To be or not to B27 positive: implications for the phenotypes of axial spondyloarthritis outcomes. Data from a large multiracial cohort from the Brazilian Registry of Spondyloarthritis.

BACKGROUND: There is a remarkable variability in the frequency of HLA-B27 positivity in patients with spondyloarthritis (SpA), which may be associated with different clinical presentations worldwide. However, there is a lack of data considering ethnicity and sex on the evaluation of the main clinical and prognostic outcomes in mixed-race populations. The aim of this study was to evaluate the frequency of HLA-B27 and its correlation with disease parameters in a large population of patients from the Brazilian Registry of Spondyloarthritis (RBE). METHODS: The RBE is a multicenter, observational, prospective cohort that enrolled patients with SpA from 46 centers representing all five geographic regions of Brazil. The inclusion criteria were as follow: (1) diagnosis of axSpA by an expert rheumatologist; (2) age ≥18 years; (3) classification according to ASAS axial. The following data were collected via a standardized protocol: demographic data, disease parameters and treatment historical. RESULTS: A total of 1096 patients were included, with 73.4% HLA-B27 positivity and a mean age of 44.4 (±13.2) years. Positive HLA-B27 was significantly associated with male sex, earlier age at disease onset and diagnosis, uveitis, and family history of SpA. Conversely, negative HLA-B27 was associated with psoriasis, higher peripheral involvement and disease activity, worse quality of life and mobility. CONCLUSIONS: Our data showed that HLA-B27 positivity was associated with a classic axSpA pattern quite similar to that of Caucasian axSpA patients around the world. Furthermore, its absence was associated with peripheral manifestations and worse outcomes, suggesting a relevant phenotypic difference in a highly miscegenated population.

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry.

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.