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Background The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare disease nomenclature/nosology). Methods A variety of approaches were used including automated matching to existing resources and extensive manual curation. To achieve the latter, a study group including clinicians, patient representatives and ontology developers from 17 countries was formed. A broad range of terms was discussed and validated during a dedicated workshop attended by 60 members of the group. Results A comprehensive, structured and well-defined set of terms has been agreed on including 1106 terms relating to ocular phenotypes (HPO) and 1202 terms relating to rare eye disease nomenclature (ORDO). These terms and their relevant annotations can be accessed in http://www.human-phenotype-ontology.org/ and http://www.orpha.net/ ; comments, corrections, suggestions and requests for new terms can be made through these websites. This is an ongoing, community-driven endeavour and both HPO and ORDO are regularly updated. Conclusions To our knowledge, this is the first effort of such scale to provide terminology standards for the rare eye disease community. We hope that this work will not only improve coding and standardise information exchange in clinical care and research, but also it will catalyse the transition to an evidence-based precision ophthalmology paradigm.

A lack of robust knowledge of the number of rare diseases and the number of people affected by them limits the development of approaches to ameliorate the substantial cumulative burden of rare diseases. Here, we call for coordinated efforts to more precisely define rare diseases.A lack of robust knowledge of the number of rare diseases and the number of people affected by them limits the development of approaches to ameliorate the substantial cumulative burden of rare diseases. Here, we call for coordinated efforts to more precisely define rare diseases.

One of the most common rare inherited bleeding disorders, congenital factor VII (FVII) deficiency typically has a milder bleeding phenotype than other rare bleeding disorders. Categorizing severity in terms of factor activity associated with hemophilia (severe <1%, moderate 1%-5%, mild 6%-40%) has led to the observation that bleeding phenotype does not follow closely with FVII activity. Over the past decade, large-scale global registries have investigated bleeding phenotype more thoroughly. The International Society on Thrombosis and Haemostasis has reclassified FVII deficiency as follows: severe, FVII <10%, risk of spontaneous major bleeding; moderate, FVII 10%-20%, risk of mild spontaneous or triggered bleeding; mild, FVII 20%-50%, mostly asymptomatic disease. Eleven illustrative cases of congenital FVII deficiency adapted from clinical practice are described to demonstrate the variability in presentation and in relation to FVII activity levels. Severe FVII deficiency usually presents at a young age and carries the risk of intracranial hemorrhage, hemarthrosis, and other major bleeds. Moderate FVII deficiency tends to present later, often in adolescence and particularly in girls as they reach menarche. Milder disease may not be apparent until found incidentally on preoperative testing, during pregnancy/childbirth, or following unexplained bleeding when faced with hemostatic challenges. It is important for health care professionals to be aware of the new definitions of severity and typical presentations of congenital FVII deficiency. Failure to appreciate the risks of major bleeding, including intracerebral hemorrhage in those with FVII activity <10%, may put particularly young children at risk.

Data-organizing methods have been in place for centuries, but very large data sets have come into being relatively recently. The authors describe terminologies, ontologies, and the changes needed to permit analyses of “big data” that might better serve medical decision making.

Japan has been facing challenges relating to specifically defined rare diseases, called Nan-Byo in Japanese (literally 'difficult'+'illness'), and has already taken measures for them since 1972. This governmental support has surely benefited Nan-Byo patients; however, those suffering from medically unidentified conditions do not fall into this scheme and thus still confront difficulty in obtaining an examination, a diagnosis, and a treatment. To identify such rare and often undiagnosed diseases, we must integrate systematic diagnosis by medical experts with phenotypic and genetic data matching. Thus, in collaboration with Nan-Byo researchers and the Japanese universal healthcare system, the Japan Agency for Medical Research and Development launched the Initiative on Rare and Undiagnosed Diseases (IRUD) in 2015. IRUD is an ambitious challenge to construct a comprehensive medical network and an internationally compatible data-sharing framework. Synergizing with existing next-generation sequencing capabilities and other infrastructure, the nationwide medical research consortium has successfully grown to accept more than 2000 undiagnosed registrants by December 2016. We also aim at expanding the concept of microattribution throughout the initiative; that is, proper credit as collaborators shall be given to local primary care physicians, nurses and paramedics, patients, their family members, and those supporting the affected individuals whenever appropriate. As it shares many challenges among similar global efforts, IRUD's future successes and lessons learned will significantly contribute to ongoing international endeavors, involving players in basic research, applied research, and societal implementation.

Rare disease detection and classification is one of the most significant challenges in the application of Natural Language Processing techniques to the analysis and extraction of information from biomedical texts. In this paper, we present a novel research focused on the detection and classification of rare diseases in clinical notes extracted from a cohort of pediatric patients from the Community of Madrid in Spain. From a set of collected and anonymized medical records, we propose a semi-supervised, keyphrase-based system to perform an initial detection of mentions of rare diseases, which is then validated and refined by experts to build a consolidated dataset concerning a subset of different rare diseases. Based on this dataset, we carry out a series of experiments for rare disease classification using both a semi-supervised technique and state-of-the-art supervised systems based on both discriminative and generative models. A detailed case analysis provides insights on which systems excel in specific scenarios and why. The validated dataset contains a total of 1900 annotated texts containing mentions to rare diseases. Experiments on this dataset show that the best supervised models improve the performance of the semi-supervised system by more than 10% (78.74% vs 67.37% micro-average F-Measure), individually enhancing the classification of a significant number of diseases in the dataset. State-of-the-art supervised systems are able to offer promising results on the detection and classification of rare diseases in clinical texts, even in cases for which the amount of annotated information is low. On the other hand, semi-supervised models present interesting capabilities for dealing with limited information and data in the field.

There is a great unmet medical need for development of molecularly characterizing modalities to assist in the complex classification of rare kidney cancers, some of which are diagnosed as unclassified renal cell carcinoma (unclassified RCC) due to complex histology. Here we show utility of the comparative transcriptome atlas as an assistive modality for complex classification of rare kidney cancers. Whereas whole genome sequencing (WGS) of 52 rare kidney cancers identifies very few clinically significant variants in a subset of cases, unsupervised clustering results of RNA-seq data from 219 renal tumors including 140 rare kidney cancers are largely consistent with the histological classification based on WHO2022 classification. Additionally, the comparative transcriptome atlas may provide an opportunity to define the molecular characteristics of unclassified RCC and might predict patient outcome. These findings support the comparative transcriptome atlas as an assistive modality for complex classification of rare kidney cancers. Rare subtypes of renal cancers can be difficult to diagnose. Here, the authors use transcriptional analysis to show utility in aiding histological classification.

Summary This article reports a taxonomic classification of rare skeletal diseases based on metabolic phenotypes. It was prepared by The Skeletal Rare Diseases Working Group of the International Osteoporosis Foundation (IOF) and includes 116 OMIM phenotypes with 86 affected genes. Introduction Rare skeletal metabolic diseases comprise a group of diseases commonly associated with severe clinical consequences. In recent years, the description of the clinical phenotypes and radiographic features of several genetic bone disorders was paralleled by the discovery of key molecular pathways involved in the regulation of bone and mineral metabolism. Including this information in the description and classification of rare skeletal diseases may improve the recognition and management of affected patients. Methods IOF recognized this need and formed a Skeletal Rare Diseases Working Group (SRD-WG) of basic and clinical scientists who developed a taxonomy of rare skeletal diseases based on their metabolic pathogenesis. Results This taxonomy of rare genetic metabolic bone disorders (RGMBDs) comprises 116 OMIM phenotypes, with 86 affected genes related to bone and mineral homeostasis. The diseases were divided into four major groups, namely, disorders due to altered osteoclast, osteoblast, or osteocyte activity; disorders due to altered bone matrix proteins; disorders due to altered bone microenvironmental regulators; and disorders due to deranged calciotropic hormonal activity. Conclusions This article provides the first comprehensive taxonomy of rare metabolic skeletal diseases based on deranged metabolic activity. This classification will help in the development of common and shared diagnostic and therapeutic pathways for these patients and also in the creation of international registries of rare skeletal diseases, the first step for the development of genetic tests based on next generation sequencing and for performing large intervention trials to assess efficacy of orphan drugs.

Background Because of their individual rarity, genetic diseases and other types of rare diseases are under-represented in healthcare coding systems; this contributes to a lack of ascertainment and recognition of their importance for healthcare planning and resource allocation, and prevents clinical research from being performed. Methods Orphanet was given the task to develop an inventory of rare diseases and a classification system which could serve as a template to update International terminologies. When the World Health Organization (WHO) launched the revision process of the International Classification of Diseases (ICD), a Topic Advisory Group for rare diseases was established, managed by Orphanet and funded by the European Commission. Results So far 5,400 rare diseases listed in the Orphanet database have an endorsed representation in the foundation layer of ICD-11, and are thus provided with a unique identifier in the Beta version of ICD-11, which is 10 times more than in ICD10. A rare disease linearization is also planned. The current beta version is open for public consultation and comments, and to be used for field testing. The adoption by the World Health Assembly is planned for 2017. Conclusions The overall revision process was carried out with very limited means considering its scope, ambition and strategic significance, and experienced significant hurdles and setbacks. The lack of funding impacted the level of professionalism that could be attained. The contrast between the initially declared goals and the currently foreseen final product is disappointing. In the context of uncertainty around the outcome of the field testing and the potential willingness of countries to adopt this new version, the European Commission Expert Group on Rare Diseases adopted in November 2014 a recommendation for health care coding systems to consider using ORPHA codes in addition to ICD10 codes for rare diseases having no specific ICD10 codes. The Orphanet terminology, classifications and mappings with other terminologies are freely available at www.orphadata.org .

ObjectivesThis study sheds light on the available global definitions, classifications, and criteria used for rare diseases (RDs), ultrarare diseases (URDs), orphan drugs (ODs) and ultraorphan drugs (UODs) and provides insights into the rationale behind these definitions.DesignA systematic literature review was conducted to identify existing definitions and the criteria used to define RDs, ODs and their subtypes.Data sourcesSearches were performed in the PubMed/Medline, Embase, Scopus and Web of Science (Science and Social Sciences Citation Index) databases covering articles published from 1985 to 2021.Eligibility criteria for selecting studiesEnglish-language studies on the general human population were included if they provided definitions or criteria for RDs, ODs and/or their subtypes without restrictions on publication year, country or jurisdiction.Data extraction and synthesisTwo independent reviewers conducted the search, screening and data extraction. Narrative synthesis, content analysis and descriptive analyses were conducted to extract and categorise definitions and criteria from these sources. Study quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools.ResultsOnline searches identified 2712 published articles. Only 93 articles met the inclusion criteria, with 209 distinct definitions extracted. Specifically, 93 of these articles pertained to 119 RDs, 11 URDs, 67 ODs and 12 UODs. These definitions varied in their reliance on prevalence based and other contextual criteria.ConclusionPrevalence-based criteria alone pose challenges, as disease frequencies differ by country. Establishing country-specific definitions can enhance understanding, support intercountry evaluations, improve healthcare efficiency and access to ODs, and strengthen equity and equality in healthcare. Such efforts would also promote research and development and support better outcomes for patients with complex and rare conditions.PROSPERO registration numberCRD42021252701.