Towards the Lowest Efficacious Dose: Results From a Multicenter Noninferiority Randomized Open-Label Controlled Trial Assessing Tocilizumab or Abatacept Injection Spacing in Rheumatoid Arthritis in Remission.

OBJECTIVE: We assess the clinical and structural impact at two years of progressively spacing tocilizumab (TCZ) or abatacept (ABA) injections versus maintenance at full dose in patients with rheumatoid arthritis in sustained remission. METHODS: This multicenter open-label noninferiority (NI) randomized clinical trial included patients with established rheumatoid arthritis in sustained remission receiving ABA or TCZ at a stable dose. Patients were randomized to treatment maintenance (M) at full dose (M-arm) or progressive injection spacing (S) driven by the Disease Activity Score in 28 joints every 3 months up to biologics discontinuation (S-arm). The primary end point was the evolution of disease activity according to the Disease Activity Score in 44 joints during the 2-year follow-up analyzed per protocol with a linear mixed-effects model, evaluated by an NI test based on the one-sided 95% confidence interval (95% CI) of the slope difference (NI margin 0.25). Other end points were flare incidence and structural damage progression. RESULTS: Overall, 202 of the 233 patients included were considered for per protocol analysis (90 in S-arm and 112 in M-arm). At the end of follow-up, 16.2% of the patients in the S-arm could discontinue their biologic disease-modifying antirheumatic drug, 46.9% tapered the dose and 36.9% returned to a full dose. NI was not demonstrated for the primary outcome, with a slope difference of 0.10 (95% CI 0.10-0.31) between the two arms. NI was not demonstrated for flare incidence (difference 42.6%, 95% CI 30.0-55.1) or rate of structural damage progression at two years (difference 13.9%, 95% CI -6.7 to 34.4). CONCLUSION: The Towards the Lowest Efficacious Dose trial failed to demonstrate NI for the proposed ABA or TCZ tapering strategy.

Cancer screening in chronic inflammatory rheumatic diseases.

Cancer is a major public health concern, and screening for cancer is a on-going issue in our practice. The risk of cancer in patients with chronic inflammatory rheumatic diseases varies according to their personal medical history, underlying rheumatic disease and its treatment. However, to date, no rheumatology learned society has established specific recommendations for cancer screening in patients with chronic inflammatory rheumatic diseases. In this review, we provide an overview of the risk of cancer in chronic inflammatory rheumatic diseases (related to the disease itself or its treatment), cancer screening in the general population and in immunocompromised subjects, and cancer screening in patients with chronic inflammatory rheumatic diseases.

A Risk Score to Detect Subclinical Rheumatoid Arthritis-Associated Interstitial Lung Disease.

OBJECTIVE: Patients at high risk of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) would benefit from being identified before the onset of respiratory symptoms; this can be done by screening patients with the use of chest high-resolution computed tomography (HRCT). Our objective was to develop and validate a risk score for patients who have subclinical RA-ILD. METHODS: Our study included a discovery population and a replication population from 2 prospective RA cohorts (ESPOIR and TRANSLATE2, respectively) without pulmonary symptoms who had received chest HRCT scans. All patients were genotyped for MUC5B rs35705950. After multiple logistic regression, a risk score based on independent risk factors for subclinical RA-ILD was developed in the discovery population and tested for validation in the replication population. RESULTS: The discovery population included 163 patients with RA, and the replication population included 89 patients with RA. The prevalence of subclinical RA-ILD was 19.0% and 16.9%, respectively. In the discovery population, independent risk factors for subclinical RA-ILD were presence of the MUC5B rs35705950 T allele (odds ratio [OR] 3.74 [95% confidence interval (95% CI) 1.37, 10.39]), male sex (OR 3.93 [95% CI 1.40, 11.39]), older age at RA onset (for each year, OR 1.10 [95% CI 1.04, 1.16]), and increased mean Disease Activity Score in 28 joints using the erythrocyte sedimentation rate (for each unit, OR 2.03 [95% CI 1.24, 3.42]). We developed and validated a derived risk score with receiver operating characteristic areas under the curve of 0.82 (95% CI 0.70-0.94) for the discovery population and 0.78 (95% CI 0.65-0.92) for the replication population. Excluding MUC5B rs35705950 from the model provided a lower goodness of fit (likelihood ratio test, P = 0.01). CONCLUSION: We developed and validated a risk score that could help identify patients at high risk of subclinical RA-ILD. Our findings support an important contribution of MUC5B rs35705950 to subclinical RA-ILD risk.

Positive impact on 10-year outcome of the window of opportunity for conventional synthetic DMARDs in rheumatoid arthritis: results from the ESPOIR cohort.

OBJECTIVE: This study aimed to assess the impact of disease-modifying antirheumatic drugs (DMARDs) on 10-year outcomes in rheumatoid arthritis (RA). METHODS: Patients with RA from the ESPOIR cohort with complete data on Disease Activity Score in 28 Joints (DAS28) and Health Assessment Questionnaire (HAQ) at 10 years (n=418) and complete radiographic data at baseline and 10 years (n=343) were included in this study. Outcomes were favourable outcome (FavOut) at 10 years, defined as DAS28 of <2.6 and HAQ score of <0.5 at 10 years, and absence of structural damage progression (AbsSDP) at 10 years, defined as change in Sharp-van der Heijde Score less than the smallest detectable change at 10 years (11.5 points). Three multivariate logistic regression models predicting 10-year outcome were built, considering (1) baseline variables only, (2) baseline variables and DMARD exposure (ever exposed, yes/no) and (3) baseline variables and DMARD exposure as weighted cumulative exposure (WCE) variables. RESULTS: Overall, 196/418 (46.9%) patients showed FavOut and 252/343 (73.5%) AbsSDP. WCE models had the best predictive performance, with area under the curve=0.80 (95% CI 0.74 to 0.87) for FavOut and 0.87 (95% CI 0.83 to 0.92) for AbsSDP. In the WCE model, the odds of FavOut and AbsSDP were reduced with conventional synthetic disease-modifying antirheumatic drug (csDMARD) initiation at 12 months versus at baseline (OR 0.78, 95% CI 0.65 to 0.94, and OR 0.89, 95% CI 0.76 to 0.98, respectively). Early biologics initiation was not significantly associated with either outcome. CONCLUSIONS: WCE models can identify and quantify the long-term benefit of early csDMARD initiation on 10-year functional and structural outcomes in patients with RA.

National reimbursement databases: Use and limitations for rheumatologic studies.

The aim was to review and analyze the current knowledge on the use and limitations of National Reimbursement Databases (NRD) in rheumatology. Three main categories of NRDs were identified, according to the data available in these databases: NRDs without details from clinical practice (such as the French and Quebec NRDs), NRDs with diagnosis details from clinical practice (such as the British NRD), and NRDs linkable to clinical databases (such as the Swedish NRD). NRDs allow the construction of cohorts with prospective data collection and with important statistical power, and therefore enable better knowledge of the rheumatic diseases' epidemiology, notably about the risk-benefit balance of available therapies as well as about the economic burden of these diseases and treatments to Society. This may have an important impact on public health decision-making as well as on development or adjustment of management guidelines. The main limitation of NRDs is the lack of exhaustive medical information, e.g., measure of disease activity, work-up results, ascertainment of diagnostic codes, etc. Other issues are related to the size and complexity of these databases and the difficulty to get access to data extractions. In conclusion, NRDs represent an important opportunity for medical research in rheumatology and the need to create scientific interactions between rheumatologists, data managers and biostatisticians. A major issue remains the lack of exhaustive clinical and paraclinical data in those databases. The latter should be addressed in the years to come by the linkage with clinical registers.

EULAR points to consider when analysing and reporting comparative effectiveness research using observational data in rheumatology.

BACKGROUND: Comparing treatment effectiveness over time in observational settings is hampered by several major threats, among them confounding and attrition bias. OBJECTIVES: To develop European Alliance of Associations for Rheumatology (EULAR) points to consider (PtC) when analysing and reporting comparative effectiveness research using observational data in rheumatology. METHODS: The PtC were developed using a three-step process according to the EULAR Standard Operating Procedures. Based on a systematic review of methods currently used in comparative effectiveness studies, the PtC were formulated through two in-person meetings of a multidisciplinary task force and a two-round online Delphi, using expert opinion and a simulation study. Finally, feedback from a larger audience was used to refine the PtC. Mean levels of agreement among the task force were calculated. RESULTS: Three overarching principles and 10 PtC were formulated, addressing, in particular, potential biases relating to attrition or confounding by indication. Building on Strengthening the Reporting of Observational Studies in Epidemiology guidelines, these PtC insist on the definition of the baseline for analysis and treatment effectiveness. They also focus on the reasons for stopping treatment as an important consideration when assessing effectiveness. Finally, the PtC recommend providing key information on missingness patterns. CONCLUSION: To improve the reliability of an increasing number of real-world comparative effectiveness studies in rheumatology, special attention is required to reduce potential biases. Adherence to clear recommendations for the analysis and reporting of observational comparative effectiveness studies will improve the trustworthiness of their results.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions.

INTRODUCTION: Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA. AREAS COVERED: In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed. EXPERT OPINION: ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

Analysing and reporting of observational data: a systematic review informing the EULAR points to consider when analysing and reporting comparative effectiveness research with observational data in rheumatology.

OBJECTIVES: To evaluate the analysis and reporting of comparative effectiveness research with observational data in rheumatology, informing European Alliance of Associations for Rheumatology points to consider. METHODS: We performed a systematic literature review searching Ovid MEDLINE for original articles comparing drug effectiveness in longitudinal observational studies, published in key rheumatology journals between 2008 and 2019. The extracted information focused on reporting and types of analyses. We evaluated if year of publication impacted results. RESULTS: From 9969 abstracts reviewed, 211 articles fulfilled the inclusion criteria. Ten per cent of studies did not adjust for confounding factors. Some studies did not explain how they chose covariates for adjustment (9%), used bivariate screening (21%) and/or stepwise selection procedures (18%). Only 33% studies reported the number of patients lost to follow-up and 25% acknowledged attrition (drop-out or treatment cessation). To account for attrition, studies used non-responder imputation, followed by last observation carried forward (LOCF) and complete case (CC) analyses. Most studies did not report the number of missing data on covariates (83%), and when addressed, 49% used CC and 11% LOCF. Date of publication did not influence the results. CONCLUSION: Most studies did not acknowledge missing data and attrition, and a tenth did not adjust for any confounding factors. When attempting to account for them, several studies used methods which potentially increase bias (LOCF, CC analysis, bivariate screening…). This study shows that there is no improvement over the last decade, highlighting the need for recommendations for the assessment and reporting of comparative drug effectiveness in observational data in rheumatology.

Wearable activity trackers and artificial intelligence in the management of rheumatic diseases : Where are we in 2021?

Wearable activity trackers are playing an increasingly important role in healthcare. In the field of rheumatic and musculoskeletal diseases (RMDs), various applications are currently possible. This review will present the use of activity trackers to promote physical activity levels in rheumatology, as well as the use of trackers to measure health parameters and detect flares using artificial intelligence. Challenges and limitations of the use of artificial intelligence will be discussed, as well as technical issues when using activity trackers in clinical practice.

Lymphoma complicating rheumatoid arthritis: results from a French case-control study.

OBJECTIVES: To study the characteristics of B-cell non-Hodgkin's lymphoma (NHL) or Hodgkin lymphoma complicating rheumatoid arthritis (RA) and to identify RA-related factors associated with their occurrence. METHODS: A multicentre case-control study was performed in France. Cases were patients with RA fulfilling ACR-EULAR 2010 criteria in whom B-cell NHL or Hodgkin lymphoma developed after the diagnosis of RA. For each case, 2 controls were assigned at random from the ESPOIR cohort and were matched on age at lymphoma diagnosis (cases)/age at the 10-year follow-up visit in the cohort (controls). Case and control characteristics were compared to identify parameters associated with the occurrence of lymphoma. RESULTS: 54 cases were included and matched to 108 controls. Lymphomas were mostly diffuse large B-cell lymphoma (DLBCL, n=27, 50.0%). On immunochemistry, 4 of 27 (14.8%) lymphoma cases were positive for Epstein-Barr virus. On univariate analysis, factors associated with the occurrence of lymphoma were male sex (OR 3.3, 95% CI 1.7 to 6.7), positivity for ACPA (OR 5.1, 95% CI 2.0 to 15.7) and rheumatoid factor (OR 3.9, 95% CI 1.6 to 12.2), and erosions on radiographs (OR 3.8, 95% CI 1.7 to 8.3) and DAS28 (OR 2.0, 95% CI 1.5 to 2.7), both at the time of matching. Methotrexate, TNF blockers and a number of previous biologics were not associated with the occurrence of lymphoma. On multivariable analysis, erosions and DAS28 remained significantly associated with increased risk of lymphoma. CONCLUSION: Lymphomas complicating RA are mostly DLBCL. Risk of lymphoma in patients with RA was increased with markers of disease activity and severity, which supports the paradigm of a continuum between autoimmunity and lymphomagenesis in RA.

Inflammation-targeted therapies and cancer.

OBJECTIVE: To review and analyze the current knowledge on the risk of malignancy associated with inflammation-targeted therapies in rheumatic diseases. METHODS: We performed a non-systematic literature review on PubMEd MEDLINE by screening randomized controlled trials, meta-analyses, reviews, and observational studies focusing on malignancies and inflammation-targeted therapies including TNF inhibitors, other biologics and JAK inhibitors in rheumatic diseases. RESULTS: Data from literature are reassuring regarding the overall risk of incident and recurrent cancer with TNF inhibitors. The risk of lymphoma is more difficult to analyze and data are controversial; however, in most of the studies, this risk does not seem to be significanlty increased. By contrast, there is probably an increased risk of non-melanoma skin cancer associated with TNF inhibitors, as with other immunosuppressants. There is no signal for an increased risk of malignancies with other biological DMARDs, but additional data are needed. A recent post-marketing surveillance study found out an increased risk of malignancies for tofacitinib compared with TNFi; additional data are, therefore, urgently needed to confirm or not these results. CONCLUSION: Data are presently reassuring regarding the overall risk of cancer, whatever the inflammation-targeted treatment. However, additional data are needed for non-TNF biologics and JAK-inhibitors.

Time to initiation of biologic disease-modifying antirheumatic drugs in the French cohort ESPOIR.

OBJECTIVE: To assess the time to initiation of biologic disease-modifying anti-rheumatic drugs (bDMARDs) in ESPOIR, the French cohort of patients with rheumatoid arthritis (RA), and factors associated with the timing of bDMARD initiation. METHODS: In total, 658 patients with early RA satisfying the 2010 ACR/EULAR criteria were included between 2003 and 2005 and followed annually for 10 years (end of follow up: 2013-2015). The timing of bDMARD introduction and predictors of use were analysed by the Kaplan-Meier method based on Cox proportional-hazard models. RESULTS: Overall, 178 patients (31.0%, 95% confidence interval [27.0-34.7]) initiated a bDMARD during the 10-year follow-up, with a mean delay of 43.6 months. The penetration rate was higher during the first 2 years of follow-up (6% between the first and second year, approximately 3.3% each year between the second and seventh year, and<2.0% after the eighth year). The first-used bDMARD was etanercept for 72 patients and adalimumab for 71. On multivariate analysis, Disease Activity Score in 28 joints, radiologic progression and positivity for anti-citrullinated protein antibodies were significantly associated with rapid initiation of a bDMARD (P<0.0001), whereas older age at first joint pain was inversely associated (P<0.0001). CONCLUSIONS: Although access to bDMARDs is widespread in France, less than one third of patients with early RA in the ESPOIR cohort initiated a bDMARD over the 10-year follow-up. Poor prognostic factors for RA were associated with more rapid initiation, as expected.

EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases.

BACKGROUND: Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs). METHODS: A multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated. RESULTS: Three overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice. CONCLUSION: These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.

Objective: To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs). Methods: A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs. Results: Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs). Conclusions: Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

A Case Of Recurrent Helicobacter cinaedi Prosthetic Joint Infection In An HIV-Infected Man.

We describe the case of an HIV-infected man who developed twice a Helicobacter cinaedi prosthetic joint infection. In our knowledge, it is the first case to date. Furthermore, it illustrates the fact that this bacterium is difficult to isolate and that recurrences can occur even after apparently successful treatment.

Lewis-Sumner syndrome in a patient with rheumatoid arthritis: Link between rheumatoid arthritis and demyelinating polyradiculoneuropathies.

Chronic inflammatory demyelinating polyradiculoneuropathies are a group of autoimmune neuropathies with a chronic course. Lewis-Sumner syndrome is a variant of this disease, characterized by an asymmetrical distal and mostly motor involvement, predominating at upper limb. We report the case of a patient who developed almost currently rheumatoid arthritis and Lewis-Sumner syndrome, which raised the problem of therapeutic intensification for his rheumatism when methotrexate proved to be ineffective. Finally, rituximab had been introduced by common consent with neurologists, and the patient noticed an improvement fifteen days after the first infusion. Even if it is striking that both dysimmune diseases had declared within a few months, the association between chronic inflammatory demyelinating polyradiculoneuropathies and rheumatoid arthritis is exceptional, since the only cases reported in the literature are secondary to TNF-alpha inhibitors. Given the potential demyelinating impact of some biologics, rituximab and perhaps abatacept seem to be the best therapeutic options when DMARDs had proven insufficient to control the rheumatism activity.