RD CDM Background
+Attention
+The RD CDM paper is currently under review. As soon as it is published, we +will provide a link to the paper here and all tables and figures will be +available in the paper.
+Introduction
+Rare diseases (RDs), though individually rare, collectively impact over 260 +million people worldwide, with over 17 million affected in Europe. These +conditions, defined by their low prevalence of fewer than 5 in 10,000 +individuals, are often genetically driven, with over 70% of cases suspected to +have a genetic cause. Despite significant advances in medical research, +RD patients still face lengthy diagnostic delays, often due to a lack of +awareness in general healthcare settings and the rarity of RD-specific knowledge +among clinicians. Misdiagnosis and underrepresentation in routine care further +compound the challenges, leaving many patients without timely and accurate +diagnoses.
+Interoperability plays a critical role in addressing these challenges, +ensuring the seamless exchange and interpretation of medical data through the +use of internationally agreed standards. In the field of rare diseases, where +data is often scarce and scattered, the importance of structured, standardized, +and reusable medical records cannot be overstated. Interoperable data formats +allow for more efficient research, better care coordination, and a clearer +understanding of complex clinical cases. However, existing medical systems often +fail to support the depth of phenotypic and genotypic data required for rare +disease research and treatment, making interoperability a crucial enabler for +improving outcomes in RD care.
+To address these needs, we introduce our RD CDM v2.0.0— a common data model +specifically designed for rare diseases. This RD CDM simplifies the capture, +storage, and exchange of complex clinical data, enabling researchers and +healthcare providers to work with harmonized datasets across different +institutions and countries. The RD CDM is based on the ERDRI-CDS, +a common data set developed by the European Rare Disease Research +Infrastructure (ERDRI) to support the collection of harmonized data for rare +disease research. By extending the ERDRI-CDS with additional concepts and +relationships, based on HL7 FHIR v4.0.1 and the GA4GH Phenopacket Schema v2.0, +the RD CDM provides a comprehensive model for capturing detailed clinical +information alongisde precise genetic data on rare diseases.
+RD CDM Overview
+
+Overview of the RD CDM v2.0.0 showing the data elements and sections. The RD CDM +does not define cardinalities or relationships to allow for nation-specific +balloting and implementation.
+Note
+The RD CDM is a community-driven project, and we welcome contributions from +researchers, clinicians, and other stakeholders in the rare disease community. +If you would like to contribute to the RD CDM, please read our contributing +guidelines.
+RD CDM Table Columns
+
+This Figure Provides an overview of the table columns used to depict our Rare +Disease Common Data Model (RD CDM). Each column’s abbreviation, further +definitions, and explanations are given. We recommend referring to this figure +when reading the tables for each section of our RD CDM.
+Note
+The table can be found in Figshare at the following link: +RD CDM v2.0.0 Excel Table.
+RD CDM Layers of harmonisation
+
+We analysed to what extent interoperability requirements were met +while harmonising data elements from the ERDRI-CDS, HL7 FHIR resources and +the GA4GH Phenopacket Schema to a single RD CDM. We identified six layers of +harmonisation on the level of each data element: (1) the Alignment Layer, +(2) the Labelling Layer, (3) the Terminology Binding Layer, (4) the Data +Type Layer, (5) the Value Set Layer, and (6) the Value Set Choice Layer. All +layers and their selection criteria are depicted in the figure below.
+While over 95% of all data elements are directly aligned with HL7 FHIR or GA4GH +Phenopackets, only one-third of terminology bindings and 80% of value types +match the specifications outlined by these standards. Our ontology-based +approach results in less than 41% of value sets being directly derived from HL7 +FHIR and GA4GH Phenopacket Schema, with slightly more than 45% of value set +choices were encoded accordingly.
+Note
+The RD CDM paper is currently under review. As soon as it is published, we +will provide a link to the paper here and all tables and figures will be +available in the paper.
+