Objectives: To investigate the association between joint involvement pattern (JIP) subgroups and treatment responses to conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and biological disease-modifying antirheumatic drugs (bDMARDs), and to compare the impact of JIP subgroups with other clinical parameters in treatment-naïve patients with early rheumatoid arthritis (RA). Methods: An individual patient data meta-analysis was conducted using 2 randomised controlled trials, NOrdic Rheumatic Diseases Strategy Trials And Registries (NORD-STAR) and Behandel-Strategieën (BeSt), including 1250 treatment-naïve patients with early RA. JIP subgroup assignment was based on 4 previously identified subgroups defined by baseline clinical characteristics, primarily joint involvement in the 66/68 joint scheme. Treatment outcomes were measured using the longitudinal Clinical Disease Activity Index (CDAI) and other disease activity indices through week 48. Associations of the JIP subgroups and other clinical predictors were evaluated using a mixed-model analysis. Results: Patients with a hand-dominant JIP (JIP-Hand) showed significantly better CDAI scores after treatment (Beta for CDAI = −1.4 [95% CI, −2.3 to −0.55]; p = .0016), whereas those with a polyarthritis pattern (JIP-Poly) exhibited worse outcomes (Beta = 0.95 [95% CI, 0.064-1.8]; p = .035). Female sex was also associated with worse CDAI scores (Beta = 1.2 [95% CI, 0.40-2.0]; p = .0031), whereas anticitrullinated protein antibodies did not show a significant association (Beta = 0.19 [95% CI, −0.69 to 1.1]; p = .67). When compared across groups, csDMARDs and combined bDMARDs were similarly effective in the respective JIP subgroups (interaction p > .10). Conclusions: In early RA, csDMARD and bDMARD treatments resulted in the greatest improvement in disease activity in JIP-Hand and the least improvement in JIP-Poly.

BackgroundOnline symptom checkers are often developed and validated on data subject to self-selection and selective attrition, potentially introducing biases in prediction models.ObjectivesTo assess recruitment, selection, and attrition patterns in a large Dutch online symptom checker for musculoskeletal complaints and to evaluate potential biases by comparing participant characteristics across recruitment sources and with external target populations.MethodsUsing data from the online Dutch Rheumatic? Questionnaire on musculoskeletal complaints, we compared baseline characteristics and key self-reported symptoms between responders to the follow-up survey and nonresponders. The survey responders were furthermore compared according to source of recruitment to the questionnaire, i.e., via primary care clinics, secondary care clinics, or via different online sources. Sex, age and BMI distributions from the total study group were compared to external data of potential target populations of primary and secondary care patients within the Netherlands.ResultsThe total study group of answers to the questionnaire comprised 31,457 responders, of which 50% (n = 15,591) responded to the follow-up survey. Study participants were predominantly female (76%), middle-aged (one-third 50–60 years), never-smokers (66%), and overweight. While participants recruited through healthcare settings resembled target populations, follow-up survey responders were older, had more rheumatic diagnoses (49% vs. 32%), and reported more symptoms than non-responders. Participant characteristics varied by recruitment source, with social media attracting younger females while healthcare routes reached more diverse populations with varying symptom presentations.ConclusionPatterns of recruitment and attrition produced differences in participant characteristics. Healthcare-based recruitment yielded participants resembling intended target populations, and follow-up survey responders differed on some points from nonresponders. Awareness of these selection processes is essential when using real-world symptom checker data for model development.

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.

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.