Rehberg, Markus, Giegerich, Clemens, Praestgaard, Amy, van Hoogstraten, Hubert, Iglesias-Rodriguez, Melitza, Curtis, Jeffrey R., Gottenberg, Jacques-Eric, Schwarting, Andreas, Castañeda, Santos, Rubbert-Roth, Andrea and Choy, Ernest H. S.  ORCID: https://orcid.org/0000-0003-4459-8609
2021.
Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data.
Rheumatology and Therapy
8
, pp. 1661-1675.
10.1007/s40744-021-00361-5
|
Preview |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (1MB) | Preview |
Preview |
PDF (Correction)
- Supplemental Material
Available under License Creative Commons Attribution. Download (153kB) | Preview |
Abstract
Introduction In rheumatoid arthritis, time spent using ineffective medications may lead to irreversible disease progression. Despite availability of targeted treatments, only a minority of patients achieve sustained remission, and little evidence exists to direct the choice of biologic disease-modifying antirheumatic drugs in individual patients. Machine learning was used to identify a rule to predict the response to sarilumab and discriminate between responses to sarilumab versus adalimumab, with a focus on clinically feasible blood biomarkers. Methods The decision tree model GUIDE was trained using a data subset from the sarilumab trial with the most biomarker data, MOBILITY, to identify a rule to predict disease activity after sarilumab 200 mg. The training set comprised 18 categorical and 24 continuous baseline variables; some data were omitted from training and used for validation by the algorithm (cross-validation). The rule was tested using full datasets from four trials (MOBILITY, MONARCH, TARGET, and ASCERTAIN), focusing on the recommended sarilumab dose of 200 mg. Results In the training set, the presence of anti-cyclic citrullinated peptide antibodies, combined with C-reactive protein > 12.3 mg/l, was identified as the “rule” that predicts American College of Rheumatology 20% response (ACR20) to sarilumab. In testing, the rule reliably predicted response to sarilumab in MOBILITY, MONARCH, and ASCERTAIN for many efficacy parameters (e.g., ACR70 and the 28-joint disease activity score using CRP [DAS28-CRP] remission). The rule applied less to TARGET, which recruited individuals refractory to tumor necrosis factor inhibitors. The potential clinical benefit of the rule was highlighted in a clinical scenario based on MONARCH data, which found that increased ACR70 rates could be achieved by treating either rule-positive patients with sarilumab or rule-negative patients with adalimumab. Conclusions Well-established and clinically feasible blood biomarkers can guide individual treatment choice. Real-world validation of the rule identified in this post hoc analysis is merited.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Medicine |
| Additional Information: | This article has been corrected: Rehberg, M., Giegerich, C., Praestgaard, A. et al. Correction to: Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data. Rheumatol Ther 8, 1921–1922 (2021). https://doi.org/10.1007/s40744-021-00389-7 |
| ISSN: | 2198-6576 |
| Related URLs: | |
| Date of First Compliant Deposit: | 7 October 2021 |
| Date of Acceptance: | 11 August 2021 |
| Last Modified: | 05 Jun 2024 09:19 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/144721 |
Citation Data
Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions
Cardiff University Information Services