BackgroundTremendous 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).MethodsA 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.ResultsThree 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.ConclusionThese EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

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.