Clinical and MRI predictors of Clinical Response in Axial Spondyloarthritis

Lead Investigator: Maureen Dubreuil, Boston University
Title of Proposal Research: Clinical and MRI predictors of Clinical Response in Axial Spondyloarthritis
Vivli Data Request: 5079
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Ankylosing spondylitis, the prototypic form of the disease, is characterized by bony changes on X-ray. Earlier or milder disease, however, may have changes visible only on MRI, and has been called non-radiographic axial spondyloarthritis (nr-axSpA). The classification of nr-axSpA remains controversial in terms of the magnetic resonance Imaging (MRI) lesions that define the presence of sacroillitis. While early studies reported that bone marrow edema and erosions were specific for nr-axSpA, subsequent work found such lesions to be present in those with mechanical causes for back pain, such as healthy athletes. Therefore, the goal of this work is to construct multivariable models predicting clinical improvement, and compare these models with novel MRI-based models clinical improvement.

The goal of this work is to improve clinical outcomes and quality of life of people with nr-axSpA, through earlier disease recognition and enhanced access to effective therapies. To this end, this application seeks to establish subject-level characteristics among nr-axSpA trial subjects who responded to Tumor necrosis factor inhibitor (TNFi) treatment or placebo. This work will leverage a unique collaboration of experts in spondyloarthritis, epidemiology and musculoskeletal imaging.

Requested Studies:

A Multicenter Study of the Efficacy and Safety of the Human Anti-TNF Monoclonal Antibody Adalimumab in Subjects With Axial Spondyloarthritis
Sponsor: Abbvie
Study ID: NCT00939003
Sponsor ID: M10-791

Update: This data request was withdrawn on 28-Mar-2022 by the researcher.