TY  - JOUR
A1  - Berger, Alexandra
A1  - Rustemeier, Anne-Kathrin
A1  - Göbel, Jens
A1  - Kadioglu, Dennis
A1  - Britz, Vanessa
A1  - Schubert, Katharina
A1  - Mohnike, Klaus
A1  - Storf, Holger
A1  - Wagner, Thomas O. F.
T1  - How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system
T2  - Orphanet journal of rare diseases
N2  - Background: About 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. Results: To 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. Conclusions: With 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.
KW  - Registry
KW  - Undiagnosed patients
KW  - Rare diseases
KW  - HPO
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63617
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-636175
SN  - 1750-1172
N1  - Open Access funding enabled and organized by Projekt DEAL.
N1  - The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
VL  - 16
IS  - art. 198
SP  - 1
EP  - 14
PB  - BioMed Central
CY  - London
ER  -