FAIRVASC: A semantic web approach to rare disease registry integration

McGlinn, Kris; Rutherford, Matthew A.; Gisslander, Karl; Hederman, Lucy; Little, Mark; O’Sullivan, Declan

doi:10.1016/j.compbiomed.2022.105313

Cited by 15 publications

(12 citation statements)

References 19 publications

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“…To allow federated analytics across multiple registries, a process of data harmonisation across data sources is needed 14 25. The FAIRVASC ontology provides a framework for harmonisation that is scalable to existing or emerging AAV registries and cohorts 22. The use of semantic web technologies in rare disease research data integration has precedents in the medical literature.…”

Section: Discussionmentioning

confidence: 99%

“…The use of ontologies is a key part of the FAIR principles of scientific data management and stewardship, an initiative putting emphasis on machine readability and reusability of data 7 21. The development of the FAIRVASC ontology and semantic integration of vasculitis data have previously been described in detail by McGlinn et al 22. This harmonisation process results in transformation of unstructured relational data to a knowledge graph data format.…”

Section: Methodsmentioning

confidence: 99%

“… 7 21 The development of the FAIRVASC ontology and semantic integration of vasculitis data have previously been described in detail by McGlinn et al . 22 This harmonisation process results in transformation of unstructured relational data to a knowledge graph data format.…”

Section: Methodsmentioning

confidence: 99%

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Data quality and patient characteristics in European ANCA-associated vasculitis registries: data retrieval by federated querying

Gisslander,

Rutherford,

Aslett

et al. 2023

Ann Rheum Dis

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ObjectivesThis study aims to describe the data structure and harmonisation process, explore data quality and define characteristics, treatment, and outcomes of patients across six federated antineutrophil cytoplasmic antibody-associated vasculitis (AAV) registries.MethodsThrough creation of the vasculitis-specific Findable, Accessible, Interoperable, Reusable, VASCulitis ontology, we harmonised the registries and enabled semantic interoperability. We assessed data quality across the domains of uniqueness, consistency, completeness and correctness. Aggregated data were retrieved using the semantic query language SPARQL Protocol and Resource Description Framework Query Language (SPARQL) and outcome rates were assessed through random effects meta-analysis.ResultsA total of 5282 cases of AAV were identified. Uniqueness and data-type consistency were 100% across all assessed variables. Completeness and correctness varied from 49%–100% to 60%–100%, respectively. There were 2754 (52.1%) cases classified as granulomatosis with polyangiitis (GPA), 1580 (29.9%) as microscopic polyangiitis and 937 (17.7%) as eosinophilic GPA. The pattern of organ involvement included: lung in 3281 (65.1%), ear-nose-throat in 2860 (56.7%) and kidney in 2534 (50.2%). Intravenous cyclophosphamide was used as remission induction therapy in 982 (50.7%), rituximab in 505 (17.7%) and pulsed intravenous glucocorticoid use was highly variable (11%–91%). Overall mortality and incidence rates of end-stage kidney disease were 28.8 (95% CI 19.7 to 42.2) and 24.8 (95% CI 19.7 to 31.1) per 1000 patient-years, respectively.ConclusionsIn the largest reported AAV cohort-study, we federated patient registries using semantic web technologies and highlighted concerns about data quality. The comparison of patient characteristics, treatment and outcomes was hampered by heterogeneous recruitment settings.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Data quality and patient characteristics in European ANCA-associated vasculitis registries: data retrieval by federated querying

Gisslander,

Rutherford,

Aslett

et al. 2023

Ann Rheum Dis

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“…Standardized disease classifications such as the Orphanet Rare Disease Ontology (ORDO), Human Phenotype Ontology (HPO), ORPHA-codes or the International Classification of Disease ICD-9, ICD-10, or ICD-11 or some combination [ 31 , 32 , 50 , 65 , 66 ] have been proposed for data collection to ensure future interoperability and registry linkages. Being able to link to other registries facilitates knowledge creation, decision making, and improvements in clinical care that may not otherwise be possible for small RD patient populations [ 33 , 36 , 49 , 51 , 67 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, the main challenge around common data elements is reaching a consensus regarding the choice, organization, and definition of the various elements [ 25 , 70 , 71 ]. Beyond simply determining the composition of the common data elements, other challenges include data coding standards (e.g., integer, float, string, date, derived data, and file names) [ 13 , 72 , 73 ], standardized data constructs, vocabulary and terminology [ 28 , 33 , 37 , 65 , 71 , 74 ], defined variable interpretation to avoid inconsistency (e.g., sex – genotypic sex or declared sex) [ 18 , 75 ] and ontology harmonization to facilitate convergence from different terms or languages [ 56 , 65 , 76 , 77 ]. The latter necessitates consistent agreed-upon disease classification standards [ 23 , 50 , 77 ].…”

Section: Resultsmentioning

confidence: 99%

Scoping review of the recommendations and guidance for improving the quality of rare disease registries

Tarride,

Okoh,

Aryal

et al. 2024

Orphanet J Rare Dis

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Background Rare disease registries (RDRs) are valuable tools for improving clinical care and advancing research. However, they often vary qualitatively, structurally, and operationally in ways that can determine their potential utility as a source of evidence to support decision-making regarding the approval and funding of new treatments for rare diseases. Objectives The goal of this research project was to review the literature on rare disease registries and identify best practices to improve the quality of RDRs. Methods In this scoping review, we searched MEDLINE and EMBASE as well as the websites of regulatory bodies and health technology assessment agencies from 2010 to April 2023 for literature offering guidance or recommendations to ensure, improve, or maintain quality RDRs. Results The search yielded 1,175 unique references, of which 64 met the inclusion criteria. The characteristics of RDRs deemed to be relevant to their quality align with three main domains and several sub-domains considered to be best practices for quality RDRs: (1) governance (registry purpose and description; governance structure; stakeholder engagement; sustainability; ethics/legal/privacy; data governance; documentation; and training and support); (2) data (standardized disease classification; common data elements; data dictionary; data collection; data quality and assurance; and data analysis and reporting); and (3) information technology (IT) infrastructure (physical and virtual infrastructure; and software infrastructure guided by FAIR principles (Findability; Accessibility; Interoperability; and Reusability). Conclusions Although RDRs face numerous challenges due to their small and dispersed populations, RDRs can generate quality data to support healthcare decision-making through the use of standards and principles on strong governance, quality data practices, and IT infrastructure.

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