<b>An ontology-based Rare Disease Common Data Model harmonising international registries, FHIR, and Phenopackets</b>

Please see our GitHub repository here: https://github.com/BIH-CEI/rd-cdm/ Please see our RD CDM documentation here: https://rd-cdm.readthedocs.io/en/latest/index.html/ Attention: The RD CDM paper is currently under review (version 2.0.0.dev0). As soon as the paper is accepted, we will publish v2.0.0. For more information please see our ChangeLog: https://rd-cdm.readthedocs.io/en/latest/changelog.htmlWe 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.Background:<br>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.<br>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.请访问我们的GitHub仓库：https://github.com/BIH-CEI/rd-cdm/；请查阅我们的罕见疾病通用数据模型（Rare Disease Common Data Model，简称RD CDM）文档：https://rd-cdm.readthedocs.io/en/latest/index.html/。注意：RD CDM相关论文目前处于审稿阶段（版本2.0.0.dev0）。论文一经录用，我们将发布v2.0.0正式版。如需了解更多信息，请查看我们的变更日志：https://rd-cdm.readthedocs.io/en/latest/changelog.html。我们在此推出RD CDM v2.0.0——一款专为罕见病设计的通用数据模型。该模型简化了复杂临床数据的采集、存储与交换流程，助力研究人员与医疗服务机构跨机构、跨国开展基于标准化数据集的研究与诊疗工作。RD CDM基于欧洲罕见病研究基础设施（European Rare Disease Research Infrastructure，简称ERDRI）开发的欧洲罕见病研究基础设施通用数据集（ERDRI-CDS），该数据集旨在支持罕见病研究的标准化数据采集。我们在ERDRI-CDS的基础上，结合HL7 快速医疗保健互操作性资源（HL7 FHIR）v4.0.1与全球基因组与健康联盟（Global Alliance for Genomics and Health，简称GA4GH）表型数据包架构（Phenopacket Schema）v2.0，扩展了新增概念与关联关系，从而构建出可全面采集罕见病详细临床信息与精准遗传数据的综合模型。背景：<br>罕见病（Rare Diseases，简称RDs）虽单病种发病率极低，但全球累计影响超2.6亿人群，其中欧洲地区受影响人群超1700万。此类疾病的定义为患病率低于每10000人中5例，多数由遗传因素驱动，超70%的病例疑似存在遗传病因。尽管医学研究已取得显著进展，但罕见病患者仍面临漫长的诊断周期：这一问题往往源于普通医疗场景对罕见病的认知不足，以及临床医师普遍缺乏罕见病专属知识。加之常规诊疗中的误诊与数据代表性不足问题，进一步加剧了诊疗困境，致使诸多患者无法获得及时且精准的诊断。互操作性是解决上述困境的关键所在，通过采用国际统一标准，可实现医疗数据的无缝交换与解读。在罕见病领域，数据往往稀缺且分散，结构化、标准化且可复用的医疗记录的重要性无论如何强调都不为过。支持互操作的数据格式能够提升研究效率、优化诊疗协作，并加深对复杂临床病例的理解。然而，现有医疗系统往往无法满足罕见病研究与诊疗所需的表型与基因型数据深度采集需求，因此互操作性成为改善罕见病诊疗结局的核心助推因素。