1. Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort.
- Author
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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, and Mohammad AJ
- Subjects
- Humans, Female, Male, Middle Aged, Aged, Cluster Analysis, Europe epidemiology, Cohort Studies, Adult, Microscopic Polyangiitis classification, Microscopic Polyangiitis epidemiology, Microscopic Polyangiitis blood, Microscopic Polyangiitis diagnosis, Microscopic Polyangiitis immunology, Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis classification, Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis diagnosis, Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis epidemiology, Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis blood, Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis immunology, Registries statistics & numerical data
- 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., Competing Interests: Declaration of interests PL has received grants or contracts from the German Research Society, Federal Ministry of Education and Research, German Society for Rheumatology, John Grube Foundation, and Vifor Pharma; consulting fees from AbbVie, AstraZeneca, GSK, Novartis, UCB, and Vifor Pharma; payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing, or educational events from AstraZeneca, Boehringer Ingelheim, GSK, Janssen, Novartis, Rheumaakademie, UCB, and Vifor Pharma; support for attending meetings or travel from Vifor Pharma; and has participated on Data Safety Monitoring Boards or Advisory Boards of AbbVie, AstraZeneca, GSK, and Vifor Pharma. XP has received grants as an investigator of academic studies of ANCA-associated vasculitis for which rituximab was provided by Roche Pharma. MS has received consulting fees from Vifor Pharma, Otsuka, and Hansa Biopharma. BT has received consulting fees from Novartis, AstraZeneca, CSL Vifor, GSK, and Boehringer Ingelheim; payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing, or educational events from Novartis, AstraZeneca, CSL Vifor, GSK, and Boehringer Ingelheim; support for attending meetings or travel from GSK, AstraZeneca, and Novartis; and participates on the Advisory Board for Amgen. KW is a member of the Advisory Board at CSL Vifor. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)
- Published
- 2024
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