Objectives: Patients with rheumatoid arthritis (RA) display different trajectories towards improvement of disease. We aimed to disentangle the heterogeneity of RA disease trajectories from the first clinical visit onwards using graph-based pseudotime analysis. Methods: We studied early patients with RA over 1.5 years in 2 data sets: Leiden (Netherlands), n = 1237, with 5017 visits, and Towards a Cure for Early Rheumatoid Arthritis (TACERA) (United Kingdom), n = 243, with 750 visits. We created a pipeline for time-independent clustering of clinical and haematologic features to identify disease states. Sequence analyses of these states defined the trajectories. We studied the predictability of the trajectories with baseline features. Results: Clustering identified 8 disease states with localised inflammation (joints) and systemic inflammation (erythrocyte sedimentation rate [ESR] or leucocytes) as the main discriminating factors. The disease state sequences consisted of 4 trajectories, which we independently replicated in TACERA: A, high ESR; B, rapid progression from many inflamed joints towards remission; C, high leucocytes; and D, many inflamed joints with poor prognosis. Systemic vs local inflammation patterns showed moderate predictability at baseline (sensitivity of 71% and precision of 0.73 for trajectory A, although lower precision of 0.52 for trajectory B), while other trajectories were less predictable. Trajectories C and D had strong resemblance with B at baseline but deteriorated into less favourable trajectories. Patients in trajectory A were more often female and on average older. The trajectories were not explained by time till disease-modifying antirheumatic drug, baseline disease activity, or symptom duration. The suboptimal trajectories coincided with worse patient-reported outcomes, even when the inflammation was mainly systemic. Conclusions: We identified 4 distinct trajectories in early RA, differentiating RA into localised vs systemic inflammation. Our results highlight potential differences in disease pathology and opportunities for further targeted treatment. Inevitably, patterns without linkage to our selected features could not be detected.

Advances in artificial intelligence (AI) are transforming patient stratification in rheumatology. In 2025, three landmark studies demonstrated how multimodal AI approaches spanning clinical, molecular and longitudinal data can uncover distinct disease subtypes and predict therapeutic response, advancing the field towards precision rheumatology.

Rheumatoid arthritis (RA) is a heterogeneous disease with variable symptoms, prognosis, and treatment response, necessitating refined patient classification. We applied multimodal deep learning and clustering to identify distinct RA phenotypes using baseline clinical data from 1,387 patients in the Leiden Rheumatology clinic. Four Joint Involvement Patterns (JIP) emerged: foot-predominant arthritis, seropositive oligoarticular disease, seronegative hand arthritis, and polyarthritis. Findings were validated in clinical trial data (n = 307) and an independent secondary care cohort (n = 515). Clusters showed high stability and significant differences in remission rates (P = 0.007) and methotrexate failure (P < 0.001). JIP-hand patients had superior outcomes (particularly in ACPA-positive patients) versus JIP-foot (HR:0.37, P < 0.001) and JIP-poly (HR:0.33, P = 0.005), independent of baseline disease activity and clinical markers. Synovial histology analysis (n = 194) revealed distinct inflammatory patterns across clusters, hinting at different underlying biological mechanisms. These validated RA phenotypes based on joint involvement patterns may enable targeted research into disease mechanisms and personalized treatment strategies.

Musculoskeletal complaints account for 30% of GP consultations, with many referred to rheumatology clinics via letters. This study developed a Machine Learning (ML) pipeline to prioritize referrals by identifying rheumatoid arthritis (RA), osteoarthritis, fibromyalgia, and patients requiring long-term care. Using 8044 referral letters from 5728 patients across 12 clinics, we trained and validated ML models in two large centers and tested their generalizability in the remaining ten. The models were robust, with RA achieving an AUC-ROC of 0.78 (CI: 0.74–0.83), osteoarthritis 0.71 (CI: 0.67–0.74), fibromyalgia 0.81 (CI: 0.77–0.85), and chronic follow-up 0.63 (CI: 0.61–0.66). The RA-classifier outperformed manual referral systems, as it prioritised RA over non-RA cases (P < 0.001), while the manual referral system could not differentiate between the two. The other classifiers showed similar prioritisation improvements, highlighting the potential to enhance care efficiency, reduce clinician workload, and facilitate earlier specialized care. Future work will focus on building clinical decision-support tools.