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Gisslander K, White A, Aslett L, Hrušková Z, Lamprecht P, Musiał J, Nazeer J, Ng J, O'Sullivan D, Puéchal X, Rutherford M, Segelmark M, Terrier B, Tesař V, Tesi M, Vaglio A, Wójcik K, Little MA, Mohammad AJ. Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort. THE LANCET. RHEUMATOLOGY 2024:S2665-9913(24)00187-5. [PMID: 39182506 DOI: 10.1016/s2665-9913(24)00187-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/19/2024] [Accepted: 06/19/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a heterogenous autoimmune disease. While traditionally stratified into two conditions, granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), the subclassification of ANCA-associated vasculitis is subject to continued debate. Here we aim to identify phenotypically distinct subgroups and develop a data-driven subclassification of ANCA-associated vasculitis, using a large real-world dataset. METHODS In the collaborative data reuse project FAIRVASC (Findable, Accessible, Interoperable, Reusable, Vasculitis), registry records of patients with ANCA-associated vasculitis were retrieved from six European vasculitis registries: the Czech Registry of ANCA-associated vasculitis (Czech Republic), the French Vasculitis Study Group Registry (FVSG; France), the Joint Vasculitis Registry in German-speaking Countries (GeVas; Germany), the Polish Vasculitis Registry (POLVAS; Poland), the Irish Rare Kidney Disease Registry (RKD; Ireland), and the Skåne Vasculitis Cohort (Sweden). We performed model-based clustering of 17 mixed-type clinical variables using a parsimonious mixture of two latent Gaussian variable models. Clinical validation of the optimal cluster solution was made through summary statistics of the clusters' demography, phenotypic and serological characteristics, and outcome. The predictive value of models featuring the cluster affiliations were compared with classifications based on clinical diagnosis and ANCA specificity. People with lived experience were involved throughout the FAIRVASVC project. FINDINGS A total of 3868 patients diagnosed with ANCA-associated vasculitis between Nov 1, 1966, and March 1, 2023, were included in the study across the six registries (Czech Registry n=371, FVSG n=1780, GeVas n=135, POLVAS n=792, RKD n=439, and Skåne Vasculitis Cohort n=351). There were 2434 (62·9%) patients with GPA and 1434 (37·1%) with MPA. Mean age at diagnosis was 57·2 years (SD 16·4); 2006 (51·9%) of 3867 patients were men and 1861 (48·1%) were women. We identified five clusters, with distinct phenotype, biochemical presentation, and disease outcome. Three clusters were characterised by kidney involvement: one severe kidney cluster (555 [14·3%] of 3868 patients) with high C-reactive protein (CRP) and serum creatinine concentrations, and variable ANCA specificity (SK cluster); one myeloperoxidase (MPO)-ANCA-positive kidney involvement cluster (782 [20·2%]) with limited extrarenal disease (MPO-K cluster); and one proteinase 3 (PR3)-ANCA-positive kidney involvement cluster (683 [17·7%]) with widespread extrarenal disease (PR3-K cluster). Two clusters were characterised by relative absence of kidney involvement: one was a predominantly PR3-ANCA-positive cluster (1202 [31·1%]) with inflammatory multisystem disease (IMS cluster), and one was a cluster (646 [16·7%]) with predominantly ear-nose-throat involvement and low CRP, with mainly younger patients (YR cluster). Compared with models fitted with clinical diagnosis or ANCA status, cluster-assigned models demonstrated improved predictive power with respect to both patient and kidney survival. INTERPRETATION Our study reinforces the view that ANCA-associated vasculitis is not merely a binary construct. Data-driven subclassification of ANCA-associated vasculitis exhibits higher predictive value than current approaches for key outcomes. FUNDING European Union's Horizon 2020 research and innovation programme under the European Joint Programme on Rare Diseases.
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Affiliation(s)
- Karl Gisslander
- Rheumatology, Department of Clinical Sciences, Lund University, Lund, Sweden.
| | - Arthur White
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland; ADAPT Centre, Trinity College Dublin, Dublin, Ireland
| | - Louis Aslett
- Department of Mathematical Sciences, Durham University, Durham, UK
| | - Zdenka Hrušková
- Department of Nephrology, General University Hospital in Prague and First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Peter Lamprecht
- Department of Rheumatology and Clinical Immunology, University of Lübeck, Lübeck, Germany
| | - Jacek Musiał
- II Chair of Internal Medicine, Faculty of Medicine, Jagiellonian University Medical College, Kraków, Poland
| | | | - James Ng
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | | | - Xavier Puéchal
- National Referral Center for Rare Systemic Autoimmune Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France; French Vasculitis Study Group, Paris, France
| | | | - Mårten Segelmark
- Department of Clinical Sciences, Lund University and Department of Endocrinology, Nephrology and Rheumatology, Skåne University Hospital, Lund, Sweden
| | - Benjamin Terrier
- National Referral Center for Rare Systemic Autoimmune Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France; French Vasculitis Study Group, Paris, France
| | - Vladimir Tesař
- Department of Nephrology, General University Hospital in Prague and First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Michelangelo Tesi
- Nephrology and Dialysis Unit, Azienda Ospedaliera Universitaria Meyer IRCCS, Florence, Italy
| | - Augusto Vaglio
- Nephrology and Dialysis Unit, Azienda Ospedaliera Universitaria Meyer IRCCS, Florence, Italy; Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Krzysztof Wójcik
- II Chair of Internal Medicine, Faculty of Medicine, Jagiellonian University Medical College, Kraków, Poland
| | - Mark A Little
- ADAPT Centre, Trinity College Dublin, Dublin, Ireland; Trinity Kidney Centre, Trinity Translational Medicine Institute, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Aladdin J Mohammad
- Rheumatology, Department of Clinical Sciences, Lund University, Lund, Sweden; Department of Medicine, University of Cambridge, Cambridge, UK
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Tarride JE, Okoh A, Aryal K, Prada C, Milinkovic D, Keepanasseril A, Iorio A. Scoping review of the recommendations and guidance for improving the quality of rare disease registries. Orphanet J Rare Dis 2024; 19:187. [PMID: 38711103 PMCID: PMC11075280 DOI: 10.1186/s13023-024-03193-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 04/19/2024] [Indexed: 05/08/2024] Open
Abstract
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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Affiliation(s)
- J E Tarride
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
- Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada
- Programs for the Assessment of Technologies in Health (PATH), The Research Institute of St. Joe's Hamilton, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - A Okoh
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - K Aryal
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - C Prada
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Deborah Milinkovic
- Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada.
| | - A Keepanasseril
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - A Iorio
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
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Scott J, White A, Walsh C, Aslett L, Rutherford MA, Ng J, Judge C, Sebastian K, O'Brien S, Kelleher J, Power J, Conlon N, Moran SM, Luqmani RA, Merkel PA, Tesar V, Hruskova Z, Little MA. Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis. RMD Open 2024; 10:e003962. [PMID: 38688690 PMCID: PMC11086371 DOI: 10.1136/rmdopen-2023-003962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/29/2024] [Indexed: 05/02/2024] Open
Abstract
OBJECTIVE ANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting. METHODS We studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse. RESULTS Development and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS. CONCLUSIONS This CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.
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Affiliation(s)
- Jennifer Scott
- Trinity Kidney Centre, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Arthur White
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
- ADAPT SFI centre, Trinity College Dublin, Dublin, Ireland
| | - Cathal Walsh
- Department of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
- National Centre for Pharmacoeconomics, St James's Hospital, Dublin, Ireland
| | - Louis Aslett
- Department of Mathematical Science, University of Durham, Durham, UK
| | | | - James Ng
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Conor Judge
- School of Medicine, College of Medicine, Nursing and Health Science, University of Galway, Galway, Ireland
| | - Kuruvilla Sebastian
- Trinity Kidney Centre, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Sorcha O'Brien
- Trinity Kidney Centre, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - John Kelleher
- Department of Statistics, Dublin Institute of Technology, Dublin, Ireland
| | - Julie Power
- Vasculitis Ireland Awareness, Dublin, Ireland
| | - Niall Conlon
- Department of Immunology, St James's Hospital, Dublin, Ireland
| | - Sarah M Moran
- Trinity Kidney Centre, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
- Department of Nephrology, Cork University Hospital, Cork, Ireland
| | - Raashid Ahmed Luqmani
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Science (NDORMs), University of Oxford, Oxford, UK
| | - Peter A Merkel
- Division of Rheumatology, Department of Medicine, Division of Epidemiology, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vladimir Tesar
- Department of Nephrology, General University Hospital, Prague, Czech Republic
- 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Zdenka Hruskova
- 1st Faculty of Medicine, Charles University, Prague, Czech Republic
- General University Hospital, Prague, Czech Republic
| | - Mark A Little
- Trinity Kidney Centre, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
- ADAPT SFI centre, Trinity College Dublin, Dublin, Ireland
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Gisslander K, Rutherford M, Aslett L, Basu N, Dradin F, Hederman L, Hruskova Z, Kardaoui H, Lamprecht P, Lichołai S, Musial J, O'Sullivan D, Puechal X, Scott J, Segelmark M, Straka R, Terrier B, Tesar V, Tesi M, Vaglio A, Wandrei D, White A, Wójcik K, Yaman B, Little MA, Mohammad AJ. Data quality and patient characteristics in European ANCA-associated vasculitis registries: data retrieval by federated querying. Ann Rheum Dis 2024; 83:112-120. [PMID: 37907255 PMCID: PMC10804071 DOI: 10.1136/ard-2023-224571] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/16/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVES This 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. METHODS Through 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. RESULTS A 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. CONCLUSIONS In 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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Affiliation(s)
- Karl Gisslander
- Clinical Sciences, Rheumatology, Lund University, Lund, Sweden
| | | | - Louis Aslett
- Department of Mathematical Science, University of Durham, Durham, UK
| | - Neil Basu
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | | | - Lucy Hederman
- ADAPT SFI Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Zdenka Hruskova
- Department of Nephrology, General University Hospital, Prague, Czech Republic
- First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Hicham Kardaoui
- National Referral Center for Rare Systemic Autoimmune Diseases, Hospital Cochin, Paris, France
- Université Paris Cité, Paris, France
| | - Peter Lamprecht
- Department of Rheumatology and Clinical Immunology, Universitat zu Lubeck, Lubeck, Germany
| | - Sabina Lichołai
- Division of Molecular Biology and Clinical Genetics, Jagiellonian University Medical College, Krakow, Poland
| | - Jacek Musial
- 2nd Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Declan O'Sullivan
- ADAPT SFI Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Xavier Puechal
- National Referral Center for Rare Systemic Autoimmune Diseases, Hospital Cochin, Paris, France
- French Vasculitis Study Group, Paris, France
| | - Jennifer Scott
- ADAPT SFI Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
- Trinity Kidney Centre, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Mårten Segelmark
- Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Endocrinology, Nephrology and Rheumatology, Skåne University Hospital, Lund, Sweden
| | - Richard Straka
- General University Hospital in Prague, Praha, Czech Republic
| | - Benjamin Terrier
- National Referral Center for Rare Systemic Autoimmune Diseases, Hospital Cochin, Paris, France
- French Vasculitis Study Group, Paris, France
| | - Vladimir Tesar
- Department of Nephrology, General University Hospital, Prague, Czech Republic
- First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Michelangelo Tesi
- Nephrology and Dialysis Unit, Meyer Children's Hospital IRCCS, Firenze, Italy
| | - Augusto Vaglio
- Nephrology and Dialysis Unit, Meyer Children's Hospital IRCCS, Firenze, Italy
| | - Dagmar Wandrei
- Clinical Trials Unit, Medical Center, University of Freiburg Faculty of Medicine, Freiburg, Germany
| | - Arthur White
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Krzysztof Wójcik
- 2nd Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Beyza Yaman
- ADAPT SFI Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Mark A Little
- ADAPT SFI Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
- Trinity Kidney Centre, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Aladdin J Mohammad
- Clinical Sciences, Rheumatology, Lund University, Lund, Sweden
- Department of Medicine, University of Cambridge, Cambridge, UK
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Gisslander K, Mohammad AJ, Vaglio A, Little MA. Overcoming challenges in rare disease registry integration using the semantic web - a clinical research perspective. Orphanet J Rare Dis 2023; 18:253. [PMID: 37644439 PMCID: PMC10466902 DOI: 10.1186/s13023-023-02841-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/21/2023] [Indexed: 08/31/2023] Open
Abstract
The growing number of disease-specific patient registries for rare diseases has highlighted the need for registry interoperability and data linkage, leading to large-scale rare disease data integration projects using Semantic Web based solutions. These technologies may be difficult to grasp for rare disease experts, leading to limited involvement by domain expertise in the data integration process. Here, we propose a data integration framework starting from the perspective of the clinical researcher, allowing for purposeful rare disease registry integration driven by clinical research questions.
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Affiliation(s)
- Karl Gisslander
- Department of Clinical Sciences - Rheumatology, Lund University, Lund, SE-221 85, Sweden.
| | - Aladdin J Mohammad
- Department of Clinical Sciences - Rheumatology, Lund University, Lund, SE-221 85, Sweden
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Augusto Vaglio
- Department of Biomedical, Experimental and Clinical Sciences, University of Florence, Florence, Italy
- Nephrology and Dialysis Unit, Meyer Children's Hospital IRCCS, Florence, Italy
| | - Mark A Little
- TTMI, Trinity Health Kidney Centre, Dublin, Ireland
- ADAPT, Trinity College Dublin, Dublin, Ireland
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Wang Y, Jiang Q, Geng Y, Hu Y, Tang Y, Li J, Zhang J, Mayer W, Liu S, Zhang HY, Yan X, Feng Z. SGMFQP: An ontology-based Swine Gut Microbiota Federated Query Platform. Methods 2023; 212:12-20. [PMID: 36858137 DOI: 10.1016/j.ymeth.2023.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/04/2023] [Accepted: 02/21/2023] [Indexed: 03/03/2023] Open
Abstract
Gut microbiota plays a crucial role in modulating pig development and health, and gut microbiota characteristics are associated with differences in feed efficiency. To answer open questions in feed efficiency analysis, biologists seek to retrieve information across multiple heterogeneous data sources. However, this is error-prone and time-consuming work since the queries can involve a sequence of multiple sub-queries over several databases. We present an implementation of an ontology-based Swine Gut Microbiota Federated Query Platform (SGMFQP) that provides a convenient, automated, and efficient query service about swine feeding and gut microbiota. The system is constructed based on a domain-specific Swine Gut Microbiota Ontology (SGMO), which facilitates the construction of queries independent of the actual organization of the data in the individual sources. This process is supported by a template-based query interface. A Datalog+-based federated query engine transforms the queries into sub-queries tailored for each individual data source, and an automated workflow orchestration mechanism executes the queries in each source database and consolidates the results. The efficiency of the system is demonstrated on several swine feeding scenarios.
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Affiliation(s)
- Ying Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qin Jiang
- College of Animal Sciences and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yilin Geng
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuren Hu
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yue Tang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jixiang Li
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Junmei Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wolfgang Mayer
- Industrial AI Research Centre, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Shanmei Liu
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hong-Yu Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Smart Farming for Agricultural Animals, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China
| | - Xianghua Yan
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; College of Animal Sciences and Technology, Huazhong Agricultural University, Wuhan 430070, China.
| | - Zaiwen Feng
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Smart Farming for Agricultural Animals, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China; Macro Agricultural Research Institute, Huazhong Agricultural University, Wuhan 430070, China.
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Gelain E, Tesi M, Mazzariol M, Vaglio A. Registries of rare diseases: current knowledge and future perspectives. Intern Emerg Med 2023; 18:19-21. [PMID: 36401715 DOI: 10.1007/s11739-022-03151-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/02/2022] [Indexed: 11/21/2022]
Affiliation(s)
- Elena Gelain
- Nephrology and Dialysis Unit, Meyer Children's Hospital, Firenze, Italy
| | - Michelangelo Tesi
- Nephrology and Dialysis Unit, Meyer Children's Hospital, Firenze, Italy
| | - Martina Mazzariol
- Nephrology and Dialysis Unit, Meyer Children's Hospital, Firenze, Italy
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Augusto Vaglio
- Nephrology and Dialysis Unit, Meyer Children's Hospital, Firenze, Italy.
- Department of Biomedical, Clinical and Experimental Sciences "Mario Serio", University of Firenze, Firenze, Italy.
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