Machine Learning to Predict Remission Between Six and 24 Months in Rheumatoid Arthritis: Insights from the JAK‐pot Collaboration
Résumé
Objective To develop, externally validate, and simplify a machine‐learning (ML) model to predict remission between six and 24 months in rheumatoid arthritis (RA) patients initiating TNF inhibitors, JAK inhibitors, IL‐6 inhibitors, abatacept, or rituximab, using data from 11 international registries in the JAK‐pot collaboration. Methods We analyzed 21,675 treatment courses for model training, 5418 courses for internal validation, and 1807 courses from Switzerland for external validation. Remission was defined as a Clinical Disease Activity Index (CDAI) ≤2.8 within six to 24 months after treatment initiation or switching. An XGBoost model was trained on 63 baseline demographic and clinical variables. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Predictor contributions were quantified using SHAP values. A simplified model using the top ten predictors and a logistic regression benchmark were also evaluated. Results In external validation, the full model achieved an AUC of 0.797 (95% CI: 0.774–0.821), sensitivity of 0.804, specificity of 0.650, PPV of 0.454, and NPV of 0.902. The simplified model performed comparably (AUC: 0.802). Logistic regression using the same ten predictors achieved an AUC of 0.809. Key predictors included patient global assessment, 28‐tender joint count, previous biologic/tsDMARD exposure, and HAQ‐DI. Conclusion Using routinely collected baseline data across 11 registries, prediction of remission showed limited discrimination and was best suited to ruling out remission. Performance was similar for a ten‐variable model and logistic regression, indicating limited incremental value of model complexity without richer predictors. image