To determine incidence of major adverse cardiovascular events (MACE: non-fatal myocardial infarction, non-fatal stroke, or cardiovascular death) in Sjögren’s disease (SjD); identify traditional and disease-specific factors associated with MACE; describe prevalence of cardiovascular risk factors (CVRFs); explore, descriptively, SCORE2-estimated risk by comparing predicted and observed events; estimate cardiovascular mortality; and assess association between MACE and all-cause mortality. Prospective, multicenter cohort study including 314 patients fulfilling 2002 AECG criteria for SjD, followed for median 9.5 years. Clinical, serological, and cardiovascular data were recorded. Factors associated with MACE were evaluated using multivariable logistic regression models. SCORE2 risk was calculated in patients with complete data, and expected events were compared with observed events. Cardiovascular mortality was expressed as crude rates per 1,000 patient-years. Seventeen patients (5.41

To assess the prevalence and persistence of self-reported depression and its associated factors in patients with systemic lupus erythematosus (SLE), using a patient-reported outcome measure in a large, multicentre, prospective cohort. We conducted a longitudinal analysis of patients enrolled in the RELESSER-PROS registry who responded to item 7 (“I was depressed”) of the Lupus Impact Tracker questionnaire (LITQ7) over five annual visits. Self-reported depression was defined as any response other than “none of the time.” Covariates assessed at each visit included: age, disease duration, SELENA-SLEDAI (S-SLEDAI), glucocorticoid (GC) use, SLICC/ACR Damage Index (SDI), fibromyalgia, Charlson index, BMI, smoking status, menopause, sedentary lifestyle, marital and employment status. Generalized estimating equation (GEE) models were used to examine longitudinal associations. Of 1463 patients (mean age 55 years; 90

OBJECTIVE:The objective of this study was to assess the risk of cancer in patients with RA treated with biologic and targeted synthetic DMARDs (b/tsDMARDs). METHODS:We analysed the 2000-2023 data for RA patients from the BIOBADASER III registry, a multicentre national registry of patients treated with b/tsDMARDs. Patients with a history of cancer were excluded. Incidence rates (IRs) were analysed, and adjusted Cox regression models were used to estimate hazard ratios (HRs) for all cancers excluding non-melanoma skin cancer (NMSC), as well as for NMSC. Treatment with a TNF inhibitor (TNFi) served as the reference for comparison. RESULTS:Among 4635 of the BIOBADASER III registry patients with RA (mean age 55.5 years; 79% female; median follow-up 3.6 years), 187 incident cancers were identified. For all cancers excluding NMSC, the adjusted HRs (95% CI) compared with TNFi were: 1.2 (0.8-1.6) for IL6i, 0.9 (0.5-1.4) for CD20i, 1.2 (0.8-1.8) for JAKi and 1.1 (0.8-1.6) for CTLA4-A. For NMSC, the adjusted HRs (95% CI) were 0.6 (0.2-1.5) for IL6i, 0.6 (0.2-1.8) for CD20i, 0.7 (0.2-2) for JAKi, and 1.1 (0.5-2.6) for CTLA4-A. No increased cancer risk was observed when adjusting for cardiovascular risk or treatment line, for either cancer type. CONCLUSION:In this large, real-world cohort of RA patients, we found no increased cancer risk associated with any bDMARDs or tsDMARDs compared with TNFi.

The objective of SjögrenSER Prospective (SjD-PROS) was to evaluate the improvement, stability or progression of SjD in clinical practice. SjD-PROS is an observational, longitudinal, multicenter study of SjD in Spain. Participants from the prior transversal phase were invited to a follow-up visit after 9.5 years. Data were collected via interviews and medical records. Variables were analyzed using means, medians and frequencies. Statistical associations were assessed using T student test, Kruskal–Wallis and the Chi-square test. We included 314 patients, 95

PV199 / #53 Poster Topic:AS23 - SLE-Diagnosis, Manifestations, & Outcomes To propose a definition for moderate disease activity state (MODAS) and severe disease activity state (SEDAS) in SLE and using the RELESSER-PROS cohort to describe the prevalence of both states of activity and to analyze the impact of this categorization on mortality, organ damage, flares, hospital admissions and health-related quality of life (HRQoL) We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. MODAS and SEDAS definitions are based on: cSLEDAI, presence of severe manifestations and PGA. MODAS was defined as the presence of at least 1 of the following conditions: <4 cSLEDAI < 8 or 1< PGA < 2 (without severe clinical manifestations); and SEDAS: SLEDAIc >8 or PGA > 2 or the presence of severe SLEDAI and Non-SLEDAI manifestations. We analyzed the impact of remaining in MODAS or SEDAS in terms of several robust outcomes. 1,463 patients were included, mean age (±SD): 56 (±13.5) years; mean disease duration (±SD): 14 (±8.5) years. Patients had a mean (±SD) of 4.2 (±1.2) visits and a mean (±SD) follow-up time of 3.6 (±1.4) years. Patients with at least 1 visit in MODAS or in SEDAS had significantly higher numbers of flares, worse HRQoL, more damage accrual and more hospital admissions. Both, damage accrual and hospital admissions were significantly higher in SEDAS than in MODAS (Table 1 and Table 2). When comparing MODAS and SEDAS vs low disease activity, worse were the outcomes in the moderate/severe activity states (Figure 1). Table 1. Outcomes, according to the number of visits in SEDAS. Table 2. Outcomes, according to the number of visits in MODAS. Figure 1. Comparison of risk of admissions, damage accrual, flares and quality of life between different states of activity with each other. Patients who were in MODAS or SEDAS at least once had worse outcomes in terms of numbers of flares, HRQoL, damage accrual, and hospital admissions. Furthermore, the more time spent in these states entailed greater risks. These results emphasize the importance of an adequate stratification of disease activity in SLE patients.

OBJECTIVE:Patients with SLE have a well-known increased risk of major comorbidities, although they are also very heterogeneous in terms of the prevalence of comorbid conditions. The relationships of such comorbidities with the outcomes and the severity of index diseases are less known. We aimed to evaluate the interactions between comorbid conditions, in a large multicentre SLE cohort, and their impact on severity and outcomes, using a cluster analysis. METHODS:Data on 14 cumulative comorbidities were derived from patients with SLE (American College of Rheumatology (ACR)-97 criteria) who had been included in the retrospective phase of the RELESSER (Spanish Society of Rheumatology National Register of SLE). The Severity Katz Index and the SLICC/ACR Damage Index were calculated. Unsupervised cluster analysis was performed to better characterise the relationships between comorbidities in a large multicentre cohort of patients with SLE. For intercluster differences testing, analysis of variance and Tukey tests were used to compare continuous numerical variables; a Kruskal-Wallis test to discrete variables and the χ² (or Fisher's exact test) were used for categorical ones. RESULTS:A total of 3658 patients with SLE were included. Men accounted for 9.6% of patients. The mean (SD) age was 45.9 years, and 93% were Caucasian. Four clusters, with markedly different comorbidity profiles and outcomes, were identified: in cluster 2 (n=516), patients were grouped around depression (100% of the cases); in cluster 3 (n=418) around serious infections (100%); and in cluster 4 (n=388) around cardiovascular events (also 100%). However, in cluster 1, the largest one (n=2336), no patient had any of the three defining comorbidities of the other clusters, and this cluster was associated with the best outcomes. CONCLUSIONS:Cluster analysis identifies well-differentiated subsets of patients with SLE in terms of their comorbidities. The most relevant comorbidities in SLE tend to aggregate in the most severe patient subsets.

INTRODUCTION:Accurate assessment of disease activity in SLE is crucial but challenging due to its varied clinical manifestations and severity. Current tools like the SLE Disease Activity Index (SLEDAI) have limitations, including unvalidated cut-off points, low sensitivity to certain severe features and an overemphasis on serological markers. There is a need for improved definitions of disease activity. METHODS:We analysed data from 1463 patients with SLE in the prospective, multicentre RELESSER-PROS cohort (39 Spanish hospitals) over five annual visits. A panel of lupus experts used the Delphi method to develop new definitions for moderate disease activity state (MODAS) and severe disease activity state (SEDAS). These incorporated clinical SLEDAI (cSLEDAI), selected severe non-SLEDAI manifestations (eg, neuropsychiatric involvement, proteinuria, severe haematological features) and the Physician Global Assessment. We compared the predictive performance of MODAS/SEDAS with SLEDAI for mortality, organ damage, severe flares, hospitalisations and health-related quality of life, using receiver operating characteristics curves. RESULTS:At baseline, 20% of patients met MODAS criteria and 24.6% SEDAS criteria, versus 10.5% and 3.0%, respectively, by SLEDAI. MODAS/SEDAS reclassified 19.9% of patients considered mild by SLEDAI, and 53.3% of moderate cases. MODAS/SEDAS showed modest but consistent improvement in predictive accuracy for damage (area under the curve 0.570 vs 0.550), flares (0.609 vs 0.564) and hospitalisations (0.609 vs 0.565). These definitions were associated with worse outcomes and demonstrated a dose-response relationship, although the overall predictive ability remained moderate. CONCLUSION:MODAS and SEDAS offer an alternative framework for defining moderate and severe SLE activity, with modest but consistent improvements in predictive performance compared with SLEDAI. By integrating cSLEDAI, key severe features and physician judgement, they improve prognostic performance and support a severity-based approach to clinical management and research. Their clinical utility remains preliminary, and further external validation is required before routine implementation.

ObjectiveWe aimed to quantify the mortality risk in a large, well-characterized cohort of patients with Sjögren disease (SjD) and to identify independent predictors of mortality in this population.MethodsWe included 314 patients diagnosed with SjD according to the 2002 American-European Consensus Group criteria from a prospective, multicenter SjögrenSER Prospective cohort. Detailed data on systemic manifestations, serological markers, disease activity, and mortality were collected after a median of 9.5 (IQR 9.2-9.9) years of follow-up. The primary outcome was overall mortality, and secondary analyses aimed to identify independent predictors of mortality using Cox proportional hazards models. Standardized mortality ratios were calculated by comparing the observed deaths in the SjD cohort to the expected deaths in an age- and sex-matched general population.ResultsThe study identified a 70% increased mortality risk in the SjD cohort compared to the general population, with a standard mortality ratio of 1.7. Infections (35.7%), malignancies (23.8%), and cardiovascular disease (CVD; 7.1%) were the most common causes of death. Multivariate analysis revealed that older age (HR 1.11/year, 95% CI 1.07-1.15), C4 hypocomplementemia (HR 3.75, 95% CI 1.55-9.06), elevated erythrocyte sedimentation rate (ESR; HR 1.01, 95% CI 1.00-1.03), history of heart failure (HR 4.24, 95% CI 1.02-17.58), and pulmonary involvement (HR 3.31, 95% CI 1.39-7.88) were independent predictors of mortality.ConclusionThis study found a significantly increased mortality risk in SjD, with infections, malignancies, and CVD as leading causes of death. Independent predictors of mortality include advanced age, C4 hypocomplementemia, elevated ESR, heart failure, and pulmonary involvement, underscoring the need for proactive, individualized management.

ObjectiveTo establish the predictive value of the QRESEARCH risk estimator version 3 (QRISK3) algorithm in identifying Spanish patients with ankylosing spondylitis (AS) at high risk of cardiovascular (CV) events and CV mortality. We also sought to determine whether to combine QRISK3 with another CV risk algorithm: the traditional SCORE, the modified SCORE (mSCORE) EULAR 2015/2016 or the SCORE2 may increase the identification of AS patients with high-risk CV disease.MethodsInformation of 684 patients with AS from the Spanish prospective CARdiovascular in ReuMAtology (CARMA) project who at the time of the initial visit had no history of CV events and were followed in rheumatology outpatient clinics of tertiary centers for 7.5 years was reviewed. The risk chart algorithms were retrospectively tested using baseline data.ResultsAfter 4,907 years of follow-up, 33 AS patients had experienced CV events. Linearized rate=6.73 per 1000 person-years (95% CI: 4.63, 9.44). The four CV risk scales were strongly correlated. QRISK3 correctly discriminated between people with lower and higher CV risk, although the percentage of accumulated events over 7.5 years was clearly lower than expected according to the risk established by QRISK3. Also, mSCORE EULAR 2015/2016 showed the same discrimination ability as SCORE, although the percentage of predicted events was clearly higher than the percentage of actual events. SCORE2 also had a strong discrimination capacity according to CV risk. Combining QRISK3 with any other scale improved the model. This was especially true for the combination of QRISK3 and SCORE2 which achieved the lowest AIC (406.70) and BIC (415.66), so this combination would be the best predictive model.ConclusionsIn patients from the Spanish CARMA project, the four algorithms tested accurately discriminated those AS patients with higher CV risk and those with lower CV risk. Moreover, a model that includes QRISK3 and SCORE2 combined the best discrimination ability of QRISK3 with the best calibration of SCORE2.

Objective Patients with Systemic lupus erythematosus (SLE) have a not uniform increased risk of serious infection. It is important to estimate the infection risk and balance the immunosuppression and infection risks in practice, but there is no evidence-based tool available to do it. SLESIS score, one score for prediction of severe infection, was previously developed by our group and validated in an external cohort.1 The original score incorporated up to 7 predictors and only a moderate performance of SLESIS score was observed, with AUC of 0.633. The objective of our study was to improve the SLESIS score both, as a predictor of infection and in terms of feasibility. Methods We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. A multivariable logistic model was constructed taking into account the variables already forming the SLESIS score, plus all other potential predictors identified in a literature review. Performance was analyzed using the C statistic and the area under the ROC (AUROC). Internal validation was carried out using a 100-sample bootstrapping procedure. OR were transformed into score items, and the AUROC was used to determine performance. Results A total of 1459 patients who had completed 1 year of follow-up were included (mean age, 49 ± 13 years; 90% females). Twenty-five (1.7%) had experienced ≥1 severe infection. According to the adjusted multivariate model, severe infection could be predicted from 4 variables: age (years) ≥60, previous SLE-related hospitalization, previous severe infection, and glucocorticoid dose. A score was built from the best model (table 1). AUROC:0.861 (0.777–0.946). The cut-off chosen was ≥6, which exhibited an accuracy of 85.9% and a positive LR of 5.48. Conclusions SLESIS-R is an accurate and feasible instrument for predicting infections in SLE patients. SLESIS-R could help to make informed decisions on the use of immunosuppressants and the implementation of preventive measures. Acknowledgements Funding by GSK and Spanish Foundation of Rheumatology. Reference Tejera-Segura B, Rúa-Figueroa I, Pego-Reigosa JM, et al. Can we validate a clinical score to predict the risk of severe infection in patients with systemic lupus erythematosus? A longitudinal retrospective study in a British Cohort BMJ Open 2019,14;9(6):e028697.

Objective To develop an improved score for prediction of severe infection in patients with systemic lupus erythematosus (SLE), namely, the SLE Severe Infection Score-Revised (SLESIS-R) and to validate it in a large multicentre lupus cohort.Methods We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. A multivariable logistic model was constructed taking into account the variables already forming the SLESIS score, plus all other potential predictors identified in a literature review. Performance was analysed using the C-statistic and the area under the receiver operating characteristic curve (AUROC). Internal validation was carried out using a 100-sample bootstrapping procedure. ORs were transformed into score items, and the AUROC was used to determine performance.Results A total of 1459 patients who had completed 1 year of follow-up were included in the development cohort (mean age, 49±13 years; 90% women). Twenty-five (1.7%) had experienced ≥1 severe infection. According to the adjusted multivariate model, severe infection could be predicted from four variables: age (years) ≥60, previous SLE-related hospitalisation, previous serious infection and glucocorticoid dose. A score was built from the best model, taking values from 0 to 17. The AUROC was 0.861 (0.777–0.946). The cut-off chosen was ≥6, which exhibited an accuracy of 85.9% and a positive likelihood ratio of 5.48.Conclusions SLESIS-R is an accurate and feasible instrument for predicting infections in patients with SLE. SLESIS-R could help to make informed decisions on the use of immunosuppressants and the implementation of preventive measures.

OBJECTIVES:To evaluate the prevalence of self-perceived depression and anxiety in patients with systemic lupus erythematosus (SLE) and to explore associated factors. METHODS:Cross-sectional study of unselected patients with SLE (ACR-97 criteria) and controls with chronic inflammatory rheumatic diseases. Both completed the Hospital Anxiety and Depression Scale (HADS). Demographic and clinical characteristics, comorbidity, and treatments were collected, and a multivariate analysis was performed to explore factors associated with depression and anxiety in SLE. RESULTS:The study population comprised 172 patients and 215 controls. Women accounted for 93% of the patients with SLE. Fibromyalgia was recorded in 12.8% and a history of depression in 17%. According to HADS, 37.2% fulfilled the diagnostic criteria for depression and 58.7% those for anxiety; prevalence was similar in the controls (32.6% and 55.1%, respectively). Up to a third of patients with self-perceived depression were not receiving antidepressants. There was no concordance between a previous history of depression and current depression. In the multivariate model, current depression was associated with single marital status (OR 2.69; 95% CI: 1.17-6.42; p = .022), fibromyalgia (7.69; 2.35-30.72; p = .001), smoking (3.12; 1.24-8.07; p = .016), severity of SLE (0.76; 0.6-0.94; p = .016), and organ damage (1.27; 1.01-1.61; p = .042). Current anxiety was only associated with fibromyalgia (3.97; 1.21-17.98; p = .036). CONCLUSIONS:Depression and anxiety are most likely underdiagnosed in SLE. Prevalence appears to be similar to that of other chronic inflammatory rheumatic diseases. Anxiety is associated with fibromyalgia, while depression is also associated with single marital status, smoking, organ damage, and severity of SLE.

Objectives To analyse the effect of targeted therapies, either biological (b) disease-modifying antirheumatic drugs (DMARDs), targeted synthetic (ts) DMARDs and other factors (demographics, comorbidities or COVID-19 symptoms) on the risk of COVID-19 related hospitalisation in patients with inflammatory rheumatic diseases. Methods The COVIDSER study is an observational cohort including 7782 patients with inflammatory rheumatic diseases. Multivariable logistic regression was used to estimate ORs and 95% CIs of hospitalisation. Antirheumatic medication taken immediately prior to infection, demographic characteristics, rheumatic disease diagnosis, comorbidities and COVID-19 symptoms were analysed. Results A total of 426 cases of symptomatic COVID-19 from 1 March 2020 to 13 April 2021 were included in the analyses: 106 (24.9%) were hospitalised and 19 (4.4%) died. In multivariate-adjusted models, bDMARDs and tsDMARDs in combination were not associated with hospitalisation compared with conventional synthetic DMARDs (OR 0.55, 95% CI 0.24 to 1.25 of b/tsDMARDs, p=0.15). Tumour necrosis factor inhibitors (TNF-i) were associated with a reduced likelihood of hospitalisation (OR 0.32, 95% CI 0.12 to 0.82, p=0.018), whereas rituximab showed a tendency to an increased risk of hospitalisation (OR 4.85, 95% CI 0.86 to 27.2). Glucocorticoid use was not associated with hospitalisation (OR 1.69, 95% CI 0.81 to 3.55). A mix of sociodemographic factors, comorbidities and COVID-19 symptoms contribute to patients’ hospitalisation. Conclusions The use of targeted therapies as a group is not associated with COVID-19 severity, except for rituximab, which shows a trend towards an increased risk of hospitalisation, while TNF-i was associated with decreased odds of hospitalisation in patients with rheumatic disease. Other factors like age, male gender, comorbidities and COVID-19 symptoms do play a role.

Objective. Since insulin resistance (IR) is highly prevalent in patients with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), we aimed to determine whether differences in IR exist between the two conditions. Methods. We conducted a cross-sectional study comprising 413 subjects without diabetes (186 with SLE and 227 with RA). Glucose, insulin, and C-peptide serum levels, as well as IR by the homeostatic model assessment (HOMA2) were studied. A multivariable regression analysis was performed to evaluate the differences in IR indexes between patients with SLE and RA, as well as to determine if IR risk factors or disease-related characteristics are differentially associated with IR in both populations. Results. The insulin:C-peptide molar ratio was upregulated in patients with RA compared to patients with SLE (β 0.009, 95% CI 0.005–0.014, P < 0.001) after multivariable analysis. HOMA2 indexes related to insulin sensitivity (HOMA2-%S) were found to be lower (β –27, 95% CI –46 to –9, P = 0.004) and β cell function (HOMA2-%B) showed higher IR indexes (β 38, 95% CI 23–52, P < 0.001) in RA than in SLE patients after multivariable analysis. Patients with RA more often fulfilled the definition of IR than those with SLE (OR 2.15, 95% CI 1.25–3.69, P = 0.005). The size effect of IR factors on IR indexes was found to be equal in both diseases. Conclusion. IR sensitivity is lower and β cell function is higher in RA than in SLE patients. The fact that traditional IR factors have an equal effect on IR in both SLE and RA supports the contention that these differences are related to the diseases themselves.

This study aimed at determining socio-demographic and clinical factors of primary Sjögren syndrome (pSS) associated with osteoporosis (OP) and fragility fracture. SJOGRENSER is a cross-sectional study of patients with pSS, classified according to American European consensus criteria developed in 33 Spanish rheumatology departments. Epidemiological, clinical, serological and treatment data were collected and a descriptive analysis was conducted. Bivariate and multivariate analyses were performed using a binomial logistic regression to study the factors associated with OP and fragility fracture in pSS. 437 patients were included (95% women, with a median age of 58.6 years). 300 women were menopausal (76.4%). Prevalence of OP was 18.5% [in men ( N = 21) this measured 19%]. A total of 37 fragility fractures were recorded. In the multivariate analysis, there was an association between OP and age: in the 51–64 age range (menopausal women), the OR measured 9.993 (95% CI 2301–43,399, p = 0.002); In the age > 64 years group, OR was 20.610 (4.679–90.774, p < 0.001); between OP and disease duration, OR was 1.046 (1.008–1085, p = 0.017); past treatment with corticosteroids, OR 2.548 (1.271–5.105, p = 0.008). Similarly, an association was found between fragility fractures and age: in the 51–64 age group, OR measured 5.068 (1.117–22,995, p = 0.035), age > 64 years, OR was 7.674 (1.675–35,151, p < 0.009); disease duration, OR 1.049 (CI 1.003–1097, p < 0.036) and the ESSDAI index, OR 1.080 (1.029–1134, p = 0.002). Patients with pSS can develop osteoporosis and fragility fractures over the course of the disease. Age, corticosteroids treatment and disease duration were associated with the development of OP. Disease duration and ESSDAI were associated with the development of fractures in patients with pSS.

To analyze the influence of tobacco smoking on systemic lupus erythematosus (SLE) clinical features and damage. Cross-sectional and retrospective, case–control study comparing SLE patients with and without tobacco exposure. Cumulative clinical data and comorbidities were collected, and severity (Katz index) and damage (SLICC/ACR damage index) (SDI) indices were calculated. Pack-years (PY) was used to estimate lifetime tobacco exposure. A logistic regression was carried out to explore the impact of tobacco use on retinal damage. 216 patients were included. The mean age was 49 years (± 12.7), 93% were females, and median disease duration was 17 years [interquartile range (IQR):9–25]. Fifty-three percent of patients were smokers at some point. The median PY was 13 (IQR: 6–20.5). Only 54.8% of active smokers recalled having been informed of the negative effects of smoking, versus 83.3% of never smokers (< 0.001). In a bivariant analysis, an association between tobacco use at any time and discoid lupus [OR: 3.5(95%CI 1.5–8.9); p = 0.002] photosensitivity [OR: 2.06(95%CI 1.16–3.7); p = 0.01] and peripheral arteriopathy ( p = 0.007) was found. Considering SDI item by item, a significant association with retinal damage, adjusted for age [OR: 1.03(95%CI 1–1.07); p = 0.04], was found. Using PYs, an association was found with discoid lupus ( p = 0.01), photosensitivity ( p = 0.03) and peripheral arteriopathy ( p = 0.01), global SDI > 0 ( p = 0.002) and retinal damage ( p = 0.02). In a multivariate analysis exploring factors associated with retinal damage, any previous smoking history and SDI remained associated with retinal damage. Tobacco smoking is associated with cutaneous manifestations and damage and is an independent predictor of retinal damage in SLE patients.

Objective: Describe the objectives, methods and results of the first year of the new version of the Spanish registry of adverse events involving biological therapies and synthetic drugs with an identifiable target in rheumatic diseases (BIOBADASER III). Methodology: Multicenter prospective registry of patients with rheumatic inflammatory diseases being treated with biological drugs or synthetic drugs with an identifiable target in rheumatology departments in Spain. The main objective of BIOBADASER Phase III is the registry and analysis of adverse events; moreover, a secondary objective was added consisting of assessing the effectiveness by means of the registry of activity indexes. Patients in the registry are evaluated at least once every year and whenever they experience an adverse event or a change in treatment. The collection of data for phase 10 began on 17 December 2015. Results: During the first year, 35 centers participated. The number of patients included in this new phase in December 2016 was 2,664. The mean age was 53.7 years and the median duration of treatment was 8.1 years. In all, 40.4% of the patients were diagnosed with rheumatoid arthritis. The most frequent adverse events were infections and infestations. Conclusions: BIOBADASER Phase III has been launched to adapt to a changing pharmacological environment, with the introduction of biosimilars and small molecules in the treatment of rheumatic diseases. This new stage is adapted to the changes in the reporting of adverse events and now includes information related to activity scores. (C) 2017 Elsevier Espafia, S.L.U. and Sociedad Espafiola de Reumatologia y Colegio Mexicano de Reumatologia. All rights reserved.