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.

POS0062 A RISK SCORE TO DETECT SUBCLINICAL RHEUMATOID ARTHRITIS-ASSOCIATED INTERSTITIAL LUNG DISEASE

Background

Despite a high morbi-mortality rate, there are no definite strategy for subclinical interstitial lung disease (ILD) screening in patients with rheumatoid arthritis (RA).

Objectives

Our objectives were: 1. to identify risk factors for subclinical RA-ILD in a prospective discovery cohort (ESPOIR) 2.to develop a risk score for subclinical RA-ILD and 3. to validate the risk score in an independent replication cohort (TRANSLATE 2).

Methods

Patients without pulmonary symptoms from 2 prospective RA cohorts who underwent chest HRCT scans were included. All patients were genotyped for MUC5B rs35705950. A risk score based on independent risk factors for subclinical RA-ILD was developed using multiple logistic regression in the discovery cohort. The risk score was tested for validation in the replication cohort.

Results

Discovery and replication cohorts included 163 and 89 patients, respectively. Subclinical ILD was detected in 19.0% and 16.9% of the patients, respectively. In the discovery cohort, independent risk factors for subclinical RA-ILD were the MUC5B rs35705950 T risk allele (odds ratio [OR]=3.74; 95% confidence interval [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 DAS28-ESR (for each unit, OR=2.03; 95%CI [1.24–3.42]). We developed a risk score for subclinical RA-ILD with AUC=0.82; 95%CI [0.70–0.94] (sensitivity (Se)=71.0%) and specificity (Sp)=79.6%). The risk score was validated in the replication cohort with AUC=0.78; 95%CI [0.65–0.92] (Se=86.7%, Sp=62.2%).

Conclusion

Our risk score could help identifying patients at high-risk for subclinical RA-ILD before the onset of pulmonary symptoms.

Disclosure of Interests

Pierre-Antoine Juge Speakers bureau: AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Consultant of: Bristol Myers Squibb, Benjamin Granger: None declared, Marie-Pierre Debray: None declared, Esther Ebstein: None declared, Fabienne Louis-Sidney: None declared, Joanna KEDRA: None declared, Tracy Doyle: None declared, Raphael Borie: None declared, Arnaud Constantin Consultant of: Abbvie, Amgen, Biogen, BMS, Boehringer Ingelheim, Fresenius Kabi, Galapagos, Janssen, Lilly, Medac, MSD, Mylan, Novartis, Pfizer, Procter & Gamble, Roche, Sanofi, UCB, Viatris, Bernard Combe Consultant of: AbbVie, BMS, Eli-Lilly, Gilead, Janssen, Merck, Novartis, Pfizer, Roche-Chugai, Sanofi, UCB, René-Marc Flipo Consultant of: Abbvie, Janssen, MSD and Pfizer. He reports research grants from Abbvie, Janssen, Novartis and Pfizer, Xavier Mariette Consultant of: BMS, Gilead, Janssen, Pfizer, Samsung, UCB, Olivier VITTECOQ: None declared, Alain Saraux: None declared, Guillermo CARVAJAL ALEGRIA: None declared, Jean Sibilia Consultant of: AbbVie, Lilly, MSD, Amgen, Pfizer, BMS, Janssen, Roche, Sandoz, Sanofi-Genzyme, SOBI, UCB, Novartis, Grant/research support from: AbbVie, Lilly, MSD, Amgen, Pfizer, BMS, Janssen, Roche, Sandoz, Sanofi-Genzyme, SOBI, UCB, Novartis, Francis Berenbaum: None declared, Caroline Kannengiesser: None declared, Catherine Boileau: None declared, Jeffrey Sparks Consultant of: AbbVie, Boehringer Ingelheim, Bristol Myers Squibb, Gilead, Inova Diagnostics, Janssen, Optum, and Pfizer, Grant/research support from: National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers R01 AR077607, P30 AR070253, and P30 AR072577), The R. Bruce and Joan M. Mickey Research Scholar Fund, Bristol Myers Squibb,

Bruno Crestani Speakers bureau: Boehringer Ingelheim, AstraZeneca, Roche, Sanofi, Grant/research support from: MedImmune, Roche, Boehringer Ingelheim, Bruno Fautrel Consultant of: AbbVie, Amgen, Biogen, BMS, Celgene, Fresenius Kabi, Janssen, Lilly, Medac, MSD, NORDIC Pharma, Novartis, Pfizer, Roche, SOBI, UCB, Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Philippe Dieudé Speakers bureau: Roche – Chugai, Bristol Myers Squibb, Consultant of: Pfizer, Roche – Chugai, Bristol Myers Squibb, Abbvie, MSD, Grant/research support from: Novartis

Impact of Axial Spondyloarthritis on Quality of Life: Results From the European Map of Axial Spondyloarthritis (EMAS) Study in France

The effect of axial spondyloarthritis (axSpA) on patients' quality of life (QOL) has been well assessed in terms of body structures and functions, but literature is scarce in terms of social interactions and activities, work, and fears related to social interactions or activities.¹.

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.

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.

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.

POS0095 DEVELOPPING A SCORE TO PREDICT PRECLINICAL INTERSTITIAL LUNG DISEASE IN PATIENTS WITH RHEUMATOID ARTHRITIS – A CROSS-SECTIONAL STUDY FROM THE ESPOIR COHORT

Background:

Interstitial lung disease (ILD) is an extra-articular manifestation of rheumatoid arthritis (RA) detected in 20% to 60% of patients with RA on high-resolution computed-tomography (HRCT) chest scan and is clinically significant in near 10%. Despite a high morbi-mortality rate, a definite strategy for preclinical ILD screening in patients with RA remains to be determined. To date, several factors have been reported to increase the risk of RA-ILD occurrence (i.e. older age at RA onset, ACPA positivity, male sex, RA disease activity, the MUC5B rs35705950 promoter variant...). However, none of these risk factors has been validated in a prospective cohort of patients with RA. The ESPOIR prospective cohort includes patients aged 18 to 70 years with recent arthritis (less than 6 months) and a definite or probable diagnosis of RA.

Objectives:

To identify in the ESPOIR cohort factors associated with ILD after at least 10 years of RA duration in order to develop a predictive score to identify patients with preclinical RA-ILD.

Methods:

An ILD detection by chest HRCT scan was systematically offered to every patient with definite RA after at least 10 years-follow-up. Chest HRCT scans were centrally reviewed by an experienced radiologist. Potential predictors of ILD were prospectively collected from baseline to the date of the HRCT scan, and all included patients were genotyped for MUC5B rs35705950. To take into account repeated measures, trajectories were determined for disease activity, C reactive protein, smoking, treatment exposure (i.e. prednisone, methotrexate [MTX] and biological disease modifying anti-rheumatic drugs [bDMARDs]). A logistic model was used to identify independent predictors for the occurrence of ILD on HRCT scans. Confidence intervals were estimated using sampling methods. A predictive score for preclinical ILD occurrence was developed based on the identified predictors.

Results:

163 RA patients according to 2010 ACR/EULAR classification criteria, none of whom had pulmonary symptoms, were investigated with a chest HRCT scan (128 women (78.5%), mean RA duration 13.7 ± 1.1 years, age at inclusion 47.6 y/o ± 10.4, mean disease activity score [DAS]-28 during follow up was 3.1 ± 1.0). ILD was detected in 31 patients (19.0%). The MUC5B rs35705950 minor allele frequency (MAF) was 22.2% and 10.0% in the RA-ILD and RA-noILD populations, respectively (OR univariate=2.6 CI95% [1.2-5.5], P=0.01). After logistic regression, independent predictors for preclinical RA-ILD were male sex (OR=3.9 CI95% [1.4-11.4]), older age at RA onset (OR=1.1 per year CI95% [1.0-1.2]), mean DAS-28 score during the follow-up (OR=2.0 CI95% [1.2-3.4]) and MUC5B rs35705950 T risk allele (OR=3.7 CI95% [1.4-10.4]) (Figure 1). No influence of the use of RA-related drugs (prednisone, MTX or bDMARDs) was identified as risk factor. The logistic model could predict preclinical ILD occurrence after 13 years of RA duration with an AUC=0.82 CI95% (0.72-0.91). A predictive score for preclinical RA-ILD based on the 4 identified predictive risk factors was developed (Sensitivity 80%, Specificity 56%).

Figure 1.

Factors independently associated with preclinical ILD after 13 years of RA duration

Conclusion:

In this cross-sectional study of the prospective ESPOIR cohort, we identified clinical and genetic predictors for ILD after 13 years of RA duration. We developed a predictive score that could improve risk stratification for preclinical RA-ILD and help physicians identify patients with RA in whom a HRCT scan should be performed.

Disclosure of Interests:

Pierre-Antoine Juge Consultant of: BMS, Benjamin Granger: None declared, Marie-Pierre Debray: None declared, Esther Ebstein: None declared, Fabienne Louis Sidney: None declared, Joanna KEDRA: None declared, Raphael Borie: None declared, Arnaud Constantin Consultant of: Bristol-Meyers Squibb, Chugai, Roche, Abbvie, MSD, Pfizer, and UCB, Bernard Combe Consultant of: Abbvie, Bristol-Meyers Squibb, Lilly, MSD, Janssen, Pfizer, Roche, Chigai, and Sanofi, Grant/research support from: Abbvie, Bristol-Meyers Squibb, Lilly, MSD, Janssen, Pfizer, Roche, Chugai, and Sanofi, René-Marc Flipo Consultant of: Bristol-Meyers Squibb, Roche, Chugai, Abbvie, and Pfizer, Grant/research support from: Roche, Chugai, Abbvie, and Pfizer, Xavier Mariette Consultant of: Bristol-Meyers Squibb, GSK, Janssen, Pfizer, and UCB, Olivier VITTECOQ Consultant of: Bristol Myers Squibb, Roche, Chugai, MSD, Novartis, Pfizer, Abbvie, and Lilly, Alain Saraux Consultant of: Roche, Chugai, and Bristol-Meyers Squibb, Grant/research support from: Roche, Chugai, and Bristol-Meyers Squibb, Guillermo CARVAJAL ALEGRIA: None declared, Jean Sibilia Consultant of: Roche, Chugai, Bristol-Meyers Squibb, UCB, GSK, LFB, Actelion, Pfizer, MSD, Novartis, Amgen, Hospira, AbbVie, Sandoz, Gilead, Lilly, Sanofi, Janssen, and Mylan, Francis Berenbaum Consultant of: Boehringer, Bone Therapeutics, Expanscience, Galapagos, Gilead, GSK, Elli Lilly, Merck Sereno, MSD, Nordic, Novartis, Pfizer, Regulaxis, Roche, Sandoz, Sanofi, Servier, UCB, Peptinov, TRB Chemedica, 4P Pharma, Caroline Kannengiesser: None declared, Catherine Boileau: None declared, Bruno Crestani Consultant of: Boehringer Ingelheim, Roche, Sanofi, Apellis, Astra-Zeneca, Grant/research support from: MedImmune, Boehringer Ingelheim, Bruno Fautrel Consultant of: AbbVie, Biogen, BMS, Celgene, Janssen, Lilly, Medac, MSD, NORDIC Pharma, Novartis, Pfizer, Roche, Sanofi-Aventis, SOBI, UCB, Grant/research support from: AbbVie, MSD, Pfizer, Philippe Dieudé: None declared

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.

OP0199 POINTS TO CONSIDER WHEN ANALYSING AND REPORTING COMPARATIVE EFFECTIVENESS RESEARCH WITH OBSERVATIONAL DATA IN RHEUMATOLOGY

Background:

Comparing drug effectiveness in observational settings is hampered by several major threats, among them confounding and attrition bias bias (patients who stop treatment no longer contribute information, which may overestimate true drug effectiveness).

Objectives:

To present points to consider (PtC) when analysing and reporting comparative effectiveness with observational data in rheumatology (EULAR-funded taskforce).

Methods:

The task force comprises 18 experts: epidemiologists, statisticians, rheumatologists, patients, and health professionals.

Results:

A systematic literature review of methods currently used for comparative effectiveness research in rheumatology and a statistical simulation study were used to inform the PtC (table). Overarching principles focused on defining treatment effectiveness and promoting robust and transparent epidemiological and statistical methods increase the trustworthiness of the results.

Points to consider

Reporting of comparative effectiveness observational studies must follow the STROBE guidelinesAuthors should prepare a statistical analysis plan in advanceTo provide a more complete picture of effectiveness, several outcomes across multiple health domains should be comparedLost to follow-up from the study sample must be reported by the exposure of interestThe proportion of patients who stop and/or change therapy over time, as well as the reasons for treatment discontinuation must be reportedCovariates should be chosen based on subject matter knowledge and model selection should be justifiedThe study baseline should be at treatment initiation and a description of how covariate measurements relate to baseline should be includedThe analysis should be based on all patients starting a treatment and not limited to patients remaining on treatment at a certain time pointWhen treatment discontinuation occurs before the time of outcome assessment, this attrition should be taken into account in the analysis.Sensitivity analyses should be undertaken to explore the influence of assumptions related to missingness, particularly in case of attrition

Conclusion:

The increased use of real-world comparative effectiveness studies makes it imperative to reduce divergent or contradictory results due to biases. Having clear recommendations for the analysis and reporting of these studies should promote agreement of observational studies, and improve studies’ trustworthiness, which may also facilitate meta-analysis of observational data.

Disclosure of Interests:

Delphine Courvoisier: None declared, Kim Lauper: None declared, Sytske Anne Bergstra: None declared, Maarten de Wit Grant/research support from: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Consultant of: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Speakers bureau: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB, Thomas Frisell: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Florenzo Iannone Consultant of: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Speakers bureau: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Joanna KEDRA: None declared, Pedro M Machado Consultant of: PMM: Abbvie, Celgene, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: PMM: Abbvie, BMS, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Lykke Midtbøll Ørnbjerg Grant/research support from: Novartis, Ziga Rotar Consultant of: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Speakers bureau: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Maria Jose Santos Speakers bureau: Novartis and Pfizer, Tanja Stamm Grant/research support from: AbbVie, Roche, Consultant of: AbbVie, Sanofi Genzyme, Speakers bureau: AbbVie, Roche, Sanofi, Simon Stones Consultant of: I have been a paid consultant for Envision Pharma Group and Parexel. This does not relate to this abstract., Speakers bureau: I have been a paid speaker for Actelion and Janssen. These do not relate to this abstract., Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific

OP0198 A SYSTEMATIC REVIEW TO INFORM THE EULAR POINTS TO CONSIDER WHEN ANALYSING AND REPORTING COMPARATIVE EFFECTIVENESS RESEARCH WITH OBSERVATIONAL DATA IN RHEUMATOLOGY

Background:

Comparative effectiveness studies using observational data are increasingly used. Despite their high potential for bias, there are no detailed recommendations on how these studies should best be analysed and reported in rheumatology.

Objectives:

To conduct a systematic literature review of comparative effectiveness research in rheumatology to inform the EULAR task force developing points to consider when analysing and reporting comparative effectiveness research with observational data.

Methods:

All original articles comparing drug effectiveness in longitudinal observational studies of ≥100 patients published in key rheumatology journals (Scientific Citation Index > 2) between 1.01.2008 and 25.03.2019 available in Ovid MEDLINE® were included. Titles and abstracts were screened by two reviewers for the first 1000 abstracts and independently checked to ensure sufficient agreement has been reached. The main information extracted included the types of outcomes used to assess effectiveness, and the types of analyses performed, focusing particularly on confounding and attrition.

Results:

9969 abstracts were screened, with 218 articles proceeding to full-text extraction (Figure 1), representing a number of rheumatic and musculoskeletal diseases. Agreement between the two reviewers for the first 1000 abstracts was 92.7% with a kappa of 0.6. The majority of the studies used several outcomes to evaluate effectiveness (Figure 2A). Most of the studies did not explain how they addressed missing data on the covariates (70%) (Figure 2B). When addressed (30%), 44% used complete case analysis and 10% last observation carried forward (LOCF). 25% of studies did not adjust for confounding factors and there was no clear correlation between the number of factors used to adjust and the number of participants in the studies. An important number of studies selected covariates using bivariate screening and/or stepwise selection. 86% of the studies did not acknowledge attrition (Figure 2C). When trying to correct for attrition (14%), 38% used non-responder (NR) imputation, 24% used LUNDEX1, a form of NR imputation, and 21% LOCF.

Conclusion:

Most of studies used multiple outcomes. However, the vast majority did not acknowledge missing data and attrition, and a quarter did not adjust for any confounding factors. Moreover, when attempting to account for attrition, several studies used methods which potentially increase bias (LOCF, complete case analysis, bivariate screening…). This systematic review confirms the need for the development of recommendations for the assessment and reporting of comparative drug effectiveness in observational data in rheumatology.

References:

[1]Kristensen et al. A&R. 2006 Feb;54(2):600-6.

Acknowledgments:

Support of the Standing Committee on Epidemiology and Health Services Research

Disclosure of Interests:

Kim Lauper: None declared, Joanna KEDRA: None declared, Maarten de Wit Grant/research support from: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Consultant of: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Speakers bureau: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB, Thomas Frisell: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Florenzo Iannone Consultant of: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Speakers bureau: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Pedro M Machado Consultant of: PMM: Abbvie, Celgene, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: PMM: Abbvie, BMS, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Lykke Midtbøll Ørnbjerg Grant/research support from: Novartis, Ziga Rotar Consultant of: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Speakers bureau: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Maria Jose Santos Speakers bureau: Novartis and Pfizer, Tanja Stamm Grant/research support from: AbbVie, Roche, Consultant of: AbbVie, Sanofi Genzyme, Speakers bureau: AbbVie, Roche, Sanofi, Simon Stones Consultant of: I have been a paid consultant for Envision Pharma Group and Parexel. This does not relate to this abstract., Speakers bureau: I have been a paid speaker for Actelion and Janssen. These do not relate to this abstract., Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific, Sytske Anne Bergstra: None declared, Delphine Courvoisier: None declared

THU0101 IMPACT OF TREATMENTS ON RADIOGRAPHIC PROGRESSION OVER THE FIRST 10 YEARS OF DISEASE IN EARLY RHEUMATOID ARTHRITIS: RESULTS FROM THE ESPOIR COHORT

Background:

Long-term observational studies on the prediction of structural damage progression (SDP) in rheumatoid arthritis (RA) have mostly considered patients baseline characteristics and have rarely evaluated the specific impact of treatments in real world settings.

Objectives:

To assess the impact of treatments exposure on the 10-year radiographic progression in early rheumatoid arthritis (RA).

Methods:

The 310 patients of the ESPOIR cohort fulfilling ACR/EULAR 2010 criteria at baseline and having complete radiographic data at baseline and 10 years were considered in the present study. SDP was defined at 10 years as a significant increase of the Sharp/van der Heijde score, i.e., superior to the Smallest Detectable Change of 11.5 at 10 years. Three RA treatments were considered: glucocorticoids (GC), conventional synthetic and biologic Disease-Modifying Anti-Rheumatic Drugs (csDMARDs and bDMARDs), which posologies were standardized by the mean of dose quotients (DoseQ). Drug exposure was modelled with Weighted Cumulative Exposure (WCE) variables, considering the intensity of drug exposure defined as a weighted function of past doses, and was incorporated into a logistic regression model that also included baseline clinical, biological and radiological characteristics. The predictive performance of this WCE model was compared to models considering on the one hand only baseline characteristics (BSL model) and on the other, baseline characteristics and binary treatments exposure - in other terms, “ever exposed, yes or no” (BIT model).

Results:

Overall, SDP at 10 years occurred in 85 (27.4%) patients. GC exposure was significantly associated with SDP in univariate analysis only, and therefore was not included in the final WCE model. In the final WCE model, the joint exposure to 1 DoseQ of csDMARD and 1 DoseQ of bDMARD during the 10-year follow-up was associated with a significant protective effect on SDP compared to patients receiving no treatment: OR=0.04 (95% CI: 0.002-0.72). Early csDMARD initiation was associated with a significantly lower risk of SDP compared to later initiation (Table 1).

Table 1.

odd ratios for the association of patterns of drug regimen with 10-year radiographic progression

Exposure testedReferenceOR (95%CI)Treatments intakes during the last 10 yearscsDMARD & bDMARD for last 10 yearsNo treatment for last 10 years0.04 (0.002-0.72)Testing the interest of an early initiation of csDMARDs (not combined with bDMARD)csDMARD for last 10 yearscsDMARD after month 30.79 (0.65-0.96)csDMARD after month 60.41 (0.19-0.86)csDMARD after year 10.13 (0.02-0.80)Testing the interest of an early initiation of bDMARDs (in association with csDMARD)bDMARD after month 3No treatment for last 10 years0.04 (0.002-0.72)bDMARD after month 60.04 (0.002-0.72)bDMARD after year 10.04 (0.002-0.72)bDMARD after year 20.04 (0.003-0.73)bDMARD after year 30.05 (0.03-0.81)

Initiation of a bDMARD between the 3rd month and 3rd year of follow-up in combination with a csDMARD was significantly associated with a lower risk of SDP compared to no bDMARD treatment (Table 1).

The final WCE model was better at predicting SDP at 10 years compared to the BSL and BIT models, with AUC=0.92 (95% CI: 0.89-0.95) (Figure 1).

Figure 1.

ROC curves of BSL model (A), BIT model (B) and WCE combined model (C) for 10-year radiographic progression

Conclusion:

CsDMARDs and bDMARDs have a protective effect on radiographic progression at 10 years in RA patients. This study has shown the value of considering drug exposure in the study of RA prognosis, and modeling this exposure using WCE variables.

Disclosure of Interests:

Joanna KEDRA: None declared, David Hajage: None declared, Alexandre Lafourcade: None declared, Bernard Combe Grant/research support from: Novartis, Pfizer, Roche-Chugai, Consultant of: AbbVie; Gilead Sciences, Inc.; Janssen; Eli Lilly and Company; Pfizer; Roche-Chugai; Sanofi, Speakers bureau: Bristol-Myers Squibb; Gilead Sciences, Inc.; Eli Lilly and Company; Merck Sharp & Dohme; Pfizer; Roche-Chugai; UCB, Maxime Dougados Grant/research support from: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Consultant of: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Speakers bureau: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB

OP0125 LYMPHOMAS COMPLICATING RHEUMATOID ARTHRITIS: RESULTS OF A FRENCH MULTI-CENTRE CASE-CONTROL STUDY

Background:

Rheumatoid arthritis (RA) is associated with an increased risk of non-Hodgkin B-cell lymphoma (B-cell NHL).

Objectives:

1)To study the characteristics of B-cell NHL complicating RA

2)To identify the factors associated with their occurrence.

Methods:

A multi-centre case-control study was performed in France. Cases were patients with RA fulfilling the ACR-EULAR 2010 criteria, who developed a B-cell NHL after the diagnosis of RA. Cases were reported following a call for observations by the “Club Rhumatismes et Inflammation” network, registries from the French society of Rheumatology (AIR, ORA and REGATE) and the ESPOIR cohort. For each case, 2 control patients were drawn at random from patients in the ESPOIR cohort with RA fulfilling the ACR-EULAR 2010 criteria; cases and controls were matched on age (age at lymphoma diagnosis for cases and age at the 10-year ESPOIR visit for controls). Patients with associated Sjögren’s syndrome were excluded. Cases and controls characteristics were compared for parameters associated with the occurrence of lymphoma.

Results:

A total of 54 cases were included and matched to 108 controls. Lymphomas were mostly diffuse large B-cell lymphomas (n=26, 48.2%)(Figure 1). EBV positivity was found in 4 cases among 27 tested (14.8%). Cases had a mean age of 63.5 years (SD=10.9), and had a mean RA duration of 12.4 years (SD=10.5) at the time of diagnosis of lymphoma; there was no significant difference with controls (p=0.47 and p=0.40 respectively). The mean duration of follow-up after the diagnosis of lymphoma was 5.2 years (SD=5.8). In univariate analysis, factors associated with occurrence of B-cell NHL were: male gender (OR=3.3, 95%CI: 1.7-6.7), positive ACPA (OR=5.1, 95%CI: 2.0-15.7), positive Rheumatoid Factor (RF) (OR=3.9, 95%CI=1.6-12.2), erosions on X-rays (OR=15.4, 95%CI: 6.9-37.7) and DAS28 (OR=2.0, 95%CI: 1.5-2.7). Methotrexate, TNF-blockers and the number of previous biologics were not associated with the occurrence of B-cell NHL. Hydroxychloroquine and sulfasalazine were more frequent in cases versus control, which could be linked to a date bias. Erosions and DAS28 remained significant in multivariate analysis(Table 1).

Conclusion:

This study revealed an association between markers of activity (DAS28), severity (erosions) and autoimmune B-cell activation (RF and ACPA) and the risk of B-cell NHL in patients with RA, supporting the continuum between autoimmunity and lymphomagenesis in RA.

Figure 1.

Disclosure of Interests:

Joanna KEDRA: None declared, Raphaèle Seror Consultant of: BMS UCB Pfizer Roche, Philippe Dieudé: None declared, Arnaud Constantin: None declared, ERIC TOUSSIROT: None declared, Elias Kfoury: None declared, Charles Masson: None declared, Divi Cornec: None declared, Jean-Jacques Dubost: None declared, Laurent Marguerie: None declared, Sebastien Ottaviani: None declared, Franck Grados: None declared, Rakiba Belkhir: None declared, olivier fain: None declared, Philippe Goupille Grant/research support from: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Lilly, Janssen, Medac, MSD France, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Consultant of: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Lilly, Janssen, Medac, MSD France, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Speakers bureau: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Lilly, Janssen, Medac, MSD France, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Christelle Sordet: None declared, Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB, Peggy Philippe: None declared, Muriel PIPERNO: None declared, Bernard Combe Grant/research support from: Novartis, Pfizer, Roche-Chugai, Consultant of: AbbVie; Gilead Sciences, Inc.; Janssen; Eli Lilly and Company; Pfizer; Roche-Chugai; Sanofi, Speakers bureau: Bristol-Myers Squibb; Gilead Sciences, Inc.; Eli Lilly and Company; Merck Sharp & Dohme; Pfizer; Roche-Chugai; UCB, Olivier Lambotte Consultant of: BMS France, MSD, Astra Zeneca, Incyte, Christophe Richez Consultant of: Abbvie, Amgen, Mylan, Pfizer, Sandoz and UCB., Jérémie SELLAM: None declared, Thomas Sene: None declared, Guillaume Denis: None declared, Thierry Lequerre: None declared, Xavier Mariette Consultant of: BMS, Gilead, Medimmune, Novartis, Pfizer, Servier, UCB, Gaetane Nocturne: None declared

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.

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.

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.