Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis

• Published in: Journal of clinical medicine Vol. 13; no. 13; p. 3890
• Main Authors: Salehi, Fatemeh, Lopera Gonzalez, Luis I, Bayat, Sara, Kleyer, Arnd, Zanca, Dario, Brost, Alexander, Schett, Georg, Eskofier, Bjoern M
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.07.2024
• Subjects: Datasets; Gene expression; Machine learning; Missing data; Patients; Remission (Medicine); Rheumatoid arthritis; Variables; machine learning predictive model; prediction; bDMARDs; rheumatoid arthritis; treatment response
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm13133890

Disease-modifying antirheumatic drugs (bDMARDs) have shown efficacy in treating Rheumatoid Arthritis (RA). Predicting treatment outcomes for RA is crucial as approximately 30% of patients do not respond to bDMARDs and only half achieve a sustained response. This study aims to leverage machine learning to predict both initial response at 6 months and sustained response at 12 months using baseline clinical data. Baseline clinical data were collected from 154 RA patients treated at the University Hospital in Erlangen, Germany. Five machine learning models were compared: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), Support Vector Machines (SVM), and Random Forest. Nested cross-validation was employed to ensure robustness and avoid overfitting, integrating hyperparameter tuning within its process. XGBoost achieved the highest accuracy for predicting initial response (AUC-ROC of 0.91), while AdaBoost was the most effective for sustained response (AUC-ROC of 0.84). Key predictors included the Disease Activity Score-28 using erythrocyte sedimentation rate (DAS28-ESR), with higher scores at baseline associated with lower response chances at 6 and 12 months. Shapley additive explanations (SHAP) identified the most important baseline features and visualized their directional effects on treatment response and sustained response. These findings can enhance RA treatment plans and support clinical decision-making, ultimately improving patient outcomes by predicting response before starting medication.