BackgroundSystemic sclerosis (SSc) is a severe connective tissue disease characterized by vascular and fibrotic changes in the skin and various internal organs. Pathogenesis of SSc includes early-onset vasculopathy with endothelial cell activation, microvascular injury and impaired angiogenesis.ObjectivesWe aimed to determine the association of several biological molecules reflecting endothelial cell activation or dysfunction: E- selectin (E-sel), inter-cellular adhesion molecule 1 (ICAM-1), endothelin 1 (ET-1), von Willebrand factor (vWF) and interleukin 6 (IL-6), with distinct capillaroscopic SSc patterns and with more severe disease.MethodsForty consecutive SSc patients attending our EUSTAR SSc clinic, aged [median (IQR)] 52 (18) years, male gender 4/40 (10%), diffuse cutaneous subset (dcSSc) 14/40 (35%) were enrolled in this study. Extensive clinical and nailfold capillaroscopy (NFC) pattern assessment, as well as quantification of serum E-sel, ICAM-1, ET-1, vWF, IL-6 and C-reactive protein (CRP) were performed on all patients. Associations between vascular biomarkers and disease characteristics were evaluated by Mann-Whitney U-test and Spearman correlations.ResultsNFC “late” pattern was found in 21 patients, while 6 had “early” and 13 had “active” NFC pattern. All 5 vascular biomarkers correlated with each other good to moderately, with r indices varying between 0.660 and 0.332, the only exception being ET-1 which did not correlate with E-sel. Good correlations (r 0.465 to 0.727) were also found between all 5 biomarkers and CRP. Patients with severe vasculopathy, as reflected by the NFC “late” pattern, had higher levels of IL-6 (median 12.06 vs. 3.08 pg/mL, p=0.001), ET-1 (median 2.06 vs 1.59 pg/mL, p=0.029), vWF (median 3284 vs 2730 IU/mL, p=0.013) and E-sel (median 52.6 vs. 42.3 ng/mL, p>0.05), compared to patients with NFC “early” or “active” patterns. There was a significant, negative correlation between lung transfer for carbon monoxide (DLCO) and E-sel, ICAM-1 (both p<0.001) and vWF (p=0.013). ET-1 was higher in patients with more severe disease (dcSSc, patients positive for anti-topoisomerase antibodies and patients with a history of digital ulcers – all p<0.05).ConclusionsSerum biomarkers reflecting endothelial cell activation and/or dysfunction are elevated in patients with more severe SSc-associated vasculopathy and correlate with serum CRP. Together with NFC data they might be used for assessing vasculopathy severity in SSc and identifying patients who would benefit from more aggressive vasoactive treatment.Acknowledgements This work was performed as part of the project “Development of a computer-based nailfold videocapillaroscopy (NVC) system for longitudinal evaluation of patients with systemic sclerosis” (QUANTICAP), financed by the UEFISCDI PN-II-PT-PCCA-2013–4-1589 grant. Disclosure of InterestNone declared

BackgroundSystemic sclerosis (SSc) is a connective tissue disease characterized by skin and internal organs fibrosis, microvascular impairment and frequently by disability and early retirement.ObjectivesTo assess employment status, risk factors for early retirement (ER) and the associations of ER with disease characteristics and with patients' health-related questionnaires (Scleroderma Health Assessment Questionnaire (SHAQ)) and hand function (Duruoz hand index (DHI)).MethodsThis study included patients with SSc according to the 2013 ACR/EULAR classification criteria, examined in our EUSTAR center from 11.2011 to 11.2016, who were under the legal age of retirement of in our country (62 years). Patients completed a work assessment questionnaire, the DHI and the SHAQ, as well as a full assessment as per the recommendations of EUSTAR.Logistic regression was used to investigate the associations between employment status (outcome) and potential predictors (including socio-economic status, education, disease characteristics and health-related questionnaires).ResultsThere were 66 patients (8 males, mean±SD age 49.1±9.3 years, 19 with diffuse cutaneous SSc (dcSSc), 46 with history of digital ulcers (DUs) and 23 with joint contractures) included. Forty-two patients lived in urban environments and 42 had higher education (high school or above).Twenty patients were active professionally, whereas 46 were retired, of which 32 retired because of SSc. Of those active professionally, 8 had to do manual labor, 7 had to spend many hours at work standing and 3 had a cold or moist work environment.Using logistic regression adjusted for age and gender, higher education was found to be highly associated with employment (OR (95% CI) 9.0 (1.5, 52.4)), whereas labor conditions (manual labor, stress) had no significant influence on employment status in our cohort. No association was found between employment status and disease characteristics or SHAQ and DHI questionnaires.ConclusionsSSc is associated with substantial work disability and unemployment. Completing less education than high school was associated with early retirement.Acknowledgements*This abstract was realized as part of the “Development of a computer-based nailfold videocapillaroscopy (NVC) system for longitudinal evaluation of patients with systemic sclerosis” (QUANTICAP) project, financed by the UEFIS-CDI PN-II-PT-PCCA-2013–4-1589 grant. Disclosure of InterestNone declared

BackgroundTuberculosis (TB) is a major concern in patients receiving TNF inhibitors (TNFi).ObjectivesTo assess the incidence of active TB and the efficacy of TB prevention measures in a large, single-center cohort of patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and ankylosing spondylitis (AS) receiving TNFi.MethodsData of all patients in whom treatment with TNFi was initiated in our rheumatology clinic from January 1st 2002 until December 31st 2015 have been retrospectively analysed. The cohort was divided into 2 groups per the mandatory latent TB infection (LTBI) screening method at baseline: tuberculin skin test (group TST), and QuantiFERON®-TB Gold test (group QFT). The incidence of active TB was analysed for each group and compared to TB incidence data in general population.Results653 patients were included (344 RA, 52 PsA, 257 AS); 324 patients belonged to the TST and 329 to the QFT group. The number of active TB cases/ time of exposure to TNFi (person-years, PY) was 17/2002.6 and 7/1041.2 respectively, accounting for an incidence of 848.9 and 672.3 cases per 105 PY, about 8 times higher (8.3 and 8.8 for TST, respectively QFT group) than the average TB during the period of exposure to TNFi. LTBI reactivations per total TB cases were only 4/17 and 2/7, respectively, too few to identify statistically significant differences between the 2 LTBI screening protocols. Only 10 patients had pulmonary TB, whereas the rest were disseminated TB (8 cases), TB pleurisy and/or pericarditis (4 cases), one mediastinal lymph node TB and one isolated hepatic TB. Using Pearson chi-square test, we found no significant differences between LTBI group and active TB (Table 1).Table 1.LTBI screening results and TB occurrence in the 653 TNFi-treated patients (Pearson χ2 test)TSTQFTAllp value (n=324)(n=329)(n=653) Positive immuno-diagnostic test at baseline52 (16.0%)63 (19.1%)115 (17.6%)<0.001*Active TB17 (5.2%)7 (2.1%)24 (3.7%)0.185*Reactivation TB4 (1.2%)2 (0.6%)6 (0.9%)**New infection TB13 (4.0%)5 (1.5%)18 (2.8%)0.052*Total TB incidence (per 105 PY)848.9672.3788.5–Maximal period of TNFi exposure in group2002–20162011–20162002–2016–Mean TB incidence in Romania in the respective time period (per 105 PY)102.376.7102.3–TB incidence patients/general population8.38.87.70.88†*Pearson χ2test comparing TST and QFT. **Reactivation TB cases were too few to perform statistical testing. †Pearson χ2test comparing total TB incidence in the TST and QFT groups to the average TB incidence in our region in the respective period of exposure.ConclusionsIn our cohort, new infection TB exceeds reactivation TB, suggesting the necessity of periodical LTBI re-screening.Disclosure of InterestA. M. Gheorghiu: None declared, A. Garaiman: None declared, A. Radu: None declared, A. Soare: None declared, V. Aramă: None declared, D. Bumbăcea: None declared, R. Dobrotă: None declared, R. Oneata: None declared, S. Pintilie: None declared, M. Milicescu Speakers bureau: Has received sponsoring from Abbvie, MSD and Pfizer., I. Ancuta Grant/research...

BackgroundSystemic sclerosis (ScS) has unpredictable course and high mortality. Generalised Estimating Equations (GEE) is a technique useful for longitudinal data analysis, using data from all time points and adjusting for within-patient correlation,i.e. correlation between time points within the same patient. GEE does not require a normal distribution of dependent variables, making it attractive for analyzing SSc data.ObjectivesTo identify predictive factors for death and unfavorable outcomes.MethodsData of SSc patients with ≥2 visits in our EUSTAR centre in 2004–2016 were analyzed. GEE investigated the relationship over time between outcomes (death, digital ulcers (DUs), forced vital capacity (FVC), modified Rodnan skin score (mRSS)) and potential predictors (age, gender, disease duration, cutaneous subset, mRSS at baseline, DUs history, DLCO, left ventricle ejection fraction (LVEF), proteinuria), separately for each predictor and in combined models.Results89 patients (12.4%males, mean±SD age 49.2±12.2years, disease duration 4.1±7.5years) were included, with a follow-up of up to 13years. There were 14deaths, most due to lung involvement (7/14). In multivariable GEE analysis (Table 1), predictors of death were a shorter disease duration, DUs history, and a lower LVEF. Predictors for FVC decrease over time were difuse cutaneous subset (dcSSc), younger age and lower DLCO. Younger age, shorter disease duration and higher baseline mRSS were the most important predictors for higher mRSS at follow-up. The only predictor for the development of new DUs was a history of DUs.Table 1.Prediction factors for death and for evolution over time of parameters reflecting disease severity in SScPredictorsDeathDUsFVCmRSS OR (95% CI)OR (95% CI)B (95% CI)B (95% CI)Age1.1 (0.9, 1.3)0.9 (0.9, 1.0)0.3 (0.1, 0.6)*-0.1 (-0.2, -0.01)*Disease duration0.8 (0.7, 0.9)***1.0 (0.9, 1.1)-0.0 (-0.3, 0. 3)-0.1 (-0.3, -0.03)**mRSS baseline0.9 (0.9, 1.1)1.1 (0.9, 1.1)0.2 (-0.2,0.7)0.4 (0.3, 0.6)***DLCO0.9 (0.9, 1.0)1.0 (0.9, 1.0)0.5 (0.3, 0.6)***0.1 (-0.0, 0.0)LVEF0.9 (0.90, 0.98)**0.9 (0.9, 1.1)0.2 (-0.2, 0.7)0.0 (-0.2, 0.2)Male gender1.9 (0.3, 11.2)0.5 (0.1, 1.9)0.7 (-9.3, 10.7)-1.2 (-3.9, 1.5)dcSSc6.8 (0.6, 81.7)0.7 (0.2, 2.9)-16.5 (-28.9, -4.9)**1.7 (-0.1, 3.4)DUs history13.1 (3.1, 55.8)***28.4 (2.3, 356.4)**3.2 (-2.1, 8.4)0.2 (-1.4, 1.7)Proteinuria1.1 (0.6, 1.8)0.9 (0.4, 1.9)2.2 (-2.5, 6.8)1.7 (-0.1, 3.5)*p<0.05, **p<0.01, ***p<0.001.ConclusionsPatients with shorter disease duration, dcSSc, higher mRSS, lower DLCO and LVEF and a history of DUs had a more unfavorable course. GEE is a robust technique for longitudinal data analysis, excellent for identifying prediction factors in SSc.Acknowledgements This abstract is part of the QUANTICAP project, UEFIS-CDI PN-II- PT-PCCA-2013–4-1589 grant. Disclosure of InterestNone declared