Citations

ObjectiveApproximately one third of individuals worldwide have not received a COVID‐19 vaccine. Although studies have investigated risk factors linked to severe COVID‐19 among unvaccinated people with rheumatic diseases (RDs), we know less about whether these factors changed as the pandemic progressed. We aimed to identify risk factors associated with severe COVID‐19 in unvaccinated individuals in different pandemic epochs corresponding to major variants of concern.MethodsPatients with RDs and COVID‐19 were entered into the COVID‐19 Global Rheumatology Alliance Registry between March 2020 and June 2022. An ordinal logistic regression model (not hospitalized, hospitalized, and death) was used with date of COVID‐19 diagnosis, age, sex, race and/or ethnicity, comorbidities, RD activity, medications, and the human development index (HDI) as covariates. The main analysis included all unvaccinated patients across COVID‐19 pandemic epochs; subanalyses stratified patients according to RD types.ResultsAmong 19,256 unvaccinated people with RDs and COVID‐19, those who were older, male, had more comorbidities, used glucocorticoids, had higher disease activity, or lived in lower HDI regions had worse outcomes across epochs. For those with rheumatoid arthritis, sulfasalazine and B‐cell–depleting therapy were associated with worse outcomes, and tumor necrosis factor inhibitors were associated with improved outcomes. In those with connective tissue disease or vasculitis, B‐cell–depleting therapy was associated with worse outcomes.ConclusionRisk factors for severe COVID‐19 outcomes were similar throughout pandemic epochs in unvaccinated people with RDs. Ongoing efforts, including vaccination, are needed to reduce COVID‐19 severity in this population, particularly in those with medical and social vulnerabilities identified in this study.

ObjectiveTo determine the association between race/ethnicity and COVID‐19 outcomes in individuals with systemic lupus erythematosus (SLE).MethodsIndividuals with SLE from the US with data entered into the COVID‐19 Global Rheumatology Alliance registry between March 24, 2020 and August 27, 2021 were included. Variables included age, sex, race, and ethnicity (White, Black, Hispanic, other), comorbidities, disease activity, pandemic time period, glucocorticoid dose, antimalarials, and immunosuppressive drug use. The ordinal outcome categories were: not hospitalized, hospitalized with no oxygenation, hospitalized with any ventilation or oxygenation, and death. We constructed ordinal logistic regression models evaluating the relationship between race/ethnicity and COVID‐19 severity, adjusting for possible confounders.ResultsWe included 523 patients; 473 (90.4%) were female and the mean ± SD age was 46.6 ± 14.0 years. A total of 358 patients (74.6%) were not hospitalized; 40 patients (8.3%) were hospitalized without oxygen, 64 patients (13.3%) were hospitalized with any oxygenation, and 18 (3.8%) died. In a multivariable model, Black (odds ratio [OR] 2.73 [95% confidence interval (95% CI) 1.36–5.53]) and Hispanic (OR 2.76 [95% CI 1.34–5.69]) individuals had higher odds of more severe outcomes than White individuals.ConclusionBlack and Hispanic individuals with SLE experienced more severe COVID‐19 outcomes, which is consistent with findings in the US general population. These results likely reflect socioeconomic and health disparities and suggest that more aggressive efforts are needed to prevent and treat infection in this population.

ObjectiveTo describe people with gout who were diagnosed with coronavirus disease 2019 (COVID‐19) and hospitalized and to characterize their outcomes.MethodsData on patients with gout hospitalized for COVID‐19 between March 12, 2020, and October 25, 2021, were extracted from the COVID‐19 Global Rheumatology Alliance registry. Descriptive statistics were used to describe the demographics, comorbidities, medication exposures, and COVID‐19 outcomes including oxygenation or ventilation support and death.ResultsOne hundred sixty‐three patients with gout who developed COVID‐19 and were hospitalized were included. The mean age was 63 years, and 85% were male. The majority of the group lived in the Western Pacific Region (35%) and North America (18%). Nearly half (46%) had two or more comorbidities, with hypertension (56%), cardiovascular disease (28%), diabetes mellitus (26%), chronic kidney disease (25%), and obesity (23%) being the most common. Glucocorticoids and colchicine were used pre‐COVID‐19 in 11% and 12% of the cohort, respectively. Over two thirds (68%) of the cohort required supplemental oxygen or ventilatory support during hospitalization. COVID‐19‐related death was reported in 16% of the overall cohort, with 73% of deaths documented in people with two or more comorbidities.ConclusionThis cohort of people with gout and COVID‐19 who were hospitalized had high frequencies of ventilatory support and death. This suggests that patients with gout who were hospitalized for COVID‐19 may be at risk of poor outcomes, perhaps related to known risk factors for poor outcomes, such as age and presence of comorbidity.

ObjectiveSome 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.MethodsData 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.ResultsThe 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.ConclusionWe 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.

Objective To summarise the available information on physician workforce modelling, to develop a rheumatology workforce prediction risk of bias tool and to apply it to existing studies in rheumatology. Methods A systematic literature review (SLR) was performed in key electronic databases (1946–2017) comprising an update of an SLR in rheumatology and a hierarchical SLR in other medical fields. Data on the type of workforce prediction models and the factors considered in the models were extracted. Key general as well as specific need/demand and supply factors for workforce calculation in rheumatology were identified. The workforce prediction risk of bias tool was developed and applied to existing workforce studies in rheumatology. Results In total, 14 studies in rheumatology and 10 studies in other medical fields were included. Studies used a variety of prediction models based on a heterogeneous set of need/demand and/or supply factors. Only two studies attempted empirical validation of the prediction quality of the model. Based on evidence and consensus, the newly developed risk of bias tool includes 21 factors (general, need/demand and supply). The majority of studies revealed high or moderate risk of bias for most of the factors. Conclusions The existing evidence on workforce prediction in rheumatology is scarce, heterogeneous and at moderate or high risk of bias. The new risk of bias tool should enable future evaluation of workforce prediction studies. This review informs the European League Against Rheumatism points to consider for the conduction of workforce requirement studies in rheumatology.

Objective Current methods used for forecasting workforce requirements in rheumatology are disparate, as are the parameters incorporated into workforce projection studies. The objective of these European League Against Rheumatism (EULAR points to consider (PTC) is to guide future workforce studies in adult rheumatology in order to produce valid and reliable manpower estimates. Methods The EULAR Standardised Operating Procedures were followed. A multidisciplinary task force with experts including patients with rheumatic diseases from 11 EULAR countries and the USA was assembled. A systematic literature review (SLR) was conducted to retrieve workforce models in rheumatology and other medical fields. PTC were based on expert opinion informed by the SLR, followed by group discussions with consensus obtained through informal voting. The level of agreement with the PTC was voted anonymously. Results A total of 10 PTC were formulated. The task force recommends models integrating supply (=workforce available in rheumatology), demand (=health services requested by the population) and need (=health services that are considered appropriate to serve the population). In general, projections of workforce requirements should consider all factors relevant for current and future workload in rheumatology inside and outside of direct patient care. Forecasts of workforce supply should consider demography and attrition of rheumatologists, as well as the effects of new developments in healthcare. Predictions of future need/demand should take demographic, sociocultural and epidemiological development of the population into account. Conclusion These EULAR-endorsed PTC will provide guidance on the methodology and the parameters to be applied in future national and international workforce requirement studies in rheumatology.