Antineutrophil cytoplasmic antibody-associated vasculitis classification by cluster analysis based on clinical phenotypes: a single-center retrospective cohort study.

Abstract

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) refers to a group of small vessel inflammatory disorders. Overlapping clinical phenotypes of AAV subgroups continually provoke controversies over their diagnostic and classification criteria. Using the agglomerative hierarchical clustering method, we classified 210 Korean patients diagnosed with AAV into mutually exclusive clusters according to Birmingham Vasculitis Activity Score items, ANCA specificity, sex, and age. We analyzed the resulting clusters' outcomes to investigate the clinical significance of the classification. We proposed a distance-based algorithm of patient assignment and explored its clinically relevant modification. In total, 116 patients (55%) had microscopic polyangiitis, 53 (25%) had granulomatosis with polyangiitis, and 42 (20%) had eosinophilic granulomatosis with polyangiitis. Our model grouped the patients into five clusters, namely, "limited proteinase 3 (PR3)-ANCA vasculitis," "generalized PR3-ANCA vasculitis," "ANCA-negative vasculitis," "renal-limited vasculitis," and "myeloperoxidase-ANCA vasculitis." Patients clustered under "generalized PR3-ANCA vasculitis" had a higher relapse rate (hazard ratio [HR] = 2.12, P = 0.067). The incidence of end-stage renal disease was higher in patients belonging to the "renal-limited vasculitis" cluster (HR=1.50, P=0.03), and those in the "ANCA-negative vasculitis" cluster experienced a relatively milder clinical course of AAV (mortality = 0). Because the clusters were naturally derived from their distinguished phenotypes and have different clinical courses, our clustering method may be a more clinically relevant classification system for AAV, revealing its phenotypic diversity. We also proposed a simple and intuitive distance-based assignment algorithm, which can be easily modified according to specific clinical needs. Key Points • In this study with a single-center AAV cohort, we showed that AAV can be divided into five distinct subclasses with different disease courses based on the clinical and laboratory features of the patients. • Our study revealed ethnic differences in AAV manifestation and suggests that physicians may need to analyze their own AAV patients to assess the disease status of AAV patients. • We proposed a distance-based cluster membership assignment method that can be clinically modified to fit the specific purpose of grouping patients.