Location of joint involvement differentiates Rheumatoid arthritis into different clinical subsets

To aid etiology and treatment research of the very heterogeneous rheumatoid arthritis (RA) population, we aimed to identify phenotypically distinct RA subsets using baseline clinical data. We collected baseline numerical- (hematology work-up & age) and categorical variables (serology, joint location & sex) from the Electronic Health records (EHR) repository of the Leiden University Medical Center, comprising 1,387 unique first visits to the outpatient clinic. We used deep learning and graph clustering to identify phenotypically distinct RA subsets. To ensure the robustness of our findings, we tested a) cluster stability (1000 fold) b) physician confounding, c) association with remission and methotrexate failure, d) generalizability to a second different data set (the Leiden Early Arthritis clinic; n=769). In total we identified four subsets (C1-C4) of patients with rheumatoid arthritis that were delineated on the following characteristics: C1) arthritis in feet, C2) seropositive oligo-articular disease, C3) seronegative hand arthritis, C4) polyarthritis. Our validity analyses showed high stability (mean 78%-91%), no physician confounding, and a significant difference in methotrexate failure(P-value=6.1e-4) and occurrence of remission(P-value=7.4e-3), and generalizability to a second dataset. The hand-cluster (III) had the most favorable outcomes (HR remission=1.65 (95%CI:1.20-2.29), HR methotrexate=0.48 (95%CI:0.35-0.77)), particularly the ACPA-positive patients in this cluster, while in the other clusters the ACPA-negative patients did best. The clusters outperformed standard clinical variables, which were attributed to the hand and feet differentiation. In conclusion, we discovered four phenotypically distinct subgroups of rheumatoid arthritis at baseline that associate with clinical outcomes. Furthermore, our study provides evidence for the presence of separate hand and foot subgroups in RA. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project has received funding from two large European Union Horizon grants for Europe research and innovation. First for SQUEEZE (activity No. 101095052) and secondly for SPIDERR (activity No. 101080711). There was additional financial support from the ZonMW Klinische Fellow No. 40-00703-97-19069, as well as the Zonmw Open Competitie, No. 09120012110075. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics committee of the Leiden University Medical Center (LUMC) gave ethical approval for this work (according to study protocol B18.057). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes We have made our scripts available in a public repository at: https://github.com/levrex/EHR-Clustering-RA. Study data is available upon reasonable request.