Vodencarevic A, Tascilar K, Reiser M, Hueber A, Haschka J, Bayat S, Meinderink T, Knitza J, Mendez L, Hagen M, Krönke G, Rech J, Manger B, Kleyer A, Zimmermann-Rittereiser M, Schett G, Simon D, Hartmann F (2021)
Publication Type: Journal article
Publication year: 2021
Book Volume: 23
Article Number: 67
Journal Issue: 1
DOI: 10.1186/s13075-021-02439-5
Background: Biological disease-modifying anti-rheumatic drugs (bDMARDs) can be tapered in some rheumatoid arthritis (RA) patients in sustained remission. The purpose of this study was to assess the feasibility of building a model to estimate the individual flare probability in RA patients tapering bDMARDs using machine learning methods. Methods: Longitudinal clinical data of RA patients on bDMARDs from a randomized controlled trial of treatment withdrawal (RETRO) were used to build a predictive model to estimate the probability of a flare. Four basic machine learning models were trained, and their predictions were additionally combined to train an ensemble learning method, a stacking meta-classifier model to predict the individual flare probability within 14 weeks after each visit. Prediction performance was estimated using nested cross-validation as the area under the receiver operating curve (AUROC). Predictor importance was estimated using the permutation importance approach. Results: Data of 135 visits from 41 patients were included. A model selection approach based on nested cross-validation was implemented to find the most suitable modeling formalism for the flare prediction task as well as the optimal model hyper-parameters. Moreover, an approach based on stacking different classifiers was successfully applied to create a powerful and flexible prediction model with the final measured AUROC of 0.81 (95%CI 0.73–0.89). The percent dose change of bDMARDs, clinical disease activity (DAS-28 ESR), disease duration, and inflammatory markers were the most important predictors of a flare. Conclusion: Machine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission.
APA:
Vodencarevic, A., Tascilar, K., Reiser, M., Hueber, A., Haschka, J., Bayat, S.,... Hartmann, F. (2021). Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs. Arthritis Research & Therapy, 23(1). https://doi.org/10.1186/s13075-021-02439-5
MLA:
Vodencarevic, Asmir, et al. "Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs." Arthritis Research & Therapy 23.1 (2021).
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