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ObjectivesTo investigate factors associated with severe COVID-19 in people with idiopathic inflammatory myopathy (IIM).MethodsDemographic data, clinical characteristics and COVID-19 outcome severity of adults with IIM were obtained from the COVID-19 Global Rheumatology Alliance physician-reported registry. A 3-point ordinal COVID-19 severity scale was defined: (1) no hospitalisation, (2) hospitalisation (and no death) and (3) death. ORs were estimated using multivariable ordinal logistic regression. Sensitivity analyses were performed using a 4-point ordinal scale: (1) no hospitalisation, (2) hospitalisation with no oxygen (and no death), (3) hospitalisation with oxygen/ventilation (and no death) and 4) death.ResultsOf 348 patients, 48% were not hospitalised, 39% were hospitalised (and did not die) and 13% died. Older age (OR=1.59/decade, 95% CI 1.31 to 1.91), high disease activity (OR=3.50, 95% CI 1.25 to 9.83; vs remission), ≥2 comorbidities (OR=2.63, 95% CI 1.39 to 4.98; vs none), prednisolone-equivalent dose >7.5 mg/day (OR=2.40, 95% CI 1.09 to 5.28; vs no intake) and exposure to rituximab (OR=2.71, 95% CI 1.28 to 5.72; vs conventional synthetic disease-modifying antirheumatic drugs only) were independently associated with severe COVID-19. In addition to these variables, in the sensitivity analyses, male sex (OR range: 1.65–1.83; vs female) was also significantly associated with severe outcomes, while COVID-19 diagnosis after 1 October 2020 (OR range: 0.51–0.59; vs on/before 15 June 2020) was significantly associated with less severe outcomes, but these associations were not significant in the main model (OR=1.57, 95% CI 0.95 to 2.59; and OR=0.61, 95% CI 0.37 to 1.00; respectively).ConclusionsThis is the first large registry data on outcomes of COVID-19 in people with IIM. Older age, male sex, higher comorbidity burden, high disease activity, prednisolone-equivalent dose >7.5 mg/day and rituximab exposure were associated with severe COVID-19. These findings will enable risk stratification and inform management decisions for patients with IIM.

ObjectiveWhile COVID-19 vaccination prevents severe infections, poor immunogenicity in immunocompromised people threatens vaccine effectiveness. We analysed the clinical characteristics of patients with rheumatic disease who developed breakthrough COVID-19 after vaccination against SARS-CoV-2.MethodsWe included people partially or fully vaccinated against SARS-CoV-2 who developed COVID-19 between 5 January and 30 September 2021 and were reported to the Global Rheumatology Alliance registry. Breakthrough infections were defined as occurring ≥14 days after completion of the vaccination series, specifically 14 days after the second dose in a two-dose series or 14 days after a single-dose vaccine. We analysed patients’ demographic and clinical characteristics and COVID-19 symptoms and outcomes.ResultsSARS-CoV-2 infection was reported in 197 partially or fully vaccinated people with rheumatic disease (mean age 54 years, 77% female, 56% white). The majority (n=140/197, 71%) received messenger RNA vaccines. Among the fully vaccinated (n=87), infection occurred a mean of 112 (±60) days after the second vaccine dose. Among those fully vaccinated and hospitalised (n=22, age range 36–83 years), nine had used B cell-depleting therapy (BCDT), with six as monotherapy, at the time of vaccination. Three were on mycophenolate. The majority (n=14/22, 64%) were not taking systemic glucocorticoids. Eight patients had pre-existing lung disease and five patients died.ConclusionMore than half of fully vaccinated individuals with breakthrough infections requiring hospitalisation were on BCDT or mycophenolate. Further risk mitigation strategies are likely needed to protect this selected high-risk population.

Background:An increased risk of severe COVID-19 outcomes may be seen in patients with autoimmune diseases on moderate to high daily doses of glucocorticoids, as well as in those with comorbidities. However, specific information about COVID-19 outcomes in SLE is scarce.Objectives:To determine the characteristics associated with severe COVID-19 outcomes in a multi-national cross-sectional registry of COVID-19 patients with SLE.Methods:SLE adult patients from a physician-reported registry of the COVID-19 GRA were studied. Variables collected at COVID-19 diagnosis included age, sex, race/ethnicity, region, comorbidities, disease activity, time period of COVID-19 diagnosis, glucocorticoid (GC) dose, and immunomodulatory therapy. Immunomodulatory therapy was categorized as: antimalarials only, no SLE therapy, traditional immunosuppressive (IS) drug monotherapy, biologics/targeted synthetic IS drug monotherapy, and biologic and traditional IS drug combination therapy. We used an ordinal COVID-19 severity outcome defined as: not hospitalized/hospitalized without supplementary oxygen; hospitalized with non-invasive ventilation; hospitalized with mechanical ventilation/extracorporeal membrane oxygenation; and death. An ordinal logistic regression model was constructed to assess the association between demographic characteristics, comorbidities, medications, disease activity and COVID-19 severity. This assumed that the relationship between each pair of outcome groups is of the same direction and magnitude.Results:Of 1069 SLE patients included, 1047 (89.6%) were female, with a mean age of 44.5 (SD: 14.1) years. Patient outcomes included 815 (78.8%) not hospitalized/hospitalized without supplementary oxygen; 116 (11.2) hospitalized with non-invasive ventilation, 25 (2.4%) hospitalized with mechanical ventilation/extracorporeal membrane oxygenation and 78 (7.5%) died. In a multivariate model (n=804), increased age [OR=1.03 (1.01, 1.04)], male sex [OR =1.93 (1.21, 3.08)], COVID-19 diagnosis between June 2020 and January 2021 (OR =1.87 (1.17, 3.00)), no IS drug use [OR =2.29 (1.34, 3.91)], chronic renal disease [OR =2.34 (1.48, 3.70)], cardiovascular disease [OR =1.93 (1.34, 3.91)] and moderate/high disease activity [OR =2.24 (1.46, 3.43)] were associated with more severe COVID-19 outcomes. Compared with no use of GC, patients using GC had a higher odds of poor outcome: 0-5 mg/d, OR =1.98 (1.33, 2.96); 5-10 mg/d, OR =2.88 (1.27, 6.56); >10 mg/d, OR =2.01 (1.26, 3.21) (Table 1).Table 1.Characteristics associated with more severe COVID-19 outcomes in SLE. (N=804)OR (95% CI)Age, years1.03 (1.01, 1.04)Sex, Male1.93 (1.21, 3.08)Race/Ethnicity, Non-White vs White1.47 (0.87, 2.50)RegionEuropeRef.North America0.67 (0.29, 1.54)South America0.67 (0.29, 1.54)Other1.93 (0.85, 4.39)Season, June 16th 2020-January 8th 2021 vs January-June 15th 20201.87 (1.17, 3.00)Glucocorticoids0 mg/dayRef.0-5 mg/day1.98 (1.33, 2.96)5-10 mg/day2.88 (1.27, 6.56)=>10 mg/day2.01 (1.26, 3.21)Medication CategoryAntimalarial onlyRef.No IS drugs2.29 (1.34, 3.91)Traditional IS drugs as monotherapy1.17 (0.77, 1.77)b/ts IS drugs as monotherapy1.00 (0.37, 2.71)Combination of traditional and b/ts IS1.00 (0.55, 1.82)Comorbidity BurdenNumber of Comorbidities (excluding renal and cardiovascular disease)1.39 (0.97, 1.99)Chronic renal disease2.34 (1.48, 3.70)Cardiovascular disease1.93 (1.34, 3.91)Disease Activity, Moderate/ high vs Remission/ low 2.24 (1.46, 3.43)IS: immunosuppressive. b/ts: biologics/targeted syntheticsConclusion:Increased age, male sex, glucocorticoid use, chronic renal disease, cardiovascular disease and moderate/high disease activity at time of COVID-19 diagnosis were associated with more severe COVID-19 outcomes in SLE. Potential limitations include possible selection bias (physician reporting), the cross-sectional nature of the data, and the assumptions underlying the outcomes modelling.Acknowledgements:The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance and do not necessarily represent the views of the ACR, EULAR) the UK National Health Service, the National Institute for Health Research (NIHR), or the UK Department of Health, or any other organization.Disclosure of Interests:Manuel F. Ugarte-Gil Grant/research support from: Pfizer, Janssen, Graciela S Alarcon: None declared, Andrea Seet: None declared, Zara Izadi: None declared, Cristina Reategui Sokolova: None declared, Ann E Clarke Consultant of: AstraZeneca, BristolMyersSquibb, GlaxoSmithKline, Exagen Diagnostics, Leanna Wise: None declared, Guillermo Pons-Estel: None declared, Maria Jose Santos: None declared, Sasha Bernatsky: None declared, Lauren Mathias: None declared, Nathan Lim: None declared, Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum unrelated to this work., Grant/research support from: Amgen and Bristol-Myers Squibb, Zachary Wallace Consultant of: Viela Bio and MedPace, Grant/research support from: Bristol-Myers Squibb and Principia/Sanofi, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: MS, UCB, and Pfizer, Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, Grant/research support from: AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB, Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB, Grant/research support from: Lilly, Mylan, Pfizer, Loreto Carmona: None declared, Elsa Mateus Grant/research support from: Pfizer, Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA, Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Patti Katz: None declared, Lindsay Jacobsohn: None declared, Samar Al Emadi: None declared, Emily Gilbert: None declared, Ali Duarte-Garcia: None declared, Maria Valenzuela-Almada: None declared, Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Philippe Dieudé Consultant of: Boerhinger Ingelheim, Bristol-Myers Squibb, Lilly, Sanofi, Pfizer, Chugai, Roche, Janssen unrelated to this work, Grant/research support from: Bristol-Myers Squibb, Chugaii, Pfizer, unrelated to this work, Elena Nikiphorou: None declared, Vanessa Kronzer: None declared, Namrata Singh: None declared, Beth Wallace: None declared, Akpabio Akpabio: None declared, Ranjeny Thomas: None declared, Suleman Bhana Consultant of: AbbVie, Horizon, Novartis, and Pfizer (all

Background:Acute Respiratory Distress Syndrome (ARDS) is a life-threatening complication of COVID-19 and has been reported in approximately one-third of hospitalized patients with COVID-191. Risk factors associated with the development of ARDS include older age and diabetes2. However, little is known about factors associated with ARDS in the setting of COVID-19, in patients with rheumatic disease or those receiving immunosuppressive medications. Prediction algorithms using traditional regression methods perform poorly with rare outcomes, often yielding high specificity but very low sensitivity. Machine learning algorithms optimized for rare events are an alternative approach with potentially improved sensitivity for rare events, such as ARDS in COVID-19 among patients with rheumatic disease.Objectives:We aimed to develop a prediction model for ARDS in people with COVID-19 and pre-existing rheumatic disease using a series of machine learning algorithms and to identify risk factors associated with ARDS in this population.Methods:We used data from the COVID-19 Global Rheumatology Alliance (GRA) Registry from March 24 to Nov 1, 2020. ARDS diagnosis was indicated by the reporting clinician. Five machine learning algorithms optimized for rare events predicted ARDS using 42 variables covering patient demographics, rheumatic disease diagnoses, medications used at the time of COVID-19 diagnosis, and comorbidities. Model performance was assessed using accuracy, area under curve, sensitivity, specificity, positive predictive value, and negative predictive value. Adjusted odds ratios corresponding to the 10 most influential predictors from the best performing model were derived using hierarchical multivariate mixed-effects logistic regression that accounted for within-country correlations.Results:A total of 5,931 COVID-19 cases from 67 countries were included in the analysis. Mean (SD) age was 54.9 (16.0) years, 4,152 (70.0%) were female, and 2,399 (40.5%) were hospitalized. ARDS was reported in 388 (6.5% of total and 15.6% of hospitalized) cases. Statistically significant differences in the risk of ARDS were observed by demographics, diagnoses, medications, and comorbidities using unadjusted univariate comparisons (data not shown). Gradient boosting machine (GBM) had the highest sensitivity (0.81) and was considered the best performing model (Table 1). Hypertension, interstitial lung disease, kidney disease, diabetes, older age, glucocorticoids, and anti-CD20 monoclonal antibodies were associated with the development of ARDS while tumor necrosis factor inhibitors were associated with a protective effect (Figure 1).Table 1.Performance of machine learning algorithms.GBMSVMGLMNETNNETRFAccuracy0.790.680.660.660.67AUC0.750.700.740.580.74Sensitivity0.810.680.650.680.67Specificity0.490.600.730.480.68PPV0.960.960.970.950.97NPV0.160.120.130.090.13GBM: Gradient Boosting Machine, SVM: Support vector machines, GLMNET: Lasso and Elastic-Net Regularized Generalized Linear Models, NNET: Neural Networks, RF: Random Forest. AUC: Area Under Curve; PPV: Positive Predictive Value; NPV: Negative Predictive Value.Conclusion:In this global cohort of patients with rheumatic disease, a machine learning model, GBM, predicted the onset of ARDS with 81% sensitivity using baseline information obtained at the time of COVID-19 diagnosis. These results identify patients who may be at higher risk of severe COVID-19 outcomes. Further studies are necessary to validate the proposed prediction model in external cohorts and to evaluate its clinical utility. Disclaimer: The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance, and do not necessarily represent the views of the ACR, NIH, (UK) NHS, NIHR, or the department of Health.References:[1]Tzotzos SJ, Fischer B, Fischer H, Zeitlinger M. 2020;24(1):516.[2]Wu C, Chen X, Cai Y, et al. JAMA Intern Med. 2020;180(7):934-943.Acknowledgements:The COVID-19 Global Rheumatology Alliance.Disclosure of Interests:Zara Izadi: None declared, Milena Gianfrancesco: None declared, Kimme Hyrich Speakers bureau: Abbvie and grant income from BMS, UCB, and Pfizer, all unrelated to this study., Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, outside the submitted work., Grant/research support from: A consortium of 13 companies (among them AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB) supporting the German RABBIT register., Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB., Grant/research support from: Lilly, Mylan, Pfizer, all unrelated to this study., Loreto Carmona Consultant of: Loreto Carmona’s institute works by contract for laboratories among other institutions, such as Abbvie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, S.A., Novartis, Farmaceutica, Pfizer, Roche Farma, Sanofi Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal GmbH, and UCB Pharma., Elsa Mateus Grant/research support from: LPCDR received grants from Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA and Pfizer., Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Lindsay Jacobsohn: None declared, Patti Katz: None declared, Samar Al Emadi: None declared, Leanna Wise: None declared, Emily Gilbert: None declared, Maria Valenzuela-Almada: None declared, Ali Duarte-Garcia: None declared, Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum unrelated to this work., Grant/research support from: Amgen and Bristol-Myers Squibb., Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Suleman Bhana Employee of: Suleman Bhana reports non-branded marketing campaigns for Novartis (