ArticleOrphanet journal of rare diseases2021
How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system.
Article in Orphanet journal of rare diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Minimum Data Set for Rare Diseases: Systematic Review.Journal of medical Internet research · 2023Pooled it
- European Reference Networks - a flagship activity of the EU in the field of rare and complex diseases: from 2017 to 2025.Orphanet journal of rare diseases · 2026Review
- [The role of disease-specific registries in the field of rare diseases: opportunities, benefits and pitfalls].Innere Medizin (Heidelberg, Germany) · 2026Review
- Digital Phenotyping of Rare Endocrine Diseases Across International Data Networks and the Effect of Granularity of Original Vocabulary.Yonsei medical journal · 2025Article
- Dos and don'ts in designing a computerized oral and lip squamous cell cancer registry.BMC health services research · 2023Article
- Data saves lives: optimising routinely collected clinical data for rare disease research.Orphanet journal of rare diseases · 2023Article
- Methods Used in the Development of Common Data Models for Health Data: Scoping Review.JMIR medical informatics · 2023Article
- Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review.Frontiers in public health · 2023Article
- The Korean undiagnosed diseases program phase I: expansion of the nationwide network and the development of long-term infrastructure.Orphanet journal of rare diseases · 2022Article
- The Interdisciplinary Diagnosis of Rare Diseases.Deutsches Arzteblatt international · 2022Observational
- The ongoing French BaMaRa-BNDMR cohort: implementation and deployment of a nationwide information system on rare disease.Journal of the American Medical Informatics Association : JAMIA · 2022Article
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9 authors.
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Abstract
backgroundAbout 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain.
resultsTo develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software Open-Source Registry System for Rare Diseases (OSSE) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded.
conclusionsWith the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some undiagnosed patients.
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