SPIDeRR | Stratification of Patients using advanced Integrative modeling of Data Routinely acquired for diagnosing Rheumatic complaints

Summary
Globally 1.Globally 1.71 billion people have musculoskeletal symptoms, the leading contributor to disability. Early disease stratification is important to ensure appropriate care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economical loss. This is due to insufficiently understood relations disease causes and similarities in symptoms between diseases, insufficiently distinguishing tests, trial and error approach in initial treatment.

SPIDeRR aims to disentangle the real-life complexity of early diagnosis of rheumatic diseases by considering the complete web of factors influencing patients’ symptoms. SPIDeRR’s approach will go well beyond the state-of-the-art in the following ways:
- By identifying different disease groups, requiring different therapies, amongst patients with similar symptoms in contrast to the traditional approach aiming to only capture one disease early.
- By integrating all relevant data dimensions from every healthcare level (primary and secondary care and patients seeking advice online).
- By translating and applying machine learning techniques from the “omics” field to clinical patient data, which will result in new pipelines for translational data science

SPIDERR will deliver three clinical models
-a symptom checker for patients
-a decision support tool for (primary) care providers providing guiding additional examination and referral decisions
-a patient-patient similarity network to optimise diagnostic groups in rheumatology and support treatment decision

To achieve this we additionally deliver solutions for data integration and shared analyses though GDPR compliant digital research environment and federated learning pipelines.
Finally we will test the acceptability of the models through stakeholders studies and provide an implementation scene tailored to current healthcare in Europe.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101080711
Start date: 01-05-2023
End date: 30-04-2028
Total budget - Public funding: 5 494 750,00 Euro - 5 494 750,00 Euro
Cordis data

Original description

Globally 1.Globally 1.71 billion people have musculoskeletal symptoms, the leading contributor to disability. Early disease stratification is important to ensure appropriate care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economical loss. This is due to insufficiently understood relations disease causes and similarities in symptoms between diseases, insufficiently distinguishing tests, trial and error approach in initial treatment.

SPIDeRR aims to disentangle the real-life complexity of early diagnosis of rheumatic diseases by considering the complete web of factors influencing patients’ symptoms. SPIDeRR’s approach will go well beyond the state-of-the-art in the following ways:
- By identifying different disease groups, requiring different therapies, amongst patients with similar symptoms in contrast to the traditional approach aiming to only capture one disease early.
- By integrating all relevant data dimensions from every healthcare level (primary and secondary care and patients seeking advice online).
- By translating and applying machine learning techniques from the “omics” field to clinical patient data, which will result in new pipelines for translational data science

SPIDERR will deliver three clinical models
-a symptom checker for patients
-a decision support tool for (primary) care providers providing guiding additional examination and referral decisions
-a patient-patient similarity network to optimise diagnostic groups in rheumatology and support treatment decision

To achieve this we additionally deliver solutions for data integration and shared analyses though GDPR compliant digital research environment and federated learning pipelines.
Finally we will test the acceptability of the models through stakeholders studies and provide an implementation scene tailored to current healthcare in Europe.

Status

SIGNED

Call topic

HORIZON-HLTH-2022-TOOL-12-01-two-stage

Update Date

31-07-2023
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Horizon Europe
HORIZON.2 Global Challenges and European Industrial Competitiveness
HORIZON.2.1 Health
HORIZON.2.1.0 Cross-cutting call topics
HORIZON-HLTH-2022-TOOL-12-two-stage
HORIZON-HLTH-2022-TOOL-12-01-two-stage Computational models for new patient stratification strategies
HORIZON.2.1.5 Tools, Technologies and Digital Solutions for Health and Care, including personalised medicine
HORIZON-HLTH-2022-TOOL-12-two-stage
HORIZON-HLTH-2022-TOOL-12-01-two-stage Computational models for new patient stratification strategies