No abstract

In the field of rare diseases, registries are considered power tool to develop clinical research, to facilitate the planning of appropriate clinical trials, to improve patient care and healthcare planning. Therefore high quality data of rare diseases registries is considered to be one of the most important element in the establishment and maintenance of a registry. Data quality can be defined as the totality of features and characteristics of data set that bear on its ability to satisfy the needs that result from the intended use of the data. In the context of registries, the 'product' is data, and quality refers to data quality, meaning that the data coming into the registry have been validated, and ready for use for analysis and research. Determining the quality of data is possible through data assessment against a number of dimensions: completeness, validity; coherence and comparability; accessibility; usefulness; timeliness; prevention of duplicate records. Many others factors may influence the quality of a registry: development of standardized Case Report Form and security/safety controls of informatics infrastructure. With the growing number of rare diseases registries being established, there is a need to develop a quality validation process to evaluate the quality of each registry. A clear description of the registry is the first step when assessing data quality or the registry evaluation system. Here we report a template as a guide for helping registry owners to describe their registry.

In the last decade a high frequency of other congenital anomalies has been reported in infants with congenital hypothyroidism (CH) detected by neonatal screening. In the present study the occurrence of additional congenital malformations (CM) in the population of CH infants detected in Italy between 1991 and 1998 (n = 1420) was investigated. In Italy all of the centers in charge of screening, treatment, and follow-up of CH adhere to the Italian National Registry of infants with CH. In this study a high prevalence of additional CM (8.4%), more than 4-fold higher than that reported in the Italian population (1-2%), was found in the population of CH infants. Cardiac anomalies represented the most frequent malformations associated with CH, with a prevalence of 5.5%. However, a significant association between CH and anomalies of nervous system, eyes, and multiple CM was also observed. In conclusion, the significantly higher frequency of extrathyroidal congenital malformations reported in the CH infants than in the general population represents a further argument supporting the role of a genetic component in the etiology of CH. Investigations of the molecular mechanisms underlying developmental events of formation of thyroid and other organs represent critical steps in the knowledge of CH etiology.

In rare disease (RD) research there is a huge need to systematically collect biomaterials, phenotypic and genomic data in a standardized way and to make them Findable, Accessible, Interoperable and Reusable (FAIR). RD-Connect is a 6 years global infrastructure project initiated in November 2012 that links genomic data with patient registries, biobanks, and clinical bioinformatics tools to create a central research resource for RDs. Here we present RD-Connect Registry & Biobank Finder, a tool that helps RD researchers to find RD biobanks and registries and provide information on the availability and accessibility of content in each database. The Finder concentrates information that is currently sparse on different repositories (inventories, websites, scientific journals, technical reports, etc.), including aggregated data and metadata from participating databases. Aggregated data provided by the Finder, if appropriately checked, can be used by researchers who are trying to estimate the prevalence of a RD, to organize a clinical trial on a RD, or to estimate the volume of patients seen by different clinical centers. The Finder is also a portal to other RD-Connect tools, providing a link to the RD-Connect Sample Catalogue, a large inventory of RD biological samples available in participating biobanks for RD research. There are several kinds of users and potential uses for the RD-Connect Registry & Biobank Finder, including researchers collaborating with academia and the industry, dealing with the questions of basic, translational and/or clinical research. As of November 2017 the Finder is populated with aggregated data for 222 registries and 21 biobanks.

Rare diseases are a global public health priority; they can cause significant morbidity and mortality, can gravely affect quality of life, and can confer a social and economic burden on families and communities. These conditions are, by their nature, encountered very infrequently by clinicians. Thus, clinical practice guidelines are potentially very helpful in supporting clinical decisions, health policy and resource allocation. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system is a structured and transparent approach to developing and presenting summaries of evidence, grading its quality, and then transparently interpreting the available evidence to make recommendations in health care. GRADE has been adopted widely. However, its use in creating guidelines for rare diseases – which are often plagued by a paucity of high quality evidence – has not yet been explored. RARE-Bestpractices is a project to create and populate a platform for sharing best practices for management of rare diseases. A major aim of this project is to ensure that European Union countries have the capacity to produce high quality clinical practice guidelines for rare diseases. On February 12, 2013 at the Istituto Superiore di Sanità, in Rome, Italy, the RARE-Bestpractices group held the first of a series of 2 workshops to discuss methodology for creating clinical practice guidelines, and explore issues specific to rare diseases. This paper summarizes key results of the first workshop, and explores how the current GRADE approach might (or might not) work for rare diseases. Avenues for future research are also identified.