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The aim of this trial is the caracterisation of a predicting algorithm of the answering response for patients with etanercept treatment in spondyloarthritis disease.
This algorithm will help to target patients patients who have a risk / benefit important for etanercept treatment.
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Spondyloarthritis (or spondyloarthropathy) is an inflammatory rheumatic disease that causes arthritis. It differs from other types of arthritis because it involves the sites where ligaments and tendons are attached to bones called "entheses."
All patients should get physical therapy and make exercises for joints. Exercises that promote spinal extension and mobility are the most recommended.
Non-steroidal anti-inflammatory drugs (NSAIDs) are the first line treatment for spondylarthritis. NSAIDs are effective when they are used continuously or at the request, in a short or long-term use. However, physicians have to be aware of potential cardiovascular, renal or gastro-intestinal secondary effects when they prescribe NSAIDs. After NSAIDs failure, TNF inhibitors can be used, like infliximab or etanercept. Before starting an anti-TNF treatment, a screening is mandatory. Indeed, patients treated with an anti-TNF must be followed regularly. Until now, there is no algorithm which can predict the response to TNF-inhibitors, and more especially for etanercept treatment.
In this clinical trial, the investigators want to caracterise an algorithm which can predict the response to etanercept for a cohort of patients who suffer from spondyloarthritis.
The development of a predictive algorithm for etanercept treatment will be set up from biological data and finalized with the availibility of clinical data (M6) of the patients .
A modelling by logistic regression will be used, incorporing the set of available variables.
In this clinical trial, the investigators want to caracterise an algorithm which can predict the response to TNF-inhibitors for a cohort of patients with Spondyloarthritis and for an indicated treatment of etanercept.
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50 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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