UNRAVEL

UNRAVEL integrates routine electronic health records (EHRs) with standardized biobanking and text‑mining to create a consolidated research database for studying genetic cardiomyopathies and associated clinical outcomes.

Key Features:

• Integrated Research Database: Consolidates clinical and genetic information from patients with proven or suspected cardiac diseases and their relatives, with routinely captured clinical data extracted weekly into the research database.
• Biobanking Capabilities: Collects consented data and blood samples to enable longitudinal studies and genetic analyses of cardiomyopathies.
• Data Enrichment through Text Mining: Applies a specialized text‑mining tool to extract information from unstructured clinical notes and enrich structured data fields.
• Comprehensive Data Collection: Contains data from 828 individuals (58% male, median age 57) and captures extensive temporal sequences including 18,565 electrocardiograms, 3,619 echocardiograms, over 20,000 radiological examinations, and 650,000 individual laboratory measurements.
• Facilitation of Clinical Trials: Embeds clinical trials within the electronic health record system to enable trials to be conducted directly in routine practice settings.
• Collaboration Encouragement: Supports national and international collaboration to facilitate multicenter research on genetic cardiomyopathies.

Scientific Applications:

• Genetic cardiomyopathy research: Enables large‑scale analysis of genetic determinants and molecular mechanisms underlying cardiomyopathies.
• Genotype–phenotype correlation: Supports exploration of interactions between genetic factors and clinical outcomes using integrated EHR and biobank data.
• Biomarker and diagnostic development: Facilitates discovery of diagnostic tools and biomarkers from multimodal clinical, imaging, and laboratory datasets.
• Embedded clinical trial research: Allows evaluation of interventions and trial conduct within routine clinical workflows via EHR integration.

Methodology:

Weekly extraction of routinely captured clinical data into the research database and application of a specialized text‑mining tool to extract information from unstructured clinical notes.