How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system

Orphanet Journal of Rare Diseases

Klaus Mohnike¹  Katharina Schubert¹  Alexandra Berger²  Thomas O. F. Wagner²  Vanessa Britz²  Anne-Kathrin Rustemeier³  Holger Storf⁴  Jens Göbel⁴  Dennis Kadioglu⁴ 

[1] Central-German Network for rare diseases, University Hospital Magdeburg A.Ö.R, Leipziger Straße 44, 39120, Magdeburg, Germany;Frankfurt Reference Centre for Rare Diseases, University Hospital Frankfurt, Goethe University Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany;Medical Clinic II, University Hospital Gießen and Marburg, Klinikstraße 33, 35392, Gießen, Germany;Medical Informatics Group Frankfurt, University Hospital Frankfurt, Goethe University Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany;

关键词: Registry;    Undiagnosed patients;    Rare diseases;    HPO;   

DOI  :  10.1186/s13023-021-01831-3

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY