AUTOANTIBODIES RELATED TO RHEUMATIC DISEASES AFTER COVID-19 INFECTION

BackgroundSARS-CoV-2(Severe acute respiratory syndrome coronavirus 2) has been circulating worldwide for three years. It mainly causes upper respiratory tract infection, which can manifest as pulmonary infection and even respiratory distress syndrome in severe cases. Different autoantibodies can be detected in patients infected with COVID-19.ObjectivesTo explore autoantibodies related to rheumatic diseases after COVID-19 infection.MethodsNinety-eight inpatients were tested for antinuclear antibodies (ANA), antibodies to extractable nuclear antigens(ENA), anti-neutrophil cytoplasmic antibodies（ANCA), anticardiolipin antibodies,a-β2GPI (IgG/IgM). They were from a tertiary hospital in Guangzhou during the COVID-19 epidemic. Data were described statistically.ResultsNinety-eight hospitalized patients were tested for relevant antibodies. The average age was 50.64±19.54;67 (68.4%) were male, 64 (65.3%) were COVID-19 positive, 90 (90.9%) had rheumatic diseases, and 56 of them were COVID-19 positive patients with rheumatic diseases.There were 76 patients tested for antinuclear antibodies;29 （38.16%）were negative, 18 （23.68%）had a 1/80 titre, and 29（28.16%） had a titre greater than 1:80. The 31 covid patients were positive for ANA. In the high-titer group, 19 patients with rheumatic diseases were positive for COVID-19, and 12 patients had an exacerbation of the rheumatic diseases (6 of whom had previously had pulmonary fibrosis). Of 31 covid patients, only two were non-rheumatic patients, and both were elderly, aged 85 and 100, respectively.Fifty-six patients had ENA results, and 29 for positive antibodies, 8 for ds-DNA antibodies, 2 for anti-Sm antibodies, 6 for anti-nucleosome antibodies, 12 for anti-U1RNP antibodies, 2 for anti-Scl-70 antibodies, 12 for anti-SS-A antibodies, 3 for anti-mitochondrial M2 antibodies, 2 for anti-centromere antibodies, 1 for anti-Po antibodies, and one for anti-Jo-1 antibody. All 56 patients had rheumatic diseases, and no new patients were found.There were 62 patients with ANCA data. P-ANCA was positive in 12 cases(19.35%), and MPO-ANCA was positive in 2 cases. An 85-year-old non-rheumatic COVID-19 patient was P-ANCA positive. She had a history of hypertension, colon cancer, CKD3, coronary heart disease, and atrial flutter.In the anticardiolipin antibodies group, there were 62 patients;only 6 were positive, and 2 were rheumatic patients infected with COVID-19. Antiphospholipid antibodies were detected in 33 patients, and a-β2GPI was tested in one patient, an 82-year-old COVID-19 patient with gout, diabetes, and cerebral infarction in the past. We did not find a statistical difference in the above results.ConclusionWe have not found a correlation between SARS-CoV-2 and serum autoantibodies of rheumatic immune diseases. It needs large samples and an extended follow-up to research.AcknowledgementsThis work was supported by Scientific and Technological Planning Project of Guangzhou City [202102020150], Guangdong Provincial Basic and Applied Basic Research Fund Project [2021A1515111172], National Natural Science Foundation of China Youth Fund [82201998] and Third Affiliated Hospital of Sun Yat-Sen University Cultivating Special Fund Project for National Natural Science Foundation of China [2022GZRPYQN01].Disclosure of Interestsone declared.

THE PREVALENCE AND INCIDENCE OF PSORIASIS AND PSORIATIC ARTHRITIS IN ENGLAND FROM 2009-2019: AN OBSERVATIONAL STUDY USING PRIMARY CARE DATA

BackgroundPsoriasis (PsO) and psoriatic arthritis (PsA) can greatly impact quality of life and result in substantial personal and societal costs. Complete and up to date data on the prevalence and incidence of these conditions and whether these change over time and vary by age is important for healthcare service planning so that specialist care and funding can be appropriately allocated.ObjectivesTo determine the prevalence and incidence of PsO and PsA in males and females from 2009-2019 across all age groups in England.MethodsWe used Clinical Practice Research Datalink AURUM, a primary care electronic health record database, including 20% of the English population. The codes used to identify patients with PsO and PsA were selected by rheumatologists and dermatologists and cross-checked with published code lists from other studies to ensure inclusion of all relevant codes. All included patients must have data for at least 1 year before their diagnosis. The annual incidence and point prevalence were calculated from 2009-2019 and stratified by age/sex. The study period ended in 2019 to avoid COVID-19 pandemic affecting results.ResultsThe prevalence of PsO and PsA in males and females increased annually, peaking in 2019 (PsO males 2.41% [95% confidence interval (CI) 2.40, 2.42];PsO females 2.60% [95% CI 2.59-2.61];PsA males 0.20% [95% CI 0.20-0.20];PsA females 0.21% [95% CI 0.21- 0.22]), as illustrated in Table 1. In 2019, the prevalence of PsO and PsA was highest in the over 65 years age group;PsO 4.25% [95% CI 4.22-4.28] and PsA 0.38% [95% CI 0.37-0.38]. The annual incidence (per 100,000 person years) of PsO has gradually decreased in males (from 168 (164-171) in 2009 to 148 (145-151) in 2019) but in females it has been stable with a slight annual decrease (from 180 (177-184) in 2009 to 173 (170-176) in 2019). The annual incidence for PsA has increased in both males and females (13 (12-14) in 2009 and 15 (14-16) in 2019 for males and 12 (11-13) in 2009 and 18 (17-19) in 2019 for females).ConclusionThe increasing prevalence of PsO and PsA highlights the importance of organising healthcare services to meet this need, particularly in the elderly population.ReferencesNIL.Table 1.Prevalence of PsO and PsA from 2009-2019 in EnglandYear20092010201120122013201420152016201720182019Population (n)1073383110910802110318501118036711343299112249341137842211657996119336261223432512420998PsO (n)216841229106239819250667259988268032276804286499295712304568311104PsO prevalence (%, 95%CI)-Male1.98 (1.96-1.99)2.06 (2.05- 2.07)2.13 (2.12-2.14)2.19 (2.18-2.20)2.24 (2.23- 2.25)2.33 (2.32- 2.34)2.37 (2.36- 2.38)2.39 (2.38- 2.40)2.40 (2.39- 2.41)2.40 (2.39- 2.42)2.41 (2.40- 2.42)-Female2.07 (2.05- 2.08)2.14 (2.13- 2.16)2.22 (2.21- 2.23)2.29 (2.28- 2.31)2.35 (2.33- 2.36)2.45 (2.43- 2.46)2.50 (2.49- 2.51)2.53 (2.52- 2.54)2.56 (2.54- 2.57)2.58 (2.56- 2.59)2.60 (2.59- 2.61)PsO incidence (100,000 person years)-Male168 (164-171)158 (155- 162)161 (158-165)153 (150-157)161 (157- 164)156 (153- 159)155 (152- 159)154 (151- 157)153 (150-156)150 (147-153)148 (145-151)-Female180 (177-184)176 (172-179)181 (177-184)171 (167-174)175 (171-178)176 (172-180)179 (176-183)178 (174-181)177 (174-181)174 (170-177)173 (170-176)PsA (n)1444515443164681752218545196182072021994232572451425683PsA prevalence (%, 95%CI)-Male0.14 (0.14- 0.14)0.15 (0.14- 0.15)0.15 (0.15- 0.16)0.16 (0.16- 0.16)0.17 (0.16- 0.17)0.18 (0.17- 0.18)0.18 (0.18- 0.19)0.19 (0.18- 0.19)0.19 (0.19- 0.20)0.20 (0.19- 0.20)0.20 (0.20- 0.20)-Female0.13 (0.13- 0.13)0.14 (0.13- 0.14)0.15 (0.14- 0.15)0.15 (0.15- 0.16)0.16 (0.16- 0.16)0.17 (0.17- 0.18)0.18 (0.18- 0.18)0.19 (0.19- 0.19)0.20 (0.19- 0.20)0.20 (0.20- 0.21)0.21 (0.21- 0.22)PsA incidence (100,000 person years)-Male13 (12- 14)12 (11- 13)13 (12- 14)12 (11- 13)13 (12-14)14 (13- 15)14 (13- 15)14 (13-15)1514-16)14(13- 15)15 (14-16)-Female12 (11- 13)13 (12- 14)13 (12- 14)14 (13-15)14 (13-15)15 (14-16)17 (16- 18)16 (15- 17)17 (16- 18)18 (17-19)18 (17-19)Acknowledgements:NIL.Disclosure of InterestsArani Vivekanantham: None declared, Edward Burn: None dec ared, Marta Pineda-Moncusí: None declared, Sara Khalid Grant/research support from: SK has received research grant funding from the UKRI and Alan Turing Institute outside this work. SK's research group has received grant support from Amgen and UCB Biopharma., Daniel Prieto-Alhambra Grant/research support from: DPA's department has received grant/s from Amgen, Chiesi-Taylor, Lilly, Janssen, Novartis, and UCB Biopharma. His research group has received consultancy fees from Astra Zeneca and UCB Biopharma. Amgen, Astellas, Janssen, Synapse Management Partners and UCB Biopharma have funded or supported training programmes organised by DPA's department., Laura Coates Speakers bureau: LC has been paid as a speaker for AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Medac, Novartis, Pfizer and UCB., Consultant of: LC has worked as a paid consultant for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer and UCB., Grant/research support from: LC has received grants/research support from AbbVie, Amgen, Celgene, Eli Lilly, Novartis and Pfizer.

SAFETY OUTCOMES IN PATIENTS (PTS) WITH RHEUMATOID ARTHRITIS (RA) TREATED WITH FILGOTINIB (FIL) IN FILOSOPHY: INTERIM RESULTS FROM A PROSPECTIVE OBSERVATIONAL STUDY

BackgroundConsideration is needed when using Janus kinase (JAK) inhibitors to treat RA in pts aged ≥65 years or those with cardiovascular (CV) risk factors. The JAK1 preferential inhibitor FIL was generally well tolerated in clinical trials[1];safety has not been determined in a real-world setting.ObjectivesTo report baseline characteristics and up to 6-month safety data from the first 480 pts treated with FIL in the FILOSOPHY study (NCT04871919), and in two mutually exclusive subgroups based on age and CV risk.MethodsFILOSOPHY is an ongoing, phase 4, non-interventional, European study of pts with RA who have been prescribed FIL for the first time and in accordance with the product label in daily practice. Baseline characteristics and the incidence of select adverse events (AEs) are assessed in pts aged ≥65 years and/or with ≥1 CV risk factor (Table 1), and in those aged <65 years with no CV risk factors.ResultsAs of the end of June 2022, 480 pts had been treated: 441 received FIL 200 mg and 39 received FIL 100 mg. Of the 480 pts, 148 (30.8%) were aged ≥65 years;332 (69.2%) were aged <65 years. In total, 86 (17.9%) were former smokers, 81 (16.9%) were current smokers and 203 (42.3%) were non-smokers (data were missing for 110 pts [22.9%]). In addition to smoking, the most frequent CV risk factors included a history of hypertension (32.3%), a history of dyslipidemia (10.2%) and a family history of myocardial infarction (8.5%;Table 1).23 pts (4.8%) discontinued treatment due to AEs. Of the 354 pts aged ≥65 years or with ≥1 CV risk factor, infections affected 64 pts (18.1%), 34 (9.6%) had COVID-19, 2 (0.6%) had herpes zoster, and cardiac disorders (angina pectoris, atrial fibrillation, palpitations and tachycardia) affected 5 pts (1.4%);no cases of malignancies were observed. In the subgroup aged <65 years and with no CV risk factors (n=126), infections occurred in 18 pts (14.3%) (9 [7.1%] had COVID-19;3 [2.4%] had herpes zoster) and malignancies (myeloproliferative neoplasm) affected 1 pt (0.8%);no pts had cardiac disorders. There were no cases of deep vein thrombosis or pulmonary embolism in either subgroup.ConclusionIn this interim analysis of FILOSOPHY, no unexpected safety signals emerged at up to 6 months. Although infections and cardiac disorders affected a numerically greater proportion of pts aged ≥65 years or with ≥1 CV risk vs those aged <65 years with no CV risk, longer follow-up on a broader cohort is necessary to further characterize the safety of FIL in different groups of pts with RA.Reference[1]Winthrop K, et al. Ann Rheum Dis 2022;81:184–92Table 1.Baseline characteristics and CV risk factorsBaseline demographics/CV risk factorsAll FIL-treated pts (N=480)≥65 years or with ≥1 CV risk factor (n=354)<65 years and no CV risk factor (n=126)*Female sex, n (%)351 (73.1)252 (71.2)99 (78.6)Age, years, mean (SD)57.6 (11.5)60.4 (10.8)49.6 (9.6)Rheumatoid factor positive, n (%)†228 (47.5)167 (47.2)61 (48.4)Anti-citrullinated protein antibody positive, n (%)‡243 (50.6)176 (49.7)67 (53. 2)Body mass index, kg/m2, mean (SD)27.6 (5.7) n=43728.0 (5.4) n=33126.3 (6.4) n=106RA disease duration, years, mean (SD)10.4 (9.4) n=47810.5 (9.5) n=35310.0 (8.8) n=125Tender joint count 28, mean (SD)8.6 (6.9) n=4578.7 (7.1) n=3408.3 (6.3) n=117Swollen joint count 28, mean (SD)5.6 (5.2) n=4525.7 (5.4) n=3365.4 (4.4) n=116Former smoker, n (%)§86 (17.9)86 (24.3)0Current smoker, n (%)§81 (16.9)81 (22.9)0Non-smoker, n (%)§203 (42.3)130 (36.7)73 (57.9)Family history of myocardial infarction, n (%)41 (8.5)41 (11.6)0Medical history of: n (%) CV disease33 (6.9)33 (9.3)0 Diabetes35 (7.3)35 (9.9)0 Dyslipidemia49 (10.2)49 (13.8)0 Hypertension155 (32.3)155 (43.8)0 Ischemic CNS  vascular disorders11 (2.3)11 (3.1)0 Peripheral vascular disease17 (3.5)17 (4.8)0*Includes 53 pts with missing smoking status data who were aged <65 years with no other CV risk factors.†Missing/unknown in 154 pts;‡Missing in 153 pts;§Smoking status data missing in 110 pts (22.9%).AcknowledgementsWe thank the physicia s and patients who participated in this study. The study was funded by Galapagos NV, Mechelen, Belgium. Publication coordination was provided by Fabien Debailleul, PhD, of Galapagos NV. Medical writing support was provided by Debbie Sherwood, BSc, CMPP (Aspire Scientific, Bollington, UK), and funded by Galapagos NV.Disclosure of InterestsPatrick Verschueren Speakers bureau: AbbVie, Eli Lilly, Galapagos, Roularta, Consultant of: Celltrion, Eli Lilly, Galapagos, Gilead, Nordic Pharma, Sidekick Health, Grant/research support from: Galapagos, Pfizer, Jérôme Avouac Speakers bureau: AbbVie, AstraZeneca, BMS, Eli Lilly, Galapagos, MSD, Novartis, Pfizer, Sandoz, Sanofi, Consultant of: AbbVie, Fresenius Kabi, Galapagos, Sanofi, Grant/research support from: BMS, Fresenius Kabi, Novartis, Pfizer, Karen Bevers Grant/research support from: Galapagos, Susana Romero-Yuste Speakers bureau: AbbVie, Biogen, BMS, Lilly, Pfizer, Consultant of: Sanofi, Lilly, Grant/research support from: Lilly, MSD, Roberto Caporali Speakers bureau: AbbVie, Amgen, BMS, Celltrion, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, Sandoz, UCB, Consultant of: AbbVie, Amgen, BMS, Celltrion, Eli Lilly, Fresenius Kabi, Galapagos, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Thomas Debray Consultant of: Biogen, Galapagos, Gilead, Francesco De Leonardis Employee of: Galapagos, James Galloway Speakers bureau: AbbVie, Biogen, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Roche, UCB, Consultant of: AbbVie, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Grant/research support from: AstraZeneca, Celgene, Gilead, Janssen, Medicago, Novavax, Pfizer, Monia Zignani Shareholder of: Galapagos, Employee of: Galapagos, Gerd Rüdiger Burmester Speakers bureau: AbbVie, Amgen, BMS, Chugai, Galapagos, Lilly, Pfizer, Sanofi, Consultant of: AbbVie, Amgen, BMS, Galapagos, Lilly, Pfizer, Sanofi.

Masked Student Dataset of Expressions

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://github.com/SridharSola/MSD-E. © 2022 ACM.

Design Principles and a Software Reference Architecture for Big Data Question Answering Systems

Companies continuously produce several documents containing valuable information for users. However, querying these documents is challenging, mainly because of the heterogeneity and volume of documents available. In this work, we investigate the challenge of developing a Big Data Question Answering system, i.e., a system that provides a unified, reliable, and accurate way to query documents through naturally asked questions. We define a set of design principles and introduce BigQA, the first software reference architecture to meet these design principles. The architecture consists of high-level layers and is independent of programming language, technology, querying and answering algorithms. BigQA was validated through a pharmaceutical case study managing over 18k documents from Wikipedia articles and FAQ about Coronavirus. The results demonstrated the applicability of BigQA to real-world applications. In addition, we conducted 27 experiments on three open-domain datasets and compared the recall results of the well-established BM25, TF-IDF, and Dense Passage Retriever algorithms to find the most appropriate generic querying algorithm. According to the experiments, BM25 provided the highest overall performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

COVID-VTS: Fact Extraction and Verification on Short Video Platforms

We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19-focused information from both the real world and machine generation. We propose, TwtrDetective, an effective model incorporating cross-media consistency checking to detect token-level malicious tampering in different modalities, and generate explanations. Due to the scarcity of training data, we also develop an efficient and scalable approach to automatically generate misleading video posts by event manipulation or adversarial matching. We investigate several state-of-the-art models and demonstrate the superiority of TwtrDetective. © 2023 Association for Computational Linguistics.

Misleading Beyond Visual Tricks: How People Actually Lie with Charts

Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event - Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation. © 2023 Owner/Author.

COVID-19 IN PATIENTS WITH RHEUMATIC IMMUNE-MEDIATED INFLAMMATORY DISEASES IN AMERICA: DIFFERENCES AND SIMILARITIES BETWEEN MEXICO AND ARGENTINA

BackgroundPatients with immune-mediated rheumatic diseases (IRD) have poorer outcomes of SARS-CoV-2 infection compared to the general population.ObjectivesTo assess and compare clinical course, severity and complications of SARS-CoV-2 infection in patients with rheumatic immune-mediated inflammatory diseases (IMIDs) from Mexico and Argentina.MethodsData from both national registries, CMR-COVID (Mexico) and SAR-COVID (Argentina), were combined. Briefly, adult IRD patients with SARS-CoV-2 infection were recruited between 08.2020 and 09.2022 in SAR-COVID and between 04.2020 and 06.2022 in CMR-COVID. Sociodemographic data, comorbidities, and DMARDs were recorded, as well as clinical characteristics, complications, and treatment for SARS-CoV-2 infection. Descriptive analysis. Chi square, Fisher, Student T, Mann Whitney U tests and multiple logistic regression analyses were performed.ResultsA total of 3709 patients were included, 1167 (31.5%) from the CMR-COVID registry and 2542 (68.5%) from the SAR-COVID registry. The majority (82.3%) were women, with a mean age of 50.4 years (SD 14.4). The most frequent IRD were rheumatoid arthritis (47.5%) and systemic lupus erythematosus (18.9%). Mexican patients were significantly older, had a higher female predominance and had higher prevalence of rheumatoid arthritis, antiphospholipid syndrome, and axial spondyloarthritis, while the Argentine patients had more frequently psoriatic arthritis and ANCA-associated vasculitis. In both cohorts, approximately 80% were in remission or low disease activity at the time of infection. Mexicans took glucocorticoids (43% vs 37%, p<0.001) and rituximab (6% vs 3%, p<0.001) more frequently. They also reported more comorbidities (48% vs 43%, p=0.012).More than 90% of patients presented symptoms related to SARS-CoV-2 infection. The frequency of hospitalization was comparable between the groups (23.4%), however, the Mexicans had more severe disease (Figure 1) and a higher mortality rate (9.4% vs 4.0%, p<0.0001). After adjusting for risk factors, Mexicans were more likely to die due to COVID-19 (OR 2.2, 95%CI 1.5-3.1).ConclusionIn this cohort of patients with IRD from Mexico and Argentina with SARS-CoV-2 infection, the majority presented symptoms, a quarter were hospitalized and 6% died due to COVID-19. Mexicans presented more severe disease, and after considering risk factors they were two times more likely to die.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsCarolina Ayelen Isnardi Grant/research support from: SAR-COVID is a multi- sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or infuenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database, Deshire Alpizar-Rodriguez: None declared, Marco Ulises Martínez-Martínez: None declared, Rosana Quintana: None declared, Ingrid Eleonora Petkovic: None declared, Sofia Ornella: None declared, Vanessa Viviana Castro Coello: None declared, Edson Velozo: None declared, David Zelaya: None declared, María Severina: None declared, Adriana Karina Cogo: None declared, Romina Nieto: None declared, Dora Aida Pereira: None declared, Iris Jazmin Colunga-Pedraza: None declared, Fedra Irazoque-Palazuelos: None declared, GRETA CRISTINA REYES CORDERO: None declared, Tatiana Sofía Rodriguez-Reyne: None declared, JOSE ANTONIO VELOZ ARANDA: None declared, Cassandra Michele Skinner Taylor: None declared, INGRID MARIBEL JUAREZ MORA: None declared, Beatriz Elena Zazueta Montiel: None declared, Atzintli Martínez: None declared, Cesar Francisco Pacheco Tena: None declared, Guillermo Pons-Estel: None declared.

Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users' intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent-discovery results over VIRADialogs, that highlight the difficulty of this task. © 2023 Association for Computational Linguistics.

HIGH DOSE INTRAVENOUS ANAKINRA TREATMENT IS SAFE AND EFFECTIVE IN SEVERE AND CRITICAL COVID-19 PATIENTS: A PROPENSITY SCORE MATCHED STUDY IN A SINGLE CENTER

BackgroundIn COVID-19 severe disease course such as need of intensive care unit (ICU) as well as development of mortality is mainly due to cytokine storm.ObjectivesIn this study, we aimed to evaluate the high dose intravenous anakinra treatment response and outcome in patients with severe and critical COVID-19 compared to standard of care.MethodsThis retrospective observational study was carried out at a tertiary referral center. The study population consisted of two groups as follows;the patients receiving high dose intravenous anakinra (anakinra group) between 01.09.2021 and 01.02.2022 and the patients treated with standard of care (SoC, control group) as historical control group who were hospitalized between 01.07.2021 and 01.09.2021.ResultsAfter the propensity score 1:1 matching 79 patients in anakinra and 79 patients in SoC matched and included into the analysis. Mean±SD patient age was 67.4±16.7 and 67.1±16.3 years in anakinra and SoC group, respectively (p=0.9). Male gender was 38 (48.7 %) in anakinra and 36 (46.2 %) SoC (p=0.8). Overall, ICU admission was in 14.1 % (n=11) and 30.8 % (n=24) (p=0.013;OR: 6.2), intubation in 12.8 % (n=10) and 16.7 % (n=13) patients (p=0.5), 14.1 % (n=11) and 32.1 % (n=25) patients died in anakinra and control group, respectively (p=0.008;OR: 7.1)ConclusionIn our study mortality was lower in patients receiving anakinra compared to SoC. Intravenous high dose anakinra is safe and effective treatment in patients with severe and critical COVID-19.Table 1.Baseline clinical and laboratory features of patients receiving standard of care (SoC) and Anakinra before and after propensity score (PS) matchingBefore PS matchingAfter PS matchingVariablesAnakinra (n=148)SoC (n=114)p value (OR)Anakinra (n=78)SoC (n=78)p value (OR)Age (years) (mean±SD)66.8±1763.1±170.0967.4±16.767.1±16.30.9Gender, male (n, %)78 (52.7)45 (39.5)0.033 (4.5)38 (48.7)36 (46.2)0.8Duration of hospitalization (days) (median, IQR)11 (12)9 (7.3)0.027.5 (9)11 (8)0.01Comorbidities (n, %) Diabetes mellitus41/146 (28.1)39 (34.2)0.318 (23)31 (39.7)0.025 (5) Hypertension84/143 (58.7)64 (56)0.730 (61.5)50 (64)0.7 Coronary heart disease27/143 (19)24 (21)0.718 (23)20 (25.6)0.7 Heart failure18/143 (12.6)23 (20)0.114 (18)20 (25.6)0.24 Chronic renal failure31 (21)6 (5.3)<0.001 (13.06)15 (19)6 (7.7)0.035 (4.5) Chronic obstructive lung disease23/144 (16)19 (16.7)0.914 (18)15 (19)0.8 Dementia15/117 (12.8)2 (1.8)0.001 (10.4)3/61 (5)2 (2.6)0.5 Malignancy16/146 (11)8 (7)0.39 (11.5)6 (7.7)0.4 Immunosuppressive usage18/146 (12.3)2 (1.8)0.001 (10.08)5 (6.5)2 (2.6)0.2Disease severity (n, %) NIH score 3 (severe)57 (38.5)68 (59.6)0.001 (11.5)48 (61.5)44 (56.4)0.5 NIH score 4 (critical)91 (61.5)46 (40.4)30 (38.5)34 (43.6) mcHIS score (mean±SD)3.4±1.22.64±1.5<0.0012.9±13.1±1.30.2PS: Propensity score, SoC: Standard of care, OR: Odds ratio, SD: Standard deviation, IQR: Interquartile range, mcHIS: Modified Covid hyperinflammatory syndrome score, NIH: National Institute Health, ALT: Alanin aminotransferase, AST: Aspartate aminotransferaseTable 2.Outcomes of patients receiving SoC and Anakinra before and after PS matchingBefore PS matchingAfter PS matchingVariables (n, %)Anakinra (n=148)SoC (n=114)p value (OR)Anakinra (n=78)SoC (n=78)p value (OR)Pneumothorax3/134 (2.2)00.25*2/73 (2.7)00.5*Myocardial infarction3/132 (2.3)6 (5.3)0.32/72 (2.8)2/56 (3.6)1Pulmonary embolism4/134 (3)11 (9.6)0.034 (4.8)*3/73 (4.1)7 (9)0.3*Intensive care unit60 (40.5)25 (22)0.001 (10.2)11 (14.1)24 (30.8)0.013 (6.2)Intubation54 (36.5)13 (11.4)<0.001 (21.3)10 (12.8)13 (16.7)0.5Mortality56 (37.8)27 (23.7)0.015 (5.96)11 (14.1)25 (32.1)0.008 (7.1)PS: Propensity score, SoC: Standard of care, OR: Odds ratioREFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Artificial intelligence and machine learning in clinical trial design and application

In 2020, COVID-19 changed how health care was approached both in the United States and globally. In the early phases, the vast majority of energy and attention was devoted to containing the pandemic and treating the infected. Toward the end of 2020, that attention expanded to vaccinating people across the globe. What was not being considered at the time were challenges to future health and clinical trials that power new treatments for COVID-19 and non-COVID-19 treatments. © 2023 Elsevier Inc. All rights reserved.

Is My Model Up-to-date? Detecting CoViD-19 Variants by Machine Learning

Machine learning extracts models from huge quantities of data. Models trained and validated over past data can be deployed in making forecasts as well as in classifying new incoming data. The real world which generates data may change over time, making the deployed model an obsolete one. To preserve the quality of the currently deployed model, continuous machine learning is required. Our approach retrospectively evaluates in an online fashion the behaviour of the currently deployed model. A drift detector detects any performance slump, and, in case, can replace the previous model with an up-to-date one. The approach experiments on a dataset of 8642 hematochemical examinations from hospitalized patients gathered over 6 months: the outcome of the model predicts the RT-PCR test result about CoViD-19. The method reached an area under the curve (AUC) of 0.794, 6% better than offline and 5% better than standard online-binary classification techniques. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

REVACCINATION OF PATIENTS WITH SLE OR RA WITHOUT AN INITIAL COVID-19 VACCINE RESPONSE ELICITS SEROCONVERSION IN HALF OF THE PATIENTS

BackgroundTreatment with Rituximab (RTX) in patients with rheumatic diseases (RD) has presented a challenge during the COVID-19 pandemic, as RTX leads to markedly reduced and often undetectable antibody responses after COVID-19 vaccination (1).ObjectivesTo investigate the effect of COVID-19 mRNA revaccination (two doses) on the antibody response in patients with RD who were initial vaccine non-responders. Further, to examine if B-cell levels or T-cell responses before revaccination predicted seroconversion.MethodsFrom a RD cohort (COPANARD) vaccinated with the standard two-dose COVID-19 vaccinations, we enrolled cases without detectable antibody responses (n=17) and controls with detectable antibody response (n=29). Blood donors (n=32) were included as additional controls. Samples were collected before and six weeks after completed revaccination. Total antibodies (abs) and specific IgG, IgA, and IgM against SARS-CoV-2 spike protein, SARS-CoV-2 neutralizing abs, and SARS-CoV-2 reacting CD4+ and CD8+ T-cells were measured before and after revaccination. B-cells (CD19+CD45+) were quantified before revaccination. This study was funded by the Danish Rheumatism Association.ResultsPatient demographics are given in Table 1. Forty-seven percent of cases had detectable total SARS-CoV-2 abs and neutralizing abs after revaccination. However, antibody levels were significantly lower than in controls and blood donors (p<0.008), Figure 1A+B. Revaccination induced an antibody class switch in cases with a decrease in detectable IgM abs (Baseline 11/17, Followup 3/17) and increase in IgG. No significant difference was observed in T-cell responses before and after revaccination between the three groups, Figure 1C. The proportion of cases with detectable CD4+ T cells increased from 69% to 88% (p=0.25), and for CD8+ T cells, the proportion decreased from 88% to 82% (p=1.00). Only 29% of cases had measurable B-cells compared to 100% of controls and blood donors, Figure 1D. Fifty percent of revaccinated cases who seroconverted had measurable B-cells before revaccination, Figure 1D.Univariate logistic regression analysis was performed to analyze if active RTX treatment, the presence of B-cells, or a positive T-cell response prior to revaccination predicted seroconversion of total SARS-CoV-2-abs in the patient cohort. We did not find a significant explanatory effect of either variable in the univariate logistic models, data not shown.Table 1.DemographicsCases Revaccination, n=17Controls Boost, n=29Female sex, no(%)1482%2172%Age, median (IQR)6549 - 706762 - 72Disease duration, years1510 - 18229 - 31Rheumatoid Arthritis/SLE13/410/19None DMARD529%828%Prednisone424%13%Methotrexate741%1241%Hydroxychloroquine212%414%None biologic treatment424%931%Rituximab1271%0TNF-inhibitors16%724%JAK-inhibitors0621%IL-6-inhibitors, Abatacept, Benlysta0724%Previous rituximab treatmentAny rituximab treatment1694%13%RTX within the last 15 months, no1488%0Cumulative total dose, mg134-242Time from RTX to revaccination, months95-1249Figure 1.ConclusionIn conclusion, forty-seven percent of initial non-responders were able to seroconvert after two-dose revaccination. However, plasma concentrations of the antibodies against SARS-COV-2 and the levels of neutralizing capacity remained significantly lower than in immunocompetent blood donors. B-cell levels or T-cell responses before revaccination did not predict seroconversion. Our study suggests that patients with RDs who did not mount a detectable serological response to a COVID-19 mRNA vaccine have a T cell response similar to immunocompetent controls. Future studies should establish the antibody levels that identify RD patients without sufficient protection against SARS-CoV-2 infection.References[1]Troldborg A, et al. Time Since Rituximab Treatment Is Essential for Developing a Humoral Response to COVID-19 mRNA Vaccines in Patients With Rheumatic Diseases. J Rheumatol. 2022.AcknowledgementsThe Danish Rheumatism Association [grant number R203-A7217]. We acknowledge all patients and blood donors contributing to the stud for their invaluable participation. The authors would like to thank Sif Kaas Nielsen and Mads Engelhardt Knudsen, the Laboratory of Molecular Medicine at Rigshospitalet, for their excellent technical assistance in analyzing the samples.Disclosure of InterestsNone Declared.

OUTCOME FOR PATIENTS WITH GIANT CELL ARTERITIS (GCA) TREATED WITH TOCILIZUMAB ACCORDING TO NICE GUIDANCE IN A SINGLE TERTIARY UK CENTRE

BackgroundIn 2018 NICE and NHS England approved one year of weekly subcutaneous tocilizumab for use in relapsing or refractory GCA [1, 2]. During the COVID pandemic NHS England allowed extended use of tocilizumab in selected high risk patients [3]. This extension ended in March 2022. This has created a cohort of patients who are now no longer treated with tocilizumab and may be at risk of GCA flare. Currently, NHS England does not allow retreatment with tocilizumab.ObjectivesThis service evaluation used an intention-to-treat approach to retrospectively evaluate patients, who were ratified to receive tocilizumab for GCA according to the NICE guidance. We aimed to describe this cohort of patients for whom the use of tocilizumab had been approved, and their outcomes in terms of complications and disease control.Methods49 patients were ratified to receive tocilizumab between May 2019 and April 2022 by a specialist multidisciplinary team at a single tertiary rheumatology center. Their response was assessed in terms of relapse rates, steroid usage and complications as described below.Results80% of the 49 cohort of patients consisted of females (Table 1). 55% of patients were diagnosed with GCA on combination of clinical history, laboratory and temporal artery duplex findings. 94% (46/49) had at least a week's course of tocilizumab. Around half (51%) had relapsing disease. 6% had first dose as intravenous due to critical ischaemia. 27% (13/49) of patients developed complications whilst on treatment. Six developed cytopenia, 3 acquired infections and 4 stopped due to other reasons. As per guidelines, tocilizumab was stopped after 12 months in 25 patients (51%). 16% stopped treatment early due to complications. 18% had incomplete information. 10% had ongoing treatment. One patient died several months after finishing tocilizumab. 47% had methotrexate as DMARD therapy added prior to tocilizumab commencement (Figure 1). Out of 25 patients who completeted treatment, 24% (6/25) relapsed. 83% of these relapses were diagnosed on recurrence of symptoms and high inflammatory markers. In addition, 3 patients, who had tocilizumab suspended relapsed. 2/3 of these patients had treatment suspended due to infection. 5/9 relapse patients did not have preceding DMARD therapy. 22% (2/9) of relapse patients had PET-CT due to involvement of extra-cranial disease. 56% (5/9) relapsed following a median follow-up of 11 months. Of relapsed patients, seven were treated with increased dose of prednisolone and two patients received 6 months extension of tocilizumab with adequate tolerance and efficacy.ConclusionOur data shows good tolerability of tocilizumab and a 24% flare rate amongst patients who completed treatment. This is less than the 50% rate seen in GiACTA and other cohorts, where the majority of which occurred within 6 months of stopping treatment [4]. DMARD treatment may reduce relapse rate, but this will require further study. The data describing the efficacy of treatment beyond one year is limited [3]. However, with no established guidance for treating patients following tocilizumab, extension of treatment is a plausible option.References[1]Tocilizumab for treating giant cell arteritis, NICE Technology Appraisal Guidance, 18 April 2018. https://www.nice.org.uk/guidance/ta518/resources/tocilizumab-for-treating-giant-cell-arteritis-pdf-82606786726597[2]Stone J, Tuckwell K, Dimonaco S et al.Trial of Tocilizumab in Giant-Cell Arteritis. N Engl J Med 2017;377:317-328.[3]Regola F, Cerudelli E,Bosio G. Long-term treatment with tocilizumab in giant cell arteritis: efficacy and safety in a monocentric cohort of patients Rheumatology Adv Pract 2020;0:1–9.[4]Conway R, Putman MS, Mackie SL. Benchmarking tocilizumab use for giant cell arteritis. Rheumatol Adv Pract. 2022;6(2):rkac037.Figure 1.Table 1.GenderAge at time of diagnosisIndication for stopping treatmentMaleFemale50-5960-6970-7980-89Completed treatmentComplicationsOngoing treatmentIncomplete information18313162010251058Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Health Disparities and Comparison of Psychiatric Medication Use before and after the COVID-19 Pandemic Lockdown among General Practitioner Practices in the North East of England.

BACKGROUND: Psychiatric medications play a vital role in the management of mental health disorders. However, the COVID-19 pandemic and subsequent lockdown limited access to primary care services, leading to an increase in remote assessment and treatment options to maintain social distancing. This study aimed to investigate the impact of the COVID-19 pandemic lockdown on the use of psychiatric medication in primary care settings. METHODS: We conducted a retrospective claims-based analysis of anonymized monthly aggregate practice-level data on anxiolytics and hypnotics use from 322 general practitioner (GP) practices in the North East of England, where health disparities are known to be higher. Participants were all residents who took anxiolytics and hypnotics from primary care facilities for two financial years, from 2019/20 to 2020/21. The primary outcome was the volume of Anxiolytics and Hypnotics used as the standardized, average daily quantities (ADQs) per 1000 patients. Based on the OpenPrescribing database, a random-effect model was applied to quantify the change in the level and trend of anxiolytics and hypnotics use after the UK national lockdown in March 2020. Practice characteristics extracted from the Fingertips data were assessed for their association with a reduction in medication use following the lockdown. RESULTS: This study in the North East of England found that GP practices in higher health disparate regions had a lower workload than those in less health disparate areas, potentially due to disparities in healthcare utilization and socioeconomic status. Patients in the region reported higher levels of satisfaction with healthcare services compared to the England average, but there were differences between patients living in higher versus less health disparate areas. The study highlights the need for targeted interventions to address health disparities, particularly in higher health disparate areas. The study also found that psychiatric medication use was significantly more common in residents living in higher health disparate areas. Daily anxiolytics and hypnotics use decreased by 14 items per 1000 patients between the financial years 2019/20 and 2020/21. A further nine items per 1000 decreased for higher health disparate areas during the UK national lockdown. CONCLUSIONS: People during the COVID-19 lockdown were associated with an increased risk of unmet psychiatric medication demand, especially for higher health disparate areas that had low-socioeconomic status.

Remdesivir Use in the Real-World Setting: An Overview of Available Evidence.

In the years of Coronavirus Disease 2019 (COVID-19), various treatment options have been utilized. COVID-19 continues to circulate in the global population, and the evolution of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has posed significant challenges to the treatment and prevention of infection. Remdesivir (RDV), an anti-viral agent with in vitro efficacy against coronaviruses, is a potent and safe treatment as suggested by a plethora of in vitro and in vivo studies and clinical trials. Emerging real-world data have confirmed its effectiveness, and there are currently datasets evaluating its efficacy and safety against SARS-CoV-2 infections in various clinical scenarios, including some that are not in the SmPC recommendations according for COVID-19 pharmacotherapy. Remdesivir increases the chance of recovery, reduces progression to severe disease, lowers mortality rates, and exhibits beneficial post-hospitalization outcomes, especially when used early in the course of the disease. Strong evidence suggests the expansion of remdesivir use in special populations (e.g., pregnancy, immunosuppression, renal impairment, transplantation, elderly and co-medicated patients) where the benefits of treatment outweigh the risk of adverse effects. In this article, we attempt to overview the available real-world data of remdesivir pharmacotherapy. With the unpredictable course of COVID-19, we need to utilize all available knowledge to bridge the gap between clinical research and clinical practice and be sufficiently prepared for the future.

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.

OBJECTIVES: As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue. TARGET AUDIENCE: The sheer diversity of existing methods presents a challenge for health scientists who are beginning to apply computational methods to their research. Therefore, this tutorial is aimed at scientists working with EHR data who are early entrants into the field of applying AI methodologies. SCOPE: This manuscript describes the diverse and growing AI research approaches in healthcare data science and categorizes them into 2 distinct paradigms, the bottom-up and top-down paradigms to provide health scientists venturing into artificial intelligent research with an understanding of the evolving computational methods and help in deciding on methods to pursue through the lens of real-world healthcare data.

Monintoring Student Depression by Mobile Devices with Autoencoder Networks

The timely assessment of mental health is difficult since we lack the objective measurements of symptoms, especially for the Covid-19 pandemic quarantined students. Fortunately, smart phones can capture the real-world data such as the GPS traces and the phone active time et.al that link the behavioral patterns to the mental health. However, recent studies are based on a very small size of participants and only collect fewer phone features, which means that the effective predicting models which require various features are hardly adopted. In this paper, we develop an android application to record multidimensional data of users as well as a PHQ-9 and a SAS questionary, and we distribute it to 176 college students to collect larger scale data when in quarantine period. To address the unprecise problem of handcrafted feature extraction, we design an autoencoder machine learning model to monitor the student mental health. Extensive experiments indicate that the performance of the proposed method improves its F-1 score for PHQ-9 and SAS by 5% and 6% to the state of the current studies, respectively. © 2023 IEEE.

Outcomes of Sars-Cov-2 Infection in Patients with Anca Associated Vasculitis. Data from the National Sar-Covid Registry

Objectives: To describe the clinical characteristics and outcomes of SARSCoV-2 infection in patients with systemic vasculitis. Method(s): Observational, multicenter, cross-sectional analytical study in patients 18 or older diagnosed with systemic vasculitis with confirmed SARSCoV-2 infection (RT-PCR or serology) included in the SAR-COVID registry. Patients were evaluated from July 2020 to February 2022. Patients diagnosed with ANCA-associated vasculitis (AAV), other systemic vasculitides (Giant cell arteritis, Takayasu), and a control group of patients with other rheumatological diseases matched by age, sex, comorbidities, and date of SARS-CoV-2 infection. The survival curve of the groups was studied by Kaplan-Meier and compared through the Log-Rank Test. A Cox regression model will be performed to adjust survival for different variables (sex, age, treatments for underlying disease, treatments for viral infection, smoking, obesity, d-dimer level, and disease activity). Result(s): A total of 282 out of 2694 patients in the SAR-COVID registry were included, 57.4%women with a mean age of 55.7 years (SD 14.1). Fifty-four patients in the AAV group, 32 in the other vasculitis group, and 196 controls were studied. Hospitalization was required in 53.7% of the AAV group, 37.5% in other vasculitides, and 26.2% in the control group. 5.6% of patients in the control group presented acute respiratory distress syndrome (ARDS), 15.6% in the other vasculitis group, and 22.2% in the AAV group (p alpha 0.001). Complete recovery was observed in 82.3% of patients in the control group, 75%in the other vasculitis group, and 63%in the AAV group.We observed that 5.7% of the patients in the control group died from COVID-19, 9.4%from other vasculitides, and 27.8% in the AAV group (p alpha 0.001). We found a lower survival in the AAV group compared to the control group (p alpha 0.005). In the multivariate Cox regression model, older age (HR:1.05 IC95%1.01-1.09 p = 0.01), BMI > 40 (HR:13.2 IC95% 2.1-83.2 p = 0.01), and high activity of the underlying disease (HR:16 95% CI 3.7-69.4 p alpha 0.005) were associated with lower survival. Conclusion(s): In conclusion, patients diagnosed with AAV presented a worse disease course during SARS-CoV-2 infection with a more frequent requirement for invasive mechanical ventilation. Likewise, these patients showed lower survival compared to patients with other autoimmune diseases.