Machine learning-based prediction of response to Janus kinase inhibitors in patients with rheumatoid arthritis using clinical data.

Objective: Rheumatoid arthritis (RA) is a chronic inflammatory disease with considerable heterogeneity in treatment response, leaving many patients unable to achieve remission or low disease activity. We aimed to develop a machine learning model to predict which patients with moderate-to-severe RA would respond to Janus kinase inhibitor therapy, thereby facilitating more effective and personalized treatment strategies. Methods: We retrospectively collected data from the Korean College of Rheumatology Biologics therapy (KOBIO) registry and Asan Medical Centers, including adult patients with moderate or high disease activity (DAS28-ESR≥3.2) and at least 12 months of follow-up. We trained and validated gradient boosting machine-learning models (XGBoost) to predict whether patients would achieve low disease activity or remission after 6 months of Janus kinase inhibitor therapy, using prespecified baseline covariates and stratified splits for independent training and test datasets. Results: This study included 264 patients with moderate-to-severe rheumatoid arthritis from the Korean cohorts (the KOBIO registry and Asan Medical Centers). Of these, 247 received either tofacitinib (n=123) or baricitinib (n=124). After 6 months of treatment, 65% of patients on tofacitinib and 70% on baricitinib achieved low disease activity or remission. Our machine-learning models (trained and validated separately for each drug) achieved high predictive performance (tofacitinib: ROC-AUC 0.82, accuracy 80%; baricitinib: ROC-AUC 0·88, accuracy 88%), identifying key clinical factors such as total cholesterol, CRP, and specific joint swelling or tenderness for tofacitinib, and patient global assessment, joint swelling, and co-administration of hydroxychloroquine for baricitinib. Model-guided treatment selection could have improved outcomes for an additional 15% of patients by aligning each individual's predicted response with the more suitable Janus kinase inhibitor. Conclusion: The findings suggest that ML models can accurately predict treatment response to Janus kinase inhibitors in rheumatoid arthritis and may support personalized therapy selection to improve clinical outcomes.