Dataset Information

Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis.

ABSTRACT:

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.

SUBMITTER: Novella-Navarro M

PROVIDER: S-EPMC9549195 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis.

Novella-Navarro Marta M Benavent Diego D Ruiz-Esquide Virginia V Tornero Carolina C Díaz-Almirón Mariana M Chacur Chafik Alejandro CA Peiteado Diana D Villalba Alejandro A Sanmartí Raimon R Plasencia-Rodríguez Chamaida C Balsa Alejandro A

Therapeutic advances in musculoskeletal disease 20221006

<h4>Background</h4>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).<h4>Objective</h4>To develop and validate an algorithm to predict multiple failure to biological therapy in patients with RA.<h4>Design</h4>Observational retrospective study ...[more]

PMID: 36226311

Similar Datasets

Project description:BackgroundBiological therapies have improved the clinical course and quality of life of rheumatoid arthritis (RA) patients. Despite the availability and effectiveness of these treatments, some patients experience multiple failures to biologic disease-modifying antirheumatic drugs (bDMARDs), constituting a particular challenge to clinicians.ObjectivesThis study aims to determine the percentage of rheumatoid arthritis (RA) patients who fail to respond to subsequent bDMARDs, describe their characteristics, and identify specific baseline and early features during the first bDMARD as possible predictors of consecutive multiple bDMARD failure.MethodsThis is a longitudinal study involving RA patients from the prospective biological cohort drawn from the La Paz University Hospital RA Registry (RA-Paz), starting a bDMARD during the years 2000 to 2019. Patients who presented insufficient response (due to primary or secondary inefficacy) to at least three bDMARDs or two bDMARDs with different mechanism of action were considered multi-refractory (MR-patients). Patients who achieved low disease activity or remission (by DAS-28) with the first bDMARD and maintained this over a follow-up period of at least 5 years were considered non-refractory (NR-patients).ResultsA total of 41 out of 402 (10%) patients were MR-patients and 71 (18%) NR-patients. In the multivariate analysis, the presence of erosions, younger age, higher baseline DAS-28 and mostly achieving delta-DAS < 1.2 after 6 months of the first bDMARD (OR 11.12; 95% CI 3.34-26.82) were independently associated with being MR-patients to bDMARDs.ConclusionsIn our cohort, 10% of patients with RA were observed to have multi-refractoriness to bDMARDs. This study supports the contention that younger patients with erosive disease and especially the early absence of clinical response to the first bDMARDs are predictors of multi-refractoriness to consecutive biologics. Hence, patients with these characteristics should be monitored more closely and may benefit from personalized treatments.

Project description:BackgroundSarcopenia is linked to an unfavorable prognosis in individuals with rheumatoid arthritis (RA). Early identification and treatment of sarcopenia are clinically significant. This study aimed to create and validate a nomogram for predicting sarcopenia risk in RA patients, providing clinicians with a reliable tool for the early identification of high-risk patients.MethodsPatients with RA diagnosed between August 2022 and January 2024 were included and randomized into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multifactorial logistic regression analysis were used to screen the risk variables for RA-associated muscle loss and to create an RA sarcopenia risk score. The predictive performance and clinical utility of the risk model were evaluated by plotting the receiver operating characteristic curve and calculating the area under the curve (AUC), along with the calibration curve and clinical decision curve (DCA).ResultsA total of 480 patients with RA were included in the study (90% female, with the largest number in the 45-59 age group, about 50%). In this study, four variables (body mass index, disease duration, hemoglobin, and grip strength) were included to construct a nomogram for predicting RA sarcopenia. The training and validation set AUCs were 0.915 (95% CI: 0.8795-0.9498) and 0.907 (95% CI: 0.8552-0.9597), respectively, proving that the predictive model was well discriminated. The calibration curve showed that the predicted values of the model were basically in line with the actual values, demonstrating good calibration. The DCA indicated that almost the entire range of patients with RA can benefit from this novel prediction model, suggesting good clinical utility.ConclusionThis study developed and validated a nomogram prediction model to predict the risk of sarcopenia in RA patients. The model can assist clinicians in enhancing their ability to screen for RA sarcopenia, assess patient prognosis, make early decisions, and improve the quality of life for RA patients.

Project description:Background: The treatment of rheumatoid arthritis (RA), a chronic systemic inflammatory autoimmune disease, is based on disease-modifying anti-rheumatic drugs (DMARDs). Typically, it starts with conventional synthetic DMARDs (csDMARDs), and depending on the patient's response to the treatment and the adverse events experienced, biological DMARDs (bDMARDs) are initiated. bDMARDs are more specific to inflammatory factors than csDMARDs and more efficient in inducing remission and low disease activity. Thus, this study aimed to assess the effectiveness of biological therapy in patients with rheumatoid arthritis in administrative health databases. Methods: PubMed, Embase, Lilacs, Ovid, Scopus, and Web of Science databases were searched from inception to 21 October 2021, to identify observational studies that evaluated the effectiveness of biological therapy in patients with rheumatoid arthritis using administrative databases and real-world data. The methodological quality was assessed by the methodological index for non-randomized studies (MINORS). A fixed or random-effects model estimated risk ratios with 95% confidence intervals. The analysis was divided into four groups: tumor necrosis factor inhibitors (TNFi) versus non-TNFi; TNFi versus TNFi (adalimumab, etanercept, and golimumab versus infliximab); bDMARDs versus Janus kinase inhibitors (JAKi); and bDMARDs monotherapy versus combination therapy (bDMARDs and MTX). Results: Twenty-one records were eligible for inclusion in this systematic review and meta-analysis; seven population-based cohorts, eight prospective, and six retrospective cohort studies. Overall, 182,098 rheumatoid arthritis patients were evaluated. In the meta-analysis, lower effectiveness was observed among TNFi users than in non-TNFi (RR: 0.88; 95% CI: 0.81-0.95; p < 0.01; I2 = 94.0%) and bDMARDs than in JAKi (RR: 0.86; 95% CI: 0.79-0.94; p < 0.01; I2 = 93.0%). Higher effectiveness among adalimumab, etanercept, and golimumab than in infliximab (RR: 1.19; 95% CI: 1.05-1.36; p < 0.01; I2 = 96.0%) was found. No significant differences in the effectiveness of bDMARD monotherapy compared to combination therapy (RR: 0.83; 95% CI: 0.68-1.00; p < 0.01; I2 = 81.0%) was observed. E-value analysis indicated that the estimates were not robust against unmeasured confounding. Conclusion: According to the available real-world data, our results suggest that biological therapy effectively treats patients with rheumatoid arthritis, indicating higher effectiveness with non-TNFi and JAKi than with TNFi. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID#CRD42020190838, identifier CRD42020190838.

Dataset Information

Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis.

Background

Objective

Design

Methods

Results

Conclusion

Publications

Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets