How many rare diseases are there?

Haendel, Melissa; Vasilevsky, Nicole; Unni, Deepak; Bologa, Cristian; Harris, Nomi L.; Rehm, Heidi L.; Hamosh, Ada; Baynam, Gareth; Groza, Tudor; McMurry, Julie A.; Dawkins, Hugh; Rath, Ana; Thaxon, Courtney; Bocci, Giovanni; Joachimiak, Marcin P.; Köhler, Sebastian; Robinson, Peter N.; Mungall, Chris; Oprea, Tudor I.

doi:10.1038/d41573-019-00180-y

Cited by 302 publications

(238 citation statements)

References 7 publications

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“…Although there is no absolute definition of a rare disease, the US Rare Diseases Act of 2002 defines rare diseases as those that occur in fewer than 200,000 patients worldwide, and the National Organization for Rare Disorders (NORD,) registry lists more than 1,200 diseases. Others have noted that the true number of rare diseases is unknown, and have called for more research to define them [2].…”

Section: Introductionmentioning

confidence: 99%

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria

et al. 2020

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Background With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often not diagnosed or delayed in diagnosis by clinicians who encounter them infrequently. One such rare disease that may be amenable to EHR-based detection is acute hepatic porphyria (AHP). AHP consists of a family of rare, metabolic diseases characterized by potentially life-threatening acute attacks and chronic debilitating symptoms. The goal of this study was to apply machine learning and knowledge engineering to a large extract of EHR data to determine whether they could be effective in identifying patients not previously tested for AHP who should receive a proper diagnostic workup for AHP.

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Section: Introductionmentioning

confidence: 99%

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria

et al. 2020

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“…Currently there has yet be to a definitive answer to the question, "How many rare diseases are there?" 24 Applying the UMLS globally would help with this question by enabling cross-references to be made between different rare disease resources without data discrepancy, as well as supporting further rare disease data harmonization.…”

Section: Application Of the Umls For Data Normalizationmentioning

confidence: 99%

Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization

et al. 2020

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Objective In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease. Methods We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study. Results We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%). Conclusion The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.

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“…Rare bone diseases (RBD) have, until recently, been a largely neglected area in healthcare. Their rarity and heterogeneity have unfortunately hindered their exploration at both clinical and scientific levels, even though more than 500 of the ~7,000 rare diseases are bone disorders ( 1 , 2 ). The estimated incidence of RBD can vary, from around 15.7/100000 births for skeletal dysplasias ( 3 ) which are the most common, to ultra-rare disorders of which only a few patients exist in the world, such as spondylo-ocular syndrome ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Collaboration Around Rare Bone Diseases Leads to the Unique Organizational Incentive of the Amsterdam Bone Center

et al. 2020

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In the field of rare bone diseases in particular, a broad care team of specialists embedded in multidisciplinary clinical and research environment is essential to generate new therapeutic solutions and approaches to care. Collaboration among clinical and research departments within a University Medical Center is often difficult to establish, and may be hindered by competition and non-equivalent cooperation inherent in a hierarchical structure. Here we describe the "collaborative organizational model" of the Amsterdam Bone Center (ABC), which emerged from and benefited the rare bone disease team. This team is often confronted with pathologically complex and under-investigated diseases. We describe the benefits of this model that still guarantees the autonomy of each team member, but combines and focuses our collective expertise on a clear shared goal, enabling us to capture synergistic and innovative opportunities for the patient, while avoiding self-interest and possible harmful competition.

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How many rare diseases are there?

Abstract: 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.

Cited by 302 publications

References 7 publications

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria

Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization

Collaboration Around Rare Bone Diseases Leads to the Unique Organizational Incentive of the Amsterdam Bone Center

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