FOUNTAIN
FOUNTAIN is a research platform designed to generate consistent and reliable real-world evidence about the use, effectiveness, and safety of the drug finerenone in patients with chronic kidney disease and type 2 diabetes. It connects multiple research teams and databases across countries, using shared methods and definitions to ensure results are comparable and reproducible.
At a glance
Use when
Generating consistent, generalizable real-world evidence on a specific drug across multiple countries and data sources; when harmonization of methodology and definitions is critical for regulatory or clinical decision-making.
Avoid when
Research requires broad applicability beyond a single drug or therapeutic area; when data from non-CDM-mapped or non-partner sources are essential; when rapid, single-database insights are sufficient.
Inputs
Multisource real-world data including electronic health records, claims databases, and registry data mapped to a common data model; research protocols; standardized medical definitions.
Outputs
Harmonized real-world evidence on finerenone utilization, effectiveness, safety, patient profiles, and healthcare resource use across multiple countries and data sources.
How it works
FOUNTAIN (FinerenOne mUlti-database NeTwork for evidence generAtIoN) is a harmonized, modular research platform that supports coordinated real-world evidence (RWE) generation across diverse data sources and research partners. It integrates two approaches: research partner collaborations conducting global protocol execution on local data, and common protocol execution across federated data networks using a common data model (CDM). The platform ensures methodological consistency through standardized medical definitions, analytical methods, and reproducible artifacts. Governed by a multidisciplinary executive advisory committee including patient representatives, FOUNTAIN currently includes 9 research collaborations and 8 CDM-mapped databases from 7 countries. It enables multicountry, multidatabase cohort studies to assess real-world utilization, effectiveness, and safety of finerenone across its lifecycle.
- Project
- More-EUROPA
- Funding
- Horizon Europe
- Project status
- Ongoing
- HTA domains
- Clinical Effectiveness, Safety, Organisational aspects
- Assumptions
- Data sources are representative of local populations; common data models preserve data integrity; standardized protocols ensure methodological consistency; local adaptations do not compromise comparability.
- Strengths
- Enables cross-national comparability through methodological harmonization; supports reproducibility via standardized definitions and protocols; integrates diverse RWE generation approaches; includes patient and multidisciplinary input; leverages both federated and collaborative research models.
- Limitations
- Findings may be limited to populations covered by participating databases; heterogeneity in data quality and availability across countries may affect comparability; platform focus is specific to finerenone, limiting generalizability to other interventions.
- Also known as
- FinerenOne mUlti-database NeTwork for evidence generAtIoN
Questions this answers
- › How is finerenone used in real-world clinical practice across different healthcare systems?
- › What is the effectiveness of finerenone in patients with chronic kidney disease and type 2 diabetes in routine care?
- › What are the safety outcomes associated with finerenone use in diverse real-world populations?
- › How do patient characteristics, comorbidities, and standard of care vary across regions?
- › How can real-world evidence generation be harmonized across multiple data sources and countries?
- › What is the healthcare resource use associated with finerenone treatment in different settings?
References & sources
Related methods
Similar by meaning
- CADTH Guidance for Reporting Real-World Evidence
- REPEAT Initiative (Reproducible Evidence: Practices to Enhance and Achieve Transparency)
- RedETS postlaunch evidence-generation approach for medical devices
- SPIFD2 (Structured process to identify fit-for-purpose study design and data for regulatory RWE)
- SPIFD (Structured Process to Identify Fit-For-Purpose Data)
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.