STRATA-FIT

Rheumatoid arthritis (RA) is the most common, chronic, inflammatory joint disease worldwide with a prevalence of about 1% of the adult population. RA patients suffer from pain and stiffness in joint(s); It causes joint and systemic inflammation that is reflected in local and systemic bone damage. Co-morbidities, specifically cardiovascular diseases and cancer contribute to further functional disability, impaired quality of life and excess morbidity and mortality. Effective treatment ameliorates RA symptoms, joint damage progression, and improves outcomes.

Unfortunately, even with breakthroughs in pharmacotherapy, a subgroup of patients, referred to as Difficult-to- Treat (D2T) RA does not respond well to consecutive treatments and remains symptomatic.

D2T RA is a subgroup of RA, which correspond to around 20% of RA patients and it 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 characterize these phenotypes.

Subsequently, we will execute a pilot study with a clinical decision aid based on our models to assess the effectiveness of personalized 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.

The project 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 personalized manner.

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This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101080243 from the Swiss State Secretariat for Education, Research and Innovation (SERI) and from Hungary’s National Research, Development and Innovation (NRDI) Fund.
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.