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Disease Phenotypes and Prediction of Outcome in ANCA-associated vasculitis

Gisslander, Karl LU orcid (2024) In Lund University, Faculty of Medicine Doctoral Dissertation Series
Abstract
Objectives
Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a rare systemic autoimmune disease characterised by inflammation and destruction of small blood vessels. With research into the disease suffering from small sample sizes, this thesis aims to (1) address research data fragmentation in AAV through the integration of real-world observational registries, (2) stratify patterns of symptoms at disease onset, and (3) build models for the prediction of disease outcome.
Methods
Data from six European vasculitis registries were integrated using two complementary approaches. First the registries were combined using Semantic Web technologies, allowing federated access to aggregated data through a... (More)
Objectives
Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a rare systemic autoimmune disease characterised by inflammation and destruction of small blood vessels. With research into the disease suffering from small sample sizes, this thesis aims to (1) address research data fragmentation in AAV through the integration of real-world observational registries, (2) stratify patterns of symptoms at disease onset, and (3) build models for the prediction of disease outcome.
Methods
Data from six European vasculitis registries were integrated using two complementary approaches. First the registries were combined using Semantic Web technologies, allowing federated access to aggregated data through a dedicated web interface. The quality of the underlying data was explored, and the characteristics, treatments and disease outcome of European patients described. Secondly, data was pooled in a central data storage. Using the central data, model-based clustering was used to study and stratify the diverse phenotypic presentations at disease onset. Lastly, prognostic models for key disease outcomes were built using survival modelling.
Results
The federated integration was successful, although some data quality concerns were identified, allowing access to an unprecedented cohort size of 5282 patients. Symptomatology, type of treatments used, mortality rates and rates of end-stage kidney disease were highly variable between the participating registries. Using model-based clustering, five clusters were identified, with distinct phenotypes, biochemical presentations, and disease outcomes – primarily stratified by kidney impairment and systemic inflammation. Building predictive models for disease outcome, known predictors of disease outcome were reidentified and compiled into comprehensive models, outperforming existing models in terms of predictive accuracy.
Conclusion
This thesis presents the first successful federated integration of distributed vasculitis datasets, allowing access to a cohort of unprecedented size. It further reinforces that AAV is beyond a binary construct and that the disease heterogeneity may be better described by five subcategories. While accurate prediction of disease outcome at the time of diagnosis is possible, the benefit of implementation of prediction models for the guidance of clinical decision-making needs further evaluation. (Less)
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author
supervisor
opponent
  • Professor Luqmani, Raashid, University of Oxford, United Kingdom
organization
publishing date
type
Thesis
publication status
published
subject
keywords
ANCA-associated vasculitis, Epidemiology, Cluster, Classification, Prediction, Outcome
in
Lund University, Faculty of Medicine Doctoral Dissertation Series
issue
2024:134
pages
120 pages
publisher
Lund University, Faculty of Medicine
defense location
Reumatologiska klinikens föreläsningssal, Lottasalen, Kioskgatan 5, Universitetssjukhuset i Lund. Join by Zoom: https://lu-se.zoom.us/j/63242456385
defense date
2024-11-08 09:00:00
ISSN
1652-8220
ISBN
978-91-8021-632-6
project
Disease Phenotypes and Prediction of Outcome in ANCA-associated vasculitis
language
English
LU publication?
yes
id
3229e106-57f4-4927-884e-6cb230528274
date added to LUP
2024-10-10 09:38:51
date last changed
2025-04-04 14:56:29
@phdthesis{3229e106-57f4-4927-884e-6cb230528274,
  abstract     = {{Objectives<br/>Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a rare systemic autoimmune disease characterised by inflammation and destruction of small blood vessels. With research into the disease suffering from small sample sizes, this thesis aims to (1) address research data fragmentation in AAV through the integration of real-world observational registries, (2) stratify patterns of symptoms at disease onset, and (3) build models for the prediction of disease outcome. <br/>Methods<br/>Data from six European vasculitis registries were integrated using two complementary approaches. First the registries were combined using Semantic Web technologies, allowing federated access to aggregated data through a dedicated web interface. The quality of the underlying data was explored, and the characteristics, treatments and disease outcome of European patients described. Secondly, data was pooled in a central data storage. Using the central data, model-based clustering was used to study and stratify the diverse phenotypic presentations at disease onset. Lastly, prognostic models for key disease outcomes were built using survival modelling.<br/>Results<br/>The federated integration was successful, although some data quality concerns were identified, allowing access to an unprecedented cohort size of 5282 patients. Symptomatology, type of treatments used, mortality rates and rates of end-stage kidney disease were highly variable between the participating registries. Using model-based clustering, five clusters were identified, with distinct phenotypes, biochemical presentations, and disease outcomes – primarily stratified by kidney impairment and systemic inflammation. Building predictive models for disease outcome, known predictors of disease outcome were reidentified and compiled into comprehensive models, outperforming existing models in terms of predictive accuracy.<br/>Conclusion<br/>This thesis presents the first successful federated integration of distributed vasculitis datasets, allowing access to a cohort of unprecedented size. It further reinforces that AAV is beyond a binary construct and that the disease heterogeneity may be better described by five subcategories. While accurate prediction of disease outcome at the time of diagnosis is possible, the benefit of implementation of prediction models for the guidance of clinical decision-making needs further evaluation.}},
  author       = {{Gisslander, Karl}},
  isbn         = {{978-91-8021-632-6}},
  issn         = {{1652-8220}},
  keywords     = {{ANCA-associated vasculitis; Epidemiology; Cluster; Classification; Prediction; Outcome}},
  language     = {{eng}},
  number       = {{2024:134}},
  publisher    = {{Lund University, Faculty of Medicine}},
  school       = {{Lund University}},
  series       = {{Lund University, Faculty of Medicine Doctoral Dissertation Series}},
  title        = {{Disease Phenotypes and Prediction of Outcome in ANCA-associated vasculitis}},
  url          = {{https://lup.lub.lu.se/search/files/197006401/Karl_Gisslander_-_WEBB.pdf}},
  year         = {{2024}},
}