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Competing Interests: Competing interests: None declared.

Competing Interests: Competing interests: None declared.

Competing Interests: Competing interests: None declared.

Competing Interests: Competing interests: None declared.

Non-adherence challenges efficacy and costs of healthcare. Knowledge of the underlying factors is essential to design effective intervention strategies., Objectives: To estimate the prevalence of treatment adherence in rheumatoid arthritis (RA) and to evaluate its predictors., Methods: A 6-month prospective cohort study of patients with RA selected by systematic stratified sampling (33% on first disease-modifying rheumatic drug (DMARD), 33% on second-line DMARD and 33% on biologics). The outcome measure was treatment adherence, defined by a score greater than 80% both in the Compliance Questionnaire in Rheumatology and the Reported Adherence to Medication scale, and was estimated with 95% CIs. Predictive factors included sociodemographic, psychological, clinical, drug-related, patient-doctor relationship related and logistic. Their effect on 6-month adherence was examined by multilevel logistic models adjusted for baseline covariates., Results: 180 patients were recruited (77% women, mean age 60.8). The prevalence of adherence was 59.1% (95% CI 48.1% to 71.8%). Patients on biologics showed higher adherence and perceived a higher medication need than the others; patients on second-line DMARDs had experienced more adverse events than the others. The variables explaining adherence in the final multivariate model were the type of treatment prescribed (second-line DMARDs OR=5.22, and biologics OR=3.76), agreement on treatment (OR=4.57), having received information on treatment adaptation (OR=1.42) and the physician perception of patient trust (OR=1.58). These effects were independent of disease activity., Conclusion: Treatment adherence in RA is far from complete. Psychological, communicational and logistic factors influence treatment adherence in RA to a greater extent than sociodemographic or clinical factors., Competing Interests: Competing interests: AB has received honoraria as speaker from Amgen, Gilead, Galapagos, Pfizer, Novartis, BMS, Nordic, Sanofi, Sandoz, Lilly, UCB and Roche. LC and MJGY have not received fees or personal grants from any laboratory, but their institute works by contract for laboratories among other institutions, such as Abbvie, Gebro Pharma, MSD, Novartis, Pfizer, Roche, Sanofi, Grünenthal and UCB., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support for therapy selection, and automated interpretation of clinical images. Nevertheless, such data-driven approaches could potentially model noise, or miss true clinical phenomena. One proposed solution to ensure clinically meaningful machine learning models is to involve primary stakeholders in their development and interpretation. Including patient and health care professionals' input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community. Here we describe outputs from workshops that involved healthcare professionals, and young people from the Your Rheum Young Person's Advisory Group, in the development of complex machine learning models. These were developed to better describe trajectory of early juvenile idiopathic arthritis disease, as part of the CLUSTER consortium. We further provide key instructions for reproducibility of this process.Involving people living with, and managing, a disease investigated using machine learning techniques, is feasible, impactful and empowering for all those involved., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: To compare the benefits of a tight-control/treat-to-target strategy (TC/T2T) in axial spondyloarthritis (axSpA) with those of usual care (UC)., Methods: Pragmatic, prospective, cluster-randomised, controlled, open, 1-year trial (NCT03043846). 18 centres were randomised (1:1). Patients met Axial Spondylo Arthritis International Society (ASAS) criteria for axSpA, had an Ankylosing Spondylitis Disease Activity Score (ASDAS) ≥2.1, received non-optimal treatment by non-steroidal anti-inflammatory drugs and were biologic-naive., Interventions: (1) TC/T2T : visits every 4 weeks and prespecified strategy based on treatment intensification until achieving target (ie, ASDAS <2.1); (2) UC: visits every 12 weeks and treatment at the rheumatologist's discretion., Main Outcome: Percentage of patients with a ≥30% improvement on the ASAS-Health Index (ASAS-HI). Other efficacy outcomes and adverse events were recorded. A health economic evaluation was performed., Statistical Analysis: Two-level mixed models were used to estimate efficacy outcomes. Cost-effectiveness was assessed by the incremental cost per quality-adjusted life-year (QALY) gained for TC/T2T versus UC., Results: 160 patients were included (80/group). Mean (SD) age was 37.9 (11.0) years and disease duration was 3.7 (6.2) years; 51.2% were men. ASDAS at inclusion was 3.0 (0.7), and ASAS-HI was 8.6 (3.7). ASAS-HI improved by ≥30% in 47.3% of the TC/T2T arm and in 36.1% of those receiving UC (non-significant). All secondary efficacy outcomes were more frequent in the TC/T2T arm, although not all statistically significant. Safety was similar in both arms. From a societal perspective, TC/T2T resulted in an additional 0.04 QALY, and saved €472 compared with UC., Conclusion: TC/T2T was not significantly superior to UC for the primary outcome, while many secondary efficacy outcomes favoured it, had a similar safety profile and was favourable from a societal health economic perspective., Trial Registration Number: NCT03043846., Competing Interests: Competing interests: Dr van Tubergen reports grants and personal fees from Novartis, grants from Pfizer, grants from UCB, grants from Biogen, grants from AbbVie, outside the submitted work. Dr van der Heijde reports personal fees from AbbVie, Amgen, Astellas, AstraZeneca, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli-Lilly, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, UCB Pharma, outside the submitted work; and Director of Imaging Rheumatology bv. Dr van Gaalen reports grants from Stichting vrienden van Sole Mio, grants from Stichting ASAS, grants and personal fees from Novartis, grants from UCB, personal fees from MSD, personal fees from AbbVie, personal fees from Bristol Myers Squibb, outside the submitted work. AB received a research grant to her department from AbbVie, consultation fees from Eli Lilly and Galapagos and a speakers fee from UCB, all paid to her department. Dr Van den Bosch reports personal fees from AbbVie, personal fees from Celgene, personal fees from Eli Lilly, personal fees from Galapagos, personal fees from Janssen, personal fees from Novartis, personal fees from Pfizer, personal fees from UCB, outside the submitted work. Dr Claudepierre reports personal fees from Roche Chugai, Novartis, Pfizer, MSD, grants from Roche Chugai, Novartis, Pfizer, UCB, MSD, AbbVie, Lilly, Celgene, Janssen, BMS, outside the submitted work. Dr Molto reports grants from UCB during the conduct of the study; personal fees from AbbVie, grants and personal fees from UCB, personal fees from BMS, grants and personal fees from Pfizer, grants and personal fees from MSD, personal fees from Novartis, personal fees from Gilead, personal fees from Lilly, outside the submitted work. Dr Gossec reports grants from Amgen, Lilly, Janssen, Pfizer, Sandoz, Sanofi, Galapagos, personal fees from AbbVie, Amgen, BMS, Biogen, Celgene, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, UCB, outside the submitted work. Dr Dougados reports grants from UCB, during the conduct of the study; grants and personal fees from AbbVie, grants and personal fees from Pfizer, grants and personal fees from Lilly, grants and personal fees from Novartis, grants and personal fees from Roche, grants and personal fees from BMS, grants and personal fees from Merck, outside the submitted work. Dr Dernis, Dr Ruyssen-Witrand, Dr Lenaerts, Dr Lopez-Medina, Dr Sparsa, Dr Starmans-Kool, Dr C Webers, Dr Pouplin, Dr Soubrier and Dr Joos have nothing to declare., (© 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.)

This research intended to examine electronic health record (EHR) based methods for automated estimation of morphine equivalent daily dose (MEDD) of prescribed opioids in primary care research and practice. The study leveraged the health system's audit of adults treated with long-term opioids for chronic non-cancer pain to compare two EHR-based automated MEDD calculation methods: RxSignature (active prescriptions' signature information) and RxQuantity (quantity dispensed for prescriptions issued within the past 90 days). Prescribed opioid EHR data were extracted from the target population at a large US academic health system in a 2-year assessment period. Forty-five 'target patients' were selected by the health system for a manual audit by an expert physician who then 'manually' calculated the actual MEDD over the past 90 days (RxAudit) for those with discrepancies in the MEDD calculated with RxSignature and RxQuantity. Paired samples t-test compared the MEDD generated by the RxSignature and RxQuantity methods by opioid type in the target population. The audit (n=45) revealed the RxSignature and RxQuantity methods yielded comparable MEDD results for 20 patients and discrepant results for 25 patients. The former group had opioid prescriptions issued at regular intervals for stable, scheduled doses of opioids; the latter group had opioid prescriptions issued irregularly or for changed daily dosing regimen, for as-needed use, or had changes in the dosing regimen or inactive prescriptions mislabeled as active. RxAudit of the EHR of those with discrepant MEDD results (n=25) produced consistent results with those yielded by the RxQuantity, but not the RxSignature, method. Significant differences in MEDD were found for most opioid types when the MEDD was calculated for the target population using the RxSignature and RxQuantity methods. In conclusion, different EHR-based methods for MEDD calculation can lead to vastly different estimates, with implications for research and clinical care outcomes. Standardising data extraction and MEDD calculation algorithms could overcome these challenges, enabling a more accurate and reproducible approach to the dose calculation for prescribed opioids, improving the quality of research and patient safety., Competing Interests: Competing interests: None declared., (© 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.)

Objectives: To establish evidence-based recommendations to guide health professionals using intra-articular therapies (IAT) in adult patients with peripheral arthropathies., Methods: A multidisciplinary international task force established the objectives, users and scope and the need for background information, including systematic literature reviews) and two surveys addressed to healthcare providers and patients throughout Europe. The evidence was discussed in a face-to-face meeting, recommendations were formulated and subsequently voted for anonymously in a three-round Delphi process to obtain the final agreement. The level of evidence was assigned to each recommendation with the Oxford levels of evidence., Results: Recommendations focus on practical aspects to guide health professionals before, during and after IAT in adult patients with peripheral arthropathies. Five overarching principles and 11 recommendations were established, addressing issues related to patient information, procedure and setting, accuracy, routine and special aseptic care, safety issues and precautions to be addressed in special populations, efficacy and safety of repeated joint injections, use of local anaesthetics and aftercare., Conclusion: We have developed the first evidence and expert opinion-based recommendations to guide health professionals using IAT. We hope that these recommendations will be included in different educational programmes, used by patient associations and put into practice via scientific societies to help improve uniformity and quality of care when performing IAT in peripheral adult joints., Competing Interests: Competing interests: None declared., (© 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: Clinical studies with work participation (WP) as an outcome domain pose particular methodological challenges that hamper interpretation, comparison between studies and meta-analyses., Objectives: To develop Points to Consider (PtC) for design, analysis and reporting of studies of patients with inflammatory arthritis that include WP as a primary or secondary outcome domain., Methods: The EULAR Standardised Operating Procedures were followed. A multidisciplinary taskforce with 22 experts including patients with rheumatic diseases, from 10 EULAR countries and Canada, identified methodologic areas of concern. Two systematic literature reviews (SLR) appraised the methodology across these areas. In parallel, two surveys among professional societies and experts outside the taskforce sought for additional methodological areas or existing conducting/reporting recommendations. The taskforce formulated the PtC after presentation of the SLRs and survey results, and discussion. Consensus was obtained through informal voting, with levels of agreement obtained anonymously., Results: Two overarching principles and nine PtC were formulated. The taskforce recommends to align the work-related study objective to the design, duration, and outcome domains/measurement instruments of the study (PtC: 1-3); to identify contextual factors upfront and account for them in analyses (PtC: 4); to account for interdependence of different work outcome domains and for changes in work status over time (PtC: 5-7); to present results as means as well as proportions of patients reaching predefined meaningful categories (PtC: 8) and to explicitly report volumes of productivity loss when costs are an outcome (PtC:9)., Conclusion: Adherence to these EULAR PtC will improve the methodological quality of studies evaluating WP., Competing Interests: Competing interests: AB: research grants from Abbvie and Celgene and honoraria for consultancy or speakers honoraria from UCB, Biogen, Sandoz, Eli Lilly and Galapagos (all to her department); EN has received speaker honoraria/participated in advisory boards for Celltrion, Pfizer, Sanofi, Gilead, AbbVie and Lilly; FPS: research grant from AbbVie, Novartis; consultancy or speaking fees: AbbVie, Angelini, AstraZeneca, Biogen, Eli Lilly, Laboratórios Vitória, MSD, Novartis, Tecnimed, UCB; SR: research grant from MSD; consultancy or speaking fees: AbbVie, Eli Lilly, MSD, Novartis, Sanofi, UCB; JKW: consultancy fees from Celgene, Eli Lilly, Novartis; RW: consultancy and speakers bureau Celltrion and Galapagos/Gilead., (© 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.)

Competing Interests: Competing interests: None declared.

Objective: To produce European League Against Rheumatism (EULAR) recommendations for the reporting of ultrasound studies in rheumatic and musculoskeletal diseases (RMDs)., Methods: Based on the literature reviews and expert opinion (through Delphi surveys), a taskforce of 23 members (12 experts in ultrasound in RMDs, 9 in methodology and biostatistics together with a patient research partner and a health professional in rheumatology) developed a checklist of items to be reported in every RMD study using ultrasound. This checklist was further refined by involving a panel of 79 external experts (musculoskeletal imaging experts, methodologists, journal editors), who evaluated its comprehensibility, feasibility and comprehensiveness. Agreement on each proposed item was assessed with an 11-point Likert scale, grading from 0 (total disagreement) to 10 (full agreement)., Results: Two face-to-face meetings, as well as two Delphi rounds of voting, resulted in a final checklist of 23 items, including a glossary of terminology. Twenty-one of these were considered 'mandatory' items to be reported in every study (such as blinding, development of scoring systems, definition of target pathologies) and 2 'optional' to be reported only if applicable, such as possible confounding factors (ie, ambient conditions) or experience of the sonographers., Conclusion: An EULAR taskforce developed a checklist to ensure transparent and comprehensive reporting of aspects concerning research and procedures that need to be presented in studies using ultrasound in RMDs. This checklist, if widely adopted by authors and editors, will greatly improve the interpretability of study development and results, including the assessment of validity, generalisability and applicability., Competing Interests: Competing interests: FC reports personal fees from Lilly and Novartis France. MB reports personal fees from Novartis, BMS and Pfizer. RC is a founding member of the Technical Advisory Group of OMERACT, an organization that develops outcome measures in rheumatology and receives arms-length funding from 12 companies. DvdH reports personal fees from AbbVie, Amgen, Astellas, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli-Lilly, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, UCB Pharma and is Director of Imaging Rheumatology bv. AI reports grants from Abbvie, MSD, and Alfasigma and personal fees from AbbVie, Abiogen, Alfasigma, Biogen, BMS, Celgene, Eli-Lilly, Janssen, MSD, Novartis, Sanofi, Sanofi Genzyme. RBL reports personal fees from AbbVie, Galapagos, Gilead, Jansen, Eli-Lilly, Novartis, Pfizer, UCB. PM reports grants and personal fees from AbbVie, Novartis, Janssen, personal fees from Celgene, grants from Merck Sharp & Dohme, UCB, Roche. RT reports personal fees from Amgen, AbbVie, Novartis. MADA reports personal fees from Abbvie, BMS, Novartis, Celgene, Janssen and grants from Pfizer., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Rheumatic and musculoskeletal diseases (RMDs) form a diverse group of diseases. Proper disease assessment is pivotal, for instance to make treatment choices and for optimising outcome in general. RMDs are multidimensional diseases, entrenching many, sometimes very different aspects. Composite outcome measures ('composites') have become very popular to assess RMDs, because of their claim to catch all relevant dimensions of the disease into one convenient measure.In this article we discuss dimensionality of RMDs in the context of the most popular conceptual framework of RMDs, being an inflammatory process leading to some sort of damage over time. We will argue that multidimensionality not only refers to heterogeneity in disease manifestations, but also to heterogeneity in possible outcomes. Unlike most unidimensional measures, multidimensional composites may include several disease manifestations as well as several outcome dimensions into one index. We will discuss fundamental problems of multidimensional composites in light of modern strategies such as treat-to-target and personalised medicine.Finally, we will disentangle the use of multidimensional composites in clinical trials versus their use in clinical practice, and propose simple solutions in order to overcome problems of multidimensionality and to avoid harm to our patients due to overtreatment., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: As part of European League against Rheumatism (EULAR)/European Musculoskeletal Conditions Surveillance and Information Network, 20 user-focused standards of care (SoCs) for rheumatoid arthritis (RA) addressing 16 domains of care were developed. This study aimed to explore gaps in implementation of these SoCs across Europe., Methods: Two cross-sectional surveys on the importance, level of and barriers (patients only) to implementation of each SoC (0-10, 10 highest) were designed to be conducted among patients and rheumatologists in 50 European countries. Care gaps were calculated as the difference between the actual and maximum possible score for implementation (ie, 10) multiplied by the care importance score, resulting in care gaps (0-100, maximal gap). Factors associated with the problematic care gaps (ie, gap≥30 and importance≥6 and implementation<6) and strong barriers (≥6) were further analysed in multilevel logistic regression models., Results: Overall, 26 and 31 countries provided data from 1873 patients and 1131 rheumatologists, respectively. 19 out of 20 SoCs were problematic from the perspectives of more than 20% of patients, while this was true for only 10 SoCs for rheumatologists. Rheumatologists in countries with lower gross domestic product and non-European Union countries were more likely to report problematic gaps in 15 of 20 SoCs, while virtually no differences were observed among patients. Lack of relevance of some SoCs (71%) and limited time of professionals (66%) were the most frequent implementation barriers identified by patients., Conclusions: Many problematic gaps were reported across several essential aspects of RA care. More efforts need to be devoted to implementation of EULAR SoCs., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)