BackgroundAdvances in rheumatology and new therapeutic options have certainly impacted patient survival, changing the age range, from youth to seniors. The differences between the age groups could influence the evolution of the disease and the adverse events (AEs) related to the treatments. There are few real-world data on the safety and efficacy of treatments in different age groups.ObjectivesTo evaluate the frequency of AEs and the survival of treatments according to the age in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) or ankylosing spondylitis (AS).MethodsRetrospective, observational, multicenter study of real-life data of patients included in the BIOBADASAR 3.0 registry; exposed and not exposed to original biological treatments (b-DMARDs), biosimilars, targeted synthetic drugs (ts-DMARDs). The unexposed group received treatment with conventional disease-modifying drugs (cDMARDs). A Kaplan-Meier and Log Rank Test analysis was performed to study AEs-free survival and treatment in different age groups (young people <25; young adults 25-34; mature adults 34-65; old adults >65). Factors related to treatment survival were evaluated using Cox regression models.Results5,297 patients were included, 80.3% female, mean age 43.7 years (SD 15.6) and median disease progression 14.3 [IQR 11.5]. RA 4658 (87.9%); APs 490 (9.25%) and EA 149 (2.8%). The main reason for treatment discontinuation was ineffectiveness, in 624 patients in the exposed group and in 53 (2.5%) patients in control group, followed by the presence of AEs in 352 (11.2%) and 83 (3.9%), respectively (p=0.001).A mean Charlson Score of 0.268 (SD 0.6) in the exposed group and 0.306 (SD 0.7) in the control group (p=0.095). Median EAs-free survival in the exposed group was 12.5 years [IQR 16.6] while in controls was 28 years [IQR 11], p<0.0001. Median AEs-free survival was 12 years (IQR 11) in young people, 11.5 years [IQR: 4.9] in young adults, 10 years [IQR: 3.25] in mature adults and 7.6 years [IQR: 6] in old adults with a difference statistically significant (p>0.017). The exposed group presented a median treatment survival in years of 11.25 years [IQR: 10] in young people; 12.5 years [IQR: 4.7] in young adults, 7.5 years [IQR: 12.1] in mature adults and 4.5 years [IQR: 1.14] in old adults (p>0.0001). Considering only the first line of treatment, a median survival of 11.5 years [IQR: 10] was evidenced in the age group <25; 12 years [IQR: 2.6] between 25-34 years old, 10 years [IQR: 12] in the group between 34-65 years old and 5.5 years [IQR: 1.14] in the group > 65 years old (p>0.004). (Figure 1). Considering the second line of treatment, the differences between the groups were not statistically significant (p=0.57). In the multivariate regression model for patients with RA, the factors with the greatest impact on treatment survival were female sex (HR 1.3, 95% CI 1.2-1.4), old age (HR 1.01, 95% CI 1.008-1.01), treatment with steroids (HR 1.19, 95% CI1.1-1.2) and longer disease duration (HR 1.01, 95% CI1.01 – 1.02).ConclusionIn the present study we were able to demonstrate a greater occurrence of AEs in old adults and mature adults compared to young people and young adults. Conversely, survival for b-DMARDs and ts-DMARDs were greater in youth and young adults. In patients with RA, female sex, corticosteroid therapy, old aged and longer disease duration were associated with treatment discontinuation.References[1]Souto A, et al. Rate of discontinuation and drug survival of biologic therapies in rheumatoid arthritis: a systematic review and meta-analysis of drug registries and health care databases. Rheumatology (Oxford). 2016;55(3):523–34.[2]Ray D, et al. Immune senescence, epigenetics and autoimmunity. Clin Immunol. 2018 Nov;196:59-63. doi: 10.1016/j.clim.2018.04.002. Epub 2018 Apr 11.[3]Vela P, et al. Influence of age on the occurrence of adverse events in rheumatic patients at the onset of biological treatment: data from the BIOBADASER III register. Arthritis Res Ther. 2020 Jun 15;22(1):143. doi: 10.1186/s13075-020-02231-x.Disclosure of InterestsNone declared

Introducción: un tercio de los pacientes con artritis psoriásica (APs) sufre ansiedad y/o depresión, lo que podría impactar negativamente en la actividad de la enfermedad.Los objetivos de este estudio fueron: evaluar la prevalencia de depresión y ansiedad en pacientes con APs, estudiar su asociación con diferentes factores sociodemográficos y clínicos, y determinar el efecto sobre los diferentes componentes de los índices compuestos de la actividad de la APs. Materiales y métodos: se incluyeron pacientes con APs que cumplían criterios de la Classi cation criteria of psoriatic arthritis (CASPAR). La depresión se de nió según el Patient Health Questionnaire-9 (PHQ-9) y la ansiedad según el cuestionario General Anxiety Disorder-7 (GAD-7). Se realizó regresión logística múltiple para identi car variables asociadas a ansiedad y depresión. Resultados: se incluyeron 100 pacientes con APs, de los cuales el 26% y el 59% presentaron depresión mayor y ansiedad respectivamente. Los pacientes con depresión mayor tuvieron mayor actividad de la enfermedad, dolor, fatiga, ansiedad, y menor educación formal y peor calidad de vida. Aquellos con ansiedad manifestaron mayor actividad de la enfermedad, dolor y fatiga, y peor calidad de vida y capacidad funcional. La alta actividad de la enfermedad y la menor educación formal se asociaron independientemente con depresión mayor. Conclusiones: la presencia de depresión mayor y ansiedad se asoció con mayor actividad de la enfermedad en pacientes con APs.

BackgroundMany activity indices have been developed for Systemic Lupus Erythematosus. However, they present important limitations due to the multi-organ compromise.The SLEDAI score and its different versions are widely used in daily practice and in clinical research.Diogo Jesus et al (2018) developed the SLE-DAS (Systemic Lupus Erythematosus Disease Activity Score), that include 17 items, 4 of them continuous. SLE-DAS assesses disease activity in the 28 previous days using an online calculator, with clinical characteristics non-evaluated by SLEDAI. It showed greater precision to measure disease activity, greater sensitivity to detect clinically significant changes and better performance to predict accumulated damage than SLEDAI. It has not yet been validated in Argentina.ObjectivesTo determine the validity of the SLE-DAS score in a population of patients with SLE from Argentina.MethodsA multicenter observational study was conducted. Outpatients and hospitalized patients with SLE from 9 Argentinian centers were included between July to August 2021. Socio-demographic and disease variables were studied and SLE activity was measured by physician’s global assessment (PGA), SLEDAI 2K and SLE-DAS. The disease activity categories used for SLE-DAS were: remission ≤2.08; mild activity >2.08 to 7.10, moderate and severe activity >7.10. For SLEDAI 2K, remission was considered 0, mild activity 1 to 5, moderate 6 to 10, high 11 to 19, very high >20 points.To determine construct validity and criterion validity, SLEDAI 2K and PGA were used as the gold standard and correlation between scores was analyzed with the Pearson and Spearman correlation coefficient. Sensitivity and specificity of the points that define each of the activity levels were established by ROC curves to determine the discriminative capacity of SLE-DAS.ResultsA multicenter observational study was conducted. Outpatients and hospitalized patients with SLE from 9 Argentinian centers were included between July to August 2021. Socio-demographic and disease variables were studied and SLE activity was measured by physician’s global assessment (PGA), SLEDAI 2K and SLE-DAS. The disease activity categories used for SLE-DAS were: remission ≤2.08; mild activity >2.08 to 7.10, moderate and severe activity >7.10. For SLEDAI 2K, remission was considered 0, mild activity 1 to 5, moderate 6 to 10, high 11 to 19, very high >20 points.To determine construct validity and criterion validity, SLEDAI 2K and PGA were used as the gold standard and correlation between scores was analyzed with the Pearson and Spearman correlation coefficient. Sensitivity and specificity of the points that define each of the activity levels were established by ROC curves to determine the discriminative capacity of SLE-DAS.ConclusionIn this population of lupus patients from Argentina, the SLE-DAS allowed to discriminate between remission and disease activity, being a useful and practical tool.Disclosure of InterestsNone declared

BackgroundThe clinical manifestations of patients with rheumatic diseases are highly heterogeneous. The appearance of adverse events (AEs) related to the treatments received for these diseases considerably increases the morbidity and mortality of these patients. This is why obtaining real-world data on these AEs and analyzing their causes is paramount. The BIOBADASAR registry has data from 5,676 patients over ten years of follow-up. Classifying patients in treatment with biological drugs into subgroups with different phenotypes through unsupervised grouping could provide valuable information on the characteristics associated with specific AE.ObjectivesThrough cluster analysis, this study aimed to identify different clinical phenotypes related to Adverse events in patients treated with biological drugs.MethodsRetrospective, multicenter study of patients with rheumatic diseases treated with original biological drugs, biosimilars, or original and generic targeted therapies in Argentina; follow-up from August 2010 to July 2021. Demographic and clinical data, time of initiation and completion of treatments, data on disease activity, and the AEs presented were collected.Patients were unbiasedly matched based on their clinical and phenotypic profiles using a k-means pooling method. The initiation of biological disease-modifying antirheumatic drugs (b-DMARD) was evaluated in the segregated groups to investigate each group’s clinical course and differential characteristics.ResultsA total of 5676 patients were analyzed. Three different clusters were obtained:Cluster 1: 1041 patients. Was observed an evolution time of the disease of 30.5 years (Q1 25.8; Q3 35.6) longer than other clusters and a longer delay in starting treatment at 18.3 years (Q114.4; Q3 24) p < 0.0001.Cluster 2: 2136 patients. We observed a higher frequency of patients with Systemic Lupus Erythematosus: 156 (7.3%) p<0.0001 and a lower frequency of AEs 190 (8.9%) p <0.0001Cluster 3: 2499 patients. We observed a higher mean age than in the other two clusters, 57.3 (SD 8.3) p < 0.001.The use of systemic corticosteroids was evenly distributed among the 3 clusters.ConclusionThe unsupervised grouping of patients from the BIOBADASAR registry demonstrated the existence of clusters based on clinical and demographic characteristics. Identifying high-risk patients through a combination of these parameters may be helpful for the early identification of risk factors and their association with adverse events.Based on our hierarchical cluster analysis, we identified different patient phenotypes.Our results show that within the heterogeneity of patient characteristics, common elements provide a basis for future analyses of these variables and their relationship with certain AEs.Graphics 1.K-means cluster analysis graph. Distribution of patients according to the cluster they belong to.Dim1: Principal component 1; Dim2: Principal component 2.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsJorge Alejandro Brigante Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Karen Roberts Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Carolina Isnardi Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Gimena Gómez Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Maria Haye Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Mercedes García Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Carla Gobbi Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Gustavo Casado Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Laura Lucia Holguín Arias Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Joan Manuel Dapeña Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Silvia Papasidero Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Guillermo Berbotto Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Malena Viola Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Veronica Saurit Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Ingrid Petkovic Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Ana Bertoli Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Monica Patricia Diaz Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Erika Catay Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Ida Elena Exeni Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Bernardo Pons-Estel Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Gladys Bovea Castelblanco Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Mercedes Elena De La Sota Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Maria Silvia Larroude Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Dora Aida Pereira Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Amelia Beatriz Granel Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Gustavo Medina Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Cecilia Pisoni Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., MONICA SACNUN Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Edson Velozo Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Nora Aste Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Cecilia Castro Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Eduardo Kerzberg Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Veronica Savio Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., JULIETA GAMBA Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Anastasia Secco Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Gustavo Citera Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Enrique Soriano Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Cesar Graf Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Guillermo Pons-Estel Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database., Maria de la Vega Grant/research support from: BIOBADASAR has received an unrestricted Grant from Pfizer. Pfizer has not participated in or influenced the project’s development, data collection, analysis, interpretation, or report writing. They do not have access to the information collected in the database.

Background:In 2019 ACR and EULAR published in joint collaboration the new classification criteria for Systemic Lupus Erythematosus (SLE). Compared to the previous ones, these criteria have shown higher sensitivity and specificity in multiple cohorts. To our knowledge, its performance has not been evaluated in a cohort of patients with rheumatological diseases living in Argentina.Objectives:The aim of this study was to evaluate the sensitivity and specificity of the ACR/ EULAR 2019 criteria in a cohort of patients with connective tissue diseases residing in Argentina. Secondary objectives were to determine the Likelihood Ratio (LR) of these criteria and the correlation of their global score with activity and damage indexes of the disease.Methods:Multicentre, retrospective and analytical study. Patients ≥ 18 years old with diagnosis of SLE (ACR 1997/SLICC 2012) without other associated collagen diseases (case group), and patients with other non-SLE connective tissue diseases (control group) were included. Those with active infectious disease, oncohematological disease, drug-induced lupus and overlap syndrome were excluded. Sociodemographic data, characteristics of the disease and treatment were recorded. In addition, activity and damage indexes were recorded in the group with SLE.Three SLE experts, blinded to the diagnosis determined, for every individual if the patient had SLE or another rheumatological disease. An interrater agreement of 100% (including the 3 evaluators) was considered “defined SLE” and used as gold standard. In all cases, ACR 1997/SLICC 2012/ACR / EULAR 2019 criteria were applied and compared with the gold standard. Statistical analysis: Descriptive statistics was estimated. Sensitivity, specificity, positive and negative LR of the criteria were determined. The association between the final score of the ACR-EULAR 2019 criteria and the disease activity and damage indexes were estimated with Spearman correlation test. STATA 15.0 was used for data analysis.Results:A total of 365 patients from 7 centres in Argentina were included. A One hundred and eighty-three belonged to the SLE group: 92.3% women, mean age 39 years (SD 13.3), median disease duration 92 months (IQR 37-150). The most frequent manifestations of the disease were mucocutaneous (94%), musculoskeletal (82.5%) and haematological (69%). All patients presented ANA +, 88% hypocomplementemia, 69.4% Anti-DNA and 19.5% antiphospholipid antibodies. Median SLEDAI and SLICC were 2 (IQR 0-6) and 0 (IQR 0-1), respectively.In the control group, 182 patients were recruited: 84% women, mean age 53.6 years (SD 14.2) and median disease duration 82.5 months (IQR 38-151). The most frequent diseases were Rheumatoid Arthritis (46.1%), Scleroderma (18.1%) and Sjögren’s Syndrome (16.5%) and most common manifestations were musculoskeletal (81.9%), immunological (73.6%) and constitutional (25.3%). A total of 62.6% of patients presented ANA+, 8.6% hypocomplementemia, and 1.3% Antiphospholipid antibodies.Ninety-one percent of patients in the case group were classified as defined SLE and 3.8% in the control group.The ACR / EULAR 2019 Criteria showed a 99.4% sensitivity and an 89.1% specificity, with a LR+ of 9.1 and a LR- of 0.007. The sensitivity and specificity of SLICC 2012 criteria were 98.3% and 88%, respectively with a LR+ of 8.2 and a LR- of 0.02; and the ACR 1997 criteria showed a 93.96% sensitivity and 90.1% specificity, with LR + of 8.21 and LR - of 0.07.The correlations between the ACR/EULAR 2019 Criteria global score, and activity and damage indexes were 0.19 and -0.006, respectively.Conclusion:The new ACR / EULAR 2019 criteria have shown high sensitivity, a specificity comparable to its predecessors, and a higher ability to distinguish SLE from other diseases and to exclude it in non-SLE patients. No correlation was observed between the criteria scores and activity and damage indexes.References:[1]Aringer M, Costenbader K, Daikh D, et al 2019 EULAR/ACR classification criteria for SLE. Ann Rheum 2019; 78: 1151-1159.Disclosure of Interests:None declared