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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.

Peripheral arterial disease (PAD), a manifestation of systemic atherosclerosis, is underdiagnosed in the general population. Despite the extensive research performed to unravel its pathophysiology, inadequate knowledge exists, thus preventing the development of new treatments. This review aims to highlight the essential elements of atherosclerosis contributing to the pathophysiology of PAD. Furthermore, emphasis will be placed on the role of thrombo-inflammation, with particular focus on platelet and coagulation activation as well as cell–cell interactions. Additional insight will be then discussed to reveal the contribution of hypercoagulability to the development of vascular diseases such as PAD. Lastly, the current antithrombotic treatments will be discussed, and light will be shed on promising new targets aiming to aid the development of new treatments.

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.

ImportanceThere is limited evidence for therapeutic options for pediatric COVID-19 outside of multisystem inflammatory syndrome in children (MIS-C).ObjectiveTo determine whether the use of steroids within 2 days of admission for non–MIS-C COVID-19 in children is associated with hospital length of stay (LOS). The secondary objective was to determine their association with intensive care unit (ICU) LOS, inflammation, and fever defervescence.Design, Setting, and ParticipantsThis cohort study analyzed data retrospectively for children (<18 years) who required hospitalization for non–MIS-C COVID-19. Data from March 2020 through September 2021 were provided by 58 hospitals in 7 countries who participate in the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS) COVID-19 registry.ExposureAdministration of steroids within 2 days of admission.Main Outcomes and MeasuresLength of stay in the hospital and ICU. Adjustment for confounders was done by mixed linear regression and propensity score matching.ResultsA total of 1163 patients met inclusion criteria and had a median (IQR) age of 7 years (0.9-14.3). Almost half of all patients (601/1163, 51.7%) were male, 33.8% (392/1163) were non-Hispanic White, and 27.9% (324/1163) were Hispanic. Of the study population, 184 patients (15.8%) received steroids within 2 days of admission, and 979 (84.2%) did not receive steroids within the first 2 days. Among 1163 patients, 658 (56.5%) required respiratory support during hospitalization. Overall, patients in the steroids group were older and had greater severity of illness, and a larger proportion required respiratory and vasoactive support. On multivariable linear regression, after controlling for treatment with remdesivir within 2 days, country, race and ethnicity, obesity and comorbidity, number of abnormal inflammatory mediators, age, bacterial or viral coinfection, and disease severity according to ICU admission within first 2 days or World Health Organization ordinal scale of 4 or higher on admission, with a random intercept for the site, early steroid treatment was not significantly associated with hospital LOS (exponentiated coefficient, 0.94; 95% CI, 0.81-1.09; P = .42). Separate analyses for patients with an LOS of 2 days or longer (n = 729), those receiving respiratory support at admission (n = 286), and propensity score–matched patients also showed no significant association between steroids and LOS. Early steroid treatment was not associated with ICU LOS, fever defervescence by day 3, or normalization of inflammatory mediators.Conclusions and RelevanceSteroid treatment within 2 days of hospital admission in a heterogeneous cohort of pediatric patients hospitalized for COVID-19 without MIS-C did not have a statistically significant association with hospital LOS.

Background: The thyroid eye disease (TED) treatment landscape is rapidly evolving. How new treatment options have impacted practice is unknown. Methods: We conducted a cross-sectional electronic survey of American and European Thyroid Association members between June 2 and June 30, 2021. The survey included TED questions about resources for its management, index cases for different severities and presentations of TED, barriers for the management of TED, and participants' concerns about TED. We classified respondents into three geographic categories: North America, Europe, or other regions. Results: Two hundred fifty-two eligible participants started the survey (15% response rate), and 227 completed it. Participants were mostly men (50.2%, 114/227), white (79.7%, 181/227), endocrinologists with a thyroid focus (66.1%, 150/227), practicing in a tertiary academic center (46.7%, 106/227), caring for 10 or more TED patients over the last 12 months (40.5%, 92/227), and reported not having a multidisciplinary TED clinic in their institution (52.8%, 120/227). The majority reported that new TED cases per annum have not changed in the past 10 years (47.5%, 108/227), and that TED patients are found in practice during the management of hyperthyroidism (41.8%, 95/227). For mild active TED, participants from Europe reported a higher use of selenium (73%[96/132] vs. 32%[20/62] of respondents from North America and 24%[8/33] of respondents from other regions). For moderate-to-severe active TED, there was a modest preference for teprotumumab as first-line therapy (37%, 23/62) among North American participants and intravenous (IV) steroids (73%[96/132], and 42%[14/33]) for participants from Europe and other regions, respectively. These treatment preferences did not change in patients with moderate-to-severe active TED with poorly controlled diabetes. In contrast, participants from the three geographic categories preferred IV steroids for optic neuropathy and women planning pregnancy. The three top “very important” concerns about TED management according to participants were: the cost of TED treatment (31.3%, 71/227), lack of effective TED treatments (19.8%, 45/227), and difficulty in predicting whether TED will develop (18.9%, 43/227). Conclusions: There is a marked geographic practice variation in the management of TED. Clinicians' concerns about TED management demand ongoing research on more effective treatment, TED predictive tools, and policy changes to improve the affordability of new TED therapies.

AbstractObjectivesThe assessment of physical function is fundamental in the management of patients with idiopathic inflammatory myopathies (IIMs). We aimed to investigate the physical function of patients with IIMs compared with those with non-IIM autoimmune rheumatic diseases (AIRDs) utilizing Patient-Reported Outcome Measurement Information System (PROMIS) Physical Function (PF) data obtained in the COVAD study, an international self-reported e-survey assessing the safety of COVID-19 vaccines in AIRDs.MethodsDemographics, AIRD diagnosis, disease activity, and PROMIS PF short form-10a data were extracted from the COVAD database. PROMIS PF-10a scores were compared between disease categories and stratified by disease activity. Factors affecting PROMIS PF-10a scores other than disease activity were identified by multivariable regression analysis in patients with inactive disease.ResultsA total of 1057 IIM patients, 3635 non-IIM AIRD patients and 3981 healthy controls (HCs) responded to the COVAD e-survey from April to August 2021. Using a binomial regression model, the predicted mean of PROMIS PF-10a scores was significantly lower in IIM patients compared with non-IIM AIRD patients or HCs [36.3 (95% CI 35.5, 37.1) vs 41.3 (95% CI 40.2, 42.5) vs 46.2 (95% CI 45.8, 46.6), P < 0.001], irrespective of disease activity. The independent factors for lower PROMIS PF-10a scores in patients with inactive disease were older age, female, longer disease duration, and a diagnosis of inclusion body myositis or polymyositis.ConclusionPhysical function is significantly impaired in IIMs compared with non-IIM AIRDs or HCs, even in patients with inactive disease. Our study highlights a critical need for better strategies to minimize functional disability in patients with IIMs.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Influenza is an important cause of hospitalization due to lower respiratory tract involvement for which there is no specific antiviral treatment with proven efficacy. We conducted a double-blind, randomized, placebo-controlled trial to assess the tolerability and efficacy of nebulized zanamivir (16 mg four times a day) in combination with rimantadine compared to rimantadine with nebulized saline for treating influenza in adults hospitalized with influenza. Twenty patients tolerated the inhaled zanamivir (ZNV) plus rimantadine without decline in peak expiratory flow rates compared to the 21 who received inhaled saline. The study was terminated early because the approval of ZNV made further enrolment untenable. No significant differences were observed in the proportion of patients shedding virus by treatment day 3 (57% ZNV plus rimantadine, 67% placebo plus rimantadine), or in the durations of hospitalization and supplemental oxygen use. More ZNV plus rimantadine recipients exhibited no or mild cough on day 3 of treatment (94 vs 55%, P=0.01). Two rimantadine-resistant viruses emerged during rimantadine monotherapy; no ZNV resistance was observed. Nebulized ZNV appears to be well tolerated in this hospitalized population but further studies are needed to assess its efficacy.

Abstract Purpose: Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers. Experimental Design: We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors. Results: We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1–high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases. Conclusions: Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.