Project Objectives

• Stratify difficult-to-treat RA:Identify and group MDR-RA patients using clinical, imaging, and behavioral data from large European cohorts.
• Reveal molecular drivers: Use multi-omics from biopsy samples to uncover mechanisms of drug resistance and improve prediction.
• Develop AI-powered care tools:Create the iCare-RA algorithm to integrate clinical, imaging, and molecular data for personalized treatment.
• Validate in real-world settings:Test iCare-RA in a multi-centre clinical trial to assess its clinical value and practical use.
• Evaluate health economics: Analyse cost-effectiveness and implementation pathways across European health systems.
• Build a lasting research resource: Create a rich, open-access dataset to fuel future innovation in MDR-RA care and research.

By integrating pan-European data with AI, we identify patient subgroups and disease signatures that power iCare-RA—our personalized decision-support tool, tested in clinical trials.

Our expert network and rich datasets ensure fast, reliable discovery and validation, advancing precision care for difficult-to-treat RA.

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Project Approach

By integrating molecular, clinical, psychosocial, and imaging data, we’re developing iCare-RA—a predictive tool to identify and support RA patients resistant to standard treatments. The aim: better care, less disability, and improved quality of life.
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Data Infrastructure, Harmonisation, and Advanced Analytics

Smart, Secure Data for RA Research

We’re building a secure, scalable infrastructure to analyse clinical, imaging, and molecular data from diverse patient cohorts. Partners contribute and apply insights across the project, guided by a dedicated data analysis team.

Using privacy-preserving tools like DataSHIELD, we perform parallel, non-disclosive analyses across sites—keeping data local while generating global insights.

Our focus:

Data harmonisation: Standardising diverse datasets for quality and interoperability.

Data integration: Applying advanced analytics to uncover new patterns in RA resistance.
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Deep Clinical and Imaging Phenotyping

Understanding RA Beyond Inflammation

We’re exploring the clinical and imaging features of multi-drug resistantRA—both inflammatory (MDR-IP) and extra-articular (MDR-EP) types—to better classify, track, and predict disease progression.

Key tasks include:

• Identifying clinical, imaging, and sociodemographic markers of MDR-RA.
• Combining objective and subjective assessments to build detailed patient profiles.
• Mapping inflammation and pain patterns using tools like sensory phenotyping.
• Developing a predictive model that links clinical and molecular data for iCare-RA.
  This work paves the way for more personalised, data-driven RA care.
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DeepMolecular Phenotyping of MDR-RA

Revealing the Molecular Drivers of MDR-RA

We’re using deep molecular profiling of blood and synovial tissue to uncover the biological roots of treatment resistance in RA.

Key tasks include:

• Mapping the molecular landscape ofMDR-RA across patient cohorts.
• Identifying markers that distinguish MDR-IP and MDR-EP subtypes.
• Creating predictive molecular algorithms to support personalised care.
  We begin with existing samples and expand from Month 6 with newly recruited patients—ensuring a scalable, data-rich approach to advance clinical tools like iCare-RA.
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Building the First Comprehensive MDR-RA Biomedical Resource

Building the First MDR-RA Cohort

We’re creating the first real-world, prospective cohort of patients with multi-drug resistant RA—linking clinical, imaging, and molecular data in one powerful resource.

Key tasks include:

• Recruiting and tracking MDR-RA patients to gather longitudinal data.
• Standardising sample collection and assessments across sites.
• Validating findings to refine the iCare-RA tool.
• Establishing a long-term database to drive future research and care.
  This unique resource will enable data-driven breakthroughs in MDR-RA diagnosis and treatment.
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IntegratingData into the iCare-RA Clinical Decision Tool

iCare-RA: Turning Data into Action

We’re translating cutting-edge clinical and molecular research into iCare-RA—anAI-powered tool to support real-world management of multi-drug resistant RA.

Key tasks include:

• Designing the tool around clinical workflows.
• Embedding AI algorithms frompatient data.
• Adding features like e-health and clinical guidelines.
• Refining and validating with real-world feedback.
  Our goal: equip clinicians with smart, data-driven support to improve treatment and outcomes in MDR-RA.
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Real-World Validation of the iCare-RA Tool in Clinical Practice

Real-World Trial of iCare-RA

We’re testing iCare-RA in areal-world clinical trial, comparing it to standard care for managing multi-drug resistant RA—both groups receiving equal treatment options.

Key tasks include:

• Defining standard care across all sites.
• Setting up and running the trial at16 centres, led by HUMANITAS RESEARCH HOSPITAL (IRCCS).
• Recruiting patients, collecting data, and ensuring secure management.
• Analysing outcomes to assess iCare-RA’s impact.
  This trial will provide vital evidence on how iCare-RA can improve care and outcomes in MDR-RA.
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Assessing the Value and Impact of iCare-RA in Healthcare Systems

Is iCare-RA Cost-Effective?

We’re assessing the economic impact of iCare-RA in the UK and Belgium, comparing it to standard care for managing MDR-RA.

Key tasks include:

• Estimating the cost and value of iCare-RA.
• Exploring how it fits into routine practice.
• Identifying what drives cost-effectiveness.
• Studying implementation challenges and enablers, with a UK case study.
  This evaluation will ensure iCare-RA delivers better care while making smart use of healthcare resources.
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AdvancedMolecular Profiling in Interventional MDR-RA Studies

Advancing Molecular Insights in MDR-RA

We’re applying next-generation phenotyping to new MDR-RA patients in both interventional and observational studies—validating earlier findings and tracking treatment effects on molecular profiles.

Key tasks include:

• Analysing synovial and blood samples with advanced technologies.
• Refining and validating biomarkers in real time during the trial.
  This work deepens our molecular understanding and supports more precise, personalised treatment for MDR-RA.
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AdvancedData Integration and Predictive Modeling for MDR-RA

From Data to Decisions: Powering iCare-RA

We’re combining data from our trial, observational cohort, and existing studies to fully capture the complexity of MDR-RA.

Key tasks include:

• Prediction and classification: Using advanced analytics to forecast outcomes and identify patient subgroups—fueling the AI behind iCare-RA.
  This integrated approach supports the development of personalised, real-world solutions for MDR-RA care.

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