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Salehi, F.* ; Lopera Gonzalez, L.I.* ; Bayat, S.* ; Kleyer, A.* ; Zanca, D.* ; Brost, A.* ; Schett, G.* ; Eskofier, B.M.

Machine learning prediction of treatment response to biological disease-modifying antirheumatic drugs in rheumatoid arthritis.

J. Clin. Med. 13:3890 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Background: 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. Methods: 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. Results: 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. Conclusions: These findings can enhance RA treatment plans and support clinical decision-making, ultimately improving patient outcomes by predicting response before starting medication.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Bdmards ; Machine Learning Predictive Model ; Prediction ; Rheumatoid Arthritis ; Treatment Response; Factor-alpha Agents; Impact; Costs
ISSN (print) / ISBN 2077-0383
e-ISSN 2077-0383
Journal Journal of Clinical Medicine
Quellenangaben Volume: 13, Issue: 13, Pages: , Article Number: 3890 Supplement: ,
Publisher MDPI
Publishing Place Basel
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Grants Digital Health Innovation Platform