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.

Aim & Objectives

The main aim of STRATA-FIT is to develop computational models to identify Difficult-to-treat rheumatoid arthritis (D2T RA) in Electronic Health Records (EHR) from routine practice and stratify these patients into clinically relevant phenotypes, then subsequently – for the first time – apply these models in clinical practice for more effective personalized treatment, and consequently, significantly reduce the disease burden and socioeconomic impact of D2T RA.

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Objectives:

• Developing the building blocks (e.g., computational identification and stratification models) for a treatment algorithm better tailoring available treatments to individual D2T RA patients and a decision aid for its implementation.
• Demonstrate the improvement in the management of D2T RA patients using the decision aid.
• Help to provide more insight into underlying disease mechanisms.

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Expected Outcomes:

STRATA-FIT will develop and pilot an innovative, actionable decision aid to stratify D2T RA patients and provide a personalised treatment advice. Thus, the outcomes of STRATA-FIT have huge potential for improving the effectiveness as well as the cost-effectiveness of the management of D2T RA. With prospective implementation, we will also further our understanding of underlying biology of the defined clinical phenotypes of D2T RA. Synergistically, STRATA-FIT will for the first time, effectively establish a Learning Healthcare System for RA in Europe. This will be the foundation of future research and evaluation of management of (specific groups) of RA patients. Our approach and infrastructure can also serve as a blueprint for other chronic conditions, thereby continuing the improvement of care for various important patient groups.

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Funded by the European Union (grant agreement no. 101080243). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
The project has also received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI) and from Hungary’s National Research, Development and Innovation (NRDI) Fund.