Rubinstein YR, Groft SC, Bartek R et alCreating a global rare disease patient registry linked to a rare diseases biorepository database: Rare Disease-HUB (RD-HUB). Contemp Clin Trials 31:394-404

Office of Rare Diseases Research National Institutes of Health, Bethesda, MD 20892, United States.
Contemporary clinical trials (Impact Factor: 1.94). 09/2010; 31(5):394-404. DOI: 10.1016/j.cct.2010.06.007
Source: PubMed


A movement to create a global patient registry for as many as 7,000 rare diseases was launched at a workshop, "Advancing Rare Disease Research: The Intersection of Patient Registries, Biospecimen Repositories, and Clinical Data." The workshop was sponsored by the Office of Rare Diseases Research (ORDR). The focus was the building of an infrastructure for an internet-based global registry linking to biorepositories. Such a registry would serve the patients, investigators, and drug companies. To aid researchers the participants suggested the creation of a centralized database of biorepositories for rare biospecimens (RD-HUB) that could be linked to the registry. Over two days of presentations and breakout sessions, several hundred attendees discussed government rules and regulations concerning privacy and patients' rights and the nature and scope of data to be entered into a central registry as well as concerns about how to validate patient and clinician-entered data to ensure data accuracy. Mechanisms for aggregating data from existing registries were also discussed. The attendees identified registry best practices, model coding systems, international systems for recruiting patients into clinical trials and novel ways of using the internet directly to invite participation in research. They also speculated about who would bear ultimate responsibility for the informatics in the registry and who would have access to the information. Hurdles associated with biospecimen collection and how to overcome them were detailed. The development of the recommendations was, in itself, an indication of the commitment of the rare disease community as never before.

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Available from: Stephen C Groft, Oct 12, 2015
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    • "The need for information systems, or registries in scientific disciplines is ubiquitous. For instance, in human rare disease [1-4] as well as plant and animal biosecurity [5,6]. In the field of human rare diseases alone, registries are used for clinical trial recruitment, surveillance, patient contact, natural disease history and longitudinal patient phenotyping. "
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    ABSTRACT: Background Information management systems are essential to capture data be it for public health and human disease, sustainable agriculture, or plant and animal biosecurity. In public health, the term patient registry is often used to describe information management systems that are used to record and track phenotypic data of patients. Appropriate design, implementation and deployment of patient registries enables rapid decision making and ongoing data mining ultimately leading to improved patient outcomes. A major bottleneck encountered is the static nature of these registries. That is, software developers are required to work with stakeholders to determine requirements, design the system, implement the required data fields and functionality for each patient registry. Additionally, software developer time is required for ongoing maintenance and customisation. It is desirable to deploy a sophisticated registry framework that can allow scientists and registry curators possessing standard computing skills to dynamically construct a complete patient registry from scratch and customise it for their specific needs with little or no need to engage a software developer at any stage. Results This paper introduces our second generation open source registry framework which builds on our previous rare disease registry framework (RDRF). This second generation RDRF is a new approach as it empowers registry administrators to construct one or more patient registries without software developer effort. New data elements for a diverse range of phenotypic and genotypic measurements can be defined at any time. Defined data elements can then be utilised in any of the created registries. Fine grained, multi-level user and workgroup access can be applied to each data element to ensure appropriate access and data privacy. We introduce the concept of derived data elements to assist the data element standards communities on how they might be best categorised. Conclusions We introduce the second generation RDRF that enables the user-driven dynamic creation of patient registries. We believe this second generation RDRF is a novel approach to patient registry design, implementation and deployment and a significant advance on existing registry systems.
    Source Code for Biology and Medicine 06/2014; 9(1):14. DOI:10.1186/1751-0473-9-14
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    • "Some countries have taken steps to develop platforms to facilitate the collection of disease-specific data. In the USA, a movement involving both researchers and patients supported the development of a global rare diseases registry for collecting a considerable amount of information on potentially thousands of diseases and linking these data with bio-repositories [20,21]. "
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    ABSTRACT: Although rare diseases have become a major public health issue, there is a paucity of population-based data on rare diseases. The aim of this epidemiological study was to provide descriptive figures referring to a sizable group of unrelated rare diseases. Data from the rare diseases registry established in the Veneto Region of north-east Italy (population 4,900,000), referring to the years from 2002 to 2012, were analyzed. The registry is based on a web-based system accessed by different users. Cases are enrolled by two different sources: clinicians working at Centers of expertise officially designated to diagnose and care patients with rare diseases and health professionals working in the Local Health Districts. Deaths of patients are monitored by Death Registry. So far, 19,547 patients with rare diseases have been registered, and 23% of them are pediatric cases. The overall raw prevalence of the rare diseases monitored in the population under study is 33.09 per 10,000 inhabitants (95% CI 32.56-33.62), whilst the overall incidence is 3.85 per 10,000 inhabitants (95% CI 3.67-4.03). The most commonly-recorded diagnoses belong to the following nosological groups: congenital malformations (Prevalence: 5.45/10,000), hematological diseases (4.83/10,000), ocular disorders (4.47/10,000), diseases of the nervous system (3.51/10,000), and metabolic disorders (2,94/10,000). Most of the deaths in the study population occur among pediatric patients with congenital malformations, and among adult cases with neurological diseases. Rare diseases of the central nervous system carry the highest fatality rate (71.36/1,000). Rare diseases explain 4.2% of general population Years of Life Lost (YLLs), comparing to 1.2% attributable to infectious diseases and 2.6% to diabetes mellitus. Our estimates of the burden of rare diseases at population level confirm that these conditions are a relevant public health issue. Our snapshot of their epidemiology is important for public health planning purposes, going to show that population-based registries are useful tools for generating health indicators relating to a considerable number of rare diseases, rather than to specific conditions.
    Orphanet Journal of Rare Diseases 03/2014; 9(1):37. DOI:10.1186/1750-1172-9-37 · 3.36 Impact Factor
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    • "A commonly propagated message is that technical challenges are insignificant hurdles in the development of RDRs [1,5] and some Information Technology experts assure the rare disease community that technology is not the stumbling block [3]. We contend that technology choices, software architecture design and software development practices, to name a few, have a dramatic impact on issues such as software sustainability, legacy software support, ease of software modification/enhancements and interoperability. "
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    ABSTRACT: Correction After publication of this work [1], we noted that we inadvertently failed to include important Acknowledgments in our final version of the manuscript. Acknowledgements The authors received funding from the Australian National Health and Medical Research Council (APP1055319) and EU FP7 Project (HEALTH.2012.2.1.1-1-C): RD Connect: An integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research. The authors wish to acknowledge their involvement in the International Rare Disease Research Consortium. References 1. [citation for].
    Source Code for Biology and Medicine 01/2014; 9(1):4. DOI:10.1186/1751-0473-9-4
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