Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort

. 2024 Nov ; 6 (11) : e762-e770. [epub] 20240822

Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid39182506
Odkazy

PubMed 39182506
DOI 10.1016/s2665-9913(24)00187-5
PII: S2665-9913(24)00187-5
Knihovny.cz E-zdroje

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.

2 Chair of Internal Medicine Faculty of Medicine Jagiellonian University Medical College Kraków Poland

ADAPT Centre Trinity College Dublin Dublin Ireland

ADAPT Centre Trinity College Dublin Dublin Ireland; Trinity Kidney Centre Trinity Translational Medicine Institute School of Medicine Trinity College Dublin Dublin Ireland

Department of Clinical Sciences Lund University and Department of Endocrinology Nephrology and Rheumatology Skåne University Hospital Lund Sweden

Department of Mathematical Sciences Durham University Durham UK

Department of Nephrology General University Hospital Prague and 1st Faculty of Medicine Charles University Prague Czech Republic

Department of Rheumatology and Clinical Immunology University of Lübeck Lübeck Germany

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

Nephrology and Dialysis Unit Azienda Ospedaliera Universitaria Meyer IRCCS Florence Italy

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

Rheumatology Department of Clinical Sciences Lund University Lund Sweden

Rheumatology Department of Clinical Sciences Lund University Lund Sweden; Department of Medicine University of Cambridge Cambridge UK

School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

School of Computer Science and Statistics Trinity College Dublin Dublin Ireland; ADAPT Centre Trinity College Dublin Dublin Ireland

School of Infection and Immunity University of Glasgow Glasgow UK

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