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 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.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Abstract Objectives The COVID-19 vaccination in autoimmune diseases (COVAD) study aimed to assess short-term COVID-19 vaccination-related adverse events (AEs) in RA patients. Methods An online self-reported questionnaire (March–December 2021) was used to capture data related to COVID-19 vaccination-related AEs in RA, other autoimmune rheumatic diseases (AIRDs) (excluding RA and inflammatory myositis), non-rheumatic autoimmune diseases (nrAIDs) and healthy controls (HCs). Descriptive and multivariable regression analyses were performed. Results Of the 9462 complete respondents, 14.2% (n = 1347) had been diagnosed with RA; they had a mean (s.d.) age of 50.7 (13.7) years, 74.2% were women and 49.3% were Caucasian. In total, 76.9% and 4.2% of patients with RA reported minor and major AEs, respectively. Patients with active and inactive RA had similar AE and hospitalization frequencies. Overall, AEs were reported more frequently by BNT162b2 and mRNA-1273 recipients and less frequently by BBV152 recipients compared with the rest. Major AE and hospitalization frequencies were similar across recipients of different vaccines. Patients receiving methotrexate and hydroxychloroquine reported fewer minor AEs than those patients not on them. Compared with HCs and patients with other AIRDs, patients with RA reported similar total AEs, overall minor AEs, and hospitalizations. Compared with nrAIDs, patients with RA reported lower frequencies of overall AEs, minor AEs (both odds ratio [OR] = 0.7; 95% CI: 0.5, 0.9), and injection site pain (OR = 0.6; 95% CI: 0.5, 0.8) with similar major AE and hospitalization frequencies. Conclusion Despite the differences in AE frequency across different COVID-19 vaccines, all were well tolerated in patients with RA and were comparable to HCs, providing reassurance as to the safety of COVID-19 vaccination.