Abstract
(Englisch)
|
Difficult-to-treat rheumatoid arthritis (D2T RA) is an area of huge unmet medical need with major socio-economic consequences for
patients and society. Contributing factors have been identified including co-morbidities, drug-related, biological and behavioral factors.
However, identifying these patients with specific underlying and overlapping problems, or patients at risk, is a big challenge in practice.
Currently, treatment decisions are random and not sufficiently patient tailored nor data-driven. Therefore, the STRATA-FIT consortium
sets out to develop and validate computational models to identify and stratify D2T RA patients into clinically relevant phenotypes using
real world clinical data. We will also measure biomarkers of inflammation to further characterise these phenotypes. Subsequently, we
will execute a pilot study with a clinical decision aid based on our models to assess the effectiveness of personalised treatment strategies.
In parallel we will develop a computational model to identify early RA patients at risk of developing D2T RA. By doing so, not only
will we provide better treatment for patients with D2T RA, but also work towards its prevention in early RA patients. STRATA-FIT
will establish a unique European Learning Healthcare System, using a privacy-proof, state-of-the-art federated learning infrastructure in
which patients with, or at risk of D2T RA are identified, stratified and treated in a personalised manner. STRATA-FIT builds on previous
work by consortium partners, who initiated and lead the European Task Force on developing points to consider for managing D2T RA.
It brings together clinical experts, patient research partners and clinical-, biological-, data- and computer-scientists to tackle this major
clinical challenge. When successful, STRATA-FIT will lead to more (cost-) effective D2T RA care and will greatly improve the quality
of life of D2T RA patients while lowering the burden of D2T RA on Europe’s health care systems and society.
|
