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Rare diseases, an emerging global public health priority, require an evidence-based estimate of the global point prevalence to inform public policy. We used the publicly available epidemiological data in the Orphanet database to calculate such a prevalence estimate. Overall, Orphanet contains information on 6172 unique rare diseases; 71.9% of which are genetic and 69.9% which are exclusively pediatric onset. Global point prevalence was calculated using rare disease prevalence data for predefined geographic regions from the ‘Orphanet Epidemiological file’ (http://www.orphadata.org/cgi-bin/epidemio.html). Of the 5304 diseases defined by point prevalence, 84.5% of those analysed have a point prevalence of <1/1 000 000. However 77.3–80.7% of the population burden of rare diseases is attributable to the 4.2% (n = 149) diseases in the most common prevalence range (1–5 per 10 000). Consequently national definitions of ‘Rare Diseases’ (ranging from prevalence of 5 to 80 per 100 000) represent a variable number of rare disease patients despite sharing the majority of rare disease in their scope. Our analysis yields a conservative, evidence-based estimate for the population prevalence of rare diseases of 3.5–5.9%, which equates to 263–446 million persons affected globally at any point in time. This figure is derived from data from 67.6% of the prevalent rare diseases; using the European definition of 5 per 10 000; and excluding rare cancers, infectious diseases, and poisonings. Future registry research and the implementation of rare disease codification in healthcare systems will further refine the estimates.

Clinical practice guidelines (CPGs) for rare diseases (RDs) are scarce, may be difficult to identify through Internet searches and may vary in quality depending on the source and methodology used. In order to contribute to the improvement of the diagnosis, treatment and care of patients, Orphanet (www.orpha.net) has set up a procedure for the selection, quality evaluation and dissemination of CPGs, with the aim to provide easy access to relevant, accurate and specific recommendations for the management of RDs. This article provides an analysis of selected CPGs by medical domain coverage, prevalence of diseases, languages and type of producer, and addresses the variability in CPG quality and availability. CPGs are identified via bibliographic databases, websites of research networks, expert centres or medical societies. They are assessed according to quality criteria derived from the Appraisal of Guidelines, REsearch and Evaluation (AGREE II) Instrument. Only open access CPGs and documents for which permission from the copyright holders has been obtained are disseminated on the Orphanet website. From January 2012 to July 2015, 277 CPGs were disseminated, representing coverage of 1,122 groups of diseases, diseases or subtypes in the Orphanet database. No language restriction is applied, and so far 10 languages are represented, with a predominance of CPGs in English, French and German (92% of all CPGs). A large proportion of diseases with identified CPGs belong to rare oncologic, neurologic, hematologic diseases or developmental anomalies. The Orphanet project on CPG collection, evaluation and dissemination is a continuous process, with regular addition of new guidelines, and updates. CPGs meeting the quality criteria are integrated to the Orphanet database of rare diseases, together with other types of textual information and the appropriate services for patients, researchers and healthcare professionals in 40 countries.

Improving health and social equity for persons living with a rare disease (PLWRD) is increasingly recognized as a global policy priority. However, there is currently no international alignment on how to define and describe rare diseases. A global reference is needed to establish a mutual understanding to inform a wide range of stakeholders for actions. A multi-stakeholder, global panel of rare disease experts, came together and developed an Operational Description of Rare Diseases. This reference describes which diseases are considered rare, how many persons are affected and why the rare disease population demands specific attention. The operational description of rare diseases is framed in two parts: a core definition of rare diseases, complemented by a descriptive framework of rare diseases. The core definition includes parameters that permit the identification of which diseases are considered rare, and how many persons are affected. The descriptive framework elaborates on the impact and burden of rare diseases on patients, their caregivers and families, healthcare systems, and society overall. The Operational Description of Rare Diseases establishes a common point of reference for decision-makers across the world who strive to understand and address the unmet needs of persons living with a rare disease. Adoption of this reference is essential to improving the visibility of rare conditions in health systems across the world. Greater recognition of the burden of rare diseases will motivate new actions and policies to address the unmet needs of the rare disease community.

The scale of the “rare disease problem”—thousands of rare diseases, the vast preponderance of them with no approved treatment, and decades‐long diagnostic odysseys for many patients—led to the realization that the time had arrived for global cooperation and collaboration among the many stakeholders active in rare diseases research, to capitalize on these proofs of principle, and maximize the output of rare diseases research efforts around the world. Major public‐sector research initiatives focused in this area have emerged or expanded in many countries, most notably from the US National Institutes of Health (NIH), the European Commission (EC), and the newly formed Japan Agency for Medical Research and Development (AMED). [...]they needed to be easily understood by a wide variety of stakeholders and audiences, while also being bold and transformational. [...]it was determined to organize the process on four levels—Vision, Goals, Activities, and Metrics. [...]IRDiRC also believes that the Vision is achievable with all stakeholders’ commitment, cooperation, and collaboration. [...]the challenge inherent in the Vision is intentional, aimed at galvanizing the broad rare disease community, within IRDiRC and outside it, to not only enable universal diagnosis and treatment, but also ensure that these interventions reach people with rare diseases, and have the intended positive impact on their health and well‐being.

Background In European Union countries, any disease affecting less than 5 people in 10,000 is considered rare. As expertise is scarce and rare diseases (RD) are complex, RD patients can remain undiagnosed for many years. The period of searching for a diagnosis, called diagnostic delay, sometimes leads to a diagnostic dead end when the patient’s disease is impossible to diagnose after undergoing all available investigations. In recent years, extensive efforts have been made to support the implementation of ORPHA nomenclature in health information systems (HIS) so as to allow RD coding. Until recently, the nomenclature only encompassed codes for specific RD. Persons suffering from a suspected RD who could not be diagnosed even after full investigation, could not be coded with ORPHAcodes. The recognition of the RD status is necessary for patients, even if they do not have a precise diagnosis. It can facilitate reimbursement of care, be socially and psychologically empowering, and grant them access to scientific advances. Results The RD-CODE project aimed at making those patients identifiable in HIS in order to produce crucial epidemiological data. Undiagnosed patients were defined as patients for whom no clinically-known disorder could be confirmed by an expert center after all reasonable efforts to obtain a diagnosis according to the state-of-the-art and diagnostic capabilities available. Three recommendations for the coding of undiagnosed RD patients were produced by a multi-stakeholder panel of experts: 1/ Capture the diagnostic ascertainment for all rare disease cases; 2/ Use the newly created ORPHAcode (ORPHA:616874 “Rare disorder without a determined diagnosis after full investigation”), available in the Orphanet nomenclature: as the code is new, guidelines are essential to ensure its correct and homogeneous use for undiagnosed patients’ identification in Europe and beyond; 3/ Use additional descriptors in registries. Conclusions The recommendations can now be implemented in HIS (electronic health records and/or registries) and could be a game-changer for patients, clinicians and researchers in the field, enabling assessment of the RD population, including undiagnosed patients, adaptation of policy measures including financing for care and research programs, and to improved access of undiagnosed patients to research programs.

Background Estimates of rare disease (RD) population impact in terms of number of affected patients and accurate disease definition is hampered by their under-representation in current coding systems. This study tested the use of a specific RD codification system (ORPHAcodes) in five European countries/regions (Czech Republic, Malta, Romania, Spain, Veneto region-Italy) across different data sources over the period January 2019-September 2021. Results Overall, 3133 ORPHAcodes were used to describe RD diagnoses, mainly corresponding to the disease/subtype of disease aggregation level of the Orphanet classification (82.2%). More than half of the ORPHAcodes (53.6%) described diseases having a very low prevalence (< 1 case per million), and most commonly captured rare developmental defects during embryogenesis (31.3%) and rare neurological diseases (17.6%). ORPHAcodes described disease entities more precisely than corresponding ICD-10 codes in 83.4% of cases. Conclusions ORPHAcodes were found to be a versatile resource for the coding of RD, able to assure easiness of use and inter-country comparability across population and hospital databases. Future research on the impact of ORPHAcoding as to the impact of numbers of RD patients with improved coding in health information systems is needed to inform on the real magnitude of this public health issue.

Background The Spina Bifida and other Dysraphisms working group (SBoD WG) is an interdisciplinary group, comprising experts on spinal dysraphism from 11 European countries. In 2022, the SBoD WG was tasked by 2 European Rare Disease Networks (ERN ITHACA and ERN eUROGEN) to revise the Orphanet classification of spinal dysraphism. Over the past two decades numerous subcategories of spinal dysraphism have been described in the medical literature resulting in a proliferation of terms, numerous synonyms and variously applied definitions. In the light of this, a revision of all terms and definitions was conducted by a Delphi approach in 3 steps by neurosurgeons (fetal/paediatric/adult), urologists (paediatric/adult), rehabilitation medicine specialists, fetal medicine and perinatal imaging specialists, geneticists, pathologists, nephrologists and patient representatives, all members of the International Federation for Spina Bifida and Hydrocephalus (IFSBH). Results In the first instance, 39 experts reviewed and refined the terminology that could be used to describe the anatomical characteristics of all forms of SBoD. At the second stage, 24 experts established terms and unambiguous definitions for 16 skin findings, 7 bone findings and 33 spinal cord findings that were considered essential features capable of describing all forms of spinal dysraphism. In the third stage, 29 experts validated 24 spinal dysraphic anomalies using these pre-agreed findings. All terms and definitions were validated by vote with a threshold of 80% approval (abstention was permitted). No terms with disagreement were retained in the subsequent classification. The revised SBoD classification was transferred to the Orphanet nomenclature ( ORPHA:823 ). 16 existing ORPHAcodes were deemed obsolete, 10 ORPHAcodes were updated (terms and/or textual definitions) and 25 new ORPHAcodes were created. The SBoD working group also developed a ‘decision tree’ for new users, to assist them in the practical aspects of applying the revised classification and designating appropriate ORPHAcodes. Conclusions An update of the Orphanet Classification of spinal dysraphism was conducted by a European interdisciplinary group of experts encompassing all aspects of healthcare for patients with these disorders. This new classification, based on essential skin, bone and spinal cord findings offers a more logical and reproducible means to categorise SBoD. It is hoped that this will permit more precise disease delineation, consistent diagnostic accuracy and better prognostication.

Purpose Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments. Methods We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data. Results Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs. Conclusion Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

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.

Professor Michael Larsen, who is a member of the ERN-EYE Ontology Study Group and co-chair of Workgroup on Retinal Rare Eye Diseases (WG1), was inadvertently omitted from the author list in the Acknowledgements section of the original article [1].