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Predicting response...
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Johansson, Fredrik,1988Chalmers tekniska högskola,Chalmers University of Technology
(author)
Predicting response to tocilizumab monotherapy in rheumatoid arthritis: A real-world data analysis using machine learning
- Article/chapterEnglish2021
Publisher, publication year, extent ...
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2021-05-01
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The Journal of Rheumatology,2021
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LIBRIS-ID:oai:research.chalmers.se:534a8f74-874c-4d24-b532-b386d2ce1d6d
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https://research.chalmers.se/publication/526011URI
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https://doi.org/10.3899/jrheum.201626DOI
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Language:English
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Summary in:English
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Subject category:art swepub-publicationtype
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Subject category:ref swepub-contenttype
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Objective. Tocilizumab (TCZ) has shown similar efficacy when used as monotherapy as in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCTs). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and performed an external validation of the prediction score using real-world data (RWD). Methods. We identified patients in the Corrona RA registry who used TCZm (n = 452), and matched the design and patients from 4 RCTs used in previous work (n = 853). Patients were followed to determine remission status at 24 weeks. We compared the performance of remission prediction models in RWD, first based on variables determined in our prior work in RCTs, and then using an extended variable set, comparing logistic regression and random forest models. We included patients on other biologic disease-modifying antirheumatic drug monotherapies (bDMARDm) to improve prediction. Results. The fraction of patients observed reaching remission on TCZm by their follow-up visit was 12% (n = 53) in RWD vs 15% (n = 127) in RCTs. Discrimination was good in RWD for the risk score developed in RCTs, with area under the receiver-operating characteristic curve (AUROC) of 0.69 (95% CI 0.62-0.75). Fitting the same logistic regression model to all bDMARDm patients in the RWD improved the AUROC on held-out TCZm patients to 0.72 (95% CI 0.63-0.81). Extending the variable set and adding regularization further increased it to 0.76 (95% CI 0.67-0.84). Conclusion. The remission prediction scores, derived in RCTs, discriminated patients in RWD about as well as in RCTs. Discrimination was further improved by retraining models on RWD.
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Collins, JamieBrigham and Women's Hospital
(author)
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Yau, VincentGenentech Inc.
(author)
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Guan, HongshuBrigham and Women's Hospital
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Kim, Seoyoung C.Brigham and Women's Hospital
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Losina, ElenaBrigham and Women's Hospital
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Sontag, D.Massachusetts Institute of Technology (MIT)
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Stratton, JacklynBrigham and Women's Hospital
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Trinh, HuongGenentech Inc.
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Greenberg, JeffreyNew York University
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Solomon, Daniel H.Brigham and Women's Hospital
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Chalmers tekniska högskolaBrigham and Women's Hospital
(creator_code:org_t)
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In:Journal of Rheumatology: The Journal of Rheumatology48:9, s. 1364-13701499-27520315-162X
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Johansson, Fredr ...
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Collins, Jamie
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Yau, Vincent
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Guan, Hongshu
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Kim, Seoyoung C.
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Losina, Elena
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Sontag, D.
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Stratton, Jackly ...
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Trinh, Huong
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Greenberg, Jeffr ...
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Solomon, Daniel ...
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Surgery
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Rheumatology and ...
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MEDICAL AND HEAL ...
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Journal of Rheum ...
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Chalmers University of Technology