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Title: | Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis |
Author: | Novella Navarro, Marta Benavent, Diego Ruiz Esquide, Virginia Tornero, Carolina Díaz Almirón, Mariana Chacur, Chafik Alejandro Peiteado, Diana Villalba, Alejandro Sanmartí Sala, Raimon Plasencia Rodríguez, Chamaida Balsa, Alejandro |
Keywords: | Artritis reumatoide Teoria de la predicció Terapèutica Rheumatoid arthritis Prediction theory Therapeutics |
Issue Date: | 6-Oct-2022 |
Publisher: | SAGE |
Abstract: | Despite advances in the treatment of rheumatoid arthritis (RA) and the wide range of therapies available, there is a percentage of patients whose treatment presents a challenge for clinicians due to lack of response to multiple biologic and target-specific disease-modifying antirheumatic drugs (b/tsDMARDs).To develop and validate an algorithm to predict multiple failure to biological therapy in patients with RA.Observational retrospective study involving subjects from a cohort of patients with RA receiving b/tsDMARDs.Based on the number of prior failures to b/tsDMARDs, patients were classified as either multi-refractory (MR) or non-refractory (NR). Patient characteristics were considered in the statistical analysis to design the predictive model, selecting those variables with a predictive capability. A decision algorithm known as 'classification and regression tree' (CART) was developed to create a prediction model of multi-drug resistance. Performance of the prediction algorithm was evaluated in an external independent cohort using area under the curve (AUC).A total of 136 patients were included: 51 MR and 85 NR. The CART model was able to predict multiple failures to b/tsDMARDs using disease activity score-28 (DAS-28) values at 6 months after the start time of the initial b/tsDMARD, as well as DAS-28 improvement in the first 6 months and baseline DAS-28. The CART model showed a capability to correctly classify 94.1% NR and 87.5% MR patients with a sensitivity = 0.88, a specificity = 0.94, and an AUC = 0.89 (95% CI: 0.74-1.00). In the external validation cohort, 35 MR and 47 NR patients were included. The AUC value for the CART model in this cohort was 0.82 (95% CI: 0.73-0.9).Our model correctly classified NR and MR patients based on simple measurements available in routine clinical practice, which provides the possibility to characterize and individualize patient treatments during early stages.© The Author(s), 2022. |
Note: | Reproducció del document publicat a: https://doi.org/10.1177/1759720X221124028 |
It is part of: | Therapeutic Advances In Musculoskeletal Disease, 2022, vol. 14 |
URI: | http://hdl.handle.net/2445/201108 |
Related resource: | https://doi.org/10.1177/1759720X221124028 |
ISSN: | 1759-7218 |
Appears in Collections: | Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) |
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