Agarwal, Vikas

Abstract Objectives We aimed to explore current practice and interregional differences in the treatment of idiopathic inflammatory myopathies (IIMs). We triangulated these observations considering countries' gross national income (GNI), disease subtypes, and symptoms using patient-reported information. Methods A cross-sectional ancillary analysis of the ‘COVID-19 vaccination in auto-immune disease’ (COVAD) e-survey containing demographic characteristics, IIM subtypes (DM, PM, IBM, anti-synthetase syndrome [ASSD], immune-mediated necrotizing myopathy [IMNM], overlap myopathies [OM]), current symptoms (surrogate for organ involvement) and treatments (corticosteroids [CS], immunomodulators [IM], i.e. antimalarials, immunosuppressants [IS], IVIG, biologic treatments and targeted-synthetic small molecules). Treatments were presented descriptively according to continents, GNI, IIM and organ involvement, and associated factors were analysed using multivariable binary logistic regressions. Results Of 18 851 respondents from 94 countries, 1418 with IIM were analysed (age 61 years, 62.5% females). DM (32.4%), IBM (24.5%) and OM (15.8%) were the most common subtypes. Treatment categories included IS (49.4%), CS (38.5%), IM (13.8%) and IVIG (9.4%). Notably, treatments varied across regions, GNI categories (IS mostly used in higher-middle income, IM in lower-middle income, IVIG and biologics largely limited to high-income countries), IIM subtypes (IS and CS associated with ASSD, IM with OM and DM, IVIG with IMNM, and biologic treatments with OM and ASSD) and disease manifestations (IS and CS with dyspnoea). Most inter-regional treatment disparities persisted after multivariable analysis. Conclusion We identified marked regional treatment disparities in a global cohort of IIM. These observations highlight the need for international consensus-driven management guidelines considering patient-centred care and available resources.

Abstract Background/Aims Flares following COVID-19 vaccination are an emerging concern among patients with rare rheumatic disease like idiopathic inflammatory myositis (IIMs), whereas data and understanding of this is rather limited. We aimed to study the prevalence, characteristics and determinants of IIM flares following COVID-19 vaccination. Methods CoVAD (COVID-19 Vaccination In Autoimmune Diseases) surveys are global patient self-reported e-surveys from 109 countries conducted in 2021 and 2022. Flares of IIM were defined by 4 definitions; a. patient self-reported, b. physician and immunosuppression (IS) denoted, c. sign directed (new erythematous rash, or worsening myositis or arthritis), d. MCID worsening of PROMISPF10a score between the patients who had taken both surveys. Descriptive statistics and multivariate regression were used to describe the predictors of flare. Cox-regression analysis was used to differentiate flares by IIM subtypes. Results Among the 1,278 IIM patients, aged 63 (50-71) years, 276 (21.5%) were dermatomyositis, 237 (18.5%) IBM, 899 (70.3%) were female and most were Caucasian (80.8%). Flares of IIM were seen in 123/1278 (9.6%), 163/1278 (12.7%), 112/1278 (8.7%), and 16/96 (19.6%) by definitions a-d respectively with median time to flare being 71.5 (10.7-235) days. Muscle weakness (69.1%), and fatigue (56.9%) were the most common symptoms of flare. The predictors of self-reported flare were: inactive/disease in remission prior to first dose of vaccine (OR = 4.3, 95%CI=2.4-7.6), and anxiety disorder (OR = 2.2, 95%CI=1.1-4.7). Rituximab use (OR = 0.3, 95%CI=0.1-0.7) and IBM (OR = 0.3, 95%CI=0.1-0.7) were protective. Physician defined flares were seen more often in females, mixed ethnicity, and those with asthma, ILD, and anxiety disorder (OR ranging 1.6-7.0, all p < 0.05). Notably, overlap myositis (OM) had higher HR for flare compared to polymyositis (HR = 2.3, 95%CI=1.2-4.4, p = 0.010). Conclusion Nearly one in ten individuals with IIM develop flares after vaccination, more so among women, those with overlap myositis, and inactive disease prior to vaccination. Formal definition of flares in IIM is needed. Disclosure L. Gupta: None. N. R: None. Z. Griger: None. V. Agarwal: None. P. Sen: None. M. Joshi: None. S. Saha: None. K. Jagtap: None. O. Distler: Honoraria; Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, Baecon, Blade, Bayer, Boehringer Ingelheim, ChemomAb, Corbus, CSL Behring, Galapagos, Glenmark, GSK, Horizon (Curzion), Inventiva, iQvia, Kymera, Lupin, Medac, Medscape, Mitsubishi Tanabe, Novartis, Roche, Roivant, Sanofi, Serodapharm, Topadur and UCB. E. Nikiphorou: Honoraria; Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly. Grants/research support; Pfizer and Lilly. A. Tan: Honoraria; Abbvie, Gilead, Janssen, Lilly, Novartis, Pfizer, and UCB. S. Shinjo: None. N. Ziade: Honoraria; Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and Pierre Fabre. M. Milchert: None. I. Parodis: Honoraria; Amgen, AstraZeneca, Aurinia Pharmaceuticals, Elli Lilly and Company, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis and F. Hoffmann-La Roche AG. M. Kuwana: None. A. Makol: None. J. Pauling: None. C. Wincup: None. J. Lilleker: Honoraria; Sanofi Genzyme, Roche, and Biogen. A. Nune: None. J. Day: Grants/research support; CSL Limited. H. Chinoy: Consultancies; Novartis, Eli Lilly, Orphazyme, Astra Zeneca. Honoraria; UCB, and Biogen. Grants/research support; Eli Lilly and UCB. V. Agarwal: None. R. Aggarwal: Consultancies; Bristol Myers-Squibb, Pfizer, Genentech, Octapharma, CSL Behring, Mallinckrodt, AstraZeneca, Corbus, Kezar, Abbvie, Janssen, Alexion, Argenx, Q32, EMD-Serono, Boehringer Ingelheim, and Roivant.

Abstract Background/Aims Post COVID-19 syndrome (PCS) is an emerging cause of morbidity and poor quality of life in COVID-19 survivors. We aimed to assess the prevalence, risk factors, outcomes, and association with disease flares of PCS in patients with autoimmune rheumatic diseases (AIRDs) and non-rheumatic autoimmune diseases (nrAIDs), both vulnerable groups understudied in the current literature using data from the 2nd COVID-19 Vaccination in Autoimmune Diseases (COVAD) global multicentre patient self-reported e-survey. Methods The survey was circulated from February to July 2022 by the international COVAD Study Group (157 collaborators from 106 countries), and demographics, comorbidities, AIRD/nrAID status, COVID-19 history, vaccination details, and PROMIS physical and mental function were recorded. PCS was defined as symptom resolution time >90 days following acute COVID-19. Predictors of PCS were analysed using regression models for the different groups. Results 7666 total respondents completed the survey. Of these, 2650 respondents with complete responses had positive COVID-19 infection, and 1677 (45.0% AIRDs, 12.5% nrAIDs, 42.5% HCs) completed the survey >90 days post acute COVID-19. Of these, 136 (8.1%) had PCS. Prevalence of PCS was higher in AIRDs (10.8%) than healthy controls HCs (5.3%) (OR: 2.1; 95%CI: 1.4-3.1, p = 0.002). Across the entire cohort, a higher risk of PCS was seen in women (OR: 2.9; 95%CI: 1.1-7.7, p = 0.037), patients with long duration of AIRDs/nrAIDs (OR 1.01; 95%CI: 1.0-1.02, p = 0.016), those with comorbidities (OR: 2.8; 95%CI: 1.4-5.7, p = 0.005), and patients requiring oxygen supplementation for severe acute COVID-19 (OR: 3.8; 95%CI: 1.1-13.6, p = 0.039). Among patients with AIRDs, comorbidities (OR 2.0; 95%CI: 1.08-3.6, p = 0.026), and advanced treatment (OR: 1.9; 95%CI: 1.08-3.3, p = 0.024), or intensive care (OR: 3.8; 95%CI: 1.01-14.4, p = 0.047) for severe COVID-19 were risk factors for PCS. Notably, patients who developed PCS had poorer PROMIS global physical [15 (12-17) vs 12 (9-15)] and mental health [14 (11-16) vs 11 (8-14)] scores than those without PCS. Conclusion Individuals with AIRDs have a greater risk of PCS than HCs. Associated comorbid conditions, and advanced treatment or intensive care unit admission for severe COVID-19 confer a higher risk of PCS. It is imperative to identify risk factors for PCS for immediate multidisciplinary management in anticipation of poor physical and mental health. Disclosure L. Gupta: None. P. Sen: None. N. R: None. M. Joshi: None. S. Saha: None. K. Jagtap: None. V. Agarwal: None. O. Distler: Consultancies; Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, Baecon, Blade, Bayer, Boehringer Ingelheim, ChemomAb, Corbus, CSL Behring, Galapagos, Glenmark, GSK, Horizon (Curzion), Inventiva, iQvia, Kymera, Lupin, Medac, Medscape, Mitsubishi Tanabe, Novartis, Roche, Roivant, Sanofi, Serodapharm, Topadur and UCB. E. Nikiphorou: Honoraria; Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly. Grants/research support; Pfizer and Lilly. A. Tan: Honoraria; Abbvie, Gilead, Janssen, Lilly, Novartis, Pfizer, and UCB. S. Shinjo: None. N. Ziade: Honoraria; Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and Pierre Fabre. M. Milchert: None. I. Parodis: Honoraria; Amgen, AstraZeneca, Aurinia Pharmaceuticals, Elli Lilly and Company, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis and F. Hoffmann-La Roche AG. M. Kuwana: None. A. Makol: None. J.D. Pauling: None. C. Wincup: None. J. Lilleker: Honoraria; Sanofi Genzyme, Roche, and Biogen. A. Nune: None. J. Day: Grants/research support; CSL Limited. H. Chinoy: Consultancies; Novartis, Eli Lilly, Orphazyme, Astra Zeneca. Honoraria; UCB, and Biogen. Grants/research support; Eli Lilly and UCB. V. Agarwal: None. R. Aggarwal: Consultancies; Bristol Myers-Squibb, Pfizer, Genentech, Octapharma, CSL Behring, Mallinckrodt, AstraZeneca, Corbus, Kezar, Abbvie, Janssen, Alexion, Argenx, Q32, EMD-Serono, Boehringer Ingelheim, and Roivant.

Abstract Objectives Disease flares in the post–coronavirus disease 2019 (COVID-19) vaccination period represent a prominent concern, though risk factors are poorly understood. We studied these flares among patients with idiopathic inflammatory myopathies (IIMs) and other autoimmune rheumatic diseases (AIRDs). Methods The COVAD-1 and -2 global surveys were circulated in early 2021 and 2022, respectively, and we captured demographics, comorbidities, AIRDs details, COVID-19 infection history and vaccination details. Flares of IIMs were defined as (a) patient self-reported, (b) immunosuppression (IS) denoted, (c) clinical sign directed and (d) with >7.9-point minimal clinically significant improvement difference worsening of Patient-Reported Outcomes Measurement Information System (PROMIS) PROMISPF10a score. Risk factors of flares were analysed using regression models. Results Of 15 165 total respondents, 1278 IIMs (age 63 years, 70.3% female, 80.8% Caucasians) and 3453 AIRDs were included. Flares of IIM were seen in 9.6%, 12.7%, 8.7% and 19.6% patients by definitions (a) to (d), respectively, with a median time to flare of 71.5 (10.7–235) days, similar to AIRDs. Patients with active IIMs pre-vaccination (OR 1.2; 95% CI 1.03, 1.6, P = 0.025) were prone to flares, while those receiving rituximab (OR 0.3; 95% CI 0.1, 0.7, P = 0.010) and AZA (OR 0.3, 95% CI 0.1, 0.8, P = 0.016) were at lower risk. Female gender and comorbidities predisposed to flares requiring changes in IS. Asthma (OR 1.62; 95% CI 1.05, 2.50, P = 0.028) and higher pain visual analogue score (OR 1.19; 95% CI 1.11, 1.27, P < 0.001) were associated with disparity between self-reported and IS-denoted flares. Conclusion A diagnosis of IIMs confers an equal risk of flares in the post–COVID-19 vaccination period to AIRDs, with active disease, female gender and comorbidities conferring a higher risk. Disparity between patient- and physician-reported outcomes represents a future avenue for exploration.

Abstract Objective Flares of autoimmune rheumatic diseases (AIRDs) following COVID-19 vaccination are a particular concern in vaccine-hesitant individuals. Therefore, we investigated the incidence, predictors and patterns of flares following vaccination in individuals living with AIRDs, using global COVID-19 Vaccination in Autoimmune Diseases (COVAD) surveys. Methods The COVAD surveys were used to extract data on flare demographics, comorbidities, COVID-19 history, and vaccination details for patients with AIRDs. Flares following vaccination were identified as patient-reported (a), increased immunosuppression (b), clinical exacerbations (c) and worsening of PROMIS scores (d). We studied flare characteristics and used regression models to differentiate flares among various AIRDs. Results Of 15 165 total responses, the incidence of flares in 3453 patients with AIRDs was 11.3%, 14.8%, 9.5% and 26.7% by definitions a–d, respectively. There was moderate agreement between patient-reported and immunosuppression-defined flares (K = 0.403, P = 0.022). Arthritis (61.6%) and fatigue (58.8%) were the most commonly reported symptoms. Self-reported flares were associated with higher comorbidities (P = 0.013), mental health disorders (MHDs) (P < 0.001) and autoimmune disease multimorbidity (AIDm) (P < 0.001). In regression analysis, the presence of AIDm [odds ratio (OR) = 1.4; 95% CI: 1.1, 1.7; P = 0.003), or a MHD (OR = 1.7; 95% CI: 1.1, 2.6; P = 0.007), or being a Moderna vaccine recipient (OR = 1.5; 95% CI: 1.09, 2.2; P = 0.014) were predictors of flares. Use of MMF (OR = 0.5; 95% CI: 0.3, 0.8; P = 0.009) and glucocorticoids (OR = 0.6; 95% CI: 0.5, 0.8; P = 0.003) were protective. A higher frequency of patients with AIRDs reported overall active disease post-vaccination compared with before vaccination (OR = 1.3; 95% CI: 1.1, 1.5; P < 0.001). Conclusion Flares occur in nearly 1 in 10 individuals with AIRDs after COVID vaccination; people with comorbidities (especially AIDm), MHDs and those receiving the Moderna vaccine are particularly vulnerable. Future avenues include exploring flare profiles and optimizing vaccine strategies for this group.

AbstractObjectivesTo compare pain intensity among individuals with idiopathic inflammatory myopathies (IIMs), other systemic autoimmune rheumatic diseases (AIRDs), and without rheumatic disease (wAIDs).MethodsData were collected from the COVID‐19 Vaccination in Autoimmune Diseases (COVAD) study, an international cross‐sectional online survey, from December 2020 to August 2021. Pain experienced in the preceding week was assessed using numeral rating scale (NRS). We performed a negative binomial regression analysis to assess pain in IIMs subtypes and whether demographics, disease activity, general health status, and physical function had an impact on pain scores.ResultsOf 6988 participants included, 15.1% had IIMs, 27.9% had other AIRDs, and 57.0% were wAIDs. The median pain NRS in patients with IIMs, other AIRDs, and wAIDs were 2.0 (interquartile range [IQR] = 1.0–5.0), 3.0 (IQR = 1.0–6.0), and 1.0 (IQR = 0–2.0), respectively (P < 0.001). Regression analysis adjusted for gender, age, and ethnicity revealed that overlap myositis and antisynthetase syndrome had the highest pain (NRS = 4.0, 95% CI = 3.5–4.5, and NRS = 3.6, 95% CI = 3.1–4.1, respectively). An additional association between pain and poor functional status was observed in all groups. Female gender was associated with higher pain scores in almost all scenarios. Increasing age was associated with higher pain NRS scores in some scenarios of disease activity, and Asian and Hispanic ethnicities had reduced pain scores in some functional status scenarios.ConclusionPatients with IIMs reported higher pain levels than wAIDs, but less than patients with other AIRDs. Pain is a disabling manifestation of IIMs and is associated with a poor functional status.

Abstract Objective COVID-19 vaccines have a favorable safety profile in patients with autoimmune rheumatic diseases (AIRDs) such as idiopathic inflammatory myopathies (IIMs); however, hesitancy continues to persist among these patients. Therefore, we studied the prevalence, predictors and reasons for hesitancy in patients with IIMs, other AIRDs, non-rheumatic autoimmune diseases (nrAIDs) and healthy controls (HCs), using data from the two international COVID-19 Vaccination in Autoimmune Diseases (COVAD) e-surveys. Methods The first and second COVAD patient self-reported e-surveys were circulated from March to December 2021, and February to June 2022 (ongoing). We collected data on demographics, comorbidities, COVID-19 infection and vaccination history, reasons for hesitancy, and patient reported outcomes. Predictors of hesitancy were analysed using regression models in different groups. Results We analysed data from 18 882 (COVAD-1) and 7666 (COVAD-2) respondents. Reassuringly, hesitancy decreased from 2021 (16.5%) to 2022 (5.1%) (OR: 0.26; 95% CI: 0.24, 0.30, P &lt; 0.001). However, concerns/fear over long-term safety had increased (OR: 3.6; 95% CI: 2.9, 4.6, P &lt; 0.01). We noted with concern greater skepticism over vaccine science among patients with IIMs than AIRDs (OR: 1.8; 95% CI: 1.08, 3.2, P = 0.023) and HCs (OR: 4; 95% CI: 1.9, 8.1, P &lt; 0.001), as well as more long-term safety concerns/fear (IIMs vs AIRDs – OR: 1.9; 95% CI: 1.2, 2.9, P = 0.001; IIMs vs HCs – OR: 5.4 95% CI: 3, 9.6, P &lt; 0.001). Caucasians [OR 4.2 (1.7–10.3)] were likely to be more hesitant, while those with better PROMIS physical health score were less hesitant [OR 0.9 (0.8–0.97)]. Conclusion Vaccine hesitancy has decreased from 2021 to 2022, long-term safety concerns remain among patients with IIMs, particularly in Caucasians and those with poor physical function.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Abstract Objective To determine COVID-19 vaccine-related adverse events (AEs) in the seven-day post-vaccination period in patients with SLE vs autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs), and healthy controls (HC). Methods Data were captured through the COVID-19 Vaccination in Autoimmune Diseases (COVAD) questionnaire (March–December 2021). Multivariable regression models accounted for age, gender, ethnicity, vaccine type and background treatment. Results Among 9462 complete respondents, 583 (6.2%) were SLE patients (mean age: 40.1 years; 94.5% females; 40.5% Asian; 42.9% Pfizer-recipients). Minor AEs were reported by 83.0% of SLE patients, major by 2.6%, hospitalization by 0.2%. AE and hospitalization frequencies were similar between patients with active and inactive SLE. Rashes were more frequent in SLE patients vs HC (OR; 95% CI: 1.2; 1.0, 1.5), chills less frequent in SLE vs AIRDs (0.6; 0.4, 0.8) and nrAIDs (0.5; 0.3, 0.8), and fatigue less frequent in SLE vs nrAIDs (0.6; 0.4, 0.9). Pfizer-recipients reported higher overall AE (2.2; 1.1, 4.2) and injection site pain (2.9; 1.6, 5.0) frequencies than recipients of other vaccines, Oxford/AstraZeneca-recipients more body ache, fever, chills (OR: 2.5, 3.0), Moderna-recipients more body ache, fever, chills, rashes (OR: 2.6, 4.3). Hospitalization frequencies were similar across vaccine types. AE frequencies were similar across treatment groups, although chills were less frequent in antimalarial users vs non-users (0.5; 0.3, 0.9). Conclusion While COVID-19 vaccination-related AEs were reported by four-fifths of SLE patients, those were mostly minor and comparable to AEs reported by healthy individuals, providing reassurance regarding COVID-19 vaccination safety in SLE.