Dual targeted therapy in patients with psoriatic arthritis and spondyloarthritis: a real-world multicenter experience from Spain

Dual targeted therapy (DTT) has emerged as a promising approach in patients with refractory spondyloarthritis (SpA) or psoriatic arthritis (PsA) and extra-musculoskeletal manifestations of both diseases, but its effectiveness/safety ratio still remains unclear. This is a retrospective, real-world multicenter study in refractory SpA and PsA patients with simultaneous use of two biological or synthetic targeted agents. Effectiveness was assessed using Ankylosing Spondylitis Disease Activity Score with C-reactive protein (ASDAS-CRP) and Disease Activity in Psoriatic Arthritis (DAPSA) Score. We identified 39 different DTT combinations in 36 patients (22 SpA; 14 PsA), 25 of them with concomitant inflammatory bowel disease. The most commonly used combinations were TNF inhibitor plus antagonist of the IL12/23 pathway, followed by TNF inhibitor plus IL-17 antagonist. During a median exposure of 14.86 months (IQR 8-20.2), DTT retention rate was 69.4% (n=25/36; 19 SpA, 6 PsA). Major clinical improvement (change in ASDAS-CRP > 2 or improvement > 85% in DAPSA) was achieved in 69.4% of patients (n=25/36 therapeutical combinations; 17/21 SpA, 8/15 PsA), with a 58.3% (n=21/36 combinations; 15/20 SpA, 6/13 PsA) low-activity/remission rate. Of the patients who were receiving glucocorticoids, 55% managed to withdraw them during follow-up. Interestingly, only four serious adverse events in three patients were observed, leading to DTT discontinuation

The SADDEN DEATH Study: Results from a Pilot Study in Non-ICU COVID-19 Spanish Patients

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

Outpatient readmission in rheumatology: A machine learning predictive model of patient's return to the clinic

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