Novella Navarro, MartaBenavent, DiegoRuiz Esquide, VirginiaTornero, CarolinaDíaz Almirón, MarianaChacur, Chafik AlejandroPeiteado, DianaVillalba, AlejandroSanmartí Sala, RaimonPlasencia Rodríguez, ChamaidaBalsa, Alejandro2023-07-242023-07-242022-10-061759-7218https://hdl.handle.net/2445/201108Despite 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), 2022.14 p.application/pdfengcc by-nc (c) Novella Navarro, Marta et al, 2022http://creativecommons.org/licenses/by-nc/3.0/es/Artritis reumatoideTeoria de la prediccióTerapèuticaRheumatoid arthritisPrediction theoryTherapeuticsinfo:eu-repo/semantics/article2023-06-28info:eu-repo/semantics/openAccess933303236226311