Non-component clinical feature-based machine learning for disease activity risk stratification in juvenile idiopathic arthritis: a multi-center retrospective validation study.

Background: JADAS27 is widely used to assess juvenile idiopathic arthritis (JIA) disease activity, but complete scoring is impractical in many clinical settings because it requires simultaneous physician global assessment (PhGA), patient/parent global assessment (PtGA), active joint count (AJC), and erythrocyte sedimentation rate (ESR). We asked whether non-component clinical variables alone-strictly excluding all four JADAS27 constituents-can classify JADAS27-defined activity strata. Methods: In this retrospective multi-center study, 800 patients with JIA were enrolled from CARRA ( Results: SVM achieved the best external validation performance (accuracy 0.731, 95% CI 0.657-0.797; macro AUC 0.918, 95% CI 0.887-0.945). Class-wise recall was highest for inactive (82.4%) and high activity (77.4%), with most errors between adjacent classes. SHAP and permutation importance analyses consistently identified CHAQ score, JIA subtype, pain score, limited joint count, and C-reactive protein as the most influential predictors. Removing all three proxy variables reduced accuracy to 0.619 and macro AUC to 0.843, indicating that both proxy and non-proxy features contribute independently. Sensitivity analysis with subtype-specific cutoffs yielded comparable performance (accuracy 0.713). Conclusion: Strictly non-component clinical features can stratify JADAS27-defined disease activity with clinically meaningful external performance. This approach may support early risk stratification when formal JADAS27 scoring is unavailable, and complements rather than replaces physician assessment.