stratified by group assign-
ment from recursive partitioning
analysis. Three
prognostic strata are identified:
the group with
the
best survival,
the group
survival, and the intermediate
with
the worst
groups.
have contributed to the morbidity and bimodal pattern of mortality seen in patients with lupus.“‘-“”Hypertension has been demonstrated to be a risk factor for atherosclerosis and myocardial infarction in lupus populations. Hypertension was present in 78% of patients with vascular events and in 37% of patients without vascular events in the series of Gladman and Urowitz.” Perez-Gutthann et a?’ examined cardiac risk factors in a prospective cohort of 198 patients, 13 of whom had coronary heart disease. A history of hypertension or hypercholesterolemia, as well as age and male sex, were identified as cardiac risk factors. When combined in a multivariate logistic model, hypertension had the strongest association with coronary disease (as measured by odds ratio).‘2 The
association of antiphospholipid antibodies and thrombotic disorders33.34may place a subset of lupus patients at particularly high risk of complications from hypertension. Kitagawa et al found hypertension to be the most important risk factor for stroke in IUPUS.~~ Anticardiolipin antibody was seen in 43% of the stroke patients in which it was assayed. Why systolic rather than diastolic blood pressure was more closely associated with mortality risk is unclear. A high degree of colinearity between the two variables was observed (r = .72). Other clinical correlates of systolic blood pressure included serum creatinine (r = .35), an kle e d ema (r = .34), BUN (r = .31), and systolic murmur (I = .31). Although BUN and systolic blood pressure are both correlates of renal disease, their cumulative effects on mortality risk appear to be time-dependent. BUN was associated with early mortality and systolic blood pressure was more associated with late mortality. This time-dependency is consistent with the concept of late-stage lupus nephropathy characterized by serologic inactivity, remission of nonrenal manifestations, azotemia, and hypertensioni as well as the pattern of bimodal mortality risk in SLE, in which early mortality is due to lupus nephritis and late mortality to vascular eventsi Twenty-one variables were initially identified as having significant associations with mortality risk. Because of the distinct possibility of misleading conclusions as a result of simultaneous inference, the significance level was necessarily adjusted. However, many of the known risk factors for mortality in lupus identified in prior studies were noted before the adjustment. These include BUN, creatinine, proteinuria, and anemia. Other variables seen in prior studies were not identified in our analysis because of the relative paucity of relevant subsets of patients in our population. Therefore, the effects of age, race, and socioeconomic status were not seen. Likewise, central nervous system (CNS) disease was uncommon in our cohort, and the resulting data were insufficient to identify CNS parameters associated with mortality risk. Several unlikely mortality correlates not identified in prior studies were probably selected by the statistical
78
SELEZNICK AND FRIES
algorithm as a result of chance alone, a known consequence of simultaneous inference.” Our results are compared with prior studies in Table 4. The relative contribution of demographic variables such as age, sex, race, and socioeconomic status to mortality risk remains controversial. Dubois et al first commented on the importance of socioeconomic status4 while later studies documented higher mortality in black patients.113’2~‘4 Studenski et al described the independent effects of race and socioeconomic status”; however, Reveille et al found that privately insured black patients fared worse than similarly insured white patients.‘* No difference was seen between privately and publicly insured black patients. The role of age also remains unsettled. Although study results have differed, age has had little overall influence on survival.“’ Among clinical variables studied, the presence of renal disease is uniformly a poor prognostic sign, although creatinine clearance at the time of diagnosis was not associated with mortality in the series of Reveille et al.‘* The impact of CNS disease on mortality has been more difficult to document because of the relatively small number of patients with CNS manifestations. Estes and Christian documented higher mortality in patients with organic mental syndrome (OMS).3 In a recent update, Ginzler and Berg confirmed the presence of OMS as a risk factor for mortality in only the first year.39 Anemia was uniformly a poor prognostic sign in all populations in which it was studied. Reveille et al recently identified thrombocytopenia as a risk factor for mortality12; however, Table Author
4:
Risk Factor
Year
TO
Age
Race
Este?
1971
A
-
_
Duboi?
1974
-
+
E
+
+
+
GinzlerT982
SES
other studies failed to confirm this association.40’41Platelet count, although discarded in our analysis at the .08 significance level, did correlate with other measures of disease severity, including BUN (- .22), albumin (.18), hematocrit (.18), and systolic blood pressure (- .17). Platelet count was a strong predictor of mortality in the subset of our population studied in 1975.‘j Although differing statistical methodology may account for much of this contrast, it also appears that dominant clinical trends in the groups of patients studied may have accounted for substantial variation. At the time of this analysis, many more patients in the 1975 report were reaching the second peak of bimodal mortality risk as their length of follow-up increased. One might expect to find different mortality predictors that are related to the occurrence of late vascular complications, rather than direct effects of inflammatory disease. Mouth ulcers were previously identified as an important prognostic variable in this set of patient? and in a second population in which mucous membrane lesions were more frequent in patients who died early.30 An ascertainment bias may be operative if this physical finding is recorded more frequently in seriously ill patients. Presence of mouth ulcers correlated with dry mouth (r = .24), fatigue (Y= .20), dyspnea (r = .20), fluorescent antinuclear antibody (FANA) titer (r = .17), and hematocrit (I = -.14). Of note in most published series is the lack of association with mortality of many of the classical serologic and clinical features of SLE, such as arthritis, rash, photosensitivity, serositis, Studies:
Mortality
Sex
Renal
_
DPGN
_
Creatinine Protein
_
+
+
D
+
E
-
+ _
-
Studenski”
i 987
Swaak13
1989
D
Reveille’*
1990
Seleznick
1991
+ MGN
HCT
BP
PLAT
-
+
+
+
+
+ _
+
urea
+ _
Protein
+
+
_
BUN urea
urea
urea
Tg: A, anytime in disease course; E, at study entry; D, at diagnosis. SES, socioeconomic
proliferative glomerulonephritis;
MGN, membranous glomerulonephritis;
status; DPGN, diffuse
CNS, central nervous system; HCT, hematocrit; BP, blood
pressure; PLAT, platelets; OMS, organic mental syndrome; f, risk factor; -, not a risk factor. not reported.
CNS OMS
_
Protein Abbreviations:
in SLE
Missing data indicate item not studied or
SURVIVAL
IN SLE
79
Raynaud phenomenon, and antibody to native DNA. The findings of greatest importance for diagnosis in SLE are distinct from those with the greatest prognostic importance. The selection of the same variables by both the stepwise Cox and the recursive partitioning statistical models is reassuring. The recursive model also selected complement as a prognostic variable. The recursive partitioning algorithm defined clear strata, which may allow for bedside prognostication without reliance on computational aids. This method of analysis may be more advantageous in populations with heterogeneous subset composition. This study was performed on a data base from a single tertiary care center and was limited by the demographics of the patient population. Although we believe our findings are generalizable to other tertiary care rheumatic disease clinics, marked variations among clinic populations are common.” Because specific data were not recorded in our data base, we were unable to address infectious complications or causes of death. Statistical limitations included the imperfect fit of the data to the proportional hazards model, the loss of power attributable to missing data, the inability to analyze the effects of low frequency events, and the difficulty in obtaining statistical significance because of multiple comparisons. Although we may have been overly conservative in correcting for this, we believe our results are confirmatory of prior studies when the same variables were identified before adjustment for multiple comparisons. We were unable to note any treatment effects on mortality. Nonetheless, known risk factors for mortality were confirmed, and the importance of hypertension as a risk factor was underscored. These data strongly reinforce the notion that blood pressure is an important risk factor for mortality in lupus and should be included in all future studies of mortality and morbidity in SLE. Although our study did not address the issue of intervention, prudence dictates that the clinician pay close attention to hypertension management.
ACKNOWLEDGMENT The authors would like to thank Daniel A. Bloch, Byron W. Brown, Halsted Holman, Yvonne Sherrer, and Frank Vasey for their comments, as well as Trude Feldman for secretarial assistance.
APPENDIX
1
97 Entry Variables Sex, age, alopecia, dry eyes, mouth ulcers, pedal edema, blood pressure, diastolic blood pressure, malar rash, lymphadenopathy, arthralgias, rales, foot edema, proximal muscle weakness, symmetrical polyarthritis, joint count, glucose, BUN, uric acid, creatinine, % bands, % eosinophils, Westergren sedimentation rate, Wintrobe sedimentation rate, log extractable nuclear antigen (ENA). latex fixation, Syphilis test (VDRL), dyspnea, dipstick urine protein, urine red blood cellsihpf, urine white blood cellsihpf, quantitative urine protein, C3, hematocrit, absolute lymphocyte count, % lymphocytes, platelet count, serum albumin, total protein, white blood cell count, family income source, fatigue, fever, photosensitivity, Raynaud, skin tightening, conjunctivitis, paresthesias, muscle pain, diarrhea, temperature, discoid rash, uveitis, iritis, oral ulcers, xerostomia, number of new involved joints, pleural effusion, pleural ruh. systolic murmur, pericardial rub, hepatomegaly, splenomegaly, muscle tenderness, distal weakness, motor neuropathy. sensory neuropathy, number of involved joints, calcium, phosphorus, cholesterol, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, aldolase. creatinine phosphokinase, iron, iron binding, vital capacity, expiratory volume, diffuse capacity, cerebrospinal fluid protein, LE prep, FANA, Farr anti-DNA, anti-DNA by hemagglutination, IgA, IgG, disease duration,
IgM, red blood data entry date.
cell casts.
APPENDIX
prednisone,
2
Missing Values Age (yr), 0; sex (% female), 4; systolic BP (mm Hg), 25: diastolic BP, 25; BUN (mg/dL), 113; creatinine (mg/dL), 90; erythrocyte sedimentation rate (mm/h), 252: C3 (mg/ dL), 80; dipstick urine protein (O-3+), 81; hematocrit (%), 31; albumin (g/dL), 121; total protein (g/dL), 122; WBC (Xl,000/mm’). 35; % bands (%), 83; o/6 lymphs (%), 44; platelets (X l,OOO/mm’), 144; alkaline phosphatase (U), 124; FANA (titer), 109.
REFERENCES 1. Merrill
M, Shulman
LE: Determination
of prognosis
in chronic disease. illustrated by systemic lupus erythematosus. J Chronic Dis 1:12-32, lY55 7. Kellum RE, Haserick
JR: Systemic
lupus erythemato-
sus: A statistical evaluation of mortality based on a consecutive series of 299 patients. Arch Intern Med 113:92-99, 1964 3. Estes D, Christian CL: The natural history of systemic lupus erythematosus by prospective analysis. Medicine (Baltimore) 50235-95, 1971
SELEZNICK
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4. Dubois EL, Wierzchowiecki M, Cox MB, et al: Duration and death in systemic lupus erythematosus: An analysis of 249 cases. JAMA 227:1399-1402,1974 5. Fries JF, Weyls S, Holman HR: Estimating prognosis in systemic lupus erythematosus. Am J Med 57:561-565, 1974 6. Fries JF, Holman HR: Systemic lupus erythematosus. Major Probl Intern Med 6:169-178,1975 7. Fessel WJ: Systemic lupus erythematosus in the community: Incidence, prevalence, outcome and first symptoms: The high prevalence of black women. Arch Intern Med 134:1027-1035, 1974 8. Lee P, Urowitz MB, Bookman AAM, et al: Systemic lupus erythematosus: A review of 110 cases with respect to nephritis, the nervous system, infection, aseptic necrosis and prognosis. Q J Med 46:1-32,1977 9. Grigor R, Edmonds J, Lewkonia R, et al: Systemic lupus erythematosus: A prospective analysis. Ann Rheum Dis 37:121-128,1978 10. Wallace DJ, Podell T, Weiner J, et al: Systemic lupus erythematosus-Survival patterns: Experience with 609 patients. JAMA 246:934-938,198l 11. Studenski S, Allen NB, Caldwell DS, et al: Survival in systemic lupus erythematosus. A multivariate analysis of demographic factors. Arthritis Rheum 30:1326-1332,1987 12. Reveille JD, Bartolucci A, Alarcon 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 33:37-48, 1990 13. Swaak AJ, Nossent JC, Bronsveld W, et al: Systemic lupus erythematosus. I. Outcome and survival: Dutch experience with 110 patients studied prospectively. Ann Rheum Dis 48:447-454, 1989 14. Ginzler EM, Diamond HS, Weiner M, et al: A multicenter study of outcome in systemic lupus erythematosus. I. Entry variables as predictors of prognosis. Arthritis Rheum 25:601-611,1982 15. Dixon W (ed): BMDP Statistical Software 576. 1981 16. Fries JF: Time-oriented patient records and a computer data bank. JAMA 222:1536,1972 17. Cohen AS, Reynolds WE, Franklin EC: Preliminary criteria for the classification of systemic lupus erythematosus. Bull Rheum Dis 21:643-648, 1971 18. Tan EM, Cohen AS, Fries JF, et al: The 1982 revised
criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 25:1271-1277, 1982 19. Jones DR, Rushton L: Simultaneous inference in epidemiologic studies. Int J Epidemiol 11:276, 1982 20. Pickering GW: Is there a dividing line between normal and raised blood pressure?, in Pickering GW (ed): High Blood Pressure. Philadelphia, PA, Grune & Stratton, 1968, pp 232-235 21. Kannel WB, Gordon T, Schwartz MJ: Systolic versus diastolic blood pressure and risk of coronary heart disease. Am J Cardiol27:325-346, 1971 22. Kannel WB, Castelli WP, McNamara PN, et al: Role
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of blood pressure in the development of congestive heart failure: The Framingham study. N Engl J Med 287:781-787, 1972 23. Kannel WB, Wolf PA, Verter J, et al: Epidemiologic assessment of the role of blood pressure in stroke. JAMA 214:301-310, 1970 24. Dinant HJ, Decker JL, Klippel JH, et al: Alternative modes of cyclophosphamide and azathioprine therapy in lupus nephritis. Ann Intern Med 86:728-736,1982 25. Budman DR, Steinberg AD: Hypertension and renal disease in systemic lupus erythematosus. Arch Intern Med 136:1003-1007, 1976 26. Ostrov BE, Min W, Eichenfield AH, et al: Hypertension in children with systemic lupus erythematosus. Semin Arthritis Rheum 19:90-98, 1989 27. Urowitz MB, Bookman AAM, Koehler BE, et al: The bimodal mortality pattern of systemic lupus erythematosus. Am J Med 60:221-225,1976 28. Spiera H, Rothenberg RR: Myocardial four young patients with SLE. J Rheumatol 1983
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29. Badui E, Garcia-Rubi D, Robles E, et al: Cardiovascular manifestations in systemic lupus erythematosus: Prospective study of 100 patients. Angiology 36:431-441, 1985 30. Urowitz MB, Gladman DD: Late mortality The price we pay for control. J Rheumatol7:412-416, 31. Gladman DD, Urowitz MB: Morbidity lupus erythematosus. J Rheumatol 14:223-226,
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Associated With Decreased Survival in Systemic Lupus Erythematosus Mitchel
J. Seleznick
Fifty-one deaths occurred among 310 patients with systemic lupus erythematosus (SLE) observed for 1,234 patient-years. Twenty-one of 97 entry variables at first clinic visit were associated with an increased risk of mortality. When corrected for multiple comparisons, the only risk factor that retained statistical significance was systolic blood pressure. Each millimeter unit increase in systolic blood pressure corresponded to a 2% increase in mortality risk. Stepwise covariance and recursive partitioning analyses
0
VER THE LAST few decades, numerous studies have documented an improvement in the prognosis of patients with systemic lupus erythematosus (SLE) and have identified risk factors for mortality.‘-‘3 Because of the nonuniformity of individual study populations and differing statistical methodology, results of prognostic analyses have not been generalizable. To address this deficiency, Ginzler et al pooled data from nine university centers and identified nine variables that predicted mortality.14 Many of these risk factors-such as urine protein, creatinine, and hematocrit-are strongly associated with the presence of clinically apparent renal disease. To identify variables predictive of mortality at the time the patient is first seen, possibly before the development of overt renal disease, we performed univariate and multivariate survival analyses using statistical methods not widely available at the time the multicenter data were analyzed. The Cox covariante analysis of censored survival data” allowed for identification of potential risk factors that may be amenable to therapeutic intervention and categorization of patients into prognostic subgroups. Stepwise analysis provided a better understanding of the independent contributions and interactions among predictor variables. Recursive partitioning analysis provided a more intuitive branching tree-like method to analyze subgroup variability and interactions among variables. The availability of a large data bank” allowed for consideration of variables not previously analyzed in other series and permitted comparison with previous analyses performed on this data bank in 1975.’ Semmars in ArthrtttsandRheumatism, Vol21. No 2 (October),
1991:
and James F. Fries tended to identify nonspecific prognostic variables, rather than the classic serological and diagnostic features of SLE. These data suggest that hypertension is a more significant risk factor for mortality in SLE than the more specific measures of disease severity. Copyright 0 1991 by W.B. Saunders Company INDEX WORDS: Systemic lupus erythematosus; prognosis; mortality; hypertension.
MATERIAL AND METHODS
Pertinent items from history, physical examination, and laboratory data were prospectively collected on patients with a clinical diagnosis of SLE seen in Stanford University Immunology Clinic, a tertiary care referral center, between January 1, 1970 and October 15, 1982. All patients fulfilled American College of Rheumatology (ACR) criteria”.” or had the clinical diagnosis confirmed by one of the authors according to methods previously described.’ The data were entered into the Arthritis, Rheumatism, and Aging Medical Information System (ARAMIS) data bank by a data bank manager, who attempted to contact all patients lost to follow-up by telephone and/or mail. Standard-
From the Department ofInternalMedicine and the Veteram Administration-Robert Wood Johnson Clinical Scholars Program, Stanford University College of Medicine, Stanford, CA, and Palo Alto Veterans Affairs Hospital, Palo Alto, CA; and the Department of Internal Medicine, University of South Florida College of Medicine, Tampa, FL. Supported by the Veterans Administration-Robert Wood Johnson Clinical Scholars Program, by the Northern California Chapter of the Arthritis Foundation, and by G-ant No. AM21393 from the National Institutes of Health to ARAMIS (Arthritis, Rheumatism, and Aging Medical Information System). Mitchel J. Seleznick, MD: Assistant Profe.s,wr of Internal Medicine, University of South Flotida College of Medicine; James F. Fries, MD: Associate Professor of Medicine, Stanford University College of Medicine. Address reprint requests Mitchel J. Seleznick, MD, Untversity of South Florida College of Medicine, Box 19, 12901 Bruce B. Downs Blvd, Tampa, FL, 33612. Copyright 0 1991 by W.B. Saunder.s Company 0049-017219112102-0002$5.0010
pp73.80
73
74
SELEZNICK AND FRIES
ized ARAMIS follow-up protocols were continued.16 Data collected within the first 30 days were considered to be baseline values. Ninetyseven variables chosen for adequacy for data analysis (listed in Appendix 1) were screened for statistical association with survival, using univariate Cox analysis. Variables that met the significance level of P < .Olwere selected for stepwise and recursive partitioning analysis. When missing, categorical data were assumed to be normal, and mean SLE data bank values were assigned to continuous variables. To protect against false associations due to missing data assumptions, univariate Cox analyses were performed for all significant variables using the missing value policy described above and also for the subgroup of patients with complete data. Missing data frequencies are given in Appendix 2. RESULTS
Fifty-one deaths occurred in 310 patients monitored for 1,234 patient-years. Patients known to be alive at the time of last observation were monitored for a mean of 3.8 years. Patients who died during the study period had a mean follow-up of 5.4 years. Mean duration of multisystem illness at study entry was 1.9 years. Fourteen patients were lost to follow-up, but they were included in the actuarial analyses as censored at time of last observation. Table 1 lists the demographic data at the time of study entry. Mean age was 34.4, and 86% were female. Survival 1, 5, and 10 years after the first symptom was 99%, 96%, and 89%, respectively. Survival 1,5, and 10 years after the first visit was 96%, 88%, and 64%. Although patients were initially evaluated at different points in their illness, we did not find a significant association between disease duration at first visit and survival following first visit. Twenty-one variables were found to be associated with increased mortality risk by univariate analysis (Table 2). These variables remained significant when patients with missing data were excluded. When corrected for multiple comparisons (significance adjusted to P < .OOOS),” only systolic blood pressure remained significant. To evaluate the possible prognostic contribution of other variables to systolic blood pressure, stepwise analysis was performed (Table 3). The exponen-
Table 1: Baseline Characteristics
of 310 SLE
Patients
I
Variable
Age W
Mean 34.4
Range 10 = 78
Sex (% female)
86
Systolic BP (mm Hg)
129
90 = 210
Diastolic BP (mm Hg)
83
40 = 130
BUN (mg/dL)
21
Creatinine (mg/dL)
1.1
ESR (mm/h)
39
C3 (mg/dL)
90
2 = 180 0.5 = 6.2 2=
125
10 = 212
Dipstick urine protein (O-3+)
1.2
0 = 3+
Hematocrit (%)
37
17 = 55
Albumin (g/dL)
3.6
0.8 = 5.3
Total protein (g/dL)
6.9
3.2 = 10
WBC (x 1 ,000/mm3)
6.2
1.3 = 35.9
% Bands
5.8
0 = 43
% Lymphs
20
0 = 64
268
5 = 900
Platelets (x 7 ,000/mm3) Alkaline phosphatase (U)
68
5=
195
FANA (titer)
295
0 = 640
DNA binding (U)
17.1
0 = 160
Abbreviations: BP, blood pressure; ESR, erythrocyte sedimentation rate.
tial of the coefficient of the hazard function corresponds to the relative risk per unit increase of the predictor variable. To illustrate the clinical significance of the statistical associations identified by stepwise analysis, life tables were constructed by comparing survival in patients stratified for each variable identified in the stepwise analysis. Excess mortality was seen more dramatically in later years in the patients with elevated systolic blood pressure (Fig 1). In contrast, patients with elevated blood urea nitrogen (BUN) exhibited excess mortality during the first year, followed by relatively parallel survival curves thereafter (Fig 2). Patients with mouth ulcers were at risk for both early and late mortality (Fig 3). An alternative method of multivariate analysis entailed repetitive division of patients into subgroups stratified by significant prognostic variables (recursive partitioning). Results of this analysis are given in Fig 4. Although survival rates appear to decrease monotonically in each of the terminal nodes, Fig 5 demonstrates that survival among the three middle groups was not significantly different. Therefore, although the algorithm defined five prognostic strata, significant differences in survival were
SURVIVAL
IN SLE
75
Table 2: Predictors of Mortality Systolic
in SLE
BP*
BUN+
90
Creatinine’ Mouth
ulcers7
Ankle edema’ WBCs in urine sediment* % Bands in differential* Diastolic BP* Total protein* FANA titer* C3f Hematocrit* Fatigue* Westergren
ESR’
30 0
Dipstick urine protein’ Alkaline
.o
40
Dry mouth*
phosphatase’
SYST
BP>150
0 SYST I
2
BP 5 150 I I
murmur’
Dyspnea’
i fP
urine protein’
Systolic
68’
4
YEARS
Serum albumin’ Z&Hour
I
Fig 1:
Survival rates stratified by systolic blood
pressure at the time of study entry.
< .OOOl.
‘P < ,001. ‘P < .Ol. ‘P < -05.
noted only among the best, worst, and intermediate groups. Patients with hypertension and azotemia formed the worst prognostic group. Normotensive, normocomplementemic patients without mouth ulcers formed the best prognostic group. DISCUSSION
The most significant finding of this study is the striking association of systolic blood pressure with mortality risk. Because systolic blood pressure is a major risk factor for mortality,‘” coronary disease,” congestive failure,” and stroke’? in the general population, the identification of this risk factor in a lupus population appears intuitive. Reveille et al identified both
Table 3: Results of Stepwise Cox Covariance
systohc and diastolic hypertension at the time of diagnosis as mortality risk factors by univariate, but not multivariate, analysis.” Although multivariate analysis may identify relationships and correlations within prognostic variables, variables selected by multivariate methods are not
100
r
1
I
I
1
I
8
10
90 80 70 60 50 40
l BUN
L 20
OBUN
>20
Analysis 30 :;::.:.I
1 0
2
4
6
12
YEARS Fig 2: Survival rates stratified time of study entry.
by BUN at the
SELEZNICK AND FRIES
s
50
5
MOUTH 40 60 r 0 MOUTH
necessarily more categorically important or clinically significant than univariate correlates. The importance of blood pressure as an indicator of disease severity and a risk factor for renal deterioration has been shown previously by Dinant et al; they observed renal deterioration in 5 of 13 patients with diastolic blood pressure greater than 9.5 mm Hg, and in only 1 of 24 normotensive patients.24 They suggested that uncontrolled hypertension may lead to deterioration of renal lupus and made a strong argument for vigorous antihypertensive therapy. In the series of Budman and Steinberg,“” two thirds of hypertensive lupus patients had creatinine clearance values greater than 60 mL/min and nonnephrotic proteinuria, which also indicates that hypertension may predate clinically apparent renal disease. Similar observations showing that hypertension may predate renal disease have been reported recently in a pediatric lupus population with a 68% prevalence of hypertension.2h Atherosclerosis and myocardial infarction
0 NO
30 1 0
2
ULCERS ULCERS
4
6
8
10
12
YEARS Survival rates stratified
Fig 3:
by mouth ulcers
at the time of study entry.
51
Deaths
(167%)
Mottal~ty
Rate
(310 Patients)
Svstolic B.P.
+
lam
Mouth Ulcers
-
A
A
(6 9%)
$$
(33 3%)
(40 4%)
;
;
(66.7%)
Fig 4:
G? 61
144, BUN < 40
Fig 5:
Survival rates stratified by group assign-
ment from recursive partitioning
analysis. Three
prognostic strata are identified:
the group with
the
best survival,
the group
survival, and the intermediate
with
the worst
groups.
have contributed to the morbidity and bimodal pattern of mortality seen in patients with lupus.“‘-“”Hypertension has been demonstrated to be a risk factor for atherosclerosis and myocardial infarction in lupus populations. Hypertension was present in 78% of patients with vascular events and in 37% of patients without vascular events in the series of Gladman and Urowitz.” Perez-Gutthann et a?’ examined cardiac risk factors in a prospective cohort of 198 patients, 13 of whom had coronary heart disease. A history of hypertension or hypercholesterolemia, as well as age and male sex, were identified as cardiac risk factors. When combined in a multivariate logistic model, hypertension had the strongest association with coronary disease (as measured by odds ratio).‘2 The
association of antiphospholipid antibodies and thrombotic disorders33.34may place a subset of lupus patients at particularly high risk of complications from hypertension. Kitagawa et al found hypertension to be the most important risk factor for stroke in IUPUS.~~ Anticardiolipin antibody was seen in 43% of the stroke patients in which it was assayed. Why systolic rather than diastolic blood pressure was more closely associated with mortality risk is unclear. A high degree of colinearity between the two variables was observed (r = .72). Other clinical correlates of systolic blood pressure included serum creatinine (r = .35), an kle e d ema (r = .34), BUN (r = .31), and systolic murmur (I = .31). Although BUN and systolic blood pressure are both correlates of renal disease, their cumulative effects on mortality risk appear to be time-dependent. BUN was associated with early mortality and systolic blood pressure was more associated with late mortality. This time-dependency is consistent with the concept of late-stage lupus nephropathy characterized by serologic inactivity, remission of nonrenal manifestations, azotemia, and hypertensioni as well as the pattern of bimodal mortality risk in SLE, in which early mortality is due to lupus nephritis and late mortality to vascular eventsi Twenty-one variables were initially identified as having significant associations with mortality risk. Because of the distinct possibility of misleading conclusions as a result of simultaneous inference, the significance level was necessarily adjusted. However, many of the known risk factors for mortality in lupus identified in prior studies were noted before the adjustment. These include BUN, creatinine, proteinuria, and anemia. Other variables seen in prior studies were not identified in our analysis because of the relative paucity of relevant subsets of patients in our population. Therefore, the effects of age, race, and socioeconomic status were not seen. Likewise, central nervous system (CNS) disease was uncommon in our cohort, and the resulting data were insufficient to identify CNS parameters associated with mortality risk. Several unlikely mortality correlates not identified in prior studies were probably selected by the statistical
78
SELEZNICK AND FRIES
algorithm as a result of chance alone, a known consequence of simultaneous inference.” Our results are compared with prior studies in Table 4. The relative contribution of demographic variables such as age, sex, race, and socioeconomic status to mortality risk remains controversial. Dubois et al first commented on the importance of socioeconomic status4 while later studies documented higher mortality in black patients.113’2~‘4 Studenski et al described the independent effects of race and socioeconomic status”; however, Reveille et al found that privately insured black patients fared worse than similarly insured white patients.‘* No difference was seen between privately and publicly insured black patients. The role of age also remains unsettled. Although study results have differed, age has had little overall influence on survival.“’ Among clinical variables studied, the presence of renal disease is uniformly a poor prognostic sign, although creatinine clearance at the time of diagnosis was not associated with mortality in the series of Reveille et al.‘* The impact of CNS disease on mortality has been more difficult to document because of the relatively small number of patients with CNS manifestations. Estes and Christian documented higher mortality in patients with organic mental syndrome (OMS).3 In a recent update, Ginzler and Berg confirmed the presence of OMS as a risk factor for mortality in only the first year.39 Anemia was uniformly a poor prognostic sign in all populations in which it was studied. Reveille et al recently identified thrombocytopenia as a risk factor for mortality12; however, Table Author
4:
Risk Factor
Year
TO
Age
Race
Este?
1971
A
-
_
Duboi?
1974
-
+
E
+
+
+
GinzlerT982
SES
other studies failed to confirm this association.40’41Platelet count, although discarded in our analysis at the .08 significance level, did correlate with other measures of disease severity, including BUN (- .22), albumin (.18), hematocrit (.18), and systolic blood pressure (- .17). Platelet count was a strong predictor of mortality in the subset of our population studied in 1975.‘j Although differing statistical methodology may account for much of this contrast, it also appears that dominant clinical trends in the groups of patients studied may have accounted for substantial variation. At the time of this analysis, many more patients in the 1975 report were reaching the second peak of bimodal mortality risk as their length of follow-up increased. One might expect to find different mortality predictors that are related to the occurrence of late vascular complications, rather than direct effects of inflammatory disease. Mouth ulcers were previously identified as an important prognostic variable in this set of patient? and in a second population in which mucous membrane lesions were more frequent in patients who died early.30 An ascertainment bias may be operative if this physical finding is recorded more frequently in seriously ill patients. Presence of mouth ulcers correlated with dry mouth (r = .24), fatigue (Y= .20), dyspnea (r = .20), fluorescent antinuclear antibody (FANA) titer (r = .17), and hematocrit (I = -.14). Of note in most published series is the lack of association with mortality of many of the classical serologic and clinical features of SLE, such as arthritis, rash, photosensitivity, serositis, Studies:
Mortality
Sex
Renal
_
DPGN
_
Creatinine Protein
_
+
+
D
+
E
-
+ _
-
Studenski”
i 987
Swaak13
1989
D
Reveille’*
1990
Seleznick
1991
+ MGN
HCT
BP
PLAT
-
+
+
+
+
+ _
+
urea
+ _
Protein
+
+
_
BUN urea
urea
urea
Tg: A, anytime in disease course; E, at study entry; D, at diagnosis. SES, socioeconomic
proliferative glomerulonephritis;
MGN, membranous glomerulonephritis;
status; DPGN, diffuse
CNS, central nervous system; HCT, hematocrit; BP, blood
pressure; PLAT, platelets; OMS, organic mental syndrome; f, risk factor; -, not a risk factor. not reported.
CNS OMS
_
Protein Abbreviations:
in SLE
Missing data indicate item not studied or
SURVIVAL
IN SLE
79
Raynaud phenomenon, and antibody to native DNA. The findings of greatest importance for diagnosis in SLE are distinct from those with the greatest prognostic importance. The selection of the same variables by both the stepwise Cox and the recursive partitioning statistical models is reassuring. The recursive model also selected complement as a prognostic variable. The recursive partitioning algorithm defined clear strata, which may allow for bedside prognostication without reliance on computational aids. This method of analysis may be more advantageous in populations with heterogeneous subset composition. This study was performed on a data base from a single tertiary care center and was limited by the demographics of the patient population. Although we believe our findings are generalizable to other tertiary care rheumatic disease clinics, marked variations among clinic populations are common.” Because specific data were not recorded in our data base, we were unable to address infectious complications or causes of death. Statistical limitations included the imperfect fit of the data to the proportional hazards model, the loss of power attributable to missing data, the inability to analyze the effects of low frequency events, and the difficulty in obtaining statistical significance because of multiple comparisons. Although we may have been overly conservative in correcting for this, we believe our results are confirmatory of prior studies when the same variables were identified before adjustment for multiple comparisons. We were unable to note any treatment effects on mortality. Nonetheless, known risk factors for mortality were confirmed, and the importance of hypertension as a risk factor was underscored. These data strongly reinforce the notion that blood pressure is an important risk factor for mortality in lupus and should be included in all future studies of mortality and morbidity in SLE. Although our study did not address the issue of intervention, prudence dictates that the clinician pay close attention to hypertension management.
ACKNOWLEDGMENT The authors would like to thank Daniel A. Bloch, Byron W. Brown, Halsted Holman, Yvonne Sherrer, and Frank Vasey for their comments, as well as Trude Feldman for secretarial assistance.
APPENDIX
1
97 Entry Variables Sex, age, alopecia, dry eyes, mouth ulcers, pedal edema, blood pressure, diastolic blood pressure, malar rash, lymphadenopathy, arthralgias, rales, foot edema, proximal muscle weakness, symmetrical polyarthritis, joint count, glucose, BUN, uric acid, creatinine, % bands, % eosinophils, Westergren sedimentation rate, Wintrobe sedimentation rate, log extractable nuclear antigen (ENA). latex fixation, Syphilis test (VDRL), dyspnea, dipstick urine protein, urine red blood cellsihpf, urine white blood cellsihpf, quantitative urine protein, C3, hematocrit, absolute lymphocyte count, % lymphocytes, platelet count, serum albumin, total protein, white blood cell count, family income source, fatigue, fever, photosensitivity, Raynaud, skin tightening, conjunctivitis, paresthesias, muscle pain, diarrhea, temperature, discoid rash, uveitis, iritis, oral ulcers, xerostomia, number of new involved joints, pleural effusion, pleural ruh. systolic murmur, pericardial rub, hepatomegaly, splenomegaly, muscle tenderness, distal weakness, motor neuropathy. sensory neuropathy, number of involved joints, calcium, phosphorus, cholesterol, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, aldolase. creatinine phosphokinase, iron, iron binding, vital capacity, expiratory volume, diffuse capacity, cerebrospinal fluid protein, LE prep, FANA, Farr anti-DNA, anti-DNA by hemagglutination, IgA, IgG, disease duration,
IgM, red blood data entry date.
cell casts.
APPENDIX
prednisone,
2
Missing Values Age (yr), 0; sex (% female), 4; systolic BP (mm Hg), 25: diastolic BP, 25; BUN (mg/dL), 113; creatinine (mg/dL), 90; erythrocyte sedimentation rate (mm/h), 252: C3 (mg/ dL), 80; dipstick urine protein (O-3+), 81; hematocrit (%), 31; albumin (g/dL), 121; total protein (g/dL), 122; WBC (Xl,000/mm’). 35; % bands (%), 83; o/6 lymphs (%), 44; platelets (X l,OOO/mm’), 144; alkaline phosphatase (U), 124; FANA (titer), 109.
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