10/28/2013
Abstract # 1699
Development of SLE among
“Possible SLE” Patients Seen in
Consultation: Long-Term Follow-Up
May Al Daabil, MD
Bonnie L. Bermas, MD
Alexander Fine
Hsun Tsao
Patricia Ho
Joseph F. Merola, MD
Peter H. Schur, MD
Elena M. Massarotti, MD
Karen H. Costenbader, MD, MPH
Disclosures
K. Costenbader, MD, MPH: Pfizer (investigator); Biogen
Idec and Genzyme (consulting)
Elena Massarotti, MD: Human Genome Sciences, Bristol
Myers Squibb, Sanofi (investigator); Springer Publishing
(royalties); Questcor, Amplimmune, UCB, InPractice, GoodwinJohnson Group, National Medical Consultants (consulting)
Brigham and Women’s Hospital
Harvard Medical School, Boston, MA
Evidence-Based Medicine
1. Vilá LM, et al. Clinical outcome and predictors of disease
evolution in patients with incomplete lupus erythematosus.
Lupus. 2000;9(2):110-5.
2. James JA, et al. Hydroxychloroquine sulfate treatment is
associated with later onset of systemic lupus erythematosus.
Lupus. 2007;16(6):401-9.
3. Olsen NJ, et al. Autoantibody profiling to follow evolution of
lupus syndromes. Arthritis Res Ther. 2012; 14(4):R174.
Background
Prior studies have described and followed patients with
“incomplete lupus” (< 4 ACR Criteria for SLE Classification).
Range of results:
• 87 patients followed mean 2.2 years 8 (9%) evolved to SLE.
Malar rash, oral ulcers, anti-dsDNA and decreased C4
associated with evolution to SLE. (Vilá LM, et al. Lupus, 2000)
• 28 patients in Northern Sweden followed for 10 years 16
(57%) developed definite SLE. Malar rash and anticardiolipin
antibodies predictors of SLE. (Stahl Hallengren C, et al. Lupus, 2004)
• 26 patients in Denmark followed for 8 years 7 (27%)
developed definite SLE. Most common baseline features
photosensitivity, arthritis and hematologic abnormalities. No
predictive features identified. (Laustrup H, et al. Lupus, 2007)
Background
• SLE can be challenging to diagnose.
• Rheumatology consultation for potential SLE is
common.
• In some patients, SLE can be neither confirmed nor
ruled out at initial visit.
• Currently, there is no accurate means of predicting
likelihood of developing SLE for those patients.
Aim
• To examine a large cohort of patients with
“possible SLE” at initial rheumatology
consultation to study predictors of evolution
to SLE
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10/28/2013
Methods
Methods
Study Population
• Our Lupus Center Registry: 5,032 patients received
billing code for SLE (ICD-9 710.0) and rheumatologist
review for ACR Classification Criteria
• Patients seen initially 1/1/1992 – 12/31/2012
• “Possible SLE” according to both initial treating and
reviewing rheumatologist, and < 4 ACR criteria for
classification of SLE at initial visit
• ≥ 2 visits at least ≥ 3 months apart
Data Collection
Review of medical records through 5/15/2013:
• Demographics
• Visit dates
• SLE manifestations
• ACR criteria
• Autoimmune disease serologies
• Prescriptions
• Final diagnoses of treating rheumatologists
Statistical methods
• Fisher’s exact and t-tests to assess differences between patients with
and without definitive SLE diagnoses
• Multivariable logistic regression models to identify independent
predictors of definite SLE:
• included all clinical variables and adjusted for age, sex, and
race/ethnicity
Results
Table 1. Baseline Demographics of
“Possible SLE” Patients
• Seen by 32 different attending rheumatologists
• Mean follow-up: 6.4 years (range 0.3- 17.8)
• Median time from initial presentation to new symptoms or
laboratory/immunological events: 20 months (range 0.2-15 years)
• Most common next event: anti-dsDNA (16 patients)
All Patients, n=264
Demographic Characteristic
39.2 (12.4)
Mean age (SD), years
2.7 (1.0)
Mean no. of ACR Criteria (SD)
% Female
94.3
% Race/Ethnicity
White
67.4
African American
10.9
Asian
5.7
Hispanic
6.4
Others
At last follow-up:
• 56 (21%) patients definite SLE
3 patients of 56 had < 4 ACR criteria:
– 2 lupus nephritis
– 1 transverse myelitis
• 161 (60%) still “possible SLE”
• 47 (17%) not SLE
9.5
% ANA
88.3
% Anti-dsDNA
17.1
Table 2. Clinical Characteristics by Final Diagnosis
Table 3. Initial Manifestations by Final Diagnosis
Definite SLE
(n= 56), %
Possible SLE
(n= 161), %
p value *
Not SLE
(n= 47), %
p value**
Malar Rash
14.3
11.2
0.63
12.8
1.00
Discoid Rash
1.8
1.2
1.00
0
1.00
Photosensitivity
21.4
22.4
1.00
21.3
1.00
Oral Ulcers
19.6
11.2
0.12
8.5
0.16
Arthritis
71.4
57.1
0.08
36.2
< 0.001
Serositis
21.4
13.0
0.14
14.9
0.45
Renal Disease***
10.7
0.6
0.001
0
--
44.6
30.4
0.07
31.9
0.22
Clinical Characteristics
Clinical Characteristic
Mean age at first visit (SD), years
Definite SLE Possible SLE
p value *
(n=56)
(n=161)
Not SLE
(n=47)
p value**
36.9 (11.1)
39.3 (12.8)
0.22
41.5 (12.5)
0.05
Mean follow-up (SD), years
6.4 (4.2)
5.9 (4.5)
0.49
7.6 (5.5)
0.19
Mean ACR Criteria at last visit, (SD)
4.1 (1.6)
3.0 (1.2)
<0.001
2.6 (1.4)
<0.001
94.6
95.6
0.72
89.4
0.46
% Female
% Race/Ethnicity
White
64.3
65.8
0.87
76.6
0.20
Hematologic Involvement
African American
16.1
11.2
0.35
4.3
0.06
Neurologic Involvement
3.6
4.4
1.00
10.6
0.24
Asian
7.1
5.0
0.51
6.4
1.00
ANA
100
97.5
0.57
78.7
<0.001
Hispanic
6.4
6.8
1.00
6.4
1.00
Anti-dsDNA
42.9
16.2
<0.001
8.5
<0.001
Other/Multiple
7.1
11.2
0.45
6.4
1.00
Anti-Sm
10.7
2.5
0.02
4.3
0.28
% Died in follow-up
3.6
5.6
0.73
2.1
1.00
Anti-Ro
Anti-La
28.6
10.7
15.5
8.1
0.04
0.58
19.2
10.6
0.35
1.00
*p- value for definite SLE vs. possible SLE. ** p- value for definite SLE vs. not SLE. t -tests for continuous
and Fisher's exact for categorical variables
*p- value for definite SLE vs. possible SLE. ** p- value for definite SLE vs. not SLE. Fisher's exact tests
***Renal disease: persistent proteinuria or cellular casts per ACR criteria
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Table 4. Other Common Presenting Symptoms
Definite SLE
(n=56) %
Possible SLE
(n=161) %
Raynauds
43
Alopecia
25
Low Complement
Fevers
Predictors of SLE:
Multivariable Logistic Regression
P*
Not SLE
(n= 47) %
P**
42
1.00
49
0.56
21
0.58
19
0.64
38
19
0.006
28
0.30
9
10
1.00
6
0.72
Headache
21
12
0.04
19
0.81
Fatigue
54
42
0.16
38
0.16
Vasculitis
2
3
1.00
6
0.33
Oral ulcers
Thrombosis
9
4
0.19
6
0.72
Renal disease**
Anti-dsDNA
SLE Symptoms
Miscarriages
5
5
1.00
6
1.00
Sicca symptoms
14
12
0.64
17
0.78
• Factors associated with evolution to definite SLE (vs.
still possible or not SLE) by last visit
• Logistic regression models of clinical variables, also
adjusted for age, sex, and race/ethnicity
Presenting Features
Medications
Hydroxychloroquine
MCTD 8.5%
RA 6.4%
Sjogren’s12.8%
Fibromyalgia
19.2%
Scleroderma 2.1%
APS 4.3%
Definite SLE
(n= 56 ) %
Possible SLE
(n= 161) %
p*
Not SLE
(n= 47) %
P**
80.4
64.6
0.03
61.7
0.04
Other Antimalarial
5.4
0.6
0.05
6.4
1.00
Oral Steroids
57.1
34.8
0.004
27.7
0.003
IV Steroids
3.6
0.6
0.16
2.1
1.00
Azathioprine
7.1
1.8
0.07
4.3
0.68
Mycophenolate Mofetil
12.5
2.5
0.007
3.1
0.06
Cyclophosphamide
5.4
0.6
0.05
0
0.24
Methotrexate
16.1
9.3
0.21
12.8
0.77
Sulfasalazine
3.6
5.6
0.73
10.6
0.24
0
0.6
1.00
0
1.00
1.8
3.1
1.00
4.3
0.59
Rituximab
Thyroid disease
4.3%
2.57 (1.22-5.40)
Table 5. Therapies Received During Follow-up
Other Diagnoses among those with “Not SLE”
Other diagnoses
36.1%
2.47 (1.04-5.88)
18.23 (1.73-189.93)
*CI= confidence interval
**Renal disease: persistent proteinuria or cellular casts per ACR criteria.
*p- value for definite SLE vs. possible SLE. ** p- value for definite SLE vs. not SLE. Fisher's exact
tests
Cutaneous LE
6.4%
Odds Ratio (95% CI)*
Other Biologics
*p- value for definite SLE vs. possible SLE. ** p- value for definite SLE vs. not SLE. Fisher's exact
Limitations
Strengths
• Large study population in a single, large
academic center
• Long follow-up period (mean 6.4 years)
• Clinical data well documented
• Medical record data prospectively recorded
•
•
•
•
•
Retrospective study
Variation in rheumatologist practice style
Multiple comparisons
Did not evaluate the severity or activity of SLE
Hydroxychloroquine may delay SLE
development and alter natural history of
disease onset
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Conclusions
Implications
• Among patients with “possible SLE” at initial
consultation, 21% were diagnosed with definite SLE
within mean of 6.4 years of follow up.
• Renal disease, anti-dsDNA, and oral ulcers at initial
visit predictive of development of SLE.
• 60% still thought to have “possible SLE” at last
follow-up.
• A large proportion of all patients received
hydroxychloroquine, including > 60% of those
without definite SLE at final follow-up.
• SLE manifestations develop slowly over long
follow-up period in some patients.
• A better means for earlier identification of
those who will progress to definite SLE is
necessary.
• Identification of factors and biomarkers that
could reliably predict SLE would be valuable.
Dr. Aldaabil was funded by a grant from the
Saudi Arabian cultural mission to the US.
NIAMS P60AR047782
Table 1 . Baseline Demographics of Possible SLE” Patients, n=264
Characteristics
All Patients
Female No. (%)
Age, mean (SD) years
Family history of SLE, (%)
Ethnicity
White, (%)
Black, (%)
249 (94.3)
39.2 (12.4)
Asian, (%)
Hispanic, (%)
Others, (%)
Smokers
Current, (%)
Past, (%)
Never, (%)
Alcohol Consumption
Heavy, (%)
Light, (%)
None, (%)
26(9.8)
178 (67.4)
29 (10.9)
15 (5.7)
17 (6.4)
25 (9.5)
28(14.4%)
19(9.7%)
148(75.9%)
5(2.6%)
104(53.9%)
84(43.5%)
Table2. Demographic and clinical characteristics of the study Patients at
follow up.
Characteristics
All Patients
(n= 264)
All Patients
%
Follow Up-period, mean SD years
Death
Hydroxychloroquine
Raynaud's
Malar Rash
Discoid Rash
Photosensitivity
Oral ulcers
Arthritis
Serositis
Renal disease
Neurological disease
Hematological disorders
Anti-DNA
Anti-sm
ANA
Anti-Ro/La
6.33± 4.61
12
178
115
41
3
58
33
149
40
10
14
89
54
12
250
52
6.33± 4.61
4.6%
67.4%
43.6%
15.5%
1.14%
21.9%
12.5%
56.4%
15.2%
3.8%
5.3%
33.7%
20.5%
4.5%
94.7%
19.7%
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Table 2. Characteristics at Latest Follow-up of Patients referred for "Possible
SLE" between 1992-2012 Diagnosis at last follow up, N=264
Not SLE
N= 47
p value**
Mean age at first visit (±SD)
Definite SLE
N= 56
36.9± 11.1
39.3± 12.8
0.22
41.5± 12.5
0.05
Mean age at follow up (mean
±SD)
46.5± 11.4
49.5± 13.5
0.15
53.2± 13.2
0.007
Clinical
Female, n(%)
Possible SLE
p value *
N= 161
Definite SLE
n=56
Symptoms
53 (94.6)
154 (95.6)
0.72
42 (89.4)
0.46
White, n(%)
36 (64.3)
106 (65.8)
0.87
36( 76.6)
0.20
African American, n(%)
9 (16.1)
18 (11.2)
0.35
2 (4.3)
0.06
Asian, n(%)
4(7.1)
8( 5.0)
0.51
3 (6.4)
1.00
Hispanic, n(%)
3(6.4)
11 (6.8)
1.00
3 (6.4)
1.00
Other/Multiple, n(%)
4(7.1)
18( 11.2)
0.45
3 (6.4)
1.00
Mean Follow up (±SD)
6.40± 4.21
5.94± 4.46
0.49
7.64± 5.51
0.19
Mean ACR Criteria at follow up
4.1± 1.58
2.97± 1.2
<0.001
2.6± 1.4
<0.001
2 (3.6)
9 (5.6)
0.73
1 (2.1)
1.00
Race/Ethnicity
Deaths in follow-up, n(%)
Table 3-Other Common Presenting Symptoms
Possible SLE
n=161
PValue
No SLE
n=47
PValue
Raynaud’s n(%)
24(42.9)
68(42.2)
1.00
23(48.9)
0.56
Alopecia n(%)
14(25)
34(21.1)
0.58
9(19.2)
0.64
21(37.5)
30(18.6)
0.006
13(27.7)
0.30
5(8.9)
16(9.9)
1.00
3(6.4)
0.72
Headache n(%)
12(21.4)
19(11.8)
0.04
9(19.2)
0.81
Fatigue n(%)
30(53.6)
68(42.2)
0.16
18(38.3)
0.16
Vasculitis n(%)
1(1.8)
4(2.5)
1.00
3(6.4)
0.33
Thrombosis n(%)
5(8.9)
7(4.4)
0.19
3(6.4)
0.72
Miscarriages n(%)
3(5.4)
8(4.9)
1.00
3(6.4)
1.00
SICCA n(%)
8(14.3)
19(11.8)
0.64
8(17.0)
0.78
0(0)
2(1.2)
1.00
1(2.1)
0.45
Low complements n(%)
Fevers n(%)
Low grade proteinuria or
hematuria n(%)
*p- value for Definite SLE vs. Possible SLE. ** p- Value for Definite SLE vs. Not SLE. t -tests for continuous and Fisher's exact for categorical
variables
Table 4. Therapies Received During Follow-up
Definitive SLE Possible SLE
n= 56 , %
n= 161, %
Medications
Hydroxychloroquine
45 (80.4%)
Other Antimalarial
104 (64.6%)
p*
No SLE
n= 47, %
P**
0.03
29 (61.7%)
0.04
3(5.4%)
1(0.6%)
0.05
3 (6.4%)
1.00
32(57.1%)
56(34.8%)
0.004
13 (27.7%)
0.003
IV steroids
2(3.6%)
1(0.6%)
0.16
1 (2.1%)
1.00
Azathioprine
4 (7.1%)
3(1.8%)
0.07
2 (4.3%)
0.68
Mycophenolate Mofetil
7(12.5%)
4(2.5%)
0.007
1 (3.1%)
0.06
Cyclophosphamide
3(5.4%)
1 (0.6%)
0.05
0 (0)
0.24
Methotrexate
9(16.1%)
15(9.3%)
0.21
6 (12.8%)
0.77
Sulfasalazine
2(3.6%)
9(5.6%)
0.73
5 (10.6%)
0.24
0(0%)
1(0.6%)
1.00
0(0%)
1.00
1(1.8%)
5(3.1%)
1.00
2 (4.3%)
0.59
Oral Steroids
Rituximab
Other Biologics
Deaths in Follow-up
2 (3.6%) Definite SLE: end-stage liver disease, lung cancer
9 (5.6%) Possible SLE: ovarian cancer, colorectal cancer, 4 lung
cancers, pulmonary hypertension, end-stage renal disease,
and 1unknown cause
1 (2.1%) No SLE: unknown cause
*p- value for Definite SLE vs. Possible SLE. ** p- Value for Definite SLE vs. Not SLE. t -tests for
continuous and Fisher's exact for categorical variables
Other diagnoses
Conclusions
All Patients
3.0%
1.9%
No Lupus
Cutaneo
us Lupus
6.4%
6.4%
8.5%
MCTD
1.5%
MCTD
0.0%
1.5%
Possible Lupus
3.10%
Cutaneo
us Lupus
1.20%
0% Cutaneo
us Lupus
Definitive Lupus
Cutaneou
s Lupus
12.5%
• > 60% of those without definite SLE at final
follow-up received hydroxychloroquine.
• Treatment with may retard the development of
SLE:
In retrospective cohort of 130 military recruits with data
prior to SLE diagnosis, those who received either
hydroxychloroquine and prednisone developed SLE more
slowly than those who did not. (James JA, et al. Lupus, 2007)
MCTD
1.90%
5
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HCQ may delayed the onset of SLE symptoms
Hydroxychloroquine sulfate treatment is associated with Later onset of SLE. James, JA et al2007;16(6):401-9
6