POS0731 ASSOCIATION OF PATIENT-REPORTED OUTCOMES WITH TYPE I INTERFERON GENE SIGNATURE FROM THE INTERNATIONAL SYSTEMIC LUPUS ERYTHEMATOSUS PROSPECTIVE OBSERVATIONAL COHORT STUDY (SPOCS)

Background

The aim of the Systemic Lupus Erythematosus Prospective Observational Cohort Study (SPOCS) is to examine the disease course of patients with systemic lupus erythematosus (SLE) in relation to their type I interferon gene signature (IFNGS) status.1 IFNGS has been associated with SLE disease activity.2

Objectives

To identify associations between IFNGS status and patient-reported outcomes (PROs) among patients receiving clinical care while enrolled in SPOCS.

Methods

This noninterventional, international, prospective, observational cohort study included adult patients (≥18 years) with moderate to severe SLE receiving standard therapy. Short Form 36 Health Survey version 2 (SF-36; 0–100), Lupus Quality of Life (LupusQoL; 0–100) and Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F; 0–52) were assessed at baseline, 6 months, and 12 months. Higher scores indicate better outcomes. Analyses were stratified by high or low IFNGS status (4-gene test) at baseline.

Results

Of 827 patients, mean (standard deviation [SD]) age was 45.1 (14.0) years, 771 (93%) were female, 525 (63%) were IFNGS high, and 219 (26%) were IFNGS low. IFNGS-high patients were younger than IFNGS-low (mean [SD] 43.0 [13.7] vs 50.7 [12.9] years), had fewer comorbidities (83% vs 91%) and similar baseline disease activity (mean [SD]: SLE Disease Activity Index 2000, 9.8 [4.3] vs 9.2 [5.2]; Physician’s Global Assessment, both 1.5 [0.6]). At baseline, there were some differences in PROs between IFNGS-high and -low patients. At Month 12, changes in most domains did not meet the minimal clinically important difference (MCID). Slight improvement was observed only in selected domains in the IFNGS-high group. This group was younger and had fewer comorbidities at baseline. (Table 1, Figure 1).

Table 1.

PRO Scores at Baseline and Month 12 by IFNGS Status

BaselineMonth 12Change From BaselineMCID (≥)PROTotal (n=810)IFNGS HighIFNGS Low (n=219)Total (n=431)IFNGS HighIFNGS Low (n=147)TotalIFNGS HighIFNGS Low(n=525)(n=279)SF-36Physical Component Summary37.4 (10.5)38.3 (10.5)+35.2 (10.6)139.8 (10.8)41.7 (10.4)+35.7 (10.8)2.3 (7.6)3.0 (8.1)*0.6 (6.4)2.5Mental Component Summary43.2 (11.6)43.6 (11.7)42.7 (11.6)44.9 (11.2)45.9 (10.6)43.4 (11.5)0.9 (9.2)1.4 (9.6)-0.3 (8.4)2.5LupusQoLPhysical health56.4 (27.4)58.1 (27.0)+52.1 (27.7)60.5 (26.9)65.0 (25.3)+52.5 (27.7)2.6 (18.1)5.0 (19.2)*-1.4 (16.4)3.4Pain54.3 (30.3)55.7 (29.8)50.2 (30.8)61.4 (28.5)66.0 (26.5)+52.6 (29.1)5.4 (23.8)8.1 (25.2)0.7 (21.3)8.5Planning61.4 (32.2)63.7 (31.8)+57.0 (32.2)66.3 (29.6)70.8 (27.8)+58.5 (30.8)2.9 (24.4)4.2 (24.7)-1.0 (22.9)6.5Intimate relationships58.0 (34.8)61.2 (34.1)+50.9 (34.8)59.8 (33.9)65.6 (32.3)+50.6 (34.7)-0.6 (24.5)-0.7 (25.9)-0.8 (22.3)9.2Burden to others50.7 (32.6)50.7 (32.6)50.4 (33.5)56.4 (30.7)59.7 (29.5)51.8 (31.7)3.1 (25.4)5.6 (26.0)*0.1 (23.9)5.3Emotional health66.3 (25.6)66.1 (26.0)67.8 (24.8)71.1 (24.7)72.9 (23.8)69.2 (24.7)1.8 (19.4)3.1 (20.3)-0.5 (18.1)3.4Body image62.6 (29.4)61.0 (30.1)66.4 (28.3)68.2 (27.6)70.3 (27.4)65.1 (27.0)2.0 (23.9)*4.1 (24.3)*-0.4 (22.1)1.1Fatigue48.6 (27.8)49.9 (27.6)45.5 (28.2)53.7 (26.9)57.4 (26.2)+46.9 (25.7)2.1 (19.3)3.5 (20.0)-0.9 (18.7)3.9FACIT-F25.8 (13.4)26.9 (13.4)+23.4 (12.9)28.7 (13.2)31.1 (12.7)+24.6 (12.9)2.3 (9.6)3.2 (10.2)0.6 (8.5)4.0

Data are mean (SD). Asterisks (*) indicate changes from baseline ≥ MCID. +Comparison between high and low IFNGS status by Mann-Whitney U test (nominal p-value<0.01).

Data for n are patients per subgroup and do not reflect responses per PRO assessment.

Conclusion

In this cohort study, patients with moderate to severe SLE had poor health status, health-related quality of life, and fatigue. A clinically meaningful change was not met in most PROs, suggesting patients continue to have a high need for improved treatment options.

References

[1]Hammond ER. BMJ Open 2020;10:e036563.

[2]Dall’era MC. Ann Rheum Dis 2005;64:1692–7.

Acknowledgements

Writing assistance by Shelley Harris, PhD (Fishawack). This study was sponsored by AstraZeneca.

Disclosure of Interests

Martin Aringer Speakers bureau: AbbVie, AstraZeneca, BMS, Boehringer Ingelheim, Chugai, HEXAL, Lilly, MSD, Mylan, Novartis, Roche, Sanofi, UCB, Consultant of: AbbVie, AstraZeneca, BMS, Boehringer Ingelheim, Galapagos, GSK, Pfizer, Roche, Sanofi, Laurent Arnaud Speakers bureau: AstraZeneca, Consultant of: AstraZeneca, Grant/research support from: AstraZeneca, Christine Peschken Consultant of: AstraZeneca, GSK, Grant/research support from: AstraZeneca, Richard Furie Speakers bureau: AstraZeneca, Genentech, Consultant of: AstraZeneca, Grant/research support from: AstraZeneca, Eric F. Morand Speakers bureau: GSK, Novartis, Paid instructor for: AstraZeneca, Biogen, Eli Lilly, Consultant of: AstraZeneca, Biogen, Bristol Myers Squibb, Eli Lilly, EMD Serono, Genentech, GSK, Janssen, Servier, Grant/research support from: Abbvie, AstraZeneca, Bristol Myers Squibb, GSK, Janssen, Caroline Seo Shareholder of: AstraZeneca, Employee of: AstraZeneca, Eleni Rapsomaniki Employee of: AstraZeneca, Jonatan Hedberg Shareholder of: AstraZeneca, Employee of: AstraZeneca, Jacob Knagenhjelm Shareholder of: AstraZeneca, Employee of: AstraZeneca, Tina Grünfeld Eén Shareholder of: AstraZeneca, Employee of: AstraZeneca, Barnabas Desta Shareholder of: AstraZeneca, Employee of: AstraZeneca, Raj Tummala Shareholder of: AstraZeneca, Employee of: AstraZeneca, Alessandro Sorrentino Shareholder of: Galapagov, Abbott Laboratories, Gilead Sciences, Moderna, Employee of: Janssen, Sanofi, AstraZeneca, Heide Stirnadel-Farrant Shareholder of: AstraZeneca, GSK, Employee of: AstraZeneca

POS0733 DISCORDANCE BETWEEN PATIENT GLOBAL ASSESSMENT AND PHYSICIAN GLOBAL ASSESSMENT OF DISEASE ACTIVITY IN THE MODERATE TO SEVERE SYSTEMIC LUPUS ERYTHEMATOSUS PROSPECTIVE OBSERVATIONAL COHORT STUDY (SPOCS)

Background

Emphasis in clinical research and care has been placed on the need to include evaluations that reflect the perspectives of both patients and physicians.

Objectives

The goal of this study was to analyze patient and physician assessments and to determine characteristics associated with concordance and discordance of these assessments.

Methods

Baseline data of patients with moderate to severe SLE enrolled in the Systemic Lupus Erythematosus Prospective Observational Cohort Study (SPOCS)1 were analyzed. Physician’s Global Assessment (PGA) representing disease activity during the 4-week interval prior to baseline and Patient Global Assessment (PtGA) representing the impact of disease during a 1-month interval prior to baseline were independently scored on visual analogue scales (PGA 0-3; PtGA 0-100). Spearman’s rank (rho) was calculated to assess their correlation. A difference between PGA and PtGA scores ≥25 points defined discordance (after rescaling PGA to 0–100).2 Baseline characteristics associated with concordance and discordance were assessed.

Results

827 patients were included in this analysis. At baseline, the mean PGA score was 1.5 (SD 0.6, n=824), and the mean PtGA score was 51.1 (SD 25.5, n=790). The correlation between PGA and PtGA was low (rho=0.19, P<0.001, n=787) (Figure 1). PGA and PtGA scores were discordant in 301 (38%) of patients. Among those patients exceeding the threshold defining discordance, 152 (19%) had higher PGA scores than PtGA scores, and 149 (19%) had higher PtGA scores than PGA scores. The subgroup of PGA-higher discordant patients had higher mean SLEDAI-2K scores, greater serological activity, and more frequent type 1 interferon gene signature positivity, whereas PtGA-higher discordant patients were more likely to be using analgesics and/or anti-depressants, had higher mean BMI and were less likely to be employed (Table 1).

Table 1.

Characteristics associated with PGA and PtGA scoresa

Concordant (N=486)PGA higher (N=152)PtGA higher (N=149)P-value-1bP-value-2bAge, years45.5 (13.7)42.7 (14.4)46.4 (14.0)0.0220.043Race, White326 (71%)96 (66%)94 (69%)0.1920.360BMI, kg/m227.6 (7.2)25.6 (5.4)27.2 (6.4)0.0320.011Employed249 (51%)80 (53%)51 (34%)0.0010.001SLEDAI-2K total score10.0 (4.8)10.8 (5.1)8.2 (3.0)<0.001<0.001Positive ANA/anti-dsDNA436 (90%)143 (94%)127 (85%)0.0120.041Low Complement (C3 or C4)132 (46%)58 (60%)41 (43%)0.0140.026High IFNGS313 (71%)101 (78%)89 (64%)0.0140.049≥1 mild flare115 (24%)20 (13%)45 (30%)<0.0010.001≥1 moderate flare88 (18%)37 (24%)23 (16%)0.0570.120≥1 severe flare39 (8%)11 (7%)15 (10%)0.3720.628OCS daily dose0.0520.129No OCS214 (44%)57 (38%)62 (42%)>0–7.5 mg139 (29%)38 (25%)52 (35%)>7.5–15 mg75 (15%)37 (25%)18 (12%)>15 mg57 (12%)19 (13%)17 (11%)Immunosuppressants271 (56%)80 (53%)75 (50%)0.6900.467Biologics106 (22%)25 (16%)19 (13%)0.3640.032Analgesics152 (31%)22 (14%)46 (31%)<0.001<0.001Antidepressants123 (25%)31 (20%)52 (35%)0.0050.013

aMean (SD) for continuous, n (%) for nominal variables. Missing data was <10% of patients for the variables displayed. Denominators exclude missing data. bP-value-1 compares PGA higher vs PtGA higher. P-value-2 compares all 3 groups. Based on the chi-squared test for categorical variables and t-test or ANOVA for continuous variables.

IFNGS, type 1 interferon gene signature; OCS, oral corticosteroid; PGA, physician global assessment; PtGA, patient global assessment

Conclusion

Low correlation between PGA and PtGA suggests both should be used to acquire a broad perspective of the impact of disease on the overall health of patients. Different baseline characteristics were associated with the PGA-higher compared to the PtGA-higher discordant subgroups.

References

[1]Hammond ER, et al. BMJ Open. 2020;10:e036563.

[2]Challa DNV, et al. Rheumatol Ther. 2017;4:201–8.

Acknowledgements

Editing assistance by Rebecca S. Jones, PhD (Fishawack). This study was sponsored by AstraZeneca.

Disclosure of Interests

Laurent Arnaud Speakers bureau: AstraZeneca, Consultant of: AstraZeneca, Grant/research support from: AstraZeneca, Richard Furie Speakers bureau: AstraZeneca, Genentech, Consultant of: AstraZeneca, Grant/research support from: AstraZeneca, Eric F. Morand Speakers bureau: GSK, Novartis, Paid instructor for: AstraZeneca, Biogen, Eli Lilly, Consultant of: AstraZeneca, Biogen, Bristol Myers Squibb, Eli Lilly, EMD Serono, Genentech, GSK, Janssen, Servier, Grant/research support from: Abbvie, AstraZeneca, Bristol Myers Squibb, GSK, Janssen, Christine Peschken Consultant of: AstraZeneca, GSK, Grant/research support from: AstraZeneca, Martin Aringer Speakers bureau: AbbVie, AstraZeneca, BMS, Boehringer Ingelheim, Chugai, HEXAL, Lilly, MSD, Mylan, Novartis, Roche, Sanofi, UCB, Consultant of: AbbVie, AstraZeneca, BMS, Boehringer Ingelheim, Galapagos, GSK, Pfizer, Roche, Sanofi, Eleni Rapsomaniki Employee of: AstraZeneca, Jonatan Hedberg Shareholder of: AstraZeneca, Employee of: AstraZeneca, Jacob Knagenhjelm Shareholder of: AstraZeneca, Employee of: AstraZeneca, Caroline Seo Shareholder of: AstraZeneca, Employee of: AstraZeneca, Tina Grünfeld Eén Shareholder of: AstraZeneca, Employee of: AstraZeneca, Barnabas Desta Shareholder of: AstraZeneca, Employee of: AstraZeneca, Alessandro Sorrentino Shareholder of: Galapagov, Abbott LAboratories, Gilead Sciences, Moderna, Employee of: Janssen, Sanofi, AstraZeneca, Raj Tummala Shareholder of: AstraZeneca, Employee of: AstraZeneca, Heide Stirnadel-Farrant Shareholder of: AstraZeneca, GSK, Employee of: AstraZeneca

OP0289 LLDAS (LOW LUPUS DISEASE ACTIVITY STATE), LOW DISEASE ACTIVITY (LDA) AND REMISSION (ON- OR OFF-TREATMENT) PREVENT DAMAGE ACCRUAL IN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) PATIENTS IN A MULTINATIONAL MULTICENTER COHORT

Background:

Remission, LDA and LDAS have been proposed as treatment goals for SLE. However, the independent impact of these states on damage accrual has not been fully evaluated.

Objectives:

To determine the independent impact of remission (both off & on treatment), LDA, and LLDAS on damage accrual.

Methods:

We studied a long-term longitudinal multinational SLE cohort, including patients completing at least two annual assessments. Remission off-treatment was defined as a SLEDAI (excluding serology) =0, without prednisone and immunosuppressive (IS) drugs. Remission on-treatment was defined as a SLEDAI (excluding serology) =0, prednisone daily dose<=5 mg/d and maintenance IS drugs. LDA was defined as a SLEDAI (excluding serology) <=2, without prednisone or IS drugs. LLDAS was defined as a SLEDAI <=4 with no activity in major organ systems, with no new features of lupus disease activity compared to the previous assessment, prednisone daily dose<=7.5 mg/d and maintenance IS drugs. Antimalarials were allowed in all groups. Damage accrual was ascertained with the SLICC/ACR damage index (SDI). Univariable and multivariable generalized estimated equation (GEE) negative binomial regression models were used. To create mutually exclusive groups, disease activity was divided into five states: remission off-treatment, remission on-treatment (minus remission off treatment), LDA (minus remission), LLDAS (minus remission and LDA) and not-optimally controlled. The proportion of the time that patients were in the specific state at each visit since cohort entry was determined. Possible effect modifiers and confounders adjusted for included sex, age at diagnosis, race/ethnicity, education, baseline disease duration, follow-up time, the highest-ever glucocorticoid dose prior to cohort entry, antimalarials and SDI. Time-dependent covariates were determined at the same annual visit as disease activity state; the outcome was the increase in the SDI and it was assessed at the subsequent visit.

Results:

There were 1,652 patients, 1464 (88.6%) were female, mean age at diagnosis was 34.6 (SD 13.4) years and mean baseline disease duration was 5.5 (SD 4.1) months. Patients had a mean follow-up of 6.5 (SD 4.3) years, 11686 visits were included. 763 patients (46.2%) had an increase in SDI score ≥1 during follow-up. 2483 (21.2%) of the visits were classified as remission off-treatment, 2276 (19.5%) as remission on-treatment, 544 (4.7%) as LDA, 657 (5.6%) as LLDAS and 5726 (49.0%) as not-optimally controlled. Being in remission off-treatment, remission on-treatment, LDA and LLDAS were predictive of a lower probability of damage accrual [remission off-treatment IRR=0.403, 95% CI 0.301-0.541); remission on-treatment IRR=0.313 (95% CI 0.218-0.451) LDA: IRR=0.469 (CI 95% CI 0.272-0.809); LLDAS IRR=0.440 (95% CI 0.241-0.803)]. The multivariable model is summarized in Table 1.

Table 1.

Multivariable GEE model of the impact of disease activity states on damage accrual.

Incidence Rate Ratio95% CIDisease activity stateRemission off treatment0.4030.301-0.541Remission on treatment0.3130.218-0.451LDA0.4690.272-0.809LLDAS0.4400.241-0.803Gender, male1.2741.086-1.495Age at diagnosis1.0241.020-1.029EthnicityCaucasian USRef.Caucasian other1.0170.849-1.217African1.4671.211-1.776Asian0.8630.693-1.075Hispanic1.2661.034-1.550Other1.1210.759-1.656Educational level, years0.9770.957-0.996Disease duration at baseline0.9600.801-1.150Follow-up time0.9420.923-0.960Antimalarial use0.7860.681-0.908Highest prednisone dose before baseline1.0021.001-1.007SDI before1.1001.050-1.1152

LLDAS: Low lupus disease activity state LDA: Low disease activity SDI: SLICC/ACR Damage Index

Conclusion:

Remission on- and off-treatment, LDA and LLDAS were associated with less damage accrual, even adjusting for possible confounders and effect modifiers. This highlights the importance of treating to target in SLE.

Disclosure of Interests:

Manuel F. Ugarte-Gil Grant/research support from: Pfizer, Janssen, John Hanly: None declared, Murray B Urowitz: None declared, Caroline Gordon Speakers bureau: UCB, Consultant of: Center for Disease Control, Astra-Zeneca, MFP, Sanofi, UCB, Sang-Cheol Bae: None declared, Juanita Romero-Diaz: None declared, Jorge Sanchez-Guerrero: None declared, Sasha Bernatsky: None declared, Ann E Clarke Consultant of: AstraZeneca, BristolMyersSquibb, GlaxoSmithKline, and Exagen Diagnostics, Daniel J Wallace Grant/research support from: Exagen, David Isenberg: None declared, Anisur Rahman: None declared, Joan T Merrill: None declared, Paul Fortin: None declared, Dafna D Gladman Consultant of: Abbvie, Janssen, Pfizer, Novartis, Amgen, Grant/research support from: Abbvie, Janssen, Pfizer, Novartis, Amgen, Ian N. Bruce: None declared, Michelle A Petri: None declared, Ellen M Ginzler Grant/research support from: Aurinia pharmaceutical, M.A. Dooley: None declared, Rosalind Ramsey-Goldman: None declared, Susan Manzi: None declared, Andreas Jonsen: None declared, Ronald van Vollenhoven Speakers bureau: AbbVie, Galapagos, GSK, Janssen, Pfizer, UCB, Consultant of: Abbvie, AstraZeneca, Biogen, Biotest, Celgen, Galapagos, Gilead, Janssen, Pfizer, Sanofie, Servier, UCB, Vielabo, Grant/research support from: BMS, GSK, Lilly, UCB, Cynthia Aranow: None declared, Meggan Mackay: None declared, Guillermo Ruiz-Irastorza: None declared, S. Sam Lim: None declared, Murat Inanc: None declared, Kenneth C Kalunian Consultant of: Roche, Biogen, Janssen, AstraZeneca, Eli Lilly, Genetech, Gilead, ILTOO, Nektar, Viela, Equillium, Bristol-Meyers Squibb, Soren Jacobsen Grant/research support from: BMS, Christine Peschken: None declared, Diane L Kamen: None declared, Anca Askanase Consultant of: Abbvie, Grant/research support from: Glaxo Smith Kline, Astra Zeneca, Janssen, Eli Lilly and Company, Mallinckrodt, Pfizer, Bernardo Pons-Estel Consultant of: GSK, Janssen, Graciela S Alarcon: None declared.

Study of Flare Assessment in Systemic Lupus Erythematosus Based on Paper Patients

Objective

To determine the level of agreement of disease flare severity (distinguishing severe, moderate, and mild flare and persistent disease activity) in a large paper‐patient exercise involving 988 individual cases of systemic lupus erythematosus.

Methods

A total of 988 individual lupus case histories were assessed by 3 individual physicians. Complete agreement about the degree of flare (or persistent disease activity) was obtained in 451 cases (46%), and these provided the reference standard for the second part of the study. This component used 3 flare activity instruments (the British Isles Lupus Assessment Group [BILAG] 2004, Safety of Estrogens in Lupus Erythematosus National Assessment [SELENA] flare index [SFI] and the revised SELENA flare index [rSFI]). The 451 patient case histories were distributed to 18 pairs of physicians, carefully randomized in a manner designed to ensure a fair case mix and equal distribution of flare according to severity.

Results

The 3‐physician assessment of flare matched the level of flare using the 3 indices, with 67% for BILAG 2004, 72% for SFI, and 70% for rSFI. The corresponding weighted kappa coefficients for each instrument were 0.82, 0.59, and 0.74, respectively. We undertook a detailed analysis of the discrepant cases and several factors emerged, including a tendency to score moderate flares as severe and persistent activity as flare, especially when the SFI and rSFI instruments were used. Overscoring was also driven by scoring treatment change as flare, even if there were no new or worsening clinical features.

Conclusion

Given the complexity of assessing lupus flare, we were encouraged by the overall results reported. However, the problem of capturing lupus flare accurately is not completely solved.

Breast cancer in systemic lupus

Objective There is a decreased breast cancer risk in systemic lupus erythematosus (SLE) versus the general population. We assessed a large sample of SLE patients, evaluating demographic and clinical characteristics and breast cancer risk. Methods We performed case-cohort analyses within a multi-center international SLE sample. We calculated the breast cancer hazard ratio (HR) in female SLE patients, relative to demographics, reproductive history, family history of breast cancer, and time-dependent measures of anti-dsDNA positivity, cumulative disease activity, and drugs, adjusted for SLE duration. Results There were 86 SLE breast cancers and 4498 female SLE cancer-free controls. Patients were followed on average for 7.6 years. Versus controls, SLE breast cancer cases tended to be white and older. Breast cancer cases were similar to controls regarding anti-dsDNA positivity, disease activity, and most drug exposures over time. In univariate and multivariate models, the principal factor associated with breast cancers was older age at cohort entry. Conclusions There was little evidence that breast cancer risk in this SLE sample was strongly driven by any of the clinical factors that we studied. Further search for factors that determine the lower risk of breast cancer in SLE may be warranted.

Cardiovascular events prior to or early after diagnosis of systemic lupus erythematosus in the systemic lupus international collaborating clinics cohort

Objective

To describe the frequency of myocardial infarction (MI) prior to the diagnosis of systemic lupus erythematosus (SLE) and within the first 2 years of follow-up.

Methods

The systemic lupus international collaborating clinics (SLICC) atherosclerosis inception cohort enters patients within 15 months of SLE diagnosis. MIs were reported and attributed on a specialised vascular event form. MIs were confirmed by one or more of the following: abnormal ECG, typical or atypical symptoms with ECG abnormalities and elevated enzymes (≥2 times upper limit of normal), or abnormal stress test, echocardiogram, nuclear scan or angiogram. Descriptive statistics were used.

Results

31 of 1848 patients who entered the cohort had an MI. Of those, 23 patients had an MI prior to SLE diagnosis or within the first 2 years of disease. Of the 23 patients studied, 60.9% were female, 78.3% were Caucasian, 8.7% black, 8.7% Hispanic and 4.3% other. The mean age at SLE diagnosis was 52.5±15.0 years. Of the 23 MIs that occurred, 16 MIs occurred at a mean of 6.1±7.0 years prior to diagnosis and 7 occurred within the first 2 years of follow-up. Risk factors associated with early MI in univariate analysis are male sex, Caucasian, older age at diagnosis, hypertension, hypercholesterolaemia, family history of MI and smoking. In multivariate analysis only age (OR=1.06 95% CI 1.03 to 1.09), hypertension (OR=5.01, 95% CI 1.38 to 18.23), hypercholesterolaemia (OR=4.43, 95% CI 1.51 to 12.99) and smoking (OR=7.50, 95% CI 2.38 to 23.57) remained significant risk factors.

Conclusions

In some patients with lupus, MI may develop even before the diagnosis of SLE or shortly thereafter, suggesting that there may be a link between autoimmune inflammation and atherosclerosis.

Anti-C1q antibodies in systemic lupus erythematosus

OBJECTIVE

Anti-C1q has been associated with systemic lupus erythematosus (SLE) and lupus nephritis in previous studies. We studied anti-C1q specificity for SLE (vs rheumatic disease controls) and the association with SLE manifestations in an international multicenter study.

METHODS

Information and blood samples were obtained in a cross-sectional study from patients with SLE (n = 308) and other rheumatologic diseases (n = 389) from 25 clinical sites (84% female, 68% Caucasian, 17% African descent, 8% Asian, 7% other). IgG anti-C1q against the collagen-like region was measured by ELISA.

RESULTS

Prevalence of anti-C1q was 28% (86/308) in patients with SLE and 13% (49/389) in controls (OR = 2.7, 95% CI: 1.8-4, p < 0.001). Anti-C1q was associated with proteinuria (OR = 3.0, 95% CI: 1.7-5.1, p < 0.001), red cell casts (OR = 2.6, 95% CI: 1.2-5.4, p = 0.015), anti-dsDNA (OR = 3.4, 95% CI: 1.9-6.1, p < 0.001) and anti-Smith (OR = 2.8, 95% CI: 1.5-5.0, p = 0.01). Anti-C1q was independently associated with renal involvement after adjustment for demographics, ANA, anti-dsDNA and low complement (OR = 2.3, 95% CI: 1.3-4.2, p < 0.01). Simultaneously positive anti-C1q, anti-dsDNA and low complement was strongly associated with renal involvement (OR = 14.9, 95% CI: 5.8-38.4, p < 0.01).

CONCLUSIONS

Anti-C1q was more common in patients with SLE and those of Asian race/ethnicity. We confirmed a significant association of anti-C1q with renal involvement, independent of demographics and other serologies. Anti-C1q in combination with anti-dsDNA and low complement was the strongest serological association with renal involvement. These data support the usefulness of anti-C1q in SLE, especially in lupus nephritis.

Autoantibodies as biomarkers for the prediction of neuropsychiatric events in systemic lupus erythematosus

OBJECTIVE

Neuropsychiatric events occur unpredictably in systemic lupus erythematosus (SLE) and most biomarker associations remain to be prospectively validated. This study examined a disease inception cohort of 1047 SLE patients to determine which autoantibodies at enrolment predicted subsequent neuropsychiatric events.

METHODS

Patients with a recent SLE diagnosis were assessed prospectively for up to 10 years for neuropsychiatric events using the American College of Rheumatology case definitions. Decision rules of graded stringency determined whether neuropsychiatric events were attributable to SLE. Associations between the first neuropsychiatric event and baseline autoantibodies (lupus anticoagulant (LA), anticardiolipin, anti-β(2) glycoprotein-I, anti-ribosomal P and anti-NR2 glutamate receptor) were tested by Cox proportional hazards regression.

RESULTS

Disease duration at enrolment was 5.4 ± 4.2 months, follow-up was 3.6 ± 2.6 years. Patients were 89.1% female with mean (±SD) age 35.2 ± 13.7 years. 495/1047 (47.3%) developed one or more neuropsychiatric event (total 917 events). Neuropsychiatric events attributed to SLE were 15.4% (model A) and 28.2% (model B). At enrolment 21.9% of patients had LA, 13.4% anticardiolipin, 15.1% anti-β(2) glycoprotein-I, 9.2% anti-ribosomal P and 13.7% anti-NR2 antibodies. LA at baseline was associated with subsequent intracranial thrombosis (total n=22) attributed to SLE (model B) (HR 2.54, 95% CI 1.08 to 5.94). Anti-ribosomal P antibody was associated with subsequent psychosis (total n=14) attributed to SLE (model B) (HR 3.92, 95% CI 1.23 to 12.5, p=0.02). Other autoantibodies did not predict neuropsychiatric events.

CONCLUSION

In a prospective study of 1047 recently diagnosed SLE patients, LA and anti-ribosomal P antibodies are associated with an increased future risk of intracranial thrombosis and lupus psychosis, respectively.

Work disability in systemic lupus erythematosus is prevalent and associated with socio-demographic and disease related factors

Our objectives were to examine the prevalence of work disability (WD) and factors associated with job loss in systemic lupus erythematosus (SLE) in a large, multi-centered Canadian sample to determine the current prevalence of WD and identify the contribution of disease activity, damage, and co-morbidities with respect to WD in this cohort. Cross-sectional data on WD status from the 1000 Canadian Faces of Lupus database (a multi-center multi-ethnic cohort of SLE patients) along with clinical measures (number of ACR criteria ever, SLICC Damage Index, SLAM, SLEDAI, SF-36 and Charlson Co-morbidity Index scores), demographic features (age, sex, high school education, household income, marital status, disease duration, employment status) and co-morbidities (including self-reported fibromyalgia, arthralgias, depression and fatigue) were used in bivariate and logistic regression analyses. The 1137 SLE patients had a mean age of 50 years (SE 0.75) and mean disease duration was 18 years (SE 0.70); 19.09% were work disabled and 49.78% were employed. Those with WD were more likely than non-WD SLE patients to have: a higher number of ACR criteria for SLE; not completed high school; older age; single marital status; a lower household income; longer disease duration; higher SLICC Damage Index and SLAM scores; lower SF-36 PCS and SF-36 MCS scores; less vigorous activity per week; and fibromyalgia, arthralgias, fatigue and depression (p < 0.05). This contemporary rate of WD is lower than many past reports. Socio-demographic factors, co-morbidities (fibromyalgia and fatigue) and disease related factors were strongly associated with WD. We cannot determine cause and effect as the study was cross-sectional.