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Objective: To describe whether rheumatic inflammatory diseases (RID) are associated with a higher risk of hospitalization and/or mortality from COVID-19 and identify the factors associated with hospitalization and mortality in RID and COVID-19 in different Hospitals in Andalusia., Methods: Design: Multicentre observational case-COntrol study., Patients: RID and COVID-19 from different centres in Andalusia., Controls: patients without RIS matched by sex, age and CRP-COVID. Protocol A list of patients with PCR for COVID-19 was requested from the microbiology service from March 14 to April 14, 2020. The patients who had RID were identified and then consecutively a paired control for each case. Variables The main outcome variable was hospital admission and mortality from COVID-19. Statistical analysis Bivariate followed by binary logistic regression models (DV: mortality/hospital admission)., Results: One hundred and fifty-six patients were included, 78 with RID and COVID-19 and 78 without RID with COVID-19. The patients did not present characteristics of COVID-19 disease different from the general population, nor did they present higher hospital admission or mortality. The factor associated with mortality in patients with RID was advanced age (OR [95% CI], 1.1 [1.0-1.2]; P= .025), while the factors associated with hospitalization were advanced age (OR [95% CI], 1.1 [1.0-1.1]; P = .007) and hypertension (OR [95% CI], 3.9 [1.5-6.7]; P = .003)., Conclusion: Mortality and hospital admission due to COVID-19 do not seem to increase in RID. Advanced age was associated with mortality in RID and, in addition, HTN was associated with hospital admission., (Copyright © 2021 Elsevier España, S.L.U. and Sociedad Española de Reumatología y Colegio Mexicano de Reumatología. All rights reserved.)

Background: This prospective multicenter study developed an integrative clinical and molecular longitudinal study in Rheumatoid Arthritis (RA) patients to explore changes in serologic parameters following anti-TNF therapy (TNF inhibitors, TNFi) and built on machine-learning algorithms aimed at the prediction of TNFi response, based on clinical and molecular profiles of RA patients. Methods: A total of 104 RA patients from two independent cohorts undergoing TNFi and 29 healthy donors (HD) were enrolled for the discovery and validation of prediction biomarkers. Serum samples were obtained at baseline and 6 months after treatment, and therapeutic efficacy was evaluated. Serum inflammatory profile, oxidative stress markers and NETosis-derived bioproducts were quantified and miRNomes were recognized by next-generation sequencing. Then, clinical and molecular changes induced by TNFi were delineated. Clinical and molecular signatures predictors of clinical response were assessed with supervised machine learning methods, using regularized logistic regressions. Results: Altered inflammatory, oxidative and NETosis-derived biomolecules were found in RA patients vs. HD, closely interconnected and associated with specific miRNA profiles. This altered molecular profile allowed the unsupervised division of three clusters of RA patients, showing distinctive clinical phenotypes, further linked to the TNFi effectiveness. Moreover, TNFi treatment reversed the molecular alterations in parallel to the clinical outcome. Machine-learning algorithms in the discovery cohort identified both, clinical and molecular signatures as potential predictors of response to TNFi treatment with high accuracy, which was further increased when both features were integrated in a mixed model (AUC: 0.91). These results were confirmed in the validation cohort. Conclusions: Our overall data suggest that: 1. RA patients undergoing anti-TNF-therapy conform distinctive clusters based on altered molecular profiles, which are directly linked to their clinical status at baseline. 2. Clinical effectiveness of anti-TNF therapy was divergent among these molecular clusters and associated with a specific modulation of the inflammatory response, the reestablishment of the altered oxidative status, the reduction of NETosis, and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Luque-Tévar, Perez-Sanchez, Patiño-Trives, Barbarroja, Arias de la Rosa, Abalos-Aguilera, Marin-Sanz, Ruiz-Vilchez, Ortega-Castro, Font, Lopez-Medina, Romero-Gomez, Rodriguez-Escalera, Perez-Venegas, Ruiz-Montesinos, Dominguez, Romero-Barco, Fernandez-Nebro, Mena-Vazquez, Marenco, Uceda-Montañez, Toledo-Coello, Aguirre, Escudero-Contreras, Collantes-Estevez and Lopez-Pedrera.)

Objective: To describe whether rheumatic inflammatory diseases (RID) are associated with a higher risk of hospitalization and/or mortality from COVID-19 and identify the factors associated with hospitalization and mortality in RID and COVID-19 in different Hospitals in Andalusia., Methods: Design: Multicentre observational case-control study., Patients: RID and COVID-19 from different centres in Andalusia., Controls: patients without RIS matched by sex, age and CRP-COVID. Protocol A list of patients with PCR for COVID-19 was requested from the microbiology service from March 14 to April 14, 2020. The patients who had RID were identified and then consecutively a paired control for each case. Variables The main outcome variable was hospital admission and mortality from COVID-19. Statistical analysis Bivariate followed by binary logistic regression models (DV: mortality/hospital admission)., Results: One hundred and fifty-six patients were included, 78 with RID and COVID-19 and 78 without RID with COVID-19. The patients did not present characteristics of COVID-19 disease different from the general population, nor did they present higher hospital admission or mortality. The factor associated with mortality in patients with RID was advanced age (OR [95% CI], 1.1 [1.0-1.2]; p = 0.025), while the factors associated with hospitalization were advanced age (OR [95% CI], 1.1 [1.0-1.1]; p = 0.007) and hypertension (OR [95% CI], 3.9 [1.5-6.7]; p = 0.003)., Conclusion: Mortality and hospital admission due to COVID-19 do not seem to increase in RID. Advanced age was associated with mortality in RID and, in addition, HTN was associated with hospital admission., (Copyright © 2021 Elsevier España, S.L.U. and Sociedad Española de Reumatología y Colegio Mexicano de Reumatología. All rights reserved.)