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Competing Interests: Competing interests: None declared.

Competing Interests: Competing interests: None declared.

Introduction: The treatment of Rheumatoid Arthritis (RA) has changed dramatically in recent years, especially with the use of disease modifying drugs (DMARDs). Data on the management of this disease in clinical trials are abundant, but not so in real life. The aim of our study is to describe the management of an early RA cohort in daily clinical practice, especially DMARD discontinuations and reasons., Methods: A retrospective observational study of patients with RA diagnosed between 01/07 and 12/14 followed up to 01/17, using >1 DMARD ≥ 3 months., Variables: sociodemographic, clinical, treatment, DMARD discontinuation and reason. Descriptive analysis of sociodemographic, clinical and treatment characteristics. Discontinuation incidence rate (DIR) due to survival techniques, expressed in 100 patients*year with 95% confidence interval., Results: 814 patients were included with 2388 courses of treatment, 77% women, mean age 57.5 years. First course: monotherapy (92.75%), especially Methotrexate (56.06%). In later courses there was increased combined therapy and use of biologicals (mainly Etanercept). There were 1094 discontinuations (29.5 [27.8-31.3]). The DIR was higher for adverse events (15.9 [14.7-17.3]), biologicals (49.6 [43.1-57.2]) and combined therapy. The DMAR with the lowest DIR was MTX (25.8 [23.8-28.1])., Conclusion: Methotrexate was the most used drug, biologicals increased throughout the follow-up, the most frequent being Etanercept. The DMARD DIR was 29*100 patients per year, mainly due to adverse events. It seems to be higher in the therapies that include biologicals and combined therapies. MTX is the drug with the lowest DIR., (Copyright © 2021. Published by Elsevier España, S.L.U.)

Aims: The aim of this study was to assess the effect of "outpatient readmissions" on the health-related quality of life (HR-QoL) of outpatients from a rheumatology clinic, meaning the effect of the patient's return to the outpatient clinic after having received care and been discharged., Methods: We conducted an observational longitudinal retrospective study, with patients selected from the Hospital Clínico San Carlos Musculoskeletal cohort, based on having received at least one discharge from the outpatient clinic and having returned (readmission) at least once after the discharge. The main outcomes were the patients' baseline HR-QoL (measured on the first visit of each episode) and the ΔHR-QoL (difference between the HR-QoL in the last and the first visit of each episode). Successive episodes of admission and readmission were chronologically ordered, paired and analyzed using nested linear mixed models, nested by patients and by admission-readmission tandem. We carried out bivariable and multivariable analyses to assess the effect of demographic, clinical, treatment and comorbidity-related variables in both main outcomes., Results: For the first main outcome, 5887 patients (13,772 episodes) were analyzed. Based on the multivariable level, readmission showed no significant marginal effect on the baseline HR-QoL ( p-value  = 0.17). Conversely, when analyzing the ΔHR-QoL, we did observe a negative and significant marginal effect ( p -value = 0.028), meaning that readmission was associated with a lower gain in the HR-QoL during the follow-up, compared with the previous episode., Conclusion: In the outpatient setting, readmission exerts a deleterious effect in patients undergoing this process. Identification of outpatients more likely to be readmitted could increase the value of the care provided., Competing Interests: Conflict of interest statement: The authors declare that there is no conflict of interest., (© The Author(s), 2021.)

Introduction: The worldwide pandemic, coronavirus disease 2019 (COVID-19) is a novel infection with serious clinical manifestations, including death. Our aim is to describe the first non-ICU Spanish deceased series with COVID-19, comparing specifically between unexpected and expected deaths. Methods: In this single-centre study, all deceased inpatients with laboratory-confirmed COVID-19 who had died from March 4 to April 16, 2020 were consecutively included. Demographic, clinical, treatment, and laboratory data, were analyzed and compared between groups. Factors associated with unexpected death were identified by multivariable logistic regression methods. Results: In total, 324 deceased patients were included. Median age was 82 years (IQR 76-87); 55.9% males. The most common cardiovascular risk factors were hypertension (78.4%), hyperlipidemia (57.7%), and diabetes (34.3%). Other common comorbidities were chronic kidney disease (40.1%), chronic pulmonary disease (30.3%), active cancer (13%), and immunosuppression (13%). The Confusion, BUN, Respiratory Rate, Systolic BP and age ≥65 (CURB-65) score at admission was >2 in 40.7% of patients. During hospitalization, 77.8% of patients received antivirals, 43.3% systemic corticosteroids, and 22.2% full anticoagulation. The rate of bacterial co-infection was 5.5%, and 105 (32.4%) patients had an increased level of troponin I. The median time from initiation of therapy to death was 5 days (IQR 3.0-8.0). In 45 patients (13.9%), the death was exclusively attributed to COVID-19, and in 254 patients (78.4%), both COVID-19 and the clinical status before admission contributed to death. Progressive respiratory failure was the most frequent cause of death (92.0%). Twenty-five patients (7.7%) had an unexpected death. Factors independently associated with unexpected death were male sex, chronic kidney disease, insulin-treated diabetes, and functional independence. Conclusions: This case series provides in-depth characterization of hospitalized non-ICU COVID-19 patients who died in Madrid. Male sex, insulin-treated diabetes, chronic kidney disease, and independency for activities of daily living are predictors of unexpected death.

Introduction: The treatment of rheumatoid arthritis has changed dramatically in recent years, especially with the use of disease modifying drugs (DMARDs). Data on the management of this disease in clinical trials are abundant, but not so in real life. The aim of our study is to describe the management of an early rheumatoid arthritis cohort in daily clinical practice, especially DMARD discontinuations and reasons., Methods: A retrospective observational study of patients with rheumatoid arthritis diagnosed between 01/07 and 12/14 followed up to 01/17, using>1 DMARD≥3 months., Variables: sociodemographic, clinical, treatment, DMARD discontinuation and reason. Descriptive analysis of sociodemographic, clinical and treatment characteristics. Discontinuation incidence rate (DIR) due to survival techniques, expressed in 100 patients/year with 95% confidence interval., Results: 814 patients were included with 2,388 courses of treatment, 77% women, mean age 57.5 years. First course: monotherapy (92.75%), especially methotrexate (56.06%). In later courses there was increased combined therapy and use of biologicals (mainly etanercept). There were 1,094 discontinuations (29.5 [27.8-31.3]). The DIR was higher for adverse events (15.9 [14.7-17.3]), biologicals (49.6 [43.1-57.2]) and combined therapy. The DMAR with the lowest DIR was methotrexate (25.8 [23.8-28.1])., Conclusion: Methotrexate was the most used drug, biologicals increased throughout the follow-up, the most frequent being Etanercept. The DMARD DIR was 29/100 patients per year, mainly due to adverse events. It seems to be higher in the therapies that include biologicals and combined therapies. Methotrexate is the drug with the lowest DIR., (Copyright © 2020. Publicado por Elsevier España, S.L.U.)

Objectives: To describe patients with autoimmune inflammatory rheumatic diseases (AIRD) who had COVID-19 disease; to compare patients who required hospital admission with those who did not and assess risk factors for hospital admission related to COVID-19., Methods: An observational longitudinal study was conducted during the pandemic peak of severe acute respiratory syndrome coronavirus 2 (1 March 2020 to 24 April). All patients attended at the rheumatology outpatient clinic of a tertiary hospital in Madrid, Spain with a medical diagnosis of AIRD and with symptomatic COVID-19 were included. The main outcome was hospital admission related to COVID-19. The covariates were sociodemographic, clinical and treatments. We ran a multivariable logistic regression model to assess risk factors for the hospital admission., Results: The study population included 123 patients with AIRD and COVID-19. Of these, 54 patients required hospital admission related to COVID-19. The mean age on admission was 69.7 (15.7) years, and the median time from onset of symptoms to hospital admission was 5 (3-10) days. The median length of stay was 9 (6-14) days. A total of 12 patients died (22%) during admission. Compared with outpatients, the factors independently associated with hospital admission were older age (OR: 1.08; p=0.00) and autoimmune systemic condition (vs chronic inflammatory arthritis) (OR: 3.55; p=0.01). No statistically significant findings for exposure to disease-modifying antirheumatic drugs were found in the final model., Conclusion: Our results suggest that age and having a systemic autoimmune condition increased the risk of hospital admission, whereas disease-modifying antirheumatic drugs were not associated with hospital admission., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Our objective is to develop and validate a predictive model based on the random forest algorithm to estimate the readmission risk to an outpatient rheumatology clinic after discharge. We included patients from the Hospital Clínico San Carlos rheumatology outpatient clinic, from 1 April 2007 to 30 November 2016, and followed-up until 30 November 2017. Only readmissions between 2 and 12 months after the discharge were analyzed. Discharge episodes were chronologically split into training, validation, and test datasets. Clinical and demographic variables (diagnoses, treatments, quality of life (QoL), and comorbidities) were used as predictors. Models were developed in the training dataset, using a grid search approach, and performance was compared using the area under the receiver operating characteristic curve (AUC-ROC). A total of 18,662 discharge episodes were analyzed, out of which 2528 (13.5%) were followed by outpatient readmissions. Overall, 38,059 models were developed. AUC-ROC, sensitivity, and specificity of the reduced final model were 0.653, 0.385, and 0.794, respectively. The most important variables were related to follow-up duration, being prescribed with disease-modifying anti-rheumatic drugs and corticosteroids, being diagnosed with chronic polyarthritis, occupation, and QoL. We have developed a predictive model for outpatient readmission in a rheumatology setting. Identification of patients with higher risk can optimize the allocation of healthcare resources.