Background: 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). Objective: To develop and validate an algorithm to predict multiple failure to biological therapy in patients with RA. Design: Observational retrospective study involving subjects from a cohort of patients with RA receiving b/tsDMARDs. Methods: 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). Results: 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). Conclusion: 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.
Background: this is an exploratory study to evaluate calprotectin serum levels in patients with rheumatic immune-related adverse events (irAEs) induced by immune checkpoint inhibitor (ICI) treatment. Methods: this is a retrospective observational study including patients with irAEs rheumatic syndromes. We compared the calprotectin levels to those in a control group of patients with RA and with a control group of healthy individuals. Additionally, we included a control group of patients treated with ICI but without irAEs to check calprotectin levels. We also analysed the performance of calprotectin for the identification of active rheumatic disease using receiver operating characteristic curves (ROC). Results: 18 patients with rheumatic irAEs were compared to a control group of 128 RA patients and another group of 29 healthy donors. The mean calprotectin level in the irAE group was 5.15 μg/mL, which was higher than the levels in both the RA group (3.19 μg/mL) and the healthy group (3.81 μg/mL) (cut-off 2 μg/mL). Additionally, 8 oncology patients without irAEs were included. In this group, calprotectin levels were similar to those of the healthy controls. In patients with active inflammation, the calprotectin levels in the irAE group were significantly higher (8.43 μg/mL) compared to the RA group (3.94 μg/mL). ROC curve analysis showed that calprotectin had a very good discriminatory capacity to identify inflammatory activity in patients with rheumatic irAEs (AUC of 0.864). Conclusions: the results suggest that calprotectin may serve as a marker of inflammatory activity in patients with rheumatic irAEs induced by treatment with ICIs.
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