The DZHK Feasibility Explorer: Supporting Cohort Discovery and Feasibility Assessment for Cardiovascular Research

The secondary use of clinical study data and biological samples holds substantial potential for advancing biomedical research. The German Centre for Cardiovascular Research (DZHK) research platform made the secondary use of study data possible through standardized data collections and centralized data management. However, identifying suitable datasets and estimating cohort sizes prior to formal data access requests remains challenging due to restricted access to data and metadata. To address this gap, we developed the Feasibility Explorer (FE), a privacy-preserving tool that enables researchers interested in using the DZHK research data to explore data availability and assess study feasibility.

The FE was designed based on requirements for multi-study research environments, analysis of existing cohort discovery tools, and DZHK-specific governance and data protection policies. It follows a layered architecture comprising presentation, application, and data-processing layers, enabling distributed querying across heterogeneous data sources, including clinical data, imaging data, and biological samples. A web-based user interface allows intuitive query construction via filters and presents aggregated results using predefined visualizations.

To ensure compliance with data protection regulations, including the General Data Protection Regulation (GDPR), the FE restricts outputs to aggregated data, suppresses small cohort sizes, and implements a blurring mechanism to mitigate inference attacks. Data integration is performed on demand using pseudonym mapping, and no individual-level data are exposed or persistently stored.

Since its productive deployment, the FE has supported researchers in assessing data availability and estimating cohort sizes prior to submitting formal applications. While initial experiences indicate improved transparency and usability, formal evaluation is ongoing. The Feasibility Explorer provides a practical approach to support feasibility assessments in a federated research data environment, enabling efficient and informed study planning while maintaining strict data protection constraints.