Logo

Vectra DA for the objective measurement of disease activity in patients with rheumatoid arthritis

,

Crescendo Bioscience, South San Francisco, USA. osegurado@crescendobio.com

CER7929 Submission on line
2014 Vol.32, N°5 ,Suppl.85 - PI 0029, PF 0034
Optimisation of rheumatology measures

Free to view (click on article PDF icon to read the article)

Rheumatology Article

 

Abstract

Quantitative and regular assessment of disease activity in rheumatoid arthritis (RA) is required to achieve treatment targets such as remission and to optimize clinical outcomes. To assess inflammation accurately, predict joint damage and monitor treatment response, a measure of disease activity in RA should reflect the pathological processes resulting in irreversible joint damage and functional disability. The Vectra DA blood test is an objective measure of disease activity for patients with RA. Vectra DA provides an accurate, reproducible score on a scale of 1 to 100 based on the concentrations of 12 biomarkers that reflect the pathophysiologic diversity of RA. The analytical validity, clinical validity, and clinical utility of Vectra DA have been evaluated for patients with RA in registries and prospective and retrospective clinical studies. As a biomarker-based instrument for assessing disease activity in RA, the Vectra DA test can help monitor therapeutic response to methotrexate and biologic agents and assess clinically challenging situations, such as when clinical measures are confounded by non-inflammatory pain from fibromyalgia. Vectra DA scores correlate with imaging of joint inflammation and are predictive for radiographic progression, with high Vectra DA scores being associated with more frequent and severe progression and low scores being predictive for non-progression. In summary, the Vectra DA score is an objective measure of RA disease activity that quantifies inflammatory status. By predicting risk for joint damage more effectively than conventional clinical and laboratory measures, it has the potential to complement these measures and optimise clinical decision making.

PMID: 25365086 [PubMed]

Received: 15/09/2014 - Accepted : 16/09/2014 - In Press: 30/10/2014 - Published: 03/11/2014