Diagnostic Agreement Among General Practitioners, Residents, and Senior Rheumatologists for Rheumatic Diseases.

OBJECTIVE: The aim of this study was to evaluate the concordance of the diagnoses made by senior rheumatologists and those made by residents in rheumatology and by general practitioners (GPs). METHODS: In this cohort, 497 patients referred by GPs from August 1, 2018 to December 16, 2019 were evaluated first by a second-year resident in rheumatology. After clinical rounds, the diagnoses by senior rheumatologists were assumed as the criterion standard and defined the prevalence of the rheumatic diseases, divided into 5 groups: rheumatoid arthritis, spondyloarthritis, other connective tissue diseases and vasculitis, nonautoimmune rheumatic diseases, and nonrheumatic diseases. The follow-up ended on November 30, 2020. We calculated sensibility, specificity, positive predictive value, negative predictive value, and κ coefficient of the diagnosis by GPs and residents. RESULTS: The diagnoses were changed for 58% of the referral letters. Diseases of low complexity, such as fibromyalgia and osteoarthritis, accounted for 50% of the diagnoses. Compared with senior rheumatologists, residents in rheumatology had κ > 0.6 for all the groups, whereas GPs had κ < 0.5, with the worst performance for nonautoimmune rheumatic disease (κ = -0.18) and nonrheumatic disease (κ = 0.15). In terms of level of complexity, 46% of the letters were inappropriate. CONCLUSIONS: We found a poor level of diagnostic agreement between GPs and the rheumatology team. General practitioners had difficulties diagnosing and treating rheumatic diseases, referring patients that should be treated in the primary level of health care. One year of training in rheumatology made residents' skills comparable to those of senior rheumatologists.

SARS-CoV-2 productively infects primary human immune system cells in vitro and in COVID-19 patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with a hyperinflammatory state and lymphocytopenia, a hallmark that appears as both signature and prognosis of disease severity outcome. Although cytokine storm and a sustained inflammatory state are commonly associated with immune cell depletion, it is still unclear whether direct SARS-CoV-2 infection of immune cells could also play a role in this scenario by harboring viral replication. We found that monocytes, as well as both B and T lymphocytes, were susceptible to SARS-CoV-2 infection in vitro, accumulating double-stranded RNA consistent with viral RNA replication and ultimately leading to expressive T cell apoptosis. In addition, flow cytometry and immunofluorescence analysis revealed that SARS-CoV-2 was frequently detected in monocytes and B lymphocytes from coronavirus disease 2019 (COVID-19) patients. The rates of SARS-CoV-2-infected monocytes in peripheral blood mononuclear cells from COVID-19 patients increased over time from symptom onset, with SARS-CoV-2-positive monocytes, B cells, and CD4+ T lymphocytes also detected in postmortem lung tissue. These results indicated that SARS-CoV-2 infection of blood-circulating leukocytes in COVID-19 patients might have important implications for disease pathogenesis and progression, immune dysfunction, and virus spread within the host.

Beneficial effects of colchicine for moderate to severe COVID-19: a randomised, double-blinded, placebo-controlled clinical trial.

OBJECTIVE: To evaluate whether the addition of colchicine to standard treatment for COVID-19 results in better outcomes. DESIGN: We present the results of a randomised, double-blinded, placebo-controlled clinical trial of colchicine for the treatment of moderate to severe COVID-19, with 75 patients allocated 1:1 from 11 April to 30 August 2020. Colchicine regimen was 0.5 mg thrice daily for 5 days, then 0.5 mg twice daily for 5 days. The primary endpoints were the need for supplemental oxygen, time of hospitalisation, need for admission and length of stay in intensive care unit and death rate. RESULTS: Seventy-two patients (36 for placebo and 36 for colchicine) completed the study. Median (and IQR) time of need for supplemental oxygen was 4.0 (2.0-6.0) days for the colchicine group and 6.5 (4.0-9.0) days for the placebo group (p<0.001). Median (IQR) time of hospitalisation was 7.0 (5.0-9.0) days for the colchicine group and 9.0 (7.0-12.0) days for the placebo group (p=0.003). At day 2, 67% versus 86% of patients maintained the need for supplemental oxygen, while at day 7, the values were 9% versus 42%, in the colchicine and the placebo groups, respectively (log rank; p=0.001). Two patients died, both in placebo group. Diarrhoea was more frequent in the colchicine group (p=0.26). CONCLUSION: Colchicine reduced the length of both, supplemental oxygen therapy and hospitalisation. The drug was safe and well tolerated. Once death was an uncommon event, it is not possible to ensure that colchicine reduced mortality of COVID-19. TRIAL REGISTRATION NUMBER: RBR-8jyhxh.

Inflammasomes are activated in response to SARS-CoV-2 infection and are associated with COVID-19 severity in patients.

Severe cases of COVID-19 are characterized by a strong inflammatory process that may ultimately lead to organ failure and patient death. The NLRP3 inflammasome is a molecular platform that promotes inflammation via cleavage and activation of key inflammatory molecules including active caspase-1 (Casp1p20), IL-1ß, and IL-18. Although participation of the inflammasome in COVID-19 has been highly speculated, the inflammasome activation and participation in the outcome of the disease are unknown. Here we demonstrate that the NLRP3 inflammasome is activated in response to SARS-CoV-2 infection and is active in COVID-19 patients. Studying moderate and severe COVID-19 patients, we found active NLRP3 inflammasome in PBMCs and tissues of postmortem patients upon autopsy. Inflammasome-derived products such as Casp1p20 and IL-18 in the sera correlated with the markers of COVID-19 severity, including IL-6 and LDH. Moreover, higher levels of IL-18 and Casp1p20 are associated with disease severity and poor clinical outcome. Our results suggest that inflammasomes participate in the pathophysiology of the disease, indicating that these platforms might be a marker of disease severity and a potential therapeutic target for COVID-19.

SARS-CoV-2-triggered neutrophil extracellular traps mediate COVID-19 pathology.

Severe COVID-19 patients develop acute respiratory distress syndrome that may progress to cytokine storm syndrome, organ dysfunction, and death. Considering that neutrophil extracellular traps (NETs) have been described as important mediators of tissue damage in inflammatory diseases, we investigated whether NETs would be involved in COVID-19 pathophysiology. A cohort of 32 hospitalized patients with a confirmed diagnosis of COVID-19 and healthy controls were enrolled. The concentration of NETs was augmented in plasma, tracheal aspirate, and lung autopsies tissues from COVID-19 patients, and their neutrophils released higher levels of NETs. Notably, we found that viable SARS-CoV-2 can directly induce the release of NETs by healthy neutrophils. Mechanistically, NETs triggered by SARS-CoV-2 depend on angiotensin-converting enzyme 2, serine protease, virus replication, and PAD-4. Finally, NETs released by SARS-CoV-2-activated neutrophils promote lung epithelial cell death in vitro. These results unravel a possible detrimental role of NETs in the pathophysiology of COVID-19. Therefore, the inhibition of NETs represents a potential therapeutic target for COVID-19.

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging.

BACKGROUND: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. METHODS: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with ~ 80% (46 samples, 20 positive and 26 negative) as training and ~ 20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. RESULTS: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. CONCLUSIONS: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

Abstract Background: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. Methods: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with ∼ 80% (46 samples, 20 positive and 26 negative) as training and ∼ 20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. Results: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. Conclusions: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.(AU)