Neither ultrasound synovitis nor clinical-ultrasound phenotypes of established rheumatoid arthritis predict response to targeted therapy.

Fitton, John; Melville, Andrew; Di Matteo, Andrea; Nam, Jackie; Dass, Shouvik; Saleem, Benazir; Shukla, Rudresh; Emery, Paul et al. · Rheumatology (Oxford) · 2025

prospective_cohort · Level II

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Abstract

To identify patient sub-phenotypes using clinical and imaging measures in established rheumatoid arthritis (RA) and to establish if baseline ultrasound synovitis and/or baseline patient sub-phenotypes predicts response to targeted therapy (TT). This was an observational cohort study of consecutively recruited patients with established RA starting TT. Participants received clinical assessment, 38-joint musculoskeletal ultrasound (MSUS), measuring grey scale and power Doppler (PD) synovitis/tenosynovitis, and patient reported outcomes (PRO), prior to and 6 months after treatment. Latent profile analysis of the clinical and MSUS variables identified disease clusters, and multinomial logistic regression models determined whether these and/or baseline synovitis predicted EULAR response. Of 200 recruited patients, three clusters, low to high inflammatory, were identified with median (IQR) total joint PD 2 (0-4) in cluster 1, 9 (6-13) in cluster 2 and 29.5 (21-45) in cluster 3. A health assessment questionnaire correlated with disease activity, and DAS-P score distributions differed between clusters (P = 0.001) but with identical medians. Other PROs did not differ. At 6 months relative risk ratio of EULAR response in those with baseline synovitis compared with those without was 1.05 (95% CI: 0.38, 2.93) and response compared with the lowest inflammatory cluster [1] was 1.50 (95% CI: 0.61, 3.70) for cluster 2 and 1.24 (95% CI: 0.43, 3.60) for cluster 3. This is the first study to identify RA phenotypes employing 38-joint MSUS. Strikingly, neither baseline synovitis nor inflammatory cluster predicted clinical response. Mechanistic understanding of TT response/non-response is needed and clinical trials need to still capture all these populations.

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