Development of SLE among Patients Seen in Consultation: Long-Term Follow-Up Abstract # 1699 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, Goodwin- Johnson 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 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. 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) Aim To examine a large cohort of patients with possible SLE at initial rheumatology consultation to study predictors of evolution to SLE 1
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 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 Methods 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 Table 1. Baseline Demographics of Patients Demographic Characteristic, n=264 Mean age (SD), years 39.2 (12.4) Mean no. of ACR Criteria (SD) 2.7 (1.0) % Female 94.3 % Race/Ethnicity White 67.4 African American 10.9 Asian 5.7 Hispanic 6.4 Others 9.5 % ANA 88.3 % Anti-dsDNA 17.1 Results 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) 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 Table 2. Clinical Characteristics by Final Diagnosis Clinical Characteristic (n=56) (n=161) p value * (n=47) p value** Mean age at first visit (SD), years 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 % Female 94.6 95.6 0.72 89.4 0.46 % Race/Ethnicity White 64.3 65.8 0.87 76.6 0.20 African American 16.1 11.2 0.35 4.3 0.06 Asian 7.1 5.0 0.51 6.4 1.00 Hispanic 6.4 6.8 1.00 6.4 1.00 Other/Multiple 7.1 11.2 0.45 6.4 1.00 % Died in follow-up 3.6 5.6 0.73 2.1 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 Table 3. Initial Manifestations by Final Diagnosis Clinical Characteristics p value * p value** (n= 56), % (n= 161), % (n= 47), % 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 -- Hematologic Involvement 44.6 30.4 0.07 31.9 0.22 Neurologic Involvement 3.6 4.4 1.00 10.6 0.24 ANA 100 97.5 0.57 78.7 <0.001 Anti-dsDNA 42.9 16.2 <0.001 8.5 <0.001 Anti-Sm 10.7 2.5 0.02 4.3 0.28 Anti-Ro 28.6 15.5 0.04 19.2 0.35 Anti-La 10.7 8.1 0.58 10.6 1.00 *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 2
Table 4. Other Common Presenting Symptoms SLE Symptoms (n=56) % (n=161) % P* (n= 47) % Raynauds 43 42 1.00 49 0.56 Alopecia 25 21 0.58 19 0.64 Low Complement 38 19 0.006 28 0.30 Fevers 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 Thrombosis 9 4 0.19 6 0.72 Miscarriages 5 5 1.00 6 1.00 Sicca symptoms 14 12 0.64 17 0.78 *p- value for definite SLE vs. possible SLE. ** p- value for definite SLE vs. not SLE. Fisher's exact tests P** Predictors of SLE: Multivariable Logistic Regression 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 Odds Ratio (95% CI)* Oral ulcers 2.47 (1.04-5.88) Renal disease** 18.23 (1.73-189.93) Anti-dsDNA 2.57 (1.22-5.40) *CI= confidence interval **Renal disease: persistent proteinuria or cellular casts per ACR criteria. Other Diagnoses among those with Other diagnoses 36.1% Cutaneous LE 6.4% MCTD 8.5% RA 6.4% Sjogren s12.8% Table 5. Therapies Received During Follow-up Medications (n= 56 ) % (n= 161) % p* (n= 47) % Hydroxychloroquine 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 P** Fibromyalgia 19.2% Scleroderma 2.1% APS 4.3% Thyroid disease 4.3% 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 Rituximab 0 0.6 1.00 0 1.00 Other Biologics 1.8 3.1 1.00 4.3 0.59 *p- value for definite SLE vs. possible SLE. ** p- value for definite SLE vs. not SLE. Fisher's exact Strengths Limitations 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 3
Conclusions 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. Implications 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 Patients, n=264 Characteristics Female No. (%) 249 (94.3) Age, mean (SD) years 39.2 (12.4) Family history of SLE, (%) 26(9.8) Ethnicity White, (%) 178 (67.4) Black, (%) 29 (10.9) Asian, (%) 15 (5.7) Hispanic, (%) 17 (6.4) Others, (%) 25 (9.5) Smokers Current, (%) 28(14.4%) Past, (%) 19(9.7%) Never, (%) 148(75.9%) Alcohol Consumption Heavy, (%) 5(2.6%) Light, (%) 104(53.9%) None, (%) 84(43.5%) Table2. Demographic and clinical characteristics of the study Patients at follow up. Characteristics (n= 264) % Follow Up-period, mean SD years 6.33± 4.61 6.33± 4.61 Death 12 4.6% Hydroxychloroquine 178 67.4% Raynaud's 115 43.6% Malar Rash 41 15.5% Discoid Rash 3 1.14% Photosensitivity 58 21.9% Oral ulcers 33 12.5% Arthritis 149 56.4% Serositis 40 15.2% Renal disease 10 3.8% Neurological disease 14 5.3% Hematological disorders 89 33.7% Anti-DNA 54 20.5% Anti-sm 12 4.5% ANA 250 94.7% Anti-Ro/La 52 19.7% 4
Table 2. Characteristics at Latest Follow-up of Patients referred for "Possible SLE" between 1992-2012 Diagnosis at last follow up, N=264 Clinical N= 56 N= 161 p value * N= 47 p value** Mean age at first visit (±SD) 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 Female, n(%) 53 (94.6) 154 (95.6) 0.72 42 (89.4) 0.46 Race/Ethnicity 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 Deaths in follow-up, n(%) 2 (3.6) 9 (5.6) 0.73 1 (2.1) 1.00 *p- value for vs.. ** p- Value for vs.. t -tests for continuous and Fisher's exact for categorical variables Table 3-Other Common Presenting Symptoms Symptoms n=56 n=161 P- Value No SLE n=47 P- Value 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 Low complements n(%) 21(37.5) 30(18.6) 0.006 13(27.7) 0.30 Fevers n(%) 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 Low grade proteinuria or hematuria n(%) 0(0) 2(1.2) 1.00 1(2.1) 0.45 Medications Table 4. Therapies Received During Follow-up Definitive SLE n= 56, % n= 161, % p* No SLE n= 47, % Hydroxychloroquine 45 (80.4%) 104 (64.6%) 0.03 29 (61.7%) 0.04 Other Antimalarial 3(5.4%) 1(0.6%) 0.05 3 (6.4%) 1.00 Oral Steroids 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 P** Deaths in Follow-up 2 (3.6%) : end-stage liver disease, lung cancer 9 (5.6%) : ovarian cancer, colorectal cancer, 4 lung cancers, pulmonary hypertension, end-stage renal disease, and 1unknown cause 1 (2.1%) No SLE: unknown cause 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 Rituximab 0(0%) 1(0.6%) 1.00 0(0%) 1.00 Other Biologics 1(1.8%) 5(3.1%) 1.00 2 (4.3%) 0.59 *p- value for vs.. ** p- Value for vs.. t -tests for continuous and Fisher's exact for categorical variables 3.0% Other diagnoses 1.5% 3.10% 1.9% 1.5% Possible Lupus 1.20% 0% Cutaneo us Lupus MCTD Cutaneo us Lupus 1.90% 6.4% 0.0% 12.5% 8.5% No Lupus 6.4% Definitive Lupus Cutaneo us Lupus MCTD Cutaneou s Lupus MCTD Conclusions > 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) 5
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