Prevention, screening, assessing and managing of non-adherent behaviour in people with rheumatic and musculoskeletal diseases: Systematic reviews informing the 2020 EULAR points to consider

© Objective To analyse how non-adherence to prescribed treatments might be prevented, screened, assessed and managed in people with rheumatic and musculoskeletal diseases (RMDs). Methods An overview of systematic reviews (SR) was performed in four bibliographic databases. Research questions focused on: (1) effective interventions or strategies, (2) associated factors, (3) impact of shared decision making and effective communication, (4) practical things to prevent non-adherence, (5) effect of non-adherence on outcome, (6) screening and assessment tools and (7) responsible healthcare providers. The methodological quality of the reviews was assessed using AMSTAR-2. The qualitative synthesis focused on results and on the level of evidence attained from the studies included in the reviews. Results After reviewing 9908 titles, the overview included 38 SR on medication, 29 on non-pharmacological interventions and 28 on assessment. Content and quality of the included SR was very heterogeneous. The number of factors that may influence adherence exceed 700. Among 53 intervention studies, 54.7% showed a small statistically significant effect on adherence, and all three multicomponent interventions, including different modes of patient education and delivered by a variety of healthcare providers, showed a positive result in adherence to medication. No single assessment provided a comprehensive measure of adherence to either medication or exercise. Conclusions The results underscore the complexity of non-adherence, its changing pattern and dependence on multi-level factors, the need to involve all stakeholders in all steps, the absence of a gold standard for screening and the requirement of multi-component interventions to manage it

Early experience of COVID-19 vaccination in adults with systemic rheumatic diseases : Results from the COVID-19 Global Rheumatology Alliance Vaccine Survey

Funding Information: Competing interests SES has received funding from the Vasculitis Foundation and the Vasculitis Clinical Research Consortium unrelated to this work. JL has received research grant funding from Pfizer unrelated to this work. ES is a Board Member of the Canadian Arthritis Patient Alliance, a patient run, volunteer-based organisation whose activities are primarily supported by independent grants from pharmaceutical companies. MP was supported by a Rheumatology Research Foundation Scientist Development grant. DA-R is a Scientific Advisor for GlaxoSmithKilne unrelated to this work. FB reports personal fees from Boehringer, Bone Therapeutics, Expanscience, Galapagos, Gilead, GSK, Merck Sereno, MSD, Nordic, Novartis, Pfizer, Regulaxis, Roche, Sandoz, Sanofi, Servier, UCB, Peptinov, TRB Chemedica and 4P Pharma outside of the submitted work. No funding relevant to this manuscript. RC: speakers bureau for Janssen, Roche, Sanofi, AbbVie. KD reports no COI-unpaid volunteer president of the Autoinflammatory Alliance. Any grants or funding from pharma is received by the non-profit organisation only. CLH received funding under a sponsored research agreement unrelated to the data in the paper from Vifor Pharmaceuticals. LeK has received a research grant from Lilly unrelated to this work. AHJK participated in consulting, advisory board or speaker's bureau for Alexion Pharmaceuticals, Aurinia Pharmaceuticals, Annexon Biosciences, Exagen Diagnostics and GlaxoSmithKilne and received funding under a sponsored research agreement unrelated to the data in the paper from GlaxoSmithKline. JSingh has received consultant fees from Crealta/ Horizon, Medisys, Fidia, PK Med, Two Labs, Adept Field Solutions, Clinical Care Options, Clearview Healthcare Partners, Putnam Associates, Focus Forward, Navigant Consulting, Spherix, MedIQ, Jupiter Life Science, UBM, Trio Health, Medscape, WebMD and Practice Point Communications; and the National Institutes of Health and the American College of Rheumatology. JSingh owns stock options in TPT Global Tech, Vaxart Pharmaceuticals and Charlotte’s Web Holdings. JSingh previously owned stock options in Amarin, Viking and Moderna Pharmaceuticals. JSingh is on the speaker’s bureau of Simply Speaking. JSingh is a member of the executive of Outcomes Measures in Rheumatology (OMERACT), an organisation that develops outcome measures in rheumatology and receives arms-length funding from eight companies. JSingh serves on the FDA Arthritis Advisory Committee. JSingh is the chair of the Veterans Affairs Rheumatology Field Advisory Committee. JSingh is the editor and the Director of the University of Alabama at Birmingham (UAB) Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis. NSingh is supported by funding from the Rheumatology Research Foundation Investigator Award and the American Heart Association. MFU-G has received research support from Pfizer and Janssen, unrelated to this work. SB reports personal fees from Novartis, AbbVie, Pfizer and Horizon Pharma, outside the submitted work. RG reports personal fees from AbbVie New Zealand, Cornerstones, Janssen New Zealand and personal fees and non-financial support Pfizer New Zealand (all <US$10 000) outside the submitted work. PMM reports personal fees from AbbVie, Eli Lilly, Janssen, Novartis, Pfizer and UCB, grants and personal fees from Orphazyme, outside the submitted work. PCR reports personal fees from AbbVie, Gilead, Lilly and Roche, grants and personal fees from Novartis, UCB Pharma, Janssen and Pfizer and non-financial support from BMS, outside the submitted work. PS reports honoraria from Social media editor for @ACR_Journals, outside the submitted work. ZSW reports grants from NIH, BMS and Principia/ Sanofi and personal fees from Viela Bio and MedPace, outside the submitted work. JY reports personal fees from Pfizer and Eli Lilly, and grants and personal fees from AstraZeneca, outside the submitted work. MJL reports grants from American College of Rheumatology, during the conduct of the study and consulting fees from AbbVie, Amgen, Actelion, Boehringer Ingelheim, BMS, Celgene, Gilead, J&J, Mallinckrodt, Novartis, Pfizer, Roche, Sandoz, Sanofi, Sobi and UCB, outside the submitted work. LGR was supported by the Intramural Research Program of the National Institute of Environmental Health Sciences (NIEHS; ZIAES101074) of the National Institutes of Health. JH reports grants from Childhood Arthritis and Rheumatology Research Alliance (CARRA) and Rheumatology Research Alliance, and personal fees from Novartis, Pfizer and Biogen, outside the submitted work. JSimard received research grant funding from the National Institutes of Health unrelated to this work (NIAMS: R01 AR077103 and NIAID R01 AI154533). JSparks has performed consultancy for AbbVie, Boehringer Ingelheim, Bristol-Myers Squibb, Gilead, Inova Diagnostics, Optum and Pfizer unrelated to this work. Funding Information: Funding This study was supported by the European Alliance of Associations for Rheumatology and American College of Rheumatology Research and Education Foundation. Dr. Lisa Rider's involvement was supported in part by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences. Publisher Copyright: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Background. We describe the early experiences of adults with systemic rheumatic disease who received the COVID-19 vaccine. Methods From 2 April to 30 April 2021, we conducted an online, international survey of adults with systemic rheumatic disease who received COVID-19 vaccination. We collected patient-reported data on clinician communication, beliefs and intent about discontinuing disease-modifying antirheumatic drugs (DMARDs) around the time of vaccination, and patient-reported adverse events after vaccination. Results We analysed 2860 adults with systemic rheumatic diseases who received COVID-19 vaccination (mean age 55.3 years, 86.7% female, 86.3% white). Types of COVID-19 vaccines were Pfizer-BioNTech (53.2%), Oxford/AstraZeneca (22.6%), Moderna (21.3%), Janssen/Johnson & Johnson (1.7%) and others (1.2%). The most common rheumatic disease was rheumatoid arthritis (42.3%), and 81.2% of respondents were on a DMARD. The majority (81.9%) reported communicating with clinicians about vaccination. Most (66.9%) were willing to temporarily discontinue DMARDs to improve vaccine efficacy, although many (44.3%) were concerned about rheumatic disease flares. After vaccination, the most reported patient-reported adverse events were fatigue/somnolence (33.4%), headache (27.7%), muscle/joint pains (22.8%) and fever/chills (19.9%). Rheumatic disease flares that required medication changes occurred in 4.6%. Conclusion. Among adults with systemic rheumatic disease who received COVID-19 vaccination, patient-reported adverse events were typical of those reported in the general population. Most patients were willing to temporarily discontinue DMARDs to improve vaccine efficacy. The relatively low frequency of rheumatic disease flare requiring medications was reassuring.publishersversionPeer reviewe

Prolonged COVID-19 symptom duration in people with systemic autoimmune rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance Vaccine Survey

OBJECTIVE: We investigated prolonged COVID-19 symptom duration, defined as lasting 28 days or longer, among people with systemic autoimmune rheumatic diseases (SARDs). METHODS: We analysed data from the COVID-19 Global Rheumatology Alliance Vaccine Survey (2 April 2021-15 October 2021) to identify people with SARDs reporting test-confirmed COVID-19. Participants reported COVID-19 severity and symptom duration, sociodemographics and clinical characteristics. We reported the proportion experiencing prolonged symptom duration and investigated associations with baseline characteristics using logistic regression. RESULTS: We identified 441 respondents with SARDs and COVID-19 (mean age 48.2 years, 83.7% female, 39.5% rheumatoid arthritis). The median COVID-19 symptom duration was 15 days (IQR 7, 25). Overall, 107 (24.2%) respondents had prolonged symptom duration (≥28 days); 42/429 (9.8%) reported symptoms lasting ≥90 days. Factors associated with higher odds of prolonged symptom duration included: hospitalisation for COVID-19 vs not hospitalised and mild acute symptoms (age-adjusted OR (aOR) 6.49, 95% CI 3.03 to 14.1), comorbidity count (aOR 1.11 per comorbidity, 95% CI 1.02 to 1.21) and osteoarthritis (aOR 2.11, 95% CI 1.01 to 4.27). COVID-19 onset in 2021 vs June 2020 or earlier was associated with lower odds of prolonged symptom duration (aOR 0.42, 95% CI 0.21 to 0.81). CONCLUSION: Most people with SARDs had complete symptom resolution by day 15 after COVID-19 onset. However, about 1 in 4 experienced COVID-19 symptom duration 28 days or longer; 1 in 10 experienced symptoms 90 days or longer. Future studies are needed to investigate the possible relationships between immunomodulating medications, SARD type/flare, vaccine doses and novel viral variants with prolonged COVID-19 symptoms and other postacute sequelae of COVID-19 among people with SARDs

Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect

Abstract Background A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient’s disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses. Results VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness—aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status—high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards’ diverse gene-to-gene relationships, including SuperPaths—integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of “disease SuperPaths”, generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease–disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources. Conclusions MalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation

Prevention, screening, assessing and managing of non-adherent behaviour in people with rheumatic and musculoskeletal diseases: Systematic reviews informing the 2020 EULAR points to consider

Objective To analyse how non-adherence to prescribed treatments might be prevented, screened, assessed and managed in people with rheumatic and musculoskeletal diseases (RMDs). Methods An overview of systematic reviews (SR) was performed in four bibliographic databases. Research questions focused on: (1) effective interventions or strategies, (2) associated factors, (3) impact of shared decision making and effective communication, (4) practical things to prevent non-adherence, (5) effect of non-adherence on outcome, (6) screening and assessment tools and (7) responsible healthcare providers. The methodological quality of the reviews was assessed using AMSTAR-2. The qualitative synthesis focused on results and on the level of evidence attained from the studies included in the reviews. Results After reviewing 9908 titles, the overview included 38 SR on medication, 29 on non-pharmacological interventions and 28 on assessment. Content and quality of the included SR was very heterogeneous. The number of factors that may influence adherence exceed 700. Among 53 intervention studies, 54.7% showed a small statistically significant effect on adherence, and all three multicomponent interventions, including different modes of patient education and delivered by a variety of healthcare providers, showed a positive result in adherence to medication. No single assessment provided a comprehensive measure of adherence to either medication or exercise. Conclusions The results underscore the complexity of non-adherence, its changing pattern and dependence on multi-level factors, the need to involve all stakeholders in all steps, the absence of a gold standard for screening and the requirement of multi-component interventions to manage it

Additional file 2: Table S1. of VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Gene lists and corresponding phenotypes for Phenolyzer. (XLSX 32.0 kb