In-person versus virtual administration of the American College of Rheumatology gold standard cognitive battery in systemic lupus erythematosus: Are they interchangeable?

OBJECTIVE: During the COVID-19 pandemic, many research studies were adapted, including our longitudinal study examining cognitive impairment (CI) in systemic lupus erythematosus (SLE). Cognitive testing was switched from in-person to virtual. This analysis aimed to determine if the administration method (in-person vs. virtual) of the ACR-neuropsychological battery (ACR-NB) affected participant cognitive performance and classification. METHODS: Data from our multi-visit, SLE CI study included demographic, clinical, and psychiatric characteristics, and the modified ACR-NB. Three analyses were undertaken for cognitive performance: (1) all visits, (2) non-CI group visits only and (3) intra-individual comparisons. A retrospective preferences questionnaire was given to participants who completed the ACR-NB both in-person and virtually. RESULTS: We analysed 328 SLE participants who had 801 visits (696 in-person and 105 virtual). Demographic, clinical, and psychiatric characteristics were comparable except for ethnicity, anxiety and disease-related damage. Across all three comparisons, six tests were consistently statistically significantly different. CI classification changed in 11/71 (15%) participants. 45% of participants preferred the virtual administration method and 33% preferred in-person. CONCLUSIONS: Of the 19 tests in the ACR-NB, we identified one or more problems with eight (42%) tests when moving from in-person to virtual administration. As the use of virtual cognitive testing will likely increase, these issues need to be addressed - potentially by validating a virtual version of the ACR-NB. Until then, caution must be taken when directly comparing virtual to in-person test results. If future studies use a mixed administration approach, this should be accounted for during analysis.

IMPACT OF IMMUNOSUPPRESSIVE THERAPIES ON ANTIBODY DECAY FOLLOWING MRNA VACCINATION TO SARS-COV-2 IN A PROSPECTIVE COHORT OF PATIENTS WITH CHRONIC INFLAMMATORY DISEASES

Background: Waning levels of anti-SARS-CoV-2 Spike (S) antibodies, particularly neutralizing, are associated with the risk of breakthrough infections. The impact of immunosuppression on antibody decay kinetics is unclear. We have previously reported a strong correlation between total anti-S antibodies and neutralization titers. Here, we report the decay kinetics in anti-S IgG antibodies across various immunosuppressive medications used in patients with CID. Methods: We recruited a volunteer sample of adults with confirmed CID eligible for SARS-CoV-2 vaccination in a prospective observational cohort study at two United States CID referral centers. All study participants received two doses of mRNA vaccine to SARSCoV- 2. To assess the durability of immunogenicity, anti-S IgG were measured at 7 (visit 3), 90 (visit 5), and 120 (visit 6) days after the 2nd dose of mRNA vaccine. The impact of various medications was assessed in repeated measures mixed model with the patient as a random effect, adjusting for gender and age, and using the group of patients on sulfasalazine, NSAIDs, or on no medications as a reference, using STATA. The half-life of anti-S IgG for a 50 percent reduction in titers at visit 3 was calculated for each medication class. Results: A total of 316 CID patients were recruited of which 148 (46.8%) had inflammatory bowel disease (IBD). Durability was assessed in 495 samples obtained in 293 patients. The arithmetic mean of anti-S IgG antibodies for each medication class at visits 3, 5, and 6 is shown in Figure 1. Overall, a 2-fold reduction in titers was observed from 7 to 90 days and 90 to 120 days (Table 1). The strongest decline was observed among patients on B cell depleting/ modulating therapies followed by those on combinations of biologics and/or small molecules and antimetabolites (methotrexate, leflunomide, thiopurines, mycophenolate mofetil, and teriflunomide). There was modest decline seen with TNFi (half-life 430.5 days, -2.15, 95% CI - 4.31 to - 1.07, p = 0.03). There was also a modest, but not significant, decline seen with Janus Kinase inhibitor (JAKi). No decline was seen with anti-IL-23 or anti-integrin medication classes. Conclusions: Antibody decay in patients with CID is not observed in patients on anti-integrins or anti-IL-23 while it is seen among patients on TNFi, JAKi, antimetabolites, and combinations of biologics and/or small molecules. Our data and those from other cohorts may be used to prioritize medication classes for boosting immunogenicity with additional doses of vaccination against SARS-CoV-2. Collection of antibody titers after booster doses is currently ongoing.(Table Presented) (Figure Presented) Figure 1: Durability of anti-spike IgG antibodies after vaccination against SARS-CoV-2 in patients with Chronic Inflammatory Disease

Reactogenicity of SARS-CoV-2 vaccines in patients with autoimmune and inflammatory disease

Background: Patients with autoimmune disease often require immunosuppressive medications that may increase their risk of developing severe illness from COVID-19. The importance of immunization in this population is particularly high. While the studied vaccines show efficacy in the general population, nothing is known regarding the immune response or safety profile in patients with autoimmune disease and those taking immunomodulatory medications. Objectives: To assess the safety profile and degree of adverse events from SARS-CoV-2 vaccines in patients with autoimmune and inflammatory disease. Methods: This study is part of a larger prospective observational study examining the immunogenicity and safety profile of the SARS-CoV-2 vaccine in patients with immune-mediated diseases taking immunomodulatory medications. Adults with an immune-mediated disease scheduled to receive either a Pfizer or Moderna SARS-COV-2 vaccine were enrolled in this study. Subjects participated in 3 study visits (pre-vaccine, dose 1 (D1) and dose 2 (D2)) where blood, for immunologic assays, and clinical data were collected. Assessments of adverse events (AE), including local and systemic symptoms and validated degree of AE severity were solicited within 7 days of receiving each vaccine dose. Results: To date, 70 patients with autoimmune and inflammatory disease have been enrolled. Demographic and clinical characteristics are shown in Table 1. Distribution of current immunomodulatory medications included prednisone 18.6%, conventional synthetic DMARD 55.7%, targeted synthetic DMARD 4.3%, and biologic DMARD 68.5%. Almost all participants experienced an adverse event following vaccination (D1 96%, D2 100%). Following D1 AEs were generally mild (76.5%) whereas following D2 a large portion of patients experienced AEs that were moderate (47.8%) and severe (30.5%). Injection site pain was the most common AE following both doses followed by arthralgias (D1 21.6%, D2 78.2%), fever (D1 21.6%, D2 70%) and fatigue (D1 21.6%, D2 65.2%) (Figure 1). Conclusion: Patients with autoimmune and inflammatory disease experience a significant burden of adverse events following SARS-CoV-2 vaccination with both frequency and severity appearing greater than that of the reported results from the vaccine clinical trials. Several of the endorsed AEs such as fever, fatigue and arthralgias can also be commonly seen in rheumatologic diseases, mimicking flares. While SARS-CoV-2 immunization is crucial in patients with autoimmune diseases, this study demonstrates the importance of understanding the AEs experienced by this patient population to better inform patients of possible expected side effects of SARS-CoV-2 vaccination and further management in the future.

Characteristics associated with severe COVID-19 outcomes in systemic lupus erythematosus (SLE): Results from the covid-19 global rheumatology alliance (COVID-19 GRA)

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). Conclusion: 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.

Associations of baseline use of biologic or targeted synthetic dmards with COVID-19 severity in rheumatoid arthritis: Results from the COVID-19 global rheumatology alliance

Background: Targeted DMARDs may dampen the inflammatory response in COVID-19, perhaps leading to a less severe clinical course. However, some DMARD targets may impair viral immune defenses. Due to sample size limitations, previous studies of DMARD use and COVID-19 outcomes have combined several heterogeneous rheumatic diseases and medications, investigating a single outcome (e.g., hospitalization). Objectives: To investigate the associations of baseline use of biologic or targeted synthetic (b/ts) DMARDs with a range of poor COVID-19 outcomes in rheumatoid arthritis (RA). Methods: We analyzed voluntarily reported cases of COVID-19 in patients with rheumatic diseases in the COVID-19 Global Rheumatology Alliance physician registry (March 12, 2020 -January 6, 2021). We investigated RA treated with b/ tsDMARD at the clinical onset of COVID-19 (baseline): abatacept (ABA), rituximab (RTX), Janus kinase inhibitors (JAK), interleukin-6 inhibitors (IL6i), or tumor necrosis factor inhibitors (TNFi). The outcome was an ordinal scale (1-4) for COVID-19 severity: 1) no hospitalization, 2) hospitalization without oxygen need, 3) hospitalization with any oxygen need or ventilation, or 4) death. Baseline covariates including age, sex, smoking, obesity, comorbidities (e.g., cardiovascular disease, cancer, interstitial lung disease [ILD]), concomitant non-biologic DMARD use, glucocorticoid use/ dose, RA disease activity, country, and calendar time were used to estimate propensity scores (PS) for b/tsDMARD. The primary analysis used PS matching to compare each drug class to TNFi. Ordinal logistic regression estimated ORs for the COVID-19 severity outcome. In a sensitivity analysis, we used traditional multivariable ordinal logistic regression adjusting for covariates without matching. Results: Of the 1,673 patients with RA on b/tsDMARDs at the onset of COVID-19, (mean age 56.7 years, 79.6% female) there were n=154 on ABA, n=224 on RTX, n=306 on JAK, n=180 on IL6i, and n=809 on TNFi. Overall, 498 (34.3%) were hospitalized and 112 (6.7%) died. Among all patients, 353 (25.3%) were ever smokers, 197 (11.8%) were obese, 462 (27.6%) were on glucocorticoids, 1,002 (59.8%) were on concomitant DMARDs, and 299 (21.7%) had moderate/ high RA disease activity. RTX users were more likely than TNFi users to have ILD (11.6% vs. 1.7%) and history of cancer (7.1% vs. 2.0%);JAK users were more likely than TNFi users to be obese (17.3% vs. 9.0%). After propensity score matching, RTX was strongly associated with greater odds of having a worse outcome compared to TNFi (OR 3.80, 95% CI 2.47, 5.85;Figure). Among RTX users, 42 (18.8%) died compared to 27 (3.3%) of TNFi users (Table). JAK use was also associated with greater odds of having a worse COVID-19 severity (OR 1.52, 95%CI 1.02, 2.28). ABA or IL6i use were not associated with COVID-19 severity compared to TNFi. Results were similar in the sensitivity analysis and after excluding cancer or ILD. Conclusion: In this large global registry of patients with RA and COVID-19, baseline use of RTX or JAK was associated with worse severity of COVID-19 compared to TNFi use. The very elevated odds for poor COVID-19 outcomes in RTX users highlights the urgent need for risk-mitigation strategies, such as the optimal timing of vaccination. The novel association of JAK with poor COVID-19 outcomes requires replication.

Machine learning algorithms to predict COVID-19 acute respiratory distress syndrome in patients with rheumatic diseases: Results from the global rheumatology alliance provider registry

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). 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.