How to Design a Registry for Undiagnosed Patients in the Framework of Rare Disease Diagnosis – Suggestions on Software, Data Set and Coding System

Abstract

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 is needed. We used the open source software OSSE (Open Source Registry System for Rare Diseases) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended for 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 patients.

The Korean Undiagnosed Diseases Program Phase I: Expansion of the Nationwide Network and the Development of Long-term Infrastructures

“…Approximately 7000 kinds of rare diseases (RDs) have been genetically identi ed to date, and many undiagnosed diseases are now known rare diseases that require established testing [1,2]. Unlike prior undiagnosed disease programs that focused on the diagnosis itself, current studies emphasize determining disease mechanisms and searching for therapeutic targets using various tools, such as data sharing, model organisms and supporting experiments, as well as identifying ultrarare diseases [3][4][5][6]. However, the clinical demand for RDs still exists.…”