Coding undiagnosed rare disease patients in health information systems: recommendations from the RD-CODE project

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.

ORPHAcodes use for the coding of rare diseases: comparison of the accuracy and cross country comparability

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.

Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes

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.

Harmonising phenomics information for a better interoperability in the rare disease field

HIPBI-RD (Harmonising phenomics information for a better interoperability in the rare disease field) is a three-year project which started in 2016 funded via the E-Rare 3 ERA-NET program. This project builds on three resources largely adopted by the rare disease (RD) community: Orphanet, its ontology ORDO (the Orphanet Rare Disease Ontology), HPO (the Human Phenotype Ontology) as well as PhenoTips software for the capture and sharing of structured phenotypic data for RD patients. Our project is further supported by resources developed by the European Bioinformatics Institute and the Garvan Institute. HIPBI-RD aims to provide the community with an integrated, RD-specific bioinformatics ecosystem that will harmonise the way phenomics information is stored in databases and patient files worldwide, and thereby contribute to interoperability. This ecosystem will consist of a suite of tools and ontologies, optimized to work together, and made available through commonly used software repositories. The project workplan follows three main objectives: The HIPBI-RD ecosystem will contribute to the interpretation of variants identified through exome and full genome sequencing by harmonising the way phenotypic information is collected, thus improving diagnostics and delineation of RD. The ultimate goal of HIPBI-RD is to provide a resource that will contribute to bridging genome-scale biology and a disease-centered view on human pathobiology. Achievements in Year 1.

[Orphanet and its consortium: where to find expert-validated information on rare diseases]

There are approximately 6000 rare diseases, and 80% of them are genetic. In Europe, a disease is considered rare when it affects no more than one person in 2000. In France, two to three million people are affected, while nearly 30 million others are affected across Europe (5-8% of the whole European population). The majority of rare diseases are poorly understood by health professionals. Due to the lack of sufficient scientific and medical knowledge, many patients are misdiagnosed, which results in delays in care that can be harmful. Because many rare diseases are often associated with neurological manifestations, the neurologist in his daily practice may often encounter these complex diseases that require special care as well as a multidisciplinary approach. Orphanet is the reference portal for rare diseases. Freely accessible on the Internet, it is a non-profit service officially supported by the French Ministry of Health and the European Commission. Its mission is to keep healthcare professionals and patients informed and, by so doing, it contributes to improvements in the diagnosis and treatment of rare diseases. It is currently the only project that establishes a link between diseases and any published information concerning them, and the appropriate services for patients as well as healthcare professionals. Orphanet is currently the most comprehensive site in terms of referenced and documented data, and it has in just a few years become the global reference portal for rare diseases and orphan drugs for all audiences. Orphanet generates a million page views per month. The site is available in six languages (English, French, Spanish, Italian, German and Portuguese) and offers a range of services, including: an inventory, classification and peer-reviewed encyclopedia of rare diseases along with the associated genes (more than 2000 diseases with neurological manifestations are described); a diagnostic support tool; clinical and emergency guidelines; a directory of specialised services in 37 partner countries; an encyclopedia aimed at the general public; an inventory of orphan drugs; downloadable thematic studies and reports on such subjects as the prevalence of rare diseases, orphan drugs, aids and services for patients; and numerous links to other sources of information. Five to ten new rare diseases are described every month, which represents a major challenge for health professionals in terms of keeping their knowledge up to date. The Orphanet website content is expert-validated and updated continuously to respond in real time.

Integration of distinct intracellular signaling pathways at distal regulatory elements directs T-bet expression in human CD4+ T cells

T-bet is a key regulator controlling Th1 cell development. This factor is not expressed in naive CD4(+) T cells, and the mechanisms controlling expression of T-bet are incompletely understood. In this study, we defined regulatory elements at the human T-bet locus and determined how signals originating at the TCR and at cytokine receptors are integrated to induce chromatin modifications and expression of this gene during human Th1 cell differentiation. We found that T cell activation induced two strong DNase I-hypersensitive sites (HS) and rapid histone acetylation at these elements in CD4(+) T cells. Histone acetylation and T-bet expression were strongly inhibited by cyclosporine A, and we detected binding of NF-AT to a HS in vivo. IL-12 and IFN-gamma signaling alone were not sufficient to induce T-bet expression in naive CD4(+) T cells, but enhanced T-bet expression in TCR/CD28-stimulated cells. We detected a third HS 12 kb upstream of the mRNA start site only in developing Th1 cells, which was bound by IL-12-induced STAT4. Our data suggest that T-bet locus remodeling and gene expression are initiated by TCR-induced NF-AT recruitment and amplified by IL-12-mediated STAT4 binding to distinct distal regulatory elements during human Th1 cell differentiation.

Genetic and epigenetic networks controlling T helper 1 cell differentiation

Significant progress has been made during the past years in our understanding of the mechanisms that control the differentiation of naïve CD4(+) T cells into effector T-cell subsets with distinct functional properties. Previous work allowed the identification of key molecules involved in regulating this highly complex process, such as cytokines and their receptors, signal transducers and transcription factors. More recently, the emphasis of research in this field has been to elucidate how the multiplicity of signals is integrated to shape a T helper subset-specific gene-expression program controlling differentiation and effector functions. In this review we will highlight advances that have been made in unravelling the genetic and epigenetic networks controlling differentiation of naïve CD4(+) T cells into interferon-gamma(IFN-gamma)-secreting T helper type 1 (Th1) cells.

Natural variation of model mutant phenotypes in Ciona intestinalis

BACKGROUND

The study of ascidians (Chordata, Tunicata) has made a considerable contribution to our understanding of the origin and evolution of basal chordates. To provide further information to support forward genetics in Ciona intestinalis, we used a combination of natural variation and neutral population genetics as an approach for the systematic identification of new mutations. In addition to the significance of developmental variation for phenotype-driven studies, this approach can encompass important implications in evolutionary and population biology.

METHODOLOGY/PRINCIPAL FINDINGS

Here, we report a preliminary survey for naturally occurring mutations in three geographically interconnected populations of C. intestinalis. The influence of historical, geographical and environmental factors on the distribution of abnormal phenotypes was assessed by means of 12 microsatellites. We identified 37 possible mutant loci with stereotyped defects in embryonic development that segregate in a way typical of recessive alleles. Local populations were found to differ in genetic organization and frequency distribution of phenotypic classes.

CONCLUSIONS/SIGNIFICANCE

Natural genetic polymorphism of C. intestinalis constitutes a valuable source of phenotypes for studying embryonic development in ascidians. Correlating genetic structure and the occurrence of abnormal phenotypes is a crucial focus for understanding the selective forces that shape natural finite populations, and may provide insights of great importance into the evolutionary mechanisms that generate animal diversity.