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STRATA-FIT

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Cerrado

Stratification of Rheumatoid Arthritis: CompuTational models to personalise mAna...

Stratification of Rheumatoid Arthritis: CompuTational models to personalise mAnagement strategies for difFIcult-to-Treat disease 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-relat... ver más

30/04/2029

UMC UTRECHT

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

UNIVERSITAIR MEDISCH CENTRUM UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 11 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-04-07

HORIZON-HLTH-2022-TOOL-12-01-two-stage: Computational models for new patien... ExpectedOutcome:This topic aims at supporting activities that are enabling or contributing to one or...

I+D

Cerrada hace 2 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

11 Participantes

UMC UTRECHT

2.01M€ | Lider

MEDIZINISCHE UNIVERSITAET WI...

847.78K€ | Participante

EIT Urban Mobility Innovatio...

800.34K€ | Participante

ETH Zürich

N.D. | Participante

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Duración del proyecto: 72 meses Fecha Inicio: 2023-04-07
Fecha Fin: 2029-04-30

Líder del proyecto

UNIVERSITAIR MEDISCH CENTRUM UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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